diff --git a/.gitignore b/.gitignore index 04536b5..3ea23ac 100644 --- a/.gitignore +++ b/.gitignore @@ -8,10 +8,20 @@ android/.kotlin/ android/build/ android/*/build/ go/teamspeak/.opus/build/ +go/teamspeak/.opus/install/ release/ *.apk *.aab +# gomobile 产物：由 build.bat / build.sh 第 1 步现场生成，不入库 +android/app/libs/*.aar + +# opus 的 PyTorch DNN 训练资源（含 5.3MB training_files.txt）。 +# 仅用于 LPCNet/DRED 模型训练，CMake 从不引用（dnn/*.c 只在 OPUS_DRED/OSCE/ +# DEEP_PLC 开启时编译，本项目的 build-opus-android 三个开关全关）， +# Android 构建完全用不到，历史入库纯属冗余。 +android/app/src/main/cpp/third_party/opus/dnn/torch/ + # ============================================================ # Android # ============================================================ @@ -56,6 +66,12 @@ android/.idea/libraries/ *.out go.work +# 静态库默认忽略（.opus/install 与 .opus/build 下的 libopus.a 是中间产物）。 +# 例外：.opus/lib 下的四个 ABI 预编译库是**有意入库**的， +# 用于免去每次克隆后的 opus 交叉编译，见 CLAUDE.md。 +*.a +!go/teamspeak/.opus/lib/**/*.a + # ============================================================ # Archives # ============================================================ diff --git a/CLAUDE.md b/CLAUDE.md index fb98685..1a394bb 100644 --- a/CLAUDE.md +++ b/CLAUDE.md @@ -88,8 +88,8 @@ Four routes in `NavGraph.kt`: `server_config` (connection form) → `channel_lis ### Known SDK Limitations -- **No channel CRUD events**: The SDK does not fire events for channel create/update/delete. Channel list is refreshed after 5 minutes of staleness or before channel switch operations. -- **Unreliable ChannelID in enter events**: `notifycliententerview` ChannelID is not effective per SDK docs. Client enter/leave events trigger a full `clientlist` refresh instead of incremental updates. +- **No channel CRUD events**: The SDK does not fire events for channel create/update/delete. Channel list is refreshed after 5 minutes of staleness or before channel switch operations. Staleness is tracked by `Repository.channelsFetchedAt` and surfaced in the UI via `StaleChannelBanner`. +- **Unreliable ChannelID in enter events**: `notifycliententerview` ChannelID is not effective per SDK docs. Client enter/leave events trigger a full `clientlist` refresh instead of incremental updates. To keep this from rebuilding the whole channel tree, `Repository.applyClients` does a **per-channel differential update** (`diffChannelClients`) that reuses unchanged list instances. - **Auto-reconnect disabled by default**: Frequent reconnect attempts cause server-side rate limiting/bans. ### Thread Safety @@ -151,4 +151,38 @@ AAR not compiled or not in `android/app/libs/teamspeak.aar`. Run Step 1 of manua Run from IDE with connected device or emulator. Check Logcat in IDE for runtime logs. -Go unit tests exist for the audio receive pipeline (`go/teamspeak/receive_audio_test.go`): `cd go && go test ./teamspeak/` +```bash +# Go：音频接收管线 + 跨语言契约 +cd go && go test ./teamspeak/ + +# Kotlin：契约反序列化 + 纯逻辑（无需设备） +cd android && ./gradlew testDebugUnitTest +``` + +### Cross-language contract + +Go ↔ Kotlin 的 JSON 字段约定是**编译器无法校验**的边界（改字段名只会静默拿到默认值）。 +三道机制钉住它，改动 bridge 字段时必须一起维护： + +| 文件 | 作用 | +| --- | --- | +| `go/teamspeak/contract.go` | 字段名常量的单一事实来源 + `BridgeContractVersion` | +| `go/teamspeak/contract_test.go` / `contract_golden_test.go` | 反射断言常量与 struct tag 一致；golden 样本一致性 | +| `android/.../data/BridgeContract.kt` | 启动时校验 AAR 契约版本，不匹配则阻止连接 | +| `android/app/src/test/resources/contract/` | **Go 与 Kotlin 共用**的 golden 样本 | +| `docs/bridge-contract.md` | 逐字段对照表与 targetMode 收发能力矩阵 | + +不兼容变更（字段改名/类型变更/删除）必须递增 `BridgeContractVersion` 并同步 +`BridgeContract.EXPECTED_VERSION` 与该文档。 + +### Known gotchas fixed here + +- **`ChannelInfo.order` 在 Go 侧不存在**（`channelJSON` 无 `order` 字段）， + 恒为默认 `0`，`buildChannelTree` 的 `sortedBy { it.order }` 是 no-op。 + 频道显示顺序由 TS3 `channellist` 的响应顺序决定（已真机验证与显示一致），这是正确的。 +- **`channel_order` 不是排序权重，是前驱频道的 ID**（链表指针，`0` = 本层最前）。 + 见 `docs/teamspeak-sdk-3.5.2/doc/client/channel-sort.html`。 + **不要按它数值排序**——那会打乱频道树。若要在客户端重建顺序，需按前驱指针串链。 +- **私聊（targetMode=1）只能收、不能发**。`TSBridge.sendTextMessage` 仅实现 `mode=2`。 +- **频道陈旧判定统一走 `Repository.isChannelDataStale()`**。 + 不要在 ViewModel 里另建时间戳——历史上两处时间戳不同步会导致重复刷新请求。 diff --git a/android/.gradle/8.11.1/executionHistory/executionHistory.bin b/android/.gradle/8.11.1/executionHistory/executionHistory.bin deleted file mode 100644 index 944bcb8..0000000 Binary files a/android/.gradle/8.11.1/executionHistory/executionHistory.bin and /dev/null differ diff --git a/android/.gradle/8.11.1/executionHistory/executionHistory.lock b/android/.gradle/8.11.1/executionHistory/executionHistory.lock deleted file mode 100644 index 121c073..0000000 Binary files a/android/.gradle/8.11.1/executionHistory/executionHistory.lock and /dev/null differ diff --git a/android/.gradle/8.11.1/fileHashes/fileHashes.bin b/android/.gradle/8.11.1/fileHashes/fileHashes.bin deleted file mode 100644 index 7e713cc..0000000 Binary files a/android/.gradle/8.11.1/fileHashes/fileHashes.bin and /dev/null differ diff --git a/android/.gradle/8.11.1/fileHashes/fileHashes.lock b/android/.gradle/8.11.1/fileHashes/fileHashes.lock deleted file mode 100644 index 312b00b..0000000 Binary files a/android/.gradle/8.11.1/fileHashes/fileHashes.lock and /dev/null differ diff --git a/android/.gradle/8.11.1/fileHashes/resourceHashesCache.bin b/android/.gradle/8.11.1/fileHashes/resourceHashesCache.bin deleted file mode 100644 index 0531c1a..0000000 Binary files a/android/.gradle/8.11.1/fileHashes/resourceHashesCache.bin and /dev/null differ diff --git a/android/.gradle/buildOutputCleanup/buildOutputCleanup.lock b/android/.gradle/buildOutputCleanup/buildOutputCleanup.lock deleted file mode 100644 index ac54abd..0000000 Binary files a/android/.gradle/buildOutputCleanup/buildOutputCleanup.lock and /dev/null differ diff --git a/android/.gradle/file-system.probe b/android/.gradle/file-system.probe deleted file mode 100644 index 2106df4..0000000 Binary files a/android/.gradle/file-system.probe and /dev/null differ diff --git a/android/app/build.gradle.kts b/android/app/build.gradle.kts index 856124b..a824fdf 100644 --- a/android/app/build.gradle.kts +++ b/android/app/build.gradle.kts @@ -13,8 +13,8 @@ android { applicationId = "com.tsmobile.app" minSdk = 26 targetSdk = 35 - versionCode = 12 - versionName = "1.0.12" + versionCode = 13 + versionName = "1.0.13" } signingConfigs { @@ -56,6 +56,9 @@ android { buildFeatures { compose = true + // 供 BuildConfig.DEBUG 使用：debug-only 的诊断日志靠它做常量折叠， + // R8 会把 `if (BuildConfig.DEBUG)` 块整体消除，release 包不留全量转储。 + buildConfig = true } // 引入 gomobile 生成的 AAR diff --git a/android/app/libs/teamspeak.aar b/android/app/libs/teamspeak.aar deleted file mode 100644 index 3aa1226..0000000 Binary files a/android/app/libs/teamspeak.aar and /dev/null differ diff --git a/android/app/proguard-rules.pro b/android/app/proguard-rules.pro index 5744c37..ba42638 100644 --- a/android/app/proguard-rules.pro +++ b/android/app/proguard-rules.pro @@ -1,3 +1,18 @@ # TeamSpeak Go Bridge -keep class teamspeak.** { *; } -keep class com.tsmobile.app.TSBridge { *; } + +# ── Release 日志剥离 ────────────────────────────────────────── +# 移除 verbose/debug/info/warn 四级日志调用。 +# 保留 Log.e：崩溃与错误路径需要留在 release 包中用于定位线上问题。 +# +# 注意：-assumenosideeffects 只删除调用点，不删除参数表达式的求值。 +# 因此凡是「日志参数本身很重」的位置（如遍历打印全量频道/成员列表）， +# 都已在源码侧改为只打印数量或加 BuildConfig.DEBUG 判断，避免空转。 +-assumenosideeffects class android.util.Log { + public static int v(...); + public static int d(...); + public static int i(...); + public static int w(...); + public static int println(...); +} diff --git a/android/app/src/main/cpp/third_party/opus/dnn/torch/dnntools/dnntools/__init__.py b/android/app/src/main/cpp/third_party/opus/dnn/torch/dnntools/dnntools/__init__.py deleted file mode 100644 index 117597a..0000000 --- a/android/app/src/main/cpp/third_party/opus/dnn/torch/dnntools/dnntools/__init__.py +++ /dev/null @@ -1,2 +0,0 @@ -from . import quantization -from . import sparsification \ No newline at end of file diff --git a/android/app/src/main/cpp/third_party/opus/dnn/torch/dnntools/dnntools/quantization/__init__.py b/android/app/src/main/cpp/third_party/opus/dnn/torch/dnntools/dnntools/quantization/__init__.py deleted file mode 100644 index 3b46a2e..0000000 --- a/android/app/src/main/cpp/third_party/opus/dnn/torch/dnntools/dnntools/quantization/__init__.py +++ /dev/null @@ -1 +0,0 @@ -from .softquant import soft_quant, remove_soft_quant \ No newline at end of file diff --git a/android/app/src/main/cpp/third_party/opus/dnn/torch/dnntools/dnntools/quantization/softquant.py b/android/app/src/main/cpp/third_party/opus/dnn/torch/dnntools/dnntools/quantization/softquant.py deleted file mode 100644 index 877c645..0000000 --- a/android/app/src/main/cpp/third_party/opus/dnn/torch/dnntools/dnntools/quantization/softquant.py +++ /dev/null @@ -1,113 +0,0 @@ -import torch - -@torch.no_grad() -def compute_optimal_scale(weight): - with torch.no_grad(): - n_out, n_in = weight.shape - assert n_in % 4 == 0 - if n_out % 8: - # add padding - pad = n_out - n_out % 8 - weight = torch.cat((weight, torch.zeros((pad, n_in), dtype=weight.dtype, device=weight.device)), dim=0) - - weight_max_abs, _ = torch.max(torch.abs(weight), dim=1) - weight_max_sum, _ = torch.max(torch.abs(weight[:, : n_in : 2] + weight[:, 1 : n_in : 2]), dim=1) - scale_max = weight_max_abs / 127 - scale_sum = weight_max_sum / 129 - - scale = torch.maximum(scale_max, scale_sum) - - return scale[:n_out] - -@torch.no_grad() -def q_scaled_noise(module, weight): - if isinstance(module, torch.nn.Conv1d): - w = weight.permute(0, 2, 1).flatten(1) - noise = torch.rand_like(w) - 0.5 - noise[w == 0] = 0 # ignore zero entries from sparsification - scale = compute_optimal_scale(w) - noise = noise * scale.unsqueeze(-1) - noise = noise.reshape(weight.size(0), weight.size(2), weight.size(1)).permute(0, 2, 1) - elif isinstance(module, torch.nn.ConvTranspose1d): - i, o, k = weight.shape - w = weight.permute(2, 1, 0).reshape(k * o, i) - noise = torch.rand_like(w) - 0.5 - noise[w == 0] = 0 # ignore zero entries from sparsification - scale = compute_optimal_scale(w) - noise = noise * scale.unsqueeze(-1) - noise = noise.reshape(k, o, i).permute(2, 1, 0) - elif len(weight.shape) == 2: - noise = torch.rand_like(weight) - 0.5 - noise[weight == 0] = 0 # ignore zero entries from sparsification - scale = compute_optimal_scale(weight) - noise = noise * scale.unsqueeze(-1) - else: - raise ValueError('unknown quantization setting') - - return noise - -class SoftQuant: - name: str - - def __init__(self, names: str, scale: float) -> None: - self.names = names - self.quantization_noise = None - self.scale = scale - - def __call__(self, module, inputs, *args, before=True): - if not module.training: return - - if before: - self.quantization_noise = dict() - for name in self.names: - weight = getattr(module, name) - if self.scale is None: - self.quantization_noise[name] = q_scaled_noise(module, weight) - else: - self.quantization_noise[name] = \ - self.scale * (torch.rand_like(weight) - 0.5) - with torch.no_grad(): - weight.data[:] = weight + self.quantization_noise[name] - else: - for name in self.names: - weight = getattr(module, name) - with torch.no_grad(): - weight.data[:] = weight - self.quantization_noise[name] - self.quantization_noise = None - - def apply(module, names=['weight'], scale=None): - fn = SoftQuant(names, scale) - - for name in names: - if not hasattr(module, name): - raise ValueError("") - - fn_before = lambda *x : fn(*x, before=True) - fn_after = lambda *x : fn(*x, before=False) - setattr(fn_before, 'sqm', fn) - setattr(fn_after, 'sqm', fn) - - - module.register_forward_pre_hook(fn_before) - module.register_forward_hook(fn_after) - - module - - return fn - - -def soft_quant(module, names=['weight'], scale=None): - fn = SoftQuant.apply(module, names, scale) - return module - -def remove_soft_quant(module, names=['weight']): - for k, hook in module._forward_pre_hooks.items(): - if hasattr(hook, 'sqm'): - if isinstance(hook.sqm, SoftQuant) and hook.sqm.names == names: - del module._forward_pre_hooks[k] - for k, hook in module._forward_hooks.items(): - if hasattr(hook, 'sqm'): - if isinstance(hook.sqm, SoftQuant) and hook.sqm.names == names: - del module._forward_hooks[k] - - return module \ No newline at end of file diff --git a/android/app/src/main/cpp/third_party/opus/dnn/torch/dnntools/dnntools/relegance/__init__.py b/android/app/src/main/cpp/third_party/opus/dnn/torch/dnntools/dnntools/relegance/__init__.py deleted file mode 100644 index cee0143..0000000 --- a/android/app/src/main/cpp/third_party/opus/dnn/torch/dnntools/dnntools/relegance/__init__.py +++ /dev/null @@ -1,2 +0,0 @@ -from .relegance import relegance_gradient_weighting, relegance_create_tconv_kernel, relegance_map_relevance_to_input_domain, relegance_resize_relevance_to_input_size -from .meta_critic import MetaCritic \ No newline at end of file diff --git a/android/app/src/main/cpp/third_party/opus/dnn/torch/dnntools/dnntools/relegance/meta_critic.py b/android/app/src/main/cpp/third_party/opus/dnn/torch/dnntools/dnntools/relegance/meta_critic.py deleted file mode 100644 index 1af0f8f..0000000 --- a/android/app/src/main/cpp/third_party/opus/dnn/torch/dnntools/dnntools/relegance/meta_critic.py +++ /dev/null @@ -1,85 +0,0 @@ -""" -/* Copyright (c) 2023 Amazon - Written by Jan Buethe */ -/* - Redistribution and use in source and binary forms, with or without - modification, are permitted provided that the following conditions - are met: - - - Redistributions of source code must retain the above copyright - notice, this list of conditions and the following disclaimer. - - - Redistributions in binary form must reproduce the above copyright - notice, this list of conditions and the following disclaimer in the - documentation and/or other materials provided with the distribution. - - THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS - ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT - LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR - A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER - OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, - EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, - PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR - PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF - LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING - NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS - SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. -*/ -""" - -import torch - -class MetaCritic(): - def __init__(self, normalize=False, gamma=0.9, beta=0.0, joint_stats=False): - """ Class for assessing relevance of discriminator scores - - Args: - gamma (float, optional): update rate for tracking discriminator stats. Defaults to 0.9. - beta (float, optional): Miminum confidence related threshold. Defaults to 0.0. - """ - self.normalize = normalize - self.gamma = gamma - self.beta = beta - self.joint_stats = joint_stats - - self.disc_stats = dict() - - def __call__(self, disc_id, real_scores, generated_scores): - """ calculates relevance from normalized scores - - Args: - disc_id (any valid key): id for tracking discriminator statistics - real_scores (torch.tensor): scores for real data - generated_scores (torch.tensor): scores for generated data; expecting device to match real_scores.device - - Returns: - torch.tensor: output-domain relevance - """ - - if self.normalize: - real_std = torch.std(real_scores.detach()).cpu().item() - gen_std = torch.std(generated_scores.detach()).cpu().item() - std = (real_std**2 + gen_std**2) ** .5 - mean = torch.mean(real_scores.detach()).cpu().item() - torch.mean(generated_scores.detach()).cpu().item() - - key = 0 if self.joint_stats else disc_id - - if key in self.disc_stats: - self.disc_stats[key]['std'] = self.gamma * self.disc_stats[key]['std'] + (1 - self.gamma) * std - self.disc_stats[key]['mean'] = self.gamma * self.disc_stats[key]['mean'] + (1 - self.gamma) * mean - else: - self.disc_stats[key] = { - 'std': std + 1e-5, - 'mean': mean - } - - std = self.disc_stats[key]['std'] - mean = self.disc_stats[key]['mean'] - else: - mean, std = 0, 1 - - relevance = torch.relu((real_scores - generated_scores - mean) / std + mean - self.beta) - - if False: print(f"relevance({disc_id}): {relevance.min()=} {relevance.max()=} {relevance.mean()=}") - - return relevance \ No newline at end of file diff --git a/android/app/src/main/cpp/third_party/opus/dnn/torch/dnntools/dnntools/relegance/relegance.py b/android/app/src/main/cpp/third_party/opus/dnn/torch/dnntools/dnntools/relegance/relegance.py deleted file mode 100644 index 29c5be2..0000000 --- a/android/app/src/main/cpp/third_party/opus/dnn/torch/dnntools/dnntools/relegance/relegance.py +++ /dev/null @@ -1,449 +0,0 @@ -""" -/* Copyright (c) 2023 Amazon - Written by Jan Buethe */ -/* - Redistribution and use in source and binary forms, with or without - modification, are permitted provided that the following conditions - are met: - - - Redistributions of source code must retain the above copyright - notice, this list of conditions and the following disclaimer. - - - Redistributions in binary form must reproduce the above copyright - notice, this list of conditions and the following disclaimer in the - documentation and/or other materials provided with the distribution. - - THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS - ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT - LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR - A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER - OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, - EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, - PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR - PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF - LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING - NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS - SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. -*/ -""" - -import torch -import torch.nn.functional as F - - -def view_one_hot(index, length): - vec = length * [1] - vec[index] = -1 - return vec - -def create_smoothing_kernel(widths, gamma=1.5): - """ creates a truncated gaussian smoothing kernel for the given widths - - Parameters: - ----------- - widths: list[Int] or torch.LongTensor - specifies the shape of the smoothing kernel, entries must be > 0. - - gamma: float, optional - decay factor for gaussian relative to kernel size - - Returns: - -------- - kernel: torch.FloatTensor - """ - - widths = torch.LongTensor(widths) - num_dims = len(widths) - - assert(widths.min() > 0) - - centers = widths.float() / 2 - 0.5 - sigmas = gamma * (centers + 1) - - vals = [] - - vals= [((torch.arange(widths[i]) - centers[i]) / sigmas[i]) ** 2 for i in range(num_dims)] - vals = sum([vals[i].view(view_one_hot(i, num_dims)) for i in range(num_dims)]) - - kernel = torch.exp(- vals) - kernel = kernel / kernel.sum() - - return kernel - - -def create_partition_kernel(widths, strides): - """ creates a partition kernel for mapping a convolutional network output back to the input domain - - Given a fully convolutional network with receptive field of shape widths and the given strides, this - function construncts an intorpolation kernel whose tranlations by multiples of the given strides form - a partition of one on the input domain. - - Parameter: - ---------- - widths: list[Int] or torch.LongTensor - shape of receptive field - - strides: list[Int] or torch.LongTensor - total strides of convolutional network - - Returns: - kernel: torch.FloatTensor - """ - - num_dims = len(widths) - assert num_dims == len(strides) and num_dims in {1, 2, 3} - - convs = {1 : F.conv1d, 2 : F.conv2d, 3 : F.conv3d} - - widths = torch.LongTensor(widths) - strides = torch.LongTensor(strides) - - proto_kernel = torch.ones(torch.minimum(strides, widths).tolist()) - - # create interpolation kernel eta - eta_widths = widths - strides + 1 - if eta_widths.min() <= 0: - print("[create_partition_kernel] warning: receptive field does not cover input domain") - eta_widths = torch.maximum(eta_widths, torch.ones_like(eta_widths)) - - - eta = create_smoothing_kernel(eta_widths).view(1, 1, *eta_widths.tolist()) - - padding = torch.repeat_interleave(eta_widths - 1, 2, 0).tolist()[::-1] # ordering of dimensions for padding and convolution functions is reversed in torch - padded_proto_kernel = F.pad(proto_kernel, padding) - padded_proto_kernel = padded_proto_kernel.view(1, 1, *padded_proto_kernel.shape) - kernel = convs[num_dims](padded_proto_kernel, eta) - - return kernel - - -def receptive_field(conv_model, input_shape, output_position): - """ estimates boundaries of receptive field connected to output_position via autograd - - Parameters: - ----------- - conv_model: nn.Module or autograd function - function or model implementing fully convolutional model - - input_shape: List[Int] - input shape ignoring batch dimension, i.e. [num_channels, dim1, dim2, ...] - - output_position: List[Int] - output position for which the receptive field is determined; the function raises an exception - if output_position is out of bounds for the given input_shape. - - Returns: - -------- - low: List[Int] - start indices of receptive field - - high: List[Int] - stop indices of receptive field - - """ - - x = torch.randn((1,) + tuple(input_shape), requires_grad=True) - y = conv_model(x) - - # collapse channels and remove batch dimension - y = torch.sum(y, 1)[0] - - # create mask - mask = torch.zeros_like(y) - index = [torch.tensor(i) for i in output_position] - try: - mask.index_put_(index, torch.tensor(1, dtype=mask.dtype)) - except IndexError: - raise ValueError('output_position out of bounds') - - (mask * y).sum().backward() - - # sum over channels and remove batch dimension - grad = torch.sum(x.grad, dim=1)[0] - tmp = torch.nonzero(grad, as_tuple=True) - low = [t.min().item() for t in tmp] - high = [t.max().item() for t in tmp] - - return low, high - -def estimate_conv_parameters(model, num_channels, num_dims, width, max_stride=10): - """ attempts to estimate receptive field size, strides and left paddings for given model - - - Parameters: - ----------- - model: nn.Module or autograd function - fully convolutional model for which parameters are estimated - - num_channels: Int - number of input channels for model - - num_dims: Int - number of input dimensions for model (without channel dimension) - - width: Int - width of the input tensor (a hyper-square) on which the receptive fields are derived via autograd - - max_stride: Int, optional - assumed maximal stride of the model for any dimension, when set too low the function may fail for - any value of width - - Returns: - -------- - receptive_field_size: List[Int] - receptive field size in all dimension - - strides: List[Int] - stride in all dimensions - - left_paddings: List[Int] - left padding in all dimensions; this is relevant for aligning the receptive field on the input plane - - Raises: - ------- - ValueError, KeyError - - """ - - input_shape = [num_channels] + num_dims * [width] - output_position1 = num_dims * [width // (2 * max_stride)] - output_position2 = num_dims * [width // (2 * max_stride) + 1] - - low1, high1 = receptive_field(model, input_shape, output_position1) - low2, high2 = receptive_field(model, input_shape, output_position2) - - widths1 = [h - l + 1 for l, h in zip(low1, high1)] - widths2 = [h - l + 1 for l, h in zip(low2, high2)] - - if not all([w1 - w2 == 0 for w1, w2 in zip(widths1, widths2)]) or not all([l1 != l2 for l1, l2 in zip(low1, low2)]): - raise ValueError("[estimate_strides]: widths to small to determine strides") - - receptive_field_size = widths1 - strides = [l2 - l1 for l1, l2 in zip(low1, low2)] - left_paddings = [s * p - l for l, s, p in zip(low1, strides, output_position1)] - - return receptive_field_size, strides, left_paddings - -def inspect_conv_model(model, num_channels, num_dims, max_width=10000, width_hint=None, stride_hint=None, verbose=False): - """ determines size of receptive field, strides and padding probabilistically - - - Parameters: - ----------- - model: nn.Module or autograd function - fully convolutional model for which parameters are estimated - - num_channels: Int - number of input channels for model - - num_dims: Int - number of input dimensions for model (without channel dimension) - - max_width: Int - maximum width of the input tensor (a hyper-square) on which the receptive fields are derived via autograd - - verbose: bool, optional - if true, the function prints parameters for individual trials - - Returns: - -------- - receptive_field_size: List[Int] - receptive field size in all dimension - - strides: List[Int] - stride in all dimensions - - left_paddings: List[Int] - left padding in all dimensions; this is relevant for aligning the receptive field on the input plane - - Raises: - ------- - ValueError - - """ - - max_stride = max_width // 2 - stride = max_stride // 100 - width = max_width // 100 - - if width_hint is not None: width = 2 * width_hint - if stride_hint is not None: stride = stride_hint - - did_it = False - while width < max_width and stride < max_stride: - try: - if verbose: print(f"[inspect_conv_model] trying parameters {width=}, {stride=}") - receptive_field_size, strides, left_paddings = estimate_conv_parameters(model, num_channels, num_dims, width, stride) - did_it = True - except: - pass - - if did_it: break - - width *= 2 - if width >= max_width and stride < max_stride: - stride *= 2 - width = 2 * stride - - if not did_it: - raise ValueError(f'could not determine conv parameter with given max_width={max_width}') - - return receptive_field_size, strides, left_paddings - - -class GradWeight(torch.autograd.Function): - def __init__(self): - super().__init__() - - @staticmethod - def forward(ctx, x, weight): - ctx.save_for_backward(weight) - return x.clone() - - @staticmethod - def backward(ctx, grad_output): - weight, = ctx.saved_tensors - - grad_input = grad_output * weight - - return grad_input, None - - -# API - -def relegance_gradient_weighting(x, weight): - """ - - Args: - x (torch.tensor): input tensor - weight (torch.tensor or None): weight tensor for gradients of x; if None, no gradient weighting will be applied in backward pass - - Returns: - torch.tensor: the unmodified input tensor x - - Raises: - RuntimeError: if estimation of parameters fails due to exceeded compute budget - """ - if weight is None: - return x - else: - return GradWeight.apply(x, weight) - - - -def relegance_create_tconv_kernel(model, num_channels, num_dims, width_hint=None, stride_hint=None, verbose=False): - """ creates parameters for mapping back output domain relevance to input tomain - - Args: - model (nn.Module or autograd.Function): fully convolutional model - num_channels (int): number of input channels to model - num_dims (int): number of input dimensions of model (without channel and batch dimension) - width_hint(int or None): optional hint at maximal width of receptive field - stride_hint(int or None): optional hint at maximal stride - - Returns: - dict: contains kernel, kernel dimensions, strides and left paddings for transposed convolution - """ - - max_width = int(100000 / (10 ** num_dims)) - - did_it = False - try: - receptive_field_size, strides, left_paddings = inspect_conv_model(model, num_channels, num_dims, max_width=max_width, width_hint=width_hint, stride_hint=stride_hint, verbose=verbose) - did_it = True - except: - # try once again with larger max_width - max_width *= 10 - - # crash if exception is raised - try: - if not did_it: receptive_field_size, strides, left_paddings = inspect_conv_model(model, num_channels, num_dims, max_width=max_width, width_hint=width_hint, stride_hint=stride_hint, verbose=verbose) - except: - raise RuntimeError("could not determine parameters within given compute budget") - - partition_kernel = create_partition_kernel(receptive_field_size, strides) - partition_kernel = torch.repeat_interleave(partition_kernel, num_channels, 1) - - tconv_parameters = { - 'kernel': partition_kernel, - 'receptive_field_shape': receptive_field_size, - 'stride': strides, - 'left_padding': left_paddings, - 'num_dims': num_dims - } - - return tconv_parameters - - - -def relegance_map_relevance_to_input_domain(od_relevance, tconv_parameters): - """ maps output-domain relevance to input-domain relevance via transpose convolution - - Args: - od_relevance (torch.tensor): output-domain relevance - tconv_parameters (dict): parameter dict as created by relegance_create_tconv_kernel - - Returns: - torch.tensor: input-domain relevance. The tensor is left aligned, i.e. the all-zero index of the output corresponds to the all-zero index of the discriminator input. - Otherwise, the size of the output tensor does not need to match the size of the discriminator input. Use relegance_resize_relevance_to_input_size for a - convenient way to adjust the output to the correct size. - - Raises: - ValueError: if number of dimensions is not supported - """ - - kernel = tconv_parameters['kernel'].to(od_relevance.device) - rf_shape = tconv_parameters['receptive_field_shape'] - stride = tconv_parameters['stride'] - left_padding = tconv_parameters['left_padding'] - - num_dims = len(kernel.shape) - 2 - - # repeat boundary values - od_padding = [rf_shape[i//2] // stride[i//2] + 1 for i in range(2 * num_dims)] - padded_od_relevance = F.pad(od_relevance, od_padding[::-1], mode='replicate') - od_padding = od_padding[::2] - - # apply mapping and left trimming - if num_dims == 1: - id_relevance = F.conv_transpose1d(padded_od_relevance, kernel, stride=stride) - id_relevance = id_relevance[..., left_padding[0] + stride[0] * od_padding[0] :] - elif num_dims == 2: - id_relevance = F.conv_transpose2d(padded_od_relevance, kernel, stride=stride) - id_relevance = id_relevance[..., left_padding[0] + stride[0] * od_padding[0] :, left_padding[1] + stride[1] * od_padding[1]:] - elif num_dims == 3: - id_relevance = F.conv_transpose2d(padded_od_relevance, kernel, stride=stride) - id_relevance = id_relevance[..., left_padding[0] + stride[0] * od_padding[0] :, left_padding[1] + stride[1] * od_padding[1]:, left_padding[2] + stride[2] * od_padding[2] :] - else: - raise ValueError(f'[relegance_map_to_input_domain] error: num_dims = {num_dims} not supported') - - return id_relevance - - -def relegance_resize_relevance_to_input_size(reference_input, relevance): - """ adjusts size of relevance tensor to reference input size - - Args: - reference_input (torch.tensor): discriminator input tensor for reference - relevance (torch.tensor): input-domain relevance corresponding to input tensor reference_input - - Returns: - torch.tensor: resized relevance - - Raises: - ValueError: if number of dimensions is not supported - """ - resized_relevance = torch.zeros_like(reference_input) - - num_dims = len(reference_input.shape) - 2 - with torch.no_grad(): - if num_dims == 1: - resized_relevance[:] = relevance[..., : min(reference_input.size(-1), relevance.size(-1))] - elif num_dims == 2: - resized_relevance[:] = relevance[..., : min(reference_input.size(-2), relevance.size(-2)), : min(reference_input.size(-1), relevance.size(-1))] - elif num_dims == 3: - resized_relevance[:] = relevance[..., : min(reference_input.size(-3), relevance.size(-3)), : min(reference_input.size(-2), relevance.size(-2)), : min(reference_input.size(-1), relevance.size(-1))] - else: - raise ValueError(f'[relegance_map_to_input_domain] error: num_dims = {num_dims} not supported') - - return resized_relevance \ No newline at end of file diff --git a/android/app/src/main/cpp/third_party/opus/dnn/torch/dnntools/dnntools/sparsification/__init__.py b/android/app/src/main/cpp/third_party/opus/dnn/torch/dnntools/dnntools/sparsification/__init__.py deleted file mode 100644 index fcc9174..0000000 --- a/android/app/src/main/cpp/third_party/opus/dnn/torch/dnntools/dnntools/sparsification/__init__.py +++ /dev/null @@ -1,6 +0,0 @@ -from .gru_sparsifier import GRUSparsifier -from .conv1d_sparsifier import Conv1dSparsifier -from .conv_transpose1d_sparsifier import ConvTranspose1dSparsifier -from .linear_sparsifier import LinearSparsifier -from .common import sparsify_matrix, calculate_gru_flops_per_step -from .utils import mark_for_sparsification, create_sparsifier \ No newline at end of file diff --git a/android/app/src/main/cpp/third_party/opus/dnn/torch/dnntools/dnntools/sparsification/base_sparsifier.py b/android/app/src/main/cpp/third_party/opus/dnn/torch/dnntools/dnntools/sparsification/base_sparsifier.py deleted file mode 100644 index dd62f40..0000000 --- a/android/app/src/main/cpp/third_party/opus/dnn/torch/dnntools/dnntools/sparsification/base_sparsifier.py +++ /dev/null @@ -1,58 +0,0 @@ -""" -/* Copyright (c) 2023 Amazon - Written by Jan Buethe */ -/* - Redistribution and use in source and binary forms, with or without - modification, are permitted provided that the following conditions - are met: - - - Redistributions of source code must retain the above copyright - notice, this list of conditions and the following disclaimer. - - - Redistributions in binary form must reproduce the above copyright - notice, this list of conditions and the following disclaimer in the - documentation and/or other materials provided with the distribution. - - THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS - ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT - LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR - A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER - OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, - EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, - PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR - PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF - LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING - NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS - SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. -*/ -""" - -class BaseSparsifier: - def __init__(self, task_list, start, stop, interval, exponent=3): - - # just copying parameters... - self.start = start - self.stop = stop - self.interval = interval - self.exponent = exponent - self.task_list = task_list - - # ... and setting counter to 0 - self.step_counter = 0 - - def step(self, verbose=False): - # compute current interpolation factor - self.step_counter += 1 - - if self.step_counter < self.start: - return - elif self.step_counter < self.stop: - # update only every self.interval-th interval - if self.step_counter % self.interval: - return - - alpha = ((self.stop - self.step_counter) / (self.stop - self.start)) ** self.exponent - else: - alpha = 0 - - self.sparsify(alpha, verbose=verbose) \ No newline at end of file diff --git a/android/app/src/main/cpp/third_party/opus/dnn/torch/dnntools/dnntools/sparsification/common.py b/android/app/src/main/cpp/third_party/opus/dnn/torch/dnntools/dnntools/sparsification/common.py deleted file mode 100644 index 4718180..0000000 --- a/android/app/src/main/cpp/third_party/opus/dnn/torch/dnntools/dnntools/sparsification/common.py +++ /dev/null @@ -1,123 +0,0 @@ -""" -/* Copyright (c) 2023 Amazon - Written by Jan Buethe */ -/* - Redistribution and use in source and binary forms, with or without - modification, are permitted provided that the following conditions - are met: - - - Redistributions of source code must retain the above copyright - notice, this list of conditions and the following disclaimer. - - - Redistributions in binary form must reproduce the above copyright - notice, this list of conditions and the following disclaimer in the - documentation and/or other materials provided with the distribution. - - THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS - ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT - LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR - A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER - OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, - EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, - PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR - PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF - LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING - NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS - SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. -*/ -""" - -import torch - -debug=True - -def sparsify_matrix(matrix : torch.tensor, density : float, block_size, keep_diagonal : bool=False, return_mask : bool=False): - """ sparsifies matrix with specified block size - - Parameters: - ----------- - matrix : torch.tensor - matrix to sparsify - density : int - target density - block_size : [int, int] - block size dimensions - keep_diagonal : bool - If true, the diagonal will be kept. This option requires block_size[0] == block_size[1] and defaults to False - """ - - m, n = matrix.shape - m1, n1 = block_size - - if m % m1 or n % n1: - raise ValueError(f"block size {(m1, n1)} does not divide matrix size {(m, n)}") - - # extract diagonal if keep_diagonal = True - if keep_diagonal: - if m != n: - raise ValueError("Attempting to sparsify non-square matrix with keep_diagonal=True") - - to_spare = torch.diag(torch.diag(matrix)) - matrix = matrix - to_spare - else: - to_spare = torch.zeros_like(matrix) - - # calculate energy in sub-blocks - x = torch.reshape(matrix, (m // m1, m1, n // n1, n1)) - x = x ** 2 - block_energies = torch.sum(torch.sum(x, dim=3), dim=1) - - number_of_blocks = (m * n) // (m1 * n1) - number_of_survivors = round(number_of_blocks * density) - - # masking threshold - if number_of_survivors == 0: - threshold = 0 - else: - threshold = torch.sort(torch.flatten(block_energies)).values[-number_of_survivors] - - # create mask - mask = torch.ones_like(block_energies) - mask[block_energies < threshold] = 0 - mask = torch.repeat_interleave(mask, m1, dim=0) - mask = torch.repeat_interleave(mask, n1, dim=1) - - # perform masking - masked_matrix = mask * matrix + to_spare - - if return_mask: - return masked_matrix, mask - else: - return masked_matrix - -def calculate_gru_flops_per_step(gru, sparsification_dict=dict(), drop_input=False): - input_size = gru.input_size - hidden_size = gru.hidden_size - flops = 0 - - input_density = ( - sparsification_dict.get('W_ir', [1])[0] - + sparsification_dict.get('W_in', [1])[0] - + sparsification_dict.get('W_iz', [1])[0] - ) / 3 - - recurrent_density = ( - sparsification_dict.get('W_hr', [1])[0] - + sparsification_dict.get('W_hn', [1])[0] - + sparsification_dict.get('W_hz', [1])[0] - ) / 3 - - # input matrix vector multiplications - if not drop_input: - flops += 2 * 3 * input_size * hidden_size * input_density - - # recurrent matrix vector multiplications - flops += 2 * 3 * hidden_size * hidden_size * recurrent_density - - # biases - flops += 6 * hidden_size - - # activations estimated by 10 flops per activation - flops += 30 * hidden_size - - return flops diff --git a/android/app/src/main/cpp/third_party/opus/dnn/torch/dnntools/dnntools/sparsification/conv1d_sparsifier.py b/android/app/src/main/cpp/third_party/opus/dnn/torch/dnntools/dnntools/sparsification/conv1d_sparsifier.py deleted file mode 100644 index 1ac51d0..0000000 --- a/android/app/src/main/cpp/third_party/opus/dnn/torch/dnntools/dnntools/sparsification/conv1d_sparsifier.py +++ /dev/null @@ -1,133 +0,0 @@ -""" -/* Copyright (c) 2023 Amazon - Written by Jan Buethe */ -/* - Redistribution and use in source and binary forms, with or without - modification, are permitted provided that the following conditions - are met: - - - Redistributions of source code must retain the above copyright - notice, this list of conditions and the following disclaimer. - - - Redistributions in binary form must reproduce the above copyright - notice, this list of conditions and the following disclaimer in the - documentation and/or other materials provided with the distribution. - - THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS - ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT - LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR - A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER - OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, - EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, - PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR - PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF - LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING - NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS - SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. -*/ -""" - -import torch - -from .base_sparsifier import BaseSparsifier -from .common import sparsify_matrix, debug - - -class Conv1dSparsifier(BaseSparsifier): - def __init__(self, task_list, start, stop, interval, exponent=3): - """ Sparsifier for torch.nn.GRUs - - Parameters: - ----------- - task_list : list - task_list contains a list of tuples (conv1d, params), where conv1d is an instance - of torch.nn.Conv1d and params is a tuple (density, [m, n]), - where density is the target density in [0, 1], [m, n] is the shape sub-blocks to which - sparsification is applied. - - start : int - training step after which sparsification will be started. - - stop : int - training step after which sparsification will be completed. - - interval : int - sparsification interval for steps between start and stop. After stop sparsification will be - carried out after every call to GRUSparsifier.step() - - exponent : float - Interpolation exponent for sparsification interval. In step i sparsification will be carried out - with density (alpha + target_density * (1 * alpha)), where - alpha = ((stop - i) / (start - stop)) ** exponent - - Example: - -------- - >>> import torch - >>> conv = torch.nn.Conv1d(8, 16, 8) - >>> params = (0.2, [8, 4]) - >>> sparsifier = Conv1dSparsifier([(conv, params)], 0, 100, 50) - >>> for i in range(100): - ... sparsifier.step() - """ - super().__init__(task_list, start, stop, interval, exponent=3) - - self.last_mask = None - - - def sparsify(self, alpha, verbose=False): - """ carries out sparsification step - - Call this function after optimizer.step in your - training loop. - - Parameters: - ---------- - alpha : float - density interpolation parameter (1: dense, 0: target density) - verbose : bool - if true, densities are printed out - - Returns: - -------- - None - - """ - - with torch.no_grad(): - for conv, params in self.task_list: - # reshape weight - if hasattr(conv, 'weight_v'): - weight = conv.weight_v - else: - weight = conv.weight - i, o, k = weight.shape - w = weight.permute(0, 2, 1).flatten(1) - target_density, block_size = params - density = alpha + (1 - alpha) * target_density - w, new_mask = sparsify_matrix(w, density, block_size, return_mask=True) - w = w.reshape(i, k, o).permute(0, 2, 1) - weight[:] = w - - if self.last_mask is not None: - if not torch.all(self.last_mask * new_mask == new_mask) and debug: - print("weight resurrection in conv.weight") - - self.last_mask = new_mask - - if verbose: - print(f"conv1d_sparsier[{self.step_counter}]: {density=}") - - -if __name__ == "__main__": - print("Testing sparsifier") - - import torch - conv = torch.nn.Conv1d(8, 16, 8) - params = (0.2, [8, 4]) - - sparsifier = Conv1dSparsifier([(conv, params)], 0, 100, 5) - - for i in range(100): - sparsifier.step(verbose=True) - - print(conv.weight) diff --git a/android/app/src/main/cpp/third_party/opus/dnn/torch/dnntools/dnntools/sparsification/conv_transpose1d_sparsifier.py b/android/app/src/main/cpp/third_party/opus/dnn/torch/dnntools/dnntools/sparsification/conv_transpose1d_sparsifier.py deleted file mode 100644 index 6d9398f..0000000 --- a/android/app/src/main/cpp/third_party/opus/dnn/torch/dnntools/dnntools/sparsification/conv_transpose1d_sparsifier.py +++ /dev/null @@ -1,134 +0,0 @@ -""" -/* Copyright (c) 2023 Amazon - Written by Jan Buethe */ -/* - Redistribution and use in source and binary forms, with or without - modification, are permitted provided that the following conditions - are met: - - - Redistributions of source code must retain the above copyright - notice, this list of conditions and the following disclaimer. - - - Redistributions in binary form must reproduce the above copyright - notice, this list of conditions and the following disclaimer in the - documentation and/or other materials provided with the distribution. - - THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS - ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT - LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR - A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER - OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, - EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, - PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR - PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF - LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING - NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS - SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. -*/ -""" - -import torch - - -from .base_sparsifier import BaseSparsifier -from .common import sparsify_matrix, debug - - -class ConvTranspose1dSparsifier(BaseSparsifier): - def __init__(self, task_list, start, stop, interval, exponent=3): - """ Sparsifier for torch.nn.GRUs - - Parameters: - ----------- - task_list : list - task_list contains a list of tuples (conv1d, params), where conv1d is an instance - of torch.nn.Conv1d and params is a tuple (density, [m, n]), - where density is the target density in [0, 1], [m, n] is the shape sub-blocks to which - sparsification is applied. - - start : int - training step after which sparsification will be started. - - stop : int - training step after which sparsification will be completed. - - interval : int - sparsification interval for steps between start and stop. After stop sparsification will be - carried out after every call to GRUSparsifier.step() - - exponent : float - Interpolation exponent for sparsification interval. In step i sparsification will be carried out - with density (alpha + target_density * (1 * alpha)), where - alpha = ((stop - i) / (start - stop)) ** exponent - - Example: - -------- - >>> import torch - >>> conv = torch.nn.ConvTranspose1d(8, 16, 8) - >>> params = (0.2, [8, 4]) - >>> sparsifier = ConvTranspose1dSparsifier([(conv, params)], 0, 100, 50) - >>> for i in range(100): - ... sparsifier.step() - """ - - super().__init__(task_list, start, stop, interval, exponent=3) - - self.last_mask = None - - def sparsify(self, alpha, verbose=False): - """ carries out sparsification step - - Call this function after optimizer.step in your - training loop. - - Parameters: - ---------- - alpha : float - density interpolation parameter (1: dense, 0: target density) - verbose : bool - if true, densities are printed out - - Returns: - -------- - None - - """ - - with torch.no_grad(): - for conv, params in self.task_list: - # reshape weight - if hasattr(conv, 'weight_v'): - weight = conv.weight_v - else: - weight = conv.weight - i, o, k = weight.shape - w = weight.permute(2, 1, 0).reshape(k * o, i) - target_density, block_size = params - density = alpha + (1 - alpha) * target_density - w, new_mask = sparsify_matrix(w, density, block_size, return_mask=True) - w = w.reshape(k, o, i).permute(2, 1, 0) - weight[:] = w - - if self.last_mask is not None: - if not torch.all(self.last_mask * new_mask == new_mask) and debug: - print("weight resurrection in conv.weight") - - self.last_mask = new_mask - - if verbose: - print(f"convtrans1d_sparsier[{self.step_counter}]: {density=}") - - -if __name__ == "__main__": - print("Testing sparsifier") - - import torch - conv = torch.nn.ConvTranspose1d(8, 16, 4, 4) - params = (0.2, [8, 4]) - - sparsifier = ConvTranspose1dSparsifier([(conv, params)], 0, 100, 5) - - for i in range(100): - sparsifier.step(verbose=True) - - print(conv.weight) diff --git a/android/app/src/main/cpp/third_party/opus/dnn/torch/dnntools/dnntools/sparsification/gru_sparsifier.py b/android/app/src/main/cpp/third_party/opus/dnn/torch/dnntools/dnntools/sparsification/gru_sparsifier.py deleted file mode 100644 index 417b04b..0000000 --- a/android/app/src/main/cpp/third_party/opus/dnn/torch/dnntools/dnntools/sparsification/gru_sparsifier.py +++ /dev/null @@ -1,178 +0,0 @@ -""" -/* Copyright (c) 2023 Amazon - Written by Jan Buethe */ -/* - Redistribution and use in source and binary forms, with or without - modification, are permitted provided that the following conditions - are met: - - - Redistributions of source code must retain the above copyright - notice, this list of conditions and the following disclaimer. - - - Redistributions in binary form must reproduce the above copyright - notice, this list of conditions and the following disclaimer in the - documentation and/or other materials provided with the distribution. - - THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS - ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT - LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR - A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER - OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, - EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, - PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR - PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF - LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING - NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS - SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. -*/ -""" - -import torch - -from .base_sparsifier import BaseSparsifier -from .common import sparsify_matrix, debug - - -class GRUSparsifier(BaseSparsifier): - def __init__(self, task_list, start, stop, interval, exponent=3): - """ Sparsifier for torch.nn.GRUs - - Parameters: - ----------- - task_list : list - task_list contains a list of tuples (gru, sparsify_dict), where gru is an instance - of torch.nn.GRU and sparsify_dic is a dictionary with keys in {'W_ir', 'W_iz', 'W_in', - 'W_hr', 'W_hz', 'W_hn'} corresponding to the input and recurrent weights for the reset, - update, and new gate. The values of sparsify_dict are tuples (density, [m, n], keep_diagonal), - where density is the target density in [0, 1], [m, n] is the shape sub-blocks to which - sparsification is applied and keep_diagonal is a bool variable indicating whether the diagonal - should be kept. - - start : int - training step after which sparsification will be started. - - stop : int - training step after which sparsification will be completed. - - interval : int - sparsification interval for steps between start and stop. After stop sparsification will be - carried out after every call to GRUSparsifier.step() - - exponent : float - Interpolation exponent for sparsification interval. In step i sparsification will be carried out - with density (alpha + target_density * (1 * alpha)), where - alpha = ((stop - i) / (start - stop)) ** exponent - - Example: - -------- - >>> import torch - >>> gru = torch.nn.GRU(10, 20) - >>> sparsify_dict = { - ... 'W_ir' : (0.5, [2, 2], False), - ... 'W_iz' : (0.6, [2, 2], False), - ... 'W_in' : (0.7, [2, 2], False), - ... 'W_hr' : (0.1, [4, 4], True), - ... 'W_hz' : (0.2, [4, 4], True), - ... 'W_hn' : (0.3, [4, 4], True), - ... } - >>> sparsifier = GRUSparsifier([(gru, sparsify_dict)], 0, 100, 50) - >>> for i in range(100): - ... sparsifier.step() - """ - super().__init__(task_list, start, stop, interval, exponent=3) - - self.last_masks = {key : None for key in ['W_ir', 'W_in', 'W_iz', 'W_hr', 'W_hn', 'W_hz']} - - def sparsify(self, alpha, verbose=False): - """ carries out sparsification step - - Call this function after optimizer.step in your - training loop. - - Parameters: - ---------- - alpha : float - density interpolation parameter (1: dense, 0: target density) - verbose : bool - if true, densities are printed out - - Returns: - -------- - None - - """ - - with torch.no_grad(): - for gru, params in self.task_list: - hidden_size = gru.hidden_size - - # input weights - for i, key in enumerate(['W_ir', 'W_iz', 'W_in']): - if key in params: - if hasattr(gru, 'weight_ih_l0_v'): - weight = gru.weight_ih_l0_v - else: - weight = gru.weight_ih_l0 - density = alpha + (1 - alpha) * params[key][0] - if verbose: - print(f"[{self.step_counter}]: {key} density: {density}") - - weight[i * hidden_size : (i+1) * hidden_size, : ], new_mask = sparsify_matrix( - weight[i * hidden_size : (i + 1) * hidden_size, : ], - density, # density - params[key][1], # block_size - params[key][2], # keep_diagonal (might want to set this to False) - return_mask=True - ) - - if type(self.last_masks[key]) != type(None): - if not torch.all(self.last_masks[key] * new_mask == new_mask) and debug: - print("weight resurrection in weight_ih_l0_v") - - self.last_masks[key] = new_mask - - # recurrent weights - for i, key in enumerate(['W_hr', 'W_hz', 'W_hn']): - if key in params: - if hasattr(gru, 'weight_hh_l0_v'): - weight = gru.weight_hh_l0_v - else: - weight = gru.weight_hh_l0 - density = alpha + (1 - alpha) * params[key][0] - if verbose: - print(f"[{self.step_counter}]: {key} density: {density}") - weight[i * hidden_size : (i+1) * hidden_size, : ], new_mask = sparsify_matrix( - weight[i * hidden_size : (i + 1) * hidden_size, : ], - density, - params[key][1], # block_size - params[key][2], # keep_diagonal (might want to set this to False) - return_mask=True - ) - - if type(self.last_masks[key]) != type(None): - if not torch.all(self.last_masks[key] * new_mask == new_mask) and True: - print("weight resurrection in weight_hh_l0_v") - - self.last_masks[key] = new_mask - - - -if __name__ == "__main__": - print("Testing sparsifier") - - gru = torch.nn.GRU(10, 20) - sparsify_dict = { - 'W_ir' : (0.5, [2, 2], False), - 'W_iz' : (0.6, [2, 2], False), - 'W_in' : (0.7, [2, 2], False), - 'W_hr' : (0.1, [4, 4], True), - 'W_hz' : (0.2, [4, 4], True), - 'W_hn' : (0.3, [4, 4], True), - } - - sparsifier = GRUSparsifier([(gru, sparsify_dict)], 0, 100, 10) - - for i in range(100): - sparsifier.step(verbose=True) - - print(gru.weight_hh_l0) diff --git a/android/app/src/main/cpp/third_party/opus/dnn/torch/dnntools/dnntools/sparsification/linear_sparsifier.py b/android/app/src/main/cpp/third_party/opus/dnn/torch/dnntools/dnntools/sparsification/linear_sparsifier.py deleted file mode 100644 index 59251dd..0000000 --- a/android/app/src/main/cpp/third_party/opus/dnn/torch/dnntools/dnntools/sparsification/linear_sparsifier.py +++ /dev/null @@ -1,128 +0,0 @@ -""" -/* Copyright (c) 2023 Amazon - Written by Jan Buethe */ -/* - Redistribution and use in source and binary forms, with or without - modification, are permitted provided that the following conditions - are met: - - - Redistributions of source code must retain the above copyright - notice, this list of conditions and the following disclaimer. - - - Redistributions in binary form must reproduce the above copyright - notice, this list of conditions and the following disclaimer in the - documentation and/or other materials provided with the distribution. - - THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS - ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT - LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR - A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER - OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, - EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, - PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR - PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF - LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING - NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS - SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. -*/ -""" - -import torch - -from .base_sparsifier import BaseSparsifier -from .common import sparsify_matrix - - -class LinearSparsifier(BaseSparsifier): - def __init__(self, task_list, start, stop, interval, exponent=3): - """ Sparsifier for torch.nn.GRUs - - Parameters: - ----------- - task_list : list - task_list contains a list of tuples (linear, params), where linear is an instance - of torch.nn.Linear and params is a tuple (density, [m, n]), - where density is the target density in [0, 1], [m, n] is the shape sub-blocks to which - sparsification is applied. - - start : int - training step after which sparsification will be started. - - stop : int - training step after which sparsification will be completed. - - interval : int - sparsification interval for steps between start and stop. After stop sparsification will be - carried out after every call to GRUSparsifier.step() - - exponent : float - Interpolation exponent for sparsification interval. In step i sparsification will be carried out - with density (alpha + target_density * (1 * alpha)), where - alpha = ((stop - i) / (start - stop)) ** exponent - - Example: - -------- - >>> import torch - >>> linear = torch.nn.Linear(8, 16) - >>> params = (0.2, [8, 4]) - >>> sparsifier = LinearSparsifier([(linear, params)], 0, 100, 50) - >>> for i in range(100): - ... sparsifier.step() - """ - - super().__init__(task_list, start, stop, interval, exponent=3) - - self.last_mask = None - - def sparsify(self, alpha, verbose=False): - """ carries out sparsification step - - Call this function after optimizer.step in your - training loop. - - Parameters: - ---------- - alpha : float - density interpolation parameter (1: dense, 0: target density) - verbose : bool - if true, densities are printed out - - Returns: - -------- - None - - """ - - with torch.no_grad(): - for linear, params in self.task_list: - if hasattr(linear, 'weight_v'): - weight = linear.weight_v - else: - weight = linear.weight - target_density, block_size = params - density = alpha + (1 - alpha) * target_density - weight[:], new_mask = sparsify_matrix(weight, density, block_size, return_mask=True) - - if self.last_mask is not None: - if not torch.all(self.last_mask * new_mask == new_mask) and debug: - print("weight resurrection in conv.weight") - - self.last_mask = new_mask - - if verbose: - print(f"linear_sparsifier[{self.step_counter}]: {density=}") - - -if __name__ == "__main__": - print("Testing sparsifier") - - import torch - linear = torch.nn.Linear(8, 16) - params = (0.2, [4, 2]) - - sparsifier = LinearSparsifier([(linear, params)], 0, 100, 5) - - for i in range(100): - sparsifier.step(verbose=True) - - print(linear.weight) diff --git a/android/app/src/main/cpp/third_party/opus/dnn/torch/dnntools/dnntools/sparsification/utils.py b/android/app/src/main/cpp/third_party/opus/dnn/torch/dnntools/dnntools/sparsification/utils.py deleted file mode 100644 index 42f2235..0000000 --- a/android/app/src/main/cpp/third_party/opus/dnn/torch/dnntools/dnntools/sparsification/utils.py +++ /dev/null @@ -1,64 +0,0 @@ -import torch - -from dnntools.sparsification import GRUSparsifier, LinearSparsifier, Conv1dSparsifier, ConvTranspose1dSparsifier - -def mark_for_sparsification(module, params): - setattr(module, 'sparsify', True) - setattr(module, 'sparsification_params', params) - return module - -def create_sparsifier(module, start, stop, interval): - sparsifier_list = [] - for m in module.modules(): - if hasattr(m, 'sparsify'): - if isinstance(m, torch.nn.GRU): - sparsifier_list.append( - GRUSparsifier([(m, m.sparsification_params)], start, stop, interval) - ) - elif isinstance(m, torch.nn.Linear): - sparsifier_list.append( - LinearSparsifier([(m, m.sparsification_params)], start, stop, interval) - ) - elif isinstance(m, torch.nn.Conv1d): - sparsifier_list.append( - Conv1dSparsifier([(m, m.sparsification_params)], start, stop, interval) - ) - elif isinstance(m, torch.nn.ConvTranspose1d): - sparsifier_list.append( - ConvTranspose1dSparsifier([(m, m.sparsification_params)], start, stop, interval) - ) - else: - print(f"[create_sparsifier] warning: module {m} marked for sparsification but no suitable sparsifier exists.") - - def sparsify(verbose=False): - for sparsifier in sparsifier_list: - sparsifier.step(verbose) - - return sparsify - - -def count_parameters(model, verbose=False): - total = 0 - for name, p in model.named_parameters(): - count = torch.ones_like(p).sum().item() - - if verbose: - print(f"{name}: {count} parameters") - - total += count - - return total - -def estimate_nonzero_parameters(module): - num_zero_parameters = 0 - if hasattr(module, 'sparsify'): - params = module.sparsification_params - if isinstance(module, torch.nn.Conv1d) or isinstance(module, torch.nn.ConvTranspose1d): - num_zero_parameters = torch.ones_like(module.weight).sum().item() * (1 - params[0]) - elif isinstance(module, torch.nn.GRU): - num_zero_parameters = module.input_size * module.hidden_size * (3 - params['W_ir'][0] - params['W_iz'][0] - params['W_in'][0]) - num_zero_parameters += module.hidden_size * module.hidden_size * (3 - params['W_hr'][0] - params['W_hz'][0] - params['W_hn'][0]) - elif isinstance(module, torch.nn.Linear): - num_zero_parameters = module.in_features * module.out_features * params[0] - else: - raise ValueError(f'unknown sparsification method for module of type {type(module)}') diff --git a/android/app/src/main/cpp/third_party/opus/dnn/torch/dnntools/requirements.txt b/android/app/src/main/cpp/third_party/opus/dnn/torch/dnntools/requirements.txt deleted file mode 100644 index 08ed5ee..0000000 --- a/android/app/src/main/cpp/third_party/opus/dnn/torch/dnntools/requirements.txt +++ /dev/null @@ -1 +0,0 @@ -torch \ No newline at end of file diff --git a/android/app/src/main/cpp/third_party/opus/dnn/torch/dnntools/setup.py b/android/app/src/main/cpp/third_party/opus/dnn/torch/dnntools/setup.py deleted file mode 100644 index bc4ef3f..0000000 --- a/android/app/src/main/cpp/third_party/opus/dnn/torch/dnntools/setup.py +++ /dev/null @@ -1,48 +0,0 @@ -""" -/* Copyright (c) 2023 Amazon - Written by Jan Buethe */ -/* - Redistribution and use in source and binary forms, with or without - modification, are permitted provided that the following conditions - are met: - - - Redistributions of source code must retain the above copyright - notice, this list of conditions and the following disclaimer. - - - Redistributions in binary form must reproduce the above copyright - notice, this list of conditions and the following disclaimer in the - documentation and/or other materials provided with the distribution. - - THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS - ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT - LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR - A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER - OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, - EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, - PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR - PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF - LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING - NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS - SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. -*/ -""" - -#!/usr/bin/env/python -import os -from setuptools import setup - -lib_folder = os.path.dirname(os.path.realpath(__file__)) - -with open(os.path.join(lib_folder, 'requirements.txt'), 'r') as f: - install_requires = list(f.read().splitlines()) - -print(install_requires) - -setup(name='dnntools', - version='1.0', - author='Jan Buethe', - author_email='jbuethe@amazon.de', - description='Non-Standard tools for deep neural network training with PyTorch', - packages=['dnntools', 'dnntools.sparsification', 'dnntools.quantization'], - install_requires=install_requires - ) diff --git a/android/app/src/main/cpp/third_party/opus/dnn/torch/fargan/README.md b/android/app/src/main/cpp/third_party/opus/dnn/torch/fargan/README.md deleted file mode 100644 index b986e38..0000000 --- a/android/app/src/main/cpp/third_party/opus/dnn/torch/fargan/README.md +++ /dev/null @@ -1,54 +0,0 @@ -# Framewise Auto-Regressive GAN (FARGAN) - -Implementation of FARGAN, a low-complexity neural vocoder. Pre-trained models -are provided as C code in the dnn/ directory with the corresponding model in -dnn/models/ directory (name starts with fargan_). If you don't want to train -a new FARGAN model, you can skip straight to the Inference section. - -## Data preparation - -For data preparation you need to build Opus as detailed in the top-level README. -You will need to use the --enable-deep-plc configure option. -The build will produce an executable named "dump_data". -To prepare the training data, run: -``` -./dump_data -train in_speech.pcm out_features.f32 out_speech.pcm -``` -Where the in_speech.pcm speech file is a raw 16-bit PCM file sampled at 16 kHz. -The speech data used for training the model can be found at: -https://media.xiph.org/lpcnet/speech/tts_speech_negative_16k.sw - -## Training - -To perform pre-training, run the following command: -``` -python ./train_fargan.py out_features.f32 out_speech.pcm output_dir --epochs 400 --batch-size 4096 --lr 0.002 --cuda-visible-devices 0 -``` -Once pre-training is complete, run adversarial training using: -``` -python adv_train_fargan.py out_features.f32 out_speech.pcm output_dir --lr 0.000002 --reg-weight 5 --batch-size 160 --cuda-visible-devices 0 --initial-checkpoint output_dir/checkpoints/fargan_400.pth -``` -The final model will be in output_dir/checkpoints/fargan_adv_50.pth. - -The model can optionally be converted to C using: -``` -python dump_fargan_weights.py output_dir/checkpoints/fargan_adv_50.pth fargan_c_dir -``` -which will create a fargan_data.c and a fargan_data.h file in the fargan_c_dir directory. -Copy these files to the opus/dnn/ directory (replacing the existing ones) and recompile Opus. - -## Inference - -To run the inference, start by generating the features from the audio using: -``` -./fargan_demo -features test_speech.pcm test_features.f32 -``` -Synthesis can be achieved either using the PyTorch code or the C code. -To synthesize from PyTorch, run: -``` -python test_fargan.py output_dir/checkpoints/fargan_adv_50.pth test_features.f32 output_speech.pcm -``` -To synthesize from the C code, run: -``` -./fargan_demo -fargan-synthesis test_features.f32 output_speech.pcm -``` diff --git a/android/app/src/main/cpp/third_party/opus/dnn/torch/fargan/adv_train_fargan.py b/android/app/src/main/cpp/third_party/opus/dnn/torch/fargan/adv_train_fargan.py deleted file mode 100644 index c297764..0000000 --- a/android/app/src/main/cpp/third_party/opus/dnn/torch/fargan/adv_train_fargan.py +++ /dev/null @@ -1,277 +0,0 @@ -import os -import argparse -import random -import numpy as np -import sys -import math as m - -import torch -from torch import nn -import torch.nn.functional as F -import tqdm - -import fargan -from dataset import FARGANDataset -from stft_loss import * - -source_dir = os.path.split(os.path.abspath(__file__))[0] -sys.path.append(os.path.join(source_dir, "../osce/")) - -import models as osce_models - - -def fmap_loss(scores_real, scores_gen): - num_discs = len(scores_real) - loss_feat = 0 - for k in range(num_discs): - num_layers = len(scores_gen[k]) - 1 - f = 4 / num_discs / num_layers - for l in range(num_layers): - loss_feat += f * F.l1_loss(scores_gen[k][l], scores_real[k][l].detach()) - - return loss_feat - -parser = argparse.ArgumentParser() - -parser.add_argument('features', type=str, help='path to feature file in .f32 format') -parser.add_argument('signal', type=str, help='path to signal file in .s16 format') -parser.add_argument('output', type=str, help='path to output folder') - -parser.add_argument('--suffix', type=str, help="model name suffix", default="") -parser.add_argument('--cuda-visible-devices', type=str, help="comma separates list of cuda visible device indices, default: CUDA_VISIBLE_DEVICES", default=None) - - -model_group = parser.add_argument_group(title="model parameters") -model_group.add_argument('--cond-size', type=int, help="first conditioning size, default: 256", default=256) -model_group.add_argument('--gamma', type=float, help="Use A(z/gamma), default: 0.9", default=0.9) - -training_group = parser.add_argument_group(title="training parameters") -training_group.add_argument('--batch-size', type=int, help="batch size, default: 128", default=128) -training_group.add_argument('--lr', type=float, help='learning rate, default: 5e-4', default=5e-4) -training_group.add_argument('--epochs', type=int, help='number of training epochs, default: 50', default=50) -training_group.add_argument('--sequence-length', type=int, help='sequence length, default: 60', default=60) -training_group.add_argument('--lr-decay', type=float, help='learning rate decay factor, default: 0.0', default=0.0) -training_group.add_argument('--initial-checkpoint', type=str, help='initial checkpoint to start training from, default: None', default=None) -training_group.add_argument('--reg-weight', type=float, help='regression loss weight, default: 1.0', default=1.0) -training_group.add_argument('--fmap-weight', type=float, help='feature matchin loss weight, default: 1.0', default=1.) - -args = parser.parse_args() - -if args.cuda_visible_devices != None: - os.environ['CUDA_VISIBLE_DEVICES'] = args.cuda_visible_devices - -# checkpoints -checkpoint_dir = os.path.join(args.output, 'checkpoints') -checkpoint = dict() -os.makedirs(checkpoint_dir, exist_ok=True) - - -# training parameters -batch_size = args.batch_size -lr = args.lr -epochs = args.epochs -sequence_length = args.sequence_length -lr_decay = args.lr_decay - -adam_betas = [0.8, 0.99] -adam_eps = 1e-8 -features_file = args.features -signal_file = args.signal - -# model parameters -cond_size = args.cond_size - - -checkpoint['batch_size'] = batch_size -checkpoint['lr'] = lr -checkpoint['lr_decay'] = lr_decay -checkpoint['epochs'] = epochs -checkpoint['sequence_length'] = sequence_length -checkpoint['adam_betas'] = adam_betas - - -device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu") - -checkpoint['model_args'] = () -checkpoint['model_kwargs'] = {'cond_size': cond_size, 'gamma': args.gamma} -print(checkpoint['model_kwargs']) -model = fargan.FARGAN(*checkpoint['model_args'], **checkpoint['model_kwargs']) - - -#discriminator -disc_name = 'fdmresdisc' -disc = osce_models.model_dict[disc_name]( - architecture='free', - design='f_down', - fft_sizes_16k=[2**n for n in range(6, 12)], - freq_roi=[0, 7400], - max_channels=256, - noise_gain=0.0 -) - -if type(args.initial_checkpoint) != type(None): - checkpoint = torch.load(args.initial_checkpoint, map_location='cpu') - model.load_state_dict(checkpoint['state_dict'], strict=False) - -checkpoint['state_dict'] = model.state_dict() - - -dataset = FARGANDataset(features_file, signal_file, sequence_length=sequence_length) -dataloader = torch.utils.data.DataLoader(dataset, batch_size=batch_size, shuffle=True, drop_last=True, num_workers=4) - - -optimizer = torch.optim.AdamW(model.parameters(), lr=lr, betas=adam_betas, eps=adam_eps) -optimizer_disc = torch.optim.AdamW([p for p in disc.parameters() if p.requires_grad], lr=lr, betas=adam_betas, eps=adam_eps) - - -# learning rate scheduler -scheduler = torch.optim.lr_scheduler.LambdaLR(optimizer=optimizer, lr_lambda=lambda x : 1 / (1 + lr_decay * x)) -scheduler_disc = torch.optim.lr_scheduler.LambdaLR(optimizer=optimizer_disc, lr_lambda=lambda x : 1 / (1 + lr_decay * x)) - -states = None - -spect_loss = MultiResolutionSTFTLoss(device).to(device) - -for param in model.parameters(): - param.requires_grad = False - -batch_count = 0 -if __name__ == '__main__': - model.to(device) - disc.to(device) - - for epoch in range(1, epochs + 1): - - m_r = 0 - m_f = 0 - s_r = 1 - s_f = 1 - - running_cont_loss = 0 - running_disc_loss = 0 - running_gen_loss = 0 - running_fmap_loss = 0 - running_reg_loss = 0 - running_wc = 0 - - print(f"training epoch {epoch}...") - with tqdm.tqdm(dataloader, unit='batch') as tepoch: - for i, (features, periods, target, lpc) in enumerate(tepoch): - if epoch == 1 and i == 400: - for param in model.parameters(): - param.requires_grad = True - for param in model.cond_net.parameters(): - param.requires_grad = False - for param in model.sig_net.cond_gain_dense.parameters(): - param.requires_grad = False - - optimizer.zero_grad() - features = features.to(device) - #lpc = lpc.to(device) - #lpc = lpc*(args.gamma**torch.arange(1,17, device=device)) - #lpc = fargan.interp_lpc(lpc, 4) - periods = periods.to(device) - if True: - target = target[:, :sequence_length*160] - #lpc = lpc[:,:sequence_length*4,:] - features = features[:,:sequence_length+4,:] - periods = periods[:,:sequence_length+4] - else: - target=target[::2, :] - #lpc=lpc[::2,:] - features=features[::2,:] - periods=periods[::2,:] - target = target.to(device) - #target = fargan.analysis_filter(target, lpc[:,:,:], nb_subframes=1, gamma=args.gamma) - - #nb_pre = random.randrange(1, 6) - nb_pre = 2 - pre = target[:, :nb_pre*160] - output, _ = model(features, periods, target.size(1)//160 - nb_pre, pre=pre, states=None) - output = torch.cat([pre, output], -1) - - - # discriminator update - scores_gen = disc(output.detach().unsqueeze(1)) - scores_real = disc(target.unsqueeze(1)) - - disc_loss = 0 - for scale in scores_gen: - disc_loss += ((scale[-1]) ** 2).mean() - m_f = 0.9 * m_f + 0.1 * scale[-1].detach().mean().cpu().item() - s_f = 0.9 * s_f + 0.1 * scale[-1].detach().std().cpu().item() - - for scale in scores_real: - disc_loss += ((1 - scale[-1]) ** 2).mean() - m_r = 0.9 * m_r + 0.1 * scale[-1].detach().mean().cpu().item() - s_r = 0.9 * s_r + 0.1 * scale[-1].detach().std().cpu().item() - - disc_loss = 0.5 * disc_loss / len(scores_gen) - winning_chance = 0.5 * m.erfc( (m_r - m_f) / m.sqrt(2 * (s_f**2 + s_r**2)) ) - running_wc += winning_chance - - disc.zero_grad() - disc_loss.backward() - optimizer_disc.step() - - # model update - scores_gen = disc(output.unsqueeze(1)) - if False: # todo: check whether that makes a difference - with torch.no_grad(): - scores_real = disc(target.unsqueeze(1)) - - cont_loss = fargan.sig_loss(target[:, nb_pre*160:nb_pre*160+80], output[:, nb_pre*160:nb_pre*160+80]) - specc_loss = spect_loss(output, target.detach()) - reg_loss = (.00*cont_loss + specc_loss) - - loss_gen = 0 - for scale in scores_gen: - loss_gen += ((1 - scale[-1]) ** 2).mean() / len(scores_gen) - - feat_loss = args.fmap_weight * fmap_loss(scores_real, scores_gen) - - reg_weight = args.reg_weight# + 15./(1 + (batch_count/7600.)) - gen_loss = reg_weight * reg_loss + feat_loss + loss_gen - - model.zero_grad() - - - gen_loss.backward() - optimizer.step() - - #model.clip_weights() - - scheduler.step() - scheduler_disc.step() - - running_cont_loss += cont_loss.detach().cpu().item() - running_gen_loss += loss_gen.detach().cpu().item() - running_disc_loss += disc_loss.detach().cpu().item() - running_fmap_loss += feat_loss.detach().cpu().item() - running_reg_loss += reg_loss.detach().cpu().item() - - - - tepoch.set_postfix(cont_loss=f"{running_cont_loss/(i+1):8.5f}", - reg_weight=f"{reg_weight:8.5f}", - gen_loss=f"{running_gen_loss/(i+1):8.5f}", - disc_loss=f"{running_disc_loss/(i+1):8.5f}", - fmap_loss=f"{running_fmap_loss/(i+1):8.5f}", - reg_loss=f"{running_reg_loss/(i+1):8.5f}", - wc = f"{running_wc/(i+1):8.5f}", - ) - batch_count = batch_count + 1 - - # save checkpoint - checkpoint_path = os.path.join(checkpoint_dir, f'fargan{args.suffix}_adv_{epoch}.pth') - checkpoint['state_dict'] = model.state_dict() - checkpoint['disc_sate_dict'] = disc.state_dict() - checkpoint['loss'] = { - 'cont': running_cont_loss / len(dataloader), - 'gen': running_gen_loss / len(dataloader), - 'disc': running_disc_loss / len(dataloader), - 'fmap': running_fmap_loss / len(dataloader), - 'reg': running_reg_loss / len(dataloader) - } - checkpoint['epoch'] = epoch - torch.save(checkpoint, checkpoint_path) diff --git a/android/app/src/main/cpp/third_party/opus/dnn/torch/fargan/dataset.py b/android/app/src/main/cpp/third_party/opus/dnn/torch/fargan/dataset.py deleted file mode 100644 index 2dfbb0b..0000000 --- a/android/app/src/main/cpp/third_party/opus/dnn/torch/fargan/dataset.py +++ /dev/null @@ -1,61 +0,0 @@ -import torch -import numpy as np -import fargan - -class FARGANDataset(torch.utils.data.Dataset): - def __init__(self, - feature_file, - signal_file, - frame_size=160, - sequence_length=15, - lookahead=1, - nb_used_features=20, - nb_features=36): - - self.frame_size = frame_size - self.sequence_length = sequence_length - self.lookahead = lookahead - self.nb_features = nb_features - self.nb_used_features = nb_used_features - pcm_chunk_size = self.frame_size*self.sequence_length - - self.data = np.memmap(signal_file, dtype='int16', mode='r') - #self.data = self.data[1::2] - self.nb_sequences = len(self.data)//(pcm_chunk_size)-4 - self.data = self.data[(4-self.lookahead)*self.frame_size:] - self.data = self.data[:self.nb_sequences*pcm_chunk_size] - - - #self.data = np.reshape(self.data, (self.nb_sequences, pcm_chunk_size)) - sizeof = self.data.strides[-1] - self.data = np.lib.stride_tricks.as_strided(self.data, shape=(self.nb_sequences, pcm_chunk_size*2), - strides=(pcm_chunk_size*sizeof, sizeof)) - - self.features = np.reshape(np.memmap(feature_file, dtype='float32', mode='r'), (-1, nb_features)) - sizeof = self.features.strides[-1] - self.features = np.lib.stride_tricks.as_strided(self.features, shape=(self.nb_sequences, self.sequence_length*2+4, nb_features), - strides=(self.sequence_length*self.nb_features*sizeof, self.nb_features*sizeof, sizeof)) - #self.periods = np.round(50*self.features[:,:,self.nb_used_features-2]+100).astype('int') - self.periods = np.round(np.clip(256./2**(self.features[:,:,self.nb_used_features-2]+1.5), 32, 255)).astype('int') - - self.lpc = self.features[:, :, self.nb_used_features:] - self.features = self.features[:, :, :self.nb_used_features] - print("lpc_size:", self.lpc.shape) - - def __len__(self): - return self.nb_sequences - - def __getitem__(self, index): - features = self.features[index, :, :].copy() - if self.lookahead != 0: - lpc = self.lpc[index, 4-self.lookahead:-self.lookahead, :].copy() - else: - lpc = self.lpc[index, 4:, :].copy() - data = self.data[index, :].copy().astype(np.float32) / 2**15 - periods = self.periods[index, :].copy() - #lpc = lpc*(self.gamma**np.arange(1,17)) - #lpc=lpc[None,:,:] - #lpc = fargan.interp_lpc(lpc, 4) - #lpc=lpc[0,:,:] - - return features, periods, data, lpc diff --git a/android/app/src/main/cpp/third_party/opus/dnn/torch/fargan/dump_fargan_weights.py b/android/app/src/main/cpp/third_party/opus/dnn/torch/fargan/dump_fargan_weights.py deleted file mode 100644 index ec1eb9a..0000000 --- a/android/app/src/main/cpp/third_party/opus/dnn/torch/fargan/dump_fargan_weights.py +++ /dev/null @@ -1,112 +0,0 @@ -import os -import sys -import argparse - -import torch -from torch import nn - - -sys.path.append(os.path.join(os.path.split(__file__)[0], '../weight-exchange')) -import wexchange.torch - -import fargan -#from models import model_dict - -unquantized = [ 'cond_net.pembed', 'cond_net.fdense1', 'sig_net.cond_gain_dense', 'sig_net.gain_dense_out' ] - -unquantized2 = [ - 'cond_net.pembed', - 'cond_net.fdense1', - 'cond_net.fconv1', - 'cond_net.fconv2', - 'cont_net.0', - 'sig_net.cond_gain_dense', - 'sig_net.fwc0.conv', - 'sig_net.fwc0.glu.gate', - 'sig_net.dense1_glu.gate', - 'sig_net.gru1_glu.gate', - 'sig_net.gru2_glu.gate', - 'sig_net.gru3_glu.gate', - 'sig_net.skip_glu.gate', - 'sig_net.skip_dense', - 'sig_net.sig_dense_out', - 'sig_net.gain_dense_out' -] - -description=f""" -This is an unsafe dumping script for FARGAN models. It assumes that all weights are included in Linear, Conv1d or GRU layer -and will fail to export any other weights. - -Furthermore, the quanitze option relies on the following explicit list of layers to be excluded: -{unquantized}. - -Modify this script manually if adjustments are needed. -""" - -parser = argparse.ArgumentParser(description=description) -parser.add_argument('weightfile', type=str, help='weight file path') -parser.add_argument('export_folder', type=str) -parser.add_argument('--export-filename', type=str, default='fargan_data', help='filename for source and header file (.c and .h will be added), defaults to fargan_data') -parser.add_argument('--struct-name', type=str, default='FARGAN', help='name for C struct, defaults to FARGAN') -parser.add_argument('--quantize', action='store_true', help='apply quantization') - -if __name__ == "__main__": - args = parser.parse_args() - - print(f"loading weights from {args.weightfile}...") - saved_gen= torch.load(args.weightfile, map_location='cpu') - saved_gen['model_args'] = () - saved_gen['model_kwargs'] = {'cond_size': 256, 'gamma': 0.9} - - model = fargan.FARGAN(*saved_gen['model_args'], **saved_gen['model_kwargs']) - model.load_state_dict(saved_gen['state_dict'], strict=False) - def _remove_weight_norm(m): - try: - torch.nn.utils.remove_weight_norm(m) - except ValueError: # this module didn't have weight norm - return - model.apply(_remove_weight_norm) - - - print("dumping model...") - quantize_model=args.quantize - - output_folder = args.export_folder - os.makedirs(output_folder, exist_ok=True) - - writer = wexchange.c_export.c_writer.CWriter(os.path.join(output_folder, args.export_filename), model_struct_name=args.struct_name) - - for name, module in model.named_modules(): - - if quantize_model: - quantize=name not in unquantized - scale = None if quantize else 1/128 - else: - quantize=False - scale=1/128 - - if isinstance(module, nn.Linear): - print(f"dumping linear layer {name}...") - wexchange.torch.dump_torch_dense_weights(writer, module, name.replace('.', '_'), quantize=quantize, scale=scale) - - elif isinstance(module, nn.Conv1d): - print(f"dumping conv1d layer {name}...") - wexchange.torch.dump_torch_conv1d_weights(writer, module, name.replace('.', '_'), quantize=quantize, scale=scale) - - elif isinstance(module, nn.GRU): - print(f"dumping GRU layer {name}...") - wexchange.torch.dump_torch_gru_weights(writer, module, name.replace('.', '_'), quantize=quantize, scale=scale, recurrent_scale=scale) - - elif isinstance(module, nn.GRUCell): - print(f"dumping GRUCell layer {name}...") - wexchange.torch.dump_torch_grucell_weights(writer, module, name.replace('.', '_'), quantize=quantize, scale=scale, recurrent_scale=scale) - - elif isinstance(module, nn.Embedding): - print(f"dumping Embedding layer {name}...") - wexchange.torch.dump_torch_embedding_weights(writer, module, name.replace('.', '_'), quantize=quantize, scale=scale) - #wexchange.torch.dump_torch_embedding_weights(writer, module) - - else: - print(f"Ignoring layer {name}...") - - writer.close() diff --git a/android/app/src/main/cpp/third_party/opus/dnn/torch/fargan/fargan.py b/android/app/src/main/cpp/third_party/opus/dnn/torch/fargan/fargan.py deleted file mode 100644 index 8dbb694..0000000 --- a/android/app/src/main/cpp/third_party/opus/dnn/torch/fargan/fargan.py +++ /dev/null @@ -1,322 +0,0 @@ -import numpy as np -import torch -from torch import nn -import torch.nn.functional as F -import filters -from torch.nn.utils import weight_norm -#from convert_lsp import lpc_to_lsp, lsp_to_lpc -from rc import lpc2rc, rc2lpc - -Fs = 16000 - -fid_dict = {} -def dump_signal(x, filename): - return - if filename in fid_dict: - fid = fid_dict[filename] - else: - fid = open(filename, "w") - fid_dict[filename] = fid - x = x.detach().numpy().astype('float32') - x.tofile(fid) - - -def sig_l1(y_true, y_pred): - return torch.mean(abs(y_true-y_pred))/torch.mean(abs(y_true)) - -def sig_loss(y_true, y_pred): - t = y_true/(1e-15+torch.norm(y_true, dim=-1, p=2, keepdim=True)) - p = y_pred/(1e-15+torch.norm(y_pred, dim=-1, p=2, keepdim=True)) - return torch.mean(1.-torch.sum(p*t, dim=-1)) - -def interp_lpc(lpc, factor): - #print(lpc.shape) - #f = (np.arange(factor)+.5*((factor+1)%2))/factor - lsp = torch.atanh(lpc2rc(lpc)) - #print("lsp0:") - #print(lsp) - shape = lsp.shape - #print("shape is", shape) - shape = (shape[0], shape[1]*factor, shape[2]) - interp_lsp = torch.zeros(shape, device=lpc.device) - for k in range(factor): - f = (k+.5*((factor+1)%2))/factor - interp = (1-f)*lsp[:,:-1,:] + f*lsp[:,1:,:] - interp_lsp[:,factor//2+k:-(factor//2):factor,:] = interp - for k in range(factor//2): - interp_lsp[:,k,:] = interp_lsp[:,factor//2,:] - for k in range((factor+1)//2): - interp_lsp[:,-k-1,:] = interp_lsp[:,-(factor+3)//2,:] - #print("lsp:") - #print(interp_lsp) - return rc2lpc(torch.tanh(interp_lsp)) - -def analysis_filter(x, lpc, nb_subframes=4, subframe_size=40, gamma=.9): - device = x.device - batch_size = lpc.size(0) - - nb_frames = lpc.shape[1] - - - sig = torch.zeros(batch_size, subframe_size+16, device=device) - x = torch.reshape(x, (batch_size, nb_frames*nb_subframes, subframe_size)) - out = torch.zeros((batch_size, 0), device=device) - - #if gamma is not None: - # bw = gamma**(torch.arange(1, 17, device=device)) - # lpc = lpc*bw[None,None,:] - ones = torch.ones((*(lpc.shape[:-1]), 1), device=device) - zeros = torch.zeros((*(lpc.shape[:-1]), subframe_size-1), device=device) - a = torch.cat([ones, lpc], -1) - a_big = torch.cat([a, zeros], -1) - fir_mat_big = filters.toeplitz_from_filter(a_big) - - #print(a_big[:,0,:]) - for n in range(nb_frames): - for k in range(nb_subframes): - - sig = torch.cat([sig[:,subframe_size:], x[:,n*nb_subframes + k, :]], 1) - exc = torch.bmm(fir_mat_big[:,n,:,:], sig[:,:,None]) - out = torch.cat([out, exc[:,-subframe_size:,0]], 1) - - return out - - -# weight initialization and clipping -def init_weights(module): - if isinstance(module, nn.GRU): - for p in module.named_parameters(): - if p[0].startswith('weight_hh_'): - nn.init.orthogonal_(p[1]) - -def gen_phase_embedding(periods, frame_size): - device = periods.device - batch_size = periods.size(0) - nb_frames = periods.size(1) - w0 = 2*torch.pi/periods - w0_shift = torch.cat([2*torch.pi*torch.rand((batch_size, 1), device=device)/frame_size, w0[:,:-1]], 1) - cum_phase = frame_size*torch.cumsum(w0_shift, 1) - fine_phase = w0[:,:,None]*torch.broadcast_to(torch.arange(frame_size, device=device), (batch_size, nb_frames, frame_size)) - embed = torch.unsqueeze(cum_phase, 2) + fine_phase - embed = torch.reshape(embed, (batch_size, -1)) - return torch.cos(embed), torch.sin(embed) - -class GLU(nn.Module): - def __init__(self, feat_size): - super(GLU, self).__init__() - - torch.manual_seed(5) - - self.gate = weight_norm(nn.Linear(feat_size, feat_size, bias=False)) - - self.init_weights() - - def init_weights(self): - - for m in self.modules(): - if isinstance(m, nn.Conv1d) or isinstance(m, nn.ConvTranspose1d)\ - or isinstance(m, nn.Linear) or isinstance(m, nn.Embedding): - nn.init.orthogonal_(m.weight.data) - - def forward(self, x): - - out = x * torch.sigmoid(self.gate(x)) - - return out - -class FWConv(nn.Module): - def __init__(self, in_size, out_size, kernel_size=2): - super(FWConv, self).__init__() - - torch.manual_seed(5) - - self.in_size = in_size - self.kernel_size = kernel_size - self.conv = weight_norm(nn.Linear(in_size*self.kernel_size, out_size, bias=False)) - self.glu = GLU(out_size) - - self.init_weights() - - def init_weights(self): - - for m in self.modules(): - if isinstance(m, nn.Conv1d) or isinstance(m, nn.ConvTranspose1d)\ - or isinstance(m, nn.Linear) or isinstance(m, nn.Embedding): - nn.init.orthogonal_(m.weight.data) - - def forward(self, x, state): - xcat = torch.cat((state, x), -1) - #print(x.shape, state.shape, xcat.shape, self.in_size, self.kernel_size) - out = self.glu(torch.tanh(self.conv(xcat))) - return out, xcat[:,self.in_size:] - -def n(x): - return torch.clamp(x + (1./127.)*(torch.rand_like(x)-.5), min=-1., max=1.) - -class FARGANCond(nn.Module): - def __init__(self, feature_dim=20, cond_size=256, pembed_dims=12): - super(FARGANCond, self).__init__() - - self.feature_dim = feature_dim - self.cond_size = cond_size - - self.pembed = nn.Embedding(224, pembed_dims) - self.fdense1 = nn.Linear(self.feature_dim + pembed_dims, 64, bias=False) - self.fconv1 = nn.Conv1d(64, 128, kernel_size=3, padding='valid', bias=False) - self.fdense2 = nn.Linear(128, 80*4, bias=False) - - self.apply(init_weights) - nb_params = sum(p.numel() for p in self.parameters()) - print(f"cond model: {nb_params} weights") - - def forward(self, features, period): - features = features[:,2:,:] - period = period[:,2:] - p = self.pembed(period-32) - features = torch.cat((features, p), -1) - tmp = torch.tanh(self.fdense1(features)) - tmp = tmp.permute(0, 2, 1) - tmp = torch.tanh(self.fconv1(tmp)) - tmp = tmp.permute(0, 2, 1) - tmp = torch.tanh(self.fdense2(tmp)) - #tmp = torch.tanh(self.fdense2(tmp)) - return tmp - -class FARGANSub(nn.Module): - def __init__(self, subframe_size=40, nb_subframes=4, cond_size=256): - super(FARGANSub, self).__init__() - - self.subframe_size = subframe_size - self.nb_subframes = nb_subframes - self.cond_size = cond_size - self.cond_gain_dense = nn.Linear(80, 1) - - #self.sig_dense1 = nn.Linear(4*self.subframe_size+self.passthrough_size+self.cond_size, self.cond_size, bias=False) - self.fwc0 = FWConv(2*self.subframe_size+80+4, 192) - self.gru1 = nn.GRUCell(192+2*self.subframe_size, 160, bias=False) - self.gru2 = nn.GRUCell(160+2*self.subframe_size, 128, bias=False) - self.gru3 = nn.GRUCell(128+2*self.subframe_size, 128, bias=False) - - self.gru1_glu = GLU(160) - self.gru2_glu = GLU(128) - self.gru3_glu = GLU(128) - self.skip_glu = GLU(128) - #self.ptaps_dense = nn.Linear(4*self.cond_size, 5) - - self.skip_dense = nn.Linear(192+160+2*128+2*self.subframe_size, 128, bias=False) - self.sig_dense_out = nn.Linear(128, self.subframe_size, bias=False) - self.gain_dense_out = nn.Linear(192, 4) - - - self.apply(init_weights) - nb_params = sum(p.numel() for p in self.parameters()) - print(f"subframe model: {nb_params} weights") - - def forward(self, cond, prev_pred, exc_mem, period, states, gain=None): - device = exc_mem.device - #print(cond.shape, prev.shape) - - cond = n(cond) - dump_signal(gain, 'gain0.f32') - gain = torch.exp(self.cond_gain_dense(cond)) - dump_signal(gain, 'gain1.f32') - idx = 256-period[:,None] - rng = torch.arange(self.subframe_size+4, device=device) - idx = idx + rng[None,:] - 2 - mask = idx >= 256 - idx = idx - mask*period[:,None] - pred = torch.gather(exc_mem, 1, idx) - pred = n(pred/(1e-5+gain)) - - prev = exc_mem[:,-self.subframe_size:] - dump_signal(prev, 'prev_in.f32') - prev = n(prev/(1e-5+gain)) - dump_signal(prev, 'pitch_exc.f32') - dump_signal(exc_mem, 'exc_mem.f32') - - tmp = torch.cat((cond, pred, prev), 1) - #fpitch = taps[:,0:1]*pred[:,:-4] + taps[:,1:2]*pred[:,1:-3] + taps[:,2:3]*pred[:,2:-2] + taps[:,3:4]*pred[:,3:-1] + taps[:,4:]*pred[:,4:] - fpitch = pred[:,2:-2] - - #tmp = self.dense1_glu(torch.tanh(self.sig_dense1(tmp))) - fwc0_out, fwc0_state = self.fwc0(tmp, states[3]) - fwc0_out = n(fwc0_out) - pitch_gain = torch.sigmoid(self.gain_dense_out(fwc0_out)) - - gru1_state = self.gru1(torch.cat([fwc0_out, pitch_gain[:,0:1]*fpitch, prev], 1), states[0]) - gru1_out = self.gru1_glu(n(gru1_state)) - gru1_out = n(gru1_out) - gru2_state = self.gru2(torch.cat([gru1_out, pitch_gain[:,1:2]*fpitch, prev], 1), states[1]) - gru2_out = self.gru2_glu(n(gru2_state)) - gru2_out = n(gru2_out) - gru3_state = self.gru3(torch.cat([gru2_out, pitch_gain[:,2:3]*fpitch, prev], 1), states[2]) - gru3_out = self.gru3_glu(n(gru3_state)) - gru3_out = n(gru3_out) - gru3_out = torch.cat([gru1_out, gru2_out, gru3_out, fwc0_out], 1) - skip_out = torch.tanh(self.skip_dense(torch.cat([gru3_out, pitch_gain[:,3:4]*fpitch, prev], 1))) - skip_out = self.skip_glu(n(skip_out)) - sig_out = torch.tanh(self.sig_dense_out(skip_out)) - dump_signal(sig_out, 'exc_out.f32') - #taps = self.ptaps_dense(gru3_out) - #taps = .2*taps + torch.exp(taps) - #taps = taps / (1e-2 + torch.sum(torch.abs(taps), dim=-1, keepdim=True)) - #dump_signal(taps, 'taps.f32') - - dump_signal(pitch_gain, 'pgain.f32') - #sig_out = (sig_out + pitch_gain*fpitch) * gain - sig_out = sig_out * gain - exc_mem = torch.cat([exc_mem[:,self.subframe_size:], sig_out], 1) - prev_pred = torch.cat([prev_pred[:,self.subframe_size:], fpitch], 1) - dump_signal(sig_out, 'sig_out.f32') - return sig_out, exc_mem, prev_pred, (gru1_state, gru2_state, gru3_state, fwc0_state) - -class FARGAN(nn.Module): - def __init__(self, subframe_size=40, nb_subframes=4, feature_dim=20, cond_size=256, passthrough_size=0, has_gain=False, gamma=None): - super(FARGAN, self).__init__() - - self.subframe_size = subframe_size - self.nb_subframes = nb_subframes - self.frame_size = self.subframe_size*self.nb_subframes - self.feature_dim = feature_dim - self.cond_size = cond_size - - self.cond_net = FARGANCond(feature_dim=feature_dim, cond_size=cond_size) - self.sig_net = FARGANSub(subframe_size=subframe_size, nb_subframes=nb_subframes, cond_size=cond_size) - - def forward(self, features, period, nb_frames, pre=None, states=None): - device = features.device - batch_size = features.size(0) - - prev = torch.zeros(batch_size, 256, device=device) - exc_mem = torch.zeros(batch_size, 256, device=device) - nb_pre_frames = pre.size(1)//self.frame_size if pre is not None else 0 - - states = ( - torch.zeros(batch_size, 160, device=device), - torch.zeros(batch_size, 128, device=device), - torch.zeros(batch_size, 128, device=device), - torch.zeros(batch_size, (2*self.subframe_size+80+4)*1, device=device) - ) - - sig = torch.zeros((batch_size, 0), device=device) - cond = self.cond_net(features, period) - if pre is not None: - exc_mem[:,-self.frame_size:] = pre[:, :self.frame_size] - start = 1 if nb_pre_frames>0 else 0 - for n in range(start, nb_frames+nb_pre_frames): - for k in range(self.nb_subframes): - pos = n*self.frame_size + k*self.subframe_size - #print("now: ", preal.shape, prev.shape, sig_in.shape) - pitch = period[:, 3+n] - gain = .03*10**(0.5*features[:, 3+n, 0:1]/np.sqrt(18.0)) - #gain = gain[:,:,None] - out, exc_mem, prev, states = self.sig_net(cond[:, n, k*80:(k+1)*80], prev, exc_mem, pitch, states, gain=gain) - - if n < nb_pre_frames: - out = pre[:, pos:pos+self.subframe_size] - exc_mem[:,-self.subframe_size:] = out - else: - sig = torch.cat([sig, out], 1) - - states = [s.detach() for s in states] - return sig, states diff --git a/android/app/src/main/cpp/third_party/opus/dnn/torch/fargan/filters.py b/android/app/src/main/cpp/third_party/opus/dnn/torch/fargan/filters.py deleted file mode 100644 index 8ec97ea..0000000 --- a/android/app/src/main/cpp/third_party/opus/dnn/torch/fargan/filters.py +++ /dev/null @@ -1,46 +0,0 @@ -import torch -from torch import nn -import torch.nn.functional as F -import math - -def toeplitz_from_filter(a): - device = a.device - L = a.size(-1) - size0 = (*(a.shape[:-1]), L, L+1) - size = (*(a.shape[:-1]), L, L) - rnge = torch.arange(0, L, dtype=torch.int64, device=device) - z = torch.tensor(0, device=device) - idx = torch.maximum(rnge[:,None] - rnge[None,:] + 1, z) - a = torch.cat([a[...,:1]*0, a], -1) - #print(a) - a = a[...,None,:] - #print(idx) - a = torch.broadcast_to(a, size0) - idx = torch.broadcast_to(idx, size) - #print(idx) - return torch.gather(a, -1, idx) - -def filter_iir_response(a, N): - device = a.device - L = a.size(-1) - ar = a.flip(dims=(2,)) - size = (*(a.shape[:-1]), N) - R = torch.zeros(size, device=device) - R[:,:,0] = torch.ones((a.shape[:-1]), device=device) - for i in range(1, L): - R[:,:,i] = - torch.sum(ar[:,:,L-i-1:-1] * R[:,:,:i], axis=-1) - #R[:,:,i] = - torch.einsum('ijk,ijk->ij', ar[:,:,L-i-1:-1], R[:,:,:i]) - for i in range(L, N): - R[:,:,i] = - torch.sum(ar[:,:,:-1] * R[:,:,i-L+1:i], axis=-1) - #R[:,:,i] = - torch.einsum('ijk,ijk->ij', ar[:,:,:-1], R[:,:,i-L+1:i]) - return R - -if __name__ == '__main__': - #a = torch.tensor([ [[1, -.9, 0.02], [1, -.8, .01]], [[1, .9, 0], [1, .8, 0]]]) - a = torch.tensor([ [[1, -.9, 0.02], [1, -.8, .01]]]) - A = toeplitz_from_filter(a) - #print(A) - R = filter_iir_response(a, 5) - - RA = toeplitz_from_filter(R) - print(RA) diff --git a/android/app/src/main/cpp/third_party/opus/dnn/torch/fargan/rc.py b/android/app/src/main/cpp/third_party/opus/dnn/torch/fargan/rc.py deleted file mode 100644 index 7f67016..0000000 --- a/android/app/src/main/cpp/third_party/opus/dnn/torch/fargan/rc.py +++ /dev/null @@ -1,29 +0,0 @@ -import torch - - - -def rc2lpc(rc): - order = rc.shape[-1] - lpc=rc[...,0:1] - for i in range(1, order): - lpc = torch.cat([lpc + rc[...,i:i+1]*torch.flip(lpc,dims=(-1,)), rc[...,i:i+1]], -1) - #print("to:", lpc) - return lpc - -def lpc2rc(lpc): - order = lpc.shape[-1] - rc = lpc[...,-1:] - for i in range(order-1, 0, -1): - ki = lpc[...,-1:] - lpc = lpc[...,:-1] - lpc = (lpc - ki*torch.flip(lpc,dims=(-1,)))/(1 - ki*ki) - rc = torch.cat([lpc[...,-1:] , rc], -1) - return rc - -if __name__ == "__main__": - rc = torch.tensor([[.5, -.5, .6, -.6]]) - print(rc) - lpc = rc2lpc(rc) - print(lpc) - rc2 = lpc2rc(lpc) - print(rc2) diff --git a/android/app/src/main/cpp/third_party/opus/dnn/torch/fargan/stft_loss.py b/android/app/src/main/cpp/third_party/opus/dnn/torch/fargan/stft_loss.py deleted file mode 100644 index 8c90405..0000000 --- a/android/app/src/main/cpp/third_party/opus/dnn/torch/fargan/stft_loss.py +++ /dev/null @@ -1,186 +0,0 @@ -"""STFT-based Loss modules.""" - -import torch -import torch.nn.functional as F -import numpy as np -import torchaudio - - -def stft(x, fft_size, hop_size, win_length, window): - """Perform STFT and convert to magnitude spectrogram. - Args: - x (Tensor): Input signal tensor (B, T). - fft_size (int): FFT size. - hop_size (int): Hop size. - win_length (int): Window length. - window (str): Window function type. - Returns: - Tensor: Magnitude spectrogram (B, #frames, fft_size // 2 + 1). - """ - - #x_stft = torch.stft(x, fft_size, hop_size, win_length, window, return_complex=False) - #real = x_stft[..., 0] - #imag = x_stft[..., 1] - - # (kan-bayashi): clamp is needed to avoid nan or inf - #return torchaudio.functional.amplitude_to_DB(torch.abs(x_stft),db_multiplier=0.0, multiplier=20,amin=1e-05,top_db=80) - #return torch.clamp(torch.abs(x_stft), min=1e-7) - - x_stft = torch.stft(x, fft_size, hop_size, win_length, window, return_complex=True) - return torch.clamp(torch.abs(x_stft), min=1e-7) - -class SpectralConvergenceLoss(torch.nn.Module): - """Spectral convergence loss module.""" - - def __init__(self): - """Initilize spectral convergence loss module.""" - super(SpectralConvergenceLoss, self).__init__() - - def forward(self, x_mag, y_mag): - """Calculate forward propagation. - Args: - x_mag (Tensor): Magnitude spectrogram of predicted signal (B, #frames, #freq_bins). - y_mag (Tensor): Magnitude spectrogram of groundtruth signal (B, #frames, #freq_bins). - Returns: - Tensor: Spectral convergence loss value. - """ - x_mag = torch.sqrt(x_mag) - y_mag = torch.sqrt(y_mag) - return torch.norm(y_mag - x_mag, p=1) / torch.norm(y_mag, p=1) - -class LogSTFTMagnitudeLoss(torch.nn.Module): - """Log STFT magnitude loss module.""" - - def __init__(self): - """Initilize los STFT magnitude loss module.""" - super(LogSTFTMagnitudeLoss, self).__init__() - - def forward(self, x, y): - """Calculate forward propagation. - Args: - x_mag (Tensor): Magnitude spectrogram of predicted signal (B, #frames, #freq_bins). - y_mag (Tensor): Magnitude spectrogram of groundtruth signal (B, #frames, #freq_bins). - Returns: - Tensor: Log STFT magnitude loss value. - """ - #F.l1_loss(torch.sqrt(y_mag), torch.sqrt(x_mag)) + - #F.l1_loss(torchaudio.functional.amplitude_to_DB(y_mag,db_multiplier=0.0, multiplier=20,amin=1e-05,top_db=80),\ - #torchaudio.functional.amplitude_to_DB(x_mag,db_multiplier=0.0, multiplier=20,amin=1e-05,top_db=80)) - - #y_mag[:,:y_mag.size(1)//2,:] = y_mag[:,:y_mag.size(1)//2,:] *0.0 - - #return F.l1_loss(torch.log(y_mag) + torch.sqrt(y_mag), torch.log(x_mag) + torch.sqrt(x_mag)) - - #return F.l1_loss(y_mag, x_mag) - - error_loss = F.l1_loss(y, x) #+ F.l1_loss(torch.sqrt(y), torch.sqrt(x))#F.l1_loss(torch.log(y), torch.log(x))# - - #x = torch.log(x) - #y = torch.log(y) - #x = x.permute(0,2,1).contiguous() - #y = y.permute(0,2,1).contiguous() - - '''mean_x = torch.mean(x, dim=1, keepdim=True) - mean_y = torch.mean(y, dim=1, keepdim=True) - - var_x = torch.var(x, dim=1, keepdim=True) - var_y = torch.var(y, dim=1, keepdim=True) - - std_x = torch.std(x, dim=1, keepdim=True) - std_y = torch.std(y, dim=1, keepdim=True) - - x_minus_mean = x - mean_x - y_minus_mean = y - mean_y - - pearson_corr = torch.sum(x_minus_mean * y_minus_mean, dim=1, keepdim=True) / \ - (torch.sqrt(torch.sum(x_minus_mean ** 2, dim=1, keepdim=True) + 1e-7) * \ - torch.sqrt(torch.sum(y_minus_mean ** 2, dim=1, keepdim=True) + 1e-7)) - - numerator = 2.0 * pearson_corr * std_x * std_y - denominator = var_x + var_y + (mean_y - mean_x)**2 - - ccc = numerator/denominator - - ccc_loss = F.l1_loss(1.0 - ccc, torch.zeros_like(ccc))''' - - return error_loss #+ ccc_loss#+ ccc_loss - - -class STFTLoss(torch.nn.Module): - """STFT loss module.""" - - def __init__(self, device, fft_size=1024, shift_size=120, win_length=600, window="hann_window"): - """Initialize STFT loss module.""" - super(STFTLoss, self).__init__() - self.fft_size = fft_size - self.shift_size = shift_size - self.win_length = win_length - self.window = getattr(torch, window)(win_length).to(device) - self.spectral_convergenge_loss = SpectralConvergenceLoss() - self.log_stft_magnitude_loss = LogSTFTMagnitudeLoss() - - def forward(self, x, y): - """Calculate forward propagation. - Args: - x (Tensor): Predicted signal (B, T). - y (Tensor): Groundtruth signal (B, T). - Returns: - Tensor: Spectral convergence loss value. - Tensor: Log STFT magnitude loss value. - """ - x_mag = stft(x, self.fft_size, self.shift_size, self.win_length, self.window) - y_mag = stft(y, self.fft_size, self.shift_size, self.win_length, self.window) - sc_loss = self.spectral_convergenge_loss(x_mag, y_mag) - mag_loss = self.log_stft_magnitude_loss(x_mag, y_mag) - - return sc_loss, mag_loss - - -class MultiResolutionSTFTLoss(torch.nn.Module): - - '''def __init__(self, - device, - fft_sizes=[2048, 1024, 512, 256, 128, 64], - hop_sizes=[512, 256, 128, 64, 32, 16], - win_lengths=[2048, 1024, 512, 256, 128, 64], - window="hann_window"):''' - - '''def __init__(self, - device, - fft_sizes=[2048, 1024, 512, 256, 128, 64], - hop_sizes=[256, 128, 64, 32, 16, 8], - win_lengths=[1024, 512, 256, 128, 64, 32], - window="hann_window"):''' - - def __init__(self, - device, - fft_sizes=[2560, 1280, 640, 320, 160, 80], - hop_sizes=[640, 320, 160, 80, 40, 20], - win_lengths=[2560, 1280, 640, 320, 160, 80], - window="hann_window"): - - super(MultiResolutionSTFTLoss, self).__init__() - assert len(fft_sizes) == len(hop_sizes) == len(win_lengths) - self.stft_losses = torch.nn.ModuleList() - for fs, ss, wl in zip(fft_sizes, hop_sizes, win_lengths): - self.stft_losses += [STFTLoss(device, fs, ss, wl, window)] - - def forward(self, x, y): - """Calculate forward propagation. - Args: - x (Tensor): Predicted signal (B, T). - y (Tensor): Groundtruth signal (B, T). - Returns: - Tensor: Multi resolution spectral convergence loss value. - Tensor: Multi resolution log STFT magnitude loss value. - """ - sc_loss = 0.0 - mag_loss = 0.0 - for f in self.stft_losses: - sc_l, mag_l = f(x, y) - sc_loss += sc_l - #mag_loss += mag_l - sc_loss /= len(self.stft_losses) - mag_loss /= len(self.stft_losses) - - return sc_loss #mag_loss #+ diff --git a/android/app/src/main/cpp/third_party/opus/dnn/torch/fargan/test_fargan.py b/android/app/src/main/cpp/third_party/opus/dnn/torch/fargan/test_fargan.py deleted file mode 100644 index d3aeb61..0000000 --- a/android/app/src/main/cpp/third_party/opus/dnn/torch/fargan/test_fargan.py +++ /dev/null @@ -1,128 +0,0 @@ -import os -import argparse -import numpy as np - -import torch -from torch import nn -import torch.nn.functional as F -import tqdm - -import fargan -from dataset import FARGANDataset - -nb_features = 36 -nb_used_features = 20 - -parser = argparse.ArgumentParser() - -parser.add_argument('model', type=str, help='CELPNet model') -parser.add_argument('features', type=str, help='path to feature file in .f32 format') -parser.add_argument('output', type=str, help='path to output file (16-bit PCM)') - -parser.add_argument('--cuda-visible-devices', type=str, help="comma separates list of cuda visible device indices, default: CUDA_VISIBLE_DEVICES", default=None) - - -model_group = parser.add_argument_group(title="model parameters") -model_group.add_argument('--cond-size', type=int, help="first conditioning size, default: 256", default=256) - -args = parser.parse_args() - -if args.cuda_visible_devices != None: - os.environ['CUDA_VISIBLE_DEVICES'] = args.cuda_visible_devices - - -features_file = args.features -signal_file = args.output - - - -device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu") - -checkpoint = torch.load(args.model, map_location='cpu') - -model = fargan.FARGAN(*checkpoint['model_args'], **checkpoint['model_kwargs']) - - -model.load_state_dict(checkpoint['state_dict'], strict=False) - -features = np.reshape(np.memmap(features_file, dtype='float32', mode='r'), (1, -1, nb_features)) -lpc = features[:,4-1:-1,nb_used_features:] -features = features[:, :, :nb_used_features] -#periods = np.round(50*features[:,:,nb_used_features-2]+100).astype('int') -periods = np.round(np.clip(256./2**(features[:,:,nb_used_features-2]+1.5), 32, 255)).astype('int') - - -nb_frames = features.shape[1] -#nb_frames = 1000 -gamma = checkpoint['model_kwargs']['gamma'] - -def lpc_synthesis_one_frame(frame, filt, buffer, weighting_vector=np.ones(16)): - - out = np.zeros_like(frame) - filt = np.flip(filt) - - inp = frame[:] - - - for i in range(0, inp.shape[0]): - - s = inp[i] - np.dot(buffer*weighting_vector, filt) - - buffer[0] = s - - buffer = np.roll(buffer, -1) - - out[i] = s - - return out - -def inverse_perceptual_weighting (pw_signal, filters, weighting_vector): - - #inverse perceptual weighting= H_preemph / W(z/gamma) - - signal = np.zeros_like(pw_signal) - buffer = np.zeros(16) - num_frames = pw_signal.shape[0] //160 - assert num_frames == filters.shape[0] - for frame_idx in range(0, num_frames): - - in_frame = pw_signal[frame_idx*160: (frame_idx+1)*160][:] - out_sig_frame = lpc_synthesis_one_frame(in_frame, filters[frame_idx, :], buffer, weighting_vector) - signal[frame_idx*160: (frame_idx+1)*160] = out_sig_frame[:] - buffer[:] = out_sig_frame[-16:] - return signal - -def inverse_perceptual_weighting40 (pw_signal, filters): - - #inverse perceptual weighting= H_preemph / W(z/gamma) - - signal = np.zeros_like(pw_signal) - buffer = np.zeros(16) - num_frames = pw_signal.shape[0] //40 - assert num_frames == filters.shape[0] - for frame_idx in range(0, num_frames): - in_frame = pw_signal[frame_idx*40: (frame_idx+1)*40][:] - out_sig_frame = lpc_synthesis_one_frame(in_frame, filters[frame_idx, :], buffer) - signal[frame_idx*40: (frame_idx+1)*40] = out_sig_frame[:] - buffer[:] = out_sig_frame[-16:] - return signal - -from scipy.signal import lfilter - -if __name__ == '__main__': - model.to(device) - features = torch.tensor(features).to(device) - #lpc = torch.tensor(lpc).to(device) - periods = torch.tensor(periods).to(device) - weighting = gamma**np.arange(1, 17) - lpc = lpc*weighting - lpc = fargan.interp_lpc(torch.tensor(lpc), 4).numpy() - - sig, _ = model(features, periods, nb_frames - 4) - #weighting_vector = np.array([gamma**i for i in range(16,0,-1)]) - sig = sig.detach().numpy().flatten() - sig = lfilter(np.array([1.]), np.array([1., -.85]), sig) - #sig = inverse_perceptual_weighting40(sig, lpc[0,:,:]) - - pcm = np.round(32768*np.clip(sig, a_max=.99, a_min=-.99)).astype('int16') - pcm.tofile(signal_file) diff --git a/android/app/src/main/cpp/third_party/opus/dnn/torch/fargan/train_fargan.py b/android/app/src/main/cpp/third_party/opus/dnn/torch/fargan/train_fargan.py deleted file mode 100644 index 1b2e200..0000000 --- a/android/app/src/main/cpp/third_party/opus/dnn/torch/fargan/train_fargan.py +++ /dev/null @@ -1,168 +0,0 @@ -import os -import argparse -import random -import numpy as np - -import torch -from torch import nn -import torch.nn.functional as F -import tqdm - -import fargan -from dataset import FARGANDataset -from stft_loss import * - -parser = argparse.ArgumentParser() - -parser.add_argument('features', type=str, help='path to feature file in .f32 format') -parser.add_argument('signal', type=str, help='path to signal file in .s16 format') -parser.add_argument('output', type=str, help='path to output folder') - -parser.add_argument('--suffix', type=str, help="model name suffix", default="") -parser.add_argument('--cuda-visible-devices', type=str, help="comma separates list of cuda visible device indices, default: CUDA_VISIBLE_DEVICES", default=None) - - -model_group = parser.add_argument_group(title="model parameters") -model_group.add_argument('--cond-size', type=int, help="first conditioning size, default: 256", default=256) -model_group.add_argument('--gamma', type=float, help="Use A(z/gamma), default: 0.9", default=0.9) - -training_group = parser.add_argument_group(title="training parameters") -training_group.add_argument('--batch-size', type=int, help="batch size, default: 512", default=512) -training_group.add_argument('--lr', type=float, help='learning rate, default: 1e-3', default=1e-3) -training_group.add_argument('--epochs', type=int, help='number of training epochs, default: 20', default=20) -training_group.add_argument('--sequence-length', type=int, help='sequence length, default: 15', default=15) -training_group.add_argument('--lr-decay', type=float, help='learning rate decay factor, default: 1e-4', default=1e-4) -training_group.add_argument('--initial-checkpoint', type=str, help='initial checkpoint to start training from, default: None', default=None) - -args = parser.parse_args() - -if args.cuda_visible_devices != None: - os.environ['CUDA_VISIBLE_DEVICES'] = args.cuda_visible_devices - -# checkpoints -checkpoint_dir = os.path.join(args.output, 'checkpoints') -checkpoint = dict() -os.makedirs(checkpoint_dir, exist_ok=True) - - -# training parameters -batch_size = args.batch_size -lr = args.lr -epochs = args.epochs -sequence_length = args.sequence_length -lr_decay = args.lr_decay - -adam_betas = [0.8, 0.95] -adam_eps = 1e-8 -features_file = args.features -signal_file = args.signal - -# model parameters -cond_size = args.cond_size - - -checkpoint['batch_size'] = batch_size -checkpoint['lr'] = lr -checkpoint['lr_decay'] = lr_decay -checkpoint['epochs'] = epochs -checkpoint['sequence_length'] = sequence_length -checkpoint['adam_betas'] = adam_betas - - -device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu") - -checkpoint['model_args'] = () -checkpoint['model_kwargs'] = {'cond_size': cond_size, 'gamma': args.gamma} -print(checkpoint['model_kwargs']) -model = fargan.FARGAN(*checkpoint['model_args'], **checkpoint['model_kwargs']) - -#model = fargan.FARGAN() -#model = nn.DataParallel(model) - -if type(args.initial_checkpoint) != type(None): - checkpoint = torch.load(args.initial_checkpoint, map_location='cpu') - model.load_state_dict(checkpoint['state_dict'], strict=False) - -checkpoint['state_dict'] = model.state_dict() - - -dataset = FARGANDataset(features_file, signal_file, sequence_length=sequence_length) -dataloader = torch.utils.data.DataLoader(dataset, batch_size=batch_size, shuffle=True, drop_last=True, num_workers=4) - - -optimizer = torch.optim.AdamW(model.parameters(), lr=lr, betas=adam_betas, eps=adam_eps) - - -# learning rate scheduler -scheduler = torch.optim.lr_scheduler.LambdaLR(optimizer=optimizer, lr_lambda=lambda x : 1 / (1 + lr_decay * x)) - -states = None - -spect_loss = MultiResolutionSTFTLoss(device).to(device) - -if __name__ == '__main__': - model.to(device) - - for epoch in range(1, epochs + 1): - - running_specc = 0 - running_cont_loss = 0 - running_loss = 0 - - print(f"training epoch {epoch}...") - with tqdm.tqdm(dataloader, unit='batch') as tepoch: - for i, (features, periods, target, lpc) in enumerate(tepoch): - optimizer.zero_grad() - features = features.to(device) - #lpc = torch.tensor(fargan.interp_lpc(lpc.numpy(), 4)) - #print("interp size", lpc.shape) - #lpc = lpc.to(device) - #lpc = lpc*(args.gamma**torch.arange(1,17, device=device)) - #lpc = fargan.interp_lpc(lpc, 4) - periods = periods.to(device) - if (np.random.rand() > 0.1): - target = target[:, :sequence_length*160] - #lpc = lpc[:,:sequence_length*4,:] - features = features[:,:sequence_length+4,:] - periods = periods[:,:sequence_length+4] - else: - target=target[::2, :] - #lpc=lpc[::2,:] - features=features[::2,:] - periods=periods[::2,:] - target = target.to(device) - #print(target.shape, lpc.shape) - #target = fargan.analysis_filter(target, lpc[:,:,:], nb_subframes=1, gamma=args.gamma) - - #nb_pre = random.randrange(1, 6) - nb_pre = 2 - pre = target[:, :nb_pre*160] - sig, states = model(features, periods, target.size(1)//160 - nb_pre, pre=pre, states=None) - sig = torch.cat([pre, sig], -1) - - cont_loss = fargan.sig_loss(target[:, nb_pre*160:nb_pre*160+160], sig[:, nb_pre*160:nb_pre*160+160]) - specc_loss = spect_loss(sig, target.detach()) - loss = .03*cont_loss + specc_loss - - loss.backward() - optimizer.step() - - #model.clip_weights() - - scheduler.step() - - running_specc += specc_loss.detach().cpu().item() - running_cont_loss += cont_loss.detach().cpu().item() - - running_loss += loss.detach().cpu().item() - tepoch.set_postfix(loss=f"{running_loss/(i+1):8.5f}", - cont_loss=f"{running_cont_loss/(i+1):8.5f}", - specc=f"{running_specc/(i+1):8.5f}", - ) - - # save checkpoint - checkpoint_path = os.path.join(checkpoint_dir, f'fargan{args.suffix}_{epoch}.pth') - checkpoint['state_dict'] = model.state_dict() - checkpoint['loss'] = running_loss / len(dataloader) - checkpoint['epoch'] = epoch - torch.save(checkpoint, checkpoint_path) diff --git a/android/app/src/main/cpp/third_party/opus/dnn/torch/fwgan/dump_model_weights.py b/android/app/src/main/cpp/third_party/opus/dnn/torch/fwgan/dump_model_weights.py deleted file mode 100644 index 64f955a..0000000 --- a/android/app/src/main/cpp/third_party/opus/dnn/torch/fwgan/dump_model_weights.py +++ /dev/null @@ -1,88 +0,0 @@ -import os -import sys -import argparse - -import torch -from torch import nn - - -sys.path.append(os.path.join(os.path.split(__file__)[0], '../weight-exchange')) -import wexchange.torch - -from models import model_dict - -unquantized = [ - 'bfcc_with_corr_upsampler.fc', - 'cont_net.0', - 'fwc6.cont_fc.0', - 'fwc6.fc.0', - 'fwc6.fc.1.gate', - 'fwc7.cont_fc.0', - 'fwc7.fc.0', - 'fwc7.fc.1.gate' -] - -description=f""" -This is an unsafe dumping script for FWGAN models. It assumes that all weights are included in Linear, Conv1d or GRU layer -and will fail to export any other weights. - -Furthermore, the quanitze option relies on the following explicit list of layers to be excluded: -{unquantized}. - -Modify this script manually if adjustments are needed. -""" - -parser = argparse.ArgumentParser(description=description) -parser.add_argument('model', choices=['fwgan400', 'fwgan500'], help='model name') -parser.add_argument('weightfile', type=str, help='weight file path') -parser.add_argument('export_folder', type=str) -parser.add_argument('--export-filename', type=str, default='fwgan_data', help='filename for source and header file (.c and .h will be added), defaults to fwgan_data') -parser.add_argument('--struct-name', type=str, default='FWGAN', help='name for C struct, defaults to FWGAN') -parser.add_argument('--quantize', action='store_true', help='apply quantization') - -if __name__ == "__main__": - args = parser.parse_args() - - model = model_dict[args.model]() - - print(f"loading weights from {args.weightfile}...") - saved_gen= torch.load(args.weightfile, map_location='cpu') - model.load_state_dict(saved_gen) - def _remove_weight_norm(m): - try: - torch.nn.utils.remove_weight_norm(m) - except ValueError: # this module didn't have weight norm - return - model.apply(_remove_weight_norm) - - - print("dumping model...") - quantize_model=args.quantize - - output_folder = args.export_folder - os.makedirs(output_folder, exist_ok=True) - - writer = wexchange.c_export.c_writer.CWriter(os.path.join(output_folder, args.export_filename), model_struct_name=args.struct_name) - - for name, module in model.named_modules(): - - if quantize_model: - quantize=name not in unquantized - scale = None if quantize else 1/128 - else: - quantize=False - scale=1/128 - - if isinstance(module, nn.Linear): - print(f"dumping linear layer {name}...") - wexchange.torch.dump_torch_dense_weights(writer, module, name.replace('.', '_'), quantize=quantize, scale=scale) - - if isinstance(module, nn.Conv1d): - print(f"dumping conv1d layer {name}...") - wexchange.torch.dump_torch_conv1d_weights(writer, module, name.replace('.', '_'), quantize=quantize, scale=scale) - - if isinstance(module, nn.GRU): - print(f"dumping GRU layer {name}...") - wexchange.torch.dump_torch_gru_weights(writer, module, name.replace('.', '_'), quantize=quantize, scale=scale, recurrent_scale=scale) - - writer.close() diff --git a/android/app/src/main/cpp/third_party/opus/dnn/torch/fwgan/inference.py b/android/app/src/main/cpp/third_party/opus/dnn/torch/fwgan/inference.py deleted file mode 100644 index c06b68b..0000000 --- a/android/app/src/main/cpp/third_party/opus/dnn/torch/fwgan/inference.py +++ /dev/null @@ -1,141 +0,0 @@ -import os -import time -import torch -import numpy as np -from scipy import signal as si -from scipy.io import wavfile -import argparse - -from models import model_dict - -parser = argparse.ArgumentParser() -parser.add_argument('model', choices=['fwgan400', 'fwgan500'], help='model name') -parser.add_argument('weightfile', type=str, help='weight file') -parser.add_argument('input', type=str, help='input: feature file or folder with feature files') -parser.add_argument('output', type=str, help='output: wav file name or folder name, depending on input') - - -########################### Signal Processing Layers ########################### - -def preemphasis(x, coef= -0.85): - - return si.lfilter(np.array([1.0, coef]), np.array([1.0]), x).astype('float32') - -def deemphasis(x, coef= -0.85): - - return si.lfilter(np.array([1.0]), np.array([1.0, coef]), x).astype('float32') - -gamma = 0.92 -weighting_vector = np.array([gamma**i for i in range(16,0,-1)]) - - -def lpc_synthesis_one_frame(frame, filt, buffer, weighting_vector=np.ones(16)): - - out = np.zeros_like(frame) - - filt = np.flip(filt) - - inp = frame[:] - - - for i in range(0, inp.shape[0]): - - s = inp[i] - np.dot(buffer*weighting_vector, filt) - - buffer[0] = s - - buffer = np.roll(buffer, -1) - - out[i] = s - - return out - -def inverse_perceptual_weighting (pw_signal, filters, weighting_vector): - - #inverse perceptual weighting= H_preemph / W(z/gamma) - - pw_signal = preemphasis(pw_signal) - - signal = np.zeros_like(pw_signal) - buffer = np.zeros(16) - num_frames = pw_signal.shape[0] //160 - assert num_frames == filters.shape[0] - - for frame_idx in range(0, num_frames): - - in_frame = pw_signal[frame_idx*160: (frame_idx+1)*160][:] - out_sig_frame = lpc_synthesis_one_frame(in_frame, filters[frame_idx, :], buffer, weighting_vector) - signal[frame_idx*160: (frame_idx+1)*160] = out_sig_frame[:] - buffer[:] = out_sig_frame[-16:] - - return signal - - -def process_item(generator, feature_filename, output_filename, verbose=False): - - feat = np.memmap(feature_filename, dtype='float32', mode='r') - - num_feat_frames = len(feat) // 36 - feat = np.reshape(feat, (num_feat_frames, 36)) - - bfcc = np.copy(feat[:, :18]) - corr = np.copy(feat[:, 19:20]) + 0.5 - bfcc_with_corr = torch.from_numpy(np.hstack((bfcc, corr))).type(torch.FloatTensor).unsqueeze(0)#.to(device) - - period = torch.from_numpy((0.1 + 50 * np.copy(feat[:, 18:19]) + 100)\ - .astype('int32')).type(torch.long).view(1,-1)#.to(device) - - lpc_filters = np.copy(feat[:, -16:]) - - start_time = time.time() - x1 = generator(period, bfcc_with_corr, torch.zeros(1,320)) #this means the vocoder runs in complete synthesis mode with zero history audio frames - end_time = time.time() - total_time = end_time - start_time - x1 = x1.squeeze(1).squeeze(0).detach().cpu().numpy() - gen_seconds = len(x1)/16000 - out = deemphasis(inverse_perceptual_weighting(x1, lpc_filters, weighting_vector)) - if verbose: - print(f"Took {total_time:.3f}s to generate {len(x1)} samples ({gen_seconds}s) -> {gen_seconds/total_time:.2f}x real time") - - out = np.clip(np.round(2**15 * out), -2**15, 2**15 -1).astype(np.int16) - wavfile.write(output_filename, 16000, out) - - -########################### The inference loop over folder containing lpcnet feature files ################################# -if __name__ == "__main__": - - args = parser.parse_args() - - generator = model_dict[args.model]() - - - #Load the FWGAN500Hz Checkpoint - saved_gen= torch.load(args.weightfile, map_location='cpu') - generator.load_state_dict(saved_gen) - - #this is just to remove the weight_norm from the model layers as it's no longer needed - def _remove_weight_norm(m): - try: - torch.nn.utils.remove_weight_norm(m) - except ValueError: # this module didn't have weight norm - return - generator.apply(_remove_weight_norm) - - #enable inference mode - generator = generator.eval() - - print('Successfully loaded the generator model ... start generation:') - - if os.path.isdir(args.input): - - os.makedirs(args.output, exist_ok=True) - - for fn in os.listdir(args.input): - print(f"processing input {fn}...") - feature_filename = os.path.join(args.input, fn) - output_filename = os.path.join(args.output, os.path.splitext(fn)[0] + f"_{args.model}.wav") - process_item(generator, feature_filename, output_filename) - else: - process_item(generator, args.input, args.output) - - print("Finished!") \ No newline at end of file diff --git a/android/app/src/main/cpp/third_party/opus/dnn/torch/fwgan/models/__init__.py b/android/app/src/main/cpp/third_party/opus/dnn/torch/fwgan/models/__init__.py deleted file mode 100644 index d52a6eb..0000000 --- a/android/app/src/main/cpp/third_party/opus/dnn/torch/fwgan/models/__init__.py +++ /dev/null @@ -1,7 +0,0 @@ -from .fwgan400 import FWGAN400ContLarge -from .fwgan500 import FWGAN500Cont - -model_dict = { - 'fwgan400': FWGAN400ContLarge, - 'fwgan500': FWGAN500Cont -} \ No newline at end of file diff --git a/android/app/src/main/cpp/third_party/opus/dnn/torch/fwgan/models/fwgan400.py b/android/app/src/main/cpp/third_party/opus/dnn/torch/fwgan/models/fwgan400.py deleted file mode 100644 index 84d9849..0000000 --- a/android/app/src/main/cpp/third_party/opus/dnn/torch/fwgan/models/fwgan400.py +++ /dev/null @@ -1,308 +0,0 @@ -import torch -import torch.nn as nn -import torch.nn.functional as F -from torch.nn.utils import weight_norm -import numpy as np - -which_norm = weight_norm - -#################### Definition of basic model components #################### - -#Convolutional layer with 1 frame look-ahead (used for feature PreCondNet) -class ConvLookahead(nn.Module): - def __init__(self, in_ch, out_ch, kernel_size, dilation=1, groups=1, bias= False): - super(ConvLookahead, self).__init__() - torch.manual_seed(5) - - self.padding_left = (kernel_size - 2) * dilation - self.padding_right = 1 * dilation - - self.conv = which_norm(nn.Conv1d(in_ch,out_ch,kernel_size,dilation=dilation, groups=groups, bias= bias)) - - self.init_weights() - - def init_weights(self): - - for m in self.modules(): - if isinstance(m, nn.Conv1d) or isinstance(m, nn.ConvTranspose1d) or isinstance(m, nn.Linear) or isinstance(m, nn.Embedding): - nn.init.orthogonal_(m.weight.data) - - def forward(self, x): - - x = F.pad(x,(self.padding_left, self.padding_right)) - conv_out = self.conv(x) - return conv_out - -#(modified) GLU Activation layer definition -class GLU(nn.Module): - def __init__(self, feat_size): - super(GLU, self).__init__() - - torch.manual_seed(5) - - self.gate = which_norm(nn.Linear(feat_size, feat_size, bias=False)) - - self.init_weights() - - def init_weights(self): - - for m in self.modules(): - if isinstance(m, nn.Conv1d) or isinstance(m, nn.ConvTranspose1d)\ - or isinstance(m, nn.Linear) or isinstance(m, nn.Embedding): - nn.init.orthogonal_(m.weight.data) - - def forward(self, x): - - out = torch.tanh(x) * torch.sigmoid(self.gate(x)) - - return out - -#GRU layer definition -class ContForwardGRU(nn.Module): - def __init__(self, input_size, hidden_size, num_layers=1): - super(ContForwardGRU, self).__init__() - - torch.manual_seed(5) - - self.hidden_size = hidden_size - - self.cont_fc = nn.Sequential(which_norm(nn.Linear(64, self.hidden_size, bias=False)), - nn.Tanh()) - - self.gru = nn.GRU(input_size=input_size, hidden_size=hidden_size, num_layers=num_layers, batch_first=True,\ - bias=False) - - self.nl = GLU(self.hidden_size) - - self.init_weights() - - def init_weights(self): - - for m in self.modules(): - if isinstance(m, nn.Conv1d) or isinstance(m, nn.ConvTranspose1d) or isinstance(m, nn.Linear) or isinstance(m, nn.Embedding): - nn.init.orthogonal_(m.weight.data) - - def forward(self, x, x0): - - self.gru.flatten_parameters() - - h0 = self.cont_fc(x0).unsqueeze(0) - - output, h0 = self.gru(x, h0) - - return self.nl(output) - -# Framewise convolution layer definition -class ContFramewiseConv(torch.nn.Module): - - def __init__(self, frame_len, out_dim, frame_kernel_size=3, act='glu', causal=True): - - super(ContFramewiseConv, self).__init__() - torch.manual_seed(5) - - self.frame_kernel_size = frame_kernel_size - self.frame_len = frame_len - - if (causal == True) or (self.frame_kernel_size == 2): - - self.required_pad_left = (self.frame_kernel_size - 1) * self.frame_len - self.required_pad_right = 0 - - self.cont_fc = nn.Sequential(which_norm(nn.Linear(64, self.required_pad_left, bias=False)), - nn.Tanh() - ) - - else: - - self.required_pad_left = (self.frame_kernel_size - 1)//2 * self.frame_len - self.required_pad_right = (self.frame_kernel_size - 1)//2 * self.frame_len - - self.fc_input_dim = self.frame_kernel_size * self.frame_len - self.fc_out_dim = out_dim - - if act=='glu': - self.fc = nn.Sequential(which_norm(nn.Linear(self.fc_input_dim, self.fc_out_dim, bias=False)), - GLU(self.fc_out_dim) - ) - if act=='tanh': - self.fc = nn.Sequential(which_norm(nn.Linear(self.fc_input_dim, self.fc_out_dim, bias=False)), - nn.Tanh() - ) - - self.init_weights() - - - def init_weights(self): - - for m in self.modules(): - if isinstance(m, nn.Conv1d) or isinstance(m, nn.ConvTranspose1d) or isinstance(m, nn.Linear) or\ - isinstance(m, nn.Embedding): - nn.init.orthogonal_(m.weight.data) - - def forward(self, x, x0): - - if self.frame_kernel_size == 1: - return self.fc(x) - - x_flat = x.reshape(x.size(0),1,-1) - pad = self.cont_fc(x0).view(x0.size(0),1,-1) - x_flat_padded = torch.cat((pad, x_flat), dim=-1).unsqueeze(2) - - x_flat_padded_unfolded = F.unfold(x_flat_padded,\ - kernel_size= (1,self.fc_input_dim), stride=self.frame_len).permute(0,2,1).contiguous() - - out = self.fc(x_flat_padded_unfolded) - return out - -# A fully-connected based upsampling layer definition -class UpsampleFC(nn.Module): - def __init__(self, in_ch, out_ch, upsample_factor): - super(UpsampleFC, self).__init__() - torch.manual_seed(5) - - self.in_ch = in_ch - self.out_ch = out_ch - self.upsample_factor = upsample_factor - self.fc = nn.Linear(in_ch, out_ch * upsample_factor, bias=False) - self.nl = nn.Tanh() - - self.init_weights() - - def init_weights(self): - - for m in self.modules(): - if isinstance(m, nn.Conv1d) or isinstance(m, nn.ConvTranspose1d) or\ - isinstance(m, nn.Linear) or isinstance(m, nn.Embedding): - nn.init.orthogonal_(m.weight.data) - - def forward(self, x): - - batch_size = x.size(0) - x = x.permute(0, 2, 1) - x = self.nl(self.fc(x)) - x = x.reshape((batch_size, -1, self.out_ch)) - x = x.permute(0, 2, 1) - return x - -########################### The complete model definition ################################# - -class FWGAN400ContLarge(nn.Module): - def __init__(self): - super().__init__() - torch.manual_seed(5) - - self.bfcc_with_corr_upsampler = UpsampleFC(19,80,4) - - self.feat_in_conv1 = ConvLookahead(160,256,kernel_size=5) - self.feat_in_nl1 = GLU(256) - - self.cont_net = nn.Sequential(which_norm(nn.Linear(321, 160, bias=False)), - nn.Tanh(), - which_norm(nn.Linear(160, 160, bias=False)), - nn.Tanh(), - which_norm(nn.Linear(160, 80, bias=False)), - nn.Tanh(), - which_norm(nn.Linear(80, 80, bias=False)), - nn.Tanh(), - which_norm(nn.Linear(80, 64, bias=False)), - nn.Tanh(), - which_norm(nn.Linear(64, 64, bias=False)), - nn.Tanh()) - - self.rnn = ContForwardGRU(256,256) - - self.fwc1 = ContFramewiseConv(256, 256) - self.fwc2 = ContFramewiseConv(256, 128) - self.fwc3 = ContFramewiseConv(128, 128) - self.fwc4 = ContFramewiseConv(128, 64) - self.fwc5 = ContFramewiseConv(64, 64) - self.fwc6 = ContFramewiseConv(64, 40) - self.fwc7 = ContFramewiseConv(40, 40) - - self.init_weights() - self.count_parameters() - - def init_weights(self): - - for m in self.modules(): - if isinstance(m, nn.Conv1d) or isinstance(m, nn.ConvTranspose1d) or isinstance(m, nn.Linear) or\ - isinstance(m, nn.Embedding): - nn.init.orthogonal_(m.weight.data) - - def count_parameters(self): - num_params = sum(p.numel() for p in self.parameters() if p.requires_grad) - print(f"Total number of {self.__class__.__name__} network parameters = {num_params}\n") - - def create_phase_signals(self, periods): - - batch_size = periods.size(0) - progression = torch.arange(1, 160 + 1, dtype=periods.dtype, device=periods.device).view((1, -1)) - progression = torch.repeat_interleave(progression, batch_size, 0) - - phase0 = torch.zeros(batch_size, dtype=periods.dtype, device=periods.device).unsqueeze(-1) - chunks = [] - for sframe in range(periods.size(1)): - f = (2.0 * torch.pi / periods[:, sframe]).unsqueeze(-1) - - chunk_sin = torch.sin(f * progression + phase0) - chunk_sin = chunk_sin.reshape(chunk_sin.size(0),-1,40) - - chunk_cos = torch.cos(f * progression + phase0) - chunk_cos = chunk_cos.reshape(chunk_cos.size(0),-1,40) - - chunk = torch.cat((chunk_sin, chunk_cos), dim = -1) - - phase0 = phase0 + 160 * f - - chunks.append(chunk) - - phase_signals = torch.cat(chunks, dim=1) - - return phase_signals - - - def gain_multiply(self, x, c0): - - gain = 10**(0.5*c0/np.sqrt(18.0)) - gain = torch.repeat_interleave(gain, 160, dim=-1) - gain = gain.reshape(gain.size(0),1,-1).squeeze(1) - - return x * gain - - def forward(self, pitch_period, bfcc_with_corr, x0): - - norm_x0 = torch.norm(x0,2, dim=-1, keepdim=True) - x0 = x0 / torch.sqrt((1e-8) + norm_x0**2) - x0 = torch.cat((torch.log(norm_x0 + 1e-7), x0), dim=-1) - - p_embed = self.create_phase_signals(pitch_period).permute(0, 2, 1).contiguous() - - envelope = self.bfcc_with_corr_upsampler(bfcc_with_corr.permute(0,2,1).contiguous()) - - feat_in = torch.cat((p_embed , envelope), dim=1) - - wav_latent1 = self.feat_in_nl1(self.feat_in_conv1(feat_in).permute(0,2,1).contiguous()) - - cont_latent = self.cont_net(x0) - - rnn_out = self.rnn(wav_latent1, cont_latent) - - fwc1_out = self.fwc1(rnn_out, cont_latent) - - fwc2_out = self.fwc2(fwc1_out, cont_latent) - - fwc3_out = self.fwc3(fwc2_out, cont_latent) - - fwc4_out = self.fwc4(fwc3_out, cont_latent) - - fwc5_out = self.fwc5(fwc4_out, cont_latent) - - fwc6_out = self.fwc6(fwc5_out, cont_latent) - - fwc7_out = self.fwc7(fwc6_out, cont_latent) - - waveform = fwc7_out.reshape(fwc7_out.size(0),1,-1).squeeze(1) - - waveform = self.gain_multiply(waveform,bfcc_with_corr[:,:,:1]) - - return waveform \ No newline at end of file diff --git a/android/app/src/main/cpp/third_party/opus/dnn/torch/fwgan/models/fwgan500.py b/android/app/src/main/cpp/third_party/opus/dnn/torch/fwgan/models/fwgan500.py deleted file mode 100644 index 2c6dea5..0000000 --- a/android/app/src/main/cpp/third_party/opus/dnn/torch/fwgan/models/fwgan500.py +++ /dev/null @@ -1,260 +0,0 @@ -import torch -import torch.nn as nn -import torch.nn.functional as F -from torch.nn.utils import weight_norm -import numpy as np - - -which_norm = weight_norm - -#################### Definition of basic model components #################### - -#Convolutional layer with 1 frame look-ahead (used for feature PreCondNet) -class ConvLookahead(nn.Module): - def __init__(self, in_ch, out_ch, kernel_size, dilation=1, groups=1, bias= False): - super(ConvLookahead, self).__init__() - torch.manual_seed(5) - - self.padding_left = (kernel_size - 2) * dilation - self.padding_right = 1 * dilation - - self.conv = which_norm(nn.Conv1d(in_ch,out_ch,kernel_size,dilation=dilation, groups=groups, bias= bias)) - - self.init_weights() - - def init_weights(self): - - for m in self.modules(): - if isinstance(m, nn.Conv1d) or isinstance(m, nn.ConvTranspose1d) or isinstance(m, nn.Linear) or isinstance(m, nn.Embedding): - nn.init.orthogonal_(m.weight.data) - - def forward(self, x): - - x = F.pad(x,(self.padding_left, self.padding_right)) - conv_out = self.conv(x) - return conv_out - -#(modified) GLU Activation layer definition -class GLU(nn.Module): - def __init__(self, feat_size): - super(GLU, self).__init__() - - torch.manual_seed(5) - - self.gate = which_norm(nn.Linear(feat_size, feat_size, bias=False)) - - self.init_weights() - - def init_weights(self): - - for m in self.modules(): - if isinstance(m, nn.Conv1d) or isinstance(m, nn.ConvTranspose1d)\ - or isinstance(m, nn.Linear) or isinstance(m, nn.Embedding): - nn.init.orthogonal_(m.weight.data) - - def forward(self, x): - - out = torch.tanh(x) * torch.sigmoid(self.gate(x)) - - return out - -#GRU layer definition -class ContForwardGRU(nn.Module): - def __init__(self, input_size, hidden_size, num_layers=1): - super(ContForwardGRU, self).__init__() - - torch.manual_seed(5) - - self.hidden_size = hidden_size - - #This is to initialize the layer with history audio samples for continuation. - self.cont_fc = nn.Sequential(which_norm(nn.Linear(320, self.hidden_size, bias=False)), - nn.Tanh()) - - self.gru = nn.GRU(input_size=input_size, hidden_size=hidden_size, num_layers=num_layers, batch_first=True,\ - bias=False) - - self.nl = GLU(self.hidden_size) - - self.init_weights() - - def init_weights(self): - - for m in self.modules(): - if isinstance(m, nn.Conv1d) or isinstance(m, nn.ConvTranspose1d) or isinstance(m, nn.Linear) or isinstance(m, nn.Embedding): - nn.init.orthogonal_(m.weight.data) - - def forward(self, x, x0): - - self.gru.flatten_parameters() - - h0 = self.cont_fc(x0).unsqueeze(0) - - output, h0 = self.gru(x, h0) - - return self.nl(output) - -# Framewise convolution layer definition -class ContFramewiseConv(torch.nn.Module): - - def __init__(self, frame_len, out_dim, frame_kernel_size=3, act='glu', causal=True): - - super(ContFramewiseConv, self).__init__() - torch.manual_seed(5) - - self.frame_kernel_size = frame_kernel_size - self.frame_len = frame_len - - if (causal == True) or (self.frame_kernel_size == 2): - - self.required_pad_left = (self.frame_kernel_size - 1) * self.frame_len - self.required_pad_right = 0 - - #This is to initialize the layer with history audio samples for continuation. - self.cont_fc = nn.Sequential(which_norm(nn.Linear(320, self.required_pad_left, bias=False)), - nn.Tanh() - ) - - else: - #This means non-causal frame-wise convolution. We don't use it at the moment - self.required_pad_left = (self.frame_kernel_size - 1)//2 * self.frame_len - self.required_pad_right = (self.frame_kernel_size - 1)//2 * self.frame_len - - self.fc_input_dim = self.frame_kernel_size * self.frame_len - self.fc_out_dim = out_dim - - if act=='glu': - self.fc = nn.Sequential(which_norm(nn.Linear(self.fc_input_dim, self.fc_out_dim, bias=False)), - GLU(self.fc_out_dim) - ) - if act=='tanh': - self.fc = nn.Sequential(which_norm(nn.Linear(self.fc_input_dim, self.fc_out_dim, bias=False)), - nn.Tanh() - ) - - self.init_weights() - - - def init_weights(self): - - for m in self.modules(): - if isinstance(m, nn.Conv1d) or isinstance(m, nn.ConvTranspose1d) or isinstance(m, nn.Linear) or\ - isinstance(m, nn.Embedding): - nn.init.orthogonal_(m.weight.data) - - def forward(self, x, x0): - - if self.frame_kernel_size == 1: - return self.fc(x) - - x_flat = x.reshape(x.size(0),1,-1) - pad = self.cont_fc(x0).view(x0.size(0),1,-1) - x_flat_padded = torch.cat((pad, x_flat), dim=-1).unsqueeze(2) - - x_flat_padded_unfolded = F.unfold(x_flat_padded,\ - kernel_size= (1,self.fc_input_dim), stride=self.frame_len).permute(0,2,1).contiguous() - - out = self.fc(x_flat_padded_unfolded) - return out - -########################### The complete model definition ################################# - -class FWGAN500Cont(nn.Module): - def __init__(self): - super().__init__() - torch.manual_seed(5) - - #PrecondNet: - self.bfcc_with_corr_upsampler = nn.Sequential(nn.ConvTranspose1d(19,64,kernel_size=5,stride=5,padding=0,\ - bias=False), - nn.Tanh()) - - self.feat_in_conv = ConvLookahead(128,256,kernel_size=5) - self.feat_in_nl = GLU(256) - - #GRU: - self.rnn = ContForwardGRU(256,256) - - #Frame-wise convolution stack: - self.fwc1 = ContFramewiseConv(256, 256) - self.fwc2 = ContFramewiseConv(256, 128) - self.fwc3 = ContFramewiseConv(128, 128) - self.fwc4 = ContFramewiseConv(128, 64) - self.fwc5 = ContFramewiseConv(64, 64) - self.fwc6 = ContFramewiseConv(64, 32) - self.fwc7 = ContFramewiseConv(32, 32, act='tanh') - - self.init_weights() - self.count_parameters() - - def init_weights(self): - - for m in self.modules(): - if isinstance(m, nn.Conv1d) or isinstance(m, nn.ConvTranspose1d) or isinstance(m, nn.Linear) or\ - isinstance(m, nn.Embedding): - nn.init.orthogonal_(m.weight.data) - - def count_parameters(self): - num_params = sum(p.numel() for p in self.parameters() if p.requires_grad) - print(f"Total number of {self.__class__.__name__} network parameters = {num_params}\n") - - def create_phase_signals(self, periods): - - batch_size = periods.size(0) - progression = torch.arange(1, 160 + 1, dtype=periods.dtype, device=periods.device).view((1, -1)) - progression = torch.repeat_interleave(progression, batch_size, 0) - - phase0 = torch.zeros(batch_size, dtype=periods.dtype, device=periods.device).unsqueeze(-1) - chunks = [] - for sframe in range(periods.size(1)): - f = (2.0 * torch.pi / periods[:, sframe]).unsqueeze(-1) - - chunk_sin = torch.sin(f * progression + phase0) - chunk_sin = chunk_sin.reshape(chunk_sin.size(0),-1,32) - - chunk_cos = torch.cos(f * progression + phase0) - chunk_cos = chunk_cos.reshape(chunk_cos.size(0),-1,32) - - chunk = torch.cat((chunk_sin, chunk_cos), dim = -1) - - phase0 = phase0 + 160 * f - - chunks.append(chunk) - - phase_signals = torch.cat(chunks, dim=1) - - return phase_signals - - - def gain_multiply(self, x, c0): - - gain = 10**(0.5*c0/np.sqrt(18.0)) - gain = torch.repeat_interleave(gain, 160, dim=-1) - gain = gain.reshape(gain.size(0),1,-1).squeeze(1) - - return x * gain - - def forward(self, pitch_period, bfcc_with_corr, x0): - - #This should create a latent representation of shape [Batch_dim, 500 frames, 256 elemets per frame] - p_embed = self.create_phase_signals(pitch_period).permute(0, 2, 1).contiguous() - envelope = self.bfcc_with_corr_upsampler(bfcc_with_corr.permute(0,2,1).contiguous()) - feat_in = torch.cat((p_embed , envelope), dim=1) - wav_latent = self.feat_in_nl(self.feat_in_conv(feat_in).permute(0,2,1).contiguous()) - - #Generation with continuation using history samples x0 starts from here: - - rnn_out = self.rnn(wav_latent, x0) - - fwc1_out = self.fwc1(rnn_out, x0) - fwc2_out = self.fwc2(fwc1_out, x0) - fwc3_out = self.fwc3(fwc2_out, x0) - fwc4_out = self.fwc4(fwc3_out, x0) - fwc5_out = self.fwc5(fwc4_out, x0) - fwc6_out = self.fwc6(fwc5_out, x0) - fwc7_out = self.fwc7(fwc6_out, x0) - - waveform_unscaled = fwc7_out.reshape(fwc7_out.size(0),1,-1).squeeze(1) - waveform = self.gain_multiply(waveform_unscaled,bfcc_with_corr[:,:,:1]) - - return waveform diff --git a/android/app/src/main/cpp/third_party/opus/dnn/torch/lossgen/README.md b/android/app/src/main/cpp/third_party/opus/dnn/torch/lossgen/README.md deleted file mode 100644 index 55c1b44..0000000 --- a/android/app/src/main/cpp/third_party/opus/dnn/torch/lossgen/README.md +++ /dev/null @@ -1,27 +0,0 @@ -#Packet loss simulator - -This code is an attempt at simulating better packet loss scenarios. The most common way of simulating -packet loss is to use a random sequence where each packet loss event is uncorrelated with previous events. -That is a simplistic model since we know that losses often occur in bursts. This model uses real data -to build a generative model for packet loss. - -We use the training data provided for the Audio Deep Packet Loss Concealment Challenge, which is available at: - -http://plcchallenge2022pub.blob.core.windows.net/plcchallengearchive/test_train.tar.gz - -To create the training data, run: - -`./process_data.sh //test_train/train/lossy_signals/` - -That will create an ascii loss\_sorted.txt file with all loss data sorted in increasing packet loss -percentage. Then just run: - -`python ./train_lossgen.py` - -to train a model - -To generate a sequence, run - -`python3 ./test_lossgen.py output.txt --length 10000` - -where is the .pth model file and is the amount of loss (e.g. 0.2 for 20% loss). diff --git a/android/app/src/main/cpp/third_party/opus/dnn/torch/lossgen/export_lossgen.py b/android/app/src/main/cpp/third_party/opus/dnn/torch/lossgen/export_lossgen.py deleted file mode 100644 index 1f7df95..0000000 --- a/android/app/src/main/cpp/third_party/opus/dnn/torch/lossgen/export_lossgen.py +++ /dev/null @@ -1,101 +0,0 @@ -""" -/* Copyright (c) 2022 Amazon - Written by Jan Buethe */ -/* - Redistribution and use in source and binary forms, with or without - modification, are permitted provided that the following conditions - are met: - - - Redistributions of source code must retain the above copyright - notice, this list of conditions and the following disclaimer. - - - Redistributions in binary form must reproduce the above copyright - notice, this list of conditions and the following disclaimer in the - documentation and/or other materials provided with the distribution. - - THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS - ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT - LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR - A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER - OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, - EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, - PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR - PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF - LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING - NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS - SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. -*/ -""" - -import os -import argparse -import sys - -sys.path.append(os.path.join(os.path.dirname(__file__), '../weight-exchange')) - - -parser = argparse.ArgumentParser() - -parser.add_argument('checkpoint', type=str, help='model checkpoint') -parser.add_argument('output_dir', type=str, help='output folder') - -args = parser.parse_args() - -import torch -import numpy as np - -import lossgen -from wexchange.torch import dump_torch_weights -from wexchange.c_export import CWriter, print_vector - -def c_export(args, model): - - message = f"Auto generated from checkpoint {os.path.basename(args.checkpoint)}" - - writer = CWriter(os.path.join(args.output_dir, "lossgen_data"), message=message, model_struct_name='LossGen') - writer.header.write( -f""" -#include "opus_types.h" -""" - ) - - dense_layers = [ - ('dense_in', "lossgen_dense_in"), - ('dense_out', "lossgen_dense_out") - ] - - - for name, export_name in dense_layers: - layer = model.get_submodule(name) - dump_torch_weights(writer, layer, name=export_name, verbose=True, quantize=False, scale=None) - - - gru_layers = [ - ("gru1", "lossgen_gru1"), - ("gru2", "lossgen_gru2"), - ] - - max_rnn_units = max([dump_torch_weights(writer, model.get_submodule(name), export_name, verbose=True, input_sparse=False, quantize=True, scale=None, recurrent_scale=None) - for name, export_name in gru_layers]) - - writer.header.write( -f""" - -#define LOSSGEN_MAX_RNN_UNITS {max_rnn_units} - -""" - ) - - writer.close() - - -if __name__ == "__main__": - - os.makedirs(args.output_dir, exist_ok=True) - checkpoint = torch.load(args.checkpoint, map_location='cpu') - model = lossgen.LossGen(*checkpoint['model_args'], **checkpoint['model_kwargs']) - model.load_state_dict(checkpoint['state_dict'], strict=False) - #model = LossGen() - #checkpoint = torch.load(args.checkpoint, map_location='cpu') - #model.load_state_dict(checkpoint['state_dict']) - c_export(args, model) diff --git a/android/app/src/main/cpp/third_party/opus/dnn/torch/lossgen/lossgen.py b/android/app/src/main/cpp/third_party/opus/dnn/torch/lossgen/lossgen.py deleted file mode 100644 index 9025165..0000000 --- a/android/app/src/main/cpp/third_party/opus/dnn/torch/lossgen/lossgen.py +++ /dev/null @@ -1,29 +0,0 @@ -import torch -from torch import nn -import torch.nn.functional as F - -class LossGen(nn.Module): - def __init__(self, gru1_size=16, gru2_size=16): - super(LossGen, self).__init__() - - self.gru1_size = gru1_size - self.gru2_size = gru2_size - self.dense_in = nn.Linear(2, 8) - self.gru1 = nn.GRU(8, self.gru1_size, batch_first=True) - self.gru2 = nn.GRU(self.gru1_size, self.gru2_size, batch_first=True) - self.dense_out = nn.Linear(self.gru2_size, 1) - - def forward(self, loss, perc, states=None): - #print(states) - device = loss.device - batch_size = loss.size(0) - if states is None: - gru1_state = torch.zeros((1, batch_size, self.gru1_size), device=device) - gru2_state = torch.zeros((1, batch_size, self.gru2_size), device=device) - else: - gru1_state = states[0] - gru2_state = states[1] - x = torch.tanh(self.dense_in(torch.cat([loss, perc], dim=-1))) - gru1_out, gru1_state = self.gru1(x, gru1_state) - gru2_out, gru2_state = self.gru2(gru1_out, gru2_state) - return self.dense_out(gru2_out), [gru1_state, gru2_state] diff --git a/android/app/src/main/cpp/third_party/opus/dnn/torch/lossgen/process_data.sh b/android/app/src/main/cpp/third_party/opus/dnn/torch/lossgen/process_data.sh deleted file mode 100644 index 308fd0a..0000000 --- a/android/app/src/main/cpp/third_party/opus/dnn/torch/lossgen/process_data.sh +++ /dev/null @@ -1,17 +0,0 @@ -#!/bin/sh - -#directory containing the loss files -datadir=$1 - -for i in $datadir/*_is_lost.txt -do - perc=`cat $i | awk '{a+=$1}END{print a/NR}'` - echo $perc $i -done > percentage_list.txt - -sort -n percentage_list.txt | awk '{print $2}' > percentage_sorted.txt - -for i in `cat percentage_sorted.txt` -do - cat $i -done > loss_sorted.txt diff --git a/android/app/src/main/cpp/third_party/opus/dnn/torch/lossgen/test_lossgen.py b/android/app/src/main/cpp/third_party/opus/dnn/torch/lossgen/test_lossgen.py deleted file mode 100644 index 95659b1..0000000 --- a/android/app/src/main/cpp/third_party/opus/dnn/torch/lossgen/test_lossgen.py +++ /dev/null @@ -1,42 +0,0 @@ -import lossgen -import os -import argparse -import torch -import numpy as np - - -parser = argparse.ArgumentParser() - -parser.add_argument('model', type=str, help='CELPNet model') -parser.add_argument('percentage', type=float, help='percentage loss') -parser.add_argument('output', type=str, help='path to output file (ascii)') - -parser.add_argument('--length', type=int, help="length of sequence to generate", default=500) - -args = parser.parse_args() - - - -checkpoint = torch.load(args.model, map_location='cpu') -model = lossgen.LossGen(*checkpoint['model_args'], **checkpoint['model_kwargs']) -model.load_state_dict(checkpoint['state_dict'], strict=False) - -states=None -last = torch.zeros((1,1,1)) -perc = torch.tensor((args.percentage,))[None,None,:] -seq = torch.zeros((0,1,1)) - -one = torch.ones((1,1,1)) -zero = torch.zeros((1,1,1)) - -if __name__ == '__main__': - for i in range(args.length): - prob, states = model(last, perc, states=states) - prob = torch.sigmoid(prob) - states[0] = states[0].detach() - states[1] = states[1].detach() - loss = one if np.random.rand() < prob else zero - last = loss - seq = torch.cat([seq, loss]) - -np.savetxt(args.output, seq[:,:,0].numpy().astype('int'), fmt='%d') diff --git a/android/app/src/main/cpp/third_party/opus/dnn/torch/lossgen/train_lossgen.py b/android/app/src/main/cpp/third_party/opus/dnn/torch/lossgen/train_lossgen.py deleted file mode 100644 index 0bbe279..0000000 --- a/android/app/src/main/cpp/third_party/opus/dnn/torch/lossgen/train_lossgen.py +++ /dev/null @@ -1,99 +0,0 @@ -import numpy as np -import torch -from torch import nn -import torch.nn.functional as F -import tqdm -from scipy.signal import lfilter -import os -import lossgen - -class LossDataset(torch.utils.data.Dataset): - def __init__(self, - loss_file, - sequence_length=997): - - self.sequence_length = sequence_length - - self.loss = np.loadtxt(loss_file, dtype='float32') - - self.nb_sequences = self.loss.shape[0]//self.sequence_length - self.loss = self.loss[:self.nb_sequences*self.sequence_length] - self.perc = lfilter(np.array([.001], dtype='float32'), np.array([1., -.999], dtype='float32'), self.loss) - - self.loss = np.reshape(self.loss, (self.nb_sequences, self.sequence_length, 1)) - self.perc = np.reshape(self.perc, (self.nb_sequences, self.sequence_length, 1)) - - def __len__(self): - return self.nb_sequences - - def __getitem__(self, index): - r0 = np.random.normal(scale=.1, size=(1,1)).astype('float32') - r1 = np.random.normal(scale=.1, size=(self.sequence_length,1)).astype('float32') - perc = self.perc[index, :, :] - perc = perc + (r0+r1)*perc*(1-perc) - return [self.loss[index, :, :], perc] - - -adam_betas = [0.8, 0.98] -adam_eps = 1e-8 -batch_size=256 -lr_decay = 0.001 -lr = 0.003 -epsilon = 1e-5 -epochs = 2000 -checkpoint_dir='checkpoint' -os.makedirs(checkpoint_dir, exist_ok=True) -checkpoint = dict() - -device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu") - -checkpoint['model_args'] = () -checkpoint['model_kwargs'] = {'gru1_size': 16, 'gru2_size': 32} -model = lossgen.LossGen(*checkpoint['model_args'], **checkpoint['model_kwargs']) -dataset = LossDataset('loss_sorted.txt') -dataloader = torch.utils.data.DataLoader(dataset, batch_size=batch_size, shuffle=True, drop_last=True, num_workers=4) - - -optimizer = torch.optim.AdamW(model.parameters(), lr=lr, betas=adam_betas, eps=adam_eps) - - -# learning rate scheduler -scheduler = torch.optim.lr_scheduler.LambdaLR(optimizer=optimizer, lr_lambda=lambda x : 1 / (1 + lr_decay * x)) - - -if __name__ == '__main__': - model.to(device) - states = None - for epoch in range(1, epochs + 1): - - running_loss = 0 - - print(f"training epoch {epoch}...") - with tqdm.tqdm(dataloader, unit='batch') as tepoch: - for i, (loss, perc) in enumerate(tepoch): - optimizer.zero_grad() - loss = loss.to(device) - perc = perc.to(device) - - out, states = model(loss, perc, states=states) - states = [state.detach() for state in states] - out = torch.sigmoid(out[:,:-1,:]) - target = loss[:,1:,:] - - loss = torch.mean(-target*torch.log(out+epsilon) - (1-target)*torch.log(1-out+epsilon)) - - loss.backward() - optimizer.step() - - scheduler.step() - - running_loss += loss.detach().cpu().item() - tepoch.set_postfix(loss=f"{running_loss/(i+1):8.5f}", - ) - - # save checkpoint - checkpoint_path = os.path.join(checkpoint_dir, f'lossgen_{epoch}.pth') - checkpoint['state_dict'] = model.state_dict() - checkpoint['loss'] = running_loss / len(dataloader) - checkpoint['epoch'] = epoch - torch.save(checkpoint, checkpoint_path) diff --git a/android/app/src/main/cpp/third_party/opus/dnn/torch/lpcnet/README.md b/android/app/src/main/cpp/third_party/opus/dnn/torch/lpcnet/README.md deleted file mode 100644 index 26d9ea1..0000000 --- a/android/app/src/main/cpp/third_party/opus/dnn/torch/lpcnet/README.md +++ /dev/null @@ -1,27 +0,0 @@ -# LPCNet - -Incomplete pytorch implementation of LPCNet - -## Data preparation -For data preparation use dump_data in github.com/xiph/LPCNet. To turn this into -a training dataset, copy data and feature file to a folder and run - -python add_dataset_config.py my_dataset_folder - - -## Training -To train a model, create and adjust a setup file, e.g. with - -python make_default_setup.py my_setup.yml --path2dataset my_dataset_folder - -Then simply run - -python train_lpcnet.py my_setup.yml my_output - -## Inference -Create feature file with dump_data from github.com/xiph/LPCNet. Then run e.g. - -python test_lpcnet.py features.f32 my_output/checkpoints/checkpoint_ep_10.pth out.wav - -Inference runs on CPU and takes usually between 3 and 20 seconds per generated second of audio, -depending on the CPU. diff --git a/android/app/src/main/cpp/third_party/opus/dnn/torch/lpcnet/add_dataset_config.py b/android/app/src/main/cpp/third_party/opus/dnn/torch/lpcnet/add_dataset_config.py deleted file mode 100644 index 1b6b4e8..0000000 --- a/android/app/src/main/cpp/third_party/opus/dnn/torch/lpcnet/add_dataset_config.py +++ /dev/null @@ -1,77 +0,0 @@ -""" -/* Copyright (c) 2023 Amazon - Written by Jan Buethe */ -/* - Redistribution and use in source and binary forms, with or without - modification, are permitted provided that the following conditions - are met: - - - Redistributions of source code must retain the above copyright - notice, this list of conditions and the following disclaimer. - - - Redistributions in binary form must reproduce the above copyright - notice, this list of conditions and the following disclaimer in the - documentation and/or other materials provided with the distribution. - - THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS - ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT - LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR - A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER - OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, - EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, - PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR - PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF - LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING - NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS - SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. -*/ -""" - -import argparse -import os - -import yaml - - -from utils.templates import dataset_template_v1, dataset_template_v2 - - - - -parser = argparse.ArgumentParser("add_dataset_config.py") - -parser.add_argument('path', type=str, help='path to folder containing feature and data file') -parser.add_argument('--version', type=int, help="dataset version, 1 for classic LPCNet with 55 feature slots, 2 for new format with 36 feature slots.", default=2) -parser.add_argument('--description', type=str, help='brief dataset description', default="I will add a description later") -args = parser.parse_args() - - -if args.version == 1: - template = dataset_template_v1 - data_extension = '.u8' -elif args.version == 2: - template = dataset_template_v2 - data_extension = '.s16' -else: - raise ValueError(f"unknown dataset version {args.version}") - -# get folder content -content = os.listdir(args.path) - -features = [c for c in content if c.endswith('.f32')] - -if len(features) != 1: - print("could not determine feature file") -else: - template['feature_file'] = features[0] - -data = [c for c in content if c.endswith(data_extension)] -if len(data) != 1: - print("could not determine data file") -else: - template['signal_file'] = data[0] - -template['description'] = args.description - -with open(os.path.join(args.path, 'info.yml'), 'w') as f: - yaml.dump(template, f) diff --git a/android/app/src/main/cpp/third_party/opus/dnn/torch/lpcnet/data/__init__.py b/android/app/src/main/cpp/third_party/opus/dnn/torch/lpcnet/data/__init__.py deleted file mode 100644 index 50bad87..0000000 --- a/android/app/src/main/cpp/third_party/opus/dnn/torch/lpcnet/data/__init__.py +++ /dev/null @@ -1 +0,0 @@ -from .lpcnet_dataset import LPCNetDataset \ No newline at end of file diff --git a/android/app/src/main/cpp/third_party/opus/dnn/torch/lpcnet/data/lpcnet_dataset.py b/android/app/src/main/cpp/third_party/opus/dnn/torch/lpcnet/data/lpcnet_dataset.py deleted file mode 100644 index dbefc57..0000000 --- a/android/app/src/main/cpp/third_party/opus/dnn/torch/lpcnet/data/lpcnet_dataset.py +++ /dev/null @@ -1,227 +0,0 @@ -""" -/* Copyright (c) 2023 Amazon - Written by Jan Buethe */ -/* - Redistribution and use in source and binary forms, with or without - modification, are permitted provided that the following conditions - are met: - - - Redistributions of source code must retain the above copyright - notice, this list of conditions and the following disclaimer. - - - Redistributions in binary form must reproduce the above copyright - notice, this list of conditions and the following disclaimer in the - documentation and/or other materials provided with the distribution. - - THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS - ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT - LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR - A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER - OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, - EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, - PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR - PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF - LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING - NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS - SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. -*/ -""" - -""" Dataset for LPCNet training """ -import os - -import yaml -import torch -import numpy as np -from torch.utils.data import Dataset - - -scale = 255.0/32768.0 -scale_1 = 32768.0/255.0 -def ulaw2lin(u): - u = u - 128 - s = np.sign(u) - u = np.abs(u) - return s*scale_1*(np.exp(u/128.*np.log(256))-1) - - -def lin2ulaw(x): - s = np.sign(x) - x = np.abs(x) - u = (s*(128*np.log(1+scale*x)/np.log(256))) - u = np.clip(128 + np.round(u), 0, 255) - return u - - -def run_lpc(signal, lpcs, frame_length=160): - num_frames, lpc_order = lpcs.shape - - prediction = np.concatenate( - [- np.convolve(signal[i * frame_length : (i + 1) * frame_length + lpc_order - 1], lpcs[i], mode='valid') for i in range(num_frames)] - ) - error = signal[lpc_order :] - prediction - - return prediction, error - -class LPCNetDataset(Dataset): - def __init__(self, - path_to_dataset, - features=['cepstrum', 'periods', 'pitch_corr'], - input_signals=['last_signal', 'prediction', 'last_error'], - target='error', - frames_per_sample=15, - feature_history=2, - feature_lookahead=2, - lpc_gamma=1): - - super(LPCNetDataset, self).__init__() - - # load dataset info - self.path_to_dataset = path_to_dataset - with open(os.path.join(path_to_dataset, 'info.yml'), 'r') as f: - dataset = yaml.load(f, yaml.FullLoader) - - # dataset version - self.version = dataset['version'] - if self.version == 1: - self.getitem = self.getitem_v1 - elif self.version == 2: - self.getitem = self.getitem_v2 - else: - raise ValueError(f"dataset version {self.version} unknown") - - # features - self.feature_history = feature_history - self.feature_lookahead = feature_lookahead - self.frame_offset = 1 + self.feature_history - self.frames_per_sample = frames_per_sample - self.input_features = features - self.feature_frame_layout = dataset['feature_frame_layout'] - self.lpc_gamma = lpc_gamma - - # load feature file - self.feature_file = os.path.join(path_to_dataset, dataset['feature_file']) - self.features = np.memmap(self.feature_file, dtype=dataset['feature_dtype']) - self.feature_frame_length = dataset['feature_frame_length'] - - assert len(self.features) % self.feature_frame_length == 0 - self.features = self.features.reshape((-1, self.feature_frame_length)) - - # derive number of samples is dataset - self.dataset_length = (len(self.features) - self.frame_offset - self.feature_lookahead - 1) // self.frames_per_sample - - # signals - self.frame_length = dataset['frame_length'] - self.signal_frame_layout = dataset['signal_frame_layout'] - self.input_signals = input_signals - self.target = target - - # load signals - self.signal_file = os.path.join(path_to_dataset, dataset['signal_file']) - self.signals = np.memmap(self.signal_file, dtype=dataset['signal_dtype']) - self.signal_frame_length = dataset['signal_frame_length'] - self.signals = self.signals.reshape((-1, self.signal_frame_length)) - assert len(self.signals) == len(self.features) * self.frame_length - - def __getitem__(self, index): - return self.getitem(index) - - def getitem_v2(self, index): - sample = dict() - - # extract features - frame_start = self.frame_offset + index * self.frames_per_sample - self.feature_history - frame_stop = self.frame_offset + (index + 1) * self.frames_per_sample + self.feature_lookahead - - for feature in self.input_features: - feature_start, feature_stop = self.feature_frame_layout[feature] - sample[feature] = self.features[frame_start : frame_stop, feature_start : feature_stop] - - # convert periods - if 'periods' in self.input_features: - sample['periods'] = (0.1 + 50 * sample['periods'] + 100).astype('int16') - - signal_start = (self.frame_offset + index * self.frames_per_sample) * self.frame_length - signal_stop = (self.frame_offset + (index + 1) * self.frames_per_sample) * self.frame_length - - # last_signal and signal are always expected to be there - sample['last_signal'] = self.signals[signal_start : signal_stop, self.signal_frame_layout['last_signal']] - sample['signal'] = self.signals[signal_start : signal_stop, self.signal_frame_layout['signal']] - - # calculate prediction and error if lpc coefficients present and prediction not given - if 'lpc' in self.feature_frame_layout and 'prediction' not in self.signal_frame_layout: - # lpc coefficients with one frame lookahead - # frame positions (start one frame early for past excitation) - frame_start = self.frame_offset + self.frames_per_sample * index - 1 - frame_stop = self.frame_offset + self.frames_per_sample * (index + 1) - - # feature positions - lpc_start, lpc_stop = self.feature_frame_layout['lpc'] - lpc_order = lpc_stop - lpc_start - lpcs = self.features[frame_start : frame_stop, lpc_start : lpc_stop] - - # LPC weighting - lpc_order = lpc_stop - lpc_start - weights = np.array([self.lpc_gamma ** (i + 1) for i in range(lpc_order)]) - lpcs = lpcs * weights - - # signal position (lpc_order samples as history) - signal_start = frame_start * self.frame_length - lpc_order + 1 - signal_stop = frame_stop * self.frame_length + 1 - noisy_signal = self.signals[signal_start : signal_stop, self.signal_frame_layout['last_signal']] - clean_signal = self.signals[signal_start - 1 : signal_stop - 1, self.signal_frame_layout['signal']] - - noisy_prediction, noisy_error = run_lpc(noisy_signal, lpcs, frame_length=self.frame_length) - - # extract signals - offset = self.frame_length - sample['prediction'] = noisy_prediction[offset : offset + self.frame_length * self.frames_per_sample] - sample['last_error'] = noisy_error[offset - 1 : offset - 1 + self.frame_length * self.frames_per_sample] - # calculate error between real signal and noisy prediction - - - sample['error'] = sample['signal'] - sample['prediction'] - - - # concatenate features - feature_keys = [key for key in self.input_features if not key.startswith("periods")] - features = torch.concat([torch.FloatTensor(sample[key]) for key in feature_keys], dim=-1) - signals = torch.cat([torch.LongTensor(lin2ulaw(sample[key])).unsqueeze(-1) for key in self.input_signals], dim=-1) - target = torch.LongTensor(lin2ulaw(sample[self.target])) - periods = torch.LongTensor(sample['periods']) - - return {'features' : features, 'periods' : periods, 'signals' : signals, 'target' : target} - - def getitem_v1(self, index): - sample = dict() - - # extract features - frame_start = self.frame_offset + index * self.frames_per_sample - self.feature_history - frame_stop = self.frame_offset + (index + 1) * self.frames_per_sample + self.feature_lookahead - - for feature in self.input_features: - feature_start, feature_stop = self.feature_frame_layout[feature] - sample[feature] = self.features[frame_start : frame_stop, feature_start : feature_stop] - - # convert periods - if 'periods' in self.input_features: - sample['periods'] = (0.1 + 50 * sample['periods'] + 100).astype('int16') - - signal_start = (self.frame_offset + index * self.frames_per_sample) * self.frame_length - signal_stop = (self.frame_offset + (index + 1) * self.frames_per_sample) * self.frame_length - - # last_signal and signal are always expected to be there - for signal_name, index in self.signal_frame_layout.items(): - sample[signal_name] = self.signals[signal_start : signal_stop, index] - - # concatenate features - feature_keys = [key for key in self.input_features if not key.startswith("periods")] - features = torch.concat([torch.FloatTensor(sample[key]) for key in feature_keys], dim=-1) - signals = torch.cat([torch.LongTensor(sample[key]).unsqueeze(-1) for key in self.input_signals], dim=-1) - target = torch.LongTensor(sample[self.target]) - periods = torch.LongTensor(sample['periods']) - - return {'features' : features, 'periods' : periods, 'signals' : signals, 'target' : target} - - def __len__(self): - return self.dataset_length diff --git a/android/app/src/main/cpp/third_party/opus/dnn/torch/lpcnet/engine/lpcnet_engine.py b/android/app/src/main/cpp/third_party/opus/dnn/torch/lpcnet/engine/lpcnet_engine.py deleted file mode 100644 index c964bdd..0000000 --- a/android/app/src/main/cpp/third_party/opus/dnn/torch/lpcnet/engine/lpcnet_engine.py +++ /dev/null @@ -1,141 +0,0 @@ -""" -/* Copyright (c) 2023 Amazon - Written by Jan Buethe */ -/* - Redistribution and use in source and binary forms, with or without - modification, are permitted provided that the following conditions - are met: - - - Redistributions of source code must retain the above copyright - notice, this list of conditions and the following disclaimer. - - - Redistributions in binary form must reproduce the above copyright - notice, this list of conditions and the following disclaimer in the - documentation and/or other materials provided with the distribution. - - THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS - ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT - LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR - A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER - OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, - EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, - PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR - PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF - LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING - NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS - SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. -*/ -""" - -import torch -from tqdm import tqdm -import sys - -def train_one_epoch(model, criterion, optimizer, dataloader, device, scheduler, log_interval=10): - - model.to(device) - model.train() - - running_loss = 0 - previous_running_loss = 0 - - # gru states - gru_a_state = torch.zeros(1, dataloader.batch_size, model.gru_a_units, device=device).to(device) - gru_b_state = torch.zeros(1, dataloader.batch_size, model.gru_b_units, device=device).to(device) - gru_states = [gru_a_state, gru_b_state] - - with tqdm(dataloader, unit='batch', file=sys.stdout) as tepoch: - - for i, batch in enumerate(tepoch): - - # set gradients to zero - optimizer.zero_grad() - - # zero out initial gru states - gru_a_state.zero_() - gru_b_state.zero_() - - # push batch to device - for key in batch: - batch[key] = batch[key].to(device) - - target = batch['target'] - - # calculate model output - output = model(batch['features'], batch['periods'], batch['signals'], gru_states) - - # calculate loss - loss = criterion(output.permute(0, 2, 1), target) - - # calculate gradients - loss.backward() - - # update weights - optimizer.step() - - # update learning rate - scheduler.step() - - # call sparsifier - model.sparsify() - - # update running loss - running_loss += float(loss.cpu()) - - # update status bar - if i % log_interval == 0: - tepoch.set_postfix(running_loss=f"{running_loss/(i + 1):8.7f}", current_loss=f"{(running_loss - previous_running_loss)/log_interval:8.7f}") - previous_running_loss = running_loss - - - running_loss /= len(dataloader) - - return running_loss - -def evaluate(model, criterion, dataloader, device, log_interval=10): - - model.to(device) - model.eval() - - running_loss = 0 - previous_running_loss = 0 - - # gru states - gru_a_state = torch.zeros(1, dataloader.batch_size, model.gru_a_units, device=device).to(device) - gru_b_state = torch.zeros(1, dataloader.batch_size, model.gru_b_units, device=device).to(device) - gru_states = [gru_a_state, gru_b_state] - - with torch.no_grad(): - with tqdm(dataloader, unit='batch', file=sys.stdout) as tepoch: - - for i, batch in enumerate(tepoch): - - - # zero out initial gru states - gru_a_state.zero_() - gru_b_state.zero_() - - # push batch to device - for key in batch: - batch[key] = batch[key].to(device) - - target = batch['target'] - - # calculate model output - output = model(batch['features'], batch['periods'], batch['signals'], gru_states) - - # calculate loss - loss = criterion(output.permute(0, 2, 1), target) - - # update running loss - running_loss += float(loss.cpu()) - - # update status bar - if i % log_interval == 0: - tepoch.set_postfix(running_loss=f"{running_loss/(i + 1):8.7f}", current_loss=f"{(running_loss - previous_running_loss)/log_interval:8.7f}") - previous_running_loss = running_loss - - - running_loss /= len(dataloader) - - return running_loss \ No newline at end of file diff --git a/android/app/src/main/cpp/third_party/opus/dnn/torch/lpcnet/make_default_setup.py b/android/app/src/main/cpp/third_party/opus/dnn/torch/lpcnet/make_default_setup.py deleted file mode 100644 index 17031d2..0000000 --- a/android/app/src/main/cpp/third_party/opus/dnn/torch/lpcnet/make_default_setup.py +++ /dev/null @@ -1,56 +0,0 @@ -""" -/* Copyright (c) 2023 Amazon - Written by Jan Buethe */ -/* - Redistribution and use in source and binary forms, with or without - modification, are permitted provided that the following conditions - are met: - - - Redistributions of source code must retain the above copyright - notice, this list of conditions and the following disclaimer. - - - Redistributions in binary form must reproduce the above copyright - notice, this list of conditions and the following disclaimer in the - documentation and/or other materials provided with the distribution. - - THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS - ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT - LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR - A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER - OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, - EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, - PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR - PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF - LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING - NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS - SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. -*/ -""" - -import argparse - -import yaml - -from utils.templates import setup_dict - -parser = argparse.ArgumentParser() - -parser.add_argument('name', type=str, help='name of default setup file') -parser.add_argument('--model', choices=['lpcnet', 'multi_rate'], help='LPCNet model name', default='lpcnet') -parser.add_argument('--path2dataset', type=str, help='dataset path', default=None) - -args = parser.parse_args() - -setup = setup_dict[args.model] - -# update dataset if given -if type(args.path2dataset) != type(None): - setup['dataset'] = args.path2dataset - -name = args.name -if not name.endswith('.yml'): - name += '.yml' - -if __name__ == '__main__': - with open(name, 'w') as f: - f.write(yaml.dump(setup)) diff --git a/android/app/src/main/cpp/third_party/opus/dnn/torch/lpcnet/make_test_config.py b/android/app/src/main/cpp/third_party/opus/dnn/torch/lpcnet/make_test_config.py deleted file mode 100644 index 7c414e0..0000000 --- a/android/app/src/main/cpp/third_party/opus/dnn/torch/lpcnet/make_test_config.py +++ /dev/null @@ -1,78 +0,0 @@ -""" -/* Copyright (c) 2023 Amazon - Written by Jan Buethe */ -/* - Redistribution and use in source and binary forms, with or without - modification, are permitted provided that the following conditions - are met: - - - Redistributions of source code must retain the above copyright - notice, this list of conditions and the following disclaimer. - - - Redistributions in binary form must reproduce the above copyright - notice, this list of conditions and the following disclaimer in the - documentation and/or other materials provided with the distribution. - - THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS - ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT - LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR - A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER - OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, - EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, - PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR - PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF - LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING - NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS - SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. -*/ -""" - -import argparse -import os -import sys - -parser = argparse.ArgumentParser() -parser.add_argument("config_name", type=str, help="name of config file (.yml will be appended)") -parser.add_argument("test_name", type=str, help="name for test result display") -parser.add_argument("checkpoint", type=str, help="checkpoint to test") -parser.add_argument("--lpcnet-demo", type=str, help="path to lpcnet_demo binary, default: /local/code/LPCNet/lpcnet_demo", default="/local/code/LPCNet/lpcnet_demo") -parser.add_argument("--lpcnext-path", type=str, help="path to lpcnext folder, defalut: dirname(__file__)", default=os.path.dirname(__file__)) -parser.add_argument("--python-exe", type=str, help='python executable path, default: sys.executable', default=sys.executable) -parser.add_argument("--pad", type=str, help="left pad of output in seconds, default: 0.015", default="0.015") -parser.add_argument("--trim", type=str, help="left trim of output in seconds, default: 0", default="0") - - - -template=''' -test: "{NAME}" -processing: - - "sox {{INPUT}} {{INPUT}}.raw" - - "{LPCNET_DEMO} -features {{INPUT}}.raw {{INPUT}}.features.f32" - - "{PYTHON} {WORKING}/test_lpcnet.py {{INPUT}}.features.f32 {CHECKPOINT} {{OUTPUT}}.ua.wav" - - "sox {{OUTPUT}}.ua.wav {{OUTPUT}}.uap.wav pad {PAD}" - - "sox {{OUTPUT}}.uap.wav {{OUTPUT}} trim {TRIM}" - - "rm {{INPUT}}.raw {{OUTPUT}}.uap.wav {{OUTPUT}}.ua.wav {{INPUT}}.features.f32" -''' - -if __name__ == "__main__": - args = parser.parse_args() - - - file_content = template.format( - NAME=args.test_name, - LPCNET_DEMO=os.path.abspath(args.lpcnet_demo), - PYTHON=os.path.abspath(args.python_exe), - PAD=args.pad, - TRIM=args.trim, - WORKING=os.path.abspath(args.lpcnext_path), - CHECKPOINT=os.path.abspath(args.checkpoint) - ) - - print(file_content) - - filename = args.config_name - if not filename.endswith(".yml"): - filename += ".yml" - - with open(filename, "w") as f: - f.write(file_content) diff --git a/android/app/src/main/cpp/third_party/opus/dnn/torch/lpcnet/models/__init__.py b/android/app/src/main/cpp/third_party/opus/dnn/torch/lpcnet/models/__init__.py deleted file mode 100644 index a26bc1c..0000000 --- a/android/app/src/main/cpp/third_party/opus/dnn/torch/lpcnet/models/__init__.py +++ /dev/null @@ -1,8 +0,0 @@ -from .lpcnet import LPCNet -from .multi_rate_lpcnet import MultiRateLPCNet - - -model_dict = { - 'lpcnet' : LPCNet, - 'multi_rate' : MultiRateLPCNet -} \ No newline at end of file diff --git a/android/app/src/main/cpp/third_party/opus/dnn/torch/lpcnet/models/lpcnet.py b/android/app/src/main/cpp/third_party/opus/dnn/torch/lpcnet/models/lpcnet.py deleted file mode 100644 index e83ae90..0000000 --- a/android/app/src/main/cpp/third_party/opus/dnn/torch/lpcnet/models/lpcnet.py +++ /dev/null @@ -1,303 +0,0 @@ -""" -/* Copyright (c) 2023 Amazon - Written by Jan Buethe */ -/* - Redistribution and use in source and binary forms, with or without - modification, are permitted provided that the following conditions - are met: - - - Redistributions of source code must retain the above copyright - notice, this list of conditions and the following disclaimer. - - - Redistributions in binary form must reproduce the above copyright - notice, this list of conditions and the following disclaimer in the - documentation and/or other materials provided with the distribution. - - THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS - ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT - LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR - A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER - OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, - EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, - PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR - PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF - LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING - NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS - SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. -*/ -""" - -import torch -from torch import nn -import numpy as np - -from utils.ulaw import lin2ulawq, ulaw2lin -from utils.sample import sample_excitation -from utils.pcm import clip_to_int16 -from utils.sparsification import GRUSparsifier, calculate_gru_flops_per_step -from utils.layers import DualFC -from utils.misc import get_pdf_from_tree - - -class LPCNet(nn.Module): - def __init__(self, config): - super(LPCNet, self).__init__() - - # - self.input_layout = config['input_layout'] - self.feature_history = config['feature_history'] - self.feature_lookahead = config['feature_lookahead'] - - # frame rate network parameters - self.feature_dimension = config['feature_dimension'] - self.period_embedding_dim = config['period_embedding_dim'] - self.period_levels = config['period_levels'] - self.feature_channels = self.feature_dimension + self.period_embedding_dim - self.feature_conditioning_dim = config['feature_conditioning_dim'] - self.feature_conv_kernel_size = config['feature_conv_kernel_size'] - - - # frame rate network layers - self.period_embedding = nn.Embedding(self.period_levels, self.period_embedding_dim) - self.feature_conv1 = nn.Conv1d(self.feature_channels, self.feature_conditioning_dim, self.feature_conv_kernel_size, padding='valid') - self.feature_conv2 = nn.Conv1d(self.feature_conditioning_dim, self.feature_conditioning_dim, self.feature_conv_kernel_size, padding='valid') - self.feature_dense1 = nn.Linear(self.feature_conditioning_dim, self.feature_conditioning_dim) - self.feature_dense2 = nn.Linear(*(2*[self.feature_conditioning_dim])) - - # sample rate network parameters - self.frame_size = config['frame_size'] - self.signal_levels = config['signal_levels'] - self.signal_embedding_dim = config['signal_embedding_dim'] - self.gru_a_units = config['gru_a_units'] - self.gru_b_units = config['gru_b_units'] - self.output_levels = config['output_levels'] - self.hsampling = config.get('hsampling', False) - - self.gru_a_input_dim = len(self.input_layout['signals']) * self.signal_embedding_dim + self.feature_conditioning_dim - self.gru_b_input_dim = self.gru_a_units + self.feature_conditioning_dim - - # sample rate network layers - self.signal_embedding = nn.Embedding(self.signal_levels, self.signal_embedding_dim) - self.gru_a = nn.GRU(self.gru_a_input_dim, self.gru_a_units, batch_first=True) - self.gru_b = nn.GRU(self.gru_b_input_dim, self.gru_b_units, batch_first=True) - self.dual_fc = DualFC(self.gru_b_units, self.output_levels) - - # sparsification - self.sparsifier = [] - - # GRU A - if 'gru_a' in config['sparsification']: - gru_config = config['sparsification']['gru_a'] - task_list = [(self.gru_a, gru_config['params'])] - self.sparsifier.append(GRUSparsifier(task_list, - gru_config['start'], - gru_config['stop'], - gru_config['interval'], - gru_config['exponent']) - ) - self.gru_a_flops_per_step = calculate_gru_flops_per_step(self.gru_a, - gru_config['params'], drop_input=True) - else: - self.gru_a_flops_per_step = calculate_gru_flops_per_step(self.gru_a, drop_input=True) - - # GRU B - if 'gru_b' in config['sparsification']: - gru_config = config['sparsification']['gru_b'] - task_list = [(self.gru_b, gru_config['params'])] - self.sparsifier.append(GRUSparsifier(task_list, - gru_config['start'], - gru_config['stop'], - gru_config['interval'], - gru_config['exponent']) - ) - self.gru_b_flops_per_step = calculate_gru_flops_per_step(self.gru_b, - gru_config['params']) - else: - self.gru_b_flops_per_step = calculate_gru_flops_per_step(self.gru_b) - - # inference parameters - self.lpc_gamma = config.get('lpc_gamma', 1) - - def sparsify(self): - for sparsifier in self.sparsifier: - sparsifier.step() - - def get_gflops(self, fs, verbose=False): - gflops = 0 - - # frame rate network - conditioning_dim = self.feature_conditioning_dim - feature_channels = self.feature_channels - frame_rate = fs / self.frame_size - frame_rate_network_complexity = 1e-9 * 2 * (5 * conditioning_dim + 3 * feature_channels) * conditioning_dim * frame_rate - if verbose: - print(f"frame rate network: {frame_rate_network_complexity} GFLOPS") - gflops += frame_rate_network_complexity - - # gru a - gru_a_rate = fs - gru_a_complexity = 1e-9 * gru_a_rate * self.gru_a_flops_per_step - if verbose: - print(f"gru A: {gru_a_complexity} GFLOPS") - gflops += gru_a_complexity - - # gru b - gru_b_rate = fs - gru_b_complexity = 1e-9 * gru_b_rate * self.gru_b_flops_per_step - if verbose: - print(f"gru B: {gru_b_complexity} GFLOPS") - gflops += gru_b_complexity - - - # dual fcs - fc = self.dual_fc - rate = fs - input_size = fc.dense1.in_features - output_size = fc.dense1.out_features - dual_fc_complexity = 1e-9 * (4 * input_size * output_size + 22 * output_size) * rate - if self.hsampling: - dual_fc_complexity /= 8 - if verbose: - print(f"dual_fc: {dual_fc_complexity} GFLOPS") - gflops += dual_fc_complexity - - if verbose: - print(f'total: {gflops} GFLOPS') - - return gflops - - def frame_rate_network(self, features, periods): - - embedded_periods = torch.flatten(self.period_embedding(periods), 2, 3) - features = torch.concat((features, embedded_periods), dim=-1) - - # convert to channels first and calculate conditioning vector - c = torch.permute(features, [0, 2, 1]) - - c = torch.tanh(self.feature_conv1(c)) - c = torch.tanh(self.feature_conv2(c)) - # back to channels last - c = torch.permute(c, [0, 2, 1]) - c = torch.tanh(self.feature_dense1(c)) - c = torch.tanh(self.feature_dense2(c)) - - return c - - def sample_rate_network(self, signals, c, gru_states): - embedded_signals = torch.flatten(self.signal_embedding(signals), 2, 3) - c_upsampled = torch.repeat_interleave(c, self.frame_size, dim=1) - - y = torch.concat((embedded_signals, c_upsampled), dim=-1) - y, gru_a_state = self.gru_a(y, gru_states[0]) - y = torch.concat((y, c_upsampled), dim=-1) - y, gru_b_state = self.gru_b(y, gru_states[1]) - - y = self.dual_fc(y) - - if self.hsampling: - y = torch.sigmoid(y) - log_probs = torch.log(get_pdf_from_tree(y) + 1e-6) - else: - log_probs = torch.log_softmax(y, dim=-1) - - return log_probs, (gru_a_state, gru_b_state) - - def decoder(self, signals, c, gru_states): - embedded_signals = torch.flatten(self.signal_embedding(signals), 2, 3) - - y = torch.concat((embedded_signals, c), dim=-1) - y, gru_a_state = self.gru_a(y, gru_states[0]) - y = torch.concat((y, c), dim=-1) - y, gru_b_state = self.gru_b(y, gru_states[1]) - - y = self.dual_fc(y) - - if self.hsampling: - y = torch.sigmoid(y) - probs = get_pdf_from_tree(y) - else: - probs = torch.softmax(y, dim=-1) - - return probs, (gru_a_state, gru_b_state) - - def forward(self, features, periods, signals, gru_states): - - c = self.frame_rate_network(features, periods) - log_probs, _ = self.sample_rate_network(signals, c, gru_states) - - return log_probs - - def generate(self, features, periods, lpcs): - - with torch.no_grad(): - device = self.parameters().__next__().device - - num_frames = features.shape[0] - self.feature_history - self.feature_lookahead - lpc_order = lpcs.shape[-1] - num_input_signals = len(self.input_layout['signals']) - pitch_corr_position = self.input_layout['features']['pitch_corr'][0] - - # signal buffers - pcm = torch.zeros((num_frames * self.frame_size + lpc_order)) - output = torch.zeros((num_frames * self.frame_size), dtype=torch.int16) - mem = 0 - - # state buffers - gru_a_state = torch.zeros((1, 1, self.gru_a_units)) - gru_b_state = torch.zeros((1, 1, self.gru_b_units)) - gru_states = [gru_a_state, gru_b_state] - - input_signals = torch.zeros((1, 1, num_input_signals), dtype=torch.long) + 128 - - # push data to device - features = features.to(device) - periods = periods.to(device) - lpcs = lpcs.to(device) - - # lpc weighting - weights = torch.FloatTensor([self.lpc_gamma ** (i + 1) for i in range(lpc_order)]).to(device) - lpcs = lpcs * weights - - # run feature encoding - c = self.frame_rate_network(features.unsqueeze(0), periods.unsqueeze(0)) - - for frame_index in range(num_frames): - frame_start = frame_index * self.frame_size - pitch_corr = features[frame_index + self.feature_history, pitch_corr_position] - a = - torch.flip(lpcs[frame_index + self.feature_history], [0]) - current_c = c[:, frame_index : frame_index + 1, :] - - for i in range(self.frame_size): - pcm_position = frame_start + i + lpc_order - output_position = frame_start + i - - # prepare input - pred = torch.sum(pcm[pcm_position - lpc_order : pcm_position] * a) - if 'prediction' in self.input_layout['signals']: - input_signals[0, 0, self.input_layout['signals']['prediction']] = lin2ulawq(pred) - - # run single step of sample rate network - probs, gru_states = self.decoder( - input_signals, - current_c, - gru_states - ) - - # sample from output - exc_ulaw = sample_excitation(probs, pitch_corr) - - # signal generation - exc = ulaw2lin(exc_ulaw) - sig = exc + pred - pcm[pcm_position] = sig - mem = 0.85 * mem + float(sig) - output[output_position] = clip_to_int16(round(mem)) - - # buffer update - if 'last_signal' in self.input_layout['signals']: - input_signals[0, 0, self.input_layout['signals']['last_signal']] = lin2ulawq(sig) - - if 'last_error' in self.input_layout['signals']: - input_signals[0, 0, self.input_layout['signals']['last_error']] = lin2ulawq(exc) - - return output diff --git a/android/app/src/main/cpp/third_party/opus/dnn/torch/lpcnet/models/multi_rate_lpcnet.py b/android/app/src/main/cpp/third_party/opus/dnn/torch/lpcnet/models/multi_rate_lpcnet.py deleted file mode 100644 index e613e74..0000000 --- a/android/app/src/main/cpp/third_party/opus/dnn/torch/lpcnet/models/multi_rate_lpcnet.py +++ /dev/null @@ -1,437 +0,0 @@ -""" -/* Copyright (c) 2023 Amazon - Written by Jan Buethe */ -/* - Redistribution and use in source and binary forms, with or without - modification, are permitted provided that the following conditions - are met: - - - Redistributions of source code must retain the above copyright - notice, this list of conditions and the following disclaimer. - - - Redistributions in binary form must reproduce the above copyright - notice, this list of conditions and the following disclaimer in the - documentation and/or other materials provided with the distribution. - - THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS - ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT - LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR - A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER - OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, - EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, - PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR - PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF - LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING - NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS - SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. -*/ -""" - -import torch -from torch import nn -from utils.layers.subconditioner import get_subconditioner -from utils.layers import DualFC - -from utils.ulaw import lin2ulawq, ulaw2lin -from utils.sample import sample_excitation -from utils.pcm import clip_to_int16 -from utils.sparsification import GRUSparsifier, calculate_gru_flops_per_step - -from utils.misc import interleave_tensors - - - - -# MultiRateLPCNet -class MultiRateLPCNet(nn.Module): - def __init__(self, config): - super(MultiRateLPCNet, self).__init__() - - # general parameters - self.input_layout = config['input_layout'] - self.feature_history = config['feature_history'] - self.feature_lookahead = config['feature_lookahead'] - self.signals = config['signals'] - - # frame rate network parameters - self.feature_dimension = config['feature_dimension'] - self.period_embedding_dim = config['period_embedding_dim'] - self.period_levels = config['period_levels'] - self.feature_channels = self.feature_dimension + self.period_embedding_dim - self.feature_conditioning_dim = config['feature_conditioning_dim'] - self.feature_conv_kernel_size = config['feature_conv_kernel_size'] - - # frame rate network layers - self.period_embedding = nn.Embedding(self.period_levels, self.period_embedding_dim) - self.feature_conv1 = nn.Conv1d(self.feature_channels, self.feature_conditioning_dim, self.feature_conv_kernel_size, padding='valid') - self.feature_conv2 = nn.Conv1d(self.feature_conditioning_dim, self.feature_conditioning_dim, self.feature_conv_kernel_size, padding='valid') - self.feature_dense1 = nn.Linear(self.feature_conditioning_dim, self.feature_conditioning_dim) - self.feature_dense2 = nn.Linear(*(2*[self.feature_conditioning_dim])) - - # sample rate network parameters - self.frame_size = config['frame_size'] - self.signal_levels = config['signal_levels'] - self.signal_embedding_dim = config['signal_embedding_dim'] - self.gru_a_units = config['gru_a_units'] - self.gru_b_units = config['gru_b_units'] - self.output_levels = config['output_levels'] - - # subconditioning B - sub_config = config['subconditioning']['subconditioning_b'] - self.substeps_b = sub_config['number_of_subsamples'] - self.subcondition_signals_b = sub_config['signals'] - self.signals_idx_b = [self.input_layout['signals'][key] for key in sub_config['signals']] - method = sub_config['method'] - kwargs = sub_config['kwargs'] - if type(kwargs) == type(None): - kwargs = dict() - - state_size = self.gru_b_units - self.subconditioner_b = get_subconditioner(method, - sub_config['number_of_subsamples'], sub_config['pcm_embedding_size'], - state_size, self.signal_levels, len(sub_config['signals']), - **sub_config['kwargs']) - - # subconditioning A - sub_config = config['subconditioning']['subconditioning_a'] - self.substeps_a = sub_config['number_of_subsamples'] - self.subcondition_signals_a = sub_config['signals'] - self.signals_idx_a = [self.input_layout['signals'][key] for key in sub_config['signals']] - method = sub_config['method'] - kwargs = sub_config['kwargs'] - if type(kwargs) == type(None): - kwargs = dict() - - state_size = self.gru_a_units - self.subconditioner_a = get_subconditioner(method, - sub_config['number_of_subsamples'], sub_config['pcm_embedding_size'], - state_size, self.signal_levels, self.substeps_b * len(sub_config['signals']), - **sub_config['kwargs']) - - - # wrap up subconditioning, group_size_gru_a holds the number - # of timesteps that are grouped as sample input for GRU A - # input and group_size_subcondition_a holds the number of samples that are - # grouped as input to pre-GRU B subconditioning - self.group_size_gru_a = self.substeps_a * self.substeps_b - self.group_size_subcondition_a = self.substeps_b - self.gru_a_rate_divider = self.group_size_gru_a - self.gru_b_rate_divider = self.substeps_b - - # gru sizes - self.gru_a_input_dim = self.group_size_gru_a * len(self.signals) * self.signal_embedding_dim + self.feature_conditioning_dim - self.gru_b_input_dim = self.subconditioner_a.get_output_dim(0) + self.feature_conditioning_dim - self.signals_idx = [self.input_layout['signals'][key] for key in self.signals] - - # sample rate network layers - self.signal_embedding = nn.Embedding(self.signal_levels, self.signal_embedding_dim) - self.gru_a = nn.GRU(self.gru_a_input_dim, self.gru_a_units, batch_first=True) - self.gru_b = nn.GRU(self.gru_b_input_dim, self.gru_b_units, batch_first=True) - - # sparsification - self.sparsifier = [] - - # GRU A - if 'gru_a' in config['sparsification']: - gru_config = config['sparsification']['gru_a'] - task_list = [(self.gru_a, gru_config['params'])] - self.sparsifier.append(GRUSparsifier(task_list, - gru_config['start'], - gru_config['stop'], - gru_config['interval'], - gru_config['exponent']) - ) - self.gru_a_flops_per_step = calculate_gru_flops_per_step(self.gru_a, - gru_config['params'], drop_input=True) - else: - self.gru_a_flops_per_step = calculate_gru_flops_per_step(self.gru_a, drop_input=True) - - # GRU B - if 'gru_b' in config['sparsification']: - gru_config = config['sparsification']['gru_b'] - task_list = [(self.gru_b, gru_config['params'])] - self.sparsifier.append(GRUSparsifier(task_list, - gru_config['start'], - gru_config['stop'], - gru_config['interval'], - gru_config['exponent']) - ) - self.gru_b_flops_per_step = calculate_gru_flops_per_step(self.gru_b, - gru_config['params']) - else: - self.gru_b_flops_per_step = calculate_gru_flops_per_step(self.gru_b) - - - - # dual FCs - self.dual_fc = [] - for i in range(self.substeps_b): - dim = self.subconditioner_b.get_output_dim(i) - self.dual_fc.append(DualFC(dim, self.output_levels)) - self.add_module(f"dual_fc_{i}", self.dual_fc[-1]) - - def get_gflops(self, fs, verbose=False, hierarchical_sampling=False): - gflops = 0 - - # frame rate network - conditioning_dim = self.feature_conditioning_dim - feature_channels = self.feature_channels - frame_rate = fs / self.frame_size - frame_rate_network_complexity = 1e-9 * 2 * (5 * conditioning_dim + 3 * feature_channels) * conditioning_dim * frame_rate - if verbose: - print(f"frame rate network: {frame_rate_network_complexity} GFLOPS") - gflops += frame_rate_network_complexity - - # gru a - gru_a_rate = fs / self.group_size_gru_a - gru_a_complexity = 1e-9 * gru_a_rate * self.gru_a_flops_per_step - if verbose: - print(f"gru A: {gru_a_complexity} GFLOPS") - gflops += gru_a_complexity - - # subconditioning a - subcond_a_rate = fs / self.substeps_b - subconditioning_a_complexity = 1e-9 * self.subconditioner_a.get_average_flops_per_step() * subcond_a_rate - if verbose: - print(f"subconditioning A: {subconditioning_a_complexity} GFLOPS") - gflops += subconditioning_a_complexity - - # gru b - gru_b_rate = fs / self.substeps_b - gru_b_complexity = 1e-9 * gru_b_rate * self.gru_b_flops_per_step - if verbose: - print(f"gru B: {gru_b_complexity} GFLOPS") - gflops += gru_b_complexity - - # subconditioning b - subcond_b_rate = fs - subconditioning_b_complexity = 1e-9 * self.subconditioner_b.get_average_flops_per_step() * subcond_b_rate - if verbose: - print(f"subconditioning B: {subconditioning_b_complexity} GFLOPS") - gflops += subconditioning_b_complexity - - # dual fcs - for i, fc in enumerate(self.dual_fc): - rate = fs / len(self.dual_fc) - input_size = fc.dense1.in_features - output_size = fc.dense1.out_features - dual_fc_complexity = 1e-9 * (4 * input_size * output_size + 22 * output_size) * rate - if hierarchical_sampling: - dual_fc_complexity /= 8 - if verbose: - print(f"dual_fc_{i}: {dual_fc_complexity} GFLOPS") - gflops += dual_fc_complexity - - if verbose: - print(f'total: {gflops} GFLOPS') - - return gflops - - - - def sparsify(self): - for sparsifier in self.sparsifier: - sparsifier.step() - - def frame_rate_network(self, features, periods): - - embedded_periods = torch.flatten(self.period_embedding(periods), 2, 3) - features = torch.concat((features, embedded_periods), dim=-1) - - # convert to channels first and calculate conditioning vector - c = torch.permute(features, [0, 2, 1]) - - c = torch.tanh(self.feature_conv1(c)) - c = torch.tanh(self.feature_conv2(c)) - # back to channels last - c = torch.permute(c, [0, 2, 1]) - c = torch.tanh(self.feature_dense1(c)) - c = torch.tanh(self.feature_dense2(c)) - - return c - - def prepare_signals(self, signals, group_size, signal_idx): - """ extracts, delays and groups signals """ - - batch_size, sequence_length, num_signals = signals.shape - - # extract signals according to position - signals = torch.cat([signals[:, :, i : i + 1] for i in signal_idx], - dim=-1) - - # roll back pcm to account for grouping - signals = torch.roll(signals, group_size - 1, -2) - - # reshape - signals = torch.reshape(signals, - (batch_size, sequence_length // group_size, group_size * len(signal_idx))) - - return signals - - - def sample_rate_network(self, signals, c, gru_states): - - signals_a = self.prepare_signals(signals, self.group_size_gru_a, self.signals_idx) - embedded_signals = torch.flatten(self.signal_embedding(signals_a), 2, 3) - # features at GRU A rate - c_upsampled_a = torch.repeat_interleave(c, self.frame_size // self.gru_a_rate_divider, dim=1) - # features at GRU B rate - c_upsampled_b = torch.repeat_interleave(c, self.frame_size // self.gru_b_rate_divider, dim=1) - - y = torch.concat((embedded_signals, c_upsampled_a), dim=-1) - y, gru_a_state = self.gru_a(y, gru_states[0]) - # first round of upsampling and subconditioning - c_signals_a = self.prepare_signals(signals, self.group_size_subcondition_a, self.signals_idx_a) - y = self.subconditioner_a(y, c_signals_a) - y = interleave_tensors(y) - - y = torch.concat((y, c_upsampled_b), dim=-1) - y, gru_b_state = self.gru_b(y, gru_states[1]) - c_signals_b = self.prepare_signals(signals, 1, self.signals_idx_b) - y = self.subconditioner_b(y, c_signals_b) - - y = [self.dual_fc[i](y[i]) for i in range(self.substeps_b)] - y = interleave_tensors(y) - - return y, (gru_a_state, gru_b_state) - - def decoder(self, signals, c, gru_states): - embedded_signals = torch.flatten(self.signal_embedding(signals), 2, 3) - - y = torch.concat((embedded_signals, c), dim=-1) - y, gru_a_state = self.gru_a(y, gru_states[0]) - y = torch.concat((y, c), dim=-1) - y, gru_b_state = self.gru_b(y, gru_states[1]) - - y = self.dual_fc(y) - - return torch.softmax(y, dim=-1), (gru_a_state, gru_b_state) - - def forward(self, features, periods, signals, gru_states): - - c = self.frame_rate_network(features, periods) - y, _ = self.sample_rate_network(signals, c, gru_states) - log_probs = torch.log_softmax(y, dim=-1) - - return log_probs - - def generate(self, features, periods, lpcs): - - with torch.no_grad(): - device = self.parameters().__next__().device - - num_frames = features.shape[0] - self.feature_history - self.feature_lookahead - lpc_order = lpcs.shape[-1] - num_input_signals = len(self.signals) - pitch_corr_position = self.input_layout['features']['pitch_corr'][0] - - # signal buffers - last_signal = torch.zeros((num_frames * self.frame_size + lpc_order + 1)) - prediction = torch.zeros((num_frames * self.frame_size + lpc_order + 1)) - last_error = torch.zeros((num_frames * self.frame_size + lpc_order + 1)) - output = torch.zeros((num_frames * self.frame_size), dtype=torch.int16) - mem = 0 - - # state buffers - gru_a_state = torch.zeros((1, 1, self.gru_a_units)) - gru_b_state = torch.zeros((1, 1, self.gru_b_units)) - - input_signals = 128 + torch.zeros(self.group_size_gru_a * num_input_signals, dtype=torch.long) - # conditioning signals for subconditioner a - c_signals_a = 128 + torch.zeros(self.group_size_subcondition_a * len(self.signals_idx_a), dtype=torch.long) - # conditioning signals for subconditioner b - c_signals_b = 128 + torch.zeros(len(self.signals_idx_b), dtype=torch.long) - - # signal dict - signal_dict = { - 'prediction' : prediction, - 'last_error' : last_error, - 'last_signal' : last_signal - } - - # push data to device - features = features.to(device) - periods = periods.to(device) - lpcs = lpcs.to(device) - - # run feature encoding - c = self.frame_rate_network(features.unsqueeze(0), periods.unsqueeze(0)) - - for frame_index in range(num_frames): - frame_start = frame_index * self.frame_size - pitch_corr = features[frame_index + self.feature_history, pitch_corr_position] - a = - torch.flip(lpcs[frame_index + self.feature_history], [0]) - current_c = c[:, frame_index : frame_index + 1, :] - - for i in range(0, self.frame_size, self.group_size_gru_a): - pcm_position = frame_start + i + lpc_order - output_position = frame_start + i - - # calculate newest prediction - prediction[pcm_position] = torch.sum(last_signal[pcm_position - lpc_order + 1: pcm_position + 1] * a) - - # prepare input - for slot in range(self.group_size_gru_a): - k = slot - self.group_size_gru_a + 1 - for idx, name in enumerate(self.signals): - input_signals[idx + slot * num_input_signals] = lin2ulawq( - signal_dict[name][pcm_position + k] - ) - - - # run GRU A - embed_signals = self.signal_embedding(input_signals.reshape((1, 1, -1))) - embed_signals = torch.flatten(embed_signals, 2) - y = torch.cat((embed_signals, current_c), dim=-1) - h_a, gru_a_state = self.gru_a(y, gru_a_state) - - # loop over substeps_a - for step_a in range(self.substeps_a): - # prepare conditioning input - for slot in range(self.group_size_subcondition_a): - k = slot - self.group_size_subcondition_a + 1 - for idx, name in enumerate(self.subcondition_signals_a): - c_signals_a[idx + slot * num_input_signals] = lin2ulawq( - signal_dict[name][pcm_position + k] - ) - - # subconditioning - h_a = self.subconditioner_a.single_step(step_a, h_a, c_signals_a.reshape((1, 1, -1))) - - # run GRU B - y = torch.cat((h_a, current_c), dim=-1) - h_b, gru_b_state = self.gru_b(y, gru_b_state) - - # loop over substeps b - for step_b in range(self.substeps_b): - # prepare subconditioning input - for idx, name in enumerate(self.subcondition_signals_b): - c_signals_b[idx] = lin2ulawq( - signal_dict[name][pcm_position] - ) - - # subcondition - h_b = self.subconditioner_b.single_step(step_b, h_b, c_signals_b.reshape((1, 1, -1))) - - # run dual FC - probs = torch.softmax(self.dual_fc[step_b](h_b), dim=-1) - - # sample - new_exc = ulaw2lin(sample_excitation(probs, pitch_corr)) - - # update signals - sig = new_exc + prediction[pcm_position] - last_error[pcm_position + 1] = new_exc - last_signal[pcm_position + 1] = sig - - mem = 0.85 * mem + float(sig) - output[output_position] = clip_to_int16(round(mem)) - - # increase positions - pcm_position += 1 - output_position += 1 - - # calculate next prediction - prediction[pcm_position] = torch.sum(last_signal[pcm_position - lpc_order + 1: pcm_position + 1] * a) - - return output diff --git a/android/app/src/main/cpp/third_party/opus/dnn/torch/lpcnet/print_lpcnet_complexity.py b/android/app/src/main/cpp/third_party/opus/dnn/torch/lpcnet/print_lpcnet_complexity.py deleted file mode 100644 index d6072dc..0000000 --- a/android/app/src/main/cpp/third_party/opus/dnn/torch/lpcnet/print_lpcnet_complexity.py +++ /dev/null @@ -1,64 +0,0 @@ -""" -/* Copyright (c) 2023 Amazon - Written by Jan Buethe */ -/* - Redistribution and use in source and binary forms, with or without - modification, are permitted provided that the following conditions - are met: - - - Redistributions of source code must retain the above copyright - notice, this list of conditions and the following disclaimer. - - - Redistributions in binary form must reproduce the above copyright - notice, this list of conditions and the following disclaimer in the - documentation and/or other materials provided with the distribution. - - THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS - ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT - LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR - A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER - OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, - EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, - PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR - PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF - LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING - NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS - SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. -*/ -""" - -import argparse - -import yaml - -from models import model_dict - - -debug = False -if debug: - args = type('dummy', (object,), - { - 'setup' : 'setups/lpcnet_m/setup_1_4_concatenative.yml', - 'hierarchical_sampling' : False - })() -else: - parser = argparse.ArgumentParser() - parser.add_argument('setup', type=str, help='setup yaml file') - parser.add_argument('--hierarchical-sampling', action="store_true", help='whether to assume hierarchical sampling (default=False)', default=False) - - args = parser.parse_args() - -with open(args.setup, 'r') as f: - setup = yaml.load(f.read(), yaml.FullLoader) - -# check model -if not 'model' in setup['lpcnet']: - print(f'warning: did not find model entry in setup, using default lpcnet') - model_name = 'lpcnet' -else: - model_name = setup['lpcnet']['model'] - -# create model -model = model_dict[model_name](setup['lpcnet']['config']) - -gflops = model.get_gflops(16000, verbose=True, hierarchical_sampling=args.hierarchical_sampling) diff --git a/android/app/src/main/cpp/third_party/opus/dnn/torch/lpcnet/scripts/collect_multi_run_results.py b/android/app/src/main/cpp/third_party/opus/dnn/torch/lpcnet/scripts/collect_multi_run_results.py deleted file mode 100644 index ae662a4..0000000 --- a/android/app/src/main/cpp/third_party/opus/dnn/torch/lpcnet/scripts/collect_multi_run_results.py +++ /dev/null @@ -1,190 +0,0 @@ -""" -/* Copyright (c) 2023 Amazon - Written by Jan Buethe */ -/* - Redistribution and use in source and binary forms, with or without - modification, are permitted provided that the following conditions - are met: - - - Redistributions of source code must retain the above copyright - notice, this list of conditions and the following disclaimer. - - - Redistributions in binary form must reproduce the above copyright - notice, this list of conditions and the following disclaimer in the - documentation and/or other materials provided with the distribution. - - THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS - ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT - LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR - A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER - OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, - EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, - PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR - PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF - LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING - NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS - SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. -*/ -""" - -import argparse -import os -from uuid import UUID -from collections import OrderedDict -import pickle - - -import torch -import numpy as np - -import utils - - - -parser = argparse.ArgumentParser() -parser.add_argument("input", type=str, help="input folder containing multi-run output") -parser.add_argument("tag", type=str, help="tag for multi-run experiment") -parser.add_argument("csv", type=str, help="name for output csv") - - -def is_uuid(val): - try: - UUID(val) - return True - except: - return False - - -def collect_results(folder): - - training_folder = os.path.join(folder, 'training') - testing_folder = os.path.join(folder, 'testing') - - # validation loss - checkpoint = torch.load(os.path.join(training_folder, 'checkpoints', 'checkpoint_finalize_epoch_1.pth'), map_location='cpu') - validation_loss = checkpoint['validation_loss'] - - # eval_warpq - eval_warpq = utils.data.parse_warpq_scores(os.path.join(training_folder, 'out_finalize.txt'))[-1] - - # testing results - testing_results = utils.data.collect_test_stats(os.path.join(testing_folder, 'final')) - - results = OrderedDict() - results['eval_loss'] = validation_loss - results['eval_warpq'] = eval_warpq - results['pesq_mean'] = testing_results['pesq'][0] - results['warpq_mean'] = testing_results['warpq'][0] - results['pitch_error_mean'] = testing_results['pitch_error'][0] - results['voicing_error_mean'] = testing_results['voicing_error'][0] - - return results - -def print_csv(path, results, tag, ranks=None, header=True): - - metrics = next(iter(results.values())).keys() - if ranks is not None: - rank_keys = next(iter(ranks.values())).keys() - else: - rank_keys = [] - - with open(path, 'w') as f: - if header: - f.write("uuid, tag") - - for metric in metrics: - f.write(f", {metric}") - - for rank in rank_keys: - f.write(f", {rank}") - - f.write("\n") - - - for uuid, values in results.items(): - f.write(f"{uuid}, {tag}") - - for val in values.values(): - f.write(f", {val:10.8f}") - - for rank in rank_keys: - f.write(f", {ranks[uuid][rank]:4d}") - - f.write("\n") - -def get_ranks(results): - - metrics = list(next(iter(results.values())).keys()) - - positive = {'pesq_mean', 'mix'} - - ranks = OrderedDict() - for key in results.keys(): - ranks[key] = OrderedDict() - - for metric in metrics: - sign = -1 if metric in positive else 1 - - x = sorted([(key, value[metric]) for key, value in results.items()], key=lambda x: sign * x[1]) - x = [y[0] for y in x] - - for key in results.keys(): - ranks[key]['rank_' + metric] = x.index(key) + 1 - - return ranks - -def analyse_metrics(results): - metrics = ['eval_loss', 'pesq_mean', 'warpq_mean', 'pitch_error_mean', 'voicing_error_mean'] - - x = [] - for metric in metrics: - x.append([val[metric] for val in results.values()]) - - x = np.array(x) - - print(x) - -def add_mix_metric(results): - metrics = ['eval_loss', 'pesq_mean', 'warpq_mean', 'pitch_error_mean', 'voicing_error_mean'] - - x = [] - for metric in metrics: - x.append([val[metric] for val in results.values()]) - - x = np.array(x).transpose() * np.array([-1, 1, -1, -1, -1]) - - z = (x - np.mean(x, axis=0)) / np.std(x, axis=0) - - print(f"covariance matrix for normalized scores of {metrics}:") - print(np.cov(z.transpose())) - - score = np.mean(z, axis=1) - - for i, key in enumerate(results.keys()): - results[key]['mix'] = score[i].item() - -if __name__ == "__main__": - args = parser.parse_args() - - uuids = sorted([x for x in os.listdir(args.input) if os.path.isdir(os.path.join(args.input, x)) and is_uuid(x)]) - - - results = OrderedDict() - - for uuid in uuids: - results[uuid] = collect_results(os.path.join(args.input, uuid)) - - - add_mix_metric(results) - - ranks = get_ranks(results) - - - - csv = args.csv if args.csv.endswith('.csv') else args.csv + '.csv' - - print_csv(args.csv, results, args.tag, ranks=ranks) - - - with open(csv[:-4] + '.pickle', 'wb') as f: - pickle.dump(results, f, protocol=pickle.HIGHEST_PROTOCOL) \ No newline at end of file diff --git a/android/app/src/main/cpp/third_party/opus/dnn/torch/lpcnet/scripts/loop_run.sh b/android/app/src/main/cpp/third_party/opus/dnn/torch/lpcnet/scripts/loop_run.sh deleted file mode 100644 index 7250f63..0000000 --- a/android/app/src/main/cpp/third_party/opus/dnn/torch/lpcnet/scripts/loop_run.sh +++ /dev/null @@ -1,52 +0,0 @@ -#!/bin/bash - - -case $# in - 9) SETUP=$1; OUTDIR=$2; NAME=$3; DEVICE=$4; ROUNDS=$5; LPCNEXT=$6; LPCNET=$7; TESTSUITE=$8; TESTITEMS=$9;; - *) echo "loop_run.sh setup outdir name device rounds lpcnext_repo lpcnet_repo testsuite_repo testitems"; exit;; -esac - - -PYTHON="/home/ubuntu/opt/miniconda3/envs/torch/bin/python" -TESTFEATURES=${LPCNEXT}/testitems/features/all_0_orig_features.f32 -WARPQREFERENCE=${LPCNEXT}/testitems/wav/all_0_orig.wav -METRICS="warpq,pesq,pitch_error,voicing_error" -LPCNETDEMO=${LPCNET}/lpcnet_demo - -for ((round = 1; round <= $ROUNDS; round++)) -do - echo - echo round $round - - UUID=$(uuidgen) - TRAINOUT=${OUTDIR}/${UUID}/training - TESTOUT=${OUTDIR}/${UUID}/testing - CHECKPOINT=${TRAINOUT}/checkpoints/checkpoint_last.pth - FINALCHECKPOINT=${TRAINOUT}/checkpoints/checkpoint_finalize_last.pth - - # run training - echo "starting training..." - $PYTHON $LPCNEXT/train_lpcnet.py $SETUP $TRAINOUT --device $DEVICE --test-features $TESTFEATURES --warpq-reference $WARPQREFERENCE - - # run finalization - echo "starting finalization..." - $PYTHON $LPCNEXT/train_lpcnet.py $SETUP $TRAINOUT \ - --device $DEVICE --test-features $TESTFEATURES \ - --warpq-reference $WARPQREFERENCE \ - --finalize --initial-checkpoint $CHECKPOINT - - # create test configs - $PYTHON $LPCNEXT/make_test_config.py ${OUTDIR}/${UUID}/testconfig.yml "$NAME $UUID" $CHECKPOINT --lpcnet-demo $LPCNETDEMO - $PYTHON $LPCNEXT/make_test_config.py ${OUTDIR}/${UUID}/testconfig_finalize.yml "$NAME $UUID finalized" $FINALCHECKPOINT --lpcnet-demo $LPCNETDEMO - - # run tests - echo "starting test 1 (no finalization)..." - $PYTHON $TESTSUITE/run_test.py ${OUTDIR}/${UUID}/testconfig.yml \ - $TESTITEMS ${TESTOUT}/prefinal --num-workers 8 \ - --num-testitems 400 --metrics $METRICS - - echo "starting test 2 (after finalization)..." - $PYTHON $TESTSUITE/run_test.py ${OUTDIR}/${UUID}/testconfig_finalize.yml \ - $TESTITEMS ${TESTOUT}/final --num-workers 8 \ - --num-testitems 400 --metrics $METRICS -done diff --git a/android/app/src/main/cpp/third_party/opus/dnn/torch/lpcnet/scripts/make_animation.py b/android/app/src/main/cpp/third_party/opus/dnn/torch/lpcnet/scripts/make_animation.py deleted file mode 100644 index 57656ef..0000000 --- a/android/app/src/main/cpp/third_party/opus/dnn/torch/lpcnet/scripts/make_animation.py +++ /dev/null @@ -1,67 +0,0 @@ -""" -/* Copyright (c) 2023 Amazon - Written by Jan Buethe */ -/* - Redistribution and use in source and binary forms, with or without - modification, are permitted provided that the following conditions - are met: - - - Redistributions of source code must retain the above copyright - notice, this list of conditions and the following disclaimer. - - - Redistributions in binary form must reproduce the above copyright - notice, this list of conditions and the following disclaimer in the - documentation and/or other materials provided with the distribution. - - THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS - ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT - LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR - A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER - OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, - EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, - PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR - PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF - LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING - NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS - SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. -*/ -""" - - -""" script for creating animations from debug data - -""" - - -import argparse - - -import sys -sys.path.append('./') - -from utils.endoscopy import make_animation, read_data - -parser = argparse.ArgumentParser() - -parser.add_argument('folder', type=str, help='endoscopy folder with debug output') -parser.add_argument('output', type=str, help='output file (will be auto-extended with .mp4)') - -parser.add_argument('--start-index', type=int, help='index of first sample to be considered', default=0) -parser.add_argument('--stop-index', type=int, help='index of last sample to be considered', default=-1) -parser.add_argument('--interval', type=int, help='interval between frames in ms', default=20) -parser.add_argument('--half-window-length', type=int, help='half size of window for displaying signals', default=80) - - -if __name__ == "__main__": - args = parser.parse_args() - - filename = args.output if args.output.endswith('.mp4') else args.output + '.mp4' - data = read_data(args.folder) - - make_animation( - data, - filename, - start_index=args.start_index, - stop_index = args.stop_index, - half_signal_window_length=args.half_window_length - ) diff --git a/android/app/src/main/cpp/third_party/opus/dnn/torch/lpcnet/scripts/modify_dataset_target.py b/android/app/src/main/cpp/third_party/opus/dnn/torch/lpcnet/scripts/modify_dataset_target.py deleted file mode 100644 index a70fe16..0000000 --- a/android/app/src/main/cpp/third_party/opus/dnn/torch/lpcnet/scripts/modify_dataset_target.py +++ /dev/null @@ -1,17 +0,0 @@ -import argparse - -import numpy as np - - -parser = argparse.ArgumentParser(description="sets s_t to augmented_s_t") - -parser.add_argument('datafile', type=str, help='data.s16 file path') - -args = parser.parse_args() - -data = np.memmap(args.datafile, dtype='int16', mode='readwrite') - -# signal is in data[1::2] -# last augmented signal is in data[0::2] - -data[1 : - 1 : 2] = data[2 : : 2] diff --git a/android/app/src/main/cpp/third_party/opus/dnn/torch/lpcnet/scripts/multi_run.sh b/android/app/src/main/cpp/third_party/opus/dnn/torch/lpcnet/scripts/multi_run.sh deleted file mode 100644 index fb0fee1..0000000 --- a/android/app/src/main/cpp/third_party/opus/dnn/torch/lpcnet/scripts/multi_run.sh +++ /dev/null @@ -1,17 +0,0 @@ -#!/bin/bash - -case $# in - 9) SETUP=$1; OUTDIR=$2; NAME=$3; NUMDEVICES=$4; ROUNDS=$5; LPCNEXT=$6; LPCNET=$7; TESTSUITE=$8; TESTITEMS=$9;; - *) echo "multi_run.sh setup outdir name num_devices rounds_per_device lpcnext_repo lpcnet_repo testsuite_repo testitems"; exit;; -esac - - -LOOPRUN=${LPCNEXT}/loop_run.sh - -mkdir -p $OUTDIR - -for ((i = 0; i < $NUMDEVICES; i++)) -do - echo "launching job queue for device $i" - nohup bash $LOOPRUN $SETUP $OUTDIR "$NAME" "cuda:$i" $ROUNDS $LPCNEXT $LPCNET $TESTSUITE $TESTITEMS > $OUTDIR/job_${i}_out.txt & -done diff --git a/android/app/src/main/cpp/third_party/opus/dnn/torch/lpcnet/scripts/run_inference_test.sh b/android/app/src/main/cpp/third_party/opus/dnn/torch/lpcnet/scripts/run_inference_test.sh deleted file mode 100644 index 9f22b03..0000000 --- a/android/app/src/main/cpp/third_party/opus/dnn/torch/lpcnet/scripts/run_inference_test.sh +++ /dev/null @@ -1,22 +0,0 @@ -#!/bin/bash - - -case $# in - 3) FEATURES=$1; FOLDER=$2; PYTHON=$3;; - *) echo "run_inference_test.sh "; exit;; -esac - - -SCRIPTFOLDER=$(dirname "$0") - -mkdir -p $FOLDER/inference_test - -# update checkpoints -for fn in $(find $FOLDER -type f -name "checkpoint*.pth") -do - tmp=$(basename $fn) - tmp=${tmp%.pth} - epoch=${tmp#checkpoint_epoch_} - echo "running inference with checkpoint $fn..." - $PYTHON $SCRIPTFOLDER/../test_lpcnet.py $FEATURES $fn $FOLDER/inference_test/output_epoch_${epoch}.wav -done diff --git a/android/app/src/main/cpp/third_party/opus/dnn/torch/lpcnet/scripts/update_checkpoints.py b/android/app/src/main/cpp/third_party/opus/dnn/torch/lpcnet/scripts/update_checkpoints.py deleted file mode 100644 index 8f00a7e..0000000 --- a/android/app/src/main/cpp/third_party/opus/dnn/torch/lpcnet/scripts/update_checkpoints.py +++ /dev/null @@ -1,54 +0,0 @@ -""" -/* Copyright (c) 2023 Amazon - Written by Jan Buethe */ -/* - Redistribution and use in source and binary forms, with or without - modification, are permitted provided that the following conditions - are met: - - - Redistributions of source code must retain the above copyright - notice, this list of conditions and the following disclaimer. - - - Redistributions in binary form must reproduce the above copyright - notice, this list of conditions and the following disclaimer in the - documentation and/or other materials provided with the distribution. - - THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS - ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT - LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR - A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER - OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, - EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, - PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR - PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF - LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING - NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS - SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. -*/ -""" - -""" script for updating checkpoints with new setup entries - - Use this script to update older outputs with newly introduced - parameters. (Saves us the trouble of backward compatibility) -""" - - -import argparse - -import torch - -parser = argparse.ArgumentParser() - -parser.add_argument('checkpoint_file', type=str, help='checkpoint to be updated') -parser.add_argument('--model', type=str, help='model update', default=None) - -args = parser.parse_args() - -checkpoint = torch.load(args.checkpoint_file, map_location='cpu') - -# update model entry -if type(args.model) != type(None): - checkpoint['setup']['lpcnet']['model'] = args.model - -torch.save(checkpoint, args.checkpoint_file) \ No newline at end of file diff --git a/android/app/src/main/cpp/third_party/opus/dnn/torch/lpcnet/scripts/update_output_folder.sh b/android/app/src/main/cpp/third_party/opus/dnn/torch/lpcnet/scripts/update_output_folder.sh deleted file mode 100644 index 487d4a2..0000000 --- a/android/app/src/main/cpp/third_party/opus/dnn/torch/lpcnet/scripts/update_output_folder.sh +++ /dev/null @@ -1,22 +0,0 @@ -#!/bin/bash - - -case $# in - 3) FOLDER=$1; MODEL=$2; PYTHON=$3;; - *) echo "update_output_folder.sh folder model python"; exit;; -esac - - -SCRIPTFOLDER=$(dirname "$0") - - -# update setup -echo "updating $FOLDER/setup.py..." -$PYTHON $SCRIPTFOLDER/update_setups.py $FOLDER/setup.yml --model $MODEL - -# update checkpoints -for fn in $(find $FOLDER -type f -name "checkpoint*.pth") -do - echo "updating $fn..." - $PYTHON $SCRIPTFOLDER/update_checkpoints.py $fn --model $MODEL -done \ No newline at end of file diff --git a/android/app/src/main/cpp/third_party/opus/dnn/torch/lpcnet/scripts/update_setups.py b/android/app/src/main/cpp/third_party/opus/dnn/torch/lpcnet/scripts/update_setups.py deleted file mode 100644 index 52f81cf..0000000 --- a/android/app/src/main/cpp/third_party/opus/dnn/torch/lpcnet/scripts/update_setups.py +++ /dev/null @@ -1,57 +0,0 @@ -""" -/* Copyright (c) 2023 Amazon - Written by Jan Buethe */ -/* - Redistribution and use in source and binary forms, with or without - modification, are permitted provided that the following conditions - are met: - - - Redistributions of source code must retain the above copyright - notice, this list of conditions and the following disclaimer. - - - Redistributions in binary form must reproduce the above copyright - notice, this list of conditions and the following disclaimer in the - documentation and/or other materials provided with the distribution. - - THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS - ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT - LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR - A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER - OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, - EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, - PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR - PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF - LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING - NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS - SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. -*/ -""" - -""" script for updating setup files with new setup entries - - Use this script to update older outputs with newly introduced - parameters. (Saves us the trouble of backward compatibility) -""" - -import argparse - -import yaml - -parser = argparse.ArgumentParser() - -parser.add_argument('setup_file', type=str, help='setup to be updated') -parser.add_argument('--model', type=str, help='model update', default=None) - -args = parser.parse_args() - -# load setup -with open(args.setup_file, 'r') as f: - setup = yaml.load(f.read(), yaml.FullLoader) - -# update model entry -if type(args.model) != type(None): - setup['lpcnet']['model'] = args.model - -# dump result -with open(args.setup_file, 'w') as f: - yaml.dump(setup, f) diff --git a/android/app/src/main/cpp/third_party/opus/dnn/torch/lpcnet/test_lpcnet.py b/android/app/src/main/cpp/third_party/opus/dnn/torch/lpcnet/test_lpcnet.py deleted file mode 100644 index 49db8b0..0000000 --- a/android/app/src/main/cpp/third_party/opus/dnn/torch/lpcnet/test_lpcnet.py +++ /dev/null @@ -1,89 +0,0 @@ -""" -/* Copyright (c) 2023 Amazon - Written by Jan Buethe */ -/* - Redistribution and use in source and binary forms, with or without - modification, are permitted provided that the following conditions - are met: - - - Redistributions of source code must retain the above copyright - notice, this list of conditions and the following disclaimer. - - - Redistributions in binary form must reproduce the above copyright - notice, this list of conditions and the following disclaimer in the - documentation and/or other materials provided with the distribution. - - THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS - ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT - LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR - A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER - OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, - EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, - PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR - PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF - LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING - NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS - SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. -*/ -""" - -import argparse - -import torch -import numpy as np - - -from models import model_dict -from utils.data import load_features -from utils.wav import wavwrite16 - -debug = False -if debug: - args = type('dummy', (object,), - { - 'features' : 'features.f32', - 'checkpoint' : 'checkpoint.pth', - 'output' : 'out.wav', - 'version' : 2 - })() -else: - parser = argparse.ArgumentParser() - - parser.add_argument('features', type=str, help='feature file') - parser.add_argument('checkpoint', type=str, help='checkpoint file') - parser.add_argument('output', type=str, help='output file') - parser.add_argument('--version', type=int, help='feature version', default=2) - - args = parser.parse_args() - - -torch.set_num_threads(2) - -version = args.version -feature_file = args.features -checkpoint_file = args.checkpoint - - - -output_file = args.output -if not output_file.endswith('.wav'): - output_file += '.wav' - -checkpoint = torch.load(checkpoint_file, map_location="cpu") - -# check model -if not 'model' in checkpoint['setup']['lpcnet']: - print(f'warning: did not find model entry in setup, using default lpcnet') - model_name = 'lpcnet' -else: - model_name = checkpoint['setup']['lpcnet']['model'] - -model = model_dict[model_name](checkpoint['setup']['lpcnet']['config']) - -model.load_state_dict(checkpoint['state_dict']) - -data = load_features(feature_file) - -output = model.generate(data['features'], data['periods'], data['lpcs']) - -wavwrite16(output_file, output.numpy(), 16000) diff --git a/android/app/src/main/cpp/third_party/opus/dnn/torch/lpcnet/train_lpcnet.py b/android/app/src/main/cpp/third_party/opus/dnn/torch/lpcnet/train_lpcnet.py deleted file mode 100644 index c6121c8..0000000 --- a/android/app/src/main/cpp/third_party/opus/dnn/torch/lpcnet/train_lpcnet.py +++ /dev/null @@ -1,272 +0,0 @@ -""" -/* Copyright (c) 2023 Amazon - Written by Jan Buethe */ -/* - Redistribution and use in source and binary forms, with or without - modification, are permitted provided that the following conditions - are met: - - - Redistributions of source code must retain the above copyright - notice, this list of conditions and the following disclaimer. - - - Redistributions in binary form must reproduce the above copyright - notice, this list of conditions and the following disclaimer in the - documentation and/or other materials provided with the distribution. - - THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS - ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT - LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR - A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER - OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, - EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, - PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR - PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF - LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING - NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS - SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. -*/ -""" - -import os -import argparse -import sys - -try: - import git - has_git = True -except: - has_git = False - -import yaml - - -import torch -from torch.optim.lr_scheduler import LambdaLR - -from data import LPCNetDataset -from models import model_dict -from engine.lpcnet_engine import train_one_epoch, evaluate -from utils.data import load_features -from utils.wav import wavwrite16 - - -debug = False -if debug: - args = type('dummy', (object,), - { - 'setup' : 'setup.yml', - 'output' : 'testout', - 'device' : None, - 'test_features' : None, - 'finalize': False, - 'initial_checkpoint': None, - 'no-redirect': False - })() -else: - parser = argparse.ArgumentParser("train_lpcnet.py") - parser.add_argument('setup', type=str, help='setup yaml file') - parser.add_argument('output', type=str, help='output path') - parser.add_argument('--device', type=str, help='compute device', default=None) - parser.add_argument('--test-features', type=str, help='test feature file in v2 format', default=None) - parser.add_argument('--finalize', action='store_true', help='run single training round with lr=1e-5') - parser.add_argument('--initial-checkpoint', type=str, help='initial checkpoint', default=None) - parser.add_argument('--no-redirect', action='store_true', help='disables re-direction of output') - - args = parser.parse_args() - - -torch.set_num_threads(4) - -with open(args.setup, 'r') as f: - setup = yaml.load(f.read(), yaml.FullLoader) - -if args.finalize: - if args.initial_checkpoint is None: - raise ValueError('finalization requires initial checkpoint') - - if 'sparsification' in setup['lpcnet']['config']: - for sp_job in setup['lpcnet']['config']['sparsification'].values(): - sp_job['start'], sp_job['stop'] = 0, 0 - - setup['training']['lr'] = 1.0e-5 - setup['training']['lr_decay_factor'] = 0.0 - setup['training']['epochs'] = 1 - - checkpoint_prefix = 'checkpoint_finalize' - output_prefix = 'output_finalize' - setup_name = 'setup_finalize.yml' - output_file='out_finalize.txt' -else: - checkpoint_prefix = 'checkpoint' - output_prefix = 'output' - setup_name = 'setup.yml' - output_file='out.txt' - - -# check model -if not 'model' in setup['lpcnet']: - print(f'warning: did not find model entry in setup, using default lpcnet') - model_name = 'lpcnet' -else: - model_name = setup['lpcnet']['model'] - -# prepare output folder -if os.path.exists(args.output) and not debug and not args.finalize: - print("warning: output folder exists") - - reply = input('continue? (y/n): ') - while reply not in {'y', 'n'}: - reply = input('continue? (y/n): ') - - if reply == 'n': - os._exit() -else: - os.makedirs(args.output, exist_ok=True) - -checkpoint_dir = os.path.join(args.output, 'checkpoints') -os.makedirs(checkpoint_dir, exist_ok=True) - - -# add repo info to setup -if has_git: - working_dir = os.path.split(__file__)[0] - try: - repo = git.Repo(working_dir) - setup['repo'] = dict() - hash = repo.head.object.hexsha - urls = list(repo.remote().urls) - is_dirty = repo.is_dirty() - - if is_dirty: - print("warning: repo is dirty") - - setup['repo']['hash'] = hash - setup['repo']['urls'] = urls - setup['repo']['dirty'] = is_dirty - except: - has_git = False - -# dump setup -with open(os.path.join(args.output, setup_name), 'w') as f: - yaml.dump(setup, f) - -# prepare inference test if wanted -run_inference_test = False -if type(args.test_features) != type(None): - test_features = load_features(args.test_features) - inference_test_dir = os.path.join(args.output, 'inference_test') - os.makedirs(inference_test_dir, exist_ok=True) - run_inference_test = True - -# training parameters -batch_size = setup['training']['batch_size'] -epochs = setup['training']['epochs'] -lr = setup['training']['lr'] -lr_decay_factor = setup['training']['lr_decay_factor'] - -# load training dataset -lpcnet_config = setup['lpcnet']['config'] -data = LPCNetDataset( setup['dataset'], - features=lpcnet_config['features'], - input_signals=lpcnet_config['signals'], - target=lpcnet_config['target'], - frames_per_sample=setup['training']['frames_per_sample'], - feature_history=lpcnet_config['feature_history'], - feature_lookahead=lpcnet_config['feature_lookahead'], - lpc_gamma=lpcnet_config.get('lpc_gamma', 1)) - -# load validation dataset if given -if 'validation_dataset' in setup: - validation_data = LPCNetDataset( setup['validation_dataset'], - features=lpcnet_config['features'], - input_signals=lpcnet_config['signals'], - target=lpcnet_config['target'], - frames_per_sample=setup['training']['frames_per_sample'], - feature_history=lpcnet_config['feature_history'], - feature_lookahead=lpcnet_config['feature_lookahead'], - lpc_gamma=lpcnet_config.get('lpc_gamma', 1)) - - validation_dataloader = torch.utils.data.DataLoader(validation_data, batch_size=batch_size, drop_last=True, num_workers=4) - - run_validation = True -else: - run_validation = False - -# create model -model = model_dict[model_name](setup['lpcnet']['config']) - -if args.initial_checkpoint is not None: - print(f"loading state dict from {args.initial_checkpoint}...") - chkpt = torch.load(args.initial_checkpoint, map_location='cpu') - model.load_state_dict(chkpt['state_dict']) - -# set compute device -if type(args.device) == type(None): - device = torch.device("cuda" if torch.cuda.is_available() else "cpu") -else: - device = torch.device(args.device) - -# push model to device -model.to(device) - -# dataloader -dataloader = torch.utils.data.DataLoader(data, batch_size=batch_size, drop_last=True, shuffle=True, num_workers=4) - -# optimizer is introduced to trainable parameters -parameters = [p for p in model.parameters() if p.requires_grad] -optimizer = torch.optim.Adam(parameters, lr=lr) - -# learning rate scheduler -scheduler = LambdaLR(optimizer=optimizer, lr_lambda=lambda x : 1 / (1 + lr_decay_factor * x)) - -# loss -criterion = torch.nn.NLLLoss() - -# model checkpoint -checkpoint = { - 'setup' : setup, - 'state_dict' : model.state_dict(), - 'loss' : -1 -} - -if not args.no_redirect: - print(f"re-directing output to {os.path.join(args.output, output_file)}") - sys.stdout = open(os.path.join(args.output, output_file), "w") - -best_loss = 1e9 - -for ep in range(1, epochs + 1): - print(f"training epoch {ep}...") - new_loss = train_one_epoch(model, criterion, optimizer, dataloader, device, scheduler) - - - # save checkpoint - checkpoint['state_dict'] = model.state_dict() - checkpoint['loss'] = new_loss - - if run_validation: - print("running validation...") - validation_loss = evaluate(model, criterion, validation_dataloader, device) - checkpoint['validation_loss'] = validation_loss - - if validation_loss < best_loss: - torch.save(checkpoint, os.path.join(checkpoint_dir, checkpoint_prefix + f'_best.pth')) - best_loss = validation_loss - - torch.save(checkpoint, os.path.join(checkpoint_dir, checkpoint_prefix + f'_epoch_{ep}.pth')) - torch.save(checkpoint, os.path.join(checkpoint_dir, checkpoint_prefix + f'_last.pth')) - - # run inference test - if run_inference_test: - model.to("cpu") - print("running inference test...") - - output = model.generate(test_features['features'], test_features['periods'], test_features['lpcs']) - - testfilename = os.path.join(inference_test_dir, output_prefix + f'_epoch_{ep}.wav') - - wavwrite16(testfilename, output.numpy(), 16000) - - model.to(device) - - print() diff --git a/android/app/src/main/cpp/third_party/opus/dnn/torch/lpcnet/utils/__init__.py b/android/app/src/main/cpp/third_party/opus/dnn/torch/lpcnet/utils/__init__.py deleted file mode 100644 index edbbe02..0000000 --- a/android/app/src/main/cpp/third_party/opus/dnn/torch/lpcnet/utils/__init__.py +++ /dev/null @@ -1,4 +0,0 @@ -from . import sparsification -from . import data -from . import pcm -from . import sample \ No newline at end of file diff --git a/android/app/src/main/cpp/third_party/opus/dnn/torch/lpcnet/utils/data.py b/android/app/src/main/cpp/third_party/opus/dnn/torch/lpcnet/utils/data.py deleted file mode 100644 index 5d6659e..0000000 --- a/android/app/src/main/cpp/third_party/opus/dnn/torch/lpcnet/utils/data.py +++ /dev/null @@ -1,141 +0,0 @@ -""" -/* Copyright (c) 2023 Amazon - Written by Jan Buethe */ -/* - Redistribution and use in source and binary forms, with or without - modification, are permitted provided that the following conditions - are met: - - - Redistributions of source code must retain the above copyright - notice, this list of conditions and the following disclaimer. - - - Redistributions in binary form must reproduce the above copyright - notice, this list of conditions and the following disclaimer in the - documentation and/or other materials provided with the distribution. - - THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS - ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT - LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR - A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER - OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, - EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, - PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR - PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF - LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING - NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS - SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. -*/ -""" - -import os - -import torch -import numpy as np - -def load_features(feature_file, version=2): - if version == 2: - layout = { - 'cepstrum': [0,18], - 'periods': [18, 19], - 'pitch_corr': [19, 20], - 'lpc': [20, 36] - } - frame_length = 36 - - elif version == 1: - layout = { - 'cepstrum': [0,18], - 'periods': [36, 37], - 'pitch_corr': [37, 38], - 'lpc': [39, 55], - } - frame_length = 55 - else: - raise ValueError(f'unknown feature version: {version}') - - - raw_features = torch.from_numpy(np.fromfile(feature_file, dtype='float32')) - raw_features = raw_features.reshape((-1, frame_length)) - - features = torch.cat( - [ - raw_features[:, layout['cepstrum'][0] : layout['cepstrum'][1]], - raw_features[:, layout['pitch_corr'][0] : layout['pitch_corr'][1]] - ], - dim=1 - ) - - lpcs = raw_features[:, layout['lpc'][0] : layout['lpc'][1]] - periods = (0.1 + 50 * raw_features[:, layout['periods'][0] : layout['periods'][1]] + 100).long() - - return {'features' : features, 'periods' : periods, 'lpcs' : lpcs} - - - -def create_new_data(signal_path, reference_data_path, new_data_path, offset=320, preemph_factor=0.85): - ref_data = np.memmap(reference_data_path, dtype=np.int16) - signal = np.memmap(signal_path, dtype=np.int16) - - signal_preemph_path = os.path.splitext(signal_path)[0] + '_preemph.raw' - signal_preemph = np.memmap(signal_preemph_path, dtype=np.int16, mode='write', shape=signal.shape) - - - assert len(signal) % 160 == 0 - num_frames = len(signal) // 160 - mem = np.zeros(1) - for fr in range(len(signal)//160): - signal_preemph[fr * 160 : (fr + 1) * 160] = np.convolve(np.concatenate((mem, signal[fr * 160 : (fr + 1) * 160])), [1, -preemph_factor], mode='valid') - mem = signal[(fr + 1) * 160 - 1 : (fr + 1) * 160] - - new_data = np.memmap(new_data_path, dtype=np.int16, mode='write', shape=ref_data.shape) - - new_data[:] = 0 - N = len(signal) - offset - new_data[1 : 2*N + 1: 2] = signal_preemph[offset:] - new_data[2 : 2*N + 2: 2] = signal_preemph[offset:] - - -def parse_warpq_scores(output_file): - """ extracts warpq scores from output file """ - - with open(output_file, "r") as f: - lines = f.readlines() - - scores = [float(line.split("WARP-Q score:")[-1]) for line in lines if line.startswith("WARP-Q score:")] - - return scores - - -def parse_stats_file(file): - - with open(file, "r") as f: - lines = f.readlines() - - mean = float(lines[0].split(":")[-1]) - bt_mean = float(lines[1].split(":")[-1]) - top_mean = float(lines[2].split(":")[-1]) - - return mean, bt_mean, top_mean - -def collect_test_stats(test_folder): - """ collects statistics for all discovered metrics from test folder """ - - metrics = {'pesq', 'warpq', 'pitch_error', 'voicing_error'} - - results = dict() - - content = os.listdir(test_folder) - - stats_files = [file for file in content if file.startswith('stats_')] - - for file in stats_files: - metric = file[len("stats_") : -len(".txt")] - - if metric not in metrics: - print(f"warning: unknown metric {metric}") - - mean, bt_mean, top_mean = parse_stats_file(os.path.join(test_folder, file)) - - results[metric] = [mean, bt_mean, top_mean] - - return results diff --git a/android/app/src/main/cpp/third_party/opus/dnn/torch/lpcnet/utils/endoscopy.py b/android/app/src/main/cpp/third_party/opus/dnn/torch/lpcnet/utils/endoscopy.py deleted file mode 100644 index 141447e..0000000 --- a/android/app/src/main/cpp/third_party/opus/dnn/torch/lpcnet/utils/endoscopy.py +++ /dev/null @@ -1,234 +0,0 @@ -""" -/* Copyright (c) 2023 Amazon - Written by Jan Buethe */ -/* - Redistribution and use in source and binary forms, with or without - modification, are permitted provided that the following conditions - are met: - - - Redistributions of source code must retain the above copyright - notice, this list of conditions and the following disclaimer. - - - Redistributions in binary form must reproduce the above copyright - notice, this list of conditions and the following disclaimer in the - documentation and/or other materials provided with the distribution. - - THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS - ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT - LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR - A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER - OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, - EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, - PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR - PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF - LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING - NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS - SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. -*/ -""" - -""" module for inspecting models during inference """ - -import os - -import yaml -import matplotlib.pyplot as plt -import matplotlib.animation as animation - -import torch -import numpy as np - -# stores entries {key : {'fid' : fid, 'fs' : fs, 'dim' : dim, 'dtype' : dtype}} -_state = dict() -_folder = 'endoscopy' - -def get_gru_gates(gru, input, state): - hidden_size = gru.hidden_size - - direct = torch.matmul(gru.weight_ih_l0, input.squeeze()) - recurrent = torch.matmul(gru.weight_hh_l0, state.squeeze()) - - # reset gate - start, stop = 0 * hidden_size, 1 * hidden_size - reset_gate = torch.sigmoid(direct[start : stop] + gru.bias_ih_l0[start : stop] + recurrent[start : stop] + gru.bias_hh_l0[start : stop]) - - # update gate - start, stop = 1 * hidden_size, 2 * hidden_size - update_gate = torch.sigmoid(direct[start : stop] + gru.bias_ih_l0[start : stop] + recurrent[start : stop] + gru.bias_hh_l0[start : stop]) - - # new gate - start, stop = 2 * hidden_size, 3 * hidden_size - new_gate = torch.tanh(direct[start : stop] + gru.bias_ih_l0[start : stop] + reset_gate * (recurrent[start : stop] + gru.bias_hh_l0[start : stop])) - - return {'reset_gate' : reset_gate, 'update_gate' : update_gate, 'new_gate' : new_gate} - - -def init(folder='endoscopy'): - """ sets up output folder for endoscopy data """ - - global _folder - _folder = folder - - if not os.path.exists(folder): - os.makedirs(folder) - else: - print(f"warning: endoscopy folder {folder} exists. Content may be lost or inconsistent results may occur.") - -def write_data(key, data, fs): - """ appends data to previous data written under key """ - - global _state - - # convert to numpy if torch.Tensor is given - if isinstance(data, torch.Tensor): - data = data.detach().numpy() - - if not key in _state: - _state[key] = { - 'fid' : open(os.path.join(_folder, key + '.bin'), 'wb'), - 'fs' : fs, - 'dim' : tuple(data.shape), - 'dtype' : str(data.dtype) - } - - with open(os.path.join(_folder, key + '.yml'), 'w') as f: - f.write(yaml.dump({'fs' : fs, 'dim' : tuple(data.shape), 'dtype' : str(data.dtype).split('.')[-1]})) - else: - if _state[key]['fs'] != fs: - raise ValueError(f"fs changed for key {key}: {_state[key]['fs']} vs. {fs}") - if _state[key]['dtype'] != str(data.dtype): - raise ValueError(f"dtype changed for key {key}: {_state[key]['dtype']} vs. {str(data.dtype)}") - if _state[key]['dim'] != tuple(data.shape): - raise ValueError(f"dim changed for key {key}: {_state[key]['dim']} vs. {tuple(data.shape)}") - - _state[key]['fid'].write(data.tobytes()) - -def close(folder='endoscopy'): - """ clean up """ - for key in _state.keys(): - _state[key]['fid'].close() - - -def read_data(folder='endoscopy'): - """ retrieves written data as numpy arrays """ - - - keys = [name[:-4] for name in os.listdir(folder) if name.endswith('.yml')] - - return_dict = dict() - - for key in keys: - with open(os.path.join(folder, key + '.yml'), 'r') as f: - value = yaml.load(f.read(), yaml.FullLoader) - - with open(os.path.join(folder, key + '.bin'), 'rb') as f: - data = np.frombuffer(f.read(), dtype=value['dtype']) - - value['data'] = data.reshape((-1,) + value['dim']) - - return_dict[key] = value - - return return_dict - -def get_best_reshape(shape, target_ratio=1): - """ calculated the best 2d reshape of shape given the target ratio (rows/cols)""" - - if len(shape) > 1: - pixel_count = 1 - for s in shape: - pixel_count *= s - else: - pixel_count = shape[0] - - if pixel_count == 1: - return (1,) - - num_columns = int((pixel_count / target_ratio)**.5) - - while (pixel_count % num_columns): - num_columns -= 1 - - num_rows = pixel_count // num_columns - - return (num_rows, num_columns) - -def get_type_and_shape(shape): - - # can happen if data is one dimensional - if len(shape) == 0: - shape = (1,) - - # calculate pixel count - if len(shape) > 1: - pixel_count = 1 - for s in shape: - pixel_count *= s - else: - pixel_count = shape[0] - - if pixel_count == 1: - return 'plot', (1, ) - - # stay with shape if already 2-dimensional - if len(shape) == 2: - if (shape[0] != pixel_count) or (shape[1] != pixel_count): - return 'image', shape - - return 'image', get_best_reshape(shape) - -def make_animation(data, filename, start_index=80, stop_index=-80, interval=20, half_signal_window_length=80): - - # determine plot setup - num_keys = len(data.keys()) - - num_rows = int((num_keys * 3/4) ** .5) - - num_cols = (num_keys + num_rows - 1) // num_rows - - fig, axs = plt.subplots(num_rows, num_cols) - fig.set_size_inches(num_cols * 5, num_rows * 5) - - display = dict() - - fs_max = max([val['fs'] for val in data.values()]) - - num_samples = max([val['data'].shape[0] for val in data.values()]) - - keys = sorted(data.keys()) - - # inspect data - for i, key in enumerate(keys): - axs[i // num_cols, i % num_cols].title.set_text(key) - - display[key] = dict() - - display[key]['type'], display[key]['shape'] = get_type_and_shape(data[key]['dim']) - display[key]['down_factor'] = data[key]['fs'] / fs_max - - start_index = max(start_index, half_signal_window_length) - while stop_index < 0: - stop_index += num_samples - - stop_index = min(stop_index, num_samples - half_signal_window_length) - - # actual plotting - frames = [] - for index in range(start_index, stop_index): - ims = [] - for i, key in enumerate(keys): - feature_index = int(round(index * display[key]['down_factor'])) - - if display[key]['type'] == 'plot': - ims.append(axs[i // num_cols, i % num_cols].plot(data[key]['data'][index - half_signal_window_length : index + half_signal_window_length], marker='P', markevery=[half_signal_window_length], animated=True, color='blue')[0]) - - elif display[key]['type'] == 'image': - ims.append(axs[i // num_cols, i % num_cols].imshow(data[key]['data'][index].reshape(display[key]['shape']), animated=True)) - - frames.append(ims) - - ani = animation.ArtistAnimation(fig, frames, interval=interval, blit=True, repeat_delay=1000) - - if not filename.endswith('.mp4'): - filename += '.mp4' - - ani.save(filename) \ No newline at end of file diff --git a/android/app/src/main/cpp/third_party/opus/dnn/torch/lpcnet/utils/layers/__init__.py b/android/app/src/main/cpp/third_party/opus/dnn/torch/lpcnet/utils/layers/__init__.py deleted file mode 100644 index 4a58f22..0000000 --- a/android/app/src/main/cpp/third_party/opus/dnn/torch/lpcnet/utils/layers/__init__.py +++ /dev/null @@ -1,3 +0,0 @@ -from .dual_fc import DualFC -from .subconditioner import AdditiveSubconditioner, ModulativeSubconditioner, ConcatenativeSubconditioner -from .pcm_embeddings import PCMEmbedding, DifferentiablePCMEmbedding \ No newline at end of file diff --git a/android/app/src/main/cpp/third_party/opus/dnn/torch/lpcnet/utils/layers/dual_fc.py b/android/app/src/main/cpp/third_party/opus/dnn/torch/lpcnet/utils/layers/dual_fc.py deleted file mode 100644 index 25d9a5f..0000000 --- a/android/app/src/main/cpp/third_party/opus/dnn/torch/lpcnet/utils/layers/dual_fc.py +++ /dev/null @@ -1,44 +0,0 @@ -""" -/* Copyright (c) 2023 Amazon - Written by Jan Buethe */ -/* - Redistribution and use in source and binary forms, with or without - modification, are permitted provided that the following conditions - are met: - - - Redistributions of source code must retain the above copyright - notice, this list of conditions and the following disclaimer. - - - Redistributions in binary form must reproduce the above copyright - notice, this list of conditions and the following disclaimer in the - documentation and/or other materials provided with the distribution. - - THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS - ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT - LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR - A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER - OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, - EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, - PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR - PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF - LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING - NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS - SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. -*/ -""" - -import torch -from torch import nn - -class DualFC(nn.Module): - def __init__(self, input_dim, output_dim): - super(DualFC, self).__init__() - - self.dense1 = nn.Linear(input_dim, output_dim) - self.dense2 = nn.Linear(input_dim, output_dim) - - self.alpha = nn.Parameter(torch.tensor([0.5]), requires_grad=True) - self.beta = nn.Parameter(torch.tensor([0.5]), requires_grad=True) - - def forward(self, x): - return self.alpha * torch.tanh(self.dense1(x)) + self.beta * torch.tanh(self.dense2(x)) diff --git a/android/app/src/main/cpp/third_party/opus/dnn/torch/lpcnet/utils/layers/pcm_embeddings.py b/android/app/src/main/cpp/third_party/opus/dnn/torch/lpcnet/utils/layers/pcm_embeddings.py deleted file mode 100644 index 603a17a..0000000 --- a/android/app/src/main/cpp/third_party/opus/dnn/torch/lpcnet/utils/layers/pcm_embeddings.py +++ /dev/null @@ -1,71 +0,0 @@ -""" -/* Copyright (c) 2023 Amazon - Written by Jan Buethe */ -/* - Redistribution and use in source and binary forms, with or without - modification, are permitted provided that the following conditions - are met: - - - Redistributions of source code must retain the above copyright - notice, this list of conditions and the following disclaimer. - - - Redistributions in binary form must reproduce the above copyright - notice, this list of conditions and the following disclaimer in the - documentation and/or other materials provided with the distribution. - - THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS - ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT - LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR - A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER - OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, - EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, - PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR - PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF - LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING - NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS - SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. -*/ -""" - -""" module implementing PCM embeddings for LPCNet """ - -import math as m - -import torch -from torch import nn - - -class PCMEmbedding(nn.Module): - def __init__(self, embed_dim=128, num_levels=256): - super(PCMEmbedding, self).__init__() - - self.embed_dim = embed_dim - self.num_levels = num_levels - - self.embedding = nn.Embedding(self.num_levels, self.num_dim) - - # initialize - with torch.no_grad(): - num_rows, num_cols = self.num_levels, self.embed_dim - a = m.sqrt(12) * (torch.rand(num_rows, num_cols) - 0.5) - for i in range(num_rows): - a[i, :] += m.sqrt(12) * (i - num_rows / 2) - self.embedding.weight[:, :] = 0.1 * a - - def forward(self, x): - return self.embeddint(x) - - -class DifferentiablePCMEmbedding(PCMEmbedding): - def __init__(self, embed_dim, num_levels=256): - super(DifferentiablePCMEmbedding, self).__init__(embed_dim, num_levels) - - def forward(self, x): - x_int = (x - torch.floor(x)).detach().long() - x_frac = x - x_int - x_next = torch.minimum(x_int + 1, self.num_levels) - - embed_0 = self.embedding(x_int) - embed_1 = self.embedding(x_next) - - return (1 - x_frac) * embed_0 + x_frac * embed_1 diff --git a/android/app/src/main/cpp/third_party/opus/dnn/torch/lpcnet/utils/layers/subconditioner.py b/android/app/src/main/cpp/third_party/opus/dnn/torch/lpcnet/utils/layers/subconditioner.py deleted file mode 100644 index 691eb44..0000000 --- a/android/app/src/main/cpp/third_party/opus/dnn/torch/lpcnet/utils/layers/subconditioner.py +++ /dev/null @@ -1,497 +0,0 @@ -""" -/* Copyright (c) 2023 Amazon - Written by Jan Buethe */ -/* - Redistribution and use in source and binary forms, with or without - modification, are permitted provided that the following conditions - are met: - - - Redistributions of source code must retain the above copyright - notice, this list of conditions and the following disclaimer. - - - Redistributions in binary form must reproduce the above copyright - notice, this list of conditions and the following disclaimer in the - documentation and/or other materials provided with the distribution. - - THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS - ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT - LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR - A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER - OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, - EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, - PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR - PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF - LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING - NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS - SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. -*/ -""" - -from re import sub -import torch -from torch import nn - - - - -def get_subconditioner( method, - number_of_subsamples, - pcm_embedding_size, - state_size, - pcm_levels, - number_of_signals, - **kwargs): - - subconditioner_dict = { - 'additive' : AdditiveSubconditioner, - 'concatenative' : ConcatenativeSubconditioner, - 'modulative' : ModulativeSubconditioner - } - - return subconditioner_dict[method](number_of_subsamples, - pcm_embedding_size, state_size, pcm_levels, number_of_signals, **kwargs) - - -class Subconditioner(nn.Module): - def __init__(self): - """ upsampling by subconditioning - - Upsamples a sequence of states conditioning on pcm signals and - optionally a feature vector. - """ - super(Subconditioner, self).__init__() - - def forward(self, states, signals, features=None): - raise Exception("Base class should not be called") - - def single_step(self, index, state, signals, features): - raise Exception("Base class should not be called") - - def get_output_dim(self, index): - raise Exception("Base class should not be called") - - -class AdditiveSubconditioner(Subconditioner): - def __init__(self, - number_of_subsamples, - pcm_embedding_size, - state_size, - pcm_levels, - number_of_signals, - **kwargs): - """ subconditioning by addition """ - - super(AdditiveSubconditioner, self).__init__() - - self.number_of_subsamples = number_of_subsamples - self.pcm_embedding_size = pcm_embedding_size - self.state_size = state_size - self.pcm_levels = pcm_levels - self.number_of_signals = number_of_signals - - if self.pcm_embedding_size != self.state_size: - raise ValueError('For additive subconditioning state and embedding ' - + f'sizes must match but but got {self.state_size} and {self.pcm_embedding_size}') - - self.embeddings = [None] - for i in range(1, self.number_of_subsamples): - embedding = nn.Embedding(self.pcm_levels, self.pcm_embedding_size) - self.add_module('pcm_embedding_' + str(i), embedding) - self.embeddings.append(embedding) - - def forward(self, states, signals): - """ creates list of subconditioned states - - Parameters: - ----------- - states : torch.tensor - states of shape (batch, seq_length // s, state_size) - signals : torch.tensor - signals of shape (batch, seq_length, number_of_signals) - - Returns: - -------- - c_states : list of torch.tensor - list of s subconditioned states - """ - - s = self.number_of_subsamples - - c_states = [states] - new_states = states - for i in range(1, self.number_of_subsamples): - embed = self.embeddings[i](signals[:, i::s]) - # reduce signal dimension - embed = torch.sum(embed, dim=2) - - new_states = new_states + embed - c_states.append(new_states) - - return c_states - - def single_step(self, index, state, signals): - """ carry out single step for inference - - Parameters: - ----------- - index : int - position in subconditioning batch - - state : torch.tensor - state to sub-condition - - signals : torch.tensor - signals for subconditioning, all but the last dimensions - must match those of state - - Returns: - c_state : torch.tensor - subconditioned state - """ - - if index == 0: - c_state = state - else: - embed_signals = self.embeddings[index](signals) - c = torch.sum(embed_signals, dim=-2) - c_state = state + c - - return c_state - - def get_output_dim(self, index): - return self.state_size - - def get_average_flops_per_step(self): - s = self.number_of_subsamples - flops = (s - 1) / s * self.number_of_signals * self.pcm_embedding_size - return flops - - -class ConcatenativeSubconditioner(Subconditioner): - def __init__(self, - number_of_subsamples, - pcm_embedding_size, - state_size, - pcm_levels, - number_of_signals, - recurrent=True, - **kwargs): - """ subconditioning by concatenation """ - - super(ConcatenativeSubconditioner, self).__init__() - - self.number_of_subsamples = number_of_subsamples - self.pcm_embedding_size = pcm_embedding_size - self.state_size = state_size - self.pcm_levels = pcm_levels - self.number_of_signals = number_of_signals - self.recurrent = recurrent - - self.embeddings = [] - start_index = 0 - if self.recurrent: - start_index = 1 - self.embeddings.append(None) - - for i in range(start_index, self.number_of_subsamples): - embedding = nn.Embedding(self.pcm_levels, self.pcm_embedding_size) - self.add_module('pcm_embedding_' + str(i), embedding) - self.embeddings.append(embedding) - - def forward(self, states, signals): - """ creates list of subconditioned states - - Parameters: - ----------- - states : torch.tensor - states of shape (batch, seq_length // s, state_size) - signals : torch.tensor - signals of shape (batch, seq_length, number_of_signals) - - Returns: - -------- - c_states : list of torch.tensor - list of s subconditioned states - """ - s = self.number_of_subsamples - - if self.recurrent: - c_states = [states] - start = 1 - else: - c_states = [] - start = 0 - - new_states = states - for i in range(start, self.number_of_subsamples): - embed = self.embeddings[i](signals[:, i::s]) - # reduce signal dimension - embed = torch.flatten(embed, -2) - - if self.recurrent: - new_states = torch.cat((new_states, embed), dim=-1) - else: - new_states = torch.cat((states, embed), dim=-1) - - c_states.append(new_states) - - return c_states - - def single_step(self, index, state, signals): - """ carry out single step for inference - - Parameters: - ----------- - index : int - position in subconditioning batch - - state : torch.tensor - state to sub-condition - - signals : torch.tensor - signals for subconditioning, all but the last dimensions - must match those of state - - Returns: - c_state : torch.tensor - subconditioned state - """ - - if index == 0 and self.recurrent: - c_state = state - else: - embed_signals = self.embeddings[index](signals) - c = torch.flatten(embed_signals, -2) - if not self.recurrent and index > 0: - # overwrite previous conditioning vector - c_state = torch.cat((state[...,:self.state_size], c), dim=-1) - else: - c_state = torch.cat((state, c), dim=-1) - return c_state - - return c_state - - def get_average_flops_per_step(self): - return 0 - - def get_output_dim(self, index): - if self.recurrent: - return self.state_size + index * self.pcm_embedding_size * self.number_of_signals - else: - return self.state_size + self.pcm_embedding_size * self.number_of_signals - -class ModulativeSubconditioner(Subconditioner): - def __init__(self, - number_of_subsamples, - pcm_embedding_size, - state_size, - pcm_levels, - number_of_signals, - state_recurrent=False, - **kwargs): - """ subconditioning by modulation """ - - super(ModulativeSubconditioner, self).__init__() - - self.number_of_subsamples = number_of_subsamples - self.pcm_embedding_size = pcm_embedding_size - self.state_size = state_size - self.pcm_levels = pcm_levels - self.number_of_signals = number_of_signals - self.state_recurrent = state_recurrent - - self.hidden_size = self.pcm_embedding_size * self.number_of_signals - - if self.state_recurrent: - self.hidden_size += self.pcm_embedding_size - self.state_transform = nn.Linear(self.state_size, self.pcm_embedding_size) - - self.embeddings = [None] - self.alphas = [None] - self.betas = [None] - - for i in range(1, self.number_of_subsamples): - embedding = nn.Embedding(self.pcm_levels, self.pcm_embedding_size) - self.add_module('pcm_embedding_' + str(i), embedding) - self.embeddings.append(embedding) - - self.alphas.append(nn.Linear(self.hidden_size, self.state_size)) - self.add_module('alpha_dense_' + str(i), self.alphas[-1]) - - self.betas.append(nn.Linear(self.hidden_size, self.state_size)) - self.add_module('beta_dense_' + str(i), self.betas[-1]) - - - - def forward(self, states, signals): - """ creates list of subconditioned states - - Parameters: - ----------- - states : torch.tensor - states of shape (batch, seq_length // s, state_size) - signals : torch.tensor - signals of shape (batch, seq_length, number_of_signals) - - Returns: - -------- - c_states : list of torch.tensor - list of s subconditioned states - """ - s = self.number_of_subsamples - - c_states = [states] - new_states = states - for i in range(1, self.number_of_subsamples): - embed = self.embeddings[i](signals[:, i::s]) - # reduce signal dimension - embed = torch.flatten(embed, -2) - - if self.state_recurrent: - comp_states = self.state_transform(new_states) - embed = torch.cat((embed, comp_states), dim=-1) - - alpha = torch.tanh(self.alphas[i](embed)) - beta = torch.tanh(self.betas[i](embed)) - - # new state obtained by modulating previous state - new_states = torch.tanh((1 + alpha) * new_states + beta) - - c_states.append(new_states) - - return c_states - - def single_step(self, index, state, signals): - """ carry out single step for inference - - Parameters: - ----------- - index : int - position in subconditioning batch - - state : torch.tensor - state to sub-condition - - signals : torch.tensor - signals for subconditioning, all but the last dimensions - must match those of state - - Returns: - c_state : torch.tensor - subconditioned state - """ - - if index == 0: - c_state = state - else: - embed_signals = self.embeddings[index](signals) - c = torch.flatten(embed_signals, -2) - if self.state_recurrent: - r_state = self.state_transform(state) - c = torch.cat((c, r_state), dim=-1) - alpha = torch.tanh(self.alphas[index](c)) - beta = torch.tanh(self.betas[index](c)) - c_state = torch.tanh((1 + alpha) * state + beta) - return c_state - - return c_state - - def get_output_dim(self, index): - return self.state_size - - def get_average_flops_per_step(self): - s = self.number_of_subsamples - - # estimate activation by 10 flops - # c_state = torch.tanh((1 + alpha) * state + beta) - flops = 13 * self.state_size - - # hidden size - hidden_size = self.number_of_signals * self.pcm_embedding_size - if self.state_recurrent: - hidden_size += self.pcm_embedding_size - - # counting 2 * A * B flops for Linear(A, B) - # alpha = torch.tanh(self.alphas[index](c)) - # beta = torch.tanh(self.betas[index](c)) - flops += 4 * hidden_size * self.state_size + 20 * self.state_size - - # r_state = self.state_transform(state) - if self.state_recurrent: - flops += 2 * self.state_size * self.pcm_embedding_size - - # average over steps - flops *= (s - 1) / s - - return flops - -class ComparitiveSubconditioner(Subconditioner): - def __init__(self, - number_of_subsamples, - pcm_embedding_size, - state_size, - pcm_levels, - number_of_signals, - error_index=-1, - apply_gate=True, - normalize=False): - """ subconditioning by comparison """ - - super(ComparitiveSubconditioner, self).__init__() - - self.comparison_size = self.pcm_embedding_size - self.error_position = error_index - self.apply_gate = apply_gate - self.normalize = normalize - - self.state_transform = nn.Linear(self.state_size, self.comparison_size) - - self.alpha_dense = nn.Linear(self.number_of_signales * self.pcm_embedding_size, self.state_size) - self.beta_dense = nn.Linear(self.number_of_signales * self.pcm_embedding_size, self.state_size) - - if self.apply_gate: - self.gate_dense = nn.Linear(self.pcm_embedding_size, self.state_size) - - # embeddings and state transforms - self.embeddings = [None] - self.alpha_denses = [None] - self.beta_denses = [None] - self.state_transforms = [nn.Linear(self.state_size, self.comparison_size)] - self.add_module('state_transform_0', self.state_transforms[0]) - - for i in range(1, self.number_of_subsamples): - embedding = nn.Embedding(self.pcm_levels, self.pcm_embedding_size) - self.add_module('pcm_embedding_' + str(i), embedding) - self.embeddings.append(embedding) - - state_transform = nn.Linear(self.state_size, self.comparison_size) - self.add_module('state_transform_' + str(i), state_transform) - self.state_transforms.append(state_transform) - - self.alpha_denses.append(nn.Linear(self.number_of_signales * self.pcm_embedding_size, self.state_size)) - self.add_module('alpha_dense_' + str(i), self.alpha_denses[-1]) - - self.beta_denses.append(nn.Linear(self.number_of_signales * self.pcm_embedding_size, self.state_size)) - self.add_module('beta_dense_' + str(i), self.beta_denses[-1]) - - def forward(self, states, signals): - s = self.number_of_subsamples - - c_states = [states] - new_states = states - for i in range(1, self.number_of_subsamples): - embed = self.embeddings[i](signals[:, i::s]) - # reduce signal dimension - embed = torch.flatten(embed, -2) - - comp_states = self.state_transforms[i](new_states) - - alpha = torch.tanh(self.alpha_dense(embed)) - beta = torch.tanh(self.beta_dense(embed)) - - # new state obtained by modulating previous state - new_states = torch.tanh((1 + alpha) * comp_states + beta) - - c_states.append(new_states) - - return c_states diff --git a/android/app/src/main/cpp/third_party/opus/dnn/torch/lpcnet/utils/misc.py b/android/app/src/main/cpp/third_party/opus/dnn/torch/lpcnet/utils/misc.py deleted file mode 100644 index b295d6c..0000000 --- a/android/app/src/main/cpp/third_party/opus/dnn/torch/lpcnet/utils/misc.py +++ /dev/null @@ -1,65 +0,0 @@ -""" -/* Copyright (c) 2023 Amazon - Written by Jan Buethe */ -/* - Redistribution and use in source and binary forms, with or without - modification, are permitted provided that the following conditions - are met: - - - Redistributions of source code must retain the above copyright - notice, this list of conditions and the following disclaimer. - - - Redistributions in binary form must reproduce the above copyright - notice, this list of conditions and the following disclaimer in the - documentation and/or other materials provided with the distribution. - - THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS - ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT - LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR - A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER - OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, - EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, - PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR - PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF - LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING - NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS - SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. -*/ -""" - -import torch - - -def find(a, v): - try: - idx = a.index(v) - except: - idx = -1 - return idx - -def interleave_tensors(tensors, dim=-2): - """ interleave list of tensors along sequence dimension """ - - x = torch.cat([x.unsqueeze(dim) for x in tensors], dim=dim) - x = torch.flatten(x, dim - 1, dim) - - return x - -def _interleave(x, pcm_levels=256): - - repeats = pcm_levels // (2*x.size(-1)) - x = x.unsqueeze(-1) - p = torch.flatten(torch.repeat_interleave(torch.cat((x, 1 - x), dim=-1), repeats, dim=-1), -2) - - return p - -def get_pdf_from_tree(x): - pcm_levels = x.size(-1) - - p = _interleave(x[..., 1:2]) - n = 4 - while n <= pcm_levels: - p = p * _interleave(x[..., n//2:n]) - n *= 2 - - return p \ No newline at end of file diff --git a/android/app/src/main/cpp/third_party/opus/dnn/torch/lpcnet/utils/pcm.py b/android/app/src/main/cpp/third_party/opus/dnn/torch/lpcnet/utils/pcm.py deleted file mode 100644 index 7e985b8..0000000 --- a/android/app/src/main/cpp/third_party/opus/dnn/torch/lpcnet/utils/pcm.py +++ /dev/null @@ -1,35 +0,0 @@ -""" -/* Copyright (c) 2023 Amazon - Written by Jan Buethe */ -/* - Redistribution and use in source and binary forms, with or without - modification, are permitted provided that the following conditions - are met: - - - Redistributions of source code must retain the above copyright - notice, this list of conditions and the following disclaimer. - - - Redistributions in binary form must reproduce the above copyright - notice, this list of conditions and the following disclaimer in the - documentation and/or other materials provided with the distribution. - - THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS - ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT - LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR - A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER - OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, - EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, - PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR - PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF - LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING - NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS - SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. -*/ -""" - - -def clip_to_int16(x): - int_min = -2**15 - int_max = 2**15 - 1 - x_clipped = max(int_min, min(x, int_max)) - return x_clipped diff --git a/android/app/src/main/cpp/third_party/opus/dnn/torch/lpcnet/utils/sample.py b/android/app/src/main/cpp/third_party/opus/dnn/torch/lpcnet/utils/sample.py deleted file mode 100644 index 63f9e52..0000000 --- a/android/app/src/main/cpp/third_party/opus/dnn/torch/lpcnet/utils/sample.py +++ /dev/null @@ -1,44 +0,0 @@ -""" -/* Copyright (c) 2023 Amazon - Written by Jan Buethe */ -/* - Redistribution and use in source and binary forms, with or without - modification, are permitted provided that the following conditions - are met: - - - Redistributions of source code must retain the above copyright - notice, this list of conditions and the following disclaimer. - - - Redistributions in binary form must reproduce the above copyright - notice, this list of conditions and the following disclaimer in the - documentation and/or other materials provided with the distribution. - - THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS - ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT - LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR - A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER - OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, - EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, - PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR - PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF - LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING - NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS - SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. -*/ -""" - -import torch - - -def sample_excitation(probs, pitch_corr): - - norm = lambda x : x / (x.sum() + 1e-18) - - # lowering the temperature - probs = norm(probs ** (1 + max(0, 1.5 * pitch_corr - 0.5))) - # cut-off tails - probs = norm(torch.maximum(probs - 0.002 , torch.FloatTensor([0]))) - # sample - exc = torch.multinomial(probs.squeeze(), 1) - - return exc diff --git a/android/app/src/main/cpp/third_party/opus/dnn/torch/lpcnet/utils/sparsification/__init__.py b/android/app/src/main/cpp/third_party/opus/dnn/torch/lpcnet/utils/sparsification/__init__.py deleted file mode 100644 index ebfa9d9..0000000 --- a/android/app/src/main/cpp/third_party/opus/dnn/torch/lpcnet/utils/sparsification/__init__.py +++ /dev/null @@ -1,2 +0,0 @@ -from .gru_sparsifier import GRUSparsifier -from .common import sparsify_matrix, calculate_gru_flops_per_step \ No newline at end of file diff --git a/android/app/src/main/cpp/third_party/opus/dnn/torch/lpcnet/utils/sparsification/common.py b/android/app/src/main/cpp/third_party/opus/dnn/torch/lpcnet/utils/sparsification/common.py deleted file mode 100644 index 2600cd0..0000000 --- a/android/app/src/main/cpp/third_party/opus/dnn/torch/lpcnet/utils/sparsification/common.py +++ /dev/null @@ -1,121 +0,0 @@ -""" -/* Copyright (c) 2023 Amazon - Written by Jan Buethe */ -/* - Redistribution and use in source and binary forms, with or without - modification, are permitted provided that the following conditions - are met: - - - Redistributions of source code must retain the above copyright - notice, this list of conditions and the following disclaimer. - - - Redistributions in binary form must reproduce the above copyright - notice, this list of conditions and the following disclaimer in the - documentation and/or other materials provided with the distribution. - - THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS - ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT - LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR - A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER - OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, - EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, - PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR - PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF - LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING - NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS - SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. -*/ -""" - -import torch - -def sparsify_matrix(matrix : torch.tensor, density : float, block_size, keep_diagonal : bool=False, return_mask : bool=False): - """ sparsifies matrix with specified block size - - Parameters: - ----------- - matrix : torch.tensor - matrix to sparsify - density : int - target density - block_size : [int, int] - block size dimensions - keep_diagonal : bool - If true, the diagonal will be kept. This option requires block_size[0] == block_size[1] and defaults to False - """ - - m, n = matrix.shape - m1, n1 = block_size - - if m % m1 or n % n1: - raise ValueError(f"block size {(m1, n1)} does not divide matrix size {(m, n)}") - - # extract diagonal if keep_diagonal = True - if keep_diagonal: - if m != n: - raise ValueError("Attempting to sparsify non-square matrix with keep_diagonal=True") - - to_spare = torch.diag(torch.diag(matrix)) - matrix = matrix - to_spare - else: - to_spare = torch.zeros_like(matrix) - - # calculate energy in sub-blocks - x = torch.reshape(matrix, (m // m1, m1, n // n1, n1)) - x = x ** 2 - block_energies = torch.sum(torch.sum(x, dim=3), dim=1) - - number_of_blocks = (m * n) // (m1 * n1) - number_of_survivors = round(number_of_blocks * density) - - # masking threshold - if number_of_survivors == 0: - threshold = 0 - else: - threshold = torch.sort(torch.flatten(block_energies)).values[-number_of_survivors] - - # create mask - mask = torch.ones_like(block_energies) - mask[block_energies < threshold] = 0 - mask = torch.repeat_interleave(mask, m1, dim=0) - mask = torch.repeat_interleave(mask, n1, dim=1) - - # perform masking - masked_matrix = mask * matrix + to_spare - - if return_mask: - return masked_matrix, mask - else: - return masked_matrix - -def calculate_gru_flops_per_step(gru, sparsification_dict=dict(), drop_input=False): - input_size = gru.input_size - hidden_size = gru.hidden_size - flops = 0 - - input_density = ( - sparsification_dict.get('W_ir', [1])[0] - + sparsification_dict.get('W_in', [1])[0] - + sparsification_dict.get('W_iz', [1])[0] - ) / 3 - - recurrent_density = ( - sparsification_dict.get('W_hr', [1])[0] - + sparsification_dict.get('W_hn', [1])[0] - + sparsification_dict.get('W_hz', [1])[0] - ) / 3 - - # input matrix vector multiplications - if not drop_input: - flops += 2 * 3 * input_size * hidden_size * input_density - - # recurrent matrix vector multiplications - flops += 2 * 3 * hidden_size * hidden_size * recurrent_density - - # biases - flops += 6 * hidden_size - - # activations estimated by 10 flops per activation - flops += 30 * hidden_size - - return flops diff --git a/android/app/src/main/cpp/third_party/opus/dnn/torch/lpcnet/utils/sparsification/gru_sparsifier.py b/android/app/src/main/cpp/third_party/opus/dnn/torch/lpcnet/utils/sparsification/gru_sparsifier.py deleted file mode 100644 index 4dfdaf0..0000000 --- a/android/app/src/main/cpp/third_party/opus/dnn/torch/lpcnet/utils/sparsification/gru_sparsifier.py +++ /dev/null @@ -1,187 +0,0 @@ -""" -/* Copyright (c) 2023 Amazon - Written by Jan Buethe */ -/* - Redistribution and use in source and binary forms, with or without - modification, are permitted provided that the following conditions - are met: - - - Redistributions of source code must retain the above copyright - notice, this list of conditions and the following disclaimer. - - - Redistributions in binary form must reproduce the above copyright - notice, this list of conditions and the following disclaimer in the - documentation and/or other materials provided with the distribution. - - THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS - ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT - LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR - A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER - OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, - EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, - PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR - PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF - LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING - NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS - SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. -*/ -""" - -import torch - -from .common import sparsify_matrix - - -class GRUSparsifier: - def __init__(self, task_list, start, stop, interval, exponent=3): - """ Sparsifier for torch.nn.GRUs - - Parameters: - ----------- - task_list : list - task_list contains a list of tuples (gru, sparsify_dict), where gru is an instance - of torch.nn.GRU and sparsify_dic is a dictionary with keys in {'W_ir', 'W_iz', 'W_in', - 'W_hr', 'W_hz', 'W_hn'} corresponding to the input and recurrent weights for the reset, - update, and new gate. The values of sparsify_dict are tuples (density, [m, n], keep_diagonal), - where density is the target density in [0, 1], [m, n] is the shape sub-blocks to which - sparsification is applied and keep_diagonal is a bool variable indicating whether the diagonal - should be kept. - - start : int - training step after which sparsification will be started. - - stop : int - training step after which sparsification will be completed. - - interval : int - sparsification interval for steps between start and stop. After stop sparsification will be - carried out after every call to GRUSparsifier.step() - - exponent : float - Interpolation exponent for sparsification interval. In step i sparsification will be carried out - with density (alpha + target_density * (1 * alpha)), where - alpha = ((stop - i) / (start - stop)) ** exponent - - Example: - -------- - >>> import torch - >>> gru = torch.nn.GRU(10, 20) - >>> sparsify_dict = { - ... 'W_ir' : (0.5, [2, 2], False), - ... 'W_iz' : (0.6, [2, 2], False), - ... 'W_in' : (0.7, [2, 2], False), - ... 'W_hr' : (0.1, [4, 4], True), - ... 'W_hz' : (0.2, [4, 4], True), - ... 'W_hn' : (0.3, [4, 4], True), - ... } - >>> sparsifier = GRUSparsifier([(gru, sparsify_dict)], 0, 100, 50) - >>> for i in range(100): - ... sparsifier.step() - """ - # just copying parameters... - self.start = start - self.stop = stop - self.interval = interval - self.exponent = exponent - self.task_list = task_list - - # ... and setting counter to 0 - self.step_counter = 0 - - self.last_masks = {key : None for key in ['W_ir', 'W_in', 'W_iz', 'W_hr', 'W_hn', 'W_hz']} - - def step(self, verbose=False): - """ carries out sparsification step - - Call this function after optimizer.step in your - training loop. - - Parameters: - ---------- - verbose : bool - if true, densities are printed out - - Returns: - -------- - None - - """ - # compute current interpolation factor - self.step_counter += 1 - - if self.step_counter < self.start: - return - elif self.step_counter < self.stop: - # update only every self.interval-th interval - if self.step_counter % self.interval: - return - - alpha = ((self.stop - self.step_counter) / (self.stop - self.start)) ** self.exponent - else: - alpha = 0 - - - with torch.no_grad(): - for gru, params in self.task_list: - hidden_size = gru.hidden_size - - # input weights - for i, key in enumerate(['W_ir', 'W_iz', 'W_in']): - if key in params: - density = alpha + (1 - alpha) * params[key][0] - if verbose: - print(f"[{self.step_counter}]: {key} density: {density}") - - gru.weight_ih_l0[i * hidden_size : (i+1) * hidden_size, : ], new_mask = sparsify_matrix( - gru.weight_ih_l0[i * hidden_size : (i + 1) * hidden_size, : ], - density, # density - params[key][1], # block_size - params[key][2], # keep_diagonal (might want to set this to False) - return_mask=True - ) - - if type(self.last_masks[key]) != type(None): - if not torch.all(self.last_masks[key] == new_mask) and self.step_counter > self.stop: - print(f"sparsification mask {key} changed for gru {gru}") - - self.last_masks[key] = new_mask - - # recurrent weights - for i, key in enumerate(['W_hr', 'W_hz', 'W_hn']): - if key in params: - density = alpha + (1 - alpha) * params[key][0] - if verbose: - print(f"[{self.step_counter}]: {key} density: {density}") - gru.weight_hh_l0[i * hidden_size : (i+1) * hidden_size, : ], new_mask = sparsify_matrix( - gru.weight_hh_l0[i * hidden_size : (i + 1) * hidden_size, : ], - density, - params[key][1], # block_size - params[key][2], # keep_diagonal (might want to set this to False) - return_mask=True - ) - - if type(self.last_masks[key]) != type(None): - if not torch.all(self.last_masks[key] == new_mask) and self.step_counter > self.stop: - print(f"sparsification mask {key} changed for gru {gru}") - - self.last_masks[key] = new_mask - - - -if __name__ == "__main__": - print("Testing sparsifier") - - gru = torch.nn.GRU(10, 20) - sparsify_dict = { - 'W_ir' : (0.5, [2, 2], False), - 'W_iz' : (0.6, [2, 2], False), - 'W_in' : (0.7, [2, 2], False), - 'W_hr' : (0.1, [4, 4], True), - 'W_hz' : (0.2, [4, 4], True), - 'W_hn' : (0.3, [4, 4], True), - } - - sparsifier = GRUSparsifier([(gru, sparsify_dict)], 0, 100, 10) - - for i in range(100): - sparsifier.step(verbose=True) diff --git a/android/app/src/main/cpp/third_party/opus/dnn/torch/lpcnet/utils/templates.py b/android/app/src/main/cpp/third_party/opus/dnn/torch/lpcnet/utils/templates.py deleted file mode 100644 index 8906856..0000000 --- a/android/app/src/main/cpp/third_party/opus/dnn/torch/lpcnet/utils/templates.py +++ /dev/null @@ -1,157 +0,0 @@ -""" -/* Copyright (c) 2023 Amazon - Written by Jan Buethe */ -/* - Redistribution and use in source and binary forms, with or without - modification, are permitted provided that the following conditions - are met: - - - Redistributions of source code must retain the above copyright - notice, this list of conditions and the following disclaimer. - - - Redistributions in binary form must reproduce the above copyright - notice, this list of conditions and the following disclaimer in the - documentation and/or other materials provided with the distribution. - - THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS - ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT - LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR - A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER - OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, - EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, - PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR - PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF - LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING - NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS - SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. -*/ -""" - -from models import multi_rate_lpcnet -import copy - -setup_dict = dict() - -dataset_template_v2 = { - 'version' : 2, - 'feature_file' : 'features.f32', - 'signal_file' : 'data.s16', - 'frame_length' : 160, - 'feature_frame_length' : 36, - 'signal_frame_length' : 2, - 'feature_dtype' : 'float32', - 'signal_dtype' : 'int16', - 'feature_frame_layout' : {'cepstrum': [0,18], 'periods': [18, 19], 'pitch_corr': [19, 20], 'lpc': [20, 36]}, - 'signal_frame_layout' : {'last_signal' : 0, 'signal': 1} # signal, last_signal, error, prediction -} - -dataset_template_v1 = { - 'version' : 1, - 'feature_file' : 'features.f32', - 'signal_file' : 'data.u8', - 'frame_length' : 160, - 'feature_frame_length' : 55, - 'signal_frame_length' : 4, - 'feature_dtype' : 'float32', - 'signal_dtype' : 'uint8', - 'feature_frame_layout' : {'cepstrum': [0,18], 'periods': [36, 37], 'pitch_corr': [37, 38], 'lpc': [39, 55]}, - 'signal_frame_layout' : {'last_signal' : 0, 'prediction' : 1, 'last_error': 2, 'error': 3} # signal, last_signal, error, prediction -} - -# lpcnet - -lpcnet_config = { - 'frame_size' : 160, - 'gru_a_units' : 384, - 'gru_b_units' : 64, - 'feature_conditioning_dim' : 128, - 'feature_conv_kernel_size' : 3, - 'period_levels' : 257, - 'period_embedding_dim' : 64, - 'signal_embedding_dim' : 128, - 'signal_levels' : 256, - 'feature_dimension' : 19, - 'output_levels' : 256, - 'lpc_gamma' : 0.9, - 'features' : ['cepstrum', 'periods', 'pitch_corr'], - 'signals' : ['last_signal', 'prediction', 'last_error'], - 'input_layout' : { 'signals' : {'last_signal' : 0, 'prediction' : 1, 'last_error' : 2}, - 'features' : {'cepstrum' : [0, 18], 'pitch_corr' : [18, 19]} }, - 'target' : 'error', - 'feature_history' : 2, - 'feature_lookahead' : 2, - 'sparsification' : { - 'gru_a' : { - 'start' : 10000, - 'stop' : 30000, - 'interval' : 100, - 'exponent' : 3, - 'params' : { - 'W_hr' : (0.05, [4, 8], True), - 'W_hz' : (0.05, [4, 8], True), - 'W_hn' : (0.2, [4, 8], True) - }, - }, - 'gru_b' : { - 'start' : 10000, - 'stop' : 30000, - 'interval' : 100, - 'exponent' : 3, - 'params' : { - 'W_ir' : (0.5, [4, 8], False), - 'W_iz' : (0.5, [4, 8], False), - 'W_in' : (0.5, [4, 8], False) - }, - } - }, - 'add_reference_phase' : False, - 'reference_phase_dim' : 0 -} - - - -# multi rate -subconditioning = { - 'subconditioning_a' : { - 'number_of_subsamples' : 2, - 'method' : 'modulative', - 'signals' : ['last_signal', 'prediction', 'last_error'], - 'pcm_embedding_size' : 64, - 'kwargs' : dict() - - }, - 'subconditioning_b' : { - 'number_of_subsamples' : 2, - 'method' : 'modulative', - 'signals' : ['last_signal', 'prediction', 'last_error'], - 'pcm_embedding_size' : 64, - 'kwargs' : dict() - } -} - -multi_rate_lpcnet_config = lpcnet_config.copy() -multi_rate_lpcnet_config['subconditioning'] = subconditioning - -training_default = { - 'batch_size' : 256, - 'epochs' : 20, - 'lr' : 1e-3, - 'lr_decay_factor' : 2.5e-5, - 'adam_betas' : [0.9, 0.99], - 'frames_per_sample' : 15 -} - -lpcnet_setup = { - 'dataset' : '/local/datasets/lpcnet_training', - 'lpcnet' : {'config' : lpcnet_config, 'model': 'lpcnet'}, - 'training' : training_default -} - -multi_rate_lpcnet_setup = copy.deepcopy(lpcnet_setup) -multi_rate_lpcnet_setup['lpcnet']['config'] = multi_rate_lpcnet_config -multi_rate_lpcnet_setup['lpcnet']['model'] = 'multi_rate' - -setup_dict = { - 'lpcnet' : lpcnet_setup, - 'multi_rate' : multi_rate_lpcnet_setup -} diff --git a/android/app/src/main/cpp/third_party/opus/dnn/torch/lpcnet/utils/ulaw.py b/android/app/src/main/cpp/third_party/opus/dnn/torch/lpcnet/utils/ulaw.py deleted file mode 100644 index 05c395e..0000000 --- a/android/app/src/main/cpp/third_party/opus/dnn/torch/lpcnet/utils/ulaw.py +++ /dev/null @@ -1,58 +0,0 @@ -""" -/* Copyright (c) 2023 Amazon - Written by Jan Buethe */ -/* - Redistribution and use in source and binary forms, with or without - modification, are permitted provided that the following conditions - are met: - - - Redistributions of source code must retain the above copyright - notice, this list of conditions and the following disclaimer. - - - Redistributions in binary form must reproduce the above copyright - notice, this list of conditions and the following disclaimer in the - documentation and/or other materials provided with the distribution. - - THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS - ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT - LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR - A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER - OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, - EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, - PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR - PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF - LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING - NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS - SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. -*/ -""" - -import math as m - -import torch - - - -def ulaw2lin(u): - scale_1 = 32768.0 / 255.0 - u = u - 128 - s = torch.sign(u) - u = torch.abs(u) - return s * scale_1 * (torch.exp(u / 128. * m.log(256)) - 1) - - -def lin2ulawq(x): - scale = 255.0 / 32768.0 - s = torch.sign(x) - x = torch.abs(x) - u = s * (128 * torch.log(1 + scale * x) / m.log(256)) - u = torch.clip(128 + torch.round(u), 0, 255) - return u - -def lin2ulaw(x): - scale = 255.0 / 32768.0 - s = torch.sign(x) - x = torch.abs(x) - u = s * (128 * torch.log(1 + scale * x) / torch.log(256)) - u = torch.clip(128 + u, 0, 255) - return u \ No newline at end of file diff --git a/android/app/src/main/cpp/third_party/opus/dnn/torch/lpcnet/utils/wav.py b/android/app/src/main/cpp/third_party/opus/dnn/torch/lpcnet/utils/wav.py deleted file mode 100644 index d955c32..0000000 --- a/android/app/src/main/cpp/third_party/opus/dnn/torch/lpcnet/utils/wav.py +++ /dev/null @@ -1,43 +0,0 @@ -""" -/* Copyright (c) 2023 Amazon - Written by Jan Buethe */ -/* - Redistribution and use in source and binary forms, with or without - modification, are permitted provided that the following conditions - are met: - - - Redistributions of source code must retain the above copyright - notice, this list of conditions and the following disclaimer. - - - Redistributions in binary form must reproduce the above copyright - notice, this list of conditions and the following disclaimer in the - documentation and/or other materials provided with the distribution. - - THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS - ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT - LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR - A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER - OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, - EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, - PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR - PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF - LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING - NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS - SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. -*/ -""" - -import wave - -def wavwrite16(filename, x, fs): - """ writes x as int16 to file with name filename - - If x.dtype is int16 x is written as is. Otherwise, - it is scaled by 2**15 - 1 and converted to int16. - """ - if x.dtype != 'int16': - x = ((2**15 - 1) * x).astype('int16') - - with wave.open(filename, 'wb') as f: - f.setparams((1, 2, fs, len(x), 'NONE', "")) - f.writeframes(x.tobytes()) \ No newline at end of file diff --git a/android/app/src/main/cpp/third_party/opus/dnn/torch/neural-pitch/README.md b/android/app/src/main/cpp/third_party/opus/dnn/torch/neural-pitch/README.md deleted file mode 100644 index 6323ead..0000000 --- a/android/app/src/main/cpp/third_party/opus/dnn/torch/neural-pitch/README.md +++ /dev/null @@ -1,18 +0,0 @@ -## Neural Pitch Estimation - -- Dataset Installation - 1. Download and unzip PTDB Dataset: - wget https://www2.spsc.tugraz.at/databases/PTDB-TUG/SPEECH_DATA_ZIPPED.zip - unzip SPEECH_DATA_ZIPPED.zip - - 2. Inside "SPEECH DATA" above, run ptdb_process.sh to combine male/female - - 3. To Download and combine demand, simply run download_demand.sh - -- LPCNet preparation - 1. To extract xcorr, add lpcnet_extractor.c and add relevant functions to lpcnet_enc.c, add source for headers/c files and Makefile.am, and compile to generate ./lpcnet_xcorr_extractor object - -- Dataset Augmentation and training (check out arguments to each of the following) - 1. Run data_augmentation.py - 2. Run training.py using augmented data - 3. Run experiments.py diff --git a/android/app/src/main/cpp/third_party/opus/dnn/torch/neural-pitch/data_augmentation.py b/android/app/src/main/cpp/third_party/opus/dnn/torch/neural-pitch/data_augmentation.py deleted file mode 100644 index ee7a3ca..0000000 --- a/android/app/src/main/cpp/third_party/opus/dnn/torch/neural-pitch/data_augmentation.py +++ /dev/null @@ -1,149 +0,0 @@ -""" -Perform Data Augmentation (Gain, Additive Noise, Random Filtering) on Input TTS Data -1. Read in chunks and compute clean pitch first -2. Then add in augmentation (Noise/Level/Response) - - Adds filtered noise from the "Demand" dataset, https://zenodo.org/record/1227121#.XRKKxYhKiUk - - When using the Demand Dataset, consider each channel as a possible noise input, and keep the first 4 minutes of noise for training -3. Use this "augmented" audio for feature computation, and compute pitch using CREPE on the clean input - -Notes: To ensure consistency with the discovered CREPE offset, we do the following -- We pad the input audio to the zero-centered CREPE estimator with 80 zeros -- We pad the input audio to our feature computation with 160 zeros to center them -""" - -import argparse -parser = argparse.ArgumentParser() - -parser.add_argument('data', type=str, help='input raw audio data') -parser.add_argument('output', type=str, help='output directory') -parser.add_argument('path_lpcnet_extractor', type=str, help='path to LPCNet extractor object file (generated on compilation)') -parser.add_argument('noise_dataset', type=str, help='Location of the Demand Datset') -parser.add_argument('--flag_xcorr', type=bool, help='Flag to additionally dump xcorr features',choices=[True,False],default = False,required = False) -parser.add_argument('--fraction_input_use', type=float, help='Fraction of input data to consider',default = 0.3,required = False) -parser.add_argument('--gpu_index', type=int, help='GPU index to use if multiple GPUs',default = 0,required = False) -parser.add_argument('--choice_augment', type=str, help='Choice of noise augmentation, either use additive synthetic noise or add noise from the demand dataset',choices = ['demand','synthetic'],default = "demand",required = False) -parser.add_argument('--fraction_clean', type=float, help='Fraction of data to keep clean (that is not augment with anything)',default = 0.2,required = False) -parser.add_argument('--chunk_size', type=int, help='Number of samples to augment with for each iteration',default = 80000,required = False) -parser.add_argument('--N', type=int, help='STFT window size',default = 320,required = False) -parser.add_argument('--H', type=int, help='STFT Hop size',default = 160,required = False) -parser.add_argument('--freq_keep', type=int, help='Number of Frequencies to keep',default = 30,required = False) - -args = parser.parse_args() - -import os -os.environ["CUDA_VISIBLE_DEVICES"] = str(args.gpu_index) - -from utils import stft, random_filter - -import numpy as np -import tqdm -import crepe -import random -import glob -import subprocess - -data_full = np.memmap(args.data, dtype=np.int16,mode = 'r') -data = data_full[:(int)(args.fraction_input_use*data_full.shape[0])] - -# list_features = [] -list_cents = [] -list_confidences = [] - -N = args.N -H = args.H -freq_keep = args.freq_keep -# Minimum/Maximum periods, decided by LPCNet -min_period = 32 -max_period = 256 -f_ref = 16000/max_period -chunk_size = args.chunk_size -num_frames_chunk = chunk_size//H -list_indices_keep = np.concatenate([np.arange(freq_keep), (N//2 + 1) + np.arange(freq_keep), 2*(N//2 + 1) + np.arange(freq_keep)]) - -output_IF = np.memmap(args.output + '_iffeat.f32', dtype=np.float32, shape=(((data.shape[0]//chunk_size - 1)//1)*num_frames_chunk,list_indices_keep.shape[0]), mode='w+') -if args.flag_xcorr: - output_xcorr = np.memmap(args.output + '_xcorr.f32', dtype=np.float32, shape=(((data.shape[0]//chunk_size - 1)//1)*num_frames_chunk,257), mode='w+') - -fraction_clean = args.fraction_clean - -noise_dataset = args.noise_dataset - -for i in tqdm.trange((data.shape[0]//chunk_size - 1)//1): - chunk = data[i*chunk_size:(i + 1)*chunk_size]/(2**15 - 1) - - # Clean Pitch/Confidence Estimate - # Padding input to CREPE by 80 samples to ensure it aligns - _, pitch, confidence, _ = crepe.predict(np.concatenate([np.zeros(80),chunk]), 16000, center=True, viterbi=True,verbose=0) - cent = 1200*np.log2(np.divide(pitch, f_ref, out=np.zeros_like(pitch), where=pitch!=0) + 1.0e-8) - - # Filter out of range pitches/confidences - confidence[pitch < 16000/max_period] = 0 - confidence[pitch > 16000/min_period] = 0 - - # Keep fraction of data clean, augment only 1 minus the fraction - if (np.random.rand() > fraction_clean): - # Response, generate controlled/random 2nd order IIR filter and filter chunk - chunk = random_filter(chunk) - - # Level/Gain response {scale by random gain between 1.0e-3 and 10} - # Generate random gain in dB and then convert to scale - g_dB = np.random.uniform(low = -60, high = 20, size = 1) - # g_dB = 0 - g = 10**(g_dB/20) - - # Noise Addition {Add random SNR 2nd order randomly colored noise} - # Generate noise SNR value and add corresponding noise - snr_dB = np.random.uniform(low = -20, high = 30, size = 1) - - if args.choice_augment == 'synthetic': - n = np.random.randn(chunk_size) - else: - list_noisefiles = noise_dataset + '*.wav' - noise_file = random.choice(glob.glob(list_noisefiles)) - n = np.memmap(noise_file, dtype=np.int16,mode = 'r')/(2**15 - 1) - rand_range = np.random.randint(low = 0, high = (n.shape[0] - 16000*60 - chunk.shape[0])) # 16000 is subtracted because we will use the last 1 minutes of noise for testing - n = n[rand_range:rand_range + chunk.shape[0]] - - # Randomly filter the sampled noise as well - n = random_filter(n) - # generate random prime number between 0,500 and make those samples of noise 0 (to prevent GRU from picking up temporal patterns) - Nprime = random.choice([2, 3, 5, 7, 11, 13, 17, 19, 23, 29, 31, 37, 41, 43, 47, 53, 59, 61, 67, 71, 73, 79, 83, 89, 97, 101, 103, 107, 109, 113, 127, 131, 137, 139, 149, 151, 157, 163, 167, 173, 179, 181, 191, 193, 197, 199, 211, 223, 227, 229, 233, 239, 241, 251, 257, 263, 269, 271, 277, 281, 283, 293, 307, 311, 313, 317, 331, 337, 347, 349, 353, 359, 367, 373, 379, 383, 389, 397, 401, 409, 419, 421, 431, 433, 439, 443, 449, 457, 461, 463, 467, 479, 487, 491, 499, 503, 509, 521, 523, 541]) - n[chunk_size - Nprime:] = np.zeros(Nprime) - snr_multiplier = np.sqrt((np.sum(np.abs(chunk)**2)/np.sum(np.abs(n)**2))*10**(-snr_dB/10)) - - chunk = g*(chunk + snr_multiplier*n) - - # Zero pad input audio by 160 to center the frames - spec = stft(x = np.concatenate([np.zeros(160),chunk]), w = 'boxcar', N = N, H = H).T - phase_diff = spec*np.conj(np.roll(spec,1,axis = -1)) - phase_diff = phase_diff/(np.abs(phase_diff) + 1.0e-8) - feature = np.concatenate([np.log(np.abs(spec) + 1.0e-8),np.real(phase_diff),np.imag(phase_diff)],axis = 0).T - feature = feature[:,list_indices_keep] - - if args.flag_xcorr: - # Dump noisy audio into temp file - data_temp = np.memmap('./temp_augment.raw', dtype=np.int16, shape=(chunk.shape[0]), mode='w+') - # data_temp[:chunk.shape[0]] = (chunk/(np.max(np.abs(chunk)))*(2**15 - 1)).astype(np.int16) - data_temp[:chunk.shape[0]] = ((chunk)*(2**15 - 1)).astype(np.int16) - - subprocess.run([args.path_lpcnet_extractor, './temp_augment.raw', './temp_augment_xcorr.f32']) - feature_xcorr = np.flip(np.fromfile('./temp_augment_xcorr.f32', dtype='float32').reshape((-1,256),order = 'C'),axis = 1) - ones_zero_lag = np.expand_dims(np.ones(feature_xcorr.shape[0]),-1) - feature_xcorr = np.concatenate([ones_zero_lag,feature_xcorr],axis = -1) - - os.remove('./temp_augment.raw') - os.remove('./temp_augment_xcorr.f32') - num_frames = min(cent.shape[0],feature.shape[0],feature_xcorr.shape[0],num_frames_chunk) - feature = feature[:num_frames,:] - cent = cent[:num_frames] - confidence = confidence[:num_frames] - feature_xcorr = feature_xcorr[:num_frames] - output_IF[i*num_frames_chunk:(i + 1)*num_frames_chunk,:] = feature - output_xcorr[i*num_frames_chunk:(i + 1)*num_frames_chunk,:] = feature_xcorr - list_cents.append(cent) - list_confidences.append(confidence) - -list_cents = np.hstack(list_cents) -list_confidences = np.hstack(list_confidences) - -np.save(args.output + '_pitches',np.vstack([list_cents,list_confidences])) diff --git a/android/app/src/main/cpp/third_party/opus/dnn/torch/neural-pitch/download_demand.sh b/android/app/src/main/cpp/third_party/opus/dnn/torch/neural-pitch/download_demand.sh deleted file mode 100644 index 0cff06a..0000000 --- a/android/app/src/main/cpp/third_party/opus/dnn/torch/neural-pitch/download_demand.sh +++ /dev/null @@ -1,43 +0,0 @@ -wget https://zenodo.org/record/1227121/files/DKITCHEN_16k.zip - -wget https://zenodo.org/record/1227121/files/DLIVING_16k.zip - -wget https://zenodo.org/record/1227121/files/DWASHING_16k.zip - -wget https://zenodo.org/record/1227121/files/NFIELD_16k.zip - -wget https://zenodo.org/record/1227121/files/NPARK_16k.zip - -wget https://zenodo.org/record/1227121/files/NRIVER_16k.zip - -wget https://zenodo.org/record/1227121/files/OHALLWAY_16k.zip - -wget https://zenodo.org/record/1227121/files/OMEETING_16k.zip - -wget https://zenodo.org/record/1227121/files/OOFFICE_16k.zip - -wget https://zenodo.org/record/1227121/files/PCAFETER_16k.zip - -wget https://zenodo.org/record/1227121/files/PRESTO_16k.zip - -wget https://zenodo.org/record/1227121/files/PSTATION_16k.zip - -wget https://zenodo.org/record/1227121/files/TMETRO_16k.zip - -wget https://zenodo.org/record/1227121/files/TCAR_16k.zip - -wget https://zenodo.org/record/1227121/files/TBUS_16k.zip - -wget https://zenodo.org/record/1227121/files/STRAFFIC_16k.zip - -wget https://zenodo.org/record/1227121/files/SPSQUARE_16k.zip - -unzip '*.zip' - -mkdir -p ./combined_demand_channels/ -for file in */*.wav; do -parentdir="$(dirname "$file")" -echo $parentdir -fname="$(basename "$file")" -cp $file ./combined_demand_channels/$parentdir+$fname -done diff --git a/android/app/src/main/cpp/third_party/opus/dnn/torch/neural-pitch/evaluation.py b/android/app/src/main/cpp/third_party/opus/dnn/torch/neural-pitch/evaluation.py deleted file mode 100644 index 38ba576..0000000 --- a/android/app/src/main/cpp/third_party/opus/dnn/torch/neural-pitch/evaluation.py +++ /dev/null @@ -1,349 +0,0 @@ -""" -Evaluation script to compute the Raw Pitch Accuracy -Procedure: - - Look at all voiced frames in file - - Compute number of pitches in those frames that lie within a 50 cent threshold - RPA = (Total number of pitches within threshold summed across all files)/(Total number of voiced frames summed accross all files) -""" - -import os -os.environ["CUDA_VISIBLE_DEVICES"] = "0" - -from prettytable import PrettyTable -import numpy as np -import glob -import random -import tqdm -import torch -import librosa -import json -from utils import stft, random_filter, feature_xform -import subprocess -import crepe - -from models import PitchDNN, PitchDNNIF, PitchDNNXcorr - -device = torch.device("cuda" if torch.cuda.is_available() else "cpu") - -def rca(reference,input,voicing,thresh = 25): - idx_voiced = np.where(voicing != 0)[0] - acc = np.where(np.abs(reference - input)[idx_voiced] < thresh)[0] - return acc.shape[0] - -def sweep_rca(reference,input,voicing,thresh = 25,ind_arr = np.arange(-10,10)): - l = [] - for i in ind_arr: - l.append(rca(reference,np.roll(input,i),voicing,thresh)) - l = np.array(l) - - return np.max(l) - -def rpa(model,device = 'cpu',data_format = 'if'): - list_files = glob.glob('/home/ubuntu/Code/Datasets/SPEECH DATA/combined_mic_16k_raw/*.raw') - dir_f0 = '/home/ubuntu/Code/Datasets/SPEECH DATA/combine_f0_ptdb/' - # random_shuffle = list(np.random.permutation(len(list_files))) - random.shuffle(list_files) - list_files = list_files[:1000] - - C_all = 0 - C_all_m = 0 - C_all_f = 0 - list_rca_model_all = [] - list_rca_male_all = [] - list_rca_female_all = [] - - thresh = 50 - N = 320 - H = 160 - freq_keep = 30 - - for idx in tqdm.trange(len(list_files)): - audio_file = list_files[idx] - file_name = os.path.basename(list_files[idx])[:-4] - - audio = np.memmap(list_files[idx], dtype=np.int16)/(2**15 - 1) - offset = 432 - audio = audio[offset:] - rmse = np.squeeze(librosa.feature.rms(y = audio,frame_length = 320,hop_length = 160)) - - spec = stft(x = np.concatenate([np.zeros(160),audio]), w = 'boxcar', N = N, H = H).T - phase_diff = spec*np.conj(np.roll(spec,1,axis = -1)) - phase_diff = phase_diff/(np.abs(phase_diff) + 1.0e-8) - idx_save = np.concatenate([np.arange(freq_keep),(N//2 + 1) + np.arange(freq_keep),2*(N//2 + 1) + np.arange(freq_keep)]) - feature = np.concatenate([np.log(np.abs(spec) + 1.0e-8),np.real(phase_diff),np.imag(phase_diff)],axis = 0).T - feature_if = feature[:,idx_save] - - data_temp = np.memmap('./temp.raw', dtype=np.int16, shape=(audio.shape[0]), mode='w+') - data_temp[:audio.shape[0]] = (audio/(np.max(np.abs(audio)))*(2**15 - 1)).astype(np.int16) - - subprocess.run(["../../../lpcnet_xcorr_extractor", './temp.raw', './temp_xcorr.f32']) - feature_xcorr = np.flip(np.fromfile('./temp_xcorr.f32', dtype='float32').reshape((-1,256),order = 'C'),axis = 1) - ones_zero_lag = np.expand_dims(np.ones(feature_xcorr.shape[0]),-1) - feature_xcorr = np.concatenate([ones_zero_lag,feature_xcorr],axis = -1) - # feature_xcorr = feature_xform(feature_xcorr) - - os.remove('./temp.raw') - os.remove('./temp_xcorr.f32') - - if data_format == 'if': - feature = feature_if - elif data_format == 'xcorr': - feature = feature_xcorr - else: - indmin = min(feature_if.shape[0],feature_xcorr.shape[0]) - feature = np.concatenate([feature_xcorr[:indmin,:],feature_if[:indmin,:]],-1) - - - pitch_file_name = dir_f0 + "ref" + os.path.basename(list_files[idx])[3:-4] + ".f0" - pitch = np.loadtxt(pitch_file_name)[:,0] - voicing = np.loadtxt(pitch_file_name)[:,1] - indmin = min(voicing.shape[0],rmse.shape[0],pitch.shape[0]) - pitch = pitch[:indmin] - voicing = voicing[:indmin] - rmse = rmse[:indmin] - voicing = voicing*(rmse > 0.05*np.max(rmse)) - if "mic_F" in audio_file: - idx_correct = np.where(pitch < 125) - voicing[idx_correct] = 0 - - cent = np.rint(1200*np.log2(np.divide(pitch, (16000/256), out=np.zeros_like(pitch), where=pitch!=0) + 1.0e-8)).astype('int') - - - model_cents = model(torch.from_numpy(np.copy(np.expand_dims(feature,0))).float().to(device)) - model_cents = 20*model_cents.argmax(dim=1).cpu().detach().squeeze().numpy() - - num_frames = min(cent.shape[0],model_cents.shape[0]) - pitch = pitch[:num_frames] - cent = cent[:num_frames] - voicing = voicing[:num_frames] - model_cents = model_cents[:num_frames] - - voicing_all = np.copy(voicing) - # Forcefully make regions where pitch is <65 or greater than 500 unvoiced for relevant accurate pitch comparisons for our model - force_out_of_pitch = np.where(np.logical_or(pitch < 65,pitch > 500)==True) - voicing_all[force_out_of_pitch] = 0 - C_all = C_all + np.where(voicing_all != 0)[0].shape[0] - - list_rca_model_all.append(rca(cent,model_cents,voicing_all,thresh)) - - if "mic_M" in audio_file: - list_rca_male_all.append(rca(cent,model_cents,voicing_all,thresh)) - C_all_m = C_all_m + np.where(voicing_all != 0)[0].shape[0] - else: - list_rca_female_all.append(rca(cent,model_cents,voicing_all,thresh)) - C_all_f = C_all_f + np.where(voicing_all != 0)[0].shape[0] - - list_rca_model_all = np.array(list_rca_model_all) - list_rca_male_all = np.array(list_rca_male_all) - list_rca_female_all = np.array(list_rca_female_all) - - - x = PrettyTable() - - x.field_names = ["Experiment", "Mean RPA"] - x.add_row(["Both all pitches", np.sum(list_rca_model_all)/C_all]) - - x.add_row(["Male all pitches", np.sum(list_rca_male_all)/C_all_m]) - - x.add_row(["Female all pitches", np.sum(list_rca_female_all)/C_all_f]) - - print(x) - - return None - -def cycle_eval(checkpoint_list, noise_type = 'synthetic', noise_dataset = None, list_snr = [-20,-15,-10,-5,0,5,10,15,20], ptdb_dataset_path = None,fraction = 0.1,thresh = 50): - """ - Cycle through SNR evaluation for list of checkpoints - """ - list_files = glob.glob(ptdb_dataset_path + 'combined_mic_16k/*.raw') - dir_f0 = ptdb_dataset_path + 'combined_reference_f0/' - random.shuffle(list_files) - list_files = list_files[:(int)(fraction*len(list_files))] - - dict_models = {} - list_snr.append(np.inf) - - for f in checkpoint_list: - if (f!='crepe') and (f!='lpcnet'): - - checkpoint = torch.load(f, map_location='cpu') - dict_params = checkpoint['config'] - if dict_params['data_format'] == 'if': - from models import large_if_ccode as model - pitch_nn = PitchDNNIF(dict_params['freq_keep']*3,dict_params['gru_dim'],dict_params['output_dim']) - elif dict_params['data_format'] == 'xcorr': - from models import large_xcorr as model - pitch_nn = PitchDNNXcorr(dict_params['xcorr_dim'],dict_params['gru_dim'],dict_params['output_dim']) - else: - from models import large_joint as model - pitch_nn = PitchDNN(dict_params['freq_keep']*3,dict_params['xcorr_dim'],dict_params['gru_dim'],dict_params['output_dim']) - - pitch_nn.load_state_dict(checkpoint['state_dict']) - - N = dict_params['window_size'] - H = dict_params['hop_factor'] - freq_keep = dict_params['freq_keep'] - - list_mean = [] - list_std = [] - for snr_dB in list_snr: - C_all = 0 - C_correct = 0 - for idx in tqdm.trange(len(list_files)): - audio_file = list_files[idx] - file_name = os.path.basename(list_files[idx])[:-4] - - audio = np.memmap(list_files[idx], dtype=np.int16)/(2**15 - 1) - offset = 432 - audio = audio[offset:] - rmse = np.squeeze(librosa.feature.rms(y = audio,frame_length = N,hop_length = H)) - - if noise_type != 'synthetic': - list_noisefiles = noise_dataset + '*.wav' - noise_file = random.choice(glob.glob(list_noisefiles)) - n = np.memmap(noise_file, dtype=np.int16,mode = 'r')/(2**15 - 1) - rand_range = np.random.randint(low = 0, high = (16000*60*5 - audio.shape[0])) # Last 1 minute of noise used for testing - n = n[rand_range:rand_range + audio.shape[0]] - else: - n = np.random.randn(audio.shape[0]) - n = random_filter(n) - - snr_multiplier = np.sqrt((np.sum(np.abs(audio)**2)/np.sum(np.abs(n)**2))*10**(-snr_dB/10)) - audio = audio + snr_multiplier*n - - spec = stft(x = np.concatenate([np.zeros(160),audio]), w = 'boxcar', N = N, H = H).T - phase_diff = spec*np.conj(np.roll(spec,1,axis = -1)) - phase_diff = phase_diff/(np.abs(phase_diff) + 1.0e-8) - idx_save = np.concatenate([np.arange(freq_keep),(N//2 + 1) + np.arange(freq_keep),2*(N//2 + 1) + np.arange(freq_keep)]) - feature = np.concatenate([np.log(np.abs(spec) + 1.0e-8),np.real(phase_diff),np.imag(phase_diff)],axis = 0).T - feature_if = feature[:,idx_save] - - data_temp = np.memmap('./temp.raw', dtype=np.int16, shape=(audio.shape[0]), mode='w+') - # data_temp[:audio.shape[0]] = (audio/(np.max(np.abs(audio)))*(2**15 - 1)).astype(np.int16) - data_temp[:audio.shape[0]] = ((audio)*(2**15 - 1)).astype(np.int16) - - subprocess.run(["../../../lpcnet_xcorr_extractor", './temp.raw', './temp_xcorr.f32']) - feature_xcorr = np.flip(np.fromfile('./temp_xcorr.f32', dtype='float32').reshape((-1,256),order = 'C'),axis = 1) - ones_zero_lag = np.expand_dims(np.ones(feature_xcorr.shape[0]),-1) - feature_xcorr = np.concatenate([ones_zero_lag,feature_xcorr],axis = -1) - - os.remove('./temp.raw') - os.remove('./temp_xcorr.f32') - - if dict_params['data_format'] == 'if': - feature = feature_if - elif dict_params['data_format'] == 'xcorr': - feature = feature_xcorr - else: - indmin = min(feature_if.shape[0],feature_xcorr.shape[0]) - feature = np.concatenate([feature_xcorr[:indmin,:],feature_if[:indmin,:]],-1) - - pitch_file_name = dir_f0 + "ref" + os.path.basename(list_files[idx])[3:-4] + ".f0" - pitch = np.loadtxt(pitch_file_name)[:,0] - voicing = np.loadtxt(pitch_file_name)[:,1] - indmin = min(voicing.shape[0],rmse.shape[0],pitch.shape[0]) - pitch = pitch[:indmin] - voicing = voicing[:indmin] - rmse = rmse[:indmin] - voicing = voicing*(rmse > 0.05*np.max(rmse)) - if "mic_F" in audio_file: - idx_correct = np.where(pitch < 125) - voicing[idx_correct] = 0 - - cent = np.rint(1200*np.log2(np.divide(pitch, (16000/256), out=np.zeros_like(pitch), where=pitch!=0) + 1.0e-8)).astype('int') - - model_cents = pitch_nn(torch.from_numpy(np.copy(np.expand_dims(feature,0))).float().to(device)) - model_cents = 20*model_cents.argmax(dim=1).cpu().detach().squeeze().numpy() - - num_frames = min(cent.shape[0],model_cents.shape[0]) - pitch = pitch[:num_frames] - cent = cent[:num_frames] - voicing = voicing[:num_frames] - model_cents = model_cents[:num_frames] - - voicing_all = np.copy(voicing) - # Forcefully make regions where pitch is <65 or greater than 500 unvoiced for relevant accurate pitch comparisons for our model - force_out_of_pitch = np.where(np.logical_or(pitch < 65,pitch > 500)==True) - voicing_all[force_out_of_pitch] = 0 - C_all = C_all + np.where(voicing_all != 0)[0].shape[0] - - C_correct = C_correct + rca(cent,model_cents,voicing_all,thresh) - list_mean.append(C_correct/C_all) - else: - fname = f - list_mean = [] - list_std = [] - for snr_dB in list_snr: - C_all = 0 - C_correct = 0 - for idx in tqdm.trange(len(list_files)): - audio_file = list_files[idx] - file_name = os.path.basename(list_files[idx])[:-4] - - audio = np.memmap(list_files[idx], dtype=np.int16)/(2**15 - 1) - offset = 432 - audio = audio[offset:] - rmse = np.squeeze(librosa.feature.rms(y = audio,frame_length = 320,hop_length = 160)) - - if noise_type != 'synthetic': - list_noisefiles = noise_dataset + '*.wav' - noise_file = random.choice(glob.glob(list_noisefiles)) - n = np.memmap(noise_file, dtype=np.int16,mode = 'r')/(2**15 - 1) - rand_range = np.random.randint(low = 0, high = (16000*60*5 - audio.shape[0])) # Last 1 minute of noise used for testing - n = n[rand_range:rand_range + audio.shape[0]] - else: - n = np.random.randn(audio.shape[0]) - n = random_filter(n) - - snr_multiplier = np.sqrt((np.sum(np.abs(audio)**2)/np.sum(np.abs(n)**2))*10**(-snr_dB/10)) - audio = audio + snr_multiplier*n - - if (f == 'crepe'): - _, model_frequency, _, _ = crepe.predict(np.concatenate([np.zeros(80),audio]), 16000, viterbi=True,center=True,verbose=0) - model_cents = 1200*np.log2(model_frequency/(16000/256) + 1.0e-8) - else: - data_temp = np.memmap('./temp.raw', dtype=np.int16, shape=(audio.shape[0]), mode='w+') - # data_temp[:audio.shape[0]] = (audio/(np.max(np.abs(audio)))*(2**15 - 1)).astype(np.int16) - data_temp[:audio.shape[0]] = ((audio)*(2**15 - 1)).astype(np.int16) - - subprocess.run(["../../../lpcnet_xcorr_extractor", './temp.raw', './temp_xcorr.f32', './temp_period.f32']) - feature_xcorr = np.fromfile('./temp_period.f32', dtype='float32') - model_cents = 1200*np.log2((256/feature_xcorr + 1.0e-8) + 1.0e-8) - - os.remove('./temp.raw') - os.remove('./temp_xcorr.f32') - os.remove('./temp_period.f32') - - - pitch_file_name = dir_f0 + "ref" + os.path.basename(list_files[idx])[3:-4] + ".f0" - pitch = np.loadtxt(pitch_file_name)[:,0] - voicing = np.loadtxt(pitch_file_name)[:,1] - indmin = min(voicing.shape[0],rmse.shape[0],pitch.shape[0]) - pitch = pitch[:indmin] - voicing = voicing[:indmin] - rmse = rmse[:indmin] - voicing = voicing*(rmse > 0.05*np.max(rmse)) - if "mic_F" in audio_file: - idx_correct = np.where(pitch < 125) - voicing[idx_correct] = 0 - - cent = np.rint(1200*np.log2(np.divide(pitch, (16000/256), out=np.zeros_like(pitch), where=pitch!=0) + 1.0e-8)).astype('int') - num_frames = min(cent.shape[0],model_cents.shape[0]) - pitch = pitch[:num_frames] - cent = cent[:num_frames] - voicing = voicing[:num_frames] - model_cents = model_cents[:num_frames] - - voicing_all = np.copy(voicing) - # Forcefully make regions where pitch is <65 or greater than 500 unvoiced for relevant accurate pitch comparisons for our model - force_out_of_pitch = np.where(np.logical_or(pitch < 65,pitch > 500)==True) - voicing_all[force_out_of_pitch] = 0 - C_all = C_all + np.where(voicing_all != 0)[0].shape[0] - - C_correct = C_correct + rca(cent,model_cents,voicing_all,thresh) - list_mean.append(C_correct/C_all) - dict_models[fname] = {} - dict_models[fname]['list_SNR'] = list_mean[:-1] - dict_models[fname]['inf'] = list_mean[-1] - - return dict_models diff --git a/android/app/src/main/cpp/third_party/opus/dnn/torch/neural-pitch/experiments.py b/android/app/src/main/cpp/third_party/opus/dnn/torch/neural-pitch/experiments.py deleted file mode 100644 index bc8ea7e..0000000 --- a/android/app/src/main/cpp/third_party/opus/dnn/torch/neural-pitch/experiments.py +++ /dev/null @@ -1,38 +0,0 @@ -""" -Running the experiments; - 1. RCA vs SNR for our models, CREPE, LPCNet -""" - -import argparse -parser = argparse.ArgumentParser() - -parser.add_argument('ptdb_root', type=str, help='Root Directory for PTDB generated by running ptdb_process.sh ') -parser.add_argument('output', type=str, help='Output dump file name') -parser.add_argument('method', type=str, help='Output Directory to save experiment dumps',choices=['model','lpcnet','crepe']) -parser.add_argument('--noise_dataset', type=str, help='Location of the Demand Datset',default = './',required=False) -parser.add_argument('--noise_type', type=str, help='Type of additive noise',default = 'synthetic',choices=['synthetic','demand'],required=False) -parser.add_argument('--pth_file', type=str, help='.pth file to analyze',default = './',required = False) -parser.add_argument('--fraction_files_analyze', type=float, help='Fraction of PTDB dataset to test on',default = 1,required = False) -parser.add_argument('--threshold_rca', type=float, help='Cent threshold when computing RCA',default = 50,required = False) -parser.add_argument('--gpu_index', type=int, help='GPU index to use if multiple GPUs',default = 0,required = False) - -args = parser.parse_args() - -import os -os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID" -os.environ["CUDA_VISIBLE_DEVICES"] = str(args.gpu_index) - -import json -from evaluation import cycle_eval - -if args.method == 'model': - dict_store = cycle_eval([args.pth_file], noise_type = args.noise_type, noise_dataset = args.noise_dataset, list_snr = [-20,-15,-10,-5,0,5,10,15,20], ptdb_dataset_path = args.ptdb_root,fraction = args.fraction_files_analyze,thresh = args.threshold_rca) -else: - dict_store = cycle_eval([args.method], noise_type = args.noise_type, noise_dataset = args.noise_dataset, list_snr = [-20,-15,-10,-5,0,5,10,15,20], ptdb_dataset_path = args.ptdb_root,fraction = args.fraction_files_analyze,thresh = args.threshold_rca) - -dict_store["method"] = args.method -if args.method == 'model': - dict_store['pth'] = args.pth_file - -with open(args.output, 'w') as fp: - json.dump(dict_store, fp) diff --git a/android/app/src/main/cpp/third_party/opus/dnn/torch/neural-pitch/export_neuralpitch_weights.py b/android/app/src/main/cpp/third_party/opus/dnn/torch/neural-pitch/export_neuralpitch_weights.py deleted file mode 100644 index 577ec88..0000000 --- a/android/app/src/main/cpp/third_party/opus/dnn/torch/neural-pitch/export_neuralpitch_weights.py +++ /dev/null @@ -1,109 +0,0 @@ -""" -/* Copyright (c) 2022 Amazon - Written by Jan Buethe */ -/* - Redistribution and use in source and binary forms, with or without - modification, are permitted provided that the following conditions - are met: - - - Redistributions of source code must retain the above copyright - notice, this list of conditions and the following disclaimer. - - - Redistributions in binary form must reproduce the above copyright - notice, this list of conditions and the following disclaimer in the - documentation and/or other materials provided with the distribution. - - THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS - ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT - LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR - A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER - OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, - EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, - PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR - PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF - LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING - NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS - SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. -*/ -""" - -import os -import argparse -import sys - -sys.path.append(os.path.join(os.path.dirname(__file__), '../weight-exchange')) - - -parser = argparse.ArgumentParser() - -parser.add_argument('checkpoint', type=str, help='model checkpoint') -parser.add_argument('output_dir', type=str, help='output folder') - -args = parser.parse_args() - -import torch -import numpy as np - -from models import PitchDNN -from wexchange.torch import dump_torch_weights -from wexchange.c_export import CWriter, print_vector - -def c_export(args, model): - - message = f"Auto generated from checkpoint {os.path.basename(args.checkpoint)}" - - writer = CWriter(os.path.join(args.output_dir, "pitchdnn_data"), message=message, model_struct_name='PitchDNN') - writer.header.write( -f""" -#include "opus_types.h" -""" - ) - - dense_layers = [ - ('if_upsample.0', "dense_if_upsampler_1"), - ('if_upsample.2', "dense_if_upsampler_2"), - ('downsample.0', "dense_downsampler"), - ("upsample.0", "dense_final_upsampler") - ] - - - for name, export_name in dense_layers: - layer = model.get_submodule(name) - dump_torch_weights(writer, layer, name=export_name, verbose=True, quantize=True, scale=None) - - conv_layers = [ - ('conv.1', "conv2d_1"), - ('conv.4', "conv2d_2") - ] - - - for name, export_name in conv_layers: - layer = model.get_submodule(name) - dump_torch_weights(writer, layer, name=export_name, verbose=True) - - - gru_layers = [ - ("GRU", "gru_1"), - ] - - max_rnn_units = max([dump_torch_weights(writer, model.get_submodule(name), export_name, verbose=True, input_sparse=False, quantize=True, scale=None, recurrent_scale=None) - for name, export_name in gru_layers]) - - writer.header.write( -f""" - -#define PITCH_DNN_MAX_RNN_UNITS {max_rnn_units} - -""" - ) - - writer.close() - - -if __name__ == "__main__": - - os.makedirs(args.output_dir, exist_ok=True) - model = PitchDNN() - checkpoint = torch.load(args.checkpoint, map_location='cpu') - model.load_state_dict(checkpoint['state_dict']) - c_export(args, model) diff --git a/android/app/src/main/cpp/third_party/opus/dnn/torch/neural-pitch/models.py b/android/app/src/main/cpp/third_party/opus/dnn/torch/neural-pitch/models.py deleted file mode 100644 index ce4977f..0000000 --- a/android/app/src/main/cpp/third_party/opus/dnn/torch/neural-pitch/models.py +++ /dev/null @@ -1,178 +0,0 @@ -""" -Pitch Estimation Models and dataloaders - - Classification Based (Input features, output logits) -""" - -import torch -import numpy as np - -class PitchDNNIF(torch.nn.Module): - - def __init__(self, input_dim=88, gru_dim=64, output_dim=192): - super().__init__() - - self.activation = torch.nn.Tanh() - self.initial = torch.nn.Linear(input_dim, gru_dim) - self.hidden = torch.nn.Linear(gru_dim, gru_dim) - self.gru = torch.nn.GRU(input_size=gru_dim, hidden_size=gru_dim, batch_first=True) - self.upsample = torch.nn.Linear(gru_dim, output_dim) - - def forward(self, x): - - x = self.initial(x) - x = self.activation(x) - x = self.hidden(x) - x = self.activation(x) - x,_ = self.gru(x) - x = self.upsample(x) - x = self.activation(x) - x = x.permute(0,2,1) - - return x - -class PitchDNNXcorr(torch.nn.Module): - - def __init__(self, input_dim=90, gru_dim=64, output_dim=192): - super().__init__() - - self.activation = torch.nn.Tanh() - - self.conv = torch.nn.Sequential( - torch.nn.ZeroPad2d((2, 0, 1, 1)), - torch.nn.Conv2d(1, 8, 3, bias=True), - self.activation, - torch.nn.ZeroPad2d((2,0,1,1)), - torch.nn.Conv2d(8, 8, 3, bias=True), - self.activation, - torch.nn.ZeroPad2d((2,0,1,1)), - torch.nn.Conv2d(8, 1, 3, bias=True), - self.activation, - ) - - self.downsample = torch.nn.Sequential( - torch.nn.Linear(input_dim, gru_dim), - self.activation - ) - self.GRU = torch.nn.GRU(input_size=gru_dim, hidden_size=gru_dim, num_layers=1, batch_first=True) - self.upsample = torch.nn.Sequential( - torch.nn.Linear(gru_dim,output_dim), - self.activation - ) - - def forward(self, x): - x = self.conv(x.unsqueeze(-1).permute(0,3,2,1)).squeeze(1) - x,_ = self.GRU(self.downsample(x.permute(0,2,1))) - x = self.upsample(x).permute(0,2,1) - - return x - -class PitchDNN(torch.nn.Module): - """ - Joint IF-xcorr - 1D CNN on IF, merge with xcorr, 2D CNN on merged + GRU - """ - - def __init__(self,input_IF_dim=88, input_xcorr_dim=224, gru_dim=64, output_dim=192): - super().__init__() - - self.activation = torch.nn.Tanh() - - self.if_upsample = torch.nn.Sequential( - torch.nn.Linear(input_IF_dim,64), - self.activation, - torch.nn.Linear(64,64), - self.activation, - ) - - self.conv = torch.nn.Sequential( - torch.nn.ZeroPad2d((2,0,1,1)), - torch.nn.Conv2d(1, 4, 3, bias=True), - self.activation, - torch.nn.ZeroPad2d((2,0,1,1)), - torch.nn.Conv2d(4, 1, 3, bias=True), - self.activation, - ) - - self.downsample = torch.nn.Sequential( - torch.nn.Linear(64 + input_xcorr_dim, gru_dim), - self.activation - ) - self.GRU = torch.nn.GRU(input_size=gru_dim, hidden_size=gru_dim, num_layers=1, batch_first=True) - self.upsample = torch.nn.Sequential( - torch.nn.Linear(gru_dim, output_dim) - ) - - def forward(self, x): - xcorr_feat = x[:,:,:224] - if_feat = x[:,:,224:] - xcorr_feat = self.conv(xcorr_feat.unsqueeze(-1).permute(0,3,2,1)).squeeze(1).permute(0,2,1) - if_feat = self.if_upsample(if_feat) - x = torch.cat([xcorr_feat,if_feat],axis = - 1) - x,_ = self.GRU(self.downsample(x)) - x = self.upsample(x).permute(0,2,1) - - return x - - -# Dataloaders -class Loader(torch.utils.data.Dataset): - def __init__(self, features_if, file_pitch, confidence_threshold=0.4, dimension_if=30, context=100): - self.if_feat = np.memmap(features_if, dtype=np.float32).reshape(-1,3*dimension_if) - - # Resolution of 20 cents - self.cents = np.rint(np.load(file_pitch)[0,:]/20) - self.cents = np.clip(self.cents,0,179) - self.confidence = np.load(file_pitch)[1,:] - - # Filter confidence for CREPE - self.confidence[self.confidence < confidence_threshold] = 0 - self.context = context - # Clip both to same size - size_common = min(self.if_feat.shape[0], self.cents.shape[0]) - self.if_feat = self.if_feat[:size_common,:] - self.cents = self.cents[:size_common] - self.confidence = self.confidence[:size_common] - - frame_max = self.if_feat.shape[0]//context - self.if_feat = np.reshape(self.if_feat[:frame_max*context, :],(frame_max, context,3*dimension_if)) - self.cents = np.reshape(self.cents[:frame_max * context],(frame_max, context)) - self.confidence = np.reshape(self.confidence[:frame_max*context],(frame_max, context)) - - def __len__(self): - return self.if_feat.shape[0] - - def __getitem__(self, index): - return torch.from_numpy(self.if_feat[index,:,:]), torch.from_numpy(self.cents[index]), torch.from_numpy(self.confidence[index]) - -class PitchDNNDataloader(torch.utils.data.Dataset): - def __init__(self, features, file_pitch, confidence_threshold=0.4, context=100, choice_data='both'): - self.feat = np.memmap(features, mode='r', dtype=np.int8).reshape(-1,312) - self.xcorr = self.feat[:,:224] - self.if_feat = self.feat[:,224:] - ground_truth = np.memmap(file_pitch, mode='r', dtype=np.float32).reshape(-1,2) - self.cents = np.rint(60*np.log2(ground_truth[:,0]/62.5)) - mask = (self.cents>=0).astype('float32') * (self.cents<=180).astype('float32') - self.cents = np.clip(self.cents,0,179) - self.confidence = ground_truth[:,1] * mask - # Filter confidence for CREPE - self.confidence[self.confidence < confidence_threshold] = 0 - self.context = context - - self.choice_data = choice_data - - frame_max = self.if_feat.shape[0]//context - self.if_feat = np.reshape(self.if_feat[:frame_max*context,:], (frame_max, context, 88)) - self.cents = np.reshape(self.cents[:frame_max*context], (frame_max,context)) - self.xcorr = np.reshape(self.xcorr[:frame_max*context,:], (frame_max,context, 224)) - self.confidence = np.reshape(self.confidence[:frame_max*context], (frame_max, context)) - - def __len__(self): - return self.if_feat.shape[0] - - def __getitem__(self, index): - if self.choice_data == 'both': - return torch.cat([torch.from_numpy((1./127)*self.xcorr[index,:,:]), torch.from_numpy((1./127)*self.if_feat[index,:,:])], dim=-1), torch.from_numpy(self.cents[index]), torch.from_numpy(self.confidence[index]) - elif self.choice_data == 'if': - return torch.from_numpy((1./127)*self.if_feat[index,:,:]),torch.from_numpy(self.cents[index]),torch.from_numpy(self.confidence[index]) - else: - return torch.from_numpy((1./127)*self.xcorr[index,:,:]),torch.from_numpy(self.cents[index]),torch.from_numpy(self.confidence[index]) diff --git a/android/app/src/main/cpp/third_party/opus/dnn/torch/neural-pitch/neural_pitch_update.py b/android/app/src/main/cpp/third_party/opus/dnn/torch/neural-pitch/neural_pitch_update.py deleted file mode 100644 index aa2caf9..0000000 --- a/android/app/src/main/cpp/third_party/opus/dnn/torch/neural-pitch/neural_pitch_update.py +++ /dev/null @@ -1,179 +0,0 @@ -import argparse -parser = argparse.ArgumentParser() - -parser.add_argument('features', type=str, help='Features generated from dump_data') -parser.add_argument('data', type=str, help='Data generated from dump_data (offset by 5ms)') -parser.add_argument('output', type=str, help='output .f32 feature file with replaced neural pitch') -parser.add_argument('checkpoint', type=str, help='model checkpoint file') -parser.add_argument('path_lpcnet_extractor', type=str, help='path to LPCNet extractor object file (generated on compilation)') -parser.add_argument('--device', type=str, help='compute device',default = None,required = False) -parser.add_argument('--replace_xcorr', type = bool, default = False, help='Replace LPCNet xcorr with updated one') - -args = parser.parse_args() - -import os - -from utils import stft, random_filter -import subprocess -import numpy as np -import json -import torch -import tqdm - -from models import PitchDNNIF, PitchDNNXcorr, PitchDNN - -device = torch.device("cuda" if torch.cuda.is_available() else "cpu") -if device is not None: - device = torch.device(args.device) - -# Loading the appropriate model -checkpoint = torch.load(args.checkpoint, map_location='cpu') -dict_params = checkpoint['config'] - -if dict_params['data_format'] == 'if': - pitch_nn = PitchDNNIF(dict_params['freq_keep']*3, dict_params['gru_dim'], dict_params['output_dim']) -elif dict_params['data_format'] == 'xcorr': - pitch_nn = PitchDNNXcorr(dict_params['xcorr_dim'], dict_params['gru_dim'], dict_params['output_dim']) -else: - pitch_nn = PitchDNN(dict_params['freq_keep']*3, dict_params['xcorr_dim'], dict_params['gru_dim'], dict_params['output_dim']) - -pitch_nn.load_state_dict(checkpoint['state_dict']) -pitch_nn = pitch_nn.to(device) - -N = dict_params['window_size'] -H = dict_params['hop_factor'] -freq_keep = dict_params['freq_keep'] - -os.environ["OMP_NUM_THREADS"] = "16" - - -def run_lpc(signal, lpcs, frame_length=160): - num_frames, lpc_order = lpcs.shape - - prediction = np.concatenate( - [- np.convolve(signal[i * frame_length : (i + 1) * frame_length + lpc_order - 1], lpcs[i], mode='valid') for i in range(num_frames)] - ) - error = signal[lpc_order :] - prediction - - return prediction, error - - -if __name__ == "__main__": - args = parser.parse_args() - - features = np.memmap(args.features, dtype=np.float32,mode = 'r').reshape((-1, 36)) - data = np.memmap(args.data, dtype=np.int16,mode = 'r').reshape((-1, 2)) - - num_frames = features.shape[0] - feature_dim = features.shape[1] - - assert feature_dim == 36 - - output = np.memmap(args.output, dtype=np.float32, shape=(num_frames, feature_dim), mode='w+') - output[:, :36] = features - - # lpc coefficients and signal - lpcs = features[:, 20:36] - sig = data[:, 1] - - # parameters - - # constants - pitch_min = 32 - pitch_max = 256 - lpc_order = 16 - fs = 16000 - frame_length = 160 - overlap_frames = 100 - chunk_size = 10000 - history_length = frame_length * overlap_frames - history = np.zeros(history_length, dtype=np.int16) - pitch_position=18 - xcorr_position=19 - conf_position=36 - - num_frames = len(sig) // 160 - 1 - - frame_start = 0 - frame_stop = min(frame_start + chunk_size, num_frames) - signal_start = 0 - signal_stop = frame_stop * frame_length - - niters = (num_frames - 1)//chunk_size - for i in tqdm.trange(niters): - if (frame_start > num_frames - 1): - break - chunk = np.concatenate((history, sig[signal_start:signal_stop])) - chunk_la = np.concatenate((history, sig[signal_start:signal_stop + 80])) - - # Feature computation - spec = stft(x = np.concatenate([np.zeros(80),chunk_la/(2**15 - 1)]), w = 'boxcar', N = N, H = H).T - phase_diff = spec*np.conj(np.roll(spec,1,axis = -1)) - phase_diff = phase_diff/(np.abs(phase_diff) + 1.0e-8) - idx_save = np.concatenate([np.arange(freq_keep),(N//2 + 1) + np.arange(freq_keep),2*(N//2 + 1) + np.arange(freq_keep)]) - feature = np.concatenate([np.log(np.abs(spec) + 1.0e-8),np.real(phase_diff),np.imag(phase_diff)],axis = 0).T - feature_if = feature[:,idx_save] - - data_temp = np.memmap('./temp_featcompute_' + dict_params['data_format'] + '_.raw', dtype=np.int16, shape=(chunk.shape[0]), mode='w+') - data_temp[:chunk.shape[0]] = chunk_la[80:].astype(np.int16) - - subprocess.run([args.path_lpcnet_extractor, './temp_featcompute_' + dict_params['data_format'] + '_.raw', './temp_featcompute_xcorr_' + dict_params['data_format'] + '_.raw']) - feature_xcorr = np.flip(np.fromfile('./temp_featcompute_xcorr_' + dict_params['data_format'] + '_.raw', dtype='float32').reshape((-1,256),order = 'C'),axis = 1) - ones_zero_lag = np.expand_dims(np.ones(feature_xcorr.shape[0]),-1) - feature_xcorr = np.concatenate([ones_zero_lag,feature_xcorr],axis = -1) - - os.remove('./temp_featcompute_' + dict_params['data_format'] + '_.raw') - os.remove('./temp_featcompute_xcorr_' + dict_params['data_format'] + '_.raw') - - if dict_params['data_format'] == 'if': - feature = feature_if - elif dict_params['data_format'] == 'xcorr': - feature = feature_xcorr - else: - indmin = min(feature_if.shape[0],feature_xcorr.shape[0]) - feature = np.concatenate([feature_xcorr[:indmin,:],feature_if[:indmin,:]],-1) - - # Compute pitch with my model - model_cents = pitch_nn(torch.from_numpy(np.copy(np.expand_dims(feature,0))).float().to(device)) - model_cents = 20*model_cents.argmax(dim=1).cpu().detach().squeeze().numpy() - frequency = 62.5*2**(model_cents/1200) - - frequency = frequency[overlap_frames : overlap_frames + frame_stop - frame_start] - - # convert frequencies to periods - periods = np.round(fs / frequency) - - periods = np.clip(periods, pitch_min, pitch_max) - - output[frame_start:frame_stop, pitch_position] = (periods - 100) / 50 - - frame_offset = (pitch_max + frame_length - 1) // frame_length - offset = frame_offset * frame_length - padding = lpc_order - - - if frame_start < frame_offset: - lpc_coeffs = np.concatenate((np.zeros((frame_offset - frame_start, lpc_order), dtype=np.float32), lpcs[:frame_stop])) - else: - lpc_coeffs = lpcs[frame_start - frame_offset : frame_stop] - - pred, error = run_lpc(chunk[history_length - offset - padding :], lpc_coeffs, frame_length=frame_length) - - xcorr = np.zeros(frame_stop - frame_start) - for i, p in enumerate(periods.astype(np.int16)): - if p > 0: - f1 = error[offset + i * frame_length : offset + (i + 1) * frame_length] - f2 = error[offset + i * frame_length - p : offset + (i + 1) * frame_length - p] - xcorr[i] = np.dot(f1, f2) / np.sqrt(np.dot(f1, f1) * np.dot(f2, f2) + 1e-6) - - output[frame_start:frame_stop, xcorr_position] = xcorr - 0.5 - - # update buffers and indices - history = chunk[-history_length :] - - frame_start += chunk_size - frame_stop += chunk_size - frame_stop = min(frame_stop, num_frames) - - signal_start = frame_start * frame_length - signal_stop = frame_stop * frame_length diff --git a/android/app/src/main/cpp/third_party/opus/dnn/torch/neural-pitch/ptdb_process.sh b/android/app/src/main/cpp/third_party/opus/dnn/torch/neural-pitch/ptdb_process.sh deleted file mode 100644 index f4df546..0000000 --- a/android/app/src/main/cpp/third_party/opus/dnn/torch/neural-pitch/ptdb_process.sh +++ /dev/null @@ -1,34 +0,0 @@ -# Copy into PTDB root directory and run to combine all the male/female raw audio/references into below directories - -# Make folder for combined audio -mkdir -p './combined_mic_16k/' -# Make folder for combined pitch reference -mkdir -p './combined_reference_f0/' - -# Resample Male Audio -for i in ./MALE/MIC/**/*.wav; do -j="$(basename "$i" .wav)" -echo $j -sox -r 48000 -b 16 -e signed-integer "$i" -r 16000 -b 16 -e signed-integer ./combined_mic_16k/$j.raw -done - -# Resample Female Audio -for i in ./FEMALE/MIC/**/*.wav; do -j="$(basename "$i" .wav)" -echo $j -sox -r 48000 -b 16 -e signed-integer "$i" -r 16000 -b 16 -e signed-integer ./combined_mic_16k/$j.raw -done - -# Shift Male reference pitch files -for i in ./MALE/REF/**/*.f0; do -j="$(basename "$i" .wav)" -echo $j -cp "$i" ./combined_reference_f0/ -done - -# Shift Female reference pitch files -for i in ./FEMALE/REF/**/*.f0; do -j="$(basename "$i" .wav)" -echo $j -cp "$i" ./combined_reference_f0/ -done \ No newline at end of file diff --git a/android/app/src/main/cpp/third_party/opus/dnn/torch/neural-pitch/run_crepe.py b/android/app/src/main/cpp/third_party/opus/dnn/torch/neural-pitch/run_crepe.py deleted file mode 100644 index 25d6524..0000000 --- a/android/app/src/main/cpp/third_party/opus/dnn/torch/neural-pitch/run_crepe.py +++ /dev/null @@ -1,72 +0,0 @@ -""" -Perform Data Augmentation (Gain, Additive Noise, Random Filtering) on Input TTS Data -1. Read in chunks and compute clean pitch first -2. Then add in augmentation (Noise/Level/Response) - - Adds filtered noise from the "Demand" dataset, https://zenodo.org/record/1227121#.XRKKxYhKiUk - - When using the Demand Dataset, consider each channel as a possible noise input, and keep the first 4 minutes of noise for training -3. Use this "augmented" audio for feature computation, and compute pitch using CREPE on the clean input - -Notes: To ensure consistency with the discovered CREPE offset, we do the following -- We pad the input audio to the zero-centered CREPE estimator with 80 zeros -- We pad the input audio to our feature computation with 160 zeros to center them -""" - -import argparse -parser = argparse.ArgumentParser() - -parser.add_argument('data', type=str, help='input raw audio data') -parser.add_argument('output', type=str, help='output directory') -parser.add_argument('--gpu-index', type=int, help='GPU index to use if multiple GPUs',default = 0,required = False) -parser.add_argument('--chunk-size-frames', type=int, help='Number of frames to process at a time',default = 100000,required = False) - -args = parser.parse_args() - -import os -os.environ["CUDA_VISIBLE_DEVICES"] = str(args.gpu_index) - -import numpy as np -import tqdm -import crepe - -data = np.memmap(args.data, dtype=np.int16,mode = 'r') - -# list_features = [] -list_cents = [] -list_confidences = [] - -min_period = 32 -max_period = 256 -f_ref = 16000/max_period -chunk_size_frames = args.chunk_size_frames -chunk_size = chunk_size_frames*160 - -nb_chunks = (data.shape[0]+79)//chunk_size+1 - -output_data = np.zeros((0,2),dtype='float32') - -for i in tqdm.trange(nb_chunks): - if i==0: - chunk = np.concatenate([np.zeros(80),data[:chunk_size-80]]) - elif i==nb_chunks-1: - chunk = data[i*chunk_size-80:] - else: - chunk = data[i*chunk_size-80:(i+1)*chunk_size-80] - chunk = chunk/np.array(32767.,dtype='float32') - - # Clean Pitch/Confidence Estimate - # Padding input to CREPE by 80 samples to ensure it aligns - _, pitch, confidence, _ = crepe.predict(chunk, 16000, center=True, viterbi=True,verbose=0) - pitch = pitch[:chunk_size_frames] - confidence = confidence[:chunk_size_frames] - - - # Filter out of range pitches/confidences - confidence[pitch < 16000/max_period] = 0 - confidence[pitch > 16000/min_period] = 0 - pitch = np.reshape(pitch, (-1, 1)) - confidence = np.reshape(confidence, (-1, 1)) - out = np.concatenate([pitch, confidence], axis=-1, dtype='float32') - output_data = np.concatenate([output_data, out], axis=0) - - -output_data.tofile(args.output) diff --git a/android/app/src/main/cpp/third_party/opus/dnn/torch/neural-pitch/training.py b/android/app/src/main/cpp/third_party/opus/dnn/torch/neural-pitch/training.py deleted file mode 100644 index 62da135..0000000 --- a/android/app/src/main/cpp/third_party/opus/dnn/torch/neural-pitch/training.py +++ /dev/null @@ -1,162 +0,0 @@ -""" -Training the neural pitch estimator - -""" - -import os -import argparse -parser = argparse.ArgumentParser() - -parser.add_argument('features', type=str, help='.f32 IF Features for training (generated by augmentation script)') -parser.add_argument('features_pitch', type=str, help='.npy Pitch file for training (generated by augmentation script)') -parser.add_argument('output_folder', type=str, help='Output directory to store the model weights and config') -parser.add_argument('data_format', type=str, help='Choice of Input Data',choices=['if','xcorr','both']) -parser.add_argument('--gpu_index', type=int, help='GPU index to use if multiple GPUs',default = 0,required = False) -parser.add_argument('--confidence_threshold', type=float, help='Confidence value below which pitch will be neglected during training',default = 0.4,required = False) -parser.add_argument('--context', type=int, help='Sequence length during training',default = 100,required = False) -parser.add_argument('--N', type=int, help='STFT window size',default = 320,required = False) -parser.add_argument('--H', type=int, help='STFT Hop size',default = 160,required = False) -parser.add_argument('--xcorr_dimension', type=int, help='Dimension of Input cross-correlation',default = 257,required = False) -parser.add_argument('--freq_keep', type=int, help='Number of Frequencies to keep',default = 30,required = False) -parser.add_argument('--gru_dim', type=int, help='GRU Dimension',default = 64,required = False) -parser.add_argument('--output_dim', type=int, help='Output dimension',default = 192,required = False) -parser.add_argument('--learning_rate', type=float, help='Learning Rate',default = 1.0e-3,required = False) -parser.add_argument('--epochs', type=int, help='Number of training epochs',default = 50,required = False) -parser.add_argument('--choice_cel', type=str, help='Choice of Cross Entropy Loss (default or robust)',choices=['default','robust'],default = 'default',required = False) -parser.add_argument('--prefix', type=str, help="prefix for model export, default: model", default='model') -parser.add_argument('--initial-checkpoint', type=str, help='initial checkpoint to start training from, default: None', default=None) - - -args = parser.parse_args() - -# import os -# os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID" -# os.environ["CUDA_VISIBLE_DEVICES"] = str(args.gpu_index) - -# Fixing the seeds for reproducability -import time -np_seed = int(time.time()) -torch_seed = int(time.time()) - -import torch -torch.manual_seed(torch_seed) -import numpy as np -np.random.seed(np_seed) -from utils import count_parameters -import tqdm -from models import PitchDNN, PitchDNNIF, PitchDNNXcorr, PitchDNNDataloader - -device = torch.device("cuda" if torch.cuda.is_available() else "cpu") - - -if args.data_format == 'if': - pitch_nn = PitchDNNIF(3 * args.freq_keep - 2, args.gru_dim, args.output_dim) -elif args.data_format == 'xcorr': - pitch_nn = PitchDNNXcorr(args.xcorr_dimension, args.gru_dim, args.output_dim) -else: - pitch_nn = PitchDNN(3 * args.freq_keep - 2, 224, args.gru_dim, args.output_dim) - -if type(args.initial_checkpoint) != type(None): - checkpoint = torch.load(args.initial_checkpoint, map_location='cpu') - pitch_nn.load_state_dict(checkpoint['state_dict'], strict=False) - - -dataset_training = PitchDNNDataloader(args.features,args.features_pitch,args.confidence_threshold,args.context,args.data_format) - -def loss_custom(logits,labels,confidence,choice = 'default',nmax = 192,q = 0.7): - logits_softmax = torch.nn.Softmax(dim = 1)(logits).permute(0,2,1) - labels_one_hot = torch.nn.functional.one_hot(labels.long(),nmax) - - if choice == 'default': - # Categorical Cross Entropy - CE = -torch.sum(torch.log(logits_softmax*labels_one_hot + 1.0e-6)*labels_one_hot,dim=-1) - CE = torch.mean(confidence*CE) - - else: - # Robust Cross Entropy - CE = (1.0/q)*(1 - torch.sum(torch.pow(logits_softmax*labels_one_hot + 1.0e-7,q),dim=-1) ) - CE = torch.sum(confidence*CE) - - return CE - -def accuracy(logits,labels,confidence,choice = 'default',nmax = 192,q = 0.7): - logits_softmax = torch.nn.Softmax(dim = 1)(logits).permute(0,2,1) - pred_pitch = torch.argmax(logits_softmax, 2) - accuracy = (pred_pitch != labels.long())*1. - return 1.-torch.mean(confidence*accuracy) - -train_dataset, test_dataset = torch.utils.data.random_split(dataset_training, [0.95,0.05], generator=torch.Generator().manual_seed(torch_seed)) - -batch_size = 256 -train_dataloader = torch.utils.data.DataLoader(dataset=train_dataset, batch_size=batch_size, shuffle=True, num_workers=0, pin_memory=False) -test_dataloader = torch.utils.data.DataLoader(dataset=test_dataset, batch_size=batch_size, shuffle=True, num_workers=0, pin_memory=False) - -pitch_nn = pitch_nn.to(device) -num_params = count_parameters(pitch_nn) -learning_rate = args.learning_rate -model_opt = torch.optim.Adam(pitch_nn.parameters(), lr = learning_rate) - -num_epochs = args.epochs - -for epoch in range(num_epochs): - losses = [] - accs = [] - pitch_nn.train() - with tqdm.tqdm(train_dataloader) as train_epoch: - for i, (xi, yi, ci) in enumerate(train_epoch): - yi, xi, ci = yi.to(device, non_blocking=True), xi.to(device, non_blocking=True), ci.to(device, non_blocking=True) - pi = pitch_nn(xi.float()) - loss = loss_custom(logits = pi,labels = yi,confidence = ci,choice = args.choice_cel,nmax = args.output_dim) - acc = accuracy(logits = pi,labels = yi,confidence = ci,choice = args.choice_cel,nmax = args.output_dim) - acc = acc.detach() - - model_opt.zero_grad() - loss.backward() - model_opt.step() - - losses.append(loss.item()) - accs.append(acc.item()) - avg_loss = np.mean(losses) - avg_acc = np.mean(accs) - train_epoch.set_postfix({"Train Epoch" : epoch, "Train Loss":avg_loss, "acc" : avg_acc.item()}) - - if epoch % 5 == 0: - pitch_nn.eval() - losses = [] - with tqdm.tqdm(test_dataloader) as test_epoch: - for i, (xi, yi, ci) in enumerate(test_epoch): - yi, xi, ci = yi.to(device, non_blocking=True), xi.to(device, non_blocking=True), ci.to(device, non_blocking=True) - pi = pitch_nn(xi.float()) - loss = loss_custom(logits = pi,labels = yi,confidence = ci,choice = args.choice_cel,nmax = args.output_dim) - losses.append(loss.item()) - avg_loss = np.mean(losses) - test_epoch.set_postfix({"Epoch" : epoch, "Test Loss":avg_loss}) - -pitch_nn.eval() - -config = dict( - data_format=args.data_format, - epochs=num_epochs, - window_size= args.N, - hop_factor= args.H, - freq_keep=args.freq_keep, - batch_size=batch_size, - learning_rate=learning_rate, - confidence_threshold=args.confidence_threshold, - model_parameters=num_params, - np_seed=np_seed, - torch_seed=torch_seed, - xcorr_dim=args.xcorr_dimension, - dim_input=3*args.freq_keep - 2, - gru_dim=args.gru_dim, - output_dim=args.output_dim, - choice_cel=args.choice_cel, - context=args.context, -) - -model_save_path = os.path.join(args.output_folder, f"{args.prefix}_{args.data_format}.pth") -checkpoint = { - 'state_dict': pitch_nn.state_dict(), - 'config': config -} -torch.save(checkpoint, model_save_path) diff --git a/android/app/src/main/cpp/third_party/opus/dnn/torch/neural-pitch/utils.py b/android/app/src/main/cpp/third_party/opus/dnn/torch/neural-pitch/utils.py deleted file mode 100644 index 8930ad1..0000000 --- a/android/app/src/main/cpp/third_party/opus/dnn/torch/neural-pitch/utils.py +++ /dev/null @@ -1,59 +0,0 @@ -""" -Utility functions that are commonly used -""" - -import numpy as np -from scipy.signal import windows, lfilter -from prettytable import PrettyTable - - -# Source: https://gist.github.com/thongonary/026210fc186eb5056f2b6f1ca362d912 -def count_parameters(model): - table = PrettyTable(["Modules", "Parameters"]) - total_params = 0 - for name, parameter in model.named_parameters(): - if not parameter.requires_grad: continue - param = parameter.numel() - table.add_row([name, param]) - total_params+=param - print(table) - print(f"Total Trainable Params: {total_params}") - return total_params - -def stft(x, w = 'boxcar', N = 320, H = 160): - x = np.concatenate([x,np.zeros(N)]) - # win_custom = np.concatenate([windows.hann(80)[:40],np.ones(240),windows.hann(80)[40:]]) - return np.stack([np.fft.rfft(x[i:i + N]*windows.get_window(w,N)) for i in np.arange(0,x.shape[0]-N,H)]) - -def random_filter(x): - # Randomly filter x with second order IIR filter with coefficients in between -3/8,3/8 - filter_coeff = np.random.uniform(low = -3.0/8, high = 3.0/8, size = 4) - b = [1,filter_coeff[0],filter_coeff[1]] - a = [1,filter_coeff[2],filter_coeff[3]] - return lfilter(b,a,x) - -def feature_xform(feature): - """ - Take as input the (N * 256) xcorr features output by LPCNet and perform the following - 1. Downsample and Upsample by 2 (followed by smoothing) - 2. Append positional embeddings (of dim k) coresponding to each xcorr lag - """ - - from scipy.signal import resample_poly, lfilter - - - feature_US = lfilter([0.25,0.5,0.25],[1],resample_poly(feature,2,1,axis = 1),axis = 1)[:,:feature.shape[1]] - feature_DS = lfilter([0.5,0.5],[1],resample_poly(feature,1,2,axis = 1),axis = 1) - Z_append = np.zeros((feature.shape[0],feature.shape[1] - feature_DS.shape[1])) - feature_DS = np.concatenate([feature_DS,Z_append],axis = -1) - - # pos_embedding = [] - # for i in range(k): - # pos_embedding.append(np.cos((2**i)*np.pi*((np.repeat(np.arange(feature.shape[1]).reshape(feature.shape[1],1),feature.shape[0],axis = 1)).T/(2*feature.shape[1])))) - - # pos_embedding = np.stack(pos_embedding,axis = -1) - - feature = np.stack((feature_DS,feature,feature_US),axis = -1) - # feature = np.concatenate((feature,pos_embedding),axis = -1) - - return feature diff --git a/android/app/src/main/cpp/third_party/opus/dnn/torch/osce/README.md b/android/app/src/main/cpp/third_party/opus/dnn/torch/osce/README.md deleted file mode 100644 index 52a715b..0000000 --- a/android/app/src/main/cpp/third_party/opus/dnn/torch/osce/README.md +++ /dev/null @@ -1,66 +0,0 @@ -# Opus Speech Coding Enhancement - -This folder hosts models for enhancing Opus SILK. - -## Environment setup -The code is tested with python 3.11. Conda setup is done via - - -`conda create -n osce python=3.11` - -`conda activate osce` - -`python -m pip install -r requirements.txt` - - -## Generating training data -First step is to convert all training items to 16 kHz and 16 bit pcm and then concatenate them. A convenient way to do this is to create a file list and then run - -`python scripts/concatenator.py filelist 16000 dataset/clean.s16 --db_min -40 --db_max 0` - -which on top provides some random scaling. Data is taken from the datasets listed in dnn/datasets.txt and the exact list of items used for training and validation is -located in dnn/torch/osce/resources. - -Second step is to run a patched version of opus_demo in the dataset folder, which will produce the coded output and add feature files. To build the patched opus_demo binary, check out the exp-neural-silk-enhancement branch and build opus_demo the usual way. Then run - -`cd dataset && /opus_demo voip 16000 1 9000 -silk_random_switching 249 clean.s16 coded.s16 ` - -The argument to -silk_random_switching specifies the number of frames after which parameters are switched randomly. - -## Regression loss based training -Create a default setup for LACE or NoLACE via - -`python make_default_setup.py model.yml --model lace/nolace --path2dataset ` - -Then run - -`python train_model.py model.yml --no-redirect` - -for running the training script in foreground or - -`nohup python train_model.py model.yml &` - -to run it in background. In the latter case the output is written to `/out.txt`. - -## Adversarial training (NoLACE only) -Create a default setup for NoLACE via - -`python make_default_setup.py nolace_adv.yml --model nolace --adversarial --path2dataset ` - -Then run - -`python adv_train_model.py nolace_adv.yml --no-redirect` - -for running the training script in foreground or - -`nohup python adv_train_model.py nolace_adv.yml &` - -to run it in background. In the latter case the output is written to `/out.txt`. - -## Inference -Generating inference data is analogous to generating training data. Given an item 'item1.wav' run -`mkdir item1.se && sox item1.wav -r 16000 -e signed-integer -b 16 item1.raw && cd item1.se && /opus_demo voip 16000 1 ../item1.raw noisy.s16` - -The folder item1.se then serves as input for the test_model.py script or for the --testdata argument of train_model.py resp. adv_train_model.py - -Checkpoints of pre-trained models are located here: https://media.xiph.org/lpcnet/models/lace-20231019.tar.gz \ No newline at end of file diff --git a/android/app/src/main/cpp/third_party/opus/dnn/torch/osce/adv_train_model.py b/android/app/src/main/cpp/third_party/opus/dnn/torch/osce/adv_train_model.py deleted file mode 100644 index 7db859e..0000000 --- a/android/app/src/main/cpp/third_party/opus/dnn/torch/osce/adv_train_model.py +++ /dev/null @@ -1,462 +0,0 @@ -""" -/* Copyright (c) 2023 Amazon - Written by Jan Buethe */ -/* - Redistribution and use in source and binary forms, with or without - modification, are permitted provided that the following conditions - are met: - - - Redistributions of source code must retain the above copyright - notice, this list of conditions and the following disclaimer. - - - Redistributions in binary form must reproduce the above copyright - notice, this list of conditions and the following disclaimer in the - documentation and/or other materials provided with the distribution. - - THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS - ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT - LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR - A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER - OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, - EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, - PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR - PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF - LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING - NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS - SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. -*/ -""" - -import os -import argparse -import sys -import math as m -import random - -import yaml - -from tqdm import tqdm - -try: - import git - has_git = True -except: - has_git = False - -import torch -from torch.optim.lr_scheduler import LambdaLR -import torch.nn.functional as F - -from scipy.io import wavfile -import numpy as np -import pesq - -from data import SilkEnhancementSet -from models import model_dict - - -from utils.silk_features import load_inference_data -from utils.misc import count_parameters, retain_grads, get_grad_norm, create_weights - -from losses.stft_loss import MRSTFTLoss, MRLogMelLoss - - -parser = argparse.ArgumentParser() - -parser.add_argument('setup', type=str, help='setup yaml file') -parser.add_argument('output', type=str, help='output path') -parser.add_argument('--device', type=str, help='compute device', default=None) -parser.add_argument('--initial-checkpoint', type=str, help='initial checkpoint', default=None) -parser.add_argument('--testdata', type=str, help='path to features and signal for testing', default=None) -parser.add_argument('--no-redirect', action='store_true', help='disables re-direction of stdout') - -args = parser.parse_args() - - -torch.set_num_threads(4) - -with open(args.setup, 'r') as f: - setup = yaml.load(f.read(), yaml.FullLoader) - -checkpoint_prefix = 'checkpoint' -output_prefix = 'output' -setup_name = 'setup.yml' -output_file='out.txt' - - -# check model -if not 'name' in setup['model']: - print(f'warning: did not find model entry in setup, using default PitchPostFilter') - model_name = 'pitchpostfilter' -else: - model_name = setup['model']['name'] - -# prepare output folder -if os.path.exists(args.output): - print("warning: output folder exists") - - reply = input('continue? (y/n): ') - while reply not in {'y', 'n'}: - reply = input('continue? (y/n): ') - - if reply == 'n': - os._exit() -else: - os.makedirs(args.output, exist_ok=True) - -checkpoint_dir = os.path.join(args.output, 'checkpoints') -os.makedirs(checkpoint_dir, exist_ok=True) - -# add repo info to setup -if has_git: - working_dir = os.path.split(__file__)[0] - try: - repo = git.Repo(working_dir, search_parent_directories=True) - setup['repo'] = dict() - hash = repo.head.object.hexsha - urls = list(repo.remote().urls) - is_dirty = repo.is_dirty() - - if is_dirty: - print("warning: repo is dirty") - - setup['repo']['hash'] = hash - setup['repo']['urls'] = urls - setup['repo']['dirty'] = is_dirty - except: - has_git = False - -# dump setup -with open(os.path.join(args.output, setup_name), 'w') as f: - yaml.dump(setup, f) - - -ref = None -if args.testdata is not None: - - testsignal, features, periods, numbits = load_inference_data(args.testdata, **setup['data']) - - inference_test = True - inference_folder = os.path.join(args.output, 'inference_test') - os.makedirs(os.path.join(args.output, 'inference_test'), exist_ok=True) - - try: - ref = np.fromfile(os.path.join(args.testdata, 'clean.s16'), dtype=np.int16) - except: - pass -else: - inference_test = False - -# training parameters -batch_size = setup['training']['batch_size'] -epochs = setup['training']['epochs'] -lr = setup['training']['lr'] -lr_decay_factor = setup['training']['lr_decay_factor'] -lr_gen = lr * setup['training']['gen_lr_reduction'] -lambda_feat = setup['training']['lambda_feat'] -lambda_reg = setup['training']['lambda_reg'] -adv_target = setup['training'].get('adv_target', 'target') - -# load training dataset -data_config = setup['data'] -data = SilkEnhancementSet(setup['dataset'], **data_config) - -# load validation dataset if given -if 'validation_dataset' in setup: - validation_data = SilkEnhancementSet(setup['validation_dataset'], **data_config) - - validation_dataloader = torch.utils.data.DataLoader(validation_data, batch_size=batch_size, drop_last=True, num_workers=4) - - run_validation = True -else: - run_validation = False - -# create model -model = model_dict[model_name](*setup['model']['args'], **setup['model']['kwargs']) - -# create discriminator -disc_name = setup['discriminator']['name'] -disc = model_dict[disc_name]( - *setup['discriminator']['args'], **setup['discriminator']['kwargs'] -) - -# set compute device -if type(args.device) == type(None): - device = torch.device("cuda" if torch.cuda.is_available() else "cpu") -else: - device = torch.device(args.device) - -# dataloader -dataloader = torch.utils.data.DataLoader(data, batch_size=batch_size, drop_last=True, shuffle=True, num_workers=4) - -# optimizer is introduced to trainable parameters -parameters = [p for p in model.parameters() if p.requires_grad] -optimizer = torch.optim.Adam(parameters, lr=lr_gen) - -# disc optimizer -parameters = [p for p in disc.parameters() if p.requires_grad] -optimizer_disc = torch.optim.Adam(parameters, lr=lr, betas=[0.5, 0.9]) - -# learning rate scheduler -scheduler = LambdaLR(optimizer=optimizer, lr_lambda=lambda x : 1 / (1 + lr_decay_factor * x)) - -if args.initial_checkpoint is not None: - print(f"loading state dict from {args.initial_checkpoint}...") - chkpt = torch.load(args.initial_checkpoint, map_location=device) - model.load_state_dict(chkpt['state_dict']) - - if 'disc_state_dict' in chkpt: - print(f"loading discriminator state dict from {args.initial_checkpoint}...") - disc.load_state_dict(chkpt['disc_state_dict']) - - if 'optimizer_state_dict' in chkpt: - print(f"loading optimizer state dict from {args.initial_checkpoint}...") - optimizer.load_state_dict(chkpt['optimizer_state_dict']) - - if 'disc_optimizer_state_dict' in chkpt: - print(f"loading discriminator optimizer state dict from {args.initial_checkpoint}...") - optimizer_disc.load_state_dict(chkpt['disc_optimizer_state_dict']) - - if 'scheduler_state_disc' in chkpt: - print(f"loading scheduler state dict from {args.initial_checkpoint}...") - scheduler.load_state_dict(chkpt['scheduler_state_dict']) - - # if 'torch_rng_state' in chkpt: - # print(f"setting torch RNG state from {args.initial_checkpoint}...") - # torch.set_rng_state(chkpt['torch_rng_state']) - - if 'numpy_rng_state' in chkpt: - print(f"setting numpy RNG state from {args.initial_checkpoint}...") - np.random.set_state(chkpt['numpy_rng_state']) - - if 'python_rng_state' in chkpt: - print(f"setting Python RNG state from {args.initial_checkpoint}...") - random.setstate(chkpt['python_rng_state']) - -# loss -w_l1 = setup['training']['loss']['w_l1'] -w_lm = setup['training']['loss']['w_lm'] -w_slm = setup['training']['loss']['w_slm'] -w_sc = setup['training']['loss']['w_sc'] -w_logmel = setup['training']['loss']['w_logmel'] -w_wsc = setup['training']['loss']['w_wsc'] -w_xcorr = setup['training']['loss']['w_xcorr'] -w_sxcorr = setup['training']['loss']['w_sxcorr'] -w_l2 = setup['training']['loss']['w_l2'] - -w_sum = w_l1 + w_lm + w_sc + w_logmel + w_wsc + w_slm + w_xcorr + w_sxcorr + w_l2 - -stftloss = MRSTFTLoss(sc_weight=w_sc, log_mag_weight=w_lm, wsc_weight=w_wsc, smooth_log_mag_weight=w_slm, sxcorr_weight=w_sxcorr).to(device) -logmelloss = MRLogMelLoss().to(device) - -def xcorr_loss(y_true, y_pred): - dims = list(range(1, len(y_true.shape))) - - loss = 1 - torch.sum(y_true * y_pred, dim=dims) / torch.sqrt(torch.sum(y_true ** 2, dim=dims) * torch.sum(y_pred ** 2, dim=dims) + 1e-9) - - return torch.mean(loss) - -def td_l2_norm(y_true, y_pred): - dims = list(range(1, len(y_true.shape))) - - loss = torch.mean((y_true - y_pred) ** 2, dim=dims) / (torch.mean(y_pred ** 2, dim=dims) ** .5 + 1e-6) - - return loss.mean() - -def td_l1(y_true, y_pred, pow=0): - dims = list(range(1, len(y_true.shape))) - tmp = torch.mean(torch.abs(y_true - y_pred), dim=dims) / ((torch.mean(torch.abs(y_pred), dim=dims) + 1e-9) ** pow) - - return torch.mean(tmp) - -def criterion(x, y): - - return (w_l1 * td_l1(x, y, pow=1) + stftloss(x, y) + w_logmel * logmelloss(x, y) - + w_xcorr * xcorr_loss(x, y) + w_l2 * td_l2_norm(x, y)) / w_sum - - -# model checkpoint -checkpoint = { - 'setup' : setup, - 'state_dict' : model.state_dict(), - 'loss' : -1 -} - - -if not args.no_redirect: - print(f"re-directing output to {os.path.join(args.output, output_file)}") - sys.stdout = open(os.path.join(args.output, output_file), "w") - - -print("summary:") - -print(f"generator: {count_parameters(model.cpu()) / 1e6:5.3f} M parameters") -if hasattr(model, 'flop_count'): - print(f"generator: {model.flop_count(16000) / 1e6:5.3f} MFLOPS") -print(f"discriminator: {count_parameters(disc.cpu()) / 1e6:5.3f} M parameters") - -if ref is not None: - noisy = np.fromfile(os.path.join(args.testdata, 'noisy.s16'), dtype=np.int16) - initial_mos = pesq.pesq(16000, ref, noisy, mode='wb') - print(f"initial MOS (PESQ): {initial_mos}") - -best_loss = 1e9 -log_interval = 10 - - -m_r = 0 -m_f = 0 -s_r = 1 -s_f = 1 - -def optimizer_to(optim, device): - for param in optim.state.values(): - if isinstance(param, torch.Tensor): - param.data = param.data.to(device) - if param._grad is not None: - param._grad.data = param._grad.data.to(device) - elif isinstance(param, dict): - for subparam in param.values(): - if isinstance(subparam, torch.Tensor): - subparam.data = subparam.data.to(device) - if subparam._grad is not None: - subparam._grad.data = subparam._grad.data.to(device) - -optimizer_to(optimizer, device) -optimizer_to(optimizer_disc, device) - -retain_grads(model) -retain_grads(disc) - -for ep in range(1, epochs + 1): - print(f"training epoch {ep}...") - - model.to(device) - disc.to(device) - model.train() - disc.train() - - running_disc_loss = 0 - running_adv_loss = 0 - running_feature_loss = 0 - running_reg_loss = 0 - running_disc_grad_norm = 0 - running_model_grad_norm = 0 - - with tqdm(dataloader, unit='batch', file=sys.stdout) as tepoch: - for i, batch in enumerate(tepoch): - - # set gradients to zero - optimizer.zero_grad() - - # push batch to device - for key in batch: - batch[key] = batch[key].to(device) - - target = batch['target'].to(device) - disc_target = batch[adv_target].to(device) - - # calculate model output - output = model(batch['signals'].permute(0, 2, 1), batch['features'], batch['periods'], batch['numbits']) - - # discriminator update - scores_gen = disc(output.detach()) - scores_real = disc(disc_target.unsqueeze(1)) - - disc_loss = 0 - for score in scores_gen: - disc_loss += (((score[-1]) ** 2)).mean() - m_f = 0.9 * m_f + 0.1 * score[-1].detach().mean().cpu().item() - s_f = 0.9 * s_f + 0.1 * score[-1].detach().std().cpu().item() - - for score in scores_real: - disc_loss += (((1 - score[-1]) ** 2)).mean() - m_r = 0.9 * m_r + 0.1 * score[-1].detach().mean().cpu().item() - s_r = 0.9 * s_r + 0.1 * score[-1].detach().std().cpu().item() - - disc_loss = 0.5 * disc_loss / len(scores_gen) - winning_chance = 0.5 * m.erfc( (m_r - m_f) / m.sqrt(2 * (s_f**2 + s_r**2)) ) - - disc.zero_grad() - disc_loss.backward() - - running_disc_grad_norm += get_grad_norm(disc).detach().cpu().item() - - optimizer_disc.step() - - # generator update - scores_gen = disc(output) - - # calculate loss - loss_reg = criterion(output.squeeze(1), target) - - num_discs = len(scores_gen) - gen_loss = 0 - for score in scores_gen: - gen_loss += (((1 - score[-1]) ** 2)).mean() / num_discs - - loss_feat = 0 - for k in range(num_discs): - num_layers = len(scores_gen[k]) - 1 - f = 4 / num_discs / num_layers - for l in range(num_layers): - loss_feat += f * F.l1_loss(scores_gen[k][l], scores_real[k][l].detach()) - - model.zero_grad() - - (gen_loss + lambda_feat * loss_feat + lambda_reg * loss_reg).backward() - - optimizer.step() - - # sparsification - if hasattr(model, 'sparsifier'): - model.sparsifier() - - running_model_grad_norm += get_grad_norm(model).detach().cpu().item() - running_adv_loss += gen_loss.detach().cpu().item() - running_disc_loss += disc_loss.detach().cpu().item() - running_feature_loss += lambda_feat * loss_feat.detach().cpu().item() - running_reg_loss += lambda_reg * loss_reg.detach().cpu().item() - - # update status bar - if i % log_interval == 0: - tepoch.set_postfix(adv_loss=f"{running_adv_loss/(i + 1):8.7f}", - disc_loss=f"{running_disc_loss/(i + 1):8.7f}", - feat_loss=f"{running_feature_loss/(i + 1):8.7f}", - reg_loss=f"{running_reg_loss/(i + 1):8.7f}", - model_gradnorm=f"{running_model_grad_norm/(i+1):8.7f}", - disc_gradnorm=f"{running_disc_grad_norm/(i+1):8.7f}", - wc=f"{100*winning_chance:5.2f}%") - - - # save checkpoint - checkpoint['state_dict'] = model.state_dict() - checkpoint['disc_state_dict'] = disc.state_dict() - checkpoint['optimizer_state_dict'] = optimizer.state_dict() - checkpoint['disc_optimizer_state_dict'] = optimizer_disc.state_dict() - checkpoint['scheduler_state_dict'] = scheduler.state_dict() - checkpoint['torch_rng_state'] = torch.get_rng_state() - checkpoint['numpy_rng_state'] = np.random.get_state() - checkpoint['python_rng_state'] = random.getstate() - checkpoint['adv_loss'] = running_adv_loss/(i + 1) - checkpoint['disc_loss'] = running_disc_loss/(i + 1) - checkpoint['feature_loss'] = running_feature_loss/(i + 1) - checkpoint['reg_loss'] = running_reg_loss/(i + 1) - - - if inference_test: - print("running inference test...") - out = model.process(testsignal, features, periods, numbits).cpu().numpy() - wavfile.write(os.path.join(inference_folder, f'{model_name}_epoch_{ep}.wav'), 16000, out) - if ref is not None: - mos = pesq.pesq(16000, ref, out, mode='wb') - print(f"MOS (PESQ): {mos}") - - - torch.save(checkpoint, os.path.join(checkpoint_dir, checkpoint_prefix + f'_epoch_{ep}.pth')) - torch.save(checkpoint, os.path.join(checkpoint_dir, checkpoint_prefix + f'_last.pth')) - - - print() - -print('Done') diff --git a/android/app/src/main/cpp/third_party/opus/dnn/torch/osce/adv_train_vocoder.py b/android/app/src/main/cpp/third_party/opus/dnn/torch/osce/adv_train_vocoder.py deleted file mode 100644 index 73e3c9b..0000000 --- a/android/app/src/main/cpp/third_party/opus/dnn/torch/osce/adv_train_vocoder.py +++ /dev/null @@ -1,451 +0,0 @@ -""" -/* Copyright (c) 2023 Amazon - Written by Jan Buethe */ -/* - Redistribution and use in source and binary forms, with or without - modification, are permitted provided that the following conditions - are met: - - - Redistributions of source code must retain the above copyright - notice, this list of conditions and the following disclaimer. - - - Redistributions in binary form must reproduce the above copyright - notice, this list of conditions and the following disclaimer in the - documentation and/or other materials provided with the distribution. - - THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS - ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT - LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR - A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER - OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, - EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, - PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR - PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF - LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING - NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS - SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. -*/ -""" - -import os -import argparse -import sys -import math as m -import random - -import yaml - -from tqdm import tqdm - -try: - import git - has_git = True -except: - has_git = False - -import torch -from torch.optim.lr_scheduler import LambdaLR -import torch.nn.functional as F - -from scipy.io import wavfile -import numpy as np -import pesq - -from data import LPCNetVocodingDataset -from models import model_dict - - -from utils.lpcnet_features import load_lpcnet_features -from utils.misc import count_parameters - -from losses.stft_loss import MRSTFTLoss, MRLogMelLoss - - -parser = argparse.ArgumentParser() - -parser.add_argument('setup', type=str, help='setup yaml file') -parser.add_argument('output', type=str, help='output path') -parser.add_argument('--device', type=str, help='compute device', default=None) -parser.add_argument('--initial-checkpoint', type=str, help='initial checkpoint', default=None) -parser.add_argument('--test-features', type=str, help='path to features for testing', default=None) -parser.add_argument('--no-redirect', action='store_true', help='disables re-direction of stdout') - -args = parser.parse_args() - - -torch.set_num_threads(4) - -with open(args.setup, 'r') as f: - setup = yaml.load(f.read(), yaml.FullLoader) - -checkpoint_prefix = 'checkpoint' -output_prefix = 'output' -setup_name = 'setup.yml' -output_file='out.txt' - - -# check model -if not 'name' in setup['model']: - print(f'warning: did not find model entry in setup, using default PitchPostFilter') - model_name = 'pitchpostfilter' -else: - model_name = setup['model']['name'] - -# prepare output folder -if os.path.exists(args.output): - print("warning: output folder exists") - - reply = input('continue? (y/n): ') - while reply not in {'y', 'n'}: - reply = input('continue? (y/n): ') - - if reply == 'n': - os._exit() -else: - os.makedirs(args.output, exist_ok=True) - -checkpoint_dir = os.path.join(args.output, 'checkpoints') -os.makedirs(checkpoint_dir, exist_ok=True) - -# add repo info to setup -if has_git: - working_dir = os.path.split(__file__)[0] - try: - repo = git.Repo(working_dir, search_parent_directories=True) - setup['repo'] = dict() - hash = repo.head.object.hexsha - urls = list(repo.remote().urls) - is_dirty = repo.is_dirty() - - if is_dirty: - print("warning: repo is dirty") - - setup['repo']['hash'] = hash - setup['repo']['urls'] = urls - setup['repo']['dirty'] = is_dirty - except: - has_git = False - -# dump setup -with open(os.path.join(args.output, setup_name), 'w') as f: - yaml.dump(setup, f) - - -ref = None -# prepare inference test if wanted -inference_test = False -if type(args.test_features) != type(None): - test_features = load_lpcnet_features(args.test_features) - features = test_features['features'] - periods = test_features['periods'] - inference_folder = os.path.join(args.output, 'inference_test') - os.makedirs(inference_folder, exist_ok=True) - inference_test = True - - -# training parameters -batch_size = setup['training']['batch_size'] -epochs = setup['training']['epochs'] -lr = setup['training']['lr'] -lr_decay_factor = setup['training']['lr_decay_factor'] -lr_gen = lr * setup['training']['gen_lr_reduction'] -lambda_feat = setup['training']['lambda_feat'] -lambda_reg = setup['training']['lambda_reg'] -adv_target = setup['training'].get('adv_target', 'target') - - -# load training dataset -data_config = setup['data'] -data = LPCNetVocodingDataset(setup['dataset'], **data_config) - -# load validation dataset if given -if 'validation_dataset' in setup: - validation_data = LPCNetVocodingDataset(setup['validation_dataset'], **data_config) - - validation_dataloader = torch.utils.data.DataLoader(validation_data, batch_size=batch_size, drop_last=True, num_workers=4) - - run_validation = True -else: - run_validation = False - -# create model -model = model_dict[model_name](*setup['model']['args'], **setup['model']['kwargs']) - - -# create discriminator -disc_name = setup['discriminator']['name'] -disc = model_dict[disc_name]( - *setup['discriminator']['args'], **setup['discriminator']['kwargs'] -) - - - -# set compute device -if type(args.device) == type(None): - device = torch.device("cuda" if torch.cuda.is_available() else "cpu") -else: - device = torch.device(args.device) - - - -# dataloader -dataloader = torch.utils.data.DataLoader(data, batch_size=batch_size, drop_last=True, shuffle=True, num_workers=4) - -# optimizer is introduced to trainable parameters -parameters = [p for p in model.parameters() if p.requires_grad] -optimizer = torch.optim.Adam(parameters, lr=lr_gen) - -# disc optimizer -parameters = [p for p in disc.parameters() if p.requires_grad] -optimizer_disc = torch.optim.Adam(parameters, lr=lr, betas=[0.5, 0.9]) - -# learning rate scheduler -scheduler = LambdaLR(optimizer=optimizer, lr_lambda=lambda x : 1 / (1 + lr_decay_factor * x)) - -if args.initial_checkpoint is not None: - print(f"loading state dict from {args.initial_checkpoint}...") - chkpt = torch.load(args.initial_checkpoint, map_location=device) - model.load_state_dict(chkpt['state_dict']) - - if 'disc_state_dict' in chkpt: - print(f"loading discriminator state dict from {args.initial_checkpoint}...") - disc.load_state_dict(chkpt['disc_state_dict']) - - if 'optimizer_state_dict' in chkpt: - print(f"loading optimizer state dict from {args.initial_checkpoint}...") - optimizer.load_state_dict(chkpt['optimizer_state_dict']) - - if 'disc_optimizer_state_dict' in chkpt: - print(f"loading discriminator optimizer state dict from {args.initial_checkpoint}...") - optimizer_disc.load_state_dict(chkpt['disc_optimizer_state_dict']) - - if 'scheduler_state_disc' in chkpt: - print(f"loading scheduler state dict from {args.initial_checkpoint}...") - scheduler.load_state_dict(chkpt['scheduler_state_dict']) - - # if 'torch_rng_state' in chkpt: - # print(f"setting torch RNG state from {args.initial_checkpoint}...") - # torch.set_rng_state(chkpt['torch_rng_state']) - - if 'numpy_rng_state' in chkpt: - print(f"setting numpy RNG state from {args.initial_checkpoint}...") - np.random.set_state(chkpt['numpy_rng_state']) - - if 'python_rng_state' in chkpt: - print(f"setting Python RNG state from {args.initial_checkpoint}...") - random.setstate(chkpt['python_rng_state']) - -# loss -w_l1 = setup['training']['loss']['w_l1'] -w_lm = setup['training']['loss']['w_lm'] -w_slm = setup['training']['loss']['w_slm'] -w_sc = setup['training']['loss']['w_sc'] -w_logmel = setup['training']['loss']['w_logmel'] -w_wsc = setup['training']['loss']['w_wsc'] -w_xcorr = setup['training']['loss']['w_xcorr'] -w_sxcorr = setup['training']['loss']['w_sxcorr'] -w_l2 = setup['training']['loss']['w_l2'] - -w_sum = w_l1 + w_lm + w_sc + w_logmel + w_wsc + w_slm + w_xcorr + w_sxcorr + w_l2 - -stftloss = MRSTFTLoss(sc_weight=w_sc, log_mag_weight=w_lm, wsc_weight=w_wsc, smooth_log_mag_weight=w_slm, sxcorr_weight=w_sxcorr).to(device) -logmelloss = MRLogMelLoss().to(device) - -def xcorr_loss(y_true, y_pred): - dims = list(range(1, len(y_true.shape))) - - loss = 1 - torch.sum(y_true * y_pred, dim=dims) / torch.sqrt(torch.sum(y_true ** 2, dim=dims) * torch.sum(y_pred ** 2, dim=dims) + 1e-9) - - return torch.mean(loss) - -def td_l2_norm(y_true, y_pred): - dims = list(range(1, len(y_true.shape))) - - loss = torch.mean((y_true - y_pred) ** 2, dim=dims) / (torch.mean(y_pred ** 2, dim=dims) ** .5 + 1e-6) - - return loss.mean() - -def td_l1(y_true, y_pred, pow=0): - dims = list(range(1, len(y_true.shape))) - tmp = torch.mean(torch.abs(y_true - y_pred), dim=dims) / ((torch.mean(torch.abs(y_pred), dim=dims) + 1e-9) ** pow) - - return torch.mean(tmp) - -def criterion(x, y): - - return (w_l1 * td_l1(x, y, pow=1) + stftloss(x, y) + w_logmel * logmelloss(x, y) - + w_xcorr * xcorr_loss(x, y) + w_l2 * td_l2_norm(x, y)) / w_sum - - -# model checkpoint -checkpoint = { - 'setup' : setup, - 'state_dict' : model.state_dict(), - 'loss' : -1 -} - - -if not args.no_redirect: - print(f"re-directing output to {os.path.join(args.output, output_file)}") - sys.stdout = open(os.path.join(args.output, output_file), "w") - - -print("summary:") - -print(f"generator: {count_parameters(model.cpu()) / 1e6:5.3f} M parameters") -if hasattr(model, 'flop_count'): - print(f"generator: {model.flop_count(16000) / 1e6:5.3f} MFLOPS") -print(f"discriminator: {count_parameters(disc.cpu()) / 1e6:5.3f} M parameters") - -if ref is not None: - noisy = np.fromfile(os.path.join(args.testdata, 'noisy.s16'), dtype=np.int16) - initial_mos = pesq.pesq(16000, ref, noisy, mode='wb') - print(f"initial MOS (PESQ): {initial_mos}") - -best_loss = 1e9 -log_interval = 10 - - -m_r = 0 -m_f = 0 -s_r = 1 -s_f = 1 - -def optimizer_to(optim, device): - for param in optim.state.values(): - if isinstance(param, torch.Tensor): - param.data = param.data.to(device) - if param._grad is not None: - param._grad.data = param._grad.data.to(device) - elif isinstance(param, dict): - for subparam in param.values(): - if isinstance(subparam, torch.Tensor): - subparam.data = subparam.data.to(device) - if subparam._grad is not None: - subparam._grad.data = subparam._grad.data.to(device) - -optimizer_to(optimizer, device) -optimizer_to(optimizer_disc, device) - - -for ep in range(1, epochs + 1): - print(f"training epoch {ep}...") - - model.to(device) - disc.to(device) - model.train() - disc.train() - - running_disc_loss = 0 - running_adv_loss = 0 - running_feature_loss = 0 - running_reg_loss = 0 - - with tqdm(dataloader, unit='batch', file=sys.stdout) as tepoch: - for i, batch in enumerate(tepoch): - - # set gradients to zero - optimizer.zero_grad() - - # push batch to device - for key in batch: - batch[key] = batch[key].to(device) - - target = batch['target'].to(device) - disc_target = batch[adv_target].to(device) - - # calculate model output - output = model(batch['features'], batch['periods']) - - # discriminator update - scores_gen = disc(output.detach()) - scores_real = disc(disc_target.unsqueeze(1)) - - disc_loss = 0 - for scale in scores_gen: - disc_loss += ((scale[-1]) ** 2).mean() - m_f = 0.9 * m_f + 0.1 * scale[-1].detach().mean().cpu().item() - s_f = 0.9 * s_f + 0.1 * scale[-1].detach().std().cpu().item() - - for scale in scores_real: - disc_loss += ((1 - scale[-1]) ** 2).mean() - m_r = 0.9 * m_r + 0.1 * scale[-1].detach().mean().cpu().item() - s_r = 0.9 * s_r + 0.1 * scale[-1].detach().std().cpu().item() - - disc_loss = 0.5 * disc_loss / len(scores_gen) - winning_chance = 0.5 * m.erfc( (m_r - m_f) / m.sqrt(2 * (s_f**2 + s_r**2)) ) - - disc.zero_grad() - disc_loss.backward() - optimizer_disc.step() - - # generator update - scores_gen = disc(output) - - - # calculate loss - loss_reg = criterion(output.squeeze(1), target) - - num_discs = len(scores_gen) - loss_gen = 0 - for scale in scores_gen: - loss_gen += ((1 - scale[-1]) ** 2).mean() / num_discs - - loss_feat = 0 - for k in range(num_discs): - num_layers = len(scores_gen[k]) - 1 - f = 4 / num_discs / num_layers - for l in range(num_layers): - loss_feat += f * F.l1_loss(scores_gen[k][l], scores_real[k][l].detach()) - - model.zero_grad() - - (loss_gen + lambda_feat * loss_feat + lambda_reg * loss_reg).backward() - - optimizer.step() - - running_adv_loss += loss_gen.detach().cpu().item() - running_disc_loss += disc_loss.detach().cpu().item() - running_feature_loss += lambda_feat * loss_feat.detach().cpu().item() - running_reg_loss += lambda_reg * loss_reg.detach().cpu().item() - - # update status bar - if i % log_interval == 0: - tepoch.set_postfix(adv_loss=f"{running_adv_loss/(i + 1):8.7f}", - disc_loss=f"{running_disc_loss/(i + 1):8.7f}", - feat_loss=f"{running_feature_loss/(i + 1):8.7f}", - reg_loss=f"{running_reg_loss/(i + 1):8.7f}", - wc=f"{100*winning_chance:5.2f}%") - - - # save checkpoint - checkpoint['state_dict'] = model.state_dict() - checkpoint['disc_state_dict'] = disc.state_dict() - checkpoint['optimizer_state_dict'] = optimizer.state_dict() - checkpoint['disc_optimizer_state_dict'] = optimizer_disc.state_dict() - checkpoint['scheduler_state_dict'] = scheduler.state_dict() - checkpoint['torch_rng_state'] = torch.get_rng_state() - checkpoint['numpy_rng_state'] = np.random.get_state() - checkpoint['python_rng_state'] = random.getstate() - checkpoint['adv_loss'] = running_adv_loss/(i + 1) - checkpoint['disc_loss'] = running_disc_loss/(i + 1) - checkpoint['feature_loss'] = running_feature_loss/(i + 1) - checkpoint['reg_loss'] = running_reg_loss/(i + 1) - - - if inference_test: - print("running inference test...") - out = model.process(features, periods).cpu().numpy() - wavfile.write(os.path.join(inference_folder, f'{model_name}_epoch_{ep}.wav'), 16000, out) - if ref is not None: - mos = pesq.pesq(16000, ref, out, mode='wb') - print(f"MOS (PESQ): {mos}") - - - torch.save(checkpoint, os.path.join(checkpoint_dir, checkpoint_prefix + f'_epoch_{ep}.pth')) - torch.save(checkpoint, os.path.join(checkpoint_dir, checkpoint_prefix + f'_last.pth')) - - - print() - -print('Done') diff --git a/android/app/src/main/cpp/third_party/opus/dnn/torch/osce/create_testvectors.py b/android/app/src/main/cpp/third_party/opus/dnn/torch/osce/create_testvectors.py deleted file mode 100644 index a037d0d..0000000 --- a/android/app/src/main/cpp/third_party/opus/dnn/torch/osce/create_testvectors.py +++ /dev/null @@ -1,165 +0,0 @@ -""" -/* Copyright (c) 2023 Amazon - Written by Jan Buethe */ -/* - Redistribution and use in source and binary forms, with or without - modification, are permitted provided that the following conditions - are met: - - - Redistributions of source code must retain the above copyright - notice, this list of conditions and the following disclaimer. - - - Redistributions in binary form must reproduce the above copyright - notice, this list of conditions and the following disclaimer in the - documentation and/or other materials provided with the distribution. - - THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS - ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT - LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR - A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER - OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, - EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, - PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR - PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF - LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING - NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS - SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. -*/ -""" - -import os -import argparse - -import torch -import numpy as np - -from models import model_dict -from utils import endoscopy - -parser = argparse.ArgumentParser() - -parser.add_argument('checkpoint_path', type=str, help='path to folder containing checkpoints "lace_checkpoint.pth" and nolace_checkpoint.pth"') -parser.add_argument('output_folder', type=str, help='output folder for testvectors') -parser.add_argument('--debug', action='store_true', help='add debug output to output folder') - - -def create_adaconv_testvector(prefix, adaconv, num_frames, debug=False): - feature_dim = adaconv.feature_dim - in_channels = adaconv.in_channels - out_channels = adaconv.out_channels - frame_size = adaconv.frame_size - - features = torch.randn((1, num_frames, feature_dim)) - x_in = torch.randn((1, in_channels, num_frames * frame_size)) - - x_out = adaconv(x_in, features, debug=debug) - - features = features[0].detach().numpy() - x_in = x_in[0].reshape(in_channels, num_frames, frame_size).permute(1, 0, 2).detach().numpy() - x_out = x_out[0].reshape(out_channels, num_frames, frame_size).permute(1, 0, 2).detach().numpy() - - features.tofile(prefix + '_features.f32') - x_in.tofile(prefix + '_x_in.f32') - x_out.tofile(prefix + '_x_out.f32') - -def create_adacomb_testvector(prefix, adacomb, num_frames, debug=False): - feature_dim = adacomb.feature_dim - in_channels = 1 - frame_size = adacomb.frame_size - - features = torch.randn((1, num_frames, feature_dim)) - x_in = torch.randn((1, in_channels, num_frames * frame_size)) - p_in = torch.randint(adacomb.kernel_size, 250, (1, num_frames)) - - x_out = adacomb(x_in, features, p_in, debug=debug) - - features = features[0].detach().numpy() - x_in = x_in[0].permute(1, 0).detach().numpy() - p_in = p_in[0].detach().numpy().astype(np.int32) - x_out = x_out[0].permute(1, 0).detach().numpy() - - features.tofile(prefix + '_features.f32') - x_in.tofile(prefix + '_x_in.f32') - p_in.tofile(prefix + '_p_in.s32') - x_out.tofile(prefix + '_x_out.f32') - -def create_adashape_testvector(prefix, adashape, num_frames): - feature_dim = adashape.feature_dim - frame_size = adashape.frame_size - - features = torch.randn((1, num_frames, feature_dim)) - x_in = torch.randn((1, 1, num_frames * frame_size)) - - x_out = adashape(x_in, features) - - features = features[0].detach().numpy() - x_in = x_in.flatten().detach().numpy() - x_out = x_out.flatten().detach().numpy() - - features.tofile(prefix + '_features.f32') - x_in.tofile(prefix + '_x_in.f32') - x_out.tofile(prefix + '_x_out.f32') - -def create_feature_net_testvector(prefix, model, num_frames): - num_features = model.num_features - num_subframes = 4 * num_frames - - input_features = torch.randn((1, num_subframes, num_features)) - periods = torch.randint(32, 300, (1, num_subframes)) - numbits = model.numbits_range[0] + torch.rand((1, num_frames, 2)) * (model.numbits_range[1] - model.numbits_range[0]) - - - pembed = model.pitch_embedding(periods) - nembed = torch.repeat_interleave(model.numbits_embedding(numbits).flatten(2), 4, dim=1) - full_features = torch.cat((input_features, pembed, nembed), dim=-1) - - cf = model.feature_net(full_features) - - input_features.float().numpy().tofile(prefix + "_in_features.f32") - periods.numpy().astype(np.int32).tofile(prefix + "_periods.s32") - numbits.float().numpy().tofile(prefix + "_numbits.f32") - full_features.detach().numpy().tofile(prefix + "_full_features.f32") - cf.detach().numpy().tofile(prefix + "_out_features.f32") - - - -if __name__ == "__main__": - args = parser.parse_args() - - os.makedirs(args.output_folder, exist_ok=True) - - lace_checkpoint = torch.load(os.path.join(args.checkpoint_path, "lace_checkpoint.pth"), map_location='cpu') - nolace_checkpoint = torch.load(os.path.join(args.checkpoint_path, "nolace_checkpoint.pth"), map_location='cpu') - - lace = model_dict['lace'](**lace_checkpoint['setup']['model']['kwargs']) - nolace = model_dict['nolace'](**nolace_checkpoint['setup']['model']['kwargs']) - - lace.load_state_dict(lace_checkpoint['state_dict']) - nolace.load_state_dict(nolace_checkpoint['state_dict']) - - if args.debug: - endoscopy.init(args.output_folder) - - # lace af1, 1 input channel, 1 output channel - create_adaconv_testvector(os.path.join(args.output_folder, "lace_af1"), lace.af1, 5, debug=args.debug) - - # nolace af1, 1 input channel, 2 output channels - create_adaconv_testvector(os.path.join(args.output_folder, "nolace_af1"), nolace.af1, 5, debug=args.debug) - - # nolace af4, 2 input channel, 1 output channels - create_adaconv_testvector(os.path.join(args.output_folder, "nolace_af4"), nolace.af4, 5, debug=args.debug) - - # nolace af2, 2 input channel, 2 output channels - create_adaconv_testvector(os.path.join(args.output_folder, "nolace_af2"), nolace.af2, 5, debug=args.debug) - - # lace cf1 - create_adacomb_testvector(os.path.join(args.output_folder, "lace_cf1"), lace.cf1, 5, debug=args.debug) - - # nolace tdshape1 - create_adashape_testvector(os.path.join(args.output_folder, "nolace_tdshape1"), nolace.tdshape1, 5) - - # lace feature net - create_feature_net_testvector(os.path.join(args.output_folder, 'lace'), lace, 5) - - if args.debug: - endoscopy.close() diff --git a/android/app/src/main/cpp/third_party/opus/dnn/torch/osce/data/__init__.py b/android/app/src/main/cpp/third_party/opus/dnn/torch/osce/data/__init__.py deleted file mode 100644 index 8df4d56..0000000 --- a/android/app/src/main/cpp/third_party/opus/dnn/torch/osce/data/__init__.py +++ /dev/null @@ -1,2 +0,0 @@ -from .silk_enhancement_set import SilkEnhancementSet -from .lpcnet_vocoding_dataset import LPCNetVocodingDataset \ No newline at end of file diff --git a/android/app/src/main/cpp/third_party/opus/dnn/torch/osce/data/lpcnet_vocoding_dataset.py b/android/app/src/main/cpp/third_party/opus/dnn/torch/osce/data/lpcnet_vocoding_dataset.py deleted file mode 100644 index 36c8c72..0000000 --- a/android/app/src/main/cpp/third_party/opus/dnn/torch/osce/data/lpcnet_vocoding_dataset.py +++ /dev/null @@ -1,225 +0,0 @@ -""" -/* Copyright (c) 2023 Amazon - Written by Jan Buethe */ -/* - Redistribution and use in source and binary forms, with or without - modification, are permitted provided that the following conditions - are met: - - - Redistributions of source code must retain the above copyright - notice, this list of conditions and the following disclaimer. - - - Redistributions in binary form must reproduce the above copyright - notice, this list of conditions and the following disclaimer in the - documentation and/or other materials provided with the distribution. - - THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS - ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT - LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR - A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER - OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, - EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, - PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR - PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF - LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING - NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS - SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. -*/ -""" - -""" Dataset for LPCNet training """ -import os - -import yaml -import torch -import numpy as np -from torch.utils.data import Dataset - - -scale = 255.0/32768.0 -scale_1 = 32768.0/255.0 -def ulaw2lin(u): - u = u - 128 - s = np.sign(u) - u = np.abs(u) - return s*scale_1*(np.exp(u/128.*np.log(256))-1) - - -def lin2ulaw(x): - s = np.sign(x) - x = np.abs(x) - u = (s*(128*np.log(1+scale*x)/np.log(256))) - u = np.clip(128 + np.round(u), 0, 255) - return u - - -def run_lpc(signal, lpcs, frame_length=160): - num_frames, lpc_order = lpcs.shape - - prediction = np.concatenate( - [- np.convolve(signal[i * frame_length : (i + 1) * frame_length + lpc_order - 1], lpcs[i], mode='valid') for i in range(num_frames)] - ) - error = signal[lpc_order :] - prediction - - return prediction, error - -class LPCNetVocodingDataset(Dataset): - def __init__(self, - path_to_dataset, - features=['cepstrum', 'periods', 'pitch_corr'], - target='signal', - frames_per_sample=100, - feature_history=0, - feature_lookahead=0, - lpc_gamma=1): - - super().__init__() - - # load dataset info - self.path_to_dataset = path_to_dataset - with open(os.path.join(path_to_dataset, 'info.yml'), 'r') as f: - dataset = yaml.load(f, yaml.FullLoader) - - # dataset version - self.version = dataset['version'] - if self.version == 1: - self.getitem = self.getitem_v1 - elif self.version == 2: - self.getitem = self.getitem_v2 - else: - raise ValueError(f"dataset version {self.version} unknown") - - # features - self.feature_history = feature_history - self.feature_lookahead = feature_lookahead - self.frame_offset = 2 + self.feature_history - self.frames_per_sample = frames_per_sample - self.input_features = features - self.feature_frame_layout = dataset['feature_frame_layout'] - self.lpc_gamma = lpc_gamma - - # load feature file - self.feature_file = os.path.join(path_to_dataset, dataset['feature_file']) - self.features = np.memmap(self.feature_file, dtype=dataset['feature_dtype']) - self.feature_frame_length = dataset['feature_frame_length'] - - assert len(self.features) % self.feature_frame_length == 0 - self.features = self.features.reshape((-1, self.feature_frame_length)) - - # derive number of samples is dataset - self.dataset_length = (len(self.features) - self.frame_offset - self.feature_lookahead - 1 - 2) // self.frames_per_sample - - # signals - self.frame_length = dataset['frame_length'] - self.signal_frame_layout = dataset['signal_frame_layout'] - self.target = target - - # load signals - self.signal_file = os.path.join(path_to_dataset, dataset['signal_file']) - self.signals = np.memmap(self.signal_file, dtype=dataset['signal_dtype']) - self.signal_frame_length = dataset['signal_frame_length'] - self.signals = self.signals.reshape((-1, self.signal_frame_length)) - assert len(self.signals) == len(self.features) * self.frame_length - - - def __getitem__(self, index): - return self.getitem(index) - - def getitem_v2(self, index): - sample = dict() - - # extract features - frame_start = self.frame_offset + index * self.frames_per_sample - self.feature_history - frame_stop = self.frame_offset + (index + 1) * self.frames_per_sample + self.feature_lookahead - - for feature in self.input_features: - feature_start, feature_stop = self.feature_frame_layout[feature] - sample[feature] = self.features[frame_start : frame_stop, feature_start : feature_stop] - - # convert periods - if 'periods' in self.input_features: - sample['periods'] = (0.1 + 50 * sample['periods'] + 100).astype('int16') - - signal_start = (self.frame_offset + index * self.frames_per_sample) * self.frame_length - signal_stop = (self.frame_offset + (index + 1) * self.frames_per_sample) * self.frame_length - - # last_signal and signal are always expected to be there - sample['last_signal'] = self.signals[signal_start : signal_stop, self.signal_frame_layout['last_signal']] - sample['signal'] = self.signals[signal_start : signal_stop, self.signal_frame_layout['signal']] - - # calculate prediction and error if lpc coefficients present and prediction not given - if 'lpc' in self.feature_frame_layout and 'prediction' not in self.signal_frame_layout: - # lpc coefficients with one frame lookahead - # frame positions (start one frame early for past excitation) - frame_start = self.frame_offset + self.frames_per_sample * index - 1 - frame_stop = self.frame_offset + self.frames_per_sample * (index + 1) - - # feature positions - lpc_start, lpc_stop = self.feature_frame_layout['lpc'] - lpc_order = lpc_stop - lpc_start - lpcs = self.features[frame_start : frame_stop, lpc_start : lpc_stop] - - # LPC weighting - lpc_order = lpc_stop - lpc_start - weights = np.array([self.lpc_gamma ** (i + 1) for i in range(lpc_order)]) - lpcs = lpcs * weights - - # signal position (lpc_order samples as history) - signal_start = frame_start * self.frame_length - lpc_order + 1 - signal_stop = frame_stop * self.frame_length + 1 - noisy_signal = self.signals[signal_start : signal_stop, self.signal_frame_layout['last_signal']] - clean_signal = self.signals[signal_start - 1 : signal_stop - 1, self.signal_frame_layout['signal']] - - noisy_prediction, noisy_error = run_lpc(noisy_signal, lpcs, frame_length=self.frame_length) - - # extract signals - offset = self.frame_length - sample['prediction'] = noisy_prediction[offset : offset + self.frame_length * self.frames_per_sample] - sample['last_error'] = noisy_error[offset - 1 : offset - 1 + self.frame_length * self.frames_per_sample] - # calculate error between real signal and noisy prediction - - - sample['error'] = sample['signal'] - sample['prediction'] - - - # concatenate features - feature_keys = [key for key in self.input_features if not key.startswith("periods")] - features = torch.concat([torch.FloatTensor(sample[key]) for key in feature_keys], dim=-1) - target = torch.FloatTensor(sample[self.target]) / 2**15 - periods = torch.LongTensor(sample['periods']) - - return {'features' : features, 'periods' : periods, 'target' : target} - - def getitem_v1(self, index): - sample = dict() - - # extract features - frame_start = self.frame_offset + index * self.frames_per_sample - self.feature_history - frame_stop = self.frame_offset + (index + 1) * self.frames_per_sample + self.feature_lookahead - - for feature in self.input_features: - feature_start, feature_stop = self.feature_frame_layout[feature] - sample[feature] = self.features[frame_start : frame_stop, feature_start : feature_stop] - - # convert periods - if 'periods' in self.input_features: - sample['periods'] = (0.1 + 50 * sample['periods'] + 100).astype('int16') - - signal_start = (self.frame_offset + index * self.frames_per_sample) * self.frame_length - signal_stop = (self.frame_offset + (index + 1) * self.frames_per_sample) * self.frame_length - - # last_signal and signal are always expected to be there - for signal_name, index in self.signal_frame_layout.items(): - sample[signal_name] = self.signals[signal_start : signal_stop, index] - - # concatenate features - feature_keys = [key for key in self.input_features if not key.startswith("periods")] - features = torch.concat([torch.FloatTensor(sample[key]) for key in feature_keys], dim=-1) - signals = torch.cat([torch.LongTensor(sample[key]).unsqueeze(-1) for key in self.input_signals], dim=-1) - target = torch.LongTensor(sample[self.target]) - periods = torch.LongTensor(sample['periods']) - - return {'features' : features, 'periods' : periods, 'signals' : signals, 'target' : target} - - def __len__(self): - return self.dataset_length diff --git a/android/app/src/main/cpp/third_party/opus/dnn/torch/osce/data/silk_conversion_set.py b/android/app/src/main/cpp/third_party/opus/dnn/torch/osce/data/silk_conversion_set.py deleted file mode 100644 index 8f64675..0000000 --- a/android/app/src/main/cpp/third_party/opus/dnn/torch/osce/data/silk_conversion_set.py +++ /dev/null @@ -1,132 +0,0 @@ -""" -/* Copyright (c) 2023 Amazon - Written by Jan Buethe */ -/* - Redistribution and use in source and binary forms, with or without - modification, are permitted provided that the following conditions - are met: - - - Redistributions of source code must retain the above copyright - notice, this list of conditions and the following disclaimer. - - - Redistributions in binary form must reproduce the above copyright - notice, this list of conditions and the following disclaimer in the - documentation and/or other materials provided with the distribution. - - THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS - ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT - LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR - A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER - OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, - EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, - PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR - PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF - LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING - NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS - SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. -*/ -""" - -import os - -from torch.utils.data import Dataset -import numpy as np - -from utils.silk_features import silk_feature_factory -from utils.pitch import hangover, calculate_acorr_window - - -class SilkEnhancementSet(Dataset): - def __init__(self, - path, - frames_per_sample=100, - no_pitch_value=9, - acorr_radius=2, - pitch_hangover=8, - num_bands_clean_spec=64, - num_bands_noisy_spec=18, - noisy_spec_scale='opus', - noisy_apply_dct=True, - add_offset=False, - add_double_lag_acorr=False - ): - - assert frames_per_sample % 4 == 0 - - self.frame_size = 80 - self.frames_per_sample = frames_per_sample - self.no_pitch_value = no_pitch_value - self.acorr_radius = acorr_radius - self.pitch_hangover = pitch_hangover - self.num_bands_clean_spec = num_bands_clean_spec - self.num_bands_noisy_spec = num_bands_noisy_spec - self.noisy_spec_scale = noisy_spec_scale - self.add_double_lag_acorr = add_double_lag_acorr - - self.lpcs = np.fromfile(os.path.join(path, 'features_lpc.f32'), dtype=np.float32).reshape(-1, 16) - self.ltps = np.fromfile(os.path.join(path, 'features_ltp.f32'), dtype=np.float32).reshape(-1, 5) - self.periods = np.fromfile(os.path.join(path, 'features_period.s16'), dtype=np.int16) - self.gains = np.fromfile(os.path.join(path, 'features_gain.f32'), dtype=np.float32) - self.num_bits = np.fromfile(os.path.join(path, 'features_num_bits.s32'), dtype=np.int32) - self.num_bits_smooth = np.fromfile(os.path.join(path, 'features_num_bits_smooth.f32'), dtype=np.float32) - self.offsets = np.fromfile(os.path.join(path, 'features_offset.f32'), dtype=np.float32) - self.lpcnet_features = np.from_file(os.path.join(path, 'features_lpcnet.f32'), dtype=np.float32).reshape(-1, 36) - - self.coded_signal = np.fromfile(os.path.join(path, 'coded.s16'), dtype=np.int16) - - self.create_features = silk_feature_factory(no_pitch_value, - acorr_radius, - pitch_hangover, - num_bands_clean_spec, - num_bands_noisy_spec, - noisy_spec_scale, - noisy_apply_dct, - add_offset, - add_double_lag_acorr) - - self.history_len = 700 if add_double_lag_acorr else 350 - # discard some frames to have enough signal history - self.skip_frames = 4 * ((self.history_len + 319) // 320 + 2) - - num_frames = self.clean_signal.shape[0] // 80 - self.skip_frames - - self.len = num_frames // frames_per_sample - - def __len__(self): - return self.len - - def __getitem__(self, index): - - frame_start = self.frames_per_sample * index + self.skip_frames - frame_stop = frame_start + self.frames_per_sample - - signal_start = frame_start * self.frame_size - self.skip - signal_stop = frame_stop * self.frame_size - self.skip - - coded_signal = self.coded_signal[signal_start : signal_stop].astype(np.float32) / 2**15 - - coded_signal_history = self.coded_signal[signal_start - self.history_len : signal_start].astype(np.float32) / 2**15 - - features, periods = self.create_features( - coded_signal, - coded_signal_history, - self.lpcs[frame_start : frame_stop], - self.gains[frame_start : frame_stop], - self.ltps[frame_start : frame_stop], - self.periods[frame_start : frame_stop], - self.offsets[frame_start : frame_stop] - ) - - lpcnet_features = self.lpcnet_features[frame_start // 2 : frame_stop // 2, :20] - - num_bits = np.repeat(self.num_bits[frame_start // 4 : frame_stop // 4], 4).astype(np.float32).reshape(-1, 1) - num_bits_smooth = np.repeat(self.num_bits_smooth[frame_start // 4 : frame_stop // 4], 4).astype(np.float32).reshape(-1, 1) - - numbits = np.concatenate((num_bits, num_bits_smooth), axis=-1) - - return { - 'silk_features' : features, - 'periods' : periods.astype(np.int64), - 'numbits' : numbits.astype(np.float32), - 'lpcnet_features' : lpcnet_features - } diff --git a/android/app/src/main/cpp/third_party/opus/dnn/torch/osce/data/silk_enhancement_set.py b/android/app/src/main/cpp/third_party/opus/dnn/torch/osce/data/silk_enhancement_set.py deleted file mode 100644 index fd18c4d..0000000 --- a/android/app/src/main/cpp/third_party/opus/dnn/torch/osce/data/silk_enhancement_set.py +++ /dev/null @@ -1,140 +0,0 @@ -""" -/* Copyright (c) 2023 Amazon - Written by Jan Buethe */ -/* - Redistribution and use in source and binary forms, with or without - modification, are permitted provided that the following conditions - are met: - - - Redistributions of source code must retain the above copyright - notice, this list of conditions and the following disclaimer. - - - Redistributions in binary form must reproduce the above copyright - notice, this list of conditions and the following disclaimer in the - documentation and/or other materials provided with the distribution. - - THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS - ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT - LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR - A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER - OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, - EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, - PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR - PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF - LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING - NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS - SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. -*/ -""" - -import os - -from torch.utils.data import Dataset -import numpy as np - -from utils.silk_features import silk_feature_factory -from utils.pitch import hangover, calculate_acorr_window - - -class SilkEnhancementSet(Dataset): - def __init__(self, - path, - frames_per_sample=100, - no_pitch_value=256, - preemph=0.85, - skip=91, - acorr_radius=2, - pitch_hangover=8, - num_bands_clean_spec=64, - num_bands_noisy_spec=18, - noisy_spec_scale='opus', - noisy_apply_dct=True, - add_double_lag_acorr=False, - ): - - assert frames_per_sample % 4 == 0 - - self.frame_size = 80 - self.frames_per_sample = frames_per_sample - self.no_pitch_value = no_pitch_value - self.preemph = preemph - self.skip = skip - self.acorr_radius = acorr_radius - self.pitch_hangover = pitch_hangover - self.num_bands_clean_spec = num_bands_clean_spec - self.num_bands_noisy_spec = num_bands_noisy_spec - self.noisy_spec_scale = noisy_spec_scale - self.add_double_lag_acorr = add_double_lag_acorr - - self.lpcs = np.fromfile(os.path.join(path, 'features_lpc.f32'), dtype=np.float32).reshape(-1, 16) - self.ltps = np.fromfile(os.path.join(path, 'features_ltp.f32'), dtype=np.float32).reshape(-1, 5) - self.periods = np.fromfile(os.path.join(path, 'features_period.s16'), dtype=np.int16) - self.gains = np.fromfile(os.path.join(path, 'features_gain.f32'), dtype=np.float32) - self.num_bits = np.fromfile(os.path.join(path, 'features_num_bits.s32'), dtype=np.int32) - self.num_bits_smooth = np.fromfile(os.path.join(path, 'features_num_bits_smooth.f32'), dtype=np.float32) - - self.clean_signal_hp = np.fromfile(os.path.join(path, 'clean_hp.s16'), dtype=np.int16) - self.clean_signal = np.fromfile(os.path.join(path, 'clean.s16'), dtype=np.int16) - self.coded_signal = np.fromfile(os.path.join(path, 'coded.s16'), dtype=np.int16) - - self.create_features = silk_feature_factory(no_pitch_value, - acorr_radius, - pitch_hangover, - num_bands_clean_spec, - num_bands_noisy_spec, - noisy_spec_scale, - noisy_apply_dct, - add_double_lag_acorr) - - self.history_len = 700 if add_double_lag_acorr else 350 - # discard some frames to have enough signal history - self.skip_frames = 4 * ((skip + self.history_len + 319) // 320 + 2) - - num_frames = self.clean_signal_hp.shape[0] // 80 - self.skip_frames - - self.len = num_frames // frames_per_sample - - def __len__(self): - return self.len - - def __getitem__(self, index): - - frame_start = self.frames_per_sample * index + self.skip_frames - frame_stop = frame_start + self.frames_per_sample - - signal_start = frame_start * self.frame_size - self.skip - signal_stop = frame_stop * self.frame_size - self.skip - - clean_signal_hp = self.clean_signal_hp[signal_start : signal_stop].astype(np.float32) / 2**15 - clean_signal = self.clean_signal[signal_start : signal_stop].astype(np.float32) / 2**15 - coded_signal = self.coded_signal[signal_start : signal_stop].astype(np.float32) / 2**15 - - coded_signal_history = self.coded_signal[signal_start - self.history_len : signal_start].astype(np.float32) / 2**15 - - features, periods = self.create_features( - coded_signal, - coded_signal_history, - self.lpcs[frame_start : frame_stop], - self.gains[frame_start : frame_stop], - self.ltps[frame_start : frame_stop], - self.periods[frame_start : frame_stop] - ) - - if self.preemph > 0: - clean_signal[1:] -= self.preemph * clean_signal[: -1] - clean_signal_hp[1:] -= self.preemph * clean_signal_hp[: -1] - coded_signal[1:] -= self.preemph * coded_signal[: -1] - - num_bits = np.repeat(self.num_bits[frame_start // 4 : frame_stop // 4], 4).astype(np.float32).reshape(-1, 1) - num_bits_smooth = np.repeat(self.num_bits_smooth[frame_start // 4 : frame_stop // 4], 4).astype(np.float32).reshape(-1, 1) - - numbits = np.concatenate((num_bits, num_bits_smooth), axis=-1) - - return { - 'features' : features, - 'periods' : periods.astype(np.int64), - 'target_orig' : clean_signal.astype(np.float32), - 'target' : clean_signal_hp.astype(np.float32), - 'signals' : coded_signal.reshape(-1, 1).astype(np.float32), - 'numbits' : numbits.astype(np.float32) - } diff --git a/android/app/src/main/cpp/third_party/opus/dnn/torch/osce/engine/engine.py b/android/app/src/main/cpp/third_party/opus/dnn/torch/osce/engine/engine.py deleted file mode 100644 index 0762c89..0000000 --- a/android/app/src/main/cpp/third_party/opus/dnn/torch/osce/engine/engine.py +++ /dev/null @@ -1,103 +0,0 @@ -import torch -from tqdm import tqdm -import sys - -def train_one_epoch(model, criterion, optimizer, dataloader, device, scheduler, log_interval=10): - - model.to(device) - model.train() - - running_loss = 0 - previous_running_loss = 0 - - - with tqdm(dataloader, unit='batch', file=sys.stdout) as tepoch: - - for i, batch in enumerate(tepoch): - - # set gradients to zero - optimizer.zero_grad() - - - # push batch to device - for key in batch: - batch[key] = batch[key].to(device) - - target = batch['target'] - - # calculate model output - output = model(batch['signals'].permute(0, 2, 1), batch['features'], batch['periods'], batch['numbits']) - - # calculate loss - if isinstance(output, list): - loss = torch.zeros(1, device=device) - for y in output: - loss = loss + criterion(target, y.squeeze(1)) - loss = loss / len(output) - else: - loss = criterion(target, output.squeeze(1)) - - # calculate gradients - loss.backward() - - # update weights - optimizer.step() - - # update learning rate - scheduler.step() - - # sparsification - if hasattr(model, 'sparsifier'): - model.sparsifier() - - # update running loss - running_loss += float(loss.cpu()) - - # update status bar - if i % log_interval == 0: - tepoch.set_postfix(running_loss=f"{running_loss/(i + 1):8.7f}", current_loss=f"{(running_loss - previous_running_loss)/log_interval:8.7f}") - previous_running_loss = running_loss - - - running_loss /= len(dataloader) - - return running_loss - -def evaluate(model, criterion, dataloader, device, log_interval=10): - - model.to(device) - model.eval() - - running_loss = 0 - previous_running_loss = 0 - - - with torch.no_grad(): - with tqdm(dataloader, unit='batch', file=sys.stdout) as tepoch: - - for i, batch in enumerate(tepoch): - - # push batch to device - for key in batch: - batch[key] = batch[key].to(device) - - target = batch['target'] - - # calculate model output - output = model(batch['signals'].permute(0, 2, 1), batch['features'], batch['periods'], batch['numbits']) - - # calculate loss - loss = criterion(target, output.squeeze(1)) - - # update running loss - running_loss += float(loss.cpu()) - - # update status bar - if i % log_interval == 0: - tepoch.set_postfix(running_loss=f"{running_loss/(i + 1):8.7f}", current_loss=f"{(running_loss - previous_running_loss)/log_interval:8.7f}") - previous_running_loss = running_loss - - - running_loss /= len(dataloader) - - return running_loss \ No newline at end of file diff --git a/android/app/src/main/cpp/third_party/opus/dnn/torch/osce/engine/vocoder_engine.py b/android/app/src/main/cpp/third_party/opus/dnn/torch/osce/engine/vocoder_engine.py deleted file mode 100644 index 9eee49e..0000000 --- a/android/app/src/main/cpp/third_party/opus/dnn/torch/osce/engine/vocoder_engine.py +++ /dev/null @@ -1,101 +0,0 @@ -import torch -from tqdm import tqdm -import sys - -def train_one_epoch(model, criterion, optimizer, dataloader, device, scheduler, log_interval=10): - - model.to(device) - model.train() - - running_loss = 0 - previous_running_loss = 0 - - - with tqdm(dataloader, unit='batch', file=sys.stdout) as tepoch: - - for i, batch in enumerate(tepoch): - - # set gradients to zero - optimizer.zero_grad() - - - # push batch to device - for key in batch: - batch[key] = batch[key].to(device) - - target = batch['target'] - - # calculate model output - output = model(batch['features'], batch['periods']) - - # calculate loss - if isinstance(output, list): - loss = torch.zeros(1, device=device) - for y in output: - loss = loss + criterion(target, y.squeeze(1)) - loss = loss / len(output) - else: - loss = criterion(target, output.squeeze(1)) - - # calculate gradients - loss.backward() - - # update weights - optimizer.step() - - # update learning rate - scheduler.step() - - # update running loss - running_loss += float(loss.cpu()) - - # update status bar - if i % log_interval == 0: - tepoch.set_postfix(running_loss=f"{running_loss/(i + 1):8.7f}", current_loss=f"{(running_loss - previous_running_loss)/log_interval:8.7f}") - previous_running_loss = running_loss - - - running_loss /= len(dataloader) - - return running_loss - -def evaluate(model, criterion, dataloader, device, log_interval=10): - - model.to(device) - model.eval() - - running_loss = 0 - previous_running_loss = 0 - - - with torch.no_grad(): - with tqdm(dataloader, unit='batch', file=sys.stdout) as tepoch: - - for i, batch in enumerate(tepoch): - - - - # push batch to device - for key in batch: - batch[key] = batch[key].to(device) - - target = batch['target'] - - # calculate model output - output = model(batch['features'], batch['periods']) - - # calculate loss - loss = criterion(target, output.squeeze(1)) - - # update running loss - running_loss += float(loss.cpu()) - - # update status bar - if i % log_interval == 0: - tepoch.set_postfix(running_loss=f"{running_loss/(i + 1):8.7f}", current_loss=f"{(running_loss - previous_running_loss)/log_interval:8.7f}") - previous_running_loss = running_loss - - - running_loss /= len(dataloader) - - return running_loss \ No newline at end of file diff --git a/android/app/src/main/cpp/third_party/opus/dnn/torch/osce/export_model_weights.py b/android/app/src/main/cpp/third_party/opus/dnn/torch/osce/export_model_weights.py deleted file mode 100644 index a504a61..0000000 --- a/android/app/src/main/cpp/third_party/opus/dnn/torch/osce/export_model_weights.py +++ /dev/null @@ -1,174 +0,0 @@ -""" -/* Copyright (c) 2023 Amazon - Written by Jan Buethe */ -/* - Redistribution and use in source and binary forms, with or without - modification, are permitted provided that the following conditions - are met: - - - Redistributions of source code must retain the above copyright - notice, this list of conditions and the following disclaimer. - - - Redistributions in binary form must reproduce the above copyright - notice, this list of conditions and the following disclaimer in the - documentation and/or other materials provided with the distribution. - - THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS - ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT - LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR - A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER - OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, - EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, - PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR - PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF - LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING - NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS - SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. -*/ -""" - -import os -import argparse -import sys - -import hashlib - -sys.path.append(os.path.join(os.path.dirname(__file__), '../weight-exchange')) - -import torch -import wexchange.torch -from wexchange.torch import dump_torch_weights -from models import model_dict - -from utils.layers.limited_adaptive_comb1d import LimitedAdaptiveComb1d -from utils.layers.limited_adaptive_conv1d import LimitedAdaptiveConv1d -from utils.layers.td_shaper import TDShaper -from utils.misc import remove_all_weight_norm -from wexchange.torch import dump_torch_weights - - - -parser = argparse.ArgumentParser() - -parser.add_argument('checkpoint', type=str, help='LACE or NoLACE model checkpoint') -parser.add_argument('output_dir', type=str, help='output folder') -parser.add_argument('--quantize', action="store_true", help='quantization according to schedule') - -sparse_default=False -schedules = { - 'nolace': [ - ('pitch_embedding', dict()), - ('feature_net.conv1', dict()), - ('feature_net.conv2', dict(quantize=True, scale=None, sparse=sparse_default)), - ('feature_net.tconv', dict(quantize=True, scale=None, sparse=sparse_default)), - ('feature_net.gru', dict(quantize=True, scale=None, recurrent_scale=None, input_sparse=sparse_default, recurrent_sparse=sparse_default)), - ('cf1', dict(quantize=True, scale=None)), - ('cf2', dict(quantize=True, scale=None)), - ('af1', dict(quantize=True, scale=None)), - ('tdshape1', dict(quantize=True, scale=None)), - ('tdshape2', dict(quantize=True, scale=None)), - ('tdshape3', dict(quantize=True, scale=None)), - ('af2', dict(quantize=True, scale=None)), - ('af3', dict(quantize=True, scale=None)), - ('af4', dict(quantize=True, scale=None)), - ('post_cf1', dict(quantize=True, scale=None, sparse=sparse_default)), - ('post_cf2', dict(quantize=True, scale=None, sparse=sparse_default)), - ('post_af1', dict(quantize=True, scale=None, sparse=sparse_default)), - ('post_af2', dict(quantize=True, scale=None, sparse=sparse_default)), - ('post_af3', dict(quantize=True, scale=None, sparse=sparse_default)) - ], - 'lace' : [ - ('pitch_embedding', dict()), - ('feature_net.conv1', dict()), - ('feature_net.conv2', dict(quantize=True, scale=None, sparse=sparse_default)), - ('feature_net.tconv', dict(quantize=True, scale=None, sparse=sparse_default)), - ('feature_net.gru', dict(quantize=True, scale=None, recurrent_scale=None, input_sparse=sparse_default, recurrent_sparse=sparse_default)), - ('cf1', dict(quantize=True, scale=None)), - ('cf2', dict(quantize=True, scale=None)), - ('af1', dict(quantize=True, scale=None)) - ] -} - - -# auxiliary functions -def sha1(filename): - BUF_SIZE = 65536 - sha1 = hashlib.sha1() - - with open(filename, 'rb') as f: - while True: - data = f.read(BUF_SIZE) - if not data: - break - sha1.update(data) - - return sha1.hexdigest() - -def osce_dump_generic(writer, name, module): - if isinstance(module, torch.nn.Linear) or isinstance(module, torch.nn.Conv1d) \ - or isinstance(module, torch.nn.ConvTranspose1d) or isinstance(module, torch.nn.Embedding) \ - or isinstance(module, LimitedAdaptiveConv1d) or isinstance(module, LimitedAdaptiveComb1d) \ - or isinstance(module, TDShaper) or isinstance(module, torch.nn.GRU): - dump_torch_weights(writer, module, name=name, verbose=True) - else: - for child_name, child in module.named_children(): - osce_dump_generic(writer, (name + "_" + child_name).replace("feature_net", "fnet"), child) - - -def export_name(name): - name = name.replace('.', '_') - name = name.replace('feature_net', 'fnet') - return name - -def osce_scheduled_dump(writer, prefix, model, schedule): - if not prefix.endswith('_'): - prefix += '_' - - for name, kwargs in schedule: - dump_torch_weights(writer, model.get_submodule(name), prefix + export_name(name), **kwargs, verbose=True) - -if __name__ == "__main__": - args = parser.parse_args() - - checkpoint_path = args.checkpoint - outdir = args.output_dir - os.makedirs(outdir, exist_ok=True) - - # dump message - message = f"Auto generated from checkpoint {os.path.basename(checkpoint_path)} (sha1: {sha1(checkpoint_path)})" - - # create model and load weights - checkpoint = torch.load(checkpoint_path, map_location='cpu') - model = model_dict[checkpoint['setup']['model']['name']](*checkpoint['setup']['model']['args'], **checkpoint['setup']['model']['kwargs']) - model.load_state_dict(checkpoint['state_dict']) - remove_all_weight_norm(model, verbose=True) - - # CWriter - model_name = checkpoint['setup']['model']['name'] - cwriter = wexchange.c_export.CWriter(os.path.join(outdir, model_name + "_data"), message=message, model_struct_name=model_name.upper() + 'Layers', add_typedef=True) - - # Add custom includes and global parameters - cwriter.header.write(f''' -#define {model_name.upper()}_PREEMPH {model.preemph}f -#define {model_name.upper()}_FRAME_SIZE {model.FRAME_SIZE} -#define {model_name.upper()}_OVERLAP_SIZE 40 -#define {model_name.upper()}_NUM_FEATURES {model.num_features} -#define {model_name.upper()}_PITCH_MAX {model.pitch_max} -#define {model_name.upper()}_PITCH_EMBEDDING_DIM {model.pitch_embedding_dim} -#define {model_name.upper()}_NUMBITS_RANGE_LOW {model.numbits_range[0]} -#define {model_name.upper()}_NUMBITS_RANGE_HIGH {model.numbits_range[1]} -#define {model_name.upper()}_NUMBITS_EMBEDDING_DIM {model.numbits_embedding_dim} -#define {model_name.upper()}_COND_DIM {model.cond_dim} -#define {model_name.upper()}_HIDDEN_FEATURE_DIM {model.hidden_feature_dim} -''') - - for i, s in enumerate(model.numbits_embedding.scale_factors): - cwriter.header.write(f"#define {model_name.upper()}_NUMBITS_SCALE_{i} {float(s.detach().cpu())}f\n") - - # dump layers - if model_name in schedules and args.quantize: - osce_scheduled_dump(cwriter, model_name, model, schedules[model_name]) - else: - osce_dump_generic(cwriter, model_name, model) - - cwriter.close() diff --git a/android/app/src/main/cpp/third_party/opus/dnn/torch/osce/losses/stft_loss.py b/android/app/src/main/cpp/third_party/opus/dnn/torch/osce/losses/stft_loss.py deleted file mode 100644 index 4c164cb..0000000 --- a/android/app/src/main/cpp/third_party/opus/dnn/torch/osce/losses/stft_loss.py +++ /dev/null @@ -1,277 +0,0 @@ -""" -/* Copyright (c) 2023 Amazon - Written by Jan Buethe */ -/* - Redistribution and use in source and binary forms, with or without - modification, are permitted provided that the following conditions - are met: - - - Redistributions of source code must retain the above copyright - notice, this list of conditions and the following disclaimer. - - - Redistributions in binary form must reproduce the above copyright - notice, this list of conditions and the following disclaimer in the - documentation and/or other materials provided with the distribution. - - THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS - ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT - LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR - A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER - OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, - EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, - PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR - PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF - LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING - NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS - SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. -*/ -""" - -"""STFT-based Loss modules.""" - -import torch -import torch.nn.functional as F -from torch import nn -import numpy as np -import torchaudio - - -def get_window(win_name, win_length, *args, **kwargs): - window_dict = { - 'bartlett_window' : torch.bartlett_window, - 'blackman_window' : torch.blackman_window, - 'hamming_window' : torch.hamming_window, - 'hann_window' : torch.hann_window, - 'kaiser_window' : torch.kaiser_window - } - - if not win_name in window_dict: - raise ValueError() - - return window_dict[win_name](win_length, *args, **kwargs) - - -def stft(x, fft_size, hop_size, win_length, window): - """Perform STFT and convert to magnitude spectrogram. - Args: - x (Tensor): Input signal tensor (B, T). - fft_size (int): FFT size. - hop_size (int): Hop size. - win_length (int): Window length. - window (str): Window function type. - Returns: - Tensor: Magnitude spectrogram (B, #frames, fft_size // 2 + 1). - """ - - win = get_window(window, win_length).to(x.device) - x_stft = torch.stft(x, fft_size, hop_size, win_length, win, return_complex=True) - - - return torch.clamp(torch.abs(x_stft), min=1e-7) - -def spectral_convergence_loss(Y_true, Y_pred): - dims=list(range(1, len(Y_pred.shape))) - return torch.mean(torch.norm(torch.abs(Y_true) - torch.abs(Y_pred), p="fro", dim=dims) / (torch.norm(Y_pred, p="fro", dim=dims) + 1e-6)) - - -def log_magnitude_loss(Y_true, Y_pred): - Y_true_log_abs = torch.log(torch.abs(Y_true) + 1e-15) - Y_pred_log_abs = torch.log(torch.abs(Y_pred) + 1e-15) - - return torch.mean(torch.abs(Y_true_log_abs - Y_pred_log_abs)) - -def spectral_xcorr_loss(Y_true, Y_pred): - Y_true = Y_true.abs() - Y_pred = Y_pred.abs() - dims=list(range(1, len(Y_pred.shape))) - xcorr = torch.sum(Y_true * Y_pred, dim=dims) / torch.sqrt(torch.sum(Y_true ** 2, dim=dims) * torch.sum(Y_pred ** 2, dim=dims) + 1e-9) - - return 1 - xcorr.mean() - - - -class MRLogMelLoss(nn.Module): - def __init__(self, - fft_sizes=[512, 256, 128, 64], - overlap=0.5, - fs=16000, - n_mels=18 - ): - - self.fft_sizes = fft_sizes - self.overlap = overlap - self.fs = fs - self.n_mels = n_mels - - super().__init__() - - self.mel_specs = [] - for fft_size in fft_sizes: - hop_size = int(round(fft_size * (1 - self.overlap))) - - n_mels = self.n_mels - if fft_size < 128: - n_mels //= 2 - - self.mel_specs.append(torchaudio.transforms.MelSpectrogram(fs, fft_size, hop_length=hop_size, n_mels=n_mels)) - - for i, mel_spec in enumerate(self.mel_specs): - self.add_module(f'mel_spec_{i+1}', mel_spec) - - def forward(self, y_true, y_pred): - - loss = torch.zeros(1, device=y_true.device) - - for mel_spec in self.mel_specs: - Y_true = mel_spec(y_true) - Y_pred = mel_spec(y_pred) - loss = loss + log_magnitude_loss(Y_true, Y_pred) - - loss = loss / len(self.mel_specs) - - return loss - -def create_weight_matrix(num_bins, bins_per_band=10): - m = torch.zeros((num_bins, num_bins), dtype=torch.float32) - - r0 = bins_per_band // 2 - r1 = bins_per_band - r0 - - for i in range(num_bins): - i0 = max(i - r0, 0) - j0 = min(i + r1, num_bins) - - m[i, i0: j0] += 1 - - if i < r0: - m[i, :r0 - i] += 1 - - if i > num_bins - r1: - m[i, num_bins - r1 - i:] += 1 - - return m / bins_per_band - -def weighted_spectral_convergence(Y_true, Y_pred, w): - - # calculate sfm based weights - logY = torch.log(torch.abs(Y_true) + 1e-9) - Y = torch.abs(Y_true) - - avg_logY = torch.matmul(logY.transpose(1, 2), w) - avg_Y = torch.matmul(Y.transpose(1, 2), w) - - sfm = torch.exp(avg_logY) / (avg_Y + 1e-9) - - weight = (torch.relu(1 - sfm) ** .5).transpose(1, 2) - - loss = torch.mean( - torch.mean(weight * torch.abs(torch.abs(Y_true) - torch.abs(Y_pred)), dim=[1, 2]) - / (torch.mean( weight * torch.abs(Y_true), dim=[1, 2]) + 1e-9) - ) - - return loss - -def gen_filterbank(N, Fs=16000): - in_freq = (np.arange(N+1, dtype='float32')/N*Fs/2)[None,:] - out_freq = (np.arange(N, dtype='float32')/N*Fs/2)[:,None] - #ERB from B.C.J Moore, An Introduction to the Psychology of Hearing, 5th Ed., page 73. - ERB_N = 24.7 + .108*in_freq - delta = np.abs(in_freq-out_freq)/ERB_N - center = (delta<.5).astype('float32') - R = -12*center*delta**2 + (1-center)*(3-12*delta) - RE = 10.**(R/10.) - norm = np.sum(RE, axis=1) - RE = RE/norm[:, np.newaxis] - return torch.from_numpy(RE) - -def smooth_log_mag(Y_true, Y_pred, filterbank): - Y_true_smooth = torch.matmul(filterbank, torch.abs(Y_true)) - Y_pred_smooth = torch.matmul(filterbank, torch.abs(Y_pred)) - - loss = torch.abs( - torch.log(Y_true_smooth + 1e-9) - torch.log(Y_pred_smooth + 1e-9) - ) - - loss = loss.mean() - - return loss - -class MRSTFTLoss(nn.Module): - def __init__(self, - fft_sizes=[2048, 1024, 512, 256, 128, 64], - overlap=0.5, - window='hann_window', - fs=16000, - log_mag_weight=1, - sc_weight=0, - wsc_weight=0, - smooth_log_mag_weight=0, - sxcorr_weight=0): - super().__init__() - - self.fft_sizes = fft_sizes - self.overlap = overlap - self.window = window - self.log_mag_weight = log_mag_weight - self.sc_weight = sc_weight - self.wsc_weight = wsc_weight - self.smooth_log_mag_weight = smooth_log_mag_weight - self.sxcorr_weight = sxcorr_weight - self.fs = fs - - # weights for SFM weighted spectral convergence loss - self.wsc_weights = torch.nn.ParameterDict() - for fft_size in fft_sizes: - width = min(11, int(1000 * fft_size / self.fs + .5)) - width += width % 2 - self.wsc_weights[str(fft_size)] = torch.nn.Parameter( - create_weight_matrix(fft_size // 2 + 1, width), - requires_grad=False - ) - - # filterbanks for smooth log magnitude loss - self.filterbanks = torch.nn.ParameterDict() - for fft_size in fft_sizes: - self.filterbanks[str(fft_size)] = torch.nn.Parameter( - gen_filterbank(fft_size//2), - requires_grad=False - ) - - - def __call__(self, y_true, y_pred): - - - lm_loss = torch.zeros(1, device=y_true.device) - sc_loss = torch.zeros(1, device=y_true.device) - wsc_loss = torch.zeros(1, device=y_true.device) - slm_loss = torch.zeros(1, device=y_true.device) - sxcorr_loss = torch.zeros(1, device=y_true.device) - - for fft_size in self.fft_sizes: - hop_size = int(round(fft_size * (1 - self.overlap))) - win_size = fft_size - - Y_true = stft(y_true, fft_size, hop_size, win_size, self.window) - Y_pred = stft(y_pred, fft_size, hop_size, win_size, self.window) - - if self.log_mag_weight > 0: - lm_loss = lm_loss + log_magnitude_loss(Y_true, Y_pred) - - if self.sc_weight > 0: - sc_loss = sc_loss + spectral_convergence_loss(Y_true, Y_pred) - - if self.wsc_weight > 0: - wsc_loss = wsc_loss + weighted_spectral_convergence(Y_true, Y_pred, self.wsc_weights[str(fft_size)]) - - if self.smooth_log_mag_weight > 0: - slm_loss = slm_loss + smooth_log_mag(Y_true, Y_pred, self.filterbanks[str(fft_size)]) - - if self.sxcorr_weight > 0: - sxcorr_loss = sxcorr_loss + spectral_xcorr_loss(Y_true, Y_pred) - - - total_loss = (self.log_mag_weight * lm_loss + self.sc_weight * sc_loss - + self.wsc_weight * wsc_loss + self.smooth_log_mag_weight * slm_loss - + self.sxcorr_weight * sxcorr_loss) / len(self.fft_sizes) - - return total_loss \ No newline at end of file diff --git a/android/app/src/main/cpp/third_party/opus/dnn/torch/osce/make_default_setup.py b/android/app/src/main/cpp/third_party/opus/dnn/torch/osce/make_default_setup.py deleted file mode 100644 index d7365ff..0000000 --- a/android/app/src/main/cpp/third_party/opus/dnn/torch/osce/make_default_setup.py +++ /dev/null @@ -1,93 +0,0 @@ -""" -/* Copyright (c) 2023 Amazon - Written by Jan Buethe */ -/* - Redistribution and use in source and binary forms, with or without - modification, are permitted provided that the following conditions - are met: - - - Redistributions of source code must retain the above copyright - notice, this list of conditions and the following disclaimer. - - - Redistributions in binary form must reproduce the above copyright - notice, this list of conditions and the following disclaimer in the - documentation and/or other materials provided with the distribution. - - THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS - ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT - LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR - A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER - OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, - EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, - PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR - PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF - LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING - NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS - SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. -*/ -""" - -""" -/* Copyright (c) 2023 Amazon - Written by Jan Buethe */ -/* - Redistribution and use in source and binary forms, with or without - modification, are permitted provided that the following conditions - are met: - - - Redistributions of source code must retain the above copyright - notice, this list of conditions and the following disclaimer. - - - Redistributions in binary form must reproduce the above copyright - notice, this list of conditions and the following disclaimer in the - documentation and/or other materials provided with the distribution. - - THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS - ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT - LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR - A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER - OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, - EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, - PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR - PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF - LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING - NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS - SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. -*/ -""" - -import sys -import argparse - -import yaml - -from utils.templates import setup_dict - -parser = argparse.ArgumentParser() - -parser.add_argument('name', type=str, help='name of default setup file') -parser.add_argument('--model', choices=['lace', 'nolace', 'lavoce'], help='model name', default='lace') -parser.add_argument('--adversarial', action='store_true', help='setup for adversarial training') -parser.add_argument('--path2dataset', type=str, help='dataset path', default=None) - -args = parser.parse_args() - -key = args.model + "_adv" if args.adversarial else args.model - -try: - setup = setup_dict[key] -except KeyError: - print("setup not found, adversarial training possibly not specified for model") - sys.exit(1) - -# update dataset if given -if type(args.path2dataset) != type(None): - setup['dataset'] = args.path2dataset - -name = args.name -if not name.endswith('.yml'): - name += '.yml' - -if __name__ == '__main__': - with open(name, 'w') as f: - f.write(yaml.dump(setup)) \ No newline at end of file diff --git a/android/app/src/main/cpp/third_party/opus/dnn/torch/osce/models/__init__.py b/android/app/src/main/cpp/third_party/opus/dnn/torch/osce/models/__init__.py deleted file mode 100644 index 859db03..0000000 --- a/android/app/src/main/cpp/third_party/opus/dnn/torch/osce/models/__init__.py +++ /dev/null @@ -1,42 +0,0 @@ -""" -/* Copyright (c) 2023 Amazon - Written by Jan Buethe */ -/* - Redistribution and use in source and binary forms, with or without - modification, are permitted provided that the following conditions - are met: - - - Redistributions of source code must retain the above copyright - notice, this list of conditions and the following disclaimer. - - - Redistributions in binary form must reproduce the above copyright - notice, this list of conditions and the following disclaimer in the - documentation and/or other materials provided with the distribution. - - THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS - ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT - LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR - A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER - OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, - EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, - PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR - PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF - LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING - NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS - SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. -*/ -""" - -from .lace import LACE -from .no_lace import NoLACE -from .lavoce import LaVoce -from .lavoce_400 import LaVoce400 -from .fd_discriminator import TFDMultiResolutionDiscriminator as FDMResDisc - -model_dict = { - 'lace': LACE, - 'nolace': NoLACE, - 'lavoce': LaVoce, - 'lavoce400': LaVoce400, - 'fdmresdisc': FDMResDisc, -} diff --git a/android/app/src/main/cpp/third_party/opus/dnn/torch/osce/models/fd_discriminator.py b/android/app/src/main/cpp/third_party/opus/dnn/torch/osce/models/fd_discriminator.py deleted file mode 100644 index 2294862..0000000 --- a/android/app/src/main/cpp/third_party/opus/dnn/torch/osce/models/fd_discriminator.py +++ /dev/null @@ -1,974 +0,0 @@ -""" -/* Copyright (c) 2023 Amazon - Written by Jan Buethe */ -/* - Redistribution and use in source and binary forms, with or without - modification, are permitted provided that the following conditions - are met: - - - Redistributions of source code must retain the above copyright - notice, this list of conditions and the following disclaimer. - - - Redistributions in binary form must reproduce the above copyright - notice, this list of conditions and the following disclaimer in the - documentation and/or other materials provided with the distribution. - - THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS - ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT - LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR - A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER - OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, - EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, - PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR - PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF - LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING - NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS - SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. -*/ -""" - -import math as m -import copy - -import torch -import torch.nn.functional as F -from torch import nn -from torch.nn.utils import weight_norm, spectral_norm -import torchaudio - -from utils.spec import gen_filterbank - -# auxiliary functions - -def remove_all_weight_norms(module): - for m in module.modules(): - if hasattr(m, 'weight_v'): - nn.utils.remove_weight_norm(m) - - -def create_smoothing_kernel(h, w, gamma=1.5): - - ch = h / 2 - 0.5 - cw = w / 2 - 0.5 - - sh = gamma * ch - sw = gamma * cw - - vx = ((torch.arange(h) - ch) / sh) ** 2 - vy = ((torch.arange(w) - cw) / sw) ** 2 - vals = vx.view(-1, 1) + vy.view(1, -1) - kernel = torch.exp(- vals) - kernel = kernel / kernel.sum() - - return kernel - - -def create_kernel(h, w, sh, sw): - # proto kernel gives disjoint partition of 1 - proto_kernel = torch.ones((sh, sw)) - - # create smoothing kernel eta - h_eta, w_eta = h - sh + 1, w - sw + 1 - assert h_eta > 0 and w_eta > 0 - eta = create_smoothing_kernel(h_eta, w_eta).view(1, 1, h_eta, w_eta) - - kernel0 = F.pad(proto_kernel, [w_eta - 1, w_eta - 1, h_eta - 1, h_eta - 1]).unsqueeze(0).unsqueeze(0) - kernel = F.conv2d(kernel0, eta) - - return kernel - -# positional embeddings -class FrequencyPositionalEmbedding(nn.Module): - def __init__(self): - - super().__init__() - - def forward(self, x): - - N = x.size(2) - args = torch.arange(0, N, dtype=x.dtype, device=x.device) * torch.pi * 2 / N - cos = torch.cos(args).reshape(1, 1, -1, 1) - sin = torch.sin(args).reshape(1, 1, -1, 1) - zeros = torch.zeros_like(x[:, 0:1, :, :]) - - y = torch.cat((x, zeros + sin, zeros + cos), dim=1) - - return y - - -class PositionalEmbedding2D(nn.Module): - def __init__(self, d=5): - - super().__init__() - - self.d = d - - def forward(self, x): - - N = x.size(2) - M = x.size(3) - - h_args = torch.arange(0, N, dtype=x.dtype, device=x.device).reshape(1, 1, -1, 1) - w_args = torch.arange(0, M, dtype=x.dtype, device=x.device).reshape(1, 1, 1, -1) - coeffs = (10000 ** (-2 * torch.arange(0, self.d, dtype=x.dtype, device=x.device) / self.d)).reshape(1, -1, 1, 1) - - h_sin = torch.sin(coeffs * h_args) - h_cos = torch.sin(coeffs * h_args) - w_sin = torch.sin(coeffs * w_args) - w_cos = torch.sin(coeffs * w_args) - - zeros = torch.zeros_like(x[:, 0:1, :, :]) - - y = torch.cat((x, zeros + h_sin, zeros + h_cos, zeros + w_sin, zeros + w_cos), dim=1) - - return y - - -# spectral discriminator base class -class SpecDiscriminatorBase(nn.Module): - RECEPTIVE_FIELD_MAX_WIDTH=10000 - def __init__(self, - layers, - resolution, - fs=16000, - freq_roi=[50, 7000], - noise_gain=1e-3, - fmap_start_index=0 - ): - super().__init__() - - - self.layers = nn.ModuleList(layers) - self.resolution = resolution - self.fs = fs - self.noise_gain = noise_gain - self.fmap_start_index = fmap_start_index - - if fmap_start_index >= len(layers): - raise ValueError(f'fmap_start_index is larger than number of layers') - - # filter bank for noise shaping - n_fft = resolution[0] - - self.filterbank = nn.Parameter( - gen_filterbank(n_fft // 2, fs, keep_size=True), - requires_grad=False - ) - - # roi bins - f_step = fs / n_fft - self.start_bin = int(m.ceil(freq_roi[0] / f_step - 0.01)) - self.stop_bin = min(int(m.floor(freq_roi[1] / f_step + 0.01)), n_fft//2 + 1) - - self.init_weights() - - # determine receptive field size, offsets and strides - - hw = 1000 - while True: - x = torch.zeros((1, hw, hw)) - with torch.no_grad(): - y = self.run_layer_stack(x)[-1] - - pos0 = [y.size(-2) // 2, y.size(-1) // 2] - pos1 = [t + 1 for t in pos0] - - hs0, ws0 = self._receptive_field((hw, hw), pos0) - hs1, ws1 = self._receptive_field((hw, hw), pos1) - - h0 = hs0[1] - hs0[0] + 1 - h1 = hs1[1] - hs1[0] + 1 - w0 = ws0[1] - ws0[0] + 1 - w1 = ws1[1] - ws1[0] + 1 - - if h0 != h1 or w0 != w1: - hw = 2 * hw - else: - - # strides - sh = hs1[0] - hs0[0] - sw = ws1[0] - ws0[0] - - if sh == 0 or sw == 0: continue - - # offsets - oh = hs0[0] - sh * pos0[0] - ow = ws0[0] - sw * pos0[1] - - # overlap factor - overlap = w0 / sw + h0 / sh - - #print(f"{w0=} {h0=} {sw=} {sh=} {overlap=}") - self.receptive_field_params = {'width': [sw, ow, w0], 'height': [sh, oh, h0], 'overlap': overlap} - - break - - if hw > self.RECEPTIVE_FIELD_MAX_WIDTH: - print("warning: exceeded max size while trying to determine receptive field") - - # create transposed convolutional kernel - #self.tconv_kernel = nn.Parameter(create_kernel(h0, w0, sw, sw), requires_grad=False) - - def run_layer_stack(self, spec): - - output = [] - - x = spec.unsqueeze(1) - - for layer in self.layers: - x = layer(x) - output.append(x) - - return output - - def forward(self, x): - """ returns array with feature maps and final score at index -1 """ - - output = [] - - x = self.spectrogram(x) - - output = self.run_layer_stack(x) - - return output[self.fmap_start_index:] - - def receptive_field(self, output_pos): - - if self.receptive_field_params is not None: - s, o, h = self.receptive_field_params['height'] - h_min = output_pos[0] * s + o + self.start_bin - h_max = h_min + h - h_min = max(h_min, self.start_bin) - h_max = min(h_max, self.stop_bin) - - s, o, w = self.receptive_field_params['width'] - w_min = output_pos[1] * s + o - w_max = w_min + w - - return (h_min, h_max), (w_min, w_max) - - else: - return None, None - - - def _receptive_field(self, input_dims, output_pos): - """ determines receptive field probabilistically via autograd (slow) """ - - x = torch.randn((1,) + input_dims, requires_grad=True) - - # run input through layers - y = self.run_layer_stack(x)[-1] - b, c, h, w = y.shape - - if output_pos[0] >= h or output_pos[1] >= w: - raise ValueError("position out of range") - - mask = torch.zeros((b, c, h, w)) - mask[0, 0, output_pos[0], output_pos[1]] = 1 - - (mask * y).sum().backward() - - hs, ws = torch.nonzero(x.grad[0], as_tuple=True) - - h_min, h_max = hs.min().item(), hs.max().item() - w_min, w_max = ws.min().item(), ws.max().item() - - return [h_min, h_max], [w_min, w_max] - - - - def init_weights(self): - - for m in self.modules(): - if isinstance(m, nn.Conv1d) or isinstance(m, nn.ConvTranspose1d) or isinstance(m, nn.Linear) or isinstance(m, nn.Embedding): - nn.init.orthogonal_(m.weight.data) - - - def spectrogram(self, x): - n_fft, hop_length, win_length = self.resolution - x = x.squeeze(1) - window = getattr(torch, 'hann_window')(win_length).to(x.device) - - x = torch.stft(x, n_fft=n_fft, hop_length=hop_length, win_length=win_length,\ - window=window, return_complex=True) #[B, F, T] - x = torch.abs(x) - - # noise floor following spectral envelope - smoothed_x = torch.matmul(self.filterbank, x) - noise = torch.randn_like(x) * smoothed_x * self.noise_gain - x = x + noise - - # frequency ROI - x = x[:, self.start_bin : self.stop_bin + 1, ...] - - return torchaudio.functional.amplitude_to_DB(x,db_multiplier=0.0, multiplier=20,amin=1e-05,top_db=80)#torch.sqrt(x) - - def grad_map(self, x): - self.zero_grad() - - n_fft, hop_length, win_length = self.resolution - - window = getattr(torch, 'hann_window')(win_length).to(x.device) - - y = torch.stft(x.squeeze(1), n_fft=n_fft, hop_length=hop_length, win_length=win_length, - window=window, return_complex=True) #[B, F, T] - y = torch.abs(y) - - specgram = torchaudio.functional.amplitude_to_DB(y,db_multiplier=0.0, multiplier=20,amin=1e-05,top_db=80) - - specgram.requires_grad = True - specgram.retain_grad() - - if specgram.grad is not None: - specgram.grad.zero_() - - y = specgram[:, self.start_bin : self.stop_bin + 1, ...] - - scores = self.run_layer_stack(y)[-1] - - loss = torch.mean((1 - scores) ** 2) - loss.backward() - - return specgram.data[0], torch.abs(specgram.grad)[0] - - def relevance_map(self, x): - - n_fft, hop_length, win_length = self.resolution - y = x.view(-1) - window = getattr(torch, 'hann_window')(win_length).to(x.device) - - y = torch.stft(y, n_fft=n_fft, hop_length=hop_length, win_length=win_length,\ - window=window, return_complex=True) #[B, F, T] - y = torch.abs(y) - - specgram = torchaudio.functional.amplitude_to_DB(y,db_multiplier=0.0, multiplier=20,amin=1e-05,top_db=80) - - - scores = self.forward(x)[-1] - - sh, _, h = self.receptive_field_params['height'] - sw, _, w = self.receptive_field_params['width'] - kernel = create_kernel(h, w, sh, sw).float().to(scores.device) - with torch.no_grad(): - pad_w = (w + sw - 1) // sw - pad_h = (h + sh - 1) // sh - padded_scores = F.pad(scores, (pad_w, pad_w, pad_h, pad_h), mode='replicate') - # CAVE: padding should be derived from offsets - rv = F.conv_transpose2d(padded_scores, kernel, bias=None, stride=(sh, sw), padding=(h//2, w//2)) - rv = rv[..., pad_h * sh : - pad_h * sh, pad_w * sw : -pad_w * sw] - - relevance = torch.zeros_like(specgram) - relevance[..., self.start_bin : self.start_bin + rv.size(-2), : rv.size(-1)] = rv - - - return specgram, relevance - - - def lrp(self, x, eps=1e-9, label='both', threshold=0.5, low=None, high=None, verbose=False): - """ layer-wise relevance propagation (https://git.tu-berlin.de/gmontavon/lrp-tutorial) """ - - # ToDo: this code is highly unsafe as it assumes that layers are nn.Sequential with suitable activations - - def newconv2d(layer,g): - - new_layer = nn.Conv2d(layer.in_channels, - layer.out_channels, - layer.kernel_size, - stride=layer.stride, - padding=layer.padding, - dilation=layer.dilation, - groups=layer.groups) - - try: new_layer.weight = nn.Parameter(g(layer.weight.data.clone())) - except AttributeError: pass - - try: new_layer.bias = nn.Parameter(g(layer.bias.data.clone())) - except AttributeError: pass - - return new_layer - - bounds = { - 64: [-85.82449722290039, 2.1755014657974243], - 128: [-84.49211349487305, 3.5078893899917607], - 256: [-80.33127822875977, 7.6687201976776125], - 512: [-73.79328079223633, 14.20672025680542], - 1024: [-67.59239501953125, 20.40760498046875], - 2048: [-62.31902580261231, 25.680974197387698], - } - - nfft = self.resolution[0] - if low is None: low = bounds[nfft][0] - if high is None: high = bounds[nfft][1] - - remove_all_weight_norms(self) - - for p in self.parameters(): - if p.grad is not None: - p.grad.zero_() - - num_layers = len(self.layers) - X = self.spectrogram(x). detach() - - - # forward pass - A = [X.unsqueeze(1)] + [None] * len(self.layers) - - for i in range(num_layers - 1): - A[i + 1] = self.layers[i](A[i]) - - # initial relevance is last layer without activation - r = A[-2] - last_layer_rs = [r] - layer = self.layers[-1] - for sublayer in list(layer)[:-1]: - r = sublayer(r) - last_layer_rs.append(r) - - - mask = torch.zeros_like(r) - mask.requires_grad_(False) - if verbose: - print(r.min(), r.max()) - if label in {'both', 'fake'}: - mask[r < -threshold] = 1 - if label in {'both', 'real'}: - mask[r > threshold] = 1 - r = r * mask - - # backward pass - R = [None] * num_layers + [r] - - for l in range(1, num_layers)[::-1]: - A[l] = (A[l]).data.requires_grad_(True) - - layer = nn.Sequential(*(list(self.layers[l])[:-1])) - z = layer(A[l]) + eps - s = (R[l+1] / z).data - (z*s).sum().backward() - c = A[l].grad - R[l] = (A[l] * c).data - - # first layer - A[0] = (A[0].data).requires_grad_(True) - - Xl = (torch.zeros_like(A[0].data) + low).requires_grad_(True) - Xh = (torch.zeros_like(A[0].data) + high).requires_grad_(True) - - if len(list(self.layers)) > 2: - # unsafe way to check for embedding layer - embed = list(self.layers[0])[0] - conv = list(self.layers[0])[1] - - layer = nn.Sequential(embed, conv) - layerl = nn.Sequential(embed, newconv2d(conv, lambda p: p.clamp(min=0))) - layerh = nn.Sequential(embed, newconv2d(conv, lambda p: p.clamp(max=0))) - - else: - layer = list(self.layers[0])[0] - layerl = newconv2d(layer, lambda p: p.clamp(min=0)) - layerh = newconv2d(layer, lambda p: p.clamp(max=0)) - - - z = layer(A[0]) - z -= layerl(Xl) + layerh(Xh) - s = (R[1] / z).data - (z * s).sum().backward() - c, cp, cm = A[0].grad, Xl.grad, Xh.grad - - R[0] = (A[0] * c + Xl * cp + Xh * cm) - #R[0] = (A[0] * c).data - - return X, R[0].mean(dim=1) - - - - - - - - - - -def create_3x3_conv_plan(num_layers : int, - f_stretch : int, - f_down : int, - t_stretch : int, - t_down : int - ): - - - """ creates a stride, dilation, padding plan for a 2d conv network - - Args: - num_layers (int): number of layers - f_stretch (int): log_2 of stretching factor along frequency axis - f_down (int): log_2 of downsampling factor along frequency axis - t_stretch (int): log_2 of stretching factor along time axis - t_down (int): log_2 of downsampling factor along time axis - - Returns: - list(list(tuple)): list containing entries [(stride_t, stride_f), (dilation_t, dilation_f), (padding_t, padding_f)] - """ - - assert num_layers > 0 and t_stretch >= 0 and t_down >= 0 and f_stretch >= 0 and f_down >= 0 - assert f_stretch < num_layers and t_stretch < num_layers - - def process_dimension(n_layers, stretch, down): - - stack_layers = n_layers - 1 - - stride_layers = min(min(down, stretch) , stack_layers) - dilation_layers = max(min(stack_layers - stride_layers - 1, stretch - stride_layers), 0) - final_stride = 2 ** (max(down - stride_layers, 0)) - - final_dilation = 1 - if stride_layers < stack_layers and stretch - stride_layers - dilation_layers > 0: - final_dilation = 2 - - strides, dilations, paddings = [], [], [] - processed_layers = 0 - current_dilation = 1 - - for _ in range(stride_layers): - # increase receptive field and downsample via stride = 2 - strides.append(2) - dilations.append(1) - paddings.append(1) - processed_layers += 1 - - if processed_layers < stack_layers: - strides.append(1) - dilations.append(1) - paddings.append(1) - processed_layers += 1 - - for _ in range(dilation_layers): - # increase receptive field via dilation = 2 - strides.append(1) - current_dilation *= 2 - dilations.append(current_dilation) - paddings.append(current_dilation) - processed_layers += 1 - - while processed_layers < n_layers - 1: - # fill up with std layers - strides.append(1) - dilations.append(current_dilation) - paddings.append(current_dilation) - processed_layers += 1 - - # final layer - strides.append(final_stride) - current_dilation * final_dilation - dilations.append(current_dilation) - paddings.append(current_dilation) - processed_layers += 1 - - assert processed_layers == n_layers - - return strides, dilations, paddings - - t_strides, t_dilations, t_paddings = process_dimension(num_layers, t_stretch, t_down) - f_strides, f_dilations, f_paddings = process_dimension(num_layers, f_stretch, f_down) - - plan = [] - - for i in range(num_layers): - plan.append([ - (f_strides[i], t_strides[i]), - (f_dilations[i], t_dilations[i]), - (f_paddings[i], t_paddings[i]), - ]) - - return plan - - -class DiscriminatorExperimental(SpecDiscriminatorBase): - - def __init__(self, - resolution, - fs=16000, - freq_roi=[50, 7400], - noise_gain=0, - num_channels=16, - max_channels=512, - num_layers=5, - use_spectral_norm=False): - - norm_f = weight_norm if use_spectral_norm == False else spectral_norm - - self.num_channels = num_channels - self.num_channels_max = max_channels - self.num_layers = num_layers - - layers = [] - stride = (2, 1) - padding= (1, 1) - in_channels = 1 + 2 - out_channels = self.num_channels - for _ in range(self.num_layers): - layers.append( - nn.Sequential( - FrequencyPositionalEmbedding(), - norm_f(nn.Conv2d(in_channels, out_channels, (3, 3), stride=stride, padding=padding)), - nn.ReLU(inplace=True) - ) - ) - in_channels = out_channels + 2 - out_channels = min(2 * out_channels, self.num_channels_max) - - layers.append( - nn.Sequential( - FrequencyPositionalEmbedding(), - norm_f(nn.Conv2d(in_channels, 1, (3, 3), padding=padding)), - nn.Sigmoid() - ) - ) - - super().__init__(layers=layers, resolution=resolution, fs=fs, freq_roi=freq_roi, noise_gain=noise_gain) - - # bias biases - bias_val = 0.1 - with torch.no_grad(): - for name, weight in self.named_parameters(): - if 'bias' in name: - weight = weight + bias_val - - -configs = { - 'f_down': { - 'stretch' : { - 64 : (0, 0), - 128: (1, 0), - 256: (2, 0), - 512: (3, 0), - 1024: (4, 0), - 2048: (5, 0) - }, - 'down' : { - 64 : (0, 0), - 128: (1, 0), - 256: (2, 0), - 512: (3, 0), - 1024: (4, 0), - 2048: (5, 0) - } - }, - 'ft_down': { - 'stretch' : { - 64 : (0, 4), - 128: (1, 3), - 256: (2, 2), - 512: (3, 1), - 1024: (4, 0), - 2048: (5, 0) - }, - 'down' : { - 64 : (0, 4), - 128: (1, 3), - 256: (2, 2), - 512: (3, 1), - 1024: (4, 0), - 2048: (5, 0) - } - }, - 'dilated': { - 'stretch' : { - 64 : (0, 4), - 128: (1, 3), - 256: (2, 2), - 512: (3, 1), - 1024: (4, 0), - 2048: (5, 0) - }, - 'down' : { - 64 : (0, 0), - 128: (0, 0), - 256: (0, 0), - 512: (0, 0), - 1024: (0, 0), - 2048: (0, 0) - } - }, - 'mixed': { - 'stretch' : { - 64 : (0, 4), - 128: (1, 3), - 256: (2, 2), - 512: (3, 1), - 1024: (4, 0), - 2048: (5, 0) - }, - 'down' : { - 64 : (0, 0), - 128: (1, 0), - 256: (2, 0), - 512: (3, 0), - 1024: (4, 0), - 2048: (5, 0) - } - }, -} - - -class DiscriminatorMagFree(SpecDiscriminatorBase): - - def __init__(self, - resolution, - fs=16000, - freq_roi=[50, 7400], - noise_gain=0, - num_channels=16, - max_channels=256, - num_layers=5, - use_spectral_norm=False, - design=None): - - if design is None: - raise ValueError('error: arch required in DiscriminatorMagFree') - - norm_f = weight_norm if use_spectral_norm == False else spectral_norm - - stretch = configs[design]['stretch'][resolution[0]] - down = configs[design]['down'][resolution[0]] - - self.num_channels = num_channels - self.num_channels_max = max_channels - self.num_layers = num_layers - self.stretch = stretch - self.down = down - - layers = [] - plan = create_3x3_conv_plan(num_layers + 1, stretch[0], down[0], stretch[1], down[1]) - in_channels = 1 + 2 - out_channels = self.num_channels - for i in range(self.num_layers): - layers.append( - nn.Sequential( - FrequencyPositionalEmbedding(), - norm_f(nn.Conv2d(in_channels, out_channels, (3, 3), stride=plan[i][0], dilation=plan[i][1], padding=plan[i][2])), - nn.ReLU(inplace=True) - ) - ) - in_channels = out_channels + 2 - # product over strides - channel_factor = plan[i][0][0] * plan[i][0][1] - out_channels = min(channel_factor * out_channels, self.num_channels_max) - - layers.append( - nn.Sequential( - FrequencyPositionalEmbedding(), - norm_f(nn.Conv2d(in_channels, 1, (3, 3), stride=plan[-1][0], dilation=plan[-1][1], padding=plan[-1][2])), - nn.Sigmoid() - ) - ) - - - - # for layer in layers: - # print(layer) - - # print("end\n\n") - - super().__init__(layers=layers, resolution=resolution, fs=fs, freq_roi=freq_roi, noise_gain=noise_gain) - - # bias biases - bias_val = 0.1 - with torch.no_grad(): - for name, weight in self.named_parameters(): - if 'bias' in name: - weight = weight + bias_val - -class DiscriminatorMagFreqPosition(SpecDiscriminatorBase): - - def __init__(self, - resolution, - fs=16000, - freq_roi=[50, 7400], - noise_gain=0, - num_channels=16, - max_channels=512, - num_layers=5, - use_spectral_norm=False): - - norm_f = weight_norm if use_spectral_norm == False else spectral_norm - - self.num_channels = num_channels - self.num_channels_max = max_channels - self.num_layers = num_layers - - layers = [] - stride = (2, 1) - padding= (1, 1) - in_channels = 1 + 2 - out_channels = self.num_channels - for _ in range(self.num_layers): - layers.append( - nn.Sequential( - FrequencyPositionalEmbedding(), - norm_f(nn.Conv2d(in_channels, out_channels, (3, 3), stride=stride, padding=padding)), - nn.LeakyReLU(0.2, inplace=True) - ) - ) - in_channels = out_channels + 2 - out_channels = min(2 * out_channels, self.num_channels_max) - - layers.append( - nn.Sequential( - FrequencyPositionalEmbedding(), - norm_f(nn.Conv2d(in_channels, 1, (3, 3), padding=padding)) - ) - ) - - super().__init__(layers=layers, resolution=resolution, fs=fs, freq_roi=freq_roi, noise_gain=noise_gain) - - - -class DiscriminatorMag2dPositional(SpecDiscriminatorBase): - - def __init__(self, - resolution, - fs=16000, - freq_roi=[50, 7400], - noise_gain=0, - num_channels=16, - max_channels=512, - num_layers=5, - d=5, - use_spectral_norm=False): - - norm_f = weight_norm if use_spectral_norm == False else spectral_norm - self.resolution = resolution - self.num_channels = num_channels - self.num_channels_max = max_channels - self.num_layers = num_layers - self.d = d - embedding_dim = 4 * d - - - layers = [] - stride = (2, 2) - padding= (1, 1) - in_channels = 1 + embedding_dim - out_channels = self.num_channels - for _ in range(self.num_layers): - layers.append( - nn.Sequential( - PositionalEmbedding2D(d), - norm_f(nn.Conv2d(in_channels, out_channels, (3, 3), stride=stride, padding=padding)), - nn.LeakyReLU(0.2, inplace=True) - ) - ) - in_channels = out_channels + embedding_dim - out_channels = min(2 * out_channels, self.num_channels_max) - - - layers.append( - nn.Sequential( - PositionalEmbedding2D(), - norm_f(nn.Conv2d(in_channels, 1, (3, 3), padding=padding)) - ) - ) - - super().__init__(layers=layers, resolution=resolution, fs=fs, freq_roi=freq_roi, noise_gain=noise_gain) - - - -class DiscriminatorMag(SpecDiscriminatorBase): - def __init__(self, - resolution, - fs=16000, - freq_roi=[50, 7400], - noise_gain=0, - num_channels=32, - num_layers=5, - use_spectral_norm=False): - - norm_f = weight_norm if use_spectral_norm == False else spectral_norm - - self.num_channels = num_channels - self.num_layers = num_layers - - layers = [] - stride = (1, 1) - padding= (1, 1) - in_channels = 1 - out_channels = self.num_channels - for _ in range(self.num_layers): - layers.append( - nn.Sequential( - norm_f(nn.Conv2d(in_channels, out_channels, (3, 3), stride=stride, padding=padding)), - nn.LeakyReLU(0.2, inplace=True) - ) - ) - in_channels = out_channels - - layers.append(norm_f(nn.Conv2d(in_channels, 1, (3, 3), padding=padding))) - - super().__init__(layers=layers, resolution=resolution, fs=fs, freq_roi=freq_roi, noise_gain=noise_gain) - - -discriminators = { - 'mag': DiscriminatorMag, - 'freqpos': DiscriminatorMagFreqPosition, - '2dpos': DiscriminatorMag2dPositional, - 'experimental': DiscriminatorExperimental, - 'free': DiscriminatorMagFree -} - -class TFDMultiResolutionDiscriminator(torch.nn.Module): - def __init__(self, - fft_sizes_16k=[64, 128, 256, 512, 1024, 2048], - architecture='mag', - fs=16000, - freq_roi=[50, 7400], - noise_gain=0, - use_spectral_norm=False, - **kwargs): - - super().__init__() - - - fft_sizes = [int(round(fft_size_16k * fs / 16000)) for fft_size_16k in fft_sizes_16k] - - resolutions = [[n_fft, n_fft // 4, n_fft] for n_fft in fft_sizes] - - - Disc = discriminators[architecture] - - discs = [Disc(resolutions[i], fs=fs, freq_roi=freq_roi, noise_gain=noise_gain, use_spectral_norm=use_spectral_norm, **kwargs) for i in range(len(resolutions))] - - self.discriminators = nn.ModuleList(discs) - - def forward(self, y): - outputs = [] - - for disc in self.discriminators: - outputs.append(disc(y)) - - return outputs - - -class FWGAN_disc_wrapper(nn.Module): - def __init__(self, disc): - super().__init__() - - self.disc = disc - - def forward(self, y, y_hat): - - out_real = self.disc(y) - out_fake = self.disc(y_hat) - - y_d_rs = [] - y_d_gs = [] - fmap_rs = [] - fmap_gs = [] - - for y_real, y_fake in zip(out_real, out_fake): - y_d_rs.append(y_real[-1]) - y_d_gs.append(y_fake[-1]) - fmap_rs.append(y_real[:-1]) - fmap_gs.append(y_fake[:-1]) - - return y_d_rs, y_d_gs, fmap_rs, fmap_gs diff --git a/android/app/src/main/cpp/third_party/opus/dnn/torch/osce/models/lace.py b/android/app/src/main/cpp/third_party/opus/dnn/torch/osce/models/lace.py deleted file mode 100644 index 51d65c3..0000000 --- a/android/app/src/main/cpp/third_party/opus/dnn/torch/osce/models/lace.py +++ /dev/null @@ -1,190 +0,0 @@ -""" -/* Copyright (c) 2023 Amazon - Written by Jan Buethe */ -/* - Redistribution and use in source and binary forms, with or without - modification, are permitted provided that the following conditions - are met: - - - Redistributions of source code must retain the above copyright - notice, this list of conditions and the following disclaimer. - - - Redistributions in binary form must reproduce the above copyright - notice, this list of conditions and the following disclaimer in the - documentation and/or other materials provided with the distribution. - - THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS - ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT - LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR - A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER - OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, - EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, - PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR - PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF - LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING - NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS - SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. -*/ -""" - -import torch -from torch import nn -import torch.nn.functional as F - -import numpy as np - -from utils.layers.limited_adaptive_comb1d import LimitedAdaptiveComb1d -from utils.layers.limited_adaptive_conv1d import LimitedAdaptiveConv1d - -from models.nns_base import NNSBase -from models.silk_feature_net_pl import SilkFeatureNetPL -from models.silk_feature_net import SilkFeatureNet -from .scale_embedding import ScaleEmbedding - -import sys -sys.path.append('../dnntools') - -from dnntools.sparsification import create_sparsifier - - -class LACE(NNSBase): - """ Linear-Adaptive Coding Enhancer """ - FRAME_SIZE=80 - - def __init__(self, - num_features=47, - pitch_embedding_dim=64, - cond_dim=256, - pitch_max=257, - kernel_size=15, - preemph=0.85, - skip=91, - comb_gain_limit_db=-6, - global_gain_limits_db=[-6, 6], - conv_gain_limits_db=[-6, 6], - numbits_range=[50, 650], - numbits_embedding_dim=8, - hidden_feature_dim=64, - partial_lookahead=True, - norm_p=2, - softquant=False, - sparsify=False, - sparsification_schedule=[10000, 30000, 100], - sparsification_density=0.5, - apply_weight_norm=False): - - super().__init__(skip=skip, preemph=preemph) - - - self.num_features = num_features - self.cond_dim = cond_dim - self.pitch_max = pitch_max - self.pitch_embedding_dim = pitch_embedding_dim - self.kernel_size = kernel_size - self.preemph = preemph - self.skip = skip - self.numbits_range = numbits_range - self.numbits_embedding_dim = numbits_embedding_dim - self.hidden_feature_dim = hidden_feature_dim - self.partial_lookahead = partial_lookahead - - # pitch embedding - self.pitch_embedding = nn.Embedding(pitch_max + 1, pitch_embedding_dim) - - # numbits embedding - self.numbits_embedding = ScaleEmbedding(numbits_embedding_dim, *numbits_range, logscale=True) - - # feature net - if partial_lookahead: - self.feature_net = SilkFeatureNetPL(num_features + pitch_embedding_dim + 2 * numbits_embedding_dim, cond_dim, hidden_feature_dim, softquant=softquant, sparsify=sparsify, sparsification_density=sparsification_density, apply_weight_norm=apply_weight_norm) - else: - self.feature_net = SilkFeatureNet(num_features + pitch_embedding_dim + 2 * numbits_embedding_dim, cond_dim) - - # comb filters - left_pad = self.kernel_size // 2 - right_pad = self.kernel_size - 1 - left_pad - self.cf1 = LimitedAdaptiveComb1d(self.kernel_size, cond_dim, frame_size=self.FRAME_SIZE, overlap_size=40, use_bias=False, padding=[left_pad, right_pad], max_lag=pitch_max + 1, gain_limit_db=comb_gain_limit_db, global_gain_limits_db=global_gain_limits_db, norm_p=norm_p, softquant=softquant, apply_weight_norm=apply_weight_norm) - self.cf2 = LimitedAdaptiveComb1d(self.kernel_size, cond_dim, frame_size=self.FRAME_SIZE, overlap_size=40, use_bias=False, padding=[left_pad, right_pad], max_lag=pitch_max + 1, gain_limit_db=comb_gain_limit_db, global_gain_limits_db=global_gain_limits_db, norm_p=norm_p, softquant=softquant, apply_weight_norm=apply_weight_norm) - - # spectral shaping - self.af1 = LimitedAdaptiveConv1d(1, 1, self.kernel_size, cond_dim, frame_size=self.FRAME_SIZE, padding=[self.kernel_size - 1, 0], gain_limits_db=conv_gain_limits_db, norm_p=norm_p, softquant=softquant, apply_weight_norm=apply_weight_norm) - - if sparsify: - self.sparsifier = create_sparsifier(self, *sparsification_schedule) - - def flop_count(self, rate=16000, verbose=False): - - frame_rate = rate / self.FRAME_SIZE - - # feature net - feature_net_flops = self.feature_net.flop_count(frame_rate) - comb_flops = self.cf1.flop_count(rate) + self.cf2.flop_count(rate) - af_flops = self.af1.flop_count(rate) - - if verbose: - print(f"feature net: {feature_net_flops / 1e6} MFLOPS") - print(f"comb filters: {comb_flops / 1e6} MFLOPS") - print(f"adaptive conv: {af_flops / 1e6} MFLOPS") - - return feature_net_flops + comb_flops + af_flops - - def forward(self, x, features, periods, numbits, debug=False): - - periods = periods.squeeze(-1) - pitch_embedding = self.pitch_embedding(periods) - numbits_embedding = self.numbits_embedding(numbits).flatten(2) - - full_features = torch.cat((features, pitch_embedding, numbits_embedding), dim=-1) - cf = self.feature_net(full_features) - - y = self.cf1(x, cf, periods, debug=debug) - - y = self.cf2(y, cf, periods, debug=debug) - - y = self.af1(y, cf, debug=debug) - - return y - - def get_impulse_responses(self, features, periods, numbits): - """ generates impoulse responses on frame centers (input without batch dimension) """ - - num_frames = features.size(0) - batch_size = 32 - max_len = 2 * (self.pitch_max + self.kernel_size) + 10 - - # spread out some pulses - x = np.zeros((batch_size, 1, num_frames * self.FRAME_SIZE)) - for b in range(batch_size): - x[b, :, self.FRAME_SIZE // 2 + b * self.FRAME_SIZE :: batch_size * self.FRAME_SIZE] = 1 - - # prepare input - x = torch.from_numpy(x).float().to(features.device) - features = torch.repeat_interleave(features.unsqueeze(0), batch_size, 0) - periods = torch.repeat_interleave(periods.unsqueeze(0), batch_size, 0) - numbits = torch.repeat_interleave(numbits.unsqueeze(0), batch_size, 0) - - # run network - with torch.no_grad(): - periods = periods.squeeze(-1) - pitch_embedding = self.pitch_embedding(periods) - numbits_embedding = self.numbits_embedding(numbits).flatten(2) - full_features = torch.cat((features, pitch_embedding, numbits_embedding), dim=-1) - cf = self.feature_net(full_features) - y = self.cf1(x, cf, periods, debug=False) - y = self.cf2(y, cf, periods, debug=False) - y = self.af1(y, cf, debug=False) - - # collect responses - y = y.detach().squeeze().cpu().numpy() - cut_frames = (max_len + self.FRAME_SIZE - 1) // self.FRAME_SIZE - num_responses = num_frames - cut_frames - responses = np.zeros((num_responses, max_len)) - - for i in range(num_responses): - b = i % batch_size - start = self.FRAME_SIZE // 2 + i * self.FRAME_SIZE - stop = start + max_len - - responses[i, :] = y[b, start:stop] - - return responses diff --git a/android/app/src/main/cpp/third_party/opus/dnn/torch/osce/models/lavoce.py b/android/app/src/main/cpp/third_party/opus/dnn/torch/osce/models/lavoce.py deleted file mode 100644 index fcfdc8b..0000000 --- a/android/app/src/main/cpp/third_party/opus/dnn/torch/osce/models/lavoce.py +++ /dev/null @@ -1,274 +0,0 @@ -""" -/* Copyright (c) 2023 Amazon - Written by Jan Buethe */ -/* - Redistribution and use in source and binary forms, with or without - modification, are permitted provided that the following conditions - are met: - - - Redistributions of source code must retain the above copyright - notice, this list of conditions and the following disclaimer. - - - Redistributions in binary form must reproduce the above copyright - notice, this list of conditions and the following disclaimer in the - documentation and/or other materials provided with the distribution. - - THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS - ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT - LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR - A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER - OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, - EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, - PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR - PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF - LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING - NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS - SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. -*/ -""" - - -import torch -from torch import nn -import torch.nn.functional as F - -import numpy as np - -from utils.layers.limited_adaptive_comb1d import LimitedAdaptiveComb1d -from utils.layers.limited_adaptive_conv1d import LimitedAdaptiveConv1d -from utils.layers.td_shaper import TDShaper -from utils.layers.noise_shaper import NoiseShaper -from utils.complexity import _conv1d_flop_count -from utils.endoscopy import write_data - -from models.nns_base import NNSBase -from models.lpcnet_feature_net import LPCNetFeatureNet -from .scale_embedding import ScaleEmbedding - -def print_channels(y, prefix="", name="", rate=16000): - num_channels = y.size(1) - for i in range(num_channels): - channel_name = f"{prefix}_c{i:02d}" - if len(name) > 0: channel_name += "_" + name - ch = y[0,i,:].detach().cpu().numpy() - ch = ((2**14) * ch / np.max(ch)).astype(np.int16) - write_data(channel_name, ch, rate) - - - -class LaVoce(nn.Module): - """ Linear-Adaptive VOCodEr """ - FEATURE_FRAME_SIZE=160 - FRAME_SIZE=80 - - def __init__(self, - num_features=20, - pitch_embedding_dim=64, - cond_dim=256, - pitch_max=300, - kernel_size=15, - preemph=0.85, - comb_gain_limit_db=-6, - global_gain_limits_db=[-6, 6], - conv_gain_limits_db=[-6, 6], - norm_p=2, - avg_pool_k=4, - pulses=False, - innovate1=True, - innovate2=False, - innovate3=False, - ftrans_k=2): - - super().__init__() - - - self.num_features = num_features - self.cond_dim = cond_dim - self.pitch_max = pitch_max - self.pitch_embedding_dim = pitch_embedding_dim - self.kernel_size = kernel_size - self.preemph = preemph - self.pulses = pulses - self.ftrans_k = ftrans_k - - assert self.FEATURE_FRAME_SIZE % self.FRAME_SIZE == 0 - self.upsamp_factor = self.FEATURE_FRAME_SIZE // self.FRAME_SIZE - - # pitch embedding - self.pitch_embedding = nn.Embedding(pitch_max + 1, pitch_embedding_dim) - - # feature net - self.feature_net = LPCNetFeatureNet(num_features + pitch_embedding_dim, cond_dim, self.upsamp_factor) - - # noise shaper - self.noise_shaper = NoiseShaper(cond_dim, self.FRAME_SIZE) - - # comb filters - left_pad = self.kernel_size // 2 - right_pad = self.kernel_size - 1 - left_pad - self.cf1 = LimitedAdaptiveComb1d(self.kernel_size, cond_dim, frame_size=self.FRAME_SIZE, overlap_size=40, use_bias=False, padding=[left_pad, right_pad], max_lag=pitch_max + 1, gain_limit_db=comb_gain_limit_db, global_gain_limits_db=global_gain_limits_db, norm_p=norm_p) - self.cf2 = LimitedAdaptiveComb1d(self.kernel_size, cond_dim, frame_size=self.FRAME_SIZE, overlap_size=40, use_bias=False, padding=[left_pad, right_pad], max_lag=pitch_max + 1, gain_limit_db=comb_gain_limit_db, global_gain_limits_db=global_gain_limits_db, norm_p=norm_p) - - - self.af_prescale = LimitedAdaptiveConv1d(2, 1, self.kernel_size, cond_dim, frame_size=self.FRAME_SIZE, use_bias=False, padding=[self.kernel_size - 1, 0], gain_limits_db=conv_gain_limits_db, norm_p=norm_p) - self.af_mix = LimitedAdaptiveConv1d(2, 2, self.kernel_size, cond_dim, frame_size=self.FRAME_SIZE, use_bias=False, padding=[self.kernel_size - 1, 0], gain_limits_db=conv_gain_limits_db, norm_p=norm_p) - - # spectral shaping - self.af1 = LimitedAdaptiveConv1d(1, 2, self.kernel_size, cond_dim, frame_size=self.FRAME_SIZE, use_bias=False, padding=[self.kernel_size - 1, 0], gain_limits_db=conv_gain_limits_db, norm_p=norm_p) - - # non-linear transforms - self.tdshape1 = TDShaper(cond_dim, frame_size=self.FRAME_SIZE, avg_pool_k=avg_pool_k, innovate=innovate1) - self.tdshape2 = TDShaper(cond_dim, frame_size=self.FRAME_SIZE, avg_pool_k=avg_pool_k, innovate=innovate2) - self.tdshape3 = TDShaper(cond_dim, frame_size=self.FRAME_SIZE, avg_pool_k=avg_pool_k, innovate=innovate3) - - # combinators - self.af2 = LimitedAdaptiveConv1d(2, 2, self.kernel_size, cond_dim, frame_size=self.FRAME_SIZE, use_bias=False, padding=[self.kernel_size - 1, 0], gain_limits_db=conv_gain_limits_db, norm_p=norm_p) - self.af3 = LimitedAdaptiveConv1d(2, 1, self.kernel_size, cond_dim, frame_size=self.FRAME_SIZE, use_bias=False, padding=[self.kernel_size - 1, 0], gain_limits_db=conv_gain_limits_db, norm_p=norm_p) - self.af4 = LimitedAdaptiveConv1d(2, 1, self.kernel_size, cond_dim, frame_size=self.FRAME_SIZE, use_bias=False, padding=[self.kernel_size - 1, 0], gain_limits_db=conv_gain_limits_db, norm_p=norm_p) - - # feature transforms - self.post_cf1 = nn.Conv1d(cond_dim, cond_dim, ftrans_k) - self.post_cf2 = nn.Conv1d(cond_dim, cond_dim, ftrans_k) - self.post_af1 = nn.Conv1d(cond_dim, cond_dim, ftrans_k) - self.post_af2 = nn.Conv1d(cond_dim, cond_dim, ftrans_k) - self.post_af3 = nn.Conv1d(cond_dim, cond_dim, ftrans_k) - - - def create_phase_signals(self, periods): - - batch_size = periods.size(0) - progression = torch.arange(1, self.FRAME_SIZE + 1, dtype=periods.dtype, device=periods.device).view((1, -1)) - progression = torch.repeat_interleave(progression, batch_size, 0) - - phase0 = torch.zeros(batch_size, dtype=periods.dtype, device=periods.device).unsqueeze(-1) - chunks = [] - for sframe in range(periods.size(1)): - f = (2.0 * torch.pi / periods[:, sframe]).unsqueeze(-1) - - if self.pulses: - alpha = torch.cos(f).view(batch_size, 1, 1) - chunk_sin = torch.sin(f * progression + phase0).view(batch_size, 1, self.FRAME_SIZE) - pulse_a = torch.relu(chunk_sin - alpha) / (1 - alpha) - pulse_b = torch.relu(-chunk_sin - alpha) / (1 - alpha) - - chunk = torch.cat((pulse_a, pulse_b), dim = 1) - else: - chunk_sin = torch.sin(f * progression + phase0).view(batch_size, 1, self.FRAME_SIZE) - chunk_cos = torch.cos(f * progression + phase0).view(batch_size, 1, self.FRAME_SIZE) - - chunk = torch.cat((chunk_sin, chunk_cos), dim = 1) - - phase0 = phase0 + self.FRAME_SIZE * f - - chunks.append(chunk) - - phase_signals = torch.cat(chunks, dim=-1) - - return phase_signals - - def flop_count(self, rate=16000, verbose=False): - - frame_rate = rate / self.FRAME_SIZE - - # feature net - feature_net_flops = self.feature_net.flop_count(frame_rate) - comb_flops = self.cf1.flop_count(rate) + self.cf2.flop_count(rate) - af_flops = self.af1.flop_count(rate) + self.af2.flop_count(rate) + self.af3.flop_count(rate) + self.af4.flop_count(rate) + self.af_prescale.flop_count(rate) + self.af_mix.flop_count(rate) - feature_flops = (_conv1d_flop_count(self.post_cf1, frame_rate) + _conv1d_flop_count(self.post_cf2, frame_rate) - + _conv1d_flop_count(self.post_af1, frame_rate) + _conv1d_flop_count(self.post_af2, frame_rate) + _conv1d_flop_count(self.post_af3, frame_rate)) - - if verbose: - print(f"feature net: {feature_net_flops / 1e6} MFLOPS") - print(f"comb filters: {comb_flops / 1e6} MFLOPS") - print(f"adaptive conv: {af_flops / 1e6} MFLOPS") - print(f"feature transforms: {feature_flops / 1e6} MFLOPS") - - return feature_net_flops + comb_flops + af_flops + feature_flops - - def feature_transform(self, f, layer): - f = f.permute(0, 2, 1) - f = F.pad(f, [self.ftrans_k - 1, 0]) - f = torch.tanh(layer(f)) - return f.permute(0, 2, 1) - - def forward(self, features, periods, debug=False): - - periods = periods.squeeze(-1) - pitch_embedding = self.pitch_embedding(periods) - - full_features = torch.cat((features, pitch_embedding), dim=-1) - cf = self.feature_net(full_features) - - # upsample periods - periods = torch.repeat_interleave(periods, self.upsamp_factor, 1) - - # pre-net - ref_phase = torch.tanh(self.create_phase_signals(periods)) - if debug: print_channels(ref_phase, prefix="lavoce_01", name="pulse") - x = self.af_prescale(ref_phase, cf) - noise = self.noise_shaper(cf) - if debug: print_channels(torch.cat((x, noise), dim=1), prefix="lavoce_02", name="inputs") - y = self.af_mix(torch.cat((x, noise), dim=1), cf) - if debug: print_channels(y, prefix="lavoce_03", name="postselect1") - - # temporal shaping + innovating - y1 = y[:, 0:1, :] - y2 = self.tdshape1(y[:, 1:2, :], cf) - if debug: print_channels(y2, prefix="lavoce_04", name="postshape1") - y = torch.cat((y1, y2), dim=1) - y = self.af2(y, cf, debug=debug) - if debug: print_channels(y, prefix="lavoce_05", name="postselect2") - cf = self.feature_transform(cf, self.post_af2) - - y1 = y[:, 0:1, :] - y2 = self.tdshape2(y[:, 1:2, :], cf) - if debug: print_channels(y2, prefix="lavoce_06", name="postshape2") - y = torch.cat((y1, y2), dim=1) - y = self.af3(y, cf, debug=debug) - if debug: print_channels(y, prefix="lavoce_07", name="postmix1") - cf = self.feature_transform(cf, self.post_af3) - - # spectral shaping - y = self.cf1(y, cf, periods, debug=debug) - if debug: print_channels(y, prefix="lavoce_08", name="postcomb1") - cf = self.feature_transform(cf, self.post_cf1) - - y = self.cf2(y, cf, periods, debug=debug) - if debug: print_channels(y, prefix="lavoce_09", name="postcomb2") - cf = self.feature_transform(cf, self.post_cf2) - - y = self.af1(y, cf, debug=debug) - if debug: print_channels(y, prefix="lavoce_10", name="postselect3") - cf = self.feature_transform(cf, self.post_af1) - - # final temporal env adjustment - y1 = y[:, 0:1, :] - y2 = self.tdshape3(y[:, 1:2, :], cf) - if debug: print_channels(y2, prefix="lavoce_11", name="postshape3") - y = torch.cat((y1, y2), dim=1) - y = self.af4(y, cf, debug=debug) - if debug: print_channels(y, prefix="lavoce_12", name="postmix2") - - return y - - def process(self, features, periods, debug=False): - - self.eval() - device = next(iter(self.parameters())).device - with torch.no_grad(): - - # run model - f = features.unsqueeze(0).to(device) - p = periods.unsqueeze(0).to(device) - - y = self.forward(f, p, debug=debug).squeeze() - - # deemphasis - if self.preemph > 0: - for i in range(len(y) - 1): - y[i + 1] += self.preemph * y[i] - - # clip to valid range - out = torch.clip((2**15) * y, -2**15, 2**15 - 1).short() - - return out \ No newline at end of file diff --git a/android/app/src/main/cpp/third_party/opus/dnn/torch/osce/models/lavoce_400.py b/android/app/src/main/cpp/third_party/opus/dnn/torch/osce/models/lavoce_400.py deleted file mode 100644 index fe8263b..0000000 --- a/android/app/src/main/cpp/third_party/opus/dnn/torch/osce/models/lavoce_400.py +++ /dev/null @@ -1,254 +0,0 @@ -""" -/* Copyright (c) 2023 Amazon - Written by Jan Buethe */ -/* - Redistribution and use in source and binary forms, with or without - modification, are permitted provided that the following conditions - are met: - - - Redistributions of source code must retain the above copyright - notice, this list of conditions and the following disclaimer. - - - Redistributions in binary form must reproduce the above copyright - notice, this list of conditions and the following disclaimer in the - documentation and/or other materials provided with the distribution. - - THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS - ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT - LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR - A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER - OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, - EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, - PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR - PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF - LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING - NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS - SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. -*/ -""" - - -import torch -from torch import nn -import torch.nn.functional as F - -import numpy as np - -from utils.layers.limited_adaptive_comb1d import LimitedAdaptiveComb1d -from utils.layers.limited_adaptive_conv1d import LimitedAdaptiveConv1d -from utils.layers.td_shaper import TDShaper -from utils.layers.noise_shaper import NoiseShaper -from utils.complexity import _conv1d_flop_count -from utils.endoscopy import write_data - -from models.nns_base import NNSBase -from models.lpcnet_feature_net import LPCNetFeatureNet -from .scale_embedding import ScaleEmbedding - -class LaVoce400(nn.Module): - """ Linear-Adaptive VOCodEr """ - FEATURE_FRAME_SIZE=160 - FRAME_SIZE=40 - - def __init__(self, - num_features=20, - pitch_embedding_dim=64, - cond_dim=256, - pitch_max=300, - kernel_size=15, - preemph=0.85, - comb_gain_limit_db=-6, - global_gain_limits_db=[-6, 6], - conv_gain_limits_db=[-6, 6], - norm_p=2, - avg_pool_k=4, - pulses=False): - - super().__init__() - - - self.num_features = num_features - self.cond_dim = cond_dim - self.pitch_max = pitch_max - self.pitch_embedding_dim = pitch_embedding_dim - self.kernel_size = kernel_size - self.preemph = preemph - self.pulses = pulses - - assert self.FEATURE_FRAME_SIZE % self.FRAME_SIZE == 0 - self.upsamp_factor = self.FEATURE_FRAME_SIZE // self.FRAME_SIZE - - # pitch embedding - self.pitch_embedding = nn.Embedding(pitch_max + 1, pitch_embedding_dim) - - # feature net - self.feature_net = LPCNetFeatureNet(num_features + pitch_embedding_dim, cond_dim, self.upsamp_factor) - - # noise shaper - self.noise_shaper = NoiseShaper(cond_dim, self.FRAME_SIZE) - - # comb filters - left_pad = self.kernel_size // 2 - right_pad = self.kernel_size - 1 - left_pad - self.cf1 = LimitedAdaptiveComb1d(self.kernel_size, cond_dim, frame_size=self.FRAME_SIZE, overlap_size=20, use_bias=False, padding=[left_pad, right_pad], max_lag=pitch_max + 1, gain_limit_db=comb_gain_limit_db, global_gain_limits_db=global_gain_limits_db, norm_p=norm_p) - self.cf2 = LimitedAdaptiveComb1d(self.kernel_size, cond_dim, frame_size=self.FRAME_SIZE, overlap_size=20, use_bias=False, padding=[left_pad, right_pad], max_lag=pitch_max + 1, gain_limit_db=comb_gain_limit_db, global_gain_limits_db=global_gain_limits_db, norm_p=norm_p) - - - self.af_prescale = LimitedAdaptiveConv1d(2, 1, self.kernel_size, cond_dim, frame_size=self.FRAME_SIZE, use_bias=False, padding=[self.kernel_size - 1, 0], gain_limits_db=conv_gain_limits_db, norm_p=norm_p) - self.af_mix = LimitedAdaptiveConv1d(2, 2, self.kernel_size, cond_dim, frame_size=self.FRAME_SIZE, use_bias=False, padding=[self.kernel_size - 1, 0], gain_limits_db=conv_gain_limits_db, norm_p=norm_p) - - # spectral shaping - self.af1 = LimitedAdaptiveConv1d(1, 2, self.kernel_size, cond_dim, frame_size=self.FRAME_SIZE, use_bias=False, padding=[self.kernel_size - 1, 0], gain_limits_db=conv_gain_limits_db, norm_p=norm_p) - - # non-linear transforms - self.tdshape1 = TDShaper(cond_dim, frame_size=self.FRAME_SIZE, avg_pool_k=avg_pool_k, innovate=True) - self.tdshape2 = TDShaper(cond_dim, frame_size=self.FRAME_SIZE, avg_pool_k=avg_pool_k) - self.tdshape3 = TDShaper(cond_dim, frame_size=self.FRAME_SIZE, avg_pool_k=avg_pool_k) - - # combinators - self.af2 = LimitedAdaptiveConv1d(2, 2, self.kernel_size, cond_dim, frame_size=self.FRAME_SIZE, use_bias=False, padding=[self.kernel_size - 1, 0], gain_limits_db=conv_gain_limits_db, norm_p=norm_p) - self.af3 = LimitedAdaptiveConv1d(2, 1, self.kernel_size, cond_dim, frame_size=self.FRAME_SIZE, use_bias=False, padding=[self.kernel_size - 1, 0], gain_limits_db=conv_gain_limits_db, norm_p=norm_p) - self.af4 = LimitedAdaptiveConv1d(2, 1, self.kernel_size, cond_dim, frame_size=self.FRAME_SIZE, use_bias=False, padding=[self.kernel_size - 1, 0], gain_limits_db=conv_gain_limits_db, norm_p=norm_p) - - # feature transforms - self.post_cf1 = nn.Conv1d(cond_dim, cond_dim, 2) - self.post_cf2 = nn.Conv1d(cond_dim, cond_dim, 2) - self.post_af1 = nn.Conv1d(cond_dim, cond_dim, 2) - self.post_af2 = nn.Conv1d(cond_dim, cond_dim, 2) - self.post_af3 = nn.Conv1d(cond_dim, cond_dim, 2) - - - def create_phase_signals(self, periods): - - batch_size = periods.size(0) - progression = torch.arange(1, self.FRAME_SIZE + 1, dtype=periods.dtype, device=periods.device).view((1, -1)) - progression = torch.repeat_interleave(progression, batch_size, 0) - - phase0 = torch.zeros(batch_size, dtype=periods.dtype, device=periods.device).unsqueeze(-1) - chunks = [] - for sframe in range(periods.size(1)): - f = (2.0 * torch.pi / periods[:, sframe]).unsqueeze(-1) - - if self.pulses: - alpha = torch.cos(f).view(batch_size, 1, 1) - chunk_sin = torch.sin(f * progression + phase0).view(batch_size, 1, self.FRAME_SIZE) - pulse_a = torch.relu(chunk_sin - alpha) / (1 - alpha) - pulse_b = torch.relu(-chunk_sin - alpha) / (1 - alpha) - - chunk = torch.cat((pulse_a, pulse_b), dim = 1) - else: - chunk_sin = torch.sin(f * progression + phase0).view(batch_size, 1, self.FRAME_SIZE) - chunk_cos = torch.cos(f * progression + phase0).view(batch_size, 1, self.FRAME_SIZE) - - chunk = torch.cat((chunk_sin, chunk_cos), dim = 1) - - phase0 = phase0 + self.FRAME_SIZE * f - - chunks.append(chunk) - - phase_signals = torch.cat(chunks, dim=-1) - - return phase_signals - - def flop_count(self, rate=16000, verbose=False): - - frame_rate = rate / self.FRAME_SIZE - - # feature net - feature_net_flops = self.feature_net.flop_count(frame_rate) - comb_flops = self.cf1.flop_count(rate) + self.cf2.flop_count(rate) - af_flops = self.af1.flop_count(rate) + self.af2.flop_count(rate) + self.af3.flop_count(rate) + self.af4.flop_count(rate) + self.af_prescale.flop_count(rate) + self.af_mix.flop_count(rate) - feature_flops = (_conv1d_flop_count(self.post_cf1, frame_rate) + _conv1d_flop_count(self.post_cf2, frame_rate) - + _conv1d_flop_count(self.post_af1, frame_rate) + _conv1d_flop_count(self.post_af2, frame_rate) + _conv1d_flop_count(self.post_af3, frame_rate)) - - if verbose: - print(f"feature net: {feature_net_flops / 1e6} MFLOPS") - print(f"comb filters: {comb_flops / 1e6} MFLOPS") - print(f"adaptive conv: {af_flops / 1e6} MFLOPS") - print(f"feature transforms: {feature_flops / 1e6} MFLOPS") - - return feature_net_flops + comb_flops + af_flops + feature_flops - - def feature_transform(self, f, layer): - f = f.permute(0, 2, 1) - f = F.pad(f, [1, 0]) - f = torch.tanh(layer(f)) - return f.permute(0, 2, 1) - - def forward(self, features, periods, debug=False): - - periods = periods.squeeze(-1) - pitch_embedding = self.pitch_embedding(periods) - - full_features = torch.cat((features, pitch_embedding), dim=-1) - cf = self.feature_net(full_features) - - # upsample periods - periods = torch.repeat_interleave(periods, self.upsamp_factor, 1) - - # pre-net - ref_phase = torch.tanh(self.create_phase_signals(periods)) - x = self.af_prescale(ref_phase, cf) - noise = self.noise_shaper(cf) - y = self.af_mix(torch.cat((x, noise), dim=1), cf) - - if debug: - ch0 = y[0,0,:].detach().cpu().numpy() - ch1 = y[0,1,:].detach().cpu().numpy() - ch0 = (2**15 * ch0 / np.max(ch0)).astype(np.int16) - ch1 = (2**15 * ch1 / np.max(ch1)).astype(np.int16) - write_data('prior_channel0', ch0, 16000) - write_data('prior_channel1', ch1, 16000) - - # temporal shaping + innovating - y1 = y[:, 0:1, :] - y2 = self.tdshape1(y[:, 1:2, :], cf) - y = torch.cat((y1, y2), dim=1) - y = self.af2(y, cf, debug=debug) - cf = self.feature_transform(cf, self.post_af2) - - y1 = y[:, 0:1, :] - y2 = self.tdshape2(y[:, 1:2, :], cf) - y = torch.cat((y1, y2), dim=1) - y = self.af3(y, cf, debug=debug) - cf = self.feature_transform(cf, self.post_af3) - - # spectral shaping - y = self.cf1(y, cf, periods, debug=debug) - cf = self.feature_transform(cf, self.post_cf1) - - y = self.cf2(y, cf, periods, debug=debug) - cf = self.feature_transform(cf, self.post_cf2) - - y = self.af1(y, cf, debug=debug) - cf = self.feature_transform(cf, self.post_af1) - - # final temporal env adjustment - y1 = y[:, 0:1, :] - y2 = self.tdshape3(y[:, 1:2, :], cf) - y = torch.cat((y1, y2), dim=1) - y = self.af4(y, cf, debug=debug) - - return y - - def process(self, features, periods, debug=False): - - self.eval() - device = next(iter(self.parameters())).device - with torch.no_grad(): - - # run model - f = features.unsqueeze(0).to(device) - p = periods.unsqueeze(0).to(device) - - y = self.forward(f, p, debug=debug).squeeze() - - # deemphasis - if self.preemph > 0: - for i in range(len(y) - 1): - y[i + 1] += self.preemph * y[i] - - # clip to valid range - out = torch.clip((2**15) * y, -2**15, 2**15 - 1).short() - - return out \ No newline at end of file diff --git a/android/app/src/main/cpp/third_party/opus/dnn/torch/osce/models/lpcnet_feature_net.py b/android/app/src/main/cpp/third_party/opus/dnn/torch/osce/models/lpcnet_feature_net.py deleted file mode 100644 index b637d74..0000000 --- a/android/app/src/main/cpp/third_party/opus/dnn/torch/osce/models/lpcnet_feature_net.py +++ /dev/null @@ -1,91 +0,0 @@ -""" -/* Copyright (c) 2023 Amazon - Written by Jan Buethe */ -/* - Redistribution and use in source and binary forms, with or without - modification, are permitted provided that the following conditions - are met: - - - Redistributions of source code must retain the above copyright - notice, this list of conditions and the following disclaimer. - - - Redistributions in binary form must reproduce the above copyright - notice, this list of conditions and the following disclaimer in the - documentation and/or other materials provided with the distribution. - - THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS - ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT - LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR - A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER - OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, - EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, - PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR - PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF - LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING - NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS - SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. -*/ -""" - -import torch -from torch import nn -import torch.nn.functional as F - -from utils.complexity import _conv1d_flop_count - -class LPCNetFeatureNet(nn.Module): - - def __init__(self, - feature_dim=84, - num_channels=256, - upsamp_factor=2, - lookahead=True): - - super().__init__() - - self.feature_dim = feature_dim - self.num_channels = num_channels - self.upsamp_factor = upsamp_factor - self.lookahead = lookahead - - self.conv1 = nn.Conv1d(feature_dim, num_channels, 3) - self.conv2 = nn.Conv1d(num_channels, num_channels, 3) - - self.gru = nn.GRU(num_channels, num_channels, batch_first=True) - - self.tconv = nn.ConvTranspose1d(num_channels, num_channels, upsamp_factor, upsamp_factor) - - def flop_count(self, rate=100): - count = 0 - for conv in self.conv1, self.conv2, self.tconv: - count += _conv1d_flop_count(conv, rate) - - count += 2 * (3 * self.gru.input_size * self.gru.hidden_size + 3 * self.gru.hidden_size * self.gru.hidden_size) * rate - - return count - - - def forward(self, features, state=None): - """ features shape: (batch_size, num_frames, feature_dim) """ - - batch_size = features.size(0) - - if state is None: - state = torch.zeros((1, batch_size, self.num_channels), device=features.device) - - - features = features.permute(0, 2, 1) - if self.lookahead: - c = torch.tanh(self.conv1(F.pad(features, [1, 1]))) - c = torch.tanh(self.conv2(F.pad(c, [2, 0]))) - else: - c = torch.tanh(self.conv1(F.pad(features, [2, 0]))) - c = torch.tanh(self.conv2(F.pad(c, [2, 0]))) - - c = torch.tanh(self.tconv(c)) - - c = c.permute(0, 2, 1) - - c, _ = self.gru(c, state) - - return c \ No newline at end of file diff --git a/android/app/src/main/cpp/third_party/opus/dnn/torch/osce/models/nns_base.py b/android/app/src/main/cpp/third_party/opus/dnn/torch/osce/models/nns_base.py deleted file mode 100644 index 6e667b9..0000000 --- a/android/app/src/main/cpp/third_party/opus/dnn/torch/osce/models/nns_base.py +++ /dev/null @@ -1,69 +0,0 @@ -""" -/* Copyright (c) 2023 Amazon - Written by Jan Buethe */ -/* - Redistribution and use in source and binary forms, with or without - modification, are permitted provided that the following conditions - are met: - - - Redistributions of source code must retain the above copyright - notice, this list of conditions and the following disclaimer. - - - Redistributions in binary form must reproduce the above copyright - notice, this list of conditions and the following disclaimer in the - documentation and/or other materials provided with the distribution. - - THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS - ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT - LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR - A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER - OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, - EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, - PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR - PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF - LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING - NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS - SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. -*/ -""" - -import torch -from torch import nn -import torch.nn.functional as F - -class NNSBase(nn.Module): - - def __init__(self, skip=91, preemph=0.85): - super().__init__() - - self.skip = skip - self.preemph = preemph - - def process(self, sig, features, periods, numbits, debug=False): - - self.eval() - has_numbits = 'numbits' in self.forward.__code__.co_varnames - device = next(iter(self.parameters())).device - with torch.no_grad(): - - # run model - x = sig.view(1, 1, -1).to(device) - f = features.unsqueeze(0).to(device) - p = periods.unsqueeze(0).to(device) - n = numbits.unsqueeze(0).to(device) - - if has_numbits: - y = self.forward(x, f, p, n, debug=debug).squeeze() - else: - y = self.forward(x, f, p, debug=debug).squeeze() - - # deemphasis - if self.preemph > 0: - for i in range(len(y) - 1): - y[i + 1] += self.preemph * y[i] - - # delay compensation - y = torch.cat((y[self.skip:], torch.zeros(self.skip, dtype=y.dtype, device=y.device))) - out = torch.clip((2**15) * y, -2**15, 2**15 - 1).short() - - return out \ No newline at end of file diff --git a/android/app/src/main/cpp/third_party/opus/dnn/torch/osce/models/no_lace.py b/android/app/src/main/cpp/third_party/opus/dnn/torch/osce/models/no_lace.py deleted file mode 100644 index 78c3a30..0000000 --- a/android/app/src/main/cpp/third_party/opus/dnn/torch/osce/models/no_lace.py +++ /dev/null @@ -1,218 +0,0 @@ -""" -/* Copyright (c) 2023 Amazon - Written by Jan Buethe */ -/* - Redistribution and use in source and binary forms, with or without - modification, are permitted provided that the following conditions - are met: - - - Redistributions of source code must retain the above copyright - notice, this list of conditions and the following disclaimer. - - - Redistributions in binary form must reproduce the above copyright - notice, this list of conditions and the following disclaimer in the - documentation and/or other materials provided with the distribution. - - THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS - ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT - LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR - A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER - OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, - EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, - PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR - PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF - LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING - NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS - SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. -*/ -""" - -import numbers - -import torch -from torch import nn -import torch.nn.functional as F -from torch.nn.utils import weight_norm - - -import numpy as np - -from utils.layers.limited_adaptive_comb1d import LimitedAdaptiveComb1d -from utils.layers.limited_adaptive_conv1d import LimitedAdaptiveConv1d -from utils.layers.td_shaper import TDShaper -from utils.complexity import _conv1d_flop_count - -from models.nns_base import NNSBase -from models.silk_feature_net_pl import SilkFeatureNetPL -from models.silk_feature_net import SilkFeatureNet -from .scale_embedding import ScaleEmbedding - -import sys -sys.path.append('../dnntools') -from dnntools.quantization import soft_quant -from dnntools.sparsification import create_sparsifier, mark_for_sparsification - -class NoLACE(NNSBase): - """ Non-Linear Adaptive Coding Enhancer """ - FRAME_SIZE=80 - - def __init__(self, - num_features=47, - pitch_embedding_dim=64, - cond_dim=256, - pitch_max=257, - kernel_size=15, - preemph=0.85, - skip=91, - comb_gain_limit_db=-6, - global_gain_limits_db=[-6, 6], - conv_gain_limits_db=[-6, 6], - numbits_range=[50, 650], - numbits_embedding_dim=8, - hidden_feature_dim=64, - partial_lookahead=True, - norm_p=2, - avg_pool_k=4, - pool_after=False, - softquant=False, - sparsify=False, - sparsification_schedule=[100, 1000, 100], - sparsification_density=0.5, - apply_weight_norm=False): - - super().__init__(skip=skip, preemph=preemph) - - self.num_features = num_features - self.cond_dim = cond_dim - self.pitch_max = pitch_max - self.pitch_embedding_dim = pitch_embedding_dim - self.kernel_size = kernel_size - self.preemph = preemph - self.skip = skip - self.numbits_range = numbits_range - self.numbits_embedding_dim = numbits_embedding_dim - self.hidden_feature_dim = hidden_feature_dim - self.partial_lookahead = partial_lookahead - - if isinstance(sparsification_density, numbers.Number): - sparsification_density = 10 * [sparsification_density] - - norm = weight_norm if apply_weight_norm else lambda x, name=None: x - - # pitch embedding - self.pitch_embedding = nn.Embedding(pitch_max + 1, pitch_embedding_dim) - - # numbits embedding - self.numbits_embedding = ScaleEmbedding(numbits_embedding_dim, *numbits_range, logscale=True) - - # feature net - if partial_lookahead: - self.feature_net = SilkFeatureNetPL(num_features + pitch_embedding_dim + 2 * numbits_embedding_dim, cond_dim, hidden_feature_dim, softquant=softquant, sparsify=sparsify, sparsification_density=sparsification_density, apply_weight_norm=apply_weight_norm) - else: - self.feature_net = SilkFeatureNet(num_features + pitch_embedding_dim + 2 * numbits_embedding_dim, cond_dim) - - # comb filters - left_pad = self.kernel_size // 2 - right_pad = self.kernel_size - 1 - left_pad - self.cf1 = LimitedAdaptiveComb1d(self.kernel_size, cond_dim, frame_size=self.FRAME_SIZE, overlap_size=40, padding=[left_pad, right_pad], max_lag=pitch_max + 1, gain_limit_db=comb_gain_limit_db, global_gain_limits_db=global_gain_limits_db, norm_p=norm_p, softquant=softquant, apply_weight_norm=apply_weight_norm) - self.cf2 = LimitedAdaptiveComb1d(self.kernel_size, cond_dim, frame_size=self.FRAME_SIZE, overlap_size=40, padding=[left_pad, right_pad], max_lag=pitch_max + 1, gain_limit_db=comb_gain_limit_db, global_gain_limits_db=global_gain_limits_db, norm_p=norm_p, softquant=softquant, apply_weight_norm=apply_weight_norm) - - # spectral shaping - self.af1 = LimitedAdaptiveConv1d(1, 2, self.kernel_size, cond_dim, frame_size=self.FRAME_SIZE, use_bias=False, padding=[self.kernel_size - 1, 0], gain_limits_db=conv_gain_limits_db, norm_p=norm_p, softquant=softquant, apply_weight_norm=apply_weight_norm) - - # non-linear transforms - self.tdshape1 = TDShaper(cond_dim, frame_size=self.FRAME_SIZE, avg_pool_k=avg_pool_k, pool_after=pool_after, softquant=softquant, apply_weight_norm=apply_weight_norm) - self.tdshape2 = TDShaper(cond_dim, frame_size=self.FRAME_SIZE, avg_pool_k=avg_pool_k, pool_after=pool_after, softquant=softquant, apply_weight_norm=apply_weight_norm) - self.tdshape3 = TDShaper(cond_dim, frame_size=self.FRAME_SIZE, avg_pool_k=avg_pool_k, pool_after=pool_after, softquant=softquant, apply_weight_norm=apply_weight_norm) - - # combinators - self.af2 = LimitedAdaptiveConv1d(2, 2, self.kernel_size, cond_dim, frame_size=self.FRAME_SIZE, use_bias=False, padding=[self.kernel_size - 1, 0], gain_limits_db=conv_gain_limits_db, norm_p=norm_p, softquant=softquant, apply_weight_norm=apply_weight_norm) - self.af3 = LimitedAdaptiveConv1d(2, 2, self.kernel_size, cond_dim, frame_size=self.FRAME_SIZE, use_bias=False, padding=[self.kernel_size - 1, 0], gain_limits_db=conv_gain_limits_db, norm_p=norm_p, softquant=softquant, apply_weight_norm=apply_weight_norm) - self.af4 = LimitedAdaptiveConv1d(2, 1, self.kernel_size, cond_dim, frame_size=self.FRAME_SIZE, use_bias=False, padding=[self.kernel_size - 1, 0], gain_limits_db=conv_gain_limits_db, norm_p=norm_p, softquant=softquant, apply_weight_norm=apply_weight_norm) - - # feature transforms - self.post_cf1 = norm(nn.Conv1d(cond_dim, cond_dim, 2)) - self.post_cf2 = norm(nn.Conv1d(cond_dim, cond_dim, 2)) - self.post_af1 = norm(nn.Conv1d(cond_dim, cond_dim, 2)) - self.post_af2 = norm(nn.Conv1d(cond_dim, cond_dim, 2)) - self.post_af3 = norm(nn.Conv1d(cond_dim, cond_dim, 2)) - - if softquant: - self.post_cf1 = soft_quant(self.post_cf1) - self.post_cf2 = soft_quant(self.post_cf2) - self.post_af1 = soft_quant(self.post_af1) - self.post_af2 = soft_quant(self.post_af2) - self.post_af3 = soft_quant(self.post_af3) - - - if sparsify: - mark_for_sparsification(self.post_cf1, (sparsification_density[4], [8, 4])) - mark_for_sparsification(self.post_cf2, (sparsification_density[5], [8, 4])) - mark_for_sparsification(self.post_af1, (sparsification_density[6], [8, 4])) - mark_for_sparsification(self.post_af2, (sparsification_density[7], [8, 4])) - mark_for_sparsification(self.post_af3, (sparsification_density[8], [8, 4])) - - self.sparsifier = create_sparsifier(self, *sparsification_schedule) - - def flop_count(self, rate=16000, verbose=False): - - frame_rate = rate / self.FRAME_SIZE - - # feature net - feature_net_flops = self.feature_net.flop_count(frame_rate) - comb_flops = self.cf1.flop_count(rate) + self.cf2.flop_count(rate) - af_flops = self.af1.flop_count(rate) + self.af2.flop_count(rate) + self.af3.flop_count(rate) + self.af4.flop_count(rate) - shape_flops = self.tdshape1.flop_count(rate) + self.tdshape2.flop_count(rate) + self.tdshape3.flop_count(rate) - feature_flops = (_conv1d_flop_count(self.post_cf1, frame_rate) + _conv1d_flop_count(self.post_cf2, frame_rate) - + _conv1d_flop_count(self.post_af1, frame_rate) + _conv1d_flop_count(self.post_af2, frame_rate) + _conv1d_flop_count(self.post_af3, frame_rate)) - - if verbose: - print(f"feature net: {feature_net_flops / 1e6} MFLOPS") - print(f"comb filters: {comb_flops / 1e6} MFLOPS") - print(f"adaptive conv: {af_flops / 1e6} MFLOPS") - print(f"feature transforms: {feature_flops / 1e6} MFLOPS") - - return feature_net_flops + comb_flops + af_flops + feature_flops + shape_flops - - def feature_transform(self, f, layer): - f0 = f.permute(0, 2, 1) - f = F.pad(f0, [1, 0]) - f = torch.tanh(layer(f)) - return f.permute(0, 2, 1) - - def forward(self, x, features, periods, numbits, debug=False): - - periods = periods.squeeze(-1) - pitch_embedding = self.pitch_embedding(periods) - numbits_embedding = self.numbits_embedding(numbits).flatten(2) - - full_features = torch.cat((features, pitch_embedding, numbits_embedding), dim=-1) - cf = self.feature_net(full_features) - - y = self.cf1(x, cf, periods, debug=debug) - cf = self.feature_transform(cf, self.post_cf1) - - y = self.cf2(y, cf, periods, debug=debug) - cf = self.feature_transform(cf, self.post_cf2) - - y = self.af1(y, cf, debug=debug) - cf = self.feature_transform(cf, self.post_af1) - - y1 = y[:, 0:1, :] - y2 = self.tdshape1(y[:, 1:2, :], cf) - y = torch.cat((y1, y2), dim=1) - y = self.af2(y, cf, debug=debug) - cf = self.feature_transform(cf, self.post_af2) - - y1 = y[:, 0:1, :] - y2 = self.tdshape2(y[:, 1:2, :], cf) - y = torch.cat((y1, y2), dim=1) - y = self.af3(y, cf, debug=debug) - cf = self.feature_transform(cf, self.post_af3) - - y1 = y[:, 0:1, :] - y2 = self.tdshape3(y[:, 1:2, :], cf) - y = torch.cat((y1, y2), dim=1) - y = self.af4(y, cf, debug=debug) - - return y diff --git a/android/app/src/main/cpp/third_party/opus/dnn/torch/osce/models/scale_embedding.py b/android/app/src/main/cpp/third_party/opus/dnn/torch/osce/models/scale_embedding.py deleted file mode 100644 index 5869530..0000000 --- a/android/app/src/main/cpp/third_party/opus/dnn/torch/osce/models/scale_embedding.py +++ /dev/null @@ -1,68 +0,0 @@ -""" -/* Copyright (c) 2023 Amazon - Written by Jan Buethe */ -/* - Redistribution and use in source and binary forms, with or without - modification, are permitted provided that the following conditions - are met: - - - Redistributions of source code must retain the above copyright - notice, this list of conditions and the following disclaimer. - - - Redistributions in binary form must reproduce the above copyright - notice, this list of conditions and the following disclaimer in the - documentation and/or other materials provided with the distribution. - - THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS - ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT - LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR - A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER - OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, - EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, - PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR - PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF - LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING - NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS - SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. -*/ -""" - -import math as m -import torch -from torch import nn - - -class ScaleEmbedding(nn.Module): - def __init__(self, - dim, - min_val, - max_val, - logscale=False): - - super().__init__() - - if min_val >= max_val: - raise ValueError('min_val must be smaller than max_val') - - if min_val <= 0 and logscale: - raise ValueError('min_val must be positive when logscale is true') - - self.dim = dim - self.logscale = logscale - self.min_val = min_val - self.max_val = max_val - - if logscale: - self.min_val = m.log(self.min_val) - self.max_val = m.log(self.max_val) - - - self.offset = (self.min_val + self.max_val) / 2 - self.scale_factors = nn.Parameter( - torch.arange(1, dim+1, dtype=torch.float32) * torch.pi / (self.max_val - self.min_val) - ) - - def forward(self, x): - if self.logscale: x = torch.log(x) - x = torch.clip(x, self.min_val, self.max_val) - self.offset - return torch.sin(x.unsqueeze(-1) * self.scale_factors - 0.5) diff --git a/android/app/src/main/cpp/third_party/opus/dnn/torch/osce/models/shape_up_48.py b/android/app/src/main/cpp/third_party/opus/dnn/torch/osce/models/shape_up_48.py deleted file mode 100644 index 6e717f0..0000000 --- a/android/app/src/main/cpp/third_party/opus/dnn/torch/osce/models/shape_up_48.py +++ /dev/null @@ -1,179 +0,0 @@ - -""" -/* Copyright (c) 2023 Amazon - Written by Jan Buethe */ -/* - Redistribution and use in source and binary forms, with or without - modification, are permitted provided that the following conditions - are met: - - - Redistributions of source code must retain the above copyright - notice, this list of conditions and the following disclaimer. - - - Redistributions in binary form must reproduce the above copyright - notice, this list of conditions and the following disclaimer in the - documentation and/or other materials provided with the distribution. - - THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS - ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT - LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR - A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER - OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, - EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, - PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR - PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF - LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING - NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS - SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. -*/ -""" - -import torch -from torch import nn -import torch.nn.functional as F - -import numpy as np - -from utils.layers.silk_upsampler import SilkUpsampler -from utils.layers.limited_adaptive_conv1d import LimitedAdaptiveConv1d -from utils.layers.td_shaper import TDShaper -from utils.layers.deemph import Deemph -from utils.misc import freeze_model - -from models.nns_base import NNSBase -from models.silk_feature_net_pl import SilkFeatureNetPL -from models.silk_feature_net import SilkFeatureNet -from .scale_embedding import ScaleEmbedding - - - -class ShapeUp48(NNSBase): - FRAME_SIZE16k=80 - - def __init__(self, - num_features=47, - pitch_embedding_dim=64, - cond_dim=256, - pitch_max=257, - kernel_size=15, - preemph=0.85, - skip=288, - conv_gain_limits_db=[-6, 6], - numbits_range=[50, 650], - numbits_embedding_dim=8, - hidden_feature_dim=64, - partial_lookahead=True, - norm_p=2, - target_fs=48000, - noise_amplitude=0, - prenet=None, - avg_pool_k=4): - - super().__init__(skip=skip, preemph=preemph) - - - self.num_features = num_features - self.cond_dim = cond_dim - self.pitch_max = pitch_max - self.pitch_embedding_dim = pitch_embedding_dim - self.kernel_size = kernel_size - self.preemph = preemph - self.skip = skip - self.numbits_range = numbits_range - self.numbits_embedding_dim = numbits_embedding_dim - self.hidden_feature_dim = hidden_feature_dim - self.partial_lookahead = partial_lookahead - self.frame_size48 = int(self.FRAME_SIZE16k * target_fs / 16000 + .1) - self.frame_size32 = self.FRAME_SIZE16k * 2 - self.noise_amplitude = noise_amplitude - self.prenet = prenet - - # freeze prenet if given - if prenet is not None: - freeze_model(self.prenet) - try: - self.deemph = Deemph(prenet.preemph) - except: - print("[warning] prenet model is expected to have preemph attribute") - self.deemph = Deemph(0) - - - - # upsampler - self.upsampler = SilkUpsampler() - - # pitch embedding - self.pitch_embedding = nn.Embedding(pitch_max + 1, pitch_embedding_dim) - - # numbits embedding - self.numbits_embedding = ScaleEmbedding(numbits_embedding_dim, *numbits_range, logscale=True) - - # feature net - if partial_lookahead: - self.feature_net = SilkFeatureNetPL(num_features + pitch_embedding_dim + 2 * numbits_embedding_dim, cond_dim, hidden_feature_dim) - else: - self.feature_net = SilkFeatureNet(num_features + pitch_embedding_dim + 2 * numbits_embedding_dim, cond_dim) - - # non-linear transforms - self.tdshape1 = TDShaper(cond_dim, frame_size=self.frame_size32, avg_pool_k=avg_pool_k) - self.tdshape2 = TDShaper(cond_dim, frame_size=self.frame_size48, avg_pool_k=avg_pool_k) - - # spectral shaping - self.af_noise = LimitedAdaptiveConv1d(1, 1, self.kernel_size, cond_dim, frame_size=self.frame_size32, overlap_size=self.frame_size32//2, use_bias=False, padding=[self.kernel_size - 1, 0], gain_limits_db=[-30, 0], norm_p=norm_p) - self.af1 = LimitedAdaptiveConv1d(1, 2, self.kernel_size, cond_dim, frame_size=self.frame_size32, overlap_size=self.frame_size32//2, use_bias=False, padding=[self.kernel_size - 1, 0], gain_limits_db=conv_gain_limits_db, norm_p=norm_p) - self.af2 = LimitedAdaptiveConv1d(3, 2, self.kernel_size, cond_dim, frame_size=self.frame_size32, overlap_size=self.frame_size32//2, use_bias=False, padding=[self.kernel_size - 1, 0], gain_limits_db=conv_gain_limits_db, norm_p=norm_p) - self.af3 = LimitedAdaptiveConv1d(2, 1, self.kernel_size, cond_dim, frame_size=self.frame_size48, overlap_size=self.frame_size48//2, use_bias=False, padding=[self.kernel_size - 1, 0], gain_limits_db=conv_gain_limits_db, norm_p=norm_p) - - - def flop_count(self, rate=16000, verbose=False): - - frame_rate = rate / self.FRAME_SIZE16k - - # feature net - feature_net_flops = self.feature_net.flop_count(frame_rate) - af_flops = self.af1.flop_count(rate) + self.af2.flop_count(2 * rate) + self.af3.flop_count(3 * rate) - - if verbose: - print(f"feature net: {feature_net_flops / 1e6} MFLOPS") - print(f"adaptive conv: {af_flops / 1e6} MFLOPS") - - return feature_net_flops + af_flops - - def forward(self, x, features, periods, numbits, debug=False): - - if self.prenet is not None: - with torch.no_grad(): - x = self.prenet(x, features, periods, numbits) - x = self.deemph(x) - - - - periods = periods.squeeze(-1) - pitch_embedding = self.pitch_embedding(periods) - numbits_embedding = self.numbits_embedding(numbits).flatten(2) - - full_features = torch.cat((features, pitch_embedding, numbits_embedding), dim=-1) - cf = self.feature_net(full_features) - - y32 = self.upsampler.hq_2x_up(x) - - noise = self.noise_amplitude * torch.randn_like(y32) - noise = self.af_noise(noise, cf) - - y32 = self.af1(y32, cf, debug=debug) - - y32_1 = y32[:, 0:1, :] - y32_2 = self.tdshape1(y32[:, 1:2, :], cf) - y32 = torch.cat((y32_1, y32_2, noise), dim=1) - - y32 = self.af2(y32, cf, debug=debug) - - y48 = self.upsampler.interpolate_3_2(y32) - - y48_1 = y48[:, 0:1, :] - y48_2 = self.tdshape2(y48[:, 1:2, :], cf) - y48 = torch.cat((y48_1, y48_2), dim=1) - - y48 = self.af3(y48, cf, debug=debug) - - return y48 diff --git a/android/app/src/main/cpp/third_party/opus/dnn/torch/osce/models/silk_feature_net.py b/android/app/src/main/cpp/third_party/opus/dnn/torch/osce/models/silk_feature_net.py deleted file mode 100644 index ed22f52..0000000 --- a/android/app/src/main/cpp/third_party/opus/dnn/torch/osce/models/silk_feature_net.py +++ /dev/null @@ -1,86 +0,0 @@ -""" -/* Copyright (c) 2023 Amazon - Written by Jan Buethe */ -/* - Redistribution and use in source and binary forms, with or without - modification, are permitted provided that the following conditions - are met: - - - Redistributions of source code must retain the above copyright - notice, this list of conditions and the following disclaimer. - - - Redistributions in binary form must reproduce the above copyright - notice, this list of conditions and the following disclaimer in the - documentation and/or other materials provided with the distribution. - - THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS - ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT - LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR - A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER - OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, - EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, - PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR - PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF - LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING - NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS - SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. -*/ -""" - - -import torch -from torch import nn -import torch.nn.functional as F - -from utils.complexity import _conv1d_flop_count - -class SilkFeatureNet(nn.Module): - - def __init__(self, - feature_dim=47, - num_channels=256, - lookahead=False): - - super(SilkFeatureNet, self).__init__() - - self.feature_dim = feature_dim - self.num_channels = num_channels - self.lookahead = lookahead - - self.conv1 = nn.Conv1d(feature_dim, num_channels, 3) - self.conv2 = nn.Conv1d(num_channels, num_channels, 3, dilation=2) - - self.gru = nn.GRU(num_channels, num_channels, batch_first=True) - - def flop_count(self, rate=200): - count = 0 - for conv in self.conv1, self.conv2: - count += _conv1d_flop_count(conv, rate) - - count += 2 * (3 * self.gru.input_size * self.gru.hidden_size + 3 * self.gru.hidden_size * self.gru.hidden_size) * rate - - return count - - - def forward(self, features, state=None): - """ features shape: (batch_size, num_frames, feature_dim) """ - - batch_size = features.size(0) - - if state is None: - state = torch.zeros((1, batch_size, self.num_channels), device=features.device) - - - features = features.permute(0, 2, 1) - if self.lookahead: - c = torch.tanh(self.conv1(F.pad(features, [1, 1]))) - c = torch.tanh(self.conv2(F.pad(c, [2, 2]))) - else: - c = torch.tanh(self.conv1(F.pad(features, [2, 0]))) - c = torch.tanh(self.conv2(F.pad(c, [4, 0]))) - - c = c.permute(0, 2, 1) - - c, _ = self.gru(c, state) - - return c \ No newline at end of file diff --git a/android/app/src/main/cpp/third_party/opus/dnn/torch/osce/models/silk_feature_net_pl.py b/android/app/src/main/cpp/third_party/opus/dnn/torch/osce/models/silk_feature_net_pl.py deleted file mode 100644 index e6a536f..0000000 --- a/android/app/src/main/cpp/third_party/opus/dnn/torch/osce/models/silk_feature_net_pl.py +++ /dev/null @@ -1,127 +0,0 @@ -""" -/* Copyright (c) 2023 Amazon - Written by Jan Buethe */ -/* - Redistribution and use in source and binary forms, with or without - modification, are permitted provided that the following conditions - are met: - - - Redistributions of source code must retain the above copyright - notice, this list of conditions and the following disclaimer. - - - Redistributions in binary form must reproduce the above copyright - notice, this list of conditions and the following disclaimer in the - documentation and/or other materials provided with the distribution. - - THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS - ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT - LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR - A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER - OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, - EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, - PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR - PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF - LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING - NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS - SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. -*/ -""" -import sys -sys.path.append('../dnntools') -import numbers - - -import torch -from torch import nn -import torch.nn.functional as F -from torch.nn.utils import weight_norm - -from utils.complexity import _conv1d_flop_count - -from dnntools.quantization.softquant import soft_quant -from dnntools.sparsification import mark_for_sparsification - -class SilkFeatureNetPL(nn.Module): - """ feature net with partial lookahead """ - def __init__(self, - feature_dim=47, - num_channels=256, - hidden_feature_dim=64, - softquant=False, - sparsify=True, - sparsification_density=0.5, - apply_weight_norm=False): - - super(SilkFeatureNetPL, self).__init__() - - if isinstance(sparsification_density, numbers.Number): - sparsification_density = 4 * [sparsification_density] - - self.feature_dim = feature_dim - self.num_channels = num_channels - self.hidden_feature_dim = hidden_feature_dim - - norm = weight_norm if apply_weight_norm else lambda x, name=None: x - - self.conv1 = norm(nn.Conv1d(feature_dim, self.hidden_feature_dim, 1)) - self.conv2 = norm(nn.Conv1d(4 * self.hidden_feature_dim, num_channels, 2)) - self.tconv = norm(nn.ConvTranspose1d(num_channels, num_channels, 4, 4)) - self.gru = norm(norm(nn.GRU(num_channels, num_channels, batch_first=True), name='weight_hh_l0'), name='weight_ih_l0') - - if softquant: - self.conv2 = soft_quant(self.conv2) - self.tconv = soft_quant(self.tconv) - self.gru = soft_quant(self.gru, names=['weight_hh_l0', 'weight_ih_l0']) - - - if sparsify: - mark_for_sparsification(self.conv2, (sparsification_density[0], [8, 4])) - mark_for_sparsification(self.tconv, (sparsification_density[1], [8, 4])) - mark_for_sparsification( - self.gru, - { - 'W_ir' : (sparsification_density[2], [8, 4], False), - 'W_iz' : (sparsification_density[2], [8, 4], False), - 'W_in' : (sparsification_density[2], [8, 4], False), - 'W_hr' : (sparsification_density[3], [8, 4], True), - 'W_hz' : (sparsification_density[3], [8, 4], True), - 'W_hn' : (sparsification_density[3], [8, 4], True), - } - ) - - - def flop_count(self, rate=200): - count = 0 - for conv in self.conv1, self.conv2, self.tconv: - count += _conv1d_flop_count(conv, rate) - - count += 2 * (3 * self.gru.input_size * self.gru.hidden_size + 3 * self.gru.hidden_size * self.gru.hidden_size) * rate - - return count - - - def forward(self, features, state=None): - """ features shape: (batch_size, num_frames, feature_dim) """ - - batch_size = features.size(0) - num_frames = features.size(1) - - if state is None: - state = torch.zeros((1, batch_size, self.num_channels), device=features.device) - - features = features.permute(0, 2, 1) - # dimensionality reduction - c = torch.tanh(self.conv1(features)) - - # frame accumulation - c = c.permute(0, 2, 1) - c = c.reshape(batch_size, num_frames // 4, -1).permute(0, 2, 1) - c = torch.tanh(self.conv2(F.pad(c, [1, 0]))) - - # upsampling - c = torch.tanh(self.tconv(c)) - c = c.permute(0, 2, 1) - - c, _ = self.gru(c, state) - - return c \ No newline at end of file diff --git a/android/app/src/main/cpp/third_party/opus/dnn/torch/osce/requirements.txt b/android/app/src/main/cpp/third_party/opus/dnn/torch/osce/requirements.txt deleted file mode 100644 index 0a92e65..0000000 --- a/android/app/src/main/cpp/third_party/opus/dnn/torch/osce/requirements.txt +++ /dev/null @@ -1,9 +0,0 @@ -pyyaml==6.0.1 -torch==2.0.1 -numpy==1.25.2 -scipy==1.11.2 -pesq==0.0.4 -gitpython==3.1.41 -matplotlib==3.7.3 -torchaudio==2.0.2 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-audio/hi_fi_tts_v0/hi_fi_tts_v0/audio/9017_clean/13130/dartagnan01_38_dumas_0142.wav -audio/kn_in_female/knf_00254_00847846395.wav -audio/hi_fi_tts_v0/hi_fi_tts_v0/audio/92_clean/9783/auntcretesemancipation_07_hill_0199.wav -audio/xh_za/xh_za/za/xho/wavs/xho_4280_4424415518.wav diff --git a/android/app/src/main/cpp/third_party/opus/dnn/torch/osce/stndrd/evaluation/create_input_data.sh b/android/app/src/main/cpp/third_party/opus/dnn/torch/osce/stndrd/evaluation/create_input_data.sh deleted file mode 100644 index 54bacb8..0000000 --- a/android/app/src/main/cpp/third_party/opus/dnn/torch/osce/stndrd/evaluation/create_input_data.sh +++ /dev/null @@ -1,25 +0,0 @@ -#!/bin/bash - - -INPUT="dataset/LibriSpeech" -OUTPUT="testdata" -OPUSDEMO="/local/experiments/ietf_enhancement_studies/bin/opus_demo_patched" -BITRATES=( 6000 7500 ) # 9000 12000 15000 18000 24000 32000 ) - - -mkdir -p $OUTPUT - -for fn in $(find $INPUT -name "*.wav") -do - name=$(basename ${fn%*.wav}) - sox $fn -r 16000 -b 16 -e signed-integer ${OUTPUT}/tmp.raw - for br in ${BITRATES[@]} - do - folder=${OUTPUT}/"${name}_${br}.se" - echo "creating ${folder}..." - mkdir -p $folder - cp ${OUTPUT}/tmp.raw ${folder}/clean.s16 - (cd ${folder} && $OPUSDEMO voip 16000 1 $br clean.s16 noisy.s16) - done - rm -f ${OUTPUT}/tmp.raw -done diff --git a/android/app/src/main/cpp/third_party/opus/dnn/torch/osce/stndrd/evaluation/env.rc b/android/app/src/main/cpp/third_party/opus/dnn/torch/osce/stndrd/evaluation/env.rc deleted file mode 100644 index f1266b6..0000000 --- a/android/app/src/main/cpp/third_party/opus/dnn/torch/osce/stndrd/evaluation/env.rc +++ /dev/null @@ -1,7 +0,0 @@ -#!/bin/bash - -export PYTHON=/home/ubuntu/opt/miniconda3/envs/torch/bin/python -export LACE="/local/experiments/ietf_enhancement_studies/checkpoints/lace_checkpoint.pth" -export NOLACE="/local/experiments/ietf_enhancement_studies/checkpoints/nolace_checkpoint.pth" -export TESTMODEL="/local/experiments/ietf_enhancement_studies/opus/dnn/torch/osce/test_model.py" -export OPUSDEMO="/local/experiments/ietf_enhancement_studies/bin/opus_demo_patched" \ No newline at end of file diff --git a/android/app/src/main/cpp/third_party/opus/dnn/torch/osce/stndrd/evaluation/evaluate.py b/android/app/src/main/cpp/third_party/opus/dnn/torch/osce/stndrd/evaluation/evaluate.py deleted file mode 100644 index 54700db..0000000 --- a/android/app/src/main/cpp/third_party/opus/dnn/torch/osce/stndrd/evaluation/evaluate.py +++ /dev/null @@ -1,113 +0,0 @@ -""" -/* Copyright (c) 2023 Amazon - Written by Jan Buethe */ -/* - Redistribution and use in source and binary forms, with or without - modification, are permitted provided that the following conditions - are met: - - - Redistributions of source code must retain the above copyright - notice, this list of conditions and the following disclaimer. - - - Redistributions in binary form must reproduce the above copyright - notice, this list of conditions and the following disclaimer in the - documentation and/or other materials provided with the distribution. - - THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS - ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT - LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR - A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER - OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, - EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, - PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR - PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF - LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING - NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS - SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. -*/ -""" - -import os -import argparse - - -from scipy.io import wavfile -from pesq import pesq -import numpy as np -from moc import compare -from moc2 import compare as compare2 -#from warpq import compute_WAPRQ as warpq -from lace_loss_metric import compare as laceloss_compare - - -parser = argparse.ArgumentParser() -parser.add_argument('folder', type=str, help='folder with processed items') -parser.add_argument('metric', type=str, choices=['pesq', 'moc', 'moc2', 'laceloss'], help='metric to be used for evaluation') - - -def get_bitrates(folder): - with open(os.path.join(folder, 'bitrates.txt')) as f: - x = f.read() - - bitrates = [int(y) for y in x.rstrip('\n').split()] - - return bitrates - -def get_itemlist(folder): - with open(os.path.join(folder, 'items.txt')) as f: - lines = f.readlines() - - items = [x.split()[0] for x in lines] - - return items - - -def process_item(folder, item, bitrate, metric): - fs, x_clean = wavfile.read(os.path.join(folder, 'clean', f"{item}_{bitrate}_clean.wav")) - fs, x_opus = wavfile.read(os.path.join(folder, 'opus', f"{item}_{bitrate}_opus.wav")) - fs, x_lace = wavfile.read(os.path.join(folder, 'lace', f"{item}_{bitrate}_lace.wav")) - fs, x_nolace = wavfile.read(os.path.join(folder, 'nolace', f"{item}_{bitrate}_nolace.wav")) - - x_clean = x_clean.astype(np.float32) / 2**15 - x_opus = x_opus.astype(np.float32) / 2**15 - x_lace = x_lace.astype(np.float32) / 2**15 - x_nolace = x_nolace.astype(np.float32) / 2**15 - - if metric == 'pesq': - result = [pesq(fs, x_clean, x_opus), pesq(fs, x_clean, x_lace), pesq(fs, x_clean, x_nolace)] - elif metric =='moc': - result = [compare(x_clean, x_opus), compare(x_clean, x_lace), compare(x_clean, x_nolace)] - elif metric =='moc2': - result = [compare2(x_clean, x_opus), compare2(x_clean, x_lace), compare2(x_clean, x_nolace)] - # elif metric == 'warpq': - # result = [warpq(x_clean, x_opus), warpq(x_clean, x_lace), warpq(x_clean, x_nolace)] - elif metric == 'laceloss': - result = [laceloss_compare(x_clean, x_opus), laceloss_compare(x_clean, x_lace), laceloss_compare(x_clean, x_nolace)] - else: - raise ValueError(f'unknown metric {metric}') - - return result - -def process_bitrate(folder, items, bitrate, metric): - results = np.zeros((len(items), 3)) - - for i, item in enumerate(items): - results[i, :] = np.array(process_item(folder, item, bitrate, metric)) - - return results - - -if __name__ == "__main__": - args = parser.parse_args() - - items = get_itemlist(args.folder) - bitrates = get_bitrates(args.folder) - - results = dict() - for br in bitrates: - print(f"processing bitrate {br}...") - results[br] = process_bitrate(args.folder, items, br, args.metric) - - np.save(os.path.join(args.folder, f'results_{args.metric}.npy'), results) - - print("Done.") diff --git a/android/app/src/main/cpp/third_party/opus/dnn/torch/osce/stndrd/evaluation/lace_loss_metric.py b/android/app/src/main/cpp/third_party/opus/dnn/torch/osce/stndrd/evaluation/lace_loss_metric.py deleted file mode 100644 index b079058..0000000 --- a/android/app/src/main/cpp/third_party/opus/dnn/torch/osce/stndrd/evaluation/lace_loss_metric.py +++ /dev/null @@ -1,330 +0,0 @@ -""" -/* Copyright (c) 2023 Amazon - Written by Jan Buethe */ -/* - Redistribution and use in source and binary forms, with or without - modification, are permitted provided that the following conditions - are met: - - - Redistributions of source code must retain the above copyright - notice, this list of conditions and the following disclaimer. - - - Redistributions in binary form must reproduce the above copyright - notice, this list of conditions and the following disclaimer in the - documentation and/or other materials provided with the distribution. - - THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS - ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT - LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR - A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER - OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, - EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, - PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR - PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF - LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING - NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS - SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. -*/ -""" - -"""STFT-based Loss modules.""" - -import torch -import torch.nn.functional as F -from torch import nn -import numpy as np -import torchaudio - - -def get_window(win_name, win_length, *args, **kwargs): - window_dict = { - 'bartlett_window' : torch.bartlett_window, - 'blackman_window' : torch.blackman_window, - 'hamming_window' : torch.hamming_window, - 'hann_window' : torch.hann_window, - 'kaiser_window' : torch.kaiser_window - } - - if not win_name in window_dict: - raise ValueError() - - return window_dict[win_name](win_length, *args, **kwargs) - - -def stft(x, fft_size, hop_size, win_length, window): - """Perform STFT and convert to magnitude spectrogram. - Args: - x (Tensor): Input signal tensor (B, T). - fft_size (int): FFT size. - hop_size (int): Hop size. - win_length (int): Window length. - window (str): Window function type. - Returns: - Tensor: Magnitude spectrogram (B, #frames, fft_size // 2 + 1). - """ - - win = get_window(window, win_length).to(x.device) - x_stft = torch.stft(x, fft_size, hop_size, win_length, win, return_complex=True) - - - return torch.clamp(torch.abs(x_stft), min=1e-7) - -def spectral_convergence_loss(Y_true, Y_pred): - dims=list(range(1, len(Y_pred.shape))) - return torch.mean(torch.norm(torch.abs(Y_true) - torch.abs(Y_pred), p="fro", dim=dims) / (torch.norm(Y_pred, p="fro", dim=dims) + 1e-6)) - - -def log_magnitude_loss(Y_true, Y_pred): - Y_true_log_abs = torch.log(torch.abs(Y_true) + 1e-15) - Y_pred_log_abs = torch.log(torch.abs(Y_pred) + 1e-15) - - return torch.mean(torch.abs(Y_true_log_abs - Y_pred_log_abs)) - -def spectral_xcorr_loss(Y_true, Y_pred): - Y_true = Y_true.abs() - Y_pred = Y_pred.abs() - dims=list(range(1, len(Y_pred.shape))) - xcorr = torch.sum(Y_true * Y_pred, dim=dims) / torch.sqrt(torch.sum(Y_true ** 2, dim=dims) * torch.sum(Y_pred ** 2, dim=dims) + 1e-9) - - return 1 - xcorr.mean() - - - -class MRLogMelLoss(nn.Module): - def __init__(self, - fft_sizes=[512, 256, 128, 64], - overlap=0.5, - fs=16000, - n_mels=18 - ): - - self.fft_sizes = fft_sizes - self.overlap = overlap - self.fs = fs - self.n_mels = n_mels - - super().__init__() - - self.mel_specs = [] - for fft_size in fft_sizes: - hop_size = int(round(fft_size * (1 - self.overlap))) - - n_mels = self.n_mels - if fft_size < 128: - n_mels //= 2 - - self.mel_specs.append(torchaudio.transforms.MelSpectrogram(fs, fft_size, hop_length=hop_size, n_mels=n_mels)) - - for i, mel_spec in enumerate(self.mel_specs): - self.add_module(f'mel_spec_{i+1}', mel_spec) - - def forward(self, y_true, y_pred): - - loss = torch.zeros(1, device=y_true.device) - - for mel_spec in self.mel_specs: - Y_true = mel_spec(y_true) - Y_pred = mel_spec(y_pred) - loss = loss + log_magnitude_loss(Y_true, Y_pred) - - loss = loss / len(self.mel_specs) - - return loss - -def create_weight_matrix(num_bins, bins_per_band=10): - m = torch.zeros((num_bins, num_bins), dtype=torch.float32) - - r0 = bins_per_band // 2 - r1 = bins_per_band - r0 - - for i in range(num_bins): - i0 = max(i - r0, 0) - j0 = min(i + r1, num_bins) - - m[i, i0: j0] += 1 - - if i < r0: - m[i, :r0 - i] += 1 - - if i > num_bins - r1: - m[i, num_bins - r1 - i:] += 1 - - return m / bins_per_band - -def weighted_spectral_convergence(Y_true, Y_pred, w): - - # calculate sfm based weights - logY = torch.log(torch.abs(Y_true) + 1e-9) - Y = torch.abs(Y_true) - - avg_logY = torch.matmul(logY.transpose(1, 2), w) - avg_Y = torch.matmul(Y.transpose(1, 2), w) - - sfm = torch.exp(avg_logY) / (avg_Y + 1e-9) - - weight = (torch.relu(1 - sfm) ** .5).transpose(1, 2) - - loss = torch.mean( - torch.mean(weight * torch.abs(torch.abs(Y_true) - torch.abs(Y_pred)), dim=[1, 2]) - / (torch.mean( weight * torch.abs(Y_true), dim=[1, 2]) + 1e-9) - ) - - return loss - -def gen_filterbank(N, Fs=16000): - in_freq = (np.arange(N+1, dtype='float32')/N*Fs/2)[None,:] - out_freq = (np.arange(N, dtype='float32')/N*Fs/2)[:,None] - #ERB from B.C.J Moore, An Introduction to the Psychology of Hearing, 5th Ed., page 73. - ERB_N = 24.7 + .108*in_freq - delta = np.abs(in_freq-out_freq)/ERB_N - center = (delta<.5).astype('float32') - R = -12*center*delta**2 + (1-center)*(3-12*delta) - RE = 10.**(R/10.) - norm = np.sum(RE, axis=1) - RE = RE/norm[:, np.newaxis] - return torch.from_numpy(RE) - -def smooth_log_mag(Y_true, Y_pred, filterbank): - Y_true_smooth = torch.matmul(filterbank, torch.abs(Y_true)) - Y_pred_smooth = torch.matmul(filterbank, torch.abs(Y_pred)) - - loss = torch.abs( - torch.log(Y_true_smooth + 1e-9) - torch.log(Y_pred_smooth + 1e-9) - ) - - loss = loss.mean() - - return loss - -class MRSTFTLoss(nn.Module): - def __init__(self, - fft_sizes=[2048, 1024, 512, 256, 128, 64], - overlap=0.5, - window='hann_window', - fs=16000, - log_mag_weight=0, - sc_weight=0, - wsc_weight=0, - smooth_log_mag_weight=2, - sxcorr_weight=1): - super().__init__() - - self.fft_sizes = fft_sizes - self.overlap = overlap - self.window = window - self.log_mag_weight = log_mag_weight - self.sc_weight = sc_weight - self.wsc_weight = wsc_weight - self.smooth_log_mag_weight = smooth_log_mag_weight - self.sxcorr_weight = sxcorr_weight - self.fs = fs - - # weights for SFM weighted spectral convergence loss - self.wsc_weights = torch.nn.ParameterDict() - for fft_size in fft_sizes: - width = min(11, int(1000 * fft_size / self.fs + .5)) - width += width % 2 - self.wsc_weights[str(fft_size)] = torch.nn.Parameter( - create_weight_matrix(fft_size // 2 + 1, width), - requires_grad=False - ) - - # filterbanks for smooth log magnitude loss - self.filterbanks = torch.nn.ParameterDict() - for fft_size in fft_sizes: - self.filterbanks[str(fft_size)] = torch.nn.Parameter( - gen_filterbank(fft_size//2), - requires_grad=False - ) - - - def __call__(self, y_true, y_pred): - - - lm_loss = torch.zeros(1, device=y_true.device) - sc_loss = torch.zeros(1, device=y_true.device) - wsc_loss = torch.zeros(1, device=y_true.device) - slm_loss = torch.zeros(1, device=y_true.device) - sxcorr_loss = torch.zeros(1, device=y_true.device) - - for fft_size in self.fft_sizes: - hop_size = int(round(fft_size * (1 - self.overlap))) - win_size = fft_size - - Y_true = stft(y_true, fft_size, hop_size, win_size, self.window) - Y_pred = stft(y_pred, fft_size, hop_size, win_size, self.window) - - if self.log_mag_weight > 0: - lm_loss = lm_loss + log_magnitude_loss(Y_true, Y_pred) - - if self.sc_weight > 0: - sc_loss = sc_loss + spectral_convergence_loss(Y_true, Y_pred) - - if self.wsc_weight > 0: - wsc_loss = wsc_loss + weighted_spectral_convergence(Y_true, Y_pred, self.wsc_weights[str(fft_size)]) - - if self.smooth_log_mag_weight > 0: - slm_loss = slm_loss + smooth_log_mag(Y_true, Y_pred, self.filterbanks[str(fft_size)]) - - if self.sxcorr_weight > 0: - sxcorr_loss = sxcorr_loss + spectral_xcorr_loss(Y_true, Y_pred) - - - total_loss = (self.log_mag_weight * lm_loss + self.sc_weight * sc_loss - + self.wsc_weight * wsc_loss + self.smooth_log_mag_weight * slm_loss - + self.sxcorr_weight * sxcorr_loss) / len(self.fft_sizes) - - return total_loss - - -def td_l2_norm(y_true, y_pred): - dims = list(range(1, len(y_true.shape))) - - loss = torch.mean((y_true - y_pred) ** 2, dim=dims) / (torch.mean(y_pred ** 2, dim=dims) ** .5 + 1e-6) - - return loss.mean() - - -class LaceLoss(nn.Module): - def __init__(self): - super().__init__() - - - self.stftloss = MRSTFTLoss(log_mag_weight=0, sc_weight=0, wsc_weight=0, smooth_log_mag_weight=2, sxcorr_weight=1) - - - def forward(self, x, y): - specloss = self.stftloss(x, y) - phaseloss = td_l2_norm(x, y) - total_loss = (specloss + 10 * phaseloss) / 13 - - return total_loss - - def compare(self, x_ref, x_deg): - # trim items to same size - n = min(len(x_ref), len(x_deg)) - x_ref = x_ref[:n].copy() - x_deg = x_deg[:n].copy() - - # pre-emphasis - x_ref[1:] -= 0.85 * x_ref[:-1] - x_deg[1:] -= 0.85 * x_deg[:-1] - - device = next(iter(self.parameters())).device - - x = torch.from_numpy(x_ref).to(device) - y = torch.from_numpy(x_deg).to(device) - - with torch.no_grad(): - dist = 10 * self.forward(x, y) - - return dist.cpu().numpy().item() - - -lace_loss = LaceLoss() -device = 'cuda' if torch.cuda.is_available() else 'cpu' -lace_loss.to(device) - -def compare(x, y): - - return lace_loss.compare(x, y) diff --git a/android/app/src/main/cpp/third_party/opus/dnn/torch/osce/stndrd/evaluation/make_boxplots.py b/android/app/src/main/cpp/third_party/opus/dnn/torch/osce/stndrd/evaluation/make_boxplots.py deleted file mode 100644 index f7ea778..0000000 --- a/android/app/src/main/cpp/third_party/opus/dnn/torch/osce/stndrd/evaluation/make_boxplots.py +++ /dev/null @@ -1,116 +0,0 @@ -""" -/* Copyright (c) 2023 Amazon - Written by Jan Buethe */ -/* - Redistribution and use in source and binary forms, with or without - modification, are permitted provided that the following conditions - are met: - - - Redistributions of source code must retain the above copyright - notice, this list of conditions and the following disclaimer. - - - Redistributions in binary form must reproduce the above copyright - notice, this list of conditions and the following disclaimer in the - documentation and/or other materials provided with the distribution. - - THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS - ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT - LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR - A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER - OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, - EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, - PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR - PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF - LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING - NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS - SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. -*/ -""" - -import os -import argparse - -import numpy as np -import matplotlib.pyplot as plt -from prettytable import PrettyTable -from matplotlib.patches import Patch - -parser = argparse.ArgumentParser() -parser.add_argument('folder', type=str, help='path to folder with pre-calculated metrics') -parser.add_argument('--metric', choices=['pesq', 'moc', 'warpq', 'nomad', 'laceloss', 'all'], default='all', help='default: all') -parser.add_argument('--output', type=str, default=None, help='alternative output folder, default: folder') - -def load_data(folder): - data = dict() - - if os.path.isfile(os.path.join(folder, 'results_moc.npy')): - data['moc'] = np.load(os.path.join(folder, 'results_moc.npy'), allow_pickle=True).item() - - if os.path.isfile(os.path.join(folder, 'results_moc2.npy')): - data['moc2'] = np.load(os.path.join(folder, 'results_moc2.npy'), allow_pickle=True).item() - - if os.path.isfile(os.path.join(folder, 'results_pesq.npy')): - data['pesq'] = np.load(os.path.join(folder, 'results_pesq.npy'), allow_pickle=True).item() - - if os.path.isfile(os.path.join(folder, 'results_warpq.npy')): - data['warpq'] = np.load(os.path.join(folder, 'results_warpq.npy'), allow_pickle=True).item() - - if os.path.isfile(os.path.join(folder, 'results_nomad.npy')): - data['nomad'] = np.load(os.path.join(folder, 'results_nomad.npy'), allow_pickle=True).item() - - if os.path.isfile(os.path.join(folder, 'results_laceloss.npy')): - data['laceloss'] = np.load(os.path.join(folder, 'results_laceloss.npy'), allow_pickle=True).item() - - return data - -def plot_data(filename, data, title=None): - compare_dict = dict() - for br in data.keys(): - compare_dict[f'Opus {br/1000:.1f} kb/s'] = data[br][:, 0] - compare_dict[f'LACE {br/1000:.1f} kb/s'] = data[br][:, 1] - compare_dict[f'NoLACE {br/1000:.1f} kb/s'] = data[br][:, 2] - - plt.rcParams.update({ - "text.usetex": True, - "font.family": "Helvetica", - "font.size": 32 - }) - - black = '#000000' - red = '#ff5745' - blue = '#007dbc' - colors = [black, red, blue] - legend_elements = [Patch(facecolor=colors[0], label='Opus SILK'), - Patch(facecolor=colors[1], label='LACE'), - Patch(facecolor=colors[2], label='NoLACE')] - - fig, ax = plt.subplots() - fig.set_size_inches(40, 20) - bplot = ax.boxplot(compare_dict.values(), showfliers=False, notch=True, patch_artist=True) - - for i, patch in enumerate(bplot['boxes']): - patch.set_facecolor(colors[i%3]) - - ax.set_xticklabels(compare_dict.keys(), rotation=290) - - if title is not None: - ax.set_title(title) - - ax.legend(handles=legend_elements) - - fig.savefig(filename, bbox_inches='tight') - -if __name__ == "__main__": - args = parser.parse_args() - data = load_data(args.folder) - - - metrics = list(data.keys()) if args.metric == 'all' else [args.metric] - folder = args.folder if args.output is None else args.output - os.makedirs(folder, exist_ok=True) - - for metric in metrics: - print(f"Plotting data for {metric} metric...") - plot_data(os.path.join(folder, f"boxplot_{metric}.png"), data[metric], title=metric.upper()) - - print("Done.") \ No newline at end of file diff --git a/android/app/src/main/cpp/third_party/opus/dnn/torch/osce/stndrd/evaluation/make_boxplots_moctest.py b/android/app/src/main/cpp/third_party/opus/dnn/torch/osce/stndrd/evaluation/make_boxplots_moctest.py deleted file mode 100644 index ca65aba..0000000 --- a/android/app/src/main/cpp/third_party/opus/dnn/torch/osce/stndrd/evaluation/make_boxplots_moctest.py +++ /dev/null @@ -1,109 +0,0 @@ -""" -/* Copyright (c) 2023 Amazon - Written by Jan Buethe */ -/* - Redistribution and use in source and binary forms, with or without - modification, are permitted provided that the following conditions - are met: - - - Redistributions of source code must retain the above copyright - notice, this list of conditions and the following disclaimer. - - - Redistributions in binary form must reproduce the above copyright - notice, this list of conditions and the following disclaimer in the - documentation and/or other materials provided with the distribution. - - THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS - ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT - LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR - A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER - OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, - EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, - PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR - PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF - LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING - NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS - SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. -*/ -""" - -import os -import argparse - -import numpy as np -import matplotlib.pyplot as plt -from prettytable import PrettyTable -from matplotlib.patches import Patch - -parser = argparse.ArgumentParser() -parser.add_argument('folder', type=str, help='path to folder with pre-calculated metrics') -parser.add_argument('--metric', choices=['pesq', 'moc', 'warpq', 'nomad', 'laceloss', 'all'], default='all', help='default: all') -parser.add_argument('--output', type=str, default=None, help='alternative output folder, default: folder') - -def load_data(folder): - data = dict() - - if os.path.isfile(os.path.join(folder, 'results_moc.npy')): - data['moc'] = np.load(os.path.join(folder, 'results_moc.npy'), allow_pickle=True).item() - - if os.path.isfile(os.path.join(folder, 'results_pesq.npy')): - data['pesq'] = np.load(os.path.join(folder, 'results_pesq.npy'), allow_pickle=True).item() - - if os.path.isfile(os.path.join(folder, 'results_warpq.npy')): - data['warpq'] = np.load(os.path.join(folder, 'results_warpq.npy'), allow_pickle=True).item() - - if os.path.isfile(os.path.join(folder, 'results_nomad.npy')): - data['nomad'] = np.load(os.path.join(folder, 'results_nomad.npy'), allow_pickle=True).item() - - if os.path.isfile(os.path.join(folder, 'results_laceloss.npy')): - data['laceloss'] = np.load(os.path.join(folder, 'results_laceloss.npy'), allow_pickle=True).item() - - return data - -def plot_data(filename, data, title=None): - compare_dict = dict() - for br in data.keys(): - compare_dict[f'Opus {br/1000:.1f} kb/s'] = data[br][:, 0] - compare_dict[f'LACE (MOC only) {br/1000:.1f} kb/s'] = data[br][:, 1] - compare_dict[f'LACE (MOC + TD) {br/1000:.1f} kb/s'] = data[br][:, 2] - - plt.rcParams.update({ - "text.usetex": True, - "font.family": "Helvetica", - "font.size": 32 - }) - colors = ['pink', 'lightblue', 'lightgreen'] - legend_elements = [Patch(facecolor=colors[0], label='Opus SILK'), - Patch(facecolor=colors[1], label='MOC loss only'), - Patch(facecolor=colors[2], label='MOC + TD loss')] - - fig, ax = plt.subplots() - fig.set_size_inches(40, 20) - bplot = ax.boxplot(compare_dict.values(), showfliers=False, notch=True, patch_artist=True) - - for i, patch in enumerate(bplot['boxes']): - patch.set_facecolor(colors[i%3]) - - ax.set_xticklabels(compare_dict.keys(), rotation=290) - - if title is not None: - ax.set_title(title) - - ax.legend(handles=legend_elements) - - fig.savefig(filename, bbox_inches='tight') - -if __name__ == "__main__": - args = parser.parse_args() - data = load_data(args.folder) - - - metrics = list(data.keys()) if args.metric == 'all' else [args.metric] - folder = args.folder if args.output is None else args.output - os.makedirs(folder, exist_ok=True) - - for metric in metrics: - print(f"Plotting data for {metric} metric...") - plot_data(os.path.join(folder, f"boxplot_{metric}.png"), data[metric], title=metric.upper()) - - print("Done.") \ No newline at end of file diff --git a/android/app/src/main/cpp/third_party/opus/dnn/torch/osce/stndrd/evaluation/make_tables.py b/android/app/src/main/cpp/third_party/opus/dnn/torch/osce/stndrd/evaluation/make_tables.py deleted file mode 100644 index 5608012..0000000 --- a/android/app/src/main/cpp/third_party/opus/dnn/torch/osce/stndrd/evaluation/make_tables.py +++ /dev/null @@ -1,124 +0,0 @@ -""" -/* Copyright (c) 2023 Amazon - Written by Jan Buethe */ -/* - Redistribution and use in source and binary forms, with or without - modification, are permitted provided that the following conditions - are met: - - - Redistributions of source code must retain the above copyright - notice, this list of conditions and the following disclaimer. - - - Redistributions in binary form must reproduce the above copyright - notice, this list of conditions and the following disclaimer in the - documentation and/or other materials provided with the distribution. - - THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS - ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT - LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR - A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER - OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, - EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, - PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR - PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF - LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING - NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS - SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. -*/ -""" - -import os -import argparse - -import numpy as np -import matplotlib.pyplot as plt -from prettytable import PrettyTable -from matplotlib.patches import Patch - -parser = argparse.ArgumentParser() -parser.add_argument('folder', type=str, help='path to folder with pre-calculated metrics') -parser.add_argument('--metric', choices=['pesq', 'moc', 'warpq', 'nomad', 'laceloss', 'all'], default='all', help='default: all') -parser.add_argument('--output', type=str, default=None, help='alternative output folder, default: folder') - -def load_data(folder): - data = dict() - - if os.path.isfile(os.path.join(folder, 'results_moc.npy')): - data['moc'] = np.load(os.path.join(folder, 'results_moc.npy'), allow_pickle=True).item() - - if os.path.isfile(os.path.join(folder, 'results_moc2.npy')): - data['moc2'] = np.load(os.path.join(folder, 'results_moc2.npy'), allow_pickle=True).item() - - if os.path.isfile(os.path.join(folder, 'results_pesq.npy')): - data['pesq'] = np.load(os.path.join(folder, 'results_pesq.npy'), allow_pickle=True).item() - - if os.path.isfile(os.path.join(folder, 'results_warpq.npy')): - data['warpq'] = np.load(os.path.join(folder, 'results_warpq.npy'), allow_pickle=True).item() - - if os.path.isfile(os.path.join(folder, 'results_nomad.npy')): - data['nomad'] = np.load(os.path.join(folder, 'results_nomad.npy'), allow_pickle=True).item() - - if os.path.isfile(os.path.join(folder, 'results_laceloss.npy')): - data['laceloss'] = np.load(os.path.join(folder, 'results_laceloss.npy'), allow_pickle=True).item() - - return data - -def make_table(filename, data, title=None): - - # mean values - tbl = PrettyTable() - tbl.field_names = ['bitrate (bps)', 'Opus', 'LACE', 'NoLACE'] - for br in data.keys(): - opus = data[br][:, 0] - lace = data[br][:, 1] - nolace = data[br][:, 2] - tbl.add_row([br, f"{float(opus.mean()):.3f} ({float(opus.std()):.2f})", f"{float(lace.mean()):.3f} ({float(lace.std()):.2f})", f"{float(nolace.mean()):.3f} ({float(nolace.std()):.2f})"]) - - with open(filename + ".txt", "w") as f: - f.write(str(tbl)) - - with open(filename + ".html", "w") as f: - f.write(tbl.get_html_string()) - - with open(filename + ".csv", "w") as f: - f.write(tbl.get_csv_string()) - - print(tbl) - - -def make_diff_table(filename, data, title=None): - - # mean values - tbl = PrettyTable() - tbl.field_names = ['bitrate (bps)', 'LACE - Opus', 'NoLACE - Opus'] - for br in data.keys(): - opus = data[br][:, 0] - lace = data[br][:, 1] - opus - nolace = data[br][:, 2] - opus - tbl.add_row([br, f"{float(lace.mean()):.3f} ({float(lace.std()):.2f})", f"{float(nolace.mean()):.3f} ({float(nolace.std()):.2f})"]) - - with open(filename + ".txt", "w") as f: - f.write(str(tbl)) - - with open(filename + ".html", "w") as f: - f.write(tbl.get_html_string()) - - with open(filename + ".csv", "w") as f: - f.write(tbl.get_csv_string()) - - print(tbl) - -if __name__ == "__main__": - args = parser.parse_args() - data = load_data(args.folder) - - metrics = list(data.keys()) if args.metric == 'all' else [args.metric] - folder = args.folder if args.output is None else args.output - os.makedirs(folder, exist_ok=True) - - for metric in metrics: - print(f"Plotting data for {metric} metric...") - make_table(os.path.join(folder, f"table_{metric}"), data[metric]) - make_diff_table(os.path.join(folder, f"table_diff_{metric}"), data[metric]) - - print("Done.") \ No newline at end of file diff --git a/android/app/src/main/cpp/third_party/opus/dnn/torch/osce/stndrd/evaluation/make_tables_moctest.py b/android/app/src/main/cpp/third_party/opus/dnn/torch/osce/stndrd/evaluation/make_tables_moctest.py deleted file mode 100644 index 3771806..0000000 --- a/android/app/src/main/cpp/third_party/opus/dnn/torch/osce/stndrd/evaluation/make_tables_moctest.py +++ /dev/null @@ -1,121 +0,0 @@ -""" -/* Copyright (c) 2023 Amazon - Written by Jan Buethe */ -/* - Redistribution and use in source and binary forms, with or without - modification, are permitted provided that the following conditions - are met: - - - Redistributions of source code must retain the above copyright - notice, this list of conditions and the following disclaimer. - - - Redistributions in binary form must reproduce the above copyright - notice, this list of conditions and the following disclaimer in the - documentation and/or other materials provided with the distribution. - - THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS - ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT - LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR - A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER - OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, - EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, - PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR - PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF - LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING - NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS - SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. -*/ -""" - -import os -import argparse - -import numpy as np -import matplotlib.pyplot as plt -from prettytable import PrettyTable -from matplotlib.patches import Patch - -parser = argparse.ArgumentParser() -parser.add_argument('folder', type=str, help='path to folder with pre-calculated metrics') -parser.add_argument('--metric', choices=['pesq', 'moc', 'warpq', 'nomad', 'laceloss', 'all'], default='all', help='default: all') -parser.add_argument('--output', type=str, default=None, help='alternative output folder, default: folder') - -def load_data(folder): - data = dict() - - if os.path.isfile(os.path.join(folder, 'results_moc.npy')): - data['moc'] = np.load(os.path.join(folder, 'results_moc.npy'), allow_pickle=True).item() - - if os.path.isfile(os.path.join(folder, 'results_pesq.npy')): - data['pesq'] = np.load(os.path.join(folder, 'results_pesq.npy'), allow_pickle=True).item() - - if os.path.isfile(os.path.join(folder, 'results_warpq.npy')): - data['warpq'] = np.load(os.path.join(folder, 'results_warpq.npy'), allow_pickle=True).item() - - if os.path.isfile(os.path.join(folder, 'results_nomad.npy')): - data['nomad'] = np.load(os.path.join(folder, 'results_nomad.npy'), allow_pickle=True).item() - - if os.path.isfile(os.path.join(folder, 'results_laceloss.npy')): - data['laceloss'] = np.load(os.path.join(folder, 'results_laceloss.npy'), allow_pickle=True).item() - - return data - -def make_table(filename, data, title=None): - - # mean values - tbl = PrettyTable() - tbl.field_names = ['bitrate (bps)', 'Opus', 'LACE', 'NoLACE'] - for br in data.keys(): - opus = data[br][:, 0] - lace = data[br][:, 1] - nolace = data[br][:, 2] - tbl.add_row([br, f"{float(opus.mean()):.3f} ({float(opus.std()):.2f})", f"{float(lace.mean()):.3f} ({float(lace.std()):.2f})", f"{float(nolace.mean()):.3f} ({float(nolace.std()):.2f})"]) - - with open(filename + ".txt", "w") as f: - f.write(str(tbl)) - - with open(filename + ".html", "w") as f: - f.write(tbl.get_html_string()) - - with open(filename + ".csv", "w") as f: - f.write(tbl.get_csv_string()) - - print(tbl) - - -def make_diff_table(filename, data, title=None): - - # mean values - tbl = PrettyTable() - tbl.field_names = ['bitrate (bps)', 'LACE - Opus', 'NoLACE - Opus'] - for br in data.keys(): - opus = data[br][:, 0] - lace = data[br][:, 1] - opus - nolace = data[br][:, 2] - opus - tbl.add_row([br, f"{float(lace.mean()):.3f} ({float(lace.std()):.2f})", f"{float(nolace.mean()):.3f} ({float(nolace.std()):.2f})"]) - - with open(filename + ".txt", "w") as f: - f.write(str(tbl)) - - with open(filename + ".html", "w") as f: - f.write(tbl.get_html_string()) - - with open(filename + ".csv", "w") as f: - f.write(tbl.get_csv_string()) - - print(tbl) - -if __name__ == "__main__": - args = parser.parse_args() - data = load_data(args.folder) - - metrics = list(data.keys()) if args.metric == 'all' else [args.metric] - folder = args.folder if args.output is None else args.output - os.makedirs(folder, exist_ok=True) - - for metric in metrics: - print(f"Plotting data for {metric} metric...") - make_table(os.path.join(folder, f"table_{metric}"), data[metric]) - make_diff_table(os.path.join(folder, f"table_diff_{metric}"), data[metric]) - - print("Done.") \ No newline at end of file diff --git a/android/app/src/main/cpp/third_party/opus/dnn/torch/osce/stndrd/evaluation/moc.py b/android/app/src/main/cpp/third_party/opus/dnn/torch/osce/stndrd/evaluation/moc.py deleted file mode 100644 index bf004de..0000000 --- a/android/app/src/main/cpp/third_party/opus/dnn/torch/osce/stndrd/evaluation/moc.py +++ /dev/null @@ -1,182 +0,0 @@ -""" -/* Copyright (c) 2023 Amazon - Written by Jan Buethe */ -/* - Redistribution and use in source and binary forms, with or without - modification, are permitted provided that the following conditions - are met: - - - Redistributions of source code must retain the above copyright - notice, this list of conditions and the following disclaimer. - - - Redistributions in binary form must reproduce the above copyright - notice, this list of conditions and the following disclaimer in the - documentation and/or other materials provided with the distribution. - - THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS - ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT - LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR - A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER - OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, - EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, - PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR - PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF - LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING - NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS - SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. -*/ -""" - -import numpy as np -import scipy.signal - -def compute_vad_mask(x, fs, stop_db=-70): - - frame_length = (fs + 49) // 50 - x = x[: frame_length * (len(x) // frame_length)] - - frames = x.reshape(-1, frame_length) - frame_energy = np.sum(frames ** 2, axis=1) - frame_energy_smooth = np.convolve(frame_energy, np.ones(5) / 5, mode='same') - - max_threshold = frame_energy.max() * 10 ** (stop_db/20) - vactive = np.ones_like(frames) - vactive[frame_energy_smooth < max_threshold, :] = 0 - vactive = vactive.reshape(-1) - - filter = np.sin(np.arange(frame_length) * np.pi / (frame_length - 1)) - filter = filter / filter.sum() - - mask = np.convolve(vactive, filter, mode='same') - - return x, mask - -def convert_mask(mask, num_frames, frame_size=160, hop_size=40): - num_samples = frame_size + (num_frames - 1) * hop_size - if len(mask) < num_samples: - mask = np.concatenate((mask, np.zeros(num_samples - len(mask))), dtype=mask.dtype) - else: - mask = mask[:num_samples] - - new_mask = np.array([np.mean(mask[i*hop_size : i*hop_size + frame_size]) for i in range(num_frames)]) - - return new_mask - -def power_spectrum(x, window_size=160, hop_size=40, window='hamming'): - num_spectra = (len(x) - window_size - hop_size) // hop_size - window = scipy.signal.get_window(window, window_size) - N = window_size // 2 - - frames = np.concatenate([x[np.newaxis, i * hop_size : i * hop_size + window_size] for i in range(num_spectra)]) * window - psd = np.abs(np.fft.fft(frames, axis=1)[:, :N + 1]) ** 2 - - return psd - - -def frequency_mask(num_bands, up_factor, down_factor): - - up_mask = np.zeros((num_bands, num_bands)) - down_mask = np.zeros((num_bands, num_bands)) - - for i in range(num_bands): - up_mask[i, : i + 1] = up_factor ** np.arange(i, -1, -1) - down_mask[i, i :] = down_factor ** np.arange(num_bands - i) - - return down_mask @ up_mask - - -def rect_fb(band_limits, num_bins=None): - num_bands = len(band_limits) - 1 - if num_bins is None: - num_bins = band_limits[-1] - - fb = np.zeros((num_bands, num_bins)) - for i in range(num_bands): - fb[i, band_limits[i]:band_limits[i+1]] = 1 - - return fb - - -def compare(x, y, apply_vad=False): - """ Modified version of opus_compare for 16 kHz mono signals - - Args: - x (np.ndarray): reference input signal scaled to [-1, 1] - y (np.ndarray): test signal scaled to [-1, 1] - - Returns: - float: perceptually weighted error - """ - # filter bank: bark scale with minimum-2-bin bands and cutoff at 7.5 kHz - band_limits = [0, 2, 4, 6, 7, 9, 11, 13, 15, 18, 22, 26, 31, 36, 43, 51, 60, 75] - num_bands = len(band_limits) - 1 - fb = rect_fb(band_limits, num_bins=81) - - # trim samples to same size - num_samples = min(len(x), len(y)) - x = x[:num_samples] * 2**15 - y = y[:num_samples] * 2**15 - - psd_x = power_spectrum(x) + 100000 - psd_y = power_spectrum(y) + 100000 - - num_frames = psd_x.shape[0] - - # average band energies - be_x = (psd_x @ fb.T) / np.sum(fb, axis=1) - - # frequecy masking - f_mask = frequency_mask(num_bands, 0.1, 0.03) - mask_x = be_x @ f_mask.T - - # temporal masking - for i in range(1, num_frames): - mask_x[i, :] += 0.5 * mask_x[i-1, :] - - # apply mask - masked_psd_x = psd_x + 0.1 * (mask_x @ fb) - masked_psd_y = psd_y + 0.1 * (mask_x @ fb) - - # 2-frame average - masked_psd_x = masked_psd_x[1:] + masked_psd_x[:-1] - masked_psd_y = masked_psd_y[1:] + masked_psd_y[:-1] - - # distortion metric - re = masked_psd_y / masked_psd_x - im = np.log(re) ** 2 - Eb = ((im @ fb.T) / np.sum(fb, axis=1)) - Ef = np.mean(Eb , axis=1) - - if apply_vad: - _, mask = compute_vad_mask(x, 16000) - mask = convert_mask(mask, Ef.shape[0]) - else: - mask = np.ones_like(Ef) - - err = np.mean(np.abs(Ef[mask > 1e-6]) ** 3) ** (1/6) - - return float(err) - -if __name__ == "__main__": - import argparse - from scipy.io import wavfile - - parser = argparse.ArgumentParser() - parser.add_argument('ref', type=str, help='reference wav file') - parser.add_argument('deg', type=str, help='degraded wav file') - parser.add_argument('--apply-vad', action='store_true') - args = parser.parse_args() - - - fs1, x = wavfile.read(args.ref) - fs2, y = wavfile.read(args.deg) - - if max(fs1, fs2) != 16000: - raise ValueError('error: encountered sampling frequency diffrent from 16kHz') - - x = x.astype(np.float32) / 2**15 - y = y.astype(np.float32) / 2**15 - - err = compare(x, y, apply_vad=args.apply_vad) - - print(f"MOC: {err}") diff --git a/android/app/src/main/cpp/third_party/opus/dnn/torch/osce/stndrd/evaluation/moc2.py b/android/app/src/main/cpp/third_party/opus/dnn/torch/osce/stndrd/evaluation/moc2.py deleted file mode 100644 index 7e155f0..0000000 --- a/android/app/src/main/cpp/third_party/opus/dnn/torch/osce/stndrd/evaluation/moc2.py +++ /dev/null @@ -1,190 +0,0 @@ -""" -/* Copyright (c) 2023 Amazon - Written by Jan Buethe */ -/* - Redistribution and use in source and binary forms, with or without - modification, are permitted provided that the following conditions - are met: - - - Redistributions of source code must retain the above copyright - notice, this list of conditions and the following disclaimer. - - - Redistributions in binary form must reproduce the above copyright - notice, this list of conditions and the following disclaimer in the - documentation and/or other materials provided with the distribution. - - THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS - ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT - LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR - A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER - OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, - EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, - PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR - PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF - LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING - NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS - SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. -*/ -""" - -import numpy as np -import scipy.signal - -def compute_vad_mask(x, fs, stop_db=-70): - - frame_length = (fs + 49) // 50 - x = x[: frame_length * (len(x) // frame_length)] - - frames = x.reshape(-1, frame_length) - frame_energy = np.sum(frames ** 2, axis=1) - frame_energy_smooth = np.convolve(frame_energy, np.ones(5) / 5, mode='same') - - max_threshold = frame_energy.max() * 10 ** (stop_db/20) - vactive = np.ones_like(frames) - vactive[frame_energy_smooth < max_threshold, :] = 0 - vactive = vactive.reshape(-1) - - filter = np.sin(np.arange(frame_length) * np.pi / (frame_length - 1)) - filter = filter / filter.sum() - - mask = np.convolve(vactive, filter, mode='same') - - return x, mask - -def convert_mask(mask, num_frames, frame_size=160, hop_size=40): - num_samples = frame_size + (num_frames - 1) * hop_size - if len(mask) < num_samples: - mask = np.concatenate((mask, np.zeros(num_samples - len(mask))), dtype=mask.dtype) - else: - mask = mask[:num_samples] - - new_mask = np.array([np.mean(mask[i*hop_size : i*hop_size + frame_size]) for i in range(num_frames)]) - - return new_mask - -def power_spectrum(x, window_size=160, hop_size=40, window='hamming'): - num_spectra = (len(x) - window_size - hop_size) // hop_size - window = scipy.signal.get_window(window, window_size) - N = window_size // 2 - - frames = np.concatenate([x[np.newaxis, i * hop_size : i * hop_size + window_size] for i in range(num_spectra)]) * window - psd = np.abs(np.fft.fft(frames, axis=1)[:, :N + 1]) ** 2 - - return psd - - -def frequency_mask(num_bands, up_factor, down_factor): - - up_mask = np.zeros((num_bands, num_bands)) - down_mask = np.zeros((num_bands, num_bands)) - - for i in range(num_bands): - up_mask[i, : i + 1] = up_factor ** np.arange(i, -1, -1) - down_mask[i, i :] = down_factor ** np.arange(num_bands - i) - - return down_mask @ up_mask - - -def rect_fb(band_limits, num_bins=None): - num_bands = len(band_limits) - 1 - if num_bins is None: - num_bins = band_limits[-1] - - fb = np.zeros((num_bands, num_bins)) - for i in range(num_bands): - fb[i, band_limits[i]:band_limits[i+1]] = 1 - - return fb - - -def _compare(x, y, apply_vad=False, factor=1): - """ Modified version of opus_compare for 16 kHz mono signals - - Args: - x (np.ndarray): reference input signal scaled to [-1, 1] - y (np.ndarray): test signal scaled to [-1, 1] - - Returns: - float: perceptually weighted error - """ - # filter bank: bark scale with minimum-2-bin bands and cutoff at 7.5 kHz - band_limits = [factor * b for b in [0, 2, 4, 6, 7, 9, 11, 13, 15, 18, 22, 26, 31, 36, 43, 51, 60, 75]] - window_size = factor * 160 - hop_size = factor * 40 - num_bins = window_size // 2 + 1 - num_bands = len(band_limits) - 1 - fb = rect_fb(band_limits, num_bins=num_bins) - - # trim samples to same size - num_samples = min(len(x), len(y)) - x = x[:num_samples].copy() * 2**15 - y = y[:num_samples].copy() * 2**15 - - psd_x = power_spectrum(x, window_size=window_size, hop_size=hop_size) + 100000 - psd_y = power_spectrum(y, window_size=window_size, hop_size=hop_size) + 100000 - - num_frames = psd_x.shape[0] - - # average band energies - be_x = (psd_x @ fb.T) / np.sum(fb, axis=1) - - # frequecy masking - f_mask = frequency_mask(num_bands, 0.1, 0.03) - mask_x = be_x @ f_mask.T - - # temporal masking - for i in range(1, num_frames): - mask_x[i, :] += (0.5 ** factor) * mask_x[i-1, :] - - # apply mask - masked_psd_x = psd_x + 0.1 * (mask_x @ fb) - masked_psd_y = psd_y + 0.1 * (mask_x @ fb) - - # 2-frame average - masked_psd_x = masked_psd_x[1:] + masked_psd_x[:-1] - masked_psd_y = masked_psd_y[1:] + masked_psd_y[:-1] - - # distortion metric - re = masked_psd_y / masked_psd_x - #im = re - np.log(re) - 1 - im = np.log(re) ** 2 - Eb = ((im @ fb.T) / np.sum(fb, axis=1)) - Ef = np.mean(Eb ** 1, axis=1) - - if apply_vad: - _, mask = compute_vad_mask(x, 16000) - mask = convert_mask(mask, Ef.shape[0]) - else: - mask = np.ones_like(Ef) - - err = np.mean(np.abs(Ef[mask > 1e-6]) ** 3) ** (1/6) - - return float(err) - -def compare(x, y, apply_vad=False): - err = np.linalg.norm([_compare(x, y, apply_vad=apply_vad, factor=1)], ord=2) - return err - -if __name__ == "__main__": - import argparse - from scipy.io import wavfile - - parser = argparse.ArgumentParser() - parser.add_argument('ref', type=str, help='reference wav file') - parser.add_argument('deg', type=str, help='degraded wav file') - parser.add_argument('--apply-vad', action='store_true') - args = parser.parse_args() - - - fs1, x = wavfile.read(args.ref) - fs2, y = wavfile.read(args.deg) - - if max(fs1, fs2) != 16000: - raise ValueError('error: encountered sampling frequency diffrent from 16kHz') - - x = x.astype(np.float32) / 2**15 - y = y.astype(np.float32) / 2**15 - - err = compare(x, y, apply_vad=args.apply_vad) - - print(f"MOC: {err}") diff --git a/android/app/src/main/cpp/third_party/opus/dnn/torch/osce/stndrd/evaluation/process_dataset.sh b/android/app/src/main/cpp/third_party/opus/dnn/torch/osce/stndrd/evaluation/process_dataset.sh deleted file mode 100644 index a490da9..0000000 --- a/android/app/src/main/cpp/third_party/opus/dnn/torch/osce/stndrd/evaluation/process_dataset.sh +++ /dev/null @@ -1,98 +0,0 @@ -#!/bin/bash - -if [ ! -f "$PYTHON" ] -then - echo "PYTHON variable does not link to a file. Please point it to your python executable." - exit 1 -fi - -if [ ! -f "$TESTMODEL" ] -then - echo "TESTMODEL variable does not link to a file. Please point it to your copy of test_model.py" - exit 1 -fi - -if [ ! -f "$OPUSDEMO" ] -then - echo "OPUSDEMO variable does not link to a file. Please point it to your patched version of opus_demo." - exit 1 -fi - -if [ ! -f "$LACE" ] -then - echo "LACE variable does not link to a file. Please point it to your copy of the LACE checkpoint." - exit 1 -fi - -if [ ! -f "$NOLACE" ] -then - echo "LACE variable does not link to a file. Please point it to your copy of the NOLACE checkpoint." - exit 1 -fi - -case $# in - 2) INPUT=$1; OUTPUT=$2;; - *) echo "process_dataset.sh "; exit 1;; -esac - -if [ -d $OUTPUT ] -then - echo "output folder $OUTPUT exists, aborting..." - exit 1 -fi - -mkdir -p $OUTPUT - -if [ "$BITRATES" == "" ] -then - BITRATES=( 6000 7500 9000 12000 15000 18000 24000 32000 ) - echo "BITRATES variable not defined. Proceeding with default bitrates ${BITRATES[@]}." -fi - - -echo "LACE=${LACE}" > ${OUTPUT}/info.txt -echo "NOLACE=${NOLACE}" >> ${OUTPUT}/info.txt - -ITEMFILE=${OUTPUT}/items.txt -BITRATEFILE=${OUTPUT}/bitrates.txt - -FPROCESSING=${OUTPUT}/processing -FCLEAN=${OUTPUT}/clean -FOPUS=${OUTPUT}/opus -FLACE=${OUTPUT}/lace -FNOLACE=${OUTPUT}/nolace - -mkdir -p $FPROCESSING $FCLEAN $FOPUS $FLACE $FNOLACE - -echo "${BITRATES[@]}" > $BITRATEFILE - -for fn in $(find $INPUT -type f -name "*.wav") -do - UUID=$(uuid) - echo "$UUID $fn" >> $ITEMFILE - PIDS=( ) - for br in ${BITRATES[@]} - do - # run opus - pfolder=${FPROCESSING}/${UUID}_${br} - mkdir -p $pfolder - sox $fn -c 1 -r 16000 -b 16 -e signed-integer $pfolder/clean.s16 - (cd ${pfolder} && $OPUSDEMO voip 16000 1 $br clean.s16 noisy.s16) - - # copy clean and opus - sox -c 1 -r 16000 -b 16 -e signed-integer $pfolder/clean.s16 $FCLEAN/${UUID}_${br}_clean.wav - sox -c 1 -r 16000 -b 16 -e signed-integer $pfolder/noisy.s16 $FOPUS/${UUID}_${br}_opus.wav - - # run LACE - $PYTHON $TESTMODEL $pfolder $LACE $FLACE/${UUID}_${br}_lace.wav & - PIDS+=( "$!" ) - - # run NoLACE - $PYTHON $TESTMODEL $pfolder $NOLACE $FNOLACE/${UUID}_${br}_nolace.wav & - PIDS+=( "$!" ) - done - for pid in ${PIDS[@]} - do - wait $pid - done -done diff --git a/android/app/src/main/cpp/third_party/opus/dnn/torch/osce/stndrd/evaluation/run_nomad.py b/android/app/src/main/cpp/third_party/opus/dnn/torch/osce/stndrd/evaluation/run_nomad.py deleted file mode 100644 index 0267bc9..0000000 --- a/android/app/src/main/cpp/third_party/opus/dnn/torch/osce/stndrd/evaluation/run_nomad.py +++ /dev/null @@ -1,138 +0,0 @@ -""" -/* Copyright (c) 2023 Amazon - Written by Jan Buethe */ -/* - Redistribution and use in source and binary forms, with or without - modification, are permitted provided that the following conditions - are met: - - - Redistributions of source code must retain the above copyright - notice, this list of conditions and the following disclaimer. - - - Redistributions in binary form must reproduce the above copyright - notice, this list of conditions and the following disclaimer in the - documentation and/or other materials provided with the distribution. - - THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS - ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT - LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR - A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER - OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, - EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, - PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR - PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF - LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING - NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS - SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. -*/ -""" - -import os -import argparse -import tempfile -import shutil - -import pandas as pd -from scipy.spatial.distance import cdist -from scipy.io import wavfile -import numpy as np - -from nomad_audio.nomad import Nomad - - -parser = argparse.ArgumentParser() -parser.add_argument('folder', type=str, help='folder with processed items') -parser.add_argument('--full-reference', action='store_true', help='use NOMAD as full-reference metric') -parser.add_argument('--device', type=str, default=None, help='device for Nomad') - - -def get_bitrates(folder): - with open(os.path.join(folder, 'bitrates.txt')) as f: - x = f.read() - - bitrates = [int(y) for y in x.rstrip('\n').split()] - - return bitrates - -def get_itemlist(folder): - with open(os.path.join(folder, 'items.txt')) as f: - lines = f.readlines() - - items = [x.split()[0] for x in lines] - - return items - - -def nomad_wrapper(ref_folder, deg_folder, full_reference=False, ref_embeddings=None, device=None): - model = Nomad(device=device) - if not full_reference: - results = model.predict(nmr=ref_folder, deg=deg_folder)[0].to_dict()['NOMAD'] - return results, None - else: - if ref_embeddings is None: - print(f"Computing reference embeddings from {ref_folder}") - ref_data = pd.DataFrame(sorted(os.listdir(ref_folder))) - ref_data.columns = ['filename'] - ref_data['filename'] = [os.path.join(ref_folder, x) for x in ref_data['filename']] - ref_embeddings = model.get_embeddings_csv(model.model, ref_data).set_index('filename') - - print(f"Computing degraded embeddings from {deg_folder}") - deg_data = pd.DataFrame(sorted(os.listdir(deg_folder))) - deg_data.columns = ['filename'] - deg_data['filename'] = [os.path.join(deg_folder, x) for x in deg_data['filename']] - deg_embeddings = model.get_embeddings_csv(model.model, deg_data).set_index('filename') - - dist = np.diag(cdist(ref_embeddings, deg_embeddings)) # wasteful - test_files = [x.split('/')[-1].split('.')[0] for x in deg_embeddings.index] - - results = dict(zip(test_files, dist)) - - return results, ref_embeddings - - - - -def nomad_process_all(folder, full_reference=False, device=None): - bitrates = get_bitrates(folder) - items = get_itemlist(folder) - with tempfile.TemporaryDirectory() as dir: - cleandir = os.path.join(dir, 'clean') - opusdir = os.path.join(dir, 'opus') - lacedir = os.path.join(dir, 'lace') - nolacedir = os.path.join(dir, 'nolace') - - # prepare files - for d in [cleandir, opusdir, lacedir, nolacedir]: os.makedirs(d) - for br in bitrates: - for item in items: - for cond in ['clean', 'opus', 'lace', 'nolace']: - shutil.copyfile(os.path.join(folder, cond, f"{item}_{br}_{cond}.wav"), os.path.join(dir, cond, f"{item}_{br}.wav")) - - nomad_opus, ref_embeddings = nomad_wrapper(cleandir, opusdir, full_reference=full_reference, ref_embeddings=None) - nomad_lace, ref_embeddings = nomad_wrapper(cleandir, lacedir, full_reference=full_reference, ref_embeddings=ref_embeddings) - nomad_nolace, ref_embeddings = nomad_wrapper(cleandir, nolacedir, full_reference=full_reference, ref_embeddings=ref_embeddings) - - results = dict() - for br in bitrates: - results[br] = np.zeros((len(items), 3)) - for i, item in enumerate(items): - key = f"{item}_{br}" - results[br][i, 0] = nomad_opus[key] - results[br][i, 1] = nomad_lace[key] - results[br][i, 2] = nomad_nolace[key] - - return results - - - -if __name__ == "__main__": - args = parser.parse_args() - - items = get_itemlist(args.folder) - bitrates = get_bitrates(args.folder) - - results = nomad_process_all(args.folder, full_reference=args.full_reference, device=args.device) - - np.save(os.path.join(args.folder, f'results_nomad.npy'), results) - - print("Done.") diff --git a/android/app/src/main/cpp/third_party/opus/dnn/torch/osce/stndrd/presentation/endoscopy.py b/android/app/src/main/cpp/third_party/opus/dnn/torch/osce/stndrd/presentation/endoscopy.py deleted file mode 100644 index 05dd475..0000000 --- a/android/app/src/main/cpp/third_party/opus/dnn/torch/osce/stndrd/presentation/endoscopy.py +++ /dev/null @@ -1,205 +0,0 @@ -""" module for inspecting models during inference """ - -import os - -import yaml -import matplotlib.pyplot as plt -import matplotlib.animation as animation - -import torch -import numpy as np - -# stores entries {key : {'fid' : fid, 'fs' : fs, 'dim' : dim, 'dtype' : dtype}} -_state = dict() -_folder = 'endoscopy' - -def get_gru_gates(gru, input, state): - hidden_size = gru.hidden_size - - direct = torch.matmul(gru.weight_ih_l0, input.squeeze()) - recurrent = torch.matmul(gru.weight_hh_l0, state.squeeze()) - - # reset gate - start, stop = 0 * hidden_size, 1 * hidden_size - reset_gate = torch.sigmoid(direct[start : stop] + gru.bias_ih_l0[start : stop] + recurrent[start : stop] + gru.bias_hh_l0[start : stop]) - - # update gate - start, stop = 1 * hidden_size, 2 * hidden_size - update_gate = torch.sigmoid(direct[start : stop] + gru.bias_ih_l0[start : stop] + recurrent[start : stop] + gru.bias_hh_l0[start : stop]) - - # new gate - start, stop = 2 * hidden_size, 3 * hidden_size - new_gate = torch.tanh(direct[start : stop] + gru.bias_ih_l0[start : stop] + reset_gate * (recurrent[start : stop] + gru.bias_hh_l0[start : stop])) - - return {'reset_gate' : reset_gate, 'update_gate' : update_gate, 'new_gate' : new_gate} - - -def init(folder='endoscopy'): - """ sets up output folder for endoscopy data """ - - global _folder - _folder = folder - - if not os.path.exists(folder): - os.makedirs(folder) - else: - print(f"warning: endoscopy folder {folder} exists. Content may be lost or inconsistent results may occur.") - -def write_data(key, data, fs): - """ appends data to previous data written under key """ - - global _state - - # convert to numpy if torch.Tensor is given - if isinstance(data, torch.Tensor): - data = data.detach().numpy() - - if not key in _state: - _state[key] = { - 'fid' : open(os.path.join(_folder, key + '.bin'), 'wb'), - 'fs' : fs, - 'dim' : tuple(data.shape), - 'dtype' : str(data.dtype) - } - - with open(os.path.join(_folder, key + '.yml'), 'w') as f: - f.write(yaml.dump({'fs' : fs, 'dim' : tuple(data.shape), 'dtype' : str(data.dtype).split('.')[-1]})) - else: - if _state[key]['fs'] != fs: - raise ValueError(f"fs changed for key {key}: {_state[key]['fs']} vs. {fs}") - if _state[key]['dtype'] != str(data.dtype): - raise ValueError(f"dtype changed for key {key}: {_state[key]['dtype']} vs. {str(data.dtype)}") - if _state[key]['dim'] != tuple(data.shape): - raise ValueError(f"dim changed for key {key}: {_state[key]['dim']} vs. {tuple(data.shape)}") - - _state[key]['fid'].write(data.tobytes()) - -def close(folder='endoscopy'): - """ clean up """ - for key in _state.keys(): - _state[key]['fid'].close() - - -def read_data(folder='endoscopy'): - """ retrieves written data as numpy arrays """ - - - keys = [name[:-4] for name in os.listdir(folder) if name.endswith('.yml')] - - return_dict = dict() - - for key in keys: - with open(os.path.join(folder, key + '.yml'), 'r') as f: - value = yaml.load(f.read(), yaml.FullLoader) - - with open(os.path.join(folder, key + '.bin'), 'rb') as f: - data = np.frombuffer(f.read(), dtype=value['dtype']) - - value['data'] = data.reshape((-1,) + value['dim']) - - return_dict[key] = value - - return return_dict - -def get_best_reshape(shape, target_ratio=1): - """ calculated the best 2d reshape of shape given the target ratio (rows/cols)""" - - if len(shape) > 1: - pixel_count = 1 - for s in shape: - pixel_count *= s - else: - pixel_count = shape[0] - - if pixel_count == 1: - return (1,) - - num_columns = int((pixel_count / target_ratio)**.5) - - while (pixel_count % num_columns): - num_columns -= 1 - - num_rows = pixel_count // num_columns - - return (num_rows, num_columns) - -def get_type_and_shape(shape): - - # can happen if data is one dimensional - if len(shape) == 0: - shape = (1,) - - # calculate pixel count - if len(shape) > 1: - pixel_count = 1 - for s in shape: - pixel_count *= s - else: - pixel_count = shape[0] - - if pixel_count == 1: - return 'plot', (1, ) - - # stay with shape if already 2-dimensional - if len(shape) == 2: - if (shape[0] != pixel_count) or (shape[1] != pixel_count): - return 'image', shape - - return 'image', get_best_reshape(shape) - -def make_animation(data, filename, start_index=80, stop_index=-80, interval=20, half_signal_window_length=80): - - # determine plot setup - num_keys = len(data.keys()) - - num_rows = int((num_keys * 3/4) ** .5) - - num_cols = (num_keys + num_rows - 1) // num_rows - - fig, axs = plt.subplots(num_rows, num_cols) - fig.set_size_inches(num_cols * 5, num_rows * 5) - - display = dict() - - fs_max = max([val['fs'] for val in data.values()]) - - num_samples = max([val['data'].shape[0] for val in data.values()]) - - keys = sorted(data.keys()) - - # inspect data - for i, key in enumerate(keys): - axs[i // num_cols, i % num_cols].title.set_text(key) - - display[key] = dict() - - display[key]['type'], display[key]['shape'] = get_type_and_shape(data[key]['dim']) - display[key]['down_factor'] = data[key]['fs'] / fs_max - - start_index = max(start_index, half_signal_window_length) - while stop_index < 0: - stop_index += num_samples - - stop_index = min(stop_index, num_samples - half_signal_window_length) - - # actual plotting - frames = [] - for index in range(start_index, stop_index): - ims = [] - for i, key in enumerate(keys): - feature_index = int(round(index * display[key]['down_factor'])) - - if display[key]['type'] == 'plot': - ims.append(axs[i // num_cols, i % num_cols].plot(data[key]['data'][index - half_signal_window_length : index + half_signal_window_length], marker='P', markevery=[half_signal_window_length], animated=True, color='blue')[0]) - - elif display[key]['type'] == 'image': - ims.append(axs[i // num_cols, i % num_cols].imshow(data[key]['data'][index].reshape(display[key]['shape']), animated=True)) - - frames.append(ims) - - ani = animation.ArtistAnimation(fig, frames, interval=interval, blit=True, repeat_delay=1000) - - if not filename.endswith('.mp4'): - filename += '.mp4' - - ani.save(filename) \ No newline at end of file diff --git a/android/app/src/main/cpp/third_party/opus/dnn/torch/osce/stndrd/presentation/lace_demo.ipynb b/android/app/src/main/cpp/third_party/opus/dnn/torch/osce/stndrd/presentation/lace_demo.ipynb deleted file mode 100644 index 5e1820a..0000000 --- a/android/app/src/main/cpp/third_party/opus/dnn/torch/osce/stndrd/presentation/lace_demo.ipynb +++ /dev/null @@ -1,313 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "import matplotlib.animation\n", - "from scipy.io import wavfile\n", - "import scipy.signal\n", - "import torch\n", - "\n", - "from playback import make_playback_animation\n", - "from endoscopy import read_data\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "plt.rcParams.update({\n", - " \"text.usetex\": True,\n", - " \"font.family\": \"Helvetica\",\n", - " \"font.size\": 24\n", - "})" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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W7H3EzqUWlKR6WgplKYIhT5xlnpOF0x5L8MhFwxqDae5xmAYYXeF0hmN4RakKqzP+9ukBz6HDXTfDm4ynpadDUFXsbMI/21GD9sKBIqe1vyLHxSFqXBJw31XcOt2qOMPP+ashIVXgbb/A69ww2cel42ySsuOtLa8akJr/HYvGORlcskZvCjamtCxdcFvBxHOlRuabPrSNe0wWjxeDToNgGMMHOihhsaoABvwalgNTbcFb4XWTnCoZ1T6f4wP9sHQIxWBheCzw53Wa4I98hS0/dARP9Aw9nJNGpyt+vknYOuoDOU2JRa4al+T4NekAWBjaqHWFR+fM+DXfG8NJ1iF4XLLGry5UMQrceuczB9vacOxvLxa/mgP+hfHYWOpVeIbNEme9saqWENL3I2hwqAqDKTgng6ewhk2vK4qihmmnK972FIucrkhF4SVSJXHrKq+ZtZuqVcVgBB6Spv16cISsEUD77BQr7jqF0dDzMpwMlPXR/Z3rJw3gS6n4bsrYWI3RKtx3Dr0Bbh2VxV5XnKJBhTBOaIF0fJMpA6eHfE4KIQNzqY1BUAHGu+n0lUyb8DxaDBTACxwHxFwVPs4dLkVhLgpOKcZWKWhqLVhcQcqUpVDApGwCoEx4SBa9ixRgdMbdeMaOs/7KQRMA3m8PKwQgbBJVMThqbB2jhzcZOxew9Qv2w6XBHs/nLQ6xw94v2PulbcBUPABgzwFRMtlcaTPs/QxnKNOaoscSCXfsXGT2isbOxVZmC0a7FIV3vUYqnIkp4F1/VZXwQSH4eHGZKgbzGrWTQ+z69wC0svGciEVzTNTc+kVyLQNcknvVqFOqwpnUeg2XbHFMFr1xdEipAq2p5M9XWaRkPaOh0nmGbge3dwV3Hq2k/rQQ5PDVkLCxmYMyZdw9B6ZcqKlmVMVSaDNLuWtUxZQNXqJtQXhjMx660OA4wsApWMo9V4oad06XBq2EorED8L6nPfEYPDXfGEs3qkKZAlMLzqnHkk17j8jJ0I4Plo+Lh9MEVSkFxGI4K6Z1unERKq1NPsks5RlCM1vjar/RGqTM0Wpq/GsAj/OAzmTs/NJe+9PS4VcXj70r2NiMrc6v9uicDSqviwhgZiirVMpW/2QbWgWVuXnYq9Ka9QDwz/YKXwxUHTwHYip5rXHPvYX3Q0AsGs/RNBiNmC+U8co96zkZFAZTLGjwx8ZSPJIm540D98no94+Jnqljlp00JmUtCqQTOZBTvKLv9LRUfAL1hUZLwf/vBaH8Q16pFrzkiL3rMRjgi55K0q2jB9DK4ErYUyiEu6ZCZbo39KA1qON7SgWW6YXfTwTN3HnKBCDdbIAPh4KF8WGraDdnxvQAtA57ZygDPkeHOVsYXfDQT1CgB7Yki49LD6cqRpswZ4NTdNjYhE0K2PqA0S+IySJXjd4FOMa1AWE7aHw6b5Gr5gxFYcmWMrGiXwX4JTl6DRexHy7ofWgslOsDyeuMjYvtd2MxeFm6FsSMogDe2YjbAZiixzl0jNmvmHWuBKG8RINUFN52lO0dGHLQiu7DlBV6Ddy43BalAeAMBY7L4vFpsThEhZ2jIG+48nnXB/Qmt1K9N7kF070L0KrAGDrUOxux6xaGjxQMB64lOYKe+PlJQJSm2iFa5Eo4PYCWcXW6NmhBMqKNqdiYQll6Vtj7QlkeMw2mtB7UQhnNVeE5ulY5Cca9cAOV7jklJMekcesV9k5hYxM6VeF1gdXE6NFXG1QYGpnxc2kwC2tHY00IzpGqp7uOGuVvFfUJfnnZ4JwMvYdCC5CdpbVxCB5OF2xshOEmt6zNS3J0COiKrcu051TFY3CYs2qJ0ZcDwZ9PwSJwczBXhU/BYWMz3nYLrCrMpKEk4MZF1EHhORp8OzncukyspqJbI68A+ObSI3BVY3Vta0yw5BuX233OVeEYLbwpjLFT9v7dZHGIwDcXOmj+q3uLvaMmOtERhUpL9z0UWl8C5xhFjc3RFCyFejnHqHHJtD8IuqG+Srxi0V1fAqOdEzWOB1swJY3nRGjBxhY8B4NzUvAa2DtCKXKlXqG8hiQzv+n6aSEUpfHGeXSGTmsJFtREI0xLAs6SgceQYZRt2etQCWaxjHuNVuMQKmKtmHOBVaY1R5focOMyHvp53XRawfJGC8W0bKI3BUbT5otczgLAIVrOrBI6m5hNUvEViNoljdDeZGxcwK5b0FkKotZkIAPnpQMA7IcJVmeklu0ExrgJXsiMDZYqLAqCFeZksesMtvy5CWunA8lyKR2ZHw2sjamQDT7OHbF95h63PuAXamVqCBVSguIUqcRNVYKGanCTNO5Cpi65VcADl69eF16khPsW0II9J3pWe0dB0+i1nC0ghsoPs0eBwt4lWC5xY9XEqtEZxmRYm+FNwhQ95uzgM2W/RhfYmnHXz+hNwpwt08IqU//MVcABB7IK2Nyaj52SdUnrY2sLdi63bOmRg1NvqDqMldZGx0HgbbdgKRpPiyfOOsCUUwqYjgPKXVX4tFj8zcnjXW+ws+uh1zlqQB84s3YMHRhd6EAEcC6UDfe6YudSw6X3fsFtN3NVSOusYG18CWXy4+IYw68NVhQY0OkCoysu0eHAr7GxCUgWuRiMJlMFytRBWWMXDp5eV3idseNIohR9hxuGtAYX2kE7MPSllIfXxIiakkaWw10TXPN+IErtD4vjwJcazVKoiaUqTFwxHaLCnS+497kFxPsu4/1APbNSFXY2Nw2AVhXvhwW5aMyMw4e8xh76jpJ9M+SqK86JMvobR0nnlumaS7FE1fwNcTZkSkZHCwyWgvhg1+q1OGK2CF7f8/MQaDdd6TJ+0/WTBnCtFAaj4TXgNdCZQtkyX8KHNbpi7xWUshgt4U5CF8z8Zba2wiqi+ZgK7J3FxtLi8abgwUQMNqE3qTUDZVF7zv6MKi0jLlA4Bc+iGLre88LddQRXtIPERaLnmYwpWbxEh85k3GCmTrrOCKCD53kZEDjAdjbiwvCLYNqnQPCHUMAqaOPvPW3MVCgL11dNPm8ytFpaUzQm2x6y0AyNIiYA0ZyoLD+FDpmDkLBOdjbi1i9UoWSDX089TtHA6grPi0yrCgug6oqdo7vjGHf8sDgYJQ0+Cui3TjVmwpQ07jpiNAjrYKUPUtn6qRDH+aEj5oJzEdZQANc6c9YNzMmiNwZdMQjJIhUDrxNcl7GtAbnqVnU8dBmh0GGSCjBVynI6TVjmzlJmNWWNyEyEO5+wd6nRB09R4TkqLNlAK6DTPfYuMk0Tje3Rc6aeOBE5J4Mbl7FzsR1YlRuJDRphHrJRVOUJXaxWYh+0QN7+jdZs9pqYTefkmqYhcpXqdMFzcNxAze1gyUVh4T0IFESl2547BY9T9HgMnvdPxsYm3PiAntfOaIk99Rg6TMniw0yH246ZFo9BYWMr/vTmgtGkxqu/BA+jK0YfoCIF3oduwa1Xja10ThQAL9nAFs387wo6vsDrrWDLeyQVjUVVLJy53zpgYymxedeXViUWADusVaNAJwrA+4F0FnKdk25QmUAmc1GI1cBzAL/1GbceuL3qkQAUc6qiwyQWrMyr9tnpIPgwm5Z5O04qelOgFT1vxc8YnPHHqnBJGmuE/LvXTxrAvQbeDxVnbiRJI1JKo73LXI5pzBk4xIKN1dhYuoFKAR8Xw6fq2hDIBThk4rLee1qYty42PjVxXCnjFvGJUsT3Fr6tubpNNy7AmoLeRMoodMacLD5OG7ppujSFotcZ975iw8GUVI0GH89bPC09i3EoAJWi4QxxVx/n8VVGoVXFxkWMNqIzCaMPjLGpBqV0lgJbLgQFjW5tlNWqYHUGFNBbtAMCWP/7ZekBrDicHGZWZ3SGmlo77qA/BWpajUbzPaPsQ+AE8LOTQHSoGntX8LbLGC1BUY1KyM9gygaRD1KrgIcutmxXmq69SdCaMNtaNUoxWKJrHPaQLS6cKQJoDB1h4JT2jDWvJdocXw5UPrurhMEoYtt4Ft0IZ/s5WByTgdEry8Iq4DkQNQ0AY8kLi5yoKgjMFLn11ItYGCeX5uUXPTFInC6YmLs9ZYOSDL0/wId95XUp6mT6HntX8Kaf4a4YRbJ3dCu5FdP96D4YcOZtSssYK69hYWgdokNvMt71M16Cx4e5g9eE8977gI2NOHMT8jk4zGVVUcbWWyDmxpIN4/mSwepGUzwFj0P0BJ/pjMx9iCMf9HuGbDYAepPxJ7szRC+Qi24NeqH+CmtDaJGylgrfk2s1K0DBsefnL9WzaD+2TlhA1F87RoUlA6dU8dBp3Pj1tbxmLUNZ+wGJsXP5e6E1xgKERBTEx5Bx7w3eDRonfq29K9g1vjrwFC1iQaMzkhALv/X6iTNwesg/JKAYevCpElUt8iK9Zph4TWVJAbEGIogiNDE7RYF54EbRKaYJc5qLwsTUwWfOcK9xRjkZAwfiVCkTNIy9ztlClcp/VrHAto1C4hVPG1cX7FzEXT9hZCFPrSRv3ncLbWx+bcoADJfHV1VH0RywiO/6Ej1lILrCm8S83tywX1nEAAWzWlfed++IpaFVQakaYwxIxSAkg1Bsg4embLCxiSiQJsOZgimpRhGT0riqdYHmSqq8X17oGW2swWiBd31qkl8vmSR/RlIfWjwGiylrXBLhkW86UrJJw+76ylUh53VZ5qz/zt+HbHBYOlyyxZfjCdtuwaAqUinoo4PRq0pyzqYF18gZueHMZy4aH2eDnSu4xSprFs7wrcttMxb+/gBBLUZVTMk2HrjRBR60ducryupjsPi0aLztM+58xYbxb9gEkw2+mTzmTL0Gy6wGz/oAg8KKw8oUx9IqVKNXKbxkm44zdnkOwr6QhEV43dJ8JdFawG03t8/rdMHexbXBy6yoc7I48UEz2IqBRSbyTI6R7s1LtPxs14a6VHxeF7wbLjiEDk+hawnVzmbsbCZmEK8fSkgKB9eIkA0+zN0Vc6TgT7bzK8GU5e8ai8bIgqNjctRD4O+76UlBfAgOU9E4JsqwH7rIVZJqz9BpYLBEl1VYpfJT0q9wb6tqg09ojVFc+vlI9/uhY5qqM9hayuSpwqorJq80N44VXgLQGfU7gJP1+kkDuCS5N542S2oSVAq+/iqwOQVsLGFDtRJO63TF+4FKlOdAVMOdA0u7KZPacUP0lIjPKZLoa/60PFxvMoyuOM6eFhh30P1VeRSywSNnfI4bKYGz0SOIrdIxthezxafzFs+hw5v+gl03I2ai9jlN2bnnn+1sxJIcHqeBhQEFVaOxNabk6ADRxC+3NiFnAxSNORNe+cwZ9Z556+IfAQAxG3y6bBALBeXry3PTc7CxeVYYxrPb92bu9raqK16qwi829PlExjwy40TocwIFXdsSOF1RUfC2o9d/iSTK6A3RFyXr7JgdtAQHpWyrPrQuGBxx7cVXQ55dhcIUPbxJcCZhcAmOA63ASgGU5Ul2mjgzBEgodooK300OO0eZpNWkqhssNcYSQ3t3XWgMJlL70mtKIJfNKE1Dp6lvEFglHLLGRdn2ek4X/GJcGhsiFI1PCwWUW28AmJYpvu1C6x3MzDQRiEWpCl0VnKLkoHIzUJ56z5WhY+jlmCx6XXDbzSQqix4iPJKGtlSGAje+6yvuuJItVRFromg8BcvCrtqEXaKGTizYEeuFJRu8BHqvzmTMgQ4vYnUU3PrUkqSYDVJ0a0+nUI/McFUi8JfQMnuzcvg9V3AFdJ8Jp6a7EQv1WHaOAnlv6qp+rgrnTJn+zhWEorBwUjlnhSlTRj1Y6uvs3NobKFXBa0oUMBt0Zq3qnC7ojYFRdLjJ7xlVcYgOz5GehzcFe1dhlMKHiXp7G6tfcdV/fP20AZwv8TCZM304evj0b3o4hD/NGXhcFHKt+MVG49aTbwBhhwYhA5esUJTCAClbuCwytSnBSqXFJKWOCCc8CyJ2LsAb4qgKh9YqyoqtKXg3TEiZFvrCDSVRhe7stXiGsubRmJZlWw6QIuCZIlUEu27C6BfcDBfEbJo0fMkksNj6Bc0LI3rM0TeGydYv8DrhFDvecJobkhQIUyLxy4k/7zlZjCbjoZuRNDdNGVLpDWXhLwupUwHCs89Ko3BDyumKuy4RJ57pV3OhbCRq7tAng8EUbC3BJMsVc+jGpQaROFXwxUC/+xw69mgh75BbH9aqpSgs0eEcPZ6Wnk2rVrhIAs0xOlhV8fXugNEtuB/OWJLD//p0jznrpiAVf49rCGXOCoeo4XXF+6Hw5yZBzt7FpqQ9Mq3x1i/c+Mt0ME4jq4FFuFFZiBK41Cfq4buB7BouyeCc6PCamPN87ylZOCbdsFOrKIlwqmDvw9pYr+QX010xeGS9avYbEdsIuUexanw/UwLy9Uj+KrcuNmjPcgKyJItj9Dgmi1M0uPWEgVfeO3O2rWEr+HqD77CaeTmuAHYutPvcFK55wDFa7FzCYDLuPB1Kcm+8LoAm75hUND4ttFc2lgLzQ0e4eqyKPUcU95OA52gwZw+nwc1zgmNEbfu4UIPeKoG/UvPICUXj28nzGi0IZY1NW1sbW+mc2KxNAQN/Jg3gh8UhFYU3fcTI/jepoq3rG6bR3vrEJAxhmRG8N14dPl8OpFvYWIuQiWr4j4YHXgGmokkAJ1x8wx4BhIcpfFrEqIb/YeROxBe5KHw3Ec1ww99gSoz5KdNwLcF4yRAn43kZMAePzHCEuNv9ehpxigZ3nuTvEweyHAg2uetnFCgcIjVVvh4XJP79u44CrYhtNh0r0HyA1flKRUgy+E03Q2tiWZRisAR6zcClvvCMiZ+cMPjVuOk0D7gEj+dlaJ9RfpeUj7oxGJxJuOHPtvVLY8aEZDFHh6d5wFPouHnGgYMzMFEoGsWZeCXa1MrBZae0onDJFp0G9i5jzhrfzxY9Zx+xKsRk2nN71ynC+XWC0kTD1JkELhog+wFudhpd0HsKAqkwb90mXILHJTliBPGBSU2yBZ5ZOaUqhmd0ayLunPiaEKXLasqw9041qt2UDOaisHXU2AXQMu1UNN6AKiVrKNMdbGK1r/Cp1waxZUaFXF4XKEvMJtIbVAyutCCgQVVnb0nmvWGfE2qUu+YhctfNzeTsEHqceE0SzY4EWnRvCqbkYGrFz8cLAGBwqZmrOZ1Zqk5Nd606nJPDjYvY2YRYFQ6B8GqxBBD4BgWsjVgD4HczBfbnaHELSly8zehsRMoGS6LK45I1YnUw0eLGpdVwDYznczLkdcbX49RsHmLRTbV4TnTofjlkBD6EwXFFGENCK7SOGphvu4hSV9WkaBWOXPlEhgHzVY+jN8QKEWjtzsuhm1qlmatCp1/3VrwuMJVdCutKLX0KFl5TVi3og/Qq9o7YTr0u6JmZFJiZ99toisBPHsBXnNvqNUB3mnDHKQmJnsr3JZMs13MXd8oaz0E16ARQOARakKOhk1dc8KjBqfDDNGDvA7bjgjfmjLt+QmdJcCONEa8TQrEEc6AiFLot4qOSmC1x60ML3ACQpWQ2azYSk2X4Q6FzEYNf4K742dPcIyUNxSwAyxmnYj7489Jj5wJGF6i042AvGLi3abWiFQyyrK8lfilWG8RMpb0E794FdDZicAGxGByTg/ipzNm0DNwq4NbTfXwOxKcmfi3wtqOAKXiqBFsxqZKssFaF72bfpOwAsTwAEinkqvAfzgPOSeMYicHw9ahhssEldK1Z5U3Cz28fm8JVGENeJzhT0LNR2CV0OJahBfEvxhMJfZJrToy1qrYZAZE5G9y6jL3LuPUB7kpiPiVHPYNENgOxGLhSgERruecD6MyiIaG6zfHvbqvnaIgPDDJP+sUYsHORKXIVe089ipfgqbxmqh9pGBJ2DCFJ8PY2Y4eFREUsCJM1UatCVQrPfEC/7ScoBZyYqmhUBYrBnGjNe5MwuoCvTMYUHc7RoefPLfTUD3OPcyIK5Manpp6Unodg4nIPyCRNI131MO67GaNJOLL6V5gh1DBFY6Q8RwcNNCz+nGyjtzpV8Ya1CfReGoWbmVYBl6RxhgTy0mwm5HrDX0zgnSlRw/brIXPzWzVLAPl+sr6jqsiKAi+ywXO0jf3T6bVpKoZgYomssTJOjFoZSdJoFfbSlDQeugTPazOwQOz6cPjx9ZMG8FKBQwQ+zhm5ViilsDUaeSBLyY2t8JUcC3+YFQ4x45m9qL4aDPauYjQVo6HTMlXgmEhZFwqJeP5gm9luk2hlU7StLB+40WhMgVIF09IjJIveJnjkliUPfoHWFZelQ8oGl+BhtSKRT7J4ZLHCu37GxkWEbDEaNmGyCTdVITL3tRSNnC20oi8iTRpR0qVMlLhT7Fr25rTFkixCtjjOA0a/YHAB1mY4N6GbRoRscDtM8KxKBNBYKgBaFi7vYXSB1hVAhTEFN/0EgLD2c3IN9xRxxL0nU6ReUyZ9iBapcHmPtWlk2Rr1yDDN1sUWRB48ZUpCbxN6mHi6eF2hbMFgVvVmLhqX6IjK5pfG6pmCxyV6fHve4sPs8cWw4L6b4UyiZi/T3AQ3F4x7tBGODzhhXFCjmRKHWy61xc2PcGDDKkWKXhub4SvRCWXjA6sE3DPjJnCWvHerCEek415XZK5MnCIG1NaF5rlTQNVedpHEPSwcO4UOU7T4tPRMUS3Y2Yg7Rc/P6cLcM43bboJnIZoEznavTcZ9f3m1H7Uie4enw9B6OU9Lj+/nDlubMbAiuLMJ70AMk28uPc6TR2fY95sPboLWgCWTXca7wVBvpxZuuhbszIKtD8BlbJ9JPM5LJXtfcsWkdSaJgDCX3rKSVSnyOjpGi15X+I6qv9VLhPoNz3AwnJRc28oazZCFqo0xpHhdWq1xiOShI/7ge0f0WSFYxEJvIp4n4tAJiDe6bn47nvF54LWnvjT+rapMk16rtVg5GzevX/s3XT9pAA+l4uOcMRoNoynTtkrkpCAPgUwGTXcdsHEGLwGYMn0ZEWXUCnxcKBN/6ES2Sk5kYsRfuXHY95H51h2m6KDViF1H/tpzdJijQ8dNzU03w7kIx6WlMQmlaPTOI2YLPVFWdNssMRPO0eHDNOBnmyPGbkHnA7QuOPMiPS09sJALocIq0rCcwUo2uPcz5uxWI6iqoSp3qk3G0M+NXvewOWLHlYQxBZela41eAEjZoFQ6PDTDNgAQokVkGp7VGbfDBem8xaF43PjQmlipaDwGjyNnkgVkuQoAvSts2GSbs51S1CzWqrbqBBAYizb4UjRi8FBXP3Pj0itam/QkZJCCM7n1B364jPh2GrF3EX+8O7N3usdNN8Pogn13BoB2KF+Cb5m6UhUn7j0IDxugLOcxGHzRrxbCsl2o8VzgUKCVA7gUNlefXzPLQ+xYkzS5ywrXbGzGm37Gnhue10ZZn5Yet5XgDum/jIyFGj5IRCfwpp/RmdQsZJ1NuIQOsRg8h65BKVu3wJnXTCeCYqjsj8Xgh2lEpzPebE7NcGpOFqfgkSpllAMLeAb2NalVwaqCjfW8Jta+j66rfuPOZ9y41D6HM6sRW60KRVHWHJgpMtjU+hk3zBwSN9DOJG6YZszZ4OPi0emKPT+rPTciY1VsUlXZ5ZCgkk/BYGspO77j4C9QZeXm9EukyowMv2i9apDL4HMkG1nxDwIIpomcGe+Yex7LagMt6743q9+SQCe3PuEUDX6YqaKbM3DjVtdCgNw/NcD2EsC7Pv4dOuT19ZMGcAXyJfBGccAluepDlzhzEsyRbtDWAlbRhnjoMjZ8klFAUOzRwdiXJhOZl+jgksWSDQabcNPRwh/V2lTpXYD3ATtFDoNT9OStkVcbVq0qcrYoRcEaUlDOzN29lrqPNmLvF+Jtl9UtUHMG3DMU8uOrsCKzFJr14Q0tWo2KwUXcDGdWJBYYluIrzpIvocNp6fBme4S1CVYwc0/NM5MNKtvP/tjBUKmKvjXZKgYbcet1c97TIGWdBGYxHFLc/NnZ3OhqdA+o7P3VYokRwC57g6Wyf+8DvrkMzStbvKUNBzF5Dc8b3NuE2+ECo8s6PaXopuwj33Xa6FoRq6dUhU+HG4h1gWHWSmbY4xw9/uN5aL7zTlXOcAq+GqgysJoggFQ0B1kKolLmh6yxs9yoY2WgTElKRTdoYikaL4zvfj1eWlMRIPrekg0mPhyMoqZchsGnhVSz4mcTC3mdf7E5IWTTGEfWFDibMPoFzmSMmSx2R+PZ43uD+35CbxP+aP+MAsUqW4dDoNcQrUHvIjU/XcISHLahw8IQoEyB6tmfPWiDAhLhbN0KScmBuOPDWCui/Q2M4wJ4RWUVZ84vxwsfcmbdM7ImXWJYj37+E6uZpXGaK4l+nhaLSybe9vsh410X0WvybJFAe9cFwsI1UTKlahLoaucim42RsvrD7Li3UrCpGorhlGNUeAoVl1zwL3YaY0eHnFBIATRrC6lAF6XhKplTCUupNzRY4+NsMGVinADUON8wQSMXxdg3sHca+JG30PX1Ewt5FH6+Ua8wHWkA1AqoCiBT2dIpmorRs8BkYEMrwtyoIxwKuIlJWV6paDTAu25GZxNuBiobc6GAZk1G3y3ougULQySpGATBulzEOEwwdvVnicGji6FJ4S+80M/Rs1KTMvrOBxhWECpVryAUg9M8IBeNXT/BWlIYAkDXpVbiU1lp0bmIzodmYiULu1ayVzWaNlatCilZFMbQtaowJsO5hJQMTnFEKnQwdTZitzlzZVEwzR2mpW+KUoCGVmgu3992M1UBSuT3BEF8N3dwquL9MCNx2UsMDSq5Ny7CMSNiZn+Xhy7goQtt84g74WNwiEV8VSp2zoGM/CMdXAwzKVWx7Rbs+rnBJJY3zmnpkTLDHwyB5aLx69MOsWjcsunXH2wmYhAFh7loPE6O2R4rn1kuw1SzraOq5IEl8yTKMc2/RA4xYT6Jv0lvMlNONUqpiFh/V5pWXw2xNSoB4JaFZjtHf3bTTe3en4LHN5cBW+sRi8Hez835slbC4o0qhDvzZyf6HWHj5+hbUCF6YEEqBqelh2fVpNEFu36CTw42FjxPA16ix9t+wpZpqj1SqxbOiawL5PMf2BcmXt1L8bJZksBWmZhLhYVdnBCJCjUVETGtSmmrVeu3OH1liVwFdtMcW+g7yGCSVLE2XlVtjehjpM/dafKDEaaNkB42dg2WuQDnBPZkAtMlFbyR4TOsmeB7ID2knU2AEjSAvs+UDZ6ChYyd+3os+HpEs2/euty80qNiOE2tfY3fdv30LBSO3YVpeApo3rdLIdrX255tTwtNwyBKj+HsDSxsqBivXntKCrBgNd9qzh+SRe8CbvZHLKHDHDzGYUI/zrA+IicL7yL5TgePGB18F2BdxDJTxuJ8gPMBwzhhmTvUww0mhk7uOo2tXzBHgln2mxPBMC7CVo3HlxtMweNmPMOYjHnpkIOGE5tcZqmIX7dleti8dG2smHMJzgdMlwHz0uFlGnCKHbMO6FBRqqLvliaCmOYef/P0BlYV3PRzU4kCQEoG09Lj8bzB4CLebI84zgOWZLnpZ5qdgFd0ssqGFd9vgLJRyRglyJ+jw8hCJJna054RH1SDTXAm48ET3ifSfcmSZu5LbBn6aYdh1c0YrGH7qsDYgof9C7xjX+tkcc99Aq0qsX2YIVIAHKPGvztW3HqNLwfFAh3H48ryq4qpgIKewEWE1dIEIVFFCvR0vHJNzFWhcGY3sQCm1yQSm7PGhbnjng+ijY1I9aoKYhhPsOKeRwk+LR2MKtjXqd0bZxKsVs1DQ7Lfb443CNlg41jdy810WWtaVUzR45vDDYwm9adc5+SIfsrCqMPSIVbdxEE7Hqknh/LOrlTRzqQ2vUrIAjHrV5RFEZ+5QvcCVbX7adj5U8bkRW5eDvweTtMgjI0j8U0SuIspnykb3PvItr+UrX+YO4SsmnHeh5ngyksyrbGdy+u5mhtL+pIpkTjQM+liYIbVhdkv954Ol1veC9fqTADIySJw813XigzVhGwrzq/beL1ayQoCIPLAb6rg5fqJ3QiBDzN9mN4ovO1JUi1OYHMhh7veFHwKGo+BbprV4Ok9wnsVehsxVYDVX/fEjAAZ/tpZymY9B2DfBWibYH2ECQ6Vmz05GWz3R8KnuwClK3wNnIG7V99DhCRv+hm9pfmX3iZsesJjS9G4TANidNgMF9ww1FEZR0vJNpz6OA9MPyQflSUbYq/0cwvg8vlisogc3ADKiINQ1nhTSgbqbMK78QRrMm63x1csmNNME4Vux0ujQHY2olbgV+ctnoNlEQQ5Bxr+XLFovETThhYIHTQUCoIjO0uWSr9rdcGgKr69DDjw2CurK77QV3apWeP//WIwGIWvxwmDi7jfnImVw/ASBQCaJbrpZuzGC2KkjepdgtYZ2+0ZzkWkZNFljV8AWILHxxM5N/5/Xui4FyvQP70BB4KKG0ec55ANLtnggd39Ql7Vq5ErNAPg3odG1wOYCYF1mooIYDaWRsSJfcOSTRN3iAdPrgpg+CpWjU9Lh9FkvCtHZhWR4OqhW1gAQw16zQerAbDpZl5fqq2xnDW+KETf+2EaMYHGp+lCxmmdTXjYHNHZiNEvuIQOLzwIYkoWG5vw1XDB6AKcKdysN/g4k7dPs6wtuikXhf8di8F35w5bF/CWqyjHgYiUpZRZJz7gvC4Y9TqicE4WoRDmLRRUBeBTcGQO5kO7pxIANzbB2PpqRJnThSxq82tPbaImUsV5jESgeD/QRKIRBYdo8HHRuPUVdz7hoaMKSwaePPP8yltX4E1to+i+m1njwdL4jwvtEc+UxI3NTcCVGHr8MDs8R2Gl0M9KZSiipTUF+rvXTxrAjaIJO2KXSAopmmk3WmIEFIhajn7uzEnBzpE8VWSoqRKZ/2/PNDHlq1F8etkASFHGcl56XEKHx+MeD/sX3N8/oWSPHB2WuUOKDtPcISWLXdUwJmG6DFfB1uB43pA8myGI+90B3TQgFY3BRmz7CZthwrhZu/w+xZblNOjDZOz3RzrteQbiA2+6lAjq0BxQG3xiCkomT5DOB3Q+YBwm5GyxBPLAlrLZ2QRrEzz/d+IMFCB4RnD+UhVStkhZYz9M6N0Cny1PZaHhB2TYIzQsdoIDoBUtJ5HXS3b0lg2rbv1CPGlVsAQKBk6T14R4oJQqYgwKYBuWK//Y97heNcq23QzvAzxXN6VbUKuGdQS3SFP45eUGIVqCyTwxKPY+4A83gRtHV4pcQwN+NzZh74XPT30RbxMi47NGS/OLG5wQCbkwPfiZM47eLHw1YYK60lACEbHsrOgTKp6XvsngNehw6DgrF+w8FoOnpeOewozORngbEdnQS+vaKiylKualJ7/y6LBc6QWkSmq8f5vRmQV7Q4Zot3OPwzziaR7amL+tJ4dNqfSO0XN2ulYIBWjKQpko9XY4w1v6HiFTg1Sagev3IrjD8+ej4RgGf3sem3pV+i4AWqNRGsWXq2epOJiHqyw2MyQBAG+62KT7tQJ3HbHXItvWnpNptD6qRiiYShNeHDqFgUTiIQ1E4F1Pz34wpfnWGAV8PUqFQL9/iKSy7bHSBH89KXw3J3zRWWwd2IaCnAvpNVeM/TddP7EXCgXhUFb+t1IruwSgGypNzIeOlE+xkMTZM9fb6oqY6cZ/OZACc+/yq24tUaQMDmGHkT2lQ/C4nDdIiTbmNPeIV74b9UgfQpqaZIOpEIrFxgXsdydYF+F9wLi5YDtekLJBjK4xRHKiTPF8GXEJHd7ePqEfiN2idEUMhAcqTbJ3rQtyMrhMAwpbu0rJ1PULNjdHWB+hXULJGjUbHD/dYrrQz6tsMHYLjEkYxgnOR2ibgKIwbC4o2SAyk6EUBWcThn5urABnyTwqc0m7sQkXVjDS89Bt7mHFyuKQ0WLHQHDCUhirrpqpY4UlxJkhIs1cagWrKev0/DN/tGVHt0qmVM+Xkex7GUKxmu1D55540V2AqiQLq0WhMA6qdMVue0SMDi/HHVVGJmHrgS9HCsC5albTOjauon9T8zihZ7qn9BQql9YCbRhdsbWhBXsxbAIAp03zz4tF49vzplET6e+pZL/AYLSUxX4/Ez799TijNxmdyUR95GzUmIJdnfCLrYXVuQ11HvoZHWfTH17u8DwNuB8u6FzEce6JgsrYt8jiBbLZ9SfKvIeprf3I3PZNN6N3gao9vn8A8DiNOCeHh361RY5F4wOrUbfc+9iwd89wpX0wqmB0EZfoGE5aJfpy2KWqoCph+qMpcOzDI6rUVDTmZMiamM3SFCjwijjH8vfT4KlcbJdMymHFimzFrClyBDSKFLnfTgaGY1HPSk2nVpsCMHxLympDboKaZ5uatakfC4mDnCbqocCNZGchU+tdoyi+7Sve9gbHCEyZPMGvLUXmoqGvSAg/vv53kdIT/k2NSKPBpQJ4wxXce7HgZJYJVgm0TG0ejbAk6HdP0aBjnwENmrhuVcHeL68m25zOI+bosURHzUIbcQndK5e70S8Y2P4SAN74IzofMIwXaC4FVWJvZZvgXYTm4DwvHULwSGxcJVlRrQo1KwTuqG92J2pk6goUha4nf+IUHGXC0a3Yly7QLkFzdBiCY6/shMRYqjEZ3UjNV20yUnBY+FBwjA3nTFmQ0Atp+nuGMQmaWQ5fbY546G0TcMjAiWP0OBeavA4A2ZGZ/R9v5yYiEZc9AI1hQaXiOk6MqFw0RmrPHFyxFD5Ejy0i3u8O6F3AZrwwd329rIswNqEWh1IMmV1l0IFWFfqOOPwy8AEALsHj28sIpwve9RM2ruChn2ngM4udlKLuf4CFz7kxUADKjmvNrGhdbX2Fa+0NuVV+N/VcMhNGGrLGUFWbXg8QW0NwZIAGYpAnR27TkwaG0KQZabVtwTBkiw3Az9RAZ9I3pKxbRjxnhzlZPPQXKAU8zQM7ORKzZTee0fmIbphQskFOpn0nqdRk2LaylN3vO2pkHkOHXD3x1KEajPLD3LMvfmxN90vwOIS+Ta7ZuIj322NT056ja1CcUWSlIDi6L6/9uTuT8XNmjMzZYC6aYTny2JbK7jmQrN0yFCENUseQ09h6aKt984Xl9rRuV/O8rBT7wZM9AFlBVIzGsjaChp7EopCUBGng3tNrX3gIswiQduxFD6zV0JQooeg509+1aT/0cxNPq/pt109OIwQoAydXL4XO0CxCAMwkIWawvv4FUInWcUCMRcpvEUsA388KO2fwJzuyZB1sZIohz6I87hulS4JXSBZz8JSVuoBL6GgTM7PicRqZdsfNiapQkkEKDsfTDt883WPXzbjbHdD5CGMzBjWj8xHOB2gOkEpVLHOHkjWG8QJjMzzLhJfTCGMTxvsX1GwQZ4/ptMH5skE9KaRooQ195nF/QrcjGMc68l2oRcHYBGUK/GaCNgWmi3BJQ5uC5Tzg2+++AIB2oBiTEaPDlA224wXWRTgb0ReFm4G+/zWnXKh4FcBLpBJvY2UjpFa6Rs5sAbB5FwkR5qLxEjTOWobQpmbalYvGiT2PlSJ+/cCT6YfNBWHucL5sUAodPNvNGRt+HgCg2STMMGVSqYrMhkTy/wA5CfZYL8NzJD0IY9640HjdparmN04BNDdPm1IVdt3cYIBcNc5LTwMreP7nmTOwweY2EGHlKxOGTeP5qKewZI07r2HZSsDb1O5/yWzPigpvc2M7yRpwvuLLfsGXAM7nESF49C6gVI2JEwmyJ1j57aVoTHOH42kDx4eFXLnoBrsIO0WmOSlV8WkeMGfDQyMK7rhxLtz3b85bjDbh/ebUmvODpYxcLuoXAHfdgruO+N6W771CxZZn0cqoN3kmtaIpIZ2qeNfFBqdIBnzrY+N7R+k9FN3k9m87sumN3ICVYP41M9wiU/i+nQweOrJHlkqRMnl6vksh9ak29FqoNJhEg/5fVL8Q8U8lWwnxX3EMoeXisRTwgbNWSqPhiWLZoNTXScz19ZNn4DTFRTXLWKspEISicOFJ54BuPN3nYInnyfiUU8QV/NXFYM6kkvIGeM8eEmIbKYq0kAyWTOyKnjeI8ItDIRx415GPhtUZRikcl5467IY8VApj2DlZlEzQSykKt8OlBRulKmqhMn9ZOmw3Z3T9AruZ4LqAfkdCkzBRBm77AO0SNu8/URYOIE8ewLbBG9dXKRola+RooU0GPKBrRr32SSgaVVcol6CVhuUM96svv0OMrkEpShWQ1zZl58YmdN0CrQsO04hT6JpLIVHQKLsbTMbXY2KZc2rsC8ky5B/KPg0Cl9lvu4AHT4s/F4U9mykBZEfwpqM1sHcBN92MoSe827kEazP6gQJMqQrOJRgf4Xoas/by6Q7z0mO/P8BLUzFn3O8OiNHhvPTIVuOrYaaSn2c13riApRjmIbPIhAOLXNe8fKVqm3rjQoaWAFs0ztFjirYN/i0gg6t7GSvG7JreGAyWXRW5Kr735KfSmwRrSqNHiiahVmpIf7F/oTFzDK3E6GhNFo1xmND1C3a706u1IjTZzL2P0zxcea0Tiydmi9M0wpuEzXCB1WRjcFx6nKMnUZxTbarOu4HWMc2ENfj1eYPEFQTZ00YWGyUoS4Mc1vmmxDF/XMjD5aFbsHGhzeWUQ18YKpYb8+TFQkQHpYBbDtYClVzb2grzSCAd0Th8WghOEi55qjLhisQ2nxbLjpwUPP1QsbWUdYsd8SWZhsOPpjax1GExTE2kTP2eD1jxcfm4+Iave4YVhYb6fpjxDusovZmpppq90p+DeUXN/PH1EzcxK259wcYSzv1pUY3FYBTw0FGWLWY0TlFXOWn6ElM2f6fRJcKMjSXnu1N0rZSRDelNxo6NnazJOMwDjqHDaCNGF/DCk8wf+gttIp2BYtZp9OYFzsUWLMX7ROuKUhQOxx2GfkY/zBiHifDJfoF1ESUaLHFoQdp1gTJql6AMBeCaDWrSKKx8bD9jMrQpyNEiB8rES7SYjxsEVjUCVI5pXaBNgQUdBDUbxMWjFgXbBbhhIeihKsLFpw7L3CFGh3nuEZNDzkTT2/qlNUcBghZkcT0z5t2z09tzNK0c3Nnc/E4u2eJp8TQKjA2GRpthWWmWr7L1W2Z19JYOWMHylckowSGwK51irq8uCpUbuxIcKmOcOVnuQ5gmDAKAu25upahjK93ILI3Afiby9yPzs0URGRJrBTiwzDzv9HEeVqoYZ4Wjo4wboJI6QUOV2iT1g9UAePixyXjHOHTPgfkSPC7B48NxD28S7sYzsVEYU5aGrbF0iChd8PJ8gw+PD41R5LixSyym1DQHwzyjFI156eBdwpv7R5SiEYJnwVjBZRpwXHoYVXA/XCjjtxEq0GEg9/Pbww1i0fiD3QEKtVFZhRYp3+UHHoJCBmmUEInUnGxx15GJcrVh18wom/M6gFriiDcFo8ntdWNVzfdbwp1StO2cLvhqWCmpGpWZIMSCsbpgs01NSSkJSKwKT4tvviryWYjpAsRM8WVg7vgpaWi10h6FznzrUmu6GoZKDsHjJdLkpoH3JUBMvVTWRvje5d84qk2unzSAx6LwYTZ46DJGK5OYwVNTwGUnGcrnSvSxjS3NVJ88funk/ZMtZVvCKhCMXPjKIlvuHWXWHZsBkUdIxr6f8P1xj4/zgI1NGG1swduzWc7IbniCkQNAzhbnuYe4uMmNDyzVHjcXdOOEkixyspinnrBK8QPvAozN6Pj3JKtOV0FKqQptE6oiFZZxRHu8PO8wTz1cF9D1C6bzgJQsFs6sw9LBmERNvitMM8wdnI8Ydmc6MKrC6bjFx+e7ViILi2GJhJ+KmZJgtd9NPWJVTTX7zPjjvc+tSVb5kEU2nDkRXPJpsfi0KACErf/hJhDzg82KLsk2Gb5nbN+4CD8sKHz4VA7achCm6BCjo6G8LlDfYPFIDH+RtwSJVcRm1yty4OtcxHa4tMz0vPQ4BeJXE/U0tYP+FHwLSjd25sypvBoGLd9djJceg2vZGICW8Qnft2dYBgA8JxkFpLTdeArUb9zxFbsGAIyhasT5CNuF9iydCHkY9w9HgwqFkdk0t1tiPoXgGSKhEX2yFnf2BWnxmKcefbdgx4O0vUvw7MGzwxmlKJwvI2K2+HL/0gZ3xGzw4bxrFEGjaa+OngyyxJtFTLdufGj35pJto/5+MUzo+L6EbPDvjjT04dYTLiye2bure3IIHh8XGVABfNGThTSwVoWlKuojgb1hQIZjEieE2idy+MiHLYlweFITyJDscVHojWYvfCJU3PKwZIF6jslgrhqfAnm1PHSBaaV0aH2YO3S64t4n0gTwIUEJK+2bp4VwdhnG/duunzSAL6Xim0vBYMS8KnPziwcNZ4XRUDOsZlZHqgpjarN9NFzWrubvdJp/XIRFUbHjMt3qgE03Nzl5KIRryoQbawq6XBCKxpLJV8KoVZkYMvl633T0cKe5xxw9Pl02GGzE3XiGswmdlPsuwpgMFIWXpxucLmPjc/uOKX7jDMWLtxaFFBxycDgdt+0+iQOhYN/95gK/mdBtL7BdwPllh8tp84rgT0Ikh1o9vv/0ph0sVmfsNlT22okarPNEaPDD/qU1WUthBkAiKIUUnBpLtjxgeR1/J7RBaezIJdN+gHXw7ZzJx/1NX3CKppXb13acC1Mbhf6prhqXcfE4Pe8bpbI1EKNFSpbuqc+kSOXvIM1HrUs7ZHPVcDpj20/UvDWZYKRE3PKb8UwHBzcvS9GcfVs8zyNCVngYz/Ds+eKywUOamjviOTp8XDxDJbV5u8jn+TA7fFo0/gtQsCg8sOM5dIjc6BQPnI0LuN8dGkwTkyX+/jQiP9/hZnPC/f0TFK+Trl+wB/B03LdEAwAdSnG1Mx76GQ5o3Pll7uC6gIErvmuV4yV0OC89qTN9QCkGpQi2W2C5CSgcfRlqMbqAKTo8TmMbpZaYSy+N8VN0eGT2D0CDMkZDroHixROLvqqYCNa48aTmFduDJsEH8ft3bMAl77EUjVsfMDCLBaBKp2ClA+4dfT6Z3AWg2SLIPzJs+ecj8LYzjUixdwkbSyMEJcheE/5k4EbHn1cO/ClpZEM+4xoEvYwsy28/YzRU49vjt14/aQDvtcLPRo2KijkTPU8y8I0l0vzA3fjeZOwBPAePc7J4CgbHqPDlQNm7+Ch7U1BAnrk0eDY3yX3IBodpgNGULYu9ZUiUoQiL4BA8+a0AzSyfAjlaM7O3CW+3ROH6Ypdbp32OHodpxMP+BePm3BqK4+YC5yIen28RssXPh29hu4AcxB1tLRsJAqmI0eJ0GdH5leIlWBoAGG6IKk0BVzbctPSI2SCet1CqUrWhSzPPiry5ctHUMGW+eq0Kh8MOh8sGzpCV7hwdYrZ4mXssxTB1TibQaPzyTBO2/4t9botbWCgAuxRyw3nHi5b8HXTr8Mt7i12v1zQzNLKLYMkaWut2uAXmEMulrwKNMQVaJ2y21Nw9HnbIiYJ7zBZnNjH7MA8YTKZD2xI/d0muMY80KnofmqthqRo/nHY4R0eGYjqjd8S/1rpCR8r6FPdJjCLRiJTyqWgcsm6+1H+4mfFH24pblqULDHPrFyhFZmaOP5s4SrbqLnh8OO0BoNm/TpcBmRucx/OG7R1c0ybIIQVQBZm4Ya2ZUQOgMZ1ysNA2YXf/gmEzYbc7NbhQDtNl7pqrZa0Kv358QypP9k0XiGrJlubHLj02NuFtv9oNyHq+62ZsXSQpPe/T+cqiQPb//+Hm0gQ7YldQKglgJGu+8aGNz7teG1sXsQU7UGaDX52pr/XHOxq6/G64MIPGNDaMN6SmlGArr0fwBynBNza1Q0L6PTIwgyxByDOfDhU+TOpqsZwYcpFBKdcmWBK8yXdfY840KvJ3XT+tnSyIgdIb4oSLg5nwHp84uI3WvAoE2iacnYGCbl9aTtRDpMbRraeffdOvrmOpaEzJtRJfzPcF39qxG9rOkdn7TUeTbnpLp/SvTyM0gK+2B3QuwrtEbAjGh+fs4G3CnhfDPPWNxheWDktw2G9PVOazurNWog3GSBCLbIyF6YVGFxIPnTZtA98mw/g3YbvPzzc4TCNuN2d4F7Abz8hF44eXW2Tm0kLV1lwV/xPDOLkyGfN5xImz+P14xnkesHB1cWafcKHXORSuloBDJDN6Mdk/RNeUbwCa0OKcLI7R4sxZeK7Ag6fZh9/JhJhhblkqADhFXiItgzYZxkf0zFsHKJjFZFuAMobw4MKlcD/M8NzAs8lin8k86cJNuF8ebrBzAW82p6bUjYyDl9Bhias4SqmK3iZ8mgcUAF/sDrA2MXU04Iuq2PK3hzjayVR6MvbKOCeDD8nhoSPIiGadZmZJ1VcijVIVTkvXoCvNkE7I5NstiQgFUY0Q6XCLPBv1FDym5LDZLxj9+ux3jKMP44RSNA4HOgyMSTDZohRD68MRPNZVhbB48gDylIH3Az2D48sOS/Do+PA9zAObQZGH99YH7LsFb1jlK66Jx6Xj70seMbmSj454jPcmY2epud0YOJWYKHnpoLDay4qS9KZbGrf/aenwHFzrne0ZppmvqkKlaI6pDDEhnQLBp3+wPbfnMCWLmSvF3mTK1rPBiVGAvSMW1ZSJjSJWuoPh6fUuNXqlUBLlEjGQYVO4l2gxJY250AyEu44sF8SPP5d/RAGcMNOKjo2pelPa1AmnK3aa+JxiCgOgUXhuXUKv1wBerjZMyOKhq7B3ptmSlqoa9ihc272LSMzLlWEGMuKoN7HNt9QAvhjpoRYomtHHzazTshLSTCHGQJoGnC4jtuMFXbfgMg1YgsPdzQG+W7DMHTBT47AUjRA9ctaYo+fmT2r4pFD3dv2E3UiZZVo8wuKRoiUee3KYgyfxj8ABUCSdvmygVcVNN8Npsh7ddDN8t8DUDJU1lrnDNPfwLsHZ2DKXc3J4iQ5vu7kNNshVY4yUcf83D5oXaWz3uLFQiubBHDS8IHPG0bGUuIB6Fhp0MIgD3Hezg1HAH24m5KKIm+4TbE9YZj/OuJxGvBz28KxGJfZMRooWYemQWNmasmkMG2HzWJ3xs+2hPTOtaerJZSH5+K5b8GZ7fLVWSyVP95h5KMSVqtWYglIUOh+RF41L9M1TvdMVN8PSkoZUNCas8ymf5h4vS4c9j2cTOqJMcT9FYgDtq4KCagZj181Ma3JzyVR8SFu2QwaAJTlM0ePNzTO6fkE/zFCqoBSDGBwMi3OE7mpcRM0a08sWh5c9no77dlDcqJVaWbLB4bSljJ790tvADPZMscnC+Yy78dRM2zqevXoIPQ7BtySKgnYiAzR2wCxQbfCE43sjGS/dKFYcc9VGz6Q0ZSiRIKSCQRtm/X4ITS1KkhoKsOdItMjnSG6a9z4gMOtEKsxOk8zfa/qu4lAptrEybFoqgTMPq5DgP1fNRIvcBs1Ilj0ljZeoOaGtHOw1QlxtQ37X9dMqMUGgv2TfGitEIHxMz6eedO1n9qc4RJoRR1zutSRzqkIbBVWo+3xMBn1VuGUYoWdLTCnXyRBowDk63DONSehLAFGdIjMYaNQUN6g4uC7R4eN5A6fJstSbhL5b0HcB3TBR5mQKup5KS4E5TsdtE9AQlatAKdUsX0Mmf5QpehJb9BNGHhARo0N4JoyuVoXORuiBSn6tc/NWOYUOc7JXC5uuxunmYBcjNT7JzdBwVUGcaZk4NDOWuvWkOrzxgd3caKPuXCAnQ52aX8lz6PDD3GNjc/NfBtBKZZk6Ts5xq2ruoePsutDUHeMiedV04gNjUY9bnJYe95wNKuYNHw47THOPcSBsG3nFamXiUuVMTjxfhG/c+wDHDACBIOZEqlvHeLhvAaaQN/nSoRQ6zJ+nDabocIy+WcEKlxegjHEwGcWRUvUyd0RR04WqPUMOilrVdmgH7gcIB1vrdfDHHD1eLht0rMYkmwjfxEW7biI9Aj9voYgq7sscX3YNdhG+vIoW02lDicFCrCTDh3ZmymytxBuP0ZF0X1U8TwMu2TY72FA0HMD4Nf2ZU8Qxl2rpaR5I7AQ0XLfqAp0sZrVONqJ5oBUbT89KfMwP0aHjezcniw/T2NSr52TxEi0euoDhqodyjWVrkGuiwC8y4QgAdvy+EvwBovY9Tz1uHNlEzNm0gSdifa1BE+2FpaJUxUO3NNVuLgqbH83NBYCX4PEcDLypeG8TerMO0K5VIeoV//9HY2ZVoNqYIEBsOOmDyuBZCoC5dYVlmGth341zoPJHPKU1wNOwC6xeJ54YXVrWIyV5LASrjJbEPrLRDqEnfnK3tOZTLgovS48CxUOPib3gbMKb8dJes1RyDjS6wDqLoiqQ0LxOxu0FxkXc2GfUqnE+bpCuKgyBY5boMCWHH6YRe7+g49PcmIzLRC6EAo+Q1F/jjc7wnuxrAeCmm+C1w/eXLeZMi2Wwsc2WLMUgRmqIyYiyCgVdaYDwkixuHPknH6NrlZCIGEROT1awHTpdsPeUPRpFjnpm4DFzqrRBuJRZAR3z9IWBMHGwEgOijaUBAtoUKJuhTIbSzGjxAXfbI4Z+bkKkkCigLsmhLwuqLi1Aef7ORhM743TatUw1Fo2XNGDJZAlMa3MVI329OWHPvQOalEMjv344b6EUDZUGgOdpwJztlXip4FQIMvLsPqhVbYpTgCiTGxdwO1zQ2cjyboPvTnvkonDfX+CvGoQh2zagQrB4gP1h+gn78dIYTpIpP59vkIqBcxE9B1xhQIXF4zwNKJWaj85kDN38qpk9R1IDF1DzU6lKTp3Z4mUaVvdLwYKlMmlBkSybvVRJV8IgUkwSXc5wYiT8+WMiKOKho6butY1xgWq+3rnQsJY3/fQqMJICW2Zerrz03hBdMJZVBCRwjkFu7DSvC8WFXFuWrLNucGG+ei8D1UxwRCwUmCYojVaHglANTtGx+HBNpze24GebGW2yFSMN8r7fTwNiUdjbf0QDHcQYfe9qw0ppigcNUb1kjQLLU0EoExdlk9MGqVC2DRAPVEH8VGoD/1PRCJWGxwpfdMf2pqLukuz0mTnj8vBDMewyF6mc4YB5ScQR3tSlsQqIG02LxeiCctE4TwO8SzCclRubcTmNrxqWAC0Aa3Ib1pCFdmcTdh1lLOJvkpJtfiXnacTEwcJosgJdkiNPbO70i7FVqgrfXjY0uqtbEJXFzFRF7wNJ8Hnwn+bPkysxL2ylaei50lzFCrQRcBKQ98KTbpi3b/df6wylabjBKbrWuBSfbBmSmxiP1FXB2oS34xl34wnGpSaMEopluTIuKlUhRYd58W1OJkDQyJYbtOJ3I57Z3VVG3fzEI3us8IzGvQvY+oCbfkLvQoOzlkRMHBnm4XV6XeEoEoNc0joQFyAfi51N2LqIU3Q8Li9jdDKs+WpeaD9hThbfXrZUlfSeR8WR22WuGpeF/E3eDBd8ycEfGaiV1kTPYqzeRcRcCB+PDkfudXQ+ICaHRz6IRr9AgfZPjA5z9Jgi8dB33Yx9R/i30YUCciGDr5gtVLKolYJ1LFQth2zwy+Dwvmr8vPVQVisJAA1SE6fAdBXU7hn3jUXhAoN9oaHjoZD/+K8nj97QaDXC3Q1ufcRdt2DvyJRMhmZorMpj8eRxWgzZaqN0CpTaM5wlPHHZQwu7D4a8moCNMpCkfSciUEyJ1uHMthFiL7uxNHVI8z4nIRDFt3OyOGVDrpV86MjvyGf7XddPDKGQidWUNE6VvL4FRxJYZErkcXDnE249NxS4bDG64nHWCAUAiIf8titQCm0e3saSy9g5adz5jJ/1hANrVIRKlp4SeASi8boAXL6UbFpZdQiesWRqbibO7gSi8CZhSjTh/babsB8mePBQAVbAHU5bxGypdOeNICWRVhXg99KpIgNtij1lBQbxqsnl2YXuNA9YkqMABeBl6XGODp8Wj1BkPBR5d4uH9ZIcpqN/xeAQoYvVGZfQ4RIdD6PlKTOVBgwAwIUzm50lBaYIYb45b2kAgQsIhTDtYhRni689j6UyMqoQvsuCDslsnKZDrWQNlTVUcEhzh+k84DINOM8DrMnw/QLfLeTR3s8t687Z4Fc/PLRDjpRwtkESTuc2hFk2qcjov7hqEArM4riXMjjSCAx29dPOVTd+uNVERT0Ez9gmDajYWGp2aVWxsbFlhSHTzNNSeTCypulNSlV0iwhGSD2cJhFQOewcsSdElk6TbohpMyeLt5sTRr/A8nDuH067Jkd3OuOeG7f7YYJR5ZWZ1ZIcPp538Do1IdfLtGmGYE+XLSYWO9UKfJjGhgPLlBynC977gDsecweAnTQpsD2M5waHiPoVoMBYKo1sA9AohAJrep1RjGrDFmQMWuGflQw5FU1+LDbiiRvS9z7A68wZuG5Z7jM/K6mWxCJYGqLfXDpmyACGLa1DoST0+1mxAVnByKZudIBLQDcYrULPDVnD674HGsPm2mCstO8MtlBmNhwou/9dIfynldIroZlVeEVDGVJVmDKND6pQNGLNpUagp0ZCxafZtOnlOzZ1orJbym/CUD/MZNn4tsvYO2KUaKw4UqmKSxrdmprfTh3OyeBn4/LKZ0NOwe8vI7wueOgnYl6wQnPjIgsUCMNe2KNaqQGbbkbnA7bjpWUhwu8GViyMcGjb3ADnCKa3eez6GfvxTLao/UJuhFU1CAGgB37XT4zll5ZVtIaOyZiZCmd1RuDxVrt+xm6YGl4sfuCSTUljVzKArlJDeMkkG94r8qZ+25dWju5NwM6Fdjhk5vNKkJPFLN974bJSgae5J4uQHOLUkwdIjshpzVLl8KlFN9fH3A5VxuGnoWHSuSocgoNW1GAS/rmYpIlsPhbzymZWaIHyM0ee7n4/nOk+MeZfq0Jsw4NtW69CE+tNQq40eUawTcNrj1wKc5s8RNk+SepTXafVnBtrAjz9aR1wLdXWFB2eQwenybrVKKqWnheyPP5ic4JRBU+XLYwujaEiWHi7r1eRQqOiKLSkJWbN4816LFxZDIwLV14bpSpYDqTSf4jJ4um4x6+PN+gMDXuYuXktnt3icyJaSKl861UvIXDD73oewJwVcqea14z0cZQC7rsFta4CnS0bYcnsUGl6bjmWSPKWCrlk/sF2Yqyemp2xaDwFYs3ceYpJ4u+dqoJBxa2TP2cGCa+/D4uFU8CWDeAkYREPmczGWbqqVyPaaiXTD1H7/qbrJzezcprVT7zQQ9aYsnik1NXGtCpk5qlaXfDgc/MSF9GHZl5xvvLpfegyel3xpl/Q6R/xiys9bF/FF5huzGALl2oalW1GlaptmoxYX1rm4G5cwDl6fH8ZG9UxFvJf7plmlrIBrsQBTVjDC+faNjYmi/NCVDSxEe1sZIZIgtZkUysURTXReDZrVoZJl9dAJyIZAG1zGPV64wLAEki5F5PFy9LjkhxegsdSdJsg36xkOYhIcF6yfaVKPC8OjgM1gCZX1qriOXhcMo1WG03GNTNK5v8tWeMUO/iFYCfrSFG6zB0u0wAA2AzUezifSJ59XaIbQ+KSf/HlN8jZ4rJ0CMniw3lHOLshEyOqWl5bLUjzUGZhSiNRfucaBhKpemV/mDl4vCw9Qlk50omb1QVkHfoYHO59xN5FbH3gQcGxqRiX5PDDZdMqQgBtrujWRWZT0IzXlDXgyMTLWbIf3nqPxNS8S/DY+gXWZHyxIWZNx5Nr5skiFcr+rS7YZgtnEjbDhE0/4WudETkROYQep+Dxdjxj280YfWiDRJayVqk06EA3Ct97F6g6u6qCxNflkqjhC5DJ2ZQsDkVjYv8PeaZSOcqhJ0ZZYvjUswgmFYW9y+gYkqu8D6WxWgHcsMOhNEjFtkEStVqBY/T4m9PY+lqjoYHJPTc1JVveutXlUBgqCsCWp2ttXWyY9pwNvucBD3vHFR8I/1+KxofZ4buJ6LUA2cpubcGWByXPnKxtbP7HhYE/BwWjVotGbwq+tK+FC0pVfFocnoLGxlJjcmPI1SxwxiuB/jGQmQ2Zr5em0ATQsmXhlDdzHP48sdDvfsmlpCgCBWLp+9dWqYKjr+pEMqPvTWoLWjKaVIhyKLia2IO+nMms6o6VdsJM8SZhih5P04jbfsKbm2cWqeSWpcs9cjYB/dzUhyLLlsCz8QvBBFdVx/VMTAA4nTd4njartSdzoQeuSq4pbrmSXWiqqg13eLk6nKymBiYgQVEhYsUYPy4Wz4F8xRN39AHwTEPgJWosBfjEI+zeXkY4m5AT8ePP09jwbGrqGazDFNgzop/gbWqueUYR3v1uc4QoMTPjtAAdaKkYxEpZ7w1T8LSqeJqH1gxzOrdmnTQcEQm++P6452zStR6MwjrcAKCSee9Sm14uzn5Cba2VmAy33YSQLT7NdFjtfWhWvpfk8Bg8bqtqn1OYUakY9C7ijSnYdjONV+MkQX4ushrzfnPGHB2+Od6slRUfJNZmWqNLR9BOjsh8QLTXKTSRR/yumwQdq02u5dFvB3by1KriEHo8LV07lOR7XxtEicJXZlh6VmYCwMBrau9W47TEv9szp37OlllM61CSVBQ6xpo3jgRa52S5d7Zmur3J+MU4Q5wNT8ngby8d9i7jxpnGzHoMBsd4HXQpuB6TaTRCx81Qqwu+5kB+iGQh4fRqS/vVUPHQUeNTsHTNAkdkQhG0rs3X6bddP/lAB6/X/yaMUrWRarGSRePOZdz6hBsnpQbZgT5H4heXCvzhlrI5MbI5J5LqSnKXK4lZnFrN40/B4zl47BwNlJUA55gu+LR0RNTHtTdCxYZ9VFIh57lvLiN6XXDfLRhtxNYvzGCgoK0yWdBanVfWBFP9rqmJIpFOxWAOvuGqSlWE6OARYV1sjJZl6QiWiIR/X0L3SpE5OprEfg70c+foeOFk3PQzdptzgxsSGzmRoCGjN2Sv+Rg8QtZ4P8w8XzTBgBZeYb6sUSRWyHzfvSmsAEwt8ChVsSSLpRjc+9z4rHMLoJUpoAX3XUYsCv/+RFDWH975V/S5wMKd3q0eL0tSrWkLkOe3CFrE04WqBnpj6V3UqriZmYktVFfL2ePS4SV63LiAN8OFXAaTwzPzku/4fkuFJQf5p8W30lkys1QVJvbl6U3Gc3As8EnN3VDYIUpVWCiUmtvnkUZpLAadztjwsxCmSc6mYeCDC+ztTnj68bJplsDCWtKqYtdP5APE04dksr1zlDFflg6HacDzMjQXRYGmxB9crsDQkRAArhtu0hQXKqTXqQXRDY9cS8zuSEYBWfNrAGCBjYhsAGavZdNYUcJYumN/dqMrRkXMsnP0XI1bRK2aIttcvaZiX6UlWzyygI7GElb2abGwipqpj8FhzgzXVGpWChb+aTEcX+gznSLpBcSLZVVYktJa+gW10sEymIyZjfp+zJz7tHjMRTWY5rddvzeAK6X+HMAzgD+utf7l/9a///FFC11EIzyeSAulp7Zhn7IRpkRUNK2og/22o7/b+9CI/rlqdJq6/N9MnoUmFNytqi0zVUrsLlOzm11LaIWHfm5G9QAgU1d6EyGG/2TYI/4IlJlNyWHfT9iz8EcYJbKhADQTIKH0vZzJ+0RsbJ1NUA0Hd/jV4xvs+wn35cD4+TrzMoLoh3MkCMTzogzSzBNoyCX0hoYWy2zNJXQ4XKhcHF3A8zzgFD3T+zJuXUS0dK+o6aP4c9J37TM9C6cLY3YErbxE26b3yD0Qi0wZVgDQ5u5MhlVk3B8LHcxOV7zrI259QMwWNjpsNhdst2dsxgtC8M2LRqCq0YUGRW26uYlcALSf/XePb8gUzb2e9g4A1SikwvLnZHHJxGBIluC2vadsd7ARuWocQ4dz9Ljh+aUjVx1Gyf1bm3ByCWtpZHtkUZ7K547JNTVhZHqe5jWtFfloO00WALUCB86QHfc2YrbN/U8y7V+93OEcHb7aHjG4gF1PFWZgS4WHzQneJIzD1JhQFwCnpYc1BbfdtA6zMGTsVaqCthXvxzNuk8WBp/10fL8T901S1oChOZ2GmVbOJGy7pR0E00RQyjlZXLLGxhIMsuc+DrHKND5yNTKahDlbfD+T6dPbjvZQrBohKXaSpHv9HBzOWWNny0pzrAqzYN8uwqDC8oF+LarpOTuWhnMsusno56x5+AIlnLEAxwgAir3xaeqPU2gZ9cwH061PnKxqltmvg5QPnJS+6xL2fp2X6nQFCq+f/1wIhYMzaq1/pZT6C6XUn9Va/+rq7/8MwF/XWv8XpdSfKaX+Za31f/ltr+dUxb3PrWt7TgSl3Mp4rqThulUIkYvGY3B4CgZ7R6UJ3QBqBsn8ywIC+hP7qgArDxRAwyELmzG97wMe+pm9m9m7oipYHoOxcIPKM7a25A2sLrhn/u9XPN2HMN/cZv/RvdK0MEqhAbs9Ubtu9gekZHGZhtYYIsOnrpknAWQ+5QwxIKzOzeY1F43NMMG5hPM0ICSL3pFydOKM3KhCJQgHgoGrgMBMDMr2CvbjBSGSsVcXHTpjVwZAJVjnwFknsM5rpG56bRulNzThRinybhYcVg4RYpkQ3SsUhTtPZfbGpkY/k4tYM4SfTtE3WEipCmVqO4BoqDO55XU2IrJEXi6pMARa6U1GAHDm7EiD2EM9VytzJuhoKZoy7/7CrJPcKrRcNY16C317XfIkyfxvShQ0H1rPwbGYaWkK1SZ4yTQcA5Fe929PW4SscdeRUZOsaaGFThPTRrmpKq6NMnAiZt0Cs6ypm27G1i0NThLhkra1BeZcNCJTVLt+wVafMfQL5sXjMg+4LMRsKfxe8r1l+IJSaM9amq3XFhfv+NkZXdhbZ2h2ANc9IA00HrT8HQnp6M9LpWcUq8KNI/vYp+ApA++W9tyfg8cpEg4/WkoYrqGYC0Oeohh1vrRETWDVXBWe2MpAcYVIlU9Gqgo/zB1K0tg7oFg0KGXv6PlLs/GcTBMtAuvczd6UdtCIAKnTBbdOt3gl/iqPgQYn02i33xZRf38G/t8C+B/5v/8awL8E8FdXf///BPD/Ukr9n0AZ+F/hd1yVv9yvJ5o2//VImRiVNPQzgjfJJUyTho/xTflu9qiVhhyTxwFxRt/3CVoR7Y0WFw/f5XJky8FWhCozY2UA8KYPTVRSsY5IkkD+3TSyVJxsbt/0M88vLDhMA54uG9yNZ2y52VayQWRPk/NMgbuxF5iPfh2oMk9RcTqTkMIqqFjJ1vPK8XC3OaNPSyuVAdrY0mTKVRgkVAKHZKEdl7e6wOiE89zjw2mHwUa83x5wXHosycKpAq2B3TBBo2Ip5DT3aemwFIVzoqxisAWmqja0Yc4GOxdx182NETAli4mHxW4MLc4pWaRC8i3i2wO/GCmjlYEbd+MJ3gfkRPNGL9MAazOcjbjZHXF/+9zYJ9PcI2WDvltgbcbxtGmK1lQM9n4mFV/oUKvCIXToDR0cMvMUoHLXGlLtyjR4yXD/43GPZ/bpsbpys6o0z/Cfq4pLcnhaqN/ynr2nxYhpzgadLti6iFAMXpYeW08K4Hf93A46aXxZVfGzDWfNmfo0wpraugiF2ioNgaukUSj9DgCI2SIVOrBE1Sn2uRJAZSiE0dQElkNALhno0fYwV6yPS9c0AcIo6k3Fm37CnsfaCQSmVcHWL+3AmZLDKTqyT/WxCWEkkD8tROGT4RgCizlNQbW3a89JmvR7F3HvF1z4QJaGu8yTfFo8+x9lGKxEiLtuQSoaL9EhZI2Zf75UtKEy8u2drti7jMSJnzQYhUkzF5LtT1kjQ8FUNMxesnuBdEXsdtG2MXkE6qlV4dZlZKsoXv3nZuAAbn/0/w/X/1NrfVZK/Q8A/ie8DuztUkr9BYC/AICt3uExKDx0xCA5JXKo87riFBU+LYBRGjtHC74zGaMhf5SXSCfSjcvoTcXX49wWT7562JqVXiFTQJebL9hTqgqKG4eJS6ReF7aszGRu4wJzWykL+u6ywTkZfDtZeINmpUoLiwJl36XGHFGqNmxbVF69C9Ad0QhLMTgyk8SZjJgNzqFr2ZmYHe10YcFPbv8GCB44L33jgk+J8F6h6gEE9xyWsWVOW780J71S6PN8uX9pzcFcST5vdUGvU2vSSkNuY2lOodxzeY+X6NCbjDfMt1eqQrHEuDcZOyfGVwrnyAs8KT5sTcteFajy2VeN9yDTqss0EIbqA+aFoJ+bzQnbzZmHGdQ2rUc80I1JCMFjeb6jLMhQtrj3tFEXhhysKciV/m4wEXtN/OuneWiZtcAax2i5x1Lha20yacN2COSMSdbGc9H41aXDjU94y3YCPWdaVpdXwyIAoLep4bvCpFLcHyLmQ2zVDPUYZhIlqYKsqP/x6bLBKXq8Hc4YfWjVXM8QUwX1PDJrDOSSPZGzRQi0Dn447fBx2sBrovjJVCoAbXBDUbQ3DcNsGSsFUSiH55lmYcpw8Cm5ptI0qrTv1rOVhGTD10wx0SIAaDMoxR9bKRbm8R51uiBrmQS1DswWuMzoipzJ1tdq29gi4m747eRbYtJrMW+jbFiqewncIQsHXZhYPLnJFp7vumo3yGJCs012xawqkCxG5qvPmRIkoVResmlEjVyBvz51r8ywfnz9vgD+DOD+t/0lQyh/VWv910qp/7tS6s9rrf/m+mcYF/9LAHiwX9RQgL2iadPih0sPE/CGhDhiGyknqVI0cm3KCjeO/z+btaHDN1oDzYBpZAzr2uLzerbdhQU88mcAlWFaOfzMZHQ2AXXlamoF/GwjZbeCUhp90c2ovlOUKQNo7IAlOmy6ueGMK/e24GZD469SsrDJYo4Ogy08jiozjzyi80szborBk7UqZ0zbbm4iFckkhRucq26ugjsXSOwUiFo3RQdvczPQqlVh62W8FeGx0jh6Cf5KZKBw4gngAw/a2LHCkYKjQVx0Y59IR/45WByTwcYW9LbAKN02fCoap0ybJBSFt53FP6s06s17OkhlhulQ5ob1nthC9Zbl9c5FaJ8w7k8Yi8IwTkjR4nDc4bz0+PXjGxqU0F9W21ZNz/kSPM7RtWa3wBVzpMBziAbHpLCz4PJahj1rxExMJwoi5CBIwQE8Ki1jNAlHboZ+OdIzpsxa44dpaGPBlqLwEjSsBh46MkB6xRkW+AWAZ5e/KRGUeMfslDk6/DBtMGeDh356xSaS1wHvE1mXziac4oinC/Vl3gyXJrbpTWQCQNfUqLlSw7uqiu8DHfTjtYUFB9jGdHERd/rUKIpKcXbMB+EUiQ0k3kgb9lwX+EP2zYU91IUnPjOV1ZvMEIrB1tEcUvJTYU+XqnCMBONRQ73iafGg2a7UU3vX094OLCybskHiprcY7524V+ZNfdUrk0vmvIp/vvz9zhFd+q9PHjtX8NAlxteF6bQOPxe18lOg/sBzUCxc/M3X7wvg/zPWLPyPAfzbH/39v6y1/mv+7/8bgP/z73oxq4CtJRJ+1RV7t7qDXbjT6zU3xYLFKWk+cYH3PTUWMp+I304eM7sQEheTZPNDVbAAm2Il7P1afshJf+aBvq+MbhR1oCWLlM0iY5pEVbgUjaeFjKCmbDDlAd9NI970M97EM3YdjVQbO8IgXy4bzMnSLE61dueFUWFtgjEZb3QhGuFl07DAJZDj4NjTaz4fKBhJSXs3nqhxp3PztpAGWc/ZMy0QyqCW6AiPHyaaOsL4ca3kBxOyaaW9UQWWB23MWeOXF4sK4NbRZxMoxXQssedM5jnY5ri2sRm9KY1+SI28jAcZkJGI+nVOBlZX/GIz494v6NjEy3cB89Tj5bQjCXey2PYTDWK2GX2lQQOXacDLYY9SFW73R1hmeVBGvrIhyCvDcuZNU9Oflp6oZsJ758MvJTrIlKLPtZRVdi39Alkjf3seYVTFjUs0PIHZD0du7PZGNygks3GY0xmmrmK1AtJIfDkkcubkodyJ33fmhEUOEHmu1axBOVfNE9kLhqufk6Hcz/PYDk7JvmulKfTWkIFayjSlCAmo1VAGbhO2ALqS8MvjHpdM7ItQNP76SEnWH20JYiAbXmDTT21I9PGywcfTHt6QM+HGBU66NMLSwV9RNYGrQ+aKPBCqbaweuffHtI7z06BKXqiKczaYim2HyD3bdRyjaTRkxwmg0QW3HFtC0XgOFt+cHVFCVcWNL7hxlWMRVevCWpEkUi4RXcWqcAiaWWBALGiBWNS5Iiy6ZN38wT3/nSRIG0vNazz+lpj6O+Itaq3/Rin133OmfSsYt1Lq39Za/xWAv2SI5K/xn8BC0QroDWXTS1b4yFnLzrHpj6ObEwvZcjqfWzYzsCw5Fg1TFWGwSgFXQtNSFQ6RcFeZnH3rF6RK/NBrCft1iSUn/5JJam9S5YGilO2Iu5icqLc+NpxX+L+h0KCIxThgXpWW1mSMnC1rXTCFrolhalV4PLJRP7MTRh9aQNaKOvjeRfh+wYN+wl2hmYY52wapGF2AAjjuCeZCm/LIFClhkCjBDw0JNk7Bo2PceesWJGNwjB4h96/9S3Rhru6KHQqrwqkCpYGtpkxzZ23DLaX6kfe2DCNIY08oc3c+sZx5wQ1PMBIus7AYnEmojqTzXUdBHiA++xJ8MwX71Yd3UKriZjxD64rn86YxEArQxsXJtKU3w6UNuf44bfCJ+cpWs38GT16XfgA1QQUqItz+fb+0deB0Qa8qeV1nw4rBFUOOlSZDyfi2vV8wcJ9BnqVTJDSh16QhDXPRqNwoFKis9wFdJSMyOiDoPe76CUYXDAyhCExmuNEci4EtTNNMBql4hGRfTfMJmdbCyK6esAm6FLwbLjyAgSCfP70hZgX5XJP9LcnkSQKmdcHQzXgAjSqTRm7I5C4oviFeF7ztpxUeqhofJuofDexx8stLj45Fek4X3PrCn4UsjqXZnpgNJf49sj/Ju5vW3cYWFE2GaimtrzEaMsV76DJXo1RtFBCVORTSnFhF5luxUAPeKuCGvVzkeROrBOxLLv6rxNiSuGZ1wT2jBc2WuRLUEwv19Lz97Sn476URXmXYf3X1Z/+K//0Mhkf+U64KIBXCqK2q2LqKWukL0wml+BSlGzyy0IOCKYkldgzqky3niktNiV7jw6zhNTmaBUM3TAQDoyU+rQQhKfNEMEC4mm5Y5D1bqcqG/25aA5vlBwPQ94mMr7rEYiGToVXB7fZIZX9yKEVhywwWaUI5F7EEj8fztp3kxhG7pZW4DCc4Ntyf5h6XpVuZLKFDLqq52AFASAYX3pBie2kNLXjwFJodByatS3Pm+zD3OCWNHxZanP98T3z7ex+xFI0fZoeqAO/ZzjWugh7h+gIiSrCtgZcqcV0bQ6XSBigVuGE1Hd1L1RqUpRjMS4/DNKINqgCZcVW2O63sl73bHFvQr5UcIgMPCBazKjlwez7YNVZ3O8m2vV4LY5Hei0uhCJQ+zYTNP3Cg3LmAOVs8B4+Z38PqgnddxCFaPAWLO5+w9zQpPRcFaI1ahTJGmXSutPYyyNVO8wHRXWWoMncSEDin4FRogrwMQ3DcM4nMoRcRk1i9hmxYQbtuf9IV+PbMNjbi3XCG0xkpG1yCR8hk4Tpn02h2HxfDEIowdkjo9nTakhVvP2MOHsd5QGS4UUzTZA8ekwFAr1lAk5q0qtwQJwYQAGzZNkP6A17LZBvdKpmJD4aNpT5V5n1N4hrgxolhlm6Zu1FEVRQYSzzEY1FIBbBaw5bK9NmKU6K/n3m7vR8yBlNwywrS90N5ZZUtLB3AIhSFTwv1hFKlIRCdBt71Ebur9fvgCRYuwKsE4MfXTy6ltxo4BaA3qk2VNkXBKQWn19l21CRQOGeNC/PFxUfcm5Xj+RgcchXPDoXekFvYvU+NkiVllHCdpTkjhjpt9h4SYNACn+Vyxmlqav0hz9OTEVAHbuDd+oQtO9n1bD3bd0sbFAyg0fZqYD76eOZmWkZUpFYzqmArA1n5EBHfk3kaoM1rxeqJmSO/Om+RqsKf3j2is5FoilA4BNrUO0fy4YHVaALpDC7QoIpimhrzxgc8dBVfDau5Tiwaj8HyJB56dk4RU+cY5ZCgJmQsq7vjkVkokhmKVUKTMzNrRuT65LNhcHPZwFsaK+d5PqQMaTAmY146PJ92OC8d7jenFQM3BUZeUxcauFB0w4oXDmJSeRFzhBSmFIQy3vMBq/kwD9xkSpV0Al6vQhwZVzcnyqgHmxqjRGiFe5ewc6kpAkVKL0F44glAzbKXD0Txqu74NUtcK74lWxznoTEwpuQQi8G+WyfxXK8hi3XqUSoZgGdV8apmTUWghdwonp/msamMP0xjGxiusXLd3/WpTZkRGt6cyZZgqKQSDnx4yjo7MQOMYKOKGxfW/pSYXDG2nUrFS+4aCycWjU+Lh3gkiUWs+GkvRSPV11x8Sdicqk3uLtCHDJQWBafJhntwFKSXokB5kcEpkgGfvhIMaiZhdPo1VLtU1aoqiUMC48xZ4xQNXuLKKfeRpvsI60QrNF3M34eF8g96ycf4ol85nwZ0CmWt0FfwhIvcgoBJFk7VRiv6bqYGwR2PUOulcaA0SiWRkNcrVUfgkr0n+fJT6FjCn9spKTzwl+CRKvDQBRqndGWE5XTGhsUIl+Ap22Zq0N7TaLDORWKUpJ5VlanZcXY+wGYy5Qdoun0phXHwjJ/dPGGJDselb00gGf5wmQYcTluiDw7k3exNwugXeJPwJ9z0uRnOjYUyMqOkVqz3WhUMHR0yMm3mHDocQ4dD8JiywRfDhJ1glMXg3x+3r0yE3vWZm3VUystk+VxovNp/OPcsQaZA6nRF4WzmzqY2ALYy3AD+3cwYOgBMPAPTsvVrrRrHacDzNOJ2uGA3kKJw9IHoV8Hj6bhvWK41uVU6IjkXlkfIurnoiYBjShbnpPGmp+nhYLjowzRgZlXq3i+47S9wZuX8B1ayEsZe0SOj6oKtIlXvd5PHwJWksDbk+x5j1xqFAFrGPbIroWShYqb1H08b9IaoiDJFSiqLTlgzRXGTcPX2LlAIaT2kAbQhJgAZp73MPTky7g4NfnucRhyza9WpiFuk4Sr3j2Cw1Qo2c79j181wZrUgyIUHK7sIrQZa90p437S+Ni40hk6twCXSgI0XVtXKe16yZqM28mnXlZqRG5uYGmwY3qApO1bRQA35LIYzd1lzAvmlStW6BgXkSa1cb7LjUPAVuPUEH0pwFSuAU3RIFZjyyp4hFpI4TJJo6d5HPFwNPAFowMOcFU5s3vY3J4dQgH++S81y4jddP2kAJ4hD4V1PE9k/zAZWUZf2KWj88pzx9WjxR1vhYK6QQK8rPSnQwzhFhRMMHjqapnOMuh0QCjQrk+YSriKhvcu4ZXP9WDS+nzo8Mz2xVJpo73TFOQ3odMGf3r6gcwlbNryXzvcTu7zFouG5/A4ZjdutVCWhBTcKAdD4MygW6mRkxsQWpgAKle+6s935gHFzgbXE/y7FoBbK4EWN92N3OeEDA2ieG0pVeJswdgsUe7Mc5wE/nLeNWdAbmq7dcwCRyTJvu4Do6N5S9kML/pQ0Ok0ZJoCG/349hFYev0SLQyQp/a0jtkosGuL3GAsJuj4txC6wusJqypxy0ThNY+OxG11wz0rX89yTuq+fiKGjC4wpyFk3auWnww09r2RRofCG1YgizhGnwVw07DDB64RQLFLWWHjwByl2FT4tHR4DKTANH9LE2yVfljnZRmeToAYAD11qG3frSOx0jh6nAHxceoSs8eU4YeRBysA6ydwzJ1sa6SIc21gyU9t1M7Z+YW57jylaAB5TqnhceiyZZjjmq+z0zi+v+NMpGwwu4J5Nr7SuOE4DLkxlfdtPbLErvioE85SrzyXP8JI1RlPwB7sjNi5gZNFbresBJ1a8G0fQpHgRTfw6z+ghk3IKVtdQIRJIUkaOjzRj1HFFLa+9Z+/0KdrGVilV4YmTs1zJ9Y/Mxuias8YPs0Fv6JlJ/+OBkMYW4F+ixZR1M9A6sSfPxuY2y9VUhVorYiWleW8qtgxt7iythe9nj62lZ7l1ZAGwY2HZd/OAYzTYuYpSKcH8HSSUnz4Dj+wjYBSw5w9J2Dfw9WiwsdTdpg2iGQdS8IxFXxgP6g03HssKr5S6ZvlEPwTfpIp3/OeaYRTBta2umLLm2ZykDv1qSOQrXjSWZBHyhoQbNmOKVAKWq41aoHDbzdh5GsZgFHFvS6WsQ+vSgqfInYXu17vQArj8bioGH097lKrJUIuNRGQmpjBNLnOHyI5ywAqtSONKPKcrmwFt+qkFem8TbvoZ83nbBhNLRmDzKukVOOExUJAdGIeULr7/EawDoGW2HVsaLEXhkkkJCwBfjzNGI4FfN1xcYBUZdCDvP7I/tcwMXZgKGYvGH9w94mZzwjhM7f6mbPB82uESPL69bFvGKPagnclNJo/1vCPWgwXGQvfsGDqkolk5Ss/Q24iRJ8HQAdPhnFyj110LvW4ZDhPGza+nsekSOv6uMu1JAuOUHB1wgdaVNNKuBTbCqhEKKPmKUHNWqzXpESEbqWYr3vQzBhexEQ8UFvhIpZOZgSLWE7LOgIpHHqHWM7VPDqyPfPhuXeYm4ppVisePVCkpa0zF4VenHT4sDg9+NTbTquIjs7sGFsiIFz3BqbpNPpIKLrW9X3lOp24U0GMia40btlAQNaXAGV5nmKqRGMYdLN3L72fLzw5tfetKcvadJV3K9fpWqjQ48ZxWnx+lKrKhxLNjhos09c88A3jgdS+VGB0GRNZ4260x85rl8uPrJx/oMFqi1VxjWHMmFdfekRvXh9lgMGQbeeMyBpPIN5dPc6tUC0SnRAvdX2FQVhFlbWczHvqJxxWxkXzVzD8t7NdMTalLNhiiRanAnhfjB/ZikAGl7waa9vJuuDTlHXE36dS2hho+S3E0xZ6Dt9ZECcxXGcEcPRlPcbZzP54RM821JMc6tu7kZiUAHA47HC6b9j3JAyO07E3GsF1v9t4m7IcLD5glOOZ52qCzETcDuRNuXMCnecA5WXjOIhaGlX6YOx4QsQ7dkF4EgHaIaZBR1ZkHGl8ycW73LiMngwxgx5mLBrEx5NpfbVQN4NvzFjJnceAgN7qIbTeTqlDx5PpE48bO0wA919bQFYMvbzIe+hkpa7ywd4cIM3Il/vWJbUaXovDQBdw2GiXRBbWpjbMMUJZqdOHGHnmjiGLX6QIHtKAkzT6vC2ZpvnG2+G6Y2pzWWknxSO6ZZDz10NU2RIKCGh2y52QbfCWbXpz4NgDz/iM6XfBxIZbHF33CwMlByAbH0x5WFfaSIThOekFWZ3yxO+D5MuJl6eELeZz0lqZaiS2rwBmHyJRW9iiKRcP+qOlWq2qfFSBW1MaYNhBjo9ccU/x0KlZLC1FVyyEsAVKag0vp2yFHrqO1CagkTlhdoIrYCKPJ8x+DwzlpfJyJf9/riiUTtVWg2+v4RXYJFIvEgXHDlE+ZvEPJjsLOErtG2HNVF2SjsHOEvT9H4tDHK4fGUlWrwSuAE1Mlf9v1k0+l39h1oDFADQCviVp45okWZIZPJ96F4Q8vMlpNpyfR+CoUUwkJz0ZTRg1sZuMNTSeRsu8QHUZWOvZ82pWqsMkG956wOKdeez0I3e2SiNIlC8obYrVsfUCtwNNlg84meMkui2Y5r+D95FWSisHL3CNV3fBIycilLC48NCBED+8DvA948/YT7vMTDi97zEvX4IOWibFnReF/ztFjzquYYtPP6LsF77ulNQVHdqYDgP6KUbJlNaoGmKttabxUsBB7g05f+dbwz83cVJLmUihUYjpdqARNFrMxzLWnVTBc2QlLedzpgttuhrgpOi3Tb3L7PrlonJcOz/PYaJ4/TANyVfhivMDr1LBeHYGiKjRnYaMl689OF6azWRyCYwZGaspJp0m+7kyBVjSNPhcaJCGBfcufRQK5wFYS0MQeogC49Qv23UIYty7NwuA5eFLmcvm+6xasAx8q3nPz/MINdBM8TmwItbEJN+wj4zR56FSn8M+ZjkeThGpjBn07DRj5PTobaQ1wJp6zaUpOYeZci5duWTAUi4HLBl8PEaEoPC0W0Sn8bJsw+oDOB3huYsZsMUWPECzOPFR41/y2y6vgq7AO4ZZhFrnQhKcd72fpE2hVm50vQIF5ShYnNpHSilz9KidlRlUco30FSdRKyd4fbulzhEyH+XeTxa0vuNUVPffMFmYj6RZnaoO2pHFM/17nGGg+dOVAKVUha/WK8y2Nb/muAKESqap/XBl4AXBJErwVAJGiE71wLgp3vuBtl1oTU1wIR7PytmslkUQoCgGixBSnw9W7AOAgyqVorXQ4iHHOmbPgfUebSuxJz5HKwh2LbRZurjVZbiH5cMgGmZWIRMeLLcN6umxwjr4pK+/GE5xNmEKHmA1u2WtE6IRiaBUyZSYDv/ccPDajgnYZ1lBJHhbfFJgN+04WL5cN0bB45uJNP/MBxHL5rPnAA0JymIJvTBgRfUgjqeMGrjcEGz2GHWIhjwanKwaBTiCDOgpmPrBCBuYMPAfi1946squle7hOCu9cQqoKT+wzsneZJzUl7PyCt9sDD7UgZs0SfLNQXZJratwmz9Z0AMp9zUXjeR4o2HBA3vmlCUpCtjwmDG2Ihdi2CgPE6NKGLbzMPTqb8MXuBdZkvNkeMEeCCKZoEYrHMVp8XCxGW7B368HkNamDNy5iZPtX8WYRKbWxRBm0prSs+LiQR7pYq84iQNPEId+yPWuu5KiYDamDAeCLDa052QeHecBgI/6rt9/D24TtcKERdZ4EUYmHRD9PGzhT8NX20PZRvWJ2iIxdKpoCGqzQm4I5WWpEmgylEjq/4HDatqlBn5aOrSsoO+1taswTwb5FJ1A5kCtLazzwIXmK7lXme+sXHKMn8y2Ak441AK6MEbpvul4NSWcRmcwRcL425bYI+GRk3ClRhen4MFTKwJXXGhOAqIoE+ZDFNUExBRuGWogQQAFdmFoyWpC8mNCq3eHvayf7D3nRRqcmpFGknqTuLpXlANrDFWqXZHLU3b6eOlMwiNtXJW5lKCTpLVA4B43oFe46MY/SZJTE7nnUzPIIoCZUyAadSc3eNVfCkEmOT4yTu36Cr5kz0woZFVYrMSY6G9H5CGOoYSgugrUqnOahBdfeRTgztUZYrQqqUlOyZyFPx4OHF84uhFmgdIX1EX3RCMEjRI8fjnukrHG/OcHogpn9n6WR1AYdZ9Pob4JxXsJqVjTY2DLqWhUyVMOaH7qAkA0+zA7eVOxZaHKMFt4UeEWZyr1PeNut6tbCm+R4RSe8YWdJpSpMIWOrCvKv6TQ1uTpm1ihFTByvSREYoodRPIKsUHZpTaFnlVRjP5ALnoF41XizOghKxSE46mgSXE/B5Hr6kjQpO5NglUaqGoppl8CKRXudkDRlUd4U/HwMLUDJwbFw5iwaAMeV4ZebIwJDZwUK1lAysDB/+xQ8lCIXQFr3K5tFnqE0E52wN3hN3VQNBzDendtnFjXwvHQN9ks8zu7jaYdvL5sWvHYuoDMU3HpL49BEnl6qws/HCUvR+ObSM85rMWQauGw5q++7BTeJ5nB6XaiaY/+Xa+M6gKreT0uHxGZSTpFHEfD6AKlV4chWEdLE3LmI76Yej8Fg51YjKhIQ0nMVb5kf5h76qqcDMCdck00yqcM1HoMn7NtmVshmxCo21Aqhavzy0mHOYM448PUokvr1AOl0xey4L8SMONFIdH7N4MX/RfbOkRvRv+36iVkoFR/mir0jvrZl5ZMwGTSXONf0K6EaihdvrpQN3HWqjRpSICqi5ZMLleAWy4G6s7ENggXIhe56Mgt5rqjmMyGn+J6ztVwJBxRcuhkQRc/NTN2YHjlraE1+J8ag2aAel4GyEDZo+vntY8Ola9uIBqelx+ACOgT03YLd9twMm0o2QAYupw2O501jVABEQ/MuwfBElpxX17nd1fDa89zj++MGg4styxe7WWgu47LBYemQq27DX8XucmNJLSusgL0LCMXgwtis2G/2JjFDgZu1Vw2nvYvkHX3VgAbAnOn4ioJGUI84xCkULjHvxhPusE4eT9mgVI3ny4jj0mPLh5f4jvwwddDKcjae0ZmEU+zwaSbmg9MFJRHHWiY3iV9J5APwnr1FlugwV9/+nqAqwxqF3HB0z1WA0NXmzBPuo0cudEAdAg2k/jD3yEU1K9Q9i8i2nsb3/YfTDhubcN8qiNSEOT2ILSOHk8ASp0De6W2ABCouyeJX512rCjYu4s1wbrM5BcY6RBpA8a432NmIAw9KkHUwyoi6SkrFN11k9gbtkfM0wttIU350waabmX5riCNvMg7R4QNXCxpoQhhxENUGLUlK3MQU47RrEgGAdqC86xe869GsEwTuFJWmRkWGarTCa81JBGX5kp0H9kb5FNaqymsejabI7iMxrVC0DEaRME2k9sLeEi8hgXnI+Iwql/GK0vzt4nBJGrc+N4jyH00G3huFP9wCz0H+n6g8n4JDrwt2LHrYudQ4u8IQ2bqMO1Na6fYcSSko3g5GA7HSiLXRFnw5BOxcxOjWyTsvy4AXpoQdosHbLhK+yaIJMc9qJT7j2e92ByhUVJAx/KfLBrGQ7F7wTaMK9v2Fsn1mQuSq0bsAaxNuhzO2ncGRG6MAkLOmEpxPXADN+hMAluBxngbEaDFER97ifIiQOyFxrMXUiqiJFkYmnGeh2KX2noNf8PaqadSx9/V02eIQPDaMlz6HDnPRrcEozb5YFUpBa6at80nXCdv0j8Jz6HDgDL1xXqtqkmGhiBWGv+54wpFWFc4k7HenRnuUEr8YttrlQy/y4UOT7mkSkskVj6zeJIMig08LHc5Oe2xdxNt+wtYtuOunJuiYM/GOxYlRnosc5DesvBSuNAVui6ela7iqZFaSgZ6ja6Zidx3ZOhyWDoPVkBF94DUHzZNhtLj2rdYP0vxdePbm9SDic3K4JIvRJu4PUcB4ZvjFR9+asKTKpSTknr3Pd/3VkItKjcz7TAIlyW5zJZHLh5kC+duOegg07Z0CrniR5KphJmqOprxODnpeBnycu7b2Nja39bUO+iiv5oAC6xSnG2b1HDipyNzw06rikg2mpHHrqWHrGFL7tHSYs8a7Xmwc9Cv/eKPICTMy0CJUw1jEtwY4RIVzAr4cNHqfW0+n53hkOKgLpi2HnBxmUWDcuo4ZNAy/bGzCaFaPJ5Hld4yRb80/IghFq4rBVJw1qZnEEIm8ezW0Wj+OOIGFTIwG4QfXSp7h4tK1sUQfPCx0+t16wBXVsKZcCArIhXBKafrsXaTS+QpXl02758k83182cKrg6/1Lo/jRZ6CBt3s/Q6TBAPDDaYebYcLglzYsmCTxxLsOMj1F0WBcrckq9f/P3L/mSpZlaWLYt5/nZWbX7r3uHhEZmdmd1WwSBARSoCBoAuQMpCn0ECRoCpqBOAMBnAF7BgR/6odIsFtdVVnxcL8vMzuP/daPtfY+1zMzqgR2IZinEIiKdPfr9jhn7bW+9T1CMHhbprZttpr8T/SwQpsIqRK05o16Uhzgy+HI77r82qlWccuX2xEAmpFWhU5iVo0ZMFoKRjj3CwYduGvTFDuH2i0o/LubRSrAnSnoWFggAJY/M0yD3Y4g1A4dYNMvSXieKJyvSfzbuqTpBeHTk3V4PL017/N1GfB8PTE85XFbR9xcjz1NRTZ2h0BpXjIr08jqIqyO0R/6rUEkLml8Xjsy1ueOctQBV+Y4U8CzwLdFEJTAEFmlK1Y4wTNeWrNEawSfkfT/G16CvacLWp24wwZsogd4jRo/b13b01Q8vKaiA4BKhOeHJDnyTePZW8yR4u4kiKZb/aolgORtW6oaPkgm4/EwzZBiv/8rNGR1whIslqRxlIHjxeh9nkxCKhlzVEhFcVpWadNwtRzwaUKvIsOQqk28n4ZtN5OTe+h1yAL3LKCbWKykEk1BT8z2OpnA3t2aHAL5cx1VpJCFd4rKCp19M6yt2FfBngTwh8NMIim+T2rq/WugQ0EJYNSFoRjB+Zq5NXhakmFcEiy9L0AqBJFVs6+joXoSyk6GqBcFWAsoodvUQ06Me4ixZxz9ryaVnvBQgWuoIH1dFAAu7VaOqRAkUr1QAOCLo26tUzuWbiT4BK5uZMD3Q4CRlI7hE+UWJsaxrUr4bpzbVreyPSrOW1MyekVdaGU8VHGIZVx2NJ4fXAoECFlBC5Lnh6TQF4HTuI+lQhQ8HCmhZ3Z9w0GrwX7hBVRgAUmGwNiv0DrCmADBWP+29ojB4DaP8NFAir0TE6KgZ4OnjbMzD/zfdaFX0+d/WCZ86Dd8mq6wHGCgEr3Wi+9otN0MtkQWvxK0ryBPCaJ/vpdUV5e8ikcCaIkylZmhBCtegdax3aJEymg3qJaZ9gMmkm96kjAm4sPda4OZBhYtvSwTLq7DXbdhsKF9zvW7moxHryQOfLjVAm1l3P2x+bW9eYslDe/gn4RRB0xM76oMF1LHpvbdjc6z53ul1BlYFvMYkTEYwoxdxb6lwKlfMRi/49fbnkIvQUZHlZLpk0LygiXn+Strh57vxUHTz+6laVRbywX5wta/HxneeG/apoVmthKpg2OiXVBlnMzR4MVZUjuriE/jDIHy1fNU+dAxk4MfsVEo9uy+I2ZL3Sn0WuKn+YCf1x69ym2HsnAHH4rAz1sHCeC+88yTD43pJd5NIQeGmQ7vhDzVr6ZTHJyc2W4WBB0ViMZCqvdrhmic8idXY/12UkENdAjsjzJHiTeP5kIIgKdQqlGDSij8HW742sNEVfdD7FnA10jQbciihTCfTGo8eHpu5F+PkEegYFQF/3LiFyd3P4JQBDZWLp1NguZCMRQBIQSWTWJNezDyUdflJ/2seyuYhZLal06UKjbtF4GXmQqv3uLCkWD1wagKMIC8JbTMOA9LM6XKnJazBIsflwkuKSxR4WPv8P3xgsF4DEwBrDAKsHfHl2VqykwlMy2QWhedcR4XbCylj4mUhEdvkZKCD4Y6vmARkqKi2wpJYegEbUlZf279u8bOIWeBqBUdKoLG2xqXVQUyMUleyrGHjKRCC0EMkcIQlZHgZfFeWEbuLkPejYqayIe/f8rNFPimJ++LD71oi9Fe5QbJhKARU4fbOvJCNyBG1dSqpVAhPPUrXtcRT8uED9ONlmzcPT9tAzKAx46YOM+uZ8N+0hRcvKVuTwdcg8GL07hyNuf3I3G0R7NCisJMFSr6gaXqpQj0hiLdqkdHpU0GT+EA0tNO5xol/vUROHUkoJEyw/kOLhj8z6/3uAbdxvJvetc+u8BTgOLdguWOfrS+ORHWKQQATNoXXlSc6Xkhm+Q6fclGa6sK15p5qST7/uiID1mSzSvfSzXmbGDmhwpkgNYmA8FNhKJl4nfTFZ0JGKxjdpXGA9+nT67DH9cOkyI5/JYlfCKHUSEKhUSLgm6iyeg30611sCmTPS9A0/gaDW4MW7kscdSRwrg5F6DCaE/ONGaJEAWBYZglKVyCwucNOBqBs6G68j6JZyvULU8642SIcEFJS1Rcjw1SSY3CuyWJz47CkSedYAEYxXg2N0IDL2kBep6gGHvn4Iq6G6oHxF+6fmUaIZnBALwZRo2oyixnV3xKkQtYZA8NwuUAW8ioqi5AAeDia74m2MiKrE9dUu2UroY19VI8qhx55G0RTBCQpeCVl3918WlVQUgKT8sEISjYtXYhvdr9UgC0Il0Lv2NLWssMlwGE482uR972LEwpM7ZgcPEdTtbh3s6w1jeVnBAF47BCqYzNWeSsMI0zlKZONWeF20zS89qJA8y0iNXGk17XN8c3vK4TfpiPGHXAoANetgFzrOZc9Oc9L1+qog8C+M1Qk8KpExkYkw+FiuKT1zgZwjbrIozYGOQv0at6OGQMMqNoCpluYbZZYt1oSuk4H/Pp5QCjyFa2HpCeP+eBl55Hnlh665EzFfgQNS8JKTm+k6WF1t53WysKfUyYNAmQLkHibAwmDjIG0Mb/jbnaC8ezUXSbaYZelh9IMmHiHMyyL9jefyeBO+xBJQjsOwGAsNS7bsMkKIxjiYamhKjx6js8po0Vh7ub4chMkepFQgIt1lW8m4oydqHR31/OuOs2fH9+bv7dKWn4oNFpg56ZWUbVDldh5VSfLel2GFyjwv90UZg08F+cN5piGcqSspAzpu/gM31OZ+txeBf0GLOEEQJnu08KSmZokZv52pIIXqpOpPUeTAyz0n1KCleTJbTYLRkq7l8dDNu9XQRbDBT87+9zW1zOXuGnRIySThL10LLo0BcBZRKUoO8boKagpvtU61opgO9Hj2qlUOsO1b5dHr9xZ38ylK06e1pw+kSU2webWq37S9evWsDrVd6RM2sO3iRIjVmji5YksUZiq1iVMRUJKwWUJHxcMEa+8aLhbAqsIurPCODcb41nWv9Ox6njZ06fqdznN9czi4DGxwzCk6+ug4saR/ZC+XQgzwhSUhrMwUBoSqCv+ZMxq90UH2gPGaV0Z/igkcvu2Fe/SMUslw/D0qTBfefQDxum4w1CZURnEQKFHMe0m12RP0rGYVrIOCpqFLbsrK9XK+pk66Vcboq6Tkd8M93ogPW2ZfOJIjAHThfh0bM+KC6T10wNjQDoBr23dDMTjEAH5ZfN4BoFTiajV7vAZXs31ku8C9pVuxy80wHT3Yacafn0skx4qQpZCAwmEPsnSyDuAceNxeKogP1uXKAkqU6B3UArZIVBR3z77n0MrDp8Y2e9Cg1VzPtt65vtKXHRa/dJzcCXrYNl/xEtxZ+M3LT/OAIYrSOL36hw8X0Lb6hwk5aZdxSx8aXJuZC4+dW7pUbExbJTZjP2g6f+b39cyXa4sr8+DZRFqhlGkzLDe4vbNjCsSAEfU7ehY43EEiw5HyqyN4jcXHw7cPhKpTL6rnHZF9fhbesxR3pm6HPYob/HjqaOKmaZeZL4MffQAri3HhLEr64Mndo0bYnEYXUColAR05hSFeqbuCOvhmnV8706GKZi4COFMNQItZyYqgnqgpUg2Naq0szDAFroa56SlMw48XNNVh252cQCgEsUen00EXe9I656qSwlKuYLh70bifb9/dL1K2PghH+Oulo7UkDoD6vFoDKlThfBWGUBNPiLAIwAhKSlghGlUQp7yVJ6hVYwlCDJrzSBCzXhYBdPJj8UWbab7VxZrv6zGxCywInpTE/b0EbPGrabCzMfssTBEhb+0/WEu37D3TDToksHPM1H3Lxto1wtgJX3rSUJOSpcc9Br2/ZXp8HOeigdiUoXFLa1h3Ndy9usoQ63dUTK8s9MraoPRUwSo/V4PL01wdB5mDFa14KW66hZU9qNIKrXxBPAlbHKiquaisszl7VXCVEUgB++tbETCNe7s6U9VBXv9bxU8mymr0TGqaMl5vsdgecphkRS5SuVbL1u20BwzDohZYkPwwJKFaKO30MhFomXbYDjzrFKoyszoCoDBxDdrMaU1csogtk+TDfkTFmkSu6OjIV/Ro0EA9hCWQCd2q2F61WniVRkYzRVKK8ye6qnzZujIJAMWiTbLqHHDpXFLPES+yYMyQA+dr7BWEqQijnzJHQyAb87vvEhktrks3hySXxvOlUbkjqRvPckL4VohL8dfZOwk5kUQXKU0FPwMCw41K6dl6818PnJEZxRn916f9T9SuW+n6wj10cVmsdPKhIraiereLJOuEVSrZ5NbGyZDLTQ8jrx1Xvhh9VgiQKXAAwKOFu2dBUFlyBxrb1PJn63xG4851nXUr/vjRl0z16hVwXf9r45IFpJvuE1i5PqIgU4vAWBxy7j0RIOrsXu9fRL169uZrUlOsWk3JVcWxIAqjkMFXcpwUkYgk+/0lR8QpS2sJn0HszQyYIH9po2vHzZ1V0k+qg2naF+eBBwWTWqUl02kFUsMQje08d80nhdx10BWCjjMSSJ5/mA3gSisnH3P+gAwyIfJclqtBSBN/Y0OfVkjdpZD8UOhhVKSFy8lMyQiv5RKmHoN+pWg4EPNRZNwEXTKHgV26zWqlVxt249VtdDy4SpX/FyO+Lq+la4axd05Pcu+IHMICrh503CSOD7MbKlJrFQ5qhxiwpvXuNgEuGCep8AgB0vn3SEFhk1F/AfYo8tSuRtQCoS/1IlKJWgdWyeI1U0E9iHYjIBVlIBq1FxnaCiWzn69SABdvHH+0NjZBpb9d7e3mG6GeRkWGGCXATuO4dO0XcassI/zJQhWZed1Q9940Jcl441hWgNBp2m78hFAx81viwjtqSb6ve9V48A0QyXaPDEifdGZkQt2SUwt6agqjFLIY8OAXAwgSROuUo4GmomvhtnDCbgbpyhNfn1bK7Dy3JApwMepytelkObdHRWeF4HEunwbmkwAQYZZ06C//t54M9CIumIqRbqmWDGg/U0YfByO5edRlpx4UtQEAD+5cG3vQJAB0blgRNsp9sBefEWz95wl504mJy+1ypzr2lKMUs8ua7Vk1QEB8UQacIIoJNESdYMkVSPJSN2ZecaCXN/8QIpA7+dSLNyi7S0/7uZprKPHU0jf7t0HI+WGErMjNnv/i6DJrGZlV9nY9ZD+5euX7WApwKsCRi1QEbdshKurWVhjrHgQi+byVUF+qtEFhC4BolU6AO3CvhuINbAfbc18n/lJqMQZa7TEVbHFkFVza2qwfyHfmuex+9HUGIQJPSdg0kUSXZzPb4sU2MEWE3ybIB8on+8HfHsLf6z8wsmtaGzvo2oKUsY7tKMon+HYFq3UgMhjIlffX6Veni9HhCCwel4hTYRiQ1vAvsmZ/7/aTFJ3VTPMWTTtGCaFnhnsbkeRtHDqGVC0vtpX0UqG8MSVS78uylxUkxuD4EQxOG1MuOOi0Qnc/PtqJ3ySdMI7LOEK3sm6W9YqKREIbWr9VCapOWxMSO+7l5fth6BYRrFE4CRCSPvNZZgKPiAC/ikI4woeOgcajBuyuRrM+nK+U8tsGOu90jZfU1qmIJji+AP7Eq4JQp8WNkvZo40ldSE86Mmn+pbMI0BEd59LtV+gaaF0AymAKaE5t035MBTJUF8lMRThUcDv4fqGFgVmfXvqeISxdjy63xAbz3O6opxWHE83OC9xeY6MjvrdruGg/XocyRVpiztXh8MTTbV9KkeIkoUHK3DyW54cz3+w/XUClHt1E/Ww4gMn1VjjZEYjL7DQ+3ywY59HBHo60GQKai8wmNKZMxMC6ywlRIFEGgwSzVlE6Lw7mFnQXUqc0OIdj/WJ4JCI0rrtAHgD4fEvk17cExBasLEWrf2RlVC08shKrPg6L2o8OKILv2pz5jeJWsprl+/dP2qBdxI4GNXUOvEGqsFJ+Ci2I1i3uVP9ipjere0+HnTWBNREVMpOGgBkwElDI6a8O3mWSIyLHe/Q89qxkKRZC4aXDcK8v3Qr3gsezLPVAsxF7GUJaAAawKipBQWq0iWvwSDp23APTvljdbBcjJPLhJWByj1NcxRO+MKdUQOJAbYn9omHI+UWh+cxWXrsW49Jc8wXqmHFcYG6qBUQk7kY1HZKvUwyBC4ugEFAkPvIAuN8rd5ws/XE3pNr9kx5lmvOVhm2uwhxUaQlLhNSWI39NGiIIJ2B1rsIobqoOcTLTnriFxpf/XAqZ2qlhnaEH1S2SpESq2wGH5YBh0xAK14VYbR58U23JMYAtWTWyFxR1a7ZVqI0Xjuk8KL6xs+WbNArShN3lz9vueoGfddW7hCZeRYCTwwBFd3EBKAAUd7/cl7//ZwJUoqe0u/uAEShbzHGVOuhlqKpz4AzArZBWAAKYfVu6K31ImCYYMalHwLHXXRGjB51w7EqDmA+NjCIu46h16F1j1D7WZT+zMiMCmCt84mYDIBJ55alSQb5l5HXINtVsyxCNgsYVSGlQmdAI7M/668cZ9pGj0y7dLK1GwwpIpfpWpRFJzFwGrVOk1VkV31TamB1HX661WBy0BiGvNaZGN9HE3G2UQ8yoIHS0gBefco+EyhMsf3xZZ3Ond6P3x3z3GFlE2Dxt4rSelgJhj4wcZGDNiSwo+b5ab1L1+/rhshCgZNieYAEMQuo66Bx3VhMHHiRfVJccxIUUwjVAKQEIStCjQu5avv2PT+RlALU6Uq7zZnhcs24nUdGLe+7JxcLqJTx77eKkNKgiyEKJAqAxtJzGunTAqy2JZp1nr0nW8HRr1MhQO2ATnpPRszU66jjrsHs9bUVder+lcAHNfku0Yd1NylxqTw0+sD4Y5FQqB8lZFZlZo5C0hJC9BDR4dd/afdWPlrfw1gZ1JI7lwsH0AhkxhnfXezZu5a69VJsvi8RYWYBSYtWyHPAF5YJTnp1B4wgApeCAbzMjUJfUiUoHOyrtmx1qtAQMKwkZBtC6ja8RiZ2/K6LsEqzly7s8osiEW0TnBLui0QFXY2QRWJ1e+l/gwDepgnHbBGQ1is9TgYx+58qhXewXoIFKzScqyZRClgrjrpA67B4seVVJUSwMfe4Zvxxv9dMHNcXA1hrt9Z7YQVd92DIUixmnmdhgXGBBgT4Fn1u3rL0vMEqRkmlKXtEqoAaWUjuN2jnD6P6tmdsyRokCHLGuDwWimWwFeh18DOhX9mil8nqaW6eNt2DPXw7hWJnCp19eetx2en8bvR4aFzOLCdQTWoq/dj/b6Xr+w0aD+TEulUJl3waGnSyACuQeEWJd68gEvAnaUsA8nPRbWtrZ95Tbf6cdMYVMH344ZOZvz+MH9VuEvZefQVXag6hHrwUlbsL+Pgvy4PnL1K5kjd+Ke+AvulUYKqjNXIKiggk6p6KvaqcOAxqZ/ePGVp/n4KHJ/ED2WRQEE7oWsCSU3n/sDcUh81rI6AKPj5doTLCn+wHp0K6KzjIp6Rs8S29sRDNR5bsJhdByFouTV1G04HeqhyFljWA0LULRuzimt66yGZawvQOChEhjEk9Fl8B+HLV5Su8+mK+8cXCJEBWeCWATFoLOuAdevRscSYvLJJcFQ9xwE0QZIPBs5bLL7DyIpH+l5oJxCCwctywJr3gILaDazcibssiaPLRaLevLU7rLi5FIVyKNN+EFWO88LfQ12wnW1oUuLIi7IqXtI6whrfDJcGE3DuNriksW4G1YXwrttgFXV+pQjce2JLVH8bI1Pr8ukQIlFKjRmTKE0GPTJDIRaJGGWjodY/a5kFUV0ut0Qd+cEEaIaTpKhiGoMfV+ZyGw+f9pDhlGmJDQAPw0LQmdrNjJoPC8gAaVSJhEuGLGCrpF+7gSX7Gpdk20F6th49f/epSPyH6xFV0n80HoPxkLIgxt3OeOAIQAo4Nhg5XWfi17wFCon2WaOU3VYVoP3R2Xp0jZor2t7I8dIyFdHyQev0VpN3Kl224ylpiQpK7iyVekiuiXJKv2x9S7W5t4SbL1Hh1U/47bTibGlnIXVpS08AyDLjzJ9vtVqgg4VYILkQm82wp5LnzAIpgE6RqG3QxF/fsoThZqs2plaRuMfwtLBGjcRNZO3SadpMtD+TVWkr27TYqwQDCsoYyi+j4L/6ErMU2vAaQebpoQh8ZhXUqDJy4cQe/gBGlTGqykbZMaU5SrgkOCCCTkPD+BkAfFlG9DrijimAdTlomeojUBCzQk3HqV0jOboRd/qy3BO2yCNcVVEqmXHdevywHPDQbfh0uO62sDxqzq7HFkjCDOup0wEQo0bOuo3R9c9J/m+bVKP8rZtEzApCZOjOQ8iMkiWWecRtGXE+XWGOtzaO16CBKiKaug2SU0MAtNdeFaU1BUjKjNX1uDmClMhfQ3+lJHty9DPPNjbIoP7qftMJKBko+Y67LivzO/+HwlJzOnCuDPVUfu59v1HupAmQOkKqjMTQUOHvKWbVFKshSxx63yK8lMxYHLEotmSwBo3/+XporxvYmSJ31sMlhTc2SOpZwXdib/bIEvmUd3z3gWl/7T1H3TwuAOyGSWL3HTmZ0ArWGgzu+g2dDuwyKdtSvWPPnrqQriZngw6Yg8UcDR+UZDVbk5yqineLGqOiwIwr+9bU7+ez61GKaAdMPXxiVvCrxvP1CKMp5FrwArXTpFytXjlI9IVXh8u6aP8yH/7MUXCNGrdwwqAjHvulfReVf7/GuoOSbYdSmSaVVixR8Jsp8uSwB5XMwSKsQ+Pbp0LRa4oPUIJG9rDyVEjh+LQNLZhEc51QIiOhLlTp9xtZkArw5iUOpqAzFEpy1LlZFPTMhqqMFqAqN2X7bEoR2Fp90xxYIxr+X/9MhSelKBh1amK3s41NeYp30+yfXr+6naxVBVaxHaugDwsgCORaSIE08vaX8inJptOU0kj8lXEi30FDP6wag1YNu6zUvS3ub7GKQepIVR+WitX94fEzTL1hQUvDUqlTMuF0uCFEjdfbEb2O+NivsCrC899RIZj6IAIU3iCDwTRStmWjxCkyZdpcB6UyxmEFEJFtgNFkSXt35/D4+AwzOGgbEJxFDgpd5yBlhrGeVWUG6d3Ctb4W8szWuLoOnYp4mGZswWALhjynfYepo5AHLRNG67B4iyUaPHsK+iXla8bB7Jaelbv61XcrCgl5XIejjphMgGdmhJIZCsAlGGRvmccLvLiuFXbqyNh6gDfxUiXYzuN4uGHdemzLBC0pDk0r6mCqKrV+5s/riIWXjBXeAfblWt38d4rYFJMOzJ9G8xyp2KUSzOh5lx5E2Z0UzFFFYG8tSb40zxDLi9VekWCMgpN182EZLX3mttoGZAUfNJ7Y0/3DMBOMx2yi9ndx6PVoXYPcbr7Ds+vIxElm/P5whWXrAACs6qSulQQ+lBaVi2j2Cl2MfM9obMnASlrM13t644nmcbo2/3DPAiYlMu6tZ8+cEaPK+G5cG5RzsA4Pw/yV/W31qdmYQmok2ShYmdtew0o6dN+2nv3PN9x1Kx6GuUW/vWwDXhNJ/i2A78aF5PuSIJCVFcZbUg3WAIDfjxRYskTVYvAcd9pHXfDYxfYdVq8Uwc3lJdB9/amn/VY11SLRIDUzWxZ48sSqGXVmd9TylVJzSRJHdiOsh1niol6bos+OCvovXf8bZGLu+M810mLsbCu+y4Y0RaDnrgwasLngGhVmxoqMpBGmf6ewHBS+ksb3LK6pG/PBeNpmp91Epzql3Y0zhn5rqsjIGOXxeONCyWZRScJtxMO27PmtJdHz6nuqeHLFwMnrRJJ02neo7nqWu/J+2JATeXuHSJCJDxqb63A8zOhGsoJNUWG5HLAuA0LUSEkh8VTxdLlDyhKfzi/oLB1Aznd4WSbCbbmoL2wRm4qExu7T8h4Dr5a5/+mpvFMTUqBr7ersu8DpugzsZAYa55geXMuOehU/701o/N6tsBdEQeP/Xzwt12JUkDz6189rcx0+z0dMxmPqXDt8Nz4Q697id+dnWgYuE7Ea+N7rmT1hma3SsfgnZnIHfPXdboPLo//8DqMtReCHNMDIgr85XRpcFDJZKtSDzucaoF1wUrsBWhMrFdEsDqQoMJq8X+p7/jARDDeyLcPiuzbqA1SMhUDTEwCA5rR6xd31NVggWOLC87OACDynDipLXvTzMlJHHOFaVz9YWqjXZ6NOpy3XMivAWy5EpIXQMiM4CVVEY2RU7HmNGp0ib5lq5LZFEt9UKvCJE+/rIV4L/xvTW39YOww64w9Hyi2dOsdsIQourovla6H31BKIRMHT1mOOumWQ3luy7liSbFTBHsDZxNYNv19AVyXlNSrcAgkHv+kT7g0tG5UoWIIhKI3vyVuUcEniGgRGVRoJo/rcCH4m9DtEoS7MBx3b7yn8d7/fE/zp9aubWW1JQPPrIQpYYZtMgkVqx0S+JvvoUIUj17h3SADxJjuZ8dhR1/dxnPcRttCXBdASMWda7nU2wBqPx3OA0gmFR64WScY33+02QauEEQsEf6E5K4S0c4vrDS1l/mqhWF9D1zko5iu/X5RKSbzuFAnb3Zgi1VnfONvaRJSkqMNmDN4Hi8466GFFKZSyU/FQen2i+a+M3KHXYlCdCA2rHSs2nrJs/inHfsWQFJTrOa5Kw2XZJh+rUsN5ATSv5jnqBkP4pDB76rCWJBsd62Qpq/HK/OrqlldVcRNz6Ak/lkhR03JtGRGSpq6KDbw2tjt93gYsUeP3xwsm62ifAVDeo9B42gaeAiSQM5Qm+KwUgSUYvHJHO5nQCnjt6LdE3duz75it5JgeSMXIRwUpOParAEqIxkFOvMwiLrbAb6cF/+L0BqNi++yFKC3PU9adwjt7VYiCu3GmqS9P9PolCYiWq2mRb4mnCuBdDBqHQjRGFqcoSS6utUDVsIf3jQgAPF9PlNfKnWx9TbWgX9YBWzJNYFT9hyYuajWAonbdPinEaBrkUV1Bq7Q9cLMgAYZ6SL9RLS62JPFlHTBo0wRFNI0R9p0YApnY4KrSH78bb+33lyJwMgaBdws+KVyCRq8ogLpSh7ekcHGWpPWJ7D+2JHC2GY8qEbzBcNV72HWJmumGVK+sEo3/XYU8GWDzL4PnoPDKwrgPXcRBpzbd1gO5Vxnmr6WAl0KpFb2hG2JNAiILeAGW0AMnm/FoY0vQqHLn/WfQlzqoGl76zmMAaFhoXVDefAebiTExDivGaWHqXcY6D/DO/hlObFh9uKwDNtfhMh/oZjne4LzZGS3tgzXthnldR8zBtoPjX9w/4zAszdhecMdYikCKCjlTBmFMCtZETOOyM15A8Wn1ionGVsuij/FwgzIBp2BQ8n4AlSIg4t5VA/QgOVYOElTS4XUd22KuMgpq53L1XcsABNDUhSGTo6AtYFYHBQuoivdjtx51bIh/NhmDjm1srph37c5/3CwmlfGH0ysFMHeehUt8wCSFkMhZ0nDIh6kJNTIhKkqQX4NpS9DqPvhhWKnj4mUhMQ8UfCa62nuO+cbLsYd+xWBcm0a0yohJtpxKw937ksln+7txZVtXA6TdaKmK0FpTokiHUFkcMauWSVo72tdlpKUsQyjvF9RJSqhcWpBD/V7p3i8tdOFsKaz44smGdeSkpd8eLw1H70zAkZudOoEplbFuHWYW8EzGIySaUurh2OnQmpE+kZjKZ8q6rBNbr1KLrhuto44+K/w0H/DTOmDgHYRPCteocWG7hu8Hh0kHjPx32JIwajoU68/+OkhFNMFeDesi8ZbhpCZyIawwKbjYKpTG1kmMP++mXIX1AIBgRtcnjour+HVdqL4yO0a/u39yATeoBTWeuHBtqjBYhQ1Hvdsy1/CGyPXNJYJfatTaL12/cqhxocWAzF+ZS0lRoItAkUQfrKG2WubW6SFSh2O4K69jxaCpOP+4kSfyb08SnQ44TvNOHZJkAxqCweVyxN35Df1xhmEBSeHRJzqL4On3eG9xWUcAQM8PUYzUudxNJHi4bkP7O26uwxIsDtbh28NlF2mI3DrsStMrhbjomZdyPmrcXIfBBEiZMA4r+mlpRSxuFpFFIPXPhqgxHW8wvYedViBLbNcJbuvw/HxPnTZ3VtXc6iCWdvDMrsfFUYxXhX6UKA16qkHCVU7uma9eH5wKOWiVW6ACQKyfGgJQF5i1a6dfp/+W/B36qPHvrwoHo/B/+FSgVCLFKR+y3bDhfH6jQ5dH24oBS7Fh6jbEpHBzHVm7ltwW0yFLvPiOuzvCWu+sb4rMQSWcrGtLUZoYVBPEVArcZDyyFq05qIdi/Uxq6vyFQ0ZCJtHHqDMGlXHQ1NkX/pyMjqSaRcHUr7CMnYao8fl2RCr75JkL0T47E0iAxrBGKdQ99yY0dsUJHoEhs4qhZoCniswwjMCaaTdiVUTXOZxO1/YMbI6sZZXMMGo3RduVobK9rmoDQCZSO6vEymoHQHuKat/wVcycKFiLaMHW5DNCkGctpnURqbipuutco0HSr1MIxzXY5p1eDzSrEsbO4dTTfXnbBrL2zXsgOfIO13jei1XP9M4ElGJxCwKdKThyA7Il1SwiGizGKEKvUosHJLYM05ujQpDVe0cyzKZwCdVCgpbsg45Nqf3HZSA/qERqz1+6ftUCTiR4iQ8dAfpHnTEniR/Xuvwj8YVVGpG/bMfybiMLDPa0i7qguvLpXR+qmWXX9/qNrFN1gncdXt/uMPQbpnEhGpjaubIxKeREX2CFEt534ilLuGKQZmJtDP2GKKvDm2re24/TrWViVh55xXAbhBLJR6UyRXrrMfZr87yuEIu21IUISWk0buswTgtOdxckVvEpy14Y84AUDObrAZuzeJqPUDLj090LclZ4vR0xWIf7+1fkRH/3hzvqdt9DKxVXfh/6UAUsP28DQpYYdGz+z71STbADoNHe1qRaAOyaBO45xaR2EnW6klkiygwl6bt3TGXMoZowJbi1x+vrHdHlKvc8KcREfGX9joObITC2CLsOW9L4/84dHGcTHnVpI/6gEnyW+PfXI44m4mQ8/wMcDGHCM0MndTI59mtTIAqQL7xLuiXffDf4r5a7VUhU1ZN1KalUwmlcIEQmcRVDWVon/O7hiZ4FmRCjxsv11Ng3tTiN1re0G8fugFKQWrLBHKCDNGXB6kSNx54ViwzpXdYRU5YYavh1oolQydyEXXfDgt76luo0ryN81I1m+MM8tcACAK1I63eFtjYJhiEPz5NY4G7TJyqIVxb5nLut7bEANBvcq++aTqBSQeln7odqymQrUNXTFZqSMsMoCskg76QEKKI2J77H6+W52zcy4/fT1sQ376/9niNtggQaHFSzT78fHAIX6zlKvAbFnk+RoZddjWzqFMA/477zOGaJhX1tfun6VQu4lgX3NmNN5Fx2MoGTNYiNsiVJMUVl9/d9cga3sLsSnkE3yS2SH+9Pm0LMwNFQd/vH+YDHqHF/vNBCz1JXW5kbpVBaTblKCO72orPISbaiXvHy6+UOMSsKOlYZx25tnRKwU6oAYOg3DP3W4tTogZDQ7HGckkbOAv2wQciMfthoMblSF9+9M9fq+nd2sFnAjitM75CTREnkkZKThJsHhK2DlAlSRxxOV0xF4Hi8IUWFy/UIYwK+//ZHaB2hbIBbBmxbT7SxfkOMujFrKq8dAG7MOqhQwKRjkzsrQeIE8qSpN+F+m9UufdIJR02d9iVoPNqAUdflT0YQ9Gf/0yNhtT8tB+Qi8NF/hikRYPSI/DoKVMpfcfqrUlYJejiVDFi48KRMRe1sEqIWzc72vV1p5iWlEjszo7JbanBHp8l9sCa91++IDKh2Wb3hA79CEmvUzWbWyIQtGYRV4bINECh4nG7oO4d161CKbFa1IdHk9NAMvQp80LhtA4Qgz6AqQpqDxYvrWxEIuWDfIlHnXRPdUxH4sk5QIuNhWJuZWJfrYUnZo+9912MmSEesIwbjmX6a2yRiQ8LT1sMHgx838in5biAs+ZXhm6dtaJ9JVUZW58A10r9PJjF9j0zTjLRt2axkQSk0uVXzJ80GcL2qnzdQyg4ZanYG9UkDif7shRsQrcif5M11LTi9MkdqR12XlncmtcV0TZlfksSDTZjqYSxKW+hXeKSgWlbTYX80splmVZ0BKUZFw7yfvcHqDDk6CmLbke3CX5EfeL2anBR0ap1MYK8A9TWFS5IasxQJyzakr0EhF4pUkoLkrAAw6byHgHK3C7DQQBOv2nYO3bhCW1peuqVHWDse2ROevjzA8SguUPA9d0MpKRpjrUNguXEd02kJtHehlWu9uQ45S9ydLtA2QCOgZIl1GahzZJWkYTMhqbg4RUmLUIZNAhs1KUWjaMkSMWjkrPDyQv7fHz99gbEbdE+w0ZAkctAYDwu9Jj6oNB9o3bji9nrC8+sZfedgTcDEQcI318PH+lAThzgVgQfrm+ugYlw1ZNlUgD7tmZml7IHTUgATQygvXuNnZ/DbcfsqAGLLAkagGfF/da9Ighwutx5P8wF3w9rMuf6UyliKgHu7hyskrgGAf8GfQS0clZFwpyOMdThbh8FEZqWQna5lxko9lMgTZReaGBNgSsBjUnheJ/xPlwOkICZDvW+tzPjIB/Et2AYv0LKUeOBV0BOTwivjzh+mGzpN7CSpEo6iwDuLbvZYXY+3dURnAk7TDadxxu+KxOI6bJ4ENvXwSkXiM//MT8OCrrF60PYgH86v0MyEqsv0ZR3wuo5sbKba8xSzhI2pFe/6vZyta6IuwtsJrhl1wBY1rtFA8ufWq4Sx29pr3JL+ys9m5CzXunQMmSCpVGQ7QGu3XBfONeC5Mooc28vWMPK629Hy3dKeQ8pjImtXmg7Ic0RwXaIDkQ6UF6fxFiQOeo8TDEXg6mmK/qZ3sMxKUvwaATQ1cMXqj3xvuyzx4ixevG5+OVoAR53xFkitnAxbPGT511PAcxG4BTa9UbRZTrw0o62w5AdnfwgGXW+86m9SUSZa3JwMhQWcOHvwZD0GHRCjhhMFwAhtAuPFDnZaoboAYSLMcUZJCjlo5KBxzzTBl9czchE4na70sA4OJQukYLAtPTbXkRLTd5isw8SUQaICkkOgVqkV6RR16/7Xjcbt+/EVmhWTwO4vIfqCkgXc1sPYgGm8IgdFPwOkIpO8yJvGSBBLkghrj+1KgRN2JGrifJ0Qo8a69RiHFecPz5Aqw3Qe/bDhyB1Nle83uCjpZthfb8YqrrhyQG/tjs7W0TIKJB8OHA1VdxSEj1d/lIIDy5srAyAUUjP2mvxTtEpI77j7pRCr5jASJU7r1ERPtLClB7vvNihF+4OUmZaZNN7WEWukUb9StywXGMHv4+Y7/P12xEO34a7bsDGdsyotPcMXLhh+DbGJwg7W4Zvev5OUl6/CLq7B4BZMi2srhQrIGiwQgIVhg8dxpp83LDD886Uo0IasR/WWMPYrLGeD9gx7lCIaH1twfNiWiRnz0FH+58RFd2gNkmC3Swkpd9gsJ0kWC/yd2qRx6tdmbVzv4Rg1fnw7w2fN3i6l5aJWO4FOkfHVgSGTLekdb06qqXELAEgSbNXnvgY6Vw/02REpYNKxuUEC9Dl2LECq99qRA77r4R6SQcyqLbGP76ZmCVoepncFMmWBTpJvzDUqPDEP+z3lT3MjU/+3wMwcgqwINajmVWj3QsYDw6Jf10S0Z4CYTAVG72KfVy//erxQAEq5qMB9zEwP2gwZWjFV52O3e1zUrq6GUtRwAZ+q8RQtxOqH+ebItP4+KR69EwcfJPilx/x2xOH+Dd1pRlx6RG+gewfVedhxg7IB44nUjZr534nZHTGqZhaVi2gy43FYYayH7TwyQxzzbcLmOnhvISNJ8YUo+PTpM3RHi8cK2eQkkbxBWHtcXk8EF6gIITOkVBAqwxjX6I5KL5DcVQNkeJWTRGLGinQWbuvw8/Mj3VgqQYeIFAxKSshKQpuA0/nSPufgDVIkTLUUkMqwiJbeUsMuqj/4xRPtsY6Rg47IAJaoWjJ4vV68xZIkQqbw6uoSkwpYJk4/4zOPuXfXQ1OILuuA5/lIXanxcN4glQ6vy4iL7zHqQIEUd6/ou60pXqUoUDHj5nqQt3XA7g2Sm9T7lmlc/9ivrD6kwpGRmty9QjUzhxkAVCxDIqfH30w3XHyHPy4DOpaThyxxXclP/mBCo6dlAGMm75zKic4QsJp42LV4L/MIKTOGaUFOFIUXoqEkp7wr/gAKT7j5jqiAisy7UpaND71VfxLebby4HlYmLL7D3bDgo3hqe5nOejyqt0aHNTpCvVcZJ2JNKVkgc8EbT2iZn9MXZzFpkvzXRWYqdIgdWuizx6CJLVK7UZ8FDkY1vnbtrJUsOytFVTuEtB9wKjVRUi4SN9djDQYP44zeeNyNvHBm+95ql1GZKdXLHcBuHQy0LFfDgkIBNH9wiRpsnr5S3lYq5MaWx9Wp8GzJ3jcX0ZwFe5XxqQ94CxTpVuPvTiahk6UV9jWR/8ov1tNf/qV//queI9dACj8jBUvnBUIGfAb6JNhWlk61NZI/AQkEAJ8kYtnl88QVLY3+M2haGFXnPqXJV7oWQTOQgVNc+mbDmrxB3Do8//QBPhjc39NouV0nhGDw9PQAgHBu5y0cy5qbJ7NMVDh65lsnVniqhMSiiMNh3rvcYAAQnl6yoCkgKaRI+ZcksZfYtr5ZzxpDsI9UCbZ3EDJDjxukicDbATlJ9KcZAJCDhlAJ98edDWOZE+69hVsHdMOKfloheQmY0wHBG0rA6SS+rBPxYQPh03VMfmK+esvNLHuEV+RlM01OsS2g76zHHdA6esfL0t7sDwBZexJlNLJQqYpJaqjALQ0tKFpyoe10bOKfJQ94up0QksSRO9QrS+uNyEigDjFL4stXvDgVCWQgBYM1Gpzs1rj11CWK5hVSCpoV7Bv7l1dVcf8u4zDzPy7L9n4BWuAerGuHxZBMo5wWvu+Vop1NzhIvz3TvSZlwXQf88XLGZDyObtspolm2A6G+n9SWeqL5en93vMCohA+HK0nU+w1959GPu/Fa8AbBG7Iv4EkzOUpKqtBHtXeNiTy64zvI7GQiRv5OqnPgAY7hDirMc7BwUXNeLfm8VJ92lyVyMLD1AHoXHVev3QJjd2OscFQ91FbOj62QT+COv9MRKue2eDYyN/63yxK3KHlKK236rHqSz05ijsC3g8KkqzUvWPizO3R+P+52A+Xd4bZGxVMfx7Nlqm1zFBzdBrx4ahDOhj4/K4Ff7r//N0jkmSMVayMpw7KC9jEL3CKNEV82g0knTDq3PLv65ztFkllSKNEpFSSFkyqR28nbFpHXA/rOY5IFkgt6LbLKBhjl4G4jLTLzvqjKmRJwAODjp88oWSJF6oSOPI4BxM1+fj3jFG84ytK65FrAdcW9tg4OXaM0Etc5IQWNkiRiIDn88XhDDAR7VIhAKY+ud7y8VEAmLFx2AXrcoAaH7DW2pzNKFpCG3mftoO4/PkEZWmLaoNENG8FB8wDbO1g+1KTMCEnDRYMPw4yHXnBwrWr+GrWzbqZV2GPherXbnlZYrIozSBijEAsarieFbIVvVAnfTzeMxreQgc4SNDHygrc6KQLA3XT7ahxNSSMl2fDf69Y3p0gAeNqmNs2RZ86Otcu8O/YpWZr9QmOgiAxrdvl7ZwNypq45JFpMAmhK1ZAlOplx1AGvvsNr0E20Vi/Hf8dD5XvrgJQl/v7pI4xM+M2Hz23vUtlJp3HGcVj5fUq8LAe8ub5Jx4lVwWHSzMGm10UCtvcaCWMCjscbbOdhxxXRG/ita1g4QKyYevA33xVP/PWn+YAkRONzv7BV8H3nYESmtCJR2j6h1453HiTXn5k9U7vdWHaYoio0fVKwalf2AiTemTjPtEDsQqGoG3YOkPzeJ4WHgewuFvbdqdNJla17PmBfvX63/q33c8ZRs5IYgFUKPgk8e4lnRxmgvSr4zRAaPTJkiX9YiQl31OTtVCGQDIFR0r1HryFDCYVOykYe8AylZAhYkfH7icVVP+EvXr8uD1wWPNgMlwX7ftdAh4wkAavq2FHemc9X3xKKWqtvMGXwSE/REP+ftwF3tsNH5n/bdziqELlRtXKSsL2DGR0tCjeL28sJznVN5JOyBJLE8eGtQRUlC4JbVvLmJjfCHsd+xd3hipQUPv/8EcfDFeNhRn+kwOFKC/LzgJLIOlaIDDNskCrDTiv8PODt5dweEms9Hj8875+bCVAm4vp8xjyP6IcVunckdJEZyAJCZ+jeoWRJqtEscXd+Q8kS2zxS7mHnyWeECwlhzQ5CJdiRXo9+3aPeQqb4OasS+ryno9BnSq/tT0UGEmWPy8rEv6682fZ7audYqvBCw2uJ3xSOyHI9pCAPkEqVS5lYEnUx1WnCy41KzZgMAD6yWKV276/L2PDpDOLlGlUl9YS3ukSy7oMhTHsw/qvF9Ns6gbIkycedMkkVbo7YKdUArCY5VZ47AJysx5GpjRVzr+lQWiWcD1fihTMkdexX1HxVoNDBHVU7jLVOFHpSBA7jgt9yQSpF4LaOcMHgjWPRTj0JyM6HK3IRvHyXGDoKGOmHFZAFOSnkqBG9gdIJx/4Kt5G3fG1qus5B6YTgDU1IWWJ2HX5YiEZYMydrmETICq4IXHmBWxOYqgqyyt8DY87VjKoKbCo0V4pAEXSIVl5+pbhWWwwlc7NSqFAJiXRItfpekUpe5FRTBPbQloPOCKwGTxBweb9XJ5Ux6gRjCrIBhDB72IwsTUksQYSBb3rf6IMV8k1cyCUKJr2nkY1sm13vzx9WgzmyE6NQ+KaP/3GhxkKI/zOAVwB/U0r5b//Cr/9XAP4GAEop/90/+rNAZlY1Gu3O7OBOyJTGUxMvYibF31EnnE3ElqkQmExJPHWVuSbq5jRHsPmoIGGhRMZhXPDwzWdindgA8I0uZEGOkh3vEpROEJ4+pFIE1pkEPPXBT3wDVHrfOKwtkXy0DkqR6dJBU+hxDAbSsCBFZQiZMZyvgCiQOgPvOgqATPVP5wvh2FnCmAgzbPRgJQk7rbDHBf35hg9ZwF0m+HmAGQkXj0tP2JooECpRAlFUmK+HZsMKkKBpGmecHl+hDKfBs+pR2wAhM07sbvjD/B0u3uLTsJJ3iExwWeHHqtLjg27lrpp8kamTvgZNoha+cQ+abtLd3J7EH/U++NQTNlq70k4HGAUApESsPjXvYZVatOrY3BvfQqMrvlll93W5LVEay0W8O3cehwUTC5akKLA6tMmpFIG+Jhvx1EF/fg+RqPFtlaFTQyxefY875pgn9uz4eR0RssR/dv+Mg1nbzzvwRJGYPUPTBnXAxQgYG4jmFwwUw15VJ/D5yyNe14lsX7utTSwVMqsUVasifNL48fUBx37FdKBmJ/KYLxX5/ujOE700GISoKRWJO/eKgW/BwieNb4eFJy3NyUhkRTCYgJgViWeibulANQtyjQpHE3HUuU1zSrBLIrNOHFP7RoZeeo5J0zK1A5teB4UHW1a6Wh3x6Uj7ndXbBnMBaElcAKkdXxwpiO9M5J0P5QpcAvvocNOYo2qwLdH7iLli+LsGgBubv9VADyszBbTzgSvA7JWgKaidNQmap1PKBxV4dhnfDhK9Ksyh/8v1FPgnCjgXb5RS/q0Q4t8IIf7rUsq//ZPf9n8vpfxf+Nf/ppTy737p55VCpH3CxsCba4E5yRZIfA0KP2+qLTtPBkwl5K1yJqN7xdv+LZHfwN8cNpyMx5HtOqeBormis8S6eLgwvCGRnWk4sVAZh/s39NOCy/M9nDc48I293CZya5sPsCri44enfZEoyeZSiNJEOfWhyjLDLz380kN3HA9mItH5tEMOGrefHwEA3bRAyILusCBsFu7tSEWkwgOZDhx6rQlCFuKCB41wGwAM7bOpv/4eWug7j8PHJ/o1fuClSljmA+brAdPxhj6uzDEn61prPX53fMPKmDAVKbNTvgR1HUEUdt4rzRdcAjiaCMt8/lJEGy8td0v19b5nbrgs8f9+O+FsEv5PhyvsuxSlhiUyh7ayQN5ftdg8v56xRoORPVUqSwKgh/dpGzDoiE/jTEpB7vKvfDCVwkIZhkdSlli8RYFAZwI6QQEISiV0wSIk2fxiqtqzTzS5fM9GZFV1LEGUvooD37yFulBB+t2Hz+QKWbMcTUAIBs/P90TZMxFCZGgToHSEMZFgua3DGiwWZsgAaNPKl5Xort+fXkl1ySyIaNRXkW2JF/7dsLX7Q/D3VCX2+zPMWD4zP+ZgcI0G/8t1gBTAo+1x33n8q/MzekPJVC4adMG0Ql4ZOdWO9ZU9Yx47h+4d//vcre3vrRYP9R/LmHrlrL8uI35cj7jrNozW48jRfNNANNLqrf/ldqSCLmjKv+928VWdDGmPQ1zsUSdeYBKUEovAoGqwuoQvAhNL4nf3TtVM91i3ilgEFIfSvCSNk2FPIdZF1Onlv7wv2JLEmpjGGH45zAH4pzvw/yOA/xf///8OwH8FoBVwIcS/AfA/cOH+s+78T69YBN6CxKj3bpfeMC2H5qjg2H3L8iJhjgq3oFrHNKoMoQt13hz8oAX4BE4t0qzmKmbuuIWJkJoKXLwSnDF/fmg4NwCsW0fb90DjYnUMpE08Fbb6cCi1J81XtVcpguENj+VyQHC2hRHrgRaOOWgkv3uXuO0eUmbYjj0vOg/NjAmCbxL8PGC7TDCdh+oC1pcT3NZhWwknH6eldU5SJSgbIFXG3f0bf0+5wTUAi4OsR+wcNEekbfMIt3WtwyK/kYyCyHxcCQsKCaiJ5TUzM2QFxx3EzMspw8U8gYM6kqJCLsiKtcZb1QzKXiX8q8NC2Yus/KuQUv2cAbBvDXWqkZdCjZttAr49v8J5g8s2IiSJt9AjZEpqIq4xTRPEZDLYtgE1EPjcbTiyf3zKEq9MQZyDoURz/fVrAgCrEz70K6sLDfnDvEsKitx5n3hxWX1MKr7+OCzodGgCs7AYGB3w8OEJyoYGoYS63Na7Mdoyj3i+njBah/M0N2hlCxZJFHx3fIMUBafpRgpiPlDugqblvgnIvDQWskCZCL8MWGcSl1nrMYwrhMxIUSMnSq730WDxHdZo8OR6Lr4RWgCDSi0yr05BIer2+cxRY4kEr53Z0W/QERMoyrAKbVIWuCUSAw06ICViAVV/8D9tAgoEetYR9CZ8RSWt909ICivz75Wg6bgqM6vIaGM6Y68yrCI7D5oY9hT6BxuaxQcV6qqeVKxaNlCy4J6dLZ+9ghJEWRxVxqRzyzh1mS2l+T6uHuBzrJJ9/K/vwAGc/+S/H//kv/8V//tZCPH/BPB/K6W8vv8NXOT/DQCc1BFGFlyDQCeBTgpoduvyiUI9ScFEH4xLErcosCaSQdNCY3czpN8jIBSNWZUfGoLBuvWYhhUPn77QA7l1tNwbHOLSY3k5tdiyWgQO09w8usv2tUxbSgrZFVkhiIKUJJZgcOgk+mFDN66wg4M0sRXRkhQky2bT1iHMA6I3SMHgejnS6Pb40iCMFDRSHOih2izm6wFvlxPh12p3KKyvuUI6tvOQOsJONJLHzSInhcLdO2SB0hG69xAmQvUO/Yc3nKJEnAeEpUc/LS1ai9g5Ej7QQtNFg1uLueKILZXJklZkcu3LsmGBtduuCrb3WZLVzKqm38Qi8MPaw8qMf326YDKUWFQ53zEqBG/RWd+gAAC43ibMrsdoHYc9F046CqjGZJ7hiJVd8JAlhI7IEK0wP7kOZ+ub58vV9c3gq2Zh1qtCPJWFsjCdtIpGKgdaq90LpKb+hCwheNHeq9AcDc/D3D53IQpOxyuMJe2BVBnSRMTNApcDmXoF0xaN1np8/81PyEkiZ4XbPCImRVixzByMHTAeFkhJ02Z0Fm9vJ1gTMBxm2N5jOF+gTIRkqFF3HolppXWCTFGhJAXvadn/0zLhxXX425mmz++G2LzPR17IliLgCxnKvTDro1OJsfCdV10DIawib5lndji87xyMzPBZt/SiXkWKxeMDok4fdY8x6IC+CFhmnymVkZLE83zEGgxefYca12ZlwsPAds2Zvs8qJiQlJ72HOSpcg0TMVZWc0DEPvNYOiYIHG5tquc4snZStE6+T66QDQpFfyfczAPAzUk3gAOBsd2bTX7r+qQL+CuDhn/g9/0sp5VUI8T+CCvX/4/0vcmf+3wLAt/ab4rPA384JvZLQUkEL4AV1oUVLTS2AWxSY456ZWS+fBTxq4sbuYki/pvHH1wec+hW//+4HKqyHBcrSQy1UAkSBPd+gBo94GxA3i5efPmBZB4zDStzbfkPOCtfbBKUyPnz4AqUjLXyCbMnyWlJAhOHRtGSB5AwSDLq7GWrcUCJBE5e//4YUdeMKZQLuPz5R18NdXfQG0VmsywhjPYbDjMP5gul4Qwy0OAreYplHbK77CpNVlwylIj7Jnxp3vV45ScTYEYzDdLHsDE0lOkPwhEDflWD1p8ZlGTk1hrMfuesMmTqRxVuSF7P3DJkYJUwyU1gEu9TFJJEN/dlOMebHC+XInejKRb36SP/d00dM1uHT+aWZJwXGoTvm25+OV0zjAuctTw0CQtCizkfdFpwP08y5qDTl1b/j4i0tT9nsvzIjlqhJVi8zTnbDsXMtZzRkheQlzsNCew+GWWbfMbarGoOiZ0orhe0q7tISFRaZsDGkcHM9+qxwmm40RfAuws0jlAnoeGqs0Ir3lr7LKmiSGT9+/g4/3054HG/tQBOi4PV2BAB8qxO63qHjQ+EUrgT1RQ1tA+zdTLCJiVBdgA0L1qc7pCtNfWZagSIaE0vrhNObZ6Myg5Ap5OAaFb44gzvTfRVdBwBn69idUVDMXvpzaICYJaVxs+vBX4OcBWPkcyDu/iB9m8xuweDZG2YBKXw8XBt/HaC9Ci1CY/OAr7BMLd41BDpk8v1WQiCVgmuQePUCZ1taYvzKXi6lEBPHyIyc6TD6u6VnTjeZ8j1Y/5XdBNnj0rRaIcnKL1+SxBo5W1XQYvxP4cL31z9VwP8H7F343wD47//Cr9cCfwYV/F+8ChfpT72CkQBQiAr4TmlEProZ32iCUKpfOOFPJP6hqCRmptScO9Cm+tPhgnFY0fWOuge+KWUXUIJGWHoqsp7+ydxVOG9hTaBQYmaIHE5Xgh9MQvIG8+sEACQ9zhLTNsDWjliWJocvWZAh09ITZp4FhvsL+qjgbgNyUc0JMawdeYyzpFebQCnz1Ro2izY6244TeIKmDruOj9xp56AQARhmm+jOQ9uA/uECNW7QZzKvQhQoUaEkCekMhE5wy4D5NuHz27kl9ghRcGNDJ8eFVBX21Tb06ydOAL8FgyAzeiSKIxOUM1hvUiMzTgbodYQGYZoxkxDoyNRD8e6Bz1liWQcM/YaHh5d2f1S3wnpT18+oMmzGw4LgDa7XQ1sIpizxxLTMujuJDBOdbWjhxVYlPPSlFR4K/ZDNVnZhIc8fL3fEqKr4at4Vq8R0kbyQ6nAygeT6OhBv3QRoSYdLypJcLL3FabpB6dSaAakjSpK4/PxAUISOUDqSHsIGmM4jOIvkDQ7DgpgUsWe4eCuZ8eH8SqHc4wJlI+yR9i2N+fTlnpbkxwUpW+S4+9zr3mNiKA4AhCI71l0RndnKlcL1QqGosW8HktbfvIXPFFl31BGP/dqUrTUguX5/jcEhiOBQpeNnVpKOZg9MocO3JxhE5vbzTlxElchwUWNl2+Y6LdVld6cD7J8wp+r0VIpoWPccabcDBTx2Ed8OBOduqYaaJFTwtVotv7gOLst39rK0F3kLmsIjZG70xbovujJUcjSJotpEgeZmQ4ivoyT/0vWPFvBSyn8nhPi/CiH+awDnusAUQvz3pZT/5k9+Hf8kDs7dNRR1zT9vApMGfjPs8U8hC7gk0RtysJujwvqOnVJVTp2im+fboaDn7XevI+6OJH8vWTSTKm0DjDNwlwOWy4FwZs6YVDpinKij6npHYxd321U4Y0ZihOiOlqLzdULfOfzumx+Jktg7WlLqhPX5DsttxDCuDYtuzAWZ0R3WRi1ElkgqQxSi/iVvsKxD+7iqf4vtPE8RXNySRMkSx4dX6N63IlavHDSCs3j5/Ih+2GCmFWpwEH2B0HRn5NeC9HqAfztgvRzI7tNEfLx7pWiy6wmLt/h5HbDyWFctC3pWyu1m/XTTu6jx4myLyAIokf6LM5ijxDe9bnQzgCLWyIifdhvfTDf0rG4thVSDzlvIhTnnknzbhSjN1ZHux4Lj6QrdeXTTgv5Y0E8LUjC4vJ7g+HCsfF5g99qpHhYZggQ8nPZTvW7qwjoXylWsvHYjM25M/fNcJCadOLbL4mwjPnau0RV91nBO4wiBTgf+h21xJe1XShZ4e7mDlBnnD880Hb17eAmvVjRJgg7uFBU+fvszvtU/ECTINgw5E7QndYQdHMnVPedqdhSscZYZZnCw91eUJJG9RrhOcLexMbeEzEChhiQnicvbCZf5wKpUiXsb2zRcC80cNf4h9DCCch7pudVfBax0srRYsbOlA7TwknDmhew3Y2AFLkEya7DQKuNhWNoCejDsPRQMAod8xCyxsFjIJ81BD77RM+s0BVSffI0n13OTEdr7qM+bZsrzyUScDDlM1gzPGpCRgXYwjVW5Cjrcqzf+myN9y9nuGL4SpZED6kVL2tKamn/s+idphKWUCon823f/23/zj/36L10SBQddGuyxsW8ynTiiFYmDjo2xoOTXUU2k+CMOZXUy6+V+o2deMoZgIHOC4lFNGsKIu9MNUhOc4l6PCJslDFUlEjMki+F0g+5pYQgAcbPc5e5+JqSWTFCR8EHdO5hpxd204g5oBTVtHS0uo2Jcmop1cz7sPUoOUFEjMzxQ4RLNI3XtuOrPUPy/qS5Amojkd2ERskRwFiWpxmvf3miUVueFOilZkK4j3PMJkBnD6Yb+SCrO5fWIdRnx+nnEC6d+UzBvZJjAsJIUbSQFqItZ2sibv+o2vul9K5gA8NPWYWPPFIC4/NUrXYqCcVihVP5qkUmwQWyK1c31WFxHoq2K3WeJhUOM+2FXFxod8c04kxI0S1yDxR/XHmcT8d3oW0wbQB3byzJhDgYfp7kFWoO79xqmCwAX/ju3pFoc18EEfDOsjQ8+B4MXN+FgArnncWd/2ciI7DcPX1h1qZg6mGFMgB3I3mEyr4ibxfZ64GmOoQdJexOpiP+vTIS7TojO4no9IASDM0iBWxgjv74dycr28RV2WnH8wz9AdgFiSkAGSpDQxxV27ptgTKgEoTPCZQSChg8Wq7fouesE0JZ/MUu8RFoCnk1sCewU9rzHlJVA1gpnE5uTH9F76Rn//fECqxLuhvmrFKDFW3Q64n68NfLA7Hq8zRN6HVtOaCoEkdQJIBWBM8v462sJmcQzHVMU/5O7l0YJrXWGlMECa5LYUrVhAL7pJSYd8ewNQiYut5EkYqrFf0sKLxstYXuVcdBkI9uKMv++JZHt8lGnd2peYubVgOa/GjMrJYBBZ7w4ugkPumDSuUUJCS62sQCRT+eOl5yhCITESeGFJPUZ9NCMKuNT2fMogZ3upHQkGKUuabqAuPQIy0gqSC4QJUv0I3Us0lDhoIdGkld4XQR2HqfTFYG75cqAUJrZHyYStgw0VaRQCfAGKu8OepXyBwAlKgQn4bYeb2+nxnfWTJkb+q3BOjsPOSNuFskZbGxaZdg6F6w6HSxTzqYVQha4Pz5AaFqMIQvY0w2Fl53u9YiwVvGGwON4o2URL2gSL11e2PtiS+Rbcd/TEqiGBlSvFIA67CUqTJo27pUiNnDRr5hg/f0Lh1acmeVT328VV2Ve+iidcP/wjEdR2pJN2wDIDD8PyFHDO1rGhqixse2qBMijgw98YkAECEF84vpeKzXuFDW0TK2z+7L1CFng+3Fp3OVUZOvqzZ8UrFQpp6Kgk6l5gmuZcOzW5g0uRIHhoIlqa5yThMwSql+YQRUR2bCs0lLN6CBMRLwNDRJZ1oFiAJlGq3SENAlIdJ/mLHF9vsPgLLoz6RaUycirQboNCNcR7joheY0UNcaHtwa9SBNxd/cGowPcF9pvVJdHehYJIiWVNUFSow5tUqvUvQ/9ijvr0bPkvtkWv4M2Uha7jUKm5yFkCVX5/zwhAQTLhaywruyNjp0+KsSuCF6Z+SNF4Ti31HYy9ffXn91FjSen8NlRvuljR41ijXZcWLwlxR4VWP3AZ2bdZAAGaN11yBJzlk0EVOP3PnYOS1Lsl7ObV0kAr17A/7V4oRQwj7sIdCxBbfzgIhp95holrkHiFoGDBka9g/wAjSa3SJj4lgQmLfH9aHDIRB/sxw39kRN5ZDWMUhApI6cMfx0xvx1JJqwy3EamUyMWGADuxkKeqCFlQn++NepWVWUKSa6EIWpc3k7onUXYuvZer29HbK7H4+MT7LjBLz05rjGGW31TwkrURcq7NJi3oY2HAGdwhr3D3mXNEnYjT5TAJla2o/dDohx64KSO0OOGUiRKlIi3Ae42wgxbEwKVLHB7ucP1etg7Wd8h5h3/BWgcfPUsqdcJSSUoDkreuLgSIkqvs2Ykft46/OwMzoaK98d+5RE0t24pg4KsMx/E9J3JtleQjAtXFoYRBULHtsuw0wrVO9jjgpIFtpcT4mYRgoF2xHzQgpSQvY646zZcfI8fFurYJYBvxhsephn/ql95NCbF4etmsUVNdrdFNAipOt+dgmmUSp/IdqAKPqrRV/cOdnLRwGqaaDxbKFSed33ffhnIxIobADUQDmzYeXC7HGCGDcqSdTAA3H14wam80g5EJyRHO56wdYjs+yNEgeb7IieFMveItxElkIXx7fM9np4eaY80rGQzsXUtExZAo80KQQlHa9T497cBShZ86ihb9MQy/pCZXhpM64Br+HJkQdfCLKFKz/zb+QAlCv6TImAU4dxKFnx3IlpkgcDiOlx8j4N1OPULtmBZsSuapF6htHvsCtqjnVgV67OEzwrzathwzDdv8ciQh5YFD7bg3kYczTtlbtDYkmzmVvQ+ZfMJOhliGE1s27Em1RajAIkQYyEBVcXj68FRD7I5Vhta8L7wL1+/agFPhdy16I2jnY7XoFsX45nx8NAl/GbYjYFqsAClfdDYHbLAiyeF1MYcz9rFpKCJRw1A9Q7msDbjKGlItKCY4tf15AXy9PSAkKjrMibg03fE6ghsfOWWgRSBTD8kCTRFq5Fz3Ep+KybiJAqmOKObVuqWWDAQtj0TE6AJIUaNy+2AXPMcbcDxcG1GXBXvrRmaxvqWFwnQ0lKK0uT5AHX/fh5gOg97vkGoDKETstugDyvmHx/x5e9+g8Ppiv44YzjOsJ3H3//9b/DGqr73wceLJ5fHUScsUeHLZiCEQSctDibhY7eRoc873+k5GGyJOvBe5TYO/7yOrcADZJDVScLAD93WEpMqywJA80InVo2G7R2ra1ngtRJUpToP8BJOiAK7eExpxe9ZYj8Yj5AUtmDIO6fb0PH+RPNi0ZjY/Fhyljj3C0LSOFryie/516onecwKKbIaURQ8dBteXY+ftg4jRwSSuIemFiUpNELrhMNhZgiP3uc67zuQnCTiZml6REBcyU2z+pckT4K0utAe7i/Q49a+/+3pjOwlrm9Hkt4fKfE+MTyCLJCzRva7EM30DsfDFcs64OXt1NwIh95BqdgCSxTvOeoE8hpo6ffdsFvG1iSeKp2vu4Itqa9SfE4MbxA1L+NkHYUwcyGsZlZKZDLSYjXlFjV6RXjyoV9x1tf2+qq5VW1EJobCqrf4LZmG27ssMXM+7KRjMx/rJE1OI0+QKy/zf9405kislF7lxqKqlh9PrO7sZF34FvQm4V7ugSQVctuS4m5+t5sgKIVq3rd9+l+/xPznvkIGfl4FHrs9+aJ2a7IQaf2oyU62ikGAWsBpUfLkLNZE2PdRF5xthBYFJ+thZcSyDgjBwG0dTOcxHBaCDIDm/a0YD4/cmQDEIqnb6qHbYE0gwQ1jrilVpgj5KCtF4hutY2OvhED+yIKLqeYbIfIDUrJo7JIKdQhFY7Q1sZkUSUldevAG821C328YprXBArfbhJQ0zudXdD111wDgl4EWW5mUZt1Ejof+5bjL+OvrSPQ+ShHv8FTKzwxJN2k62bfKFkBwZz06pQBY1DT2LUk8uR5n63Gy225BoGic7bnQkbMc8OZt48CWdw8y4cO5QQjzPLIKkVSJm+twPNxwOl9gRxJMFe4s/drBLwMCf9bV+bGxeNiDxPE0U+1KtdoTzK/bgCVQIK5VqSXsSLGHKJMBE3HKXTDtngHowaNoMIteRfzr09b+TCoSz+uAj1NGbwIO04yud5jurtC81EpBY1t6KJ0wfXiBtAF62lCiQnJke3y4f2uK2rBZpGDQH2foYYNQCTno1qj4tYNbBrxdj6Qk7YliWDULanDELJEL4tLDvxxhBzI3G28D3NbDuw4h0CRK8X6SjMPeUUuNzPh+iJx6ROIXJQusSG3Sask6PHHN0TTdQDUc26JGKBKnzrVFt4sGT+tIrobGwWeNN9dh0BH37yLuZtcjzBpj52BUhPdkh3vkaapK6us9V+mEr65Hwh7bNvMS1Kq85/Lyvy/B4BoUHmzCY7c3lc+eyuhjF2AUsVXAdc2zLN8qgoLrDqVK6+uC88XToVDzDbZE3fjJ4CsTtD+9ft1INQEcDPDkBDoFaElmL2QXW3AyFZPaaUZVDFINgowogARJqnlRBtCJf0OHn9/uYVXE1G04Ha84fnghhsfWwV9HuNuI/u4KezfDjA45KPh5gPYG91kgJQoLlpKCBUIgk59KKyxFwjvbCo82EcO0UhRV1MgxIYqC9TYheMu+3qV1k/ld4QLQ0nbI8ZCKQaWNYaCoqxQpEzN4ixA05nVESAo9i08qjqp0BHRpye2ahT5x69pUkoIhPnrv8OE3PzHTJjdGx/39K6ZxIagiKWzewkfd4IGYyWnvP79/aZS/io8bZmtcfI83Fv5UK83CC74qn4akziUDTVrtk0JIVIC63uFwvjQ8+70HSfD0HgDg7uMz7OkGM9GEFWaCHpLXiMHgNk9wfp96bsE2f4mKfw4mYLQO9+MN93yvFoY6HPuASEGZmOLdMq5mVSZPbn9aZKRCWCYUedxsiWT2k44YTGzJMHVSrP9UCft4ulE3zkVaqIwSFeLaw11HXF/uMB1njA9v9P2KQtj42xEbC8WGaYUQGZ9//ojN0SJNiYx1GZCswrF3jckC0IFegoZfu+ZaKU2CyYFVublpBGJS8EHjp+VAUJEgKAAgDHxLCoMoODPrQ6vdo6QyewT2IpZB7J6QJR57og0G9rqp18lucEnjH+Zj65o9T9xV8WkR2yK86heqEhTgvNWs0LOSVYkMXwgXBwizD1niFkxLCdNci+pFi+hd3Fd56oGZVBeGsmpGQW06KXcTWKNEYSM1y0vuNWqYLImiKHbp/NFkWFmYD//LGMqvnon52GUcTcV9SEb/MDJ3VVSl0k5NqrmFhrfURhbybi6CRkegcXvrskSrhPPdG7QNcJep0a+6YcP48NbYJTlSUaubfeouiC8qTaJ/vMHl7dSSWKTK6NijA7IgOovlNmI8LDjcXxA9hfIS7W3nLFcooGLdny/n1pWOncM3Hz/TeLn2JGGeVlbPUSdo4NBFysXsOtcKbGUcKJXw8PACbQJSohs4bB2kjuwTruEXmkzWZWhTQvQG8AZh6xC8IU+M3uE2T1hd33i0LmrEImm85QeghSFX/BJk8SkFGfl8dhZvnox7lADeQo9S6D7QgrBFJXNTv4WsEJJqnHrTe6RAn5vtPCbBUJAsmN+O2NYeh6hgAOhxg9CEFZcokbYOcbM4cPH/6XoHABhMwBoMntzAYgqFb4cN3x6uGK3jyYNEMiN7ti/b0ArP/vCSrwbAJlFZ4xosjMx47FdiuiwTR3/RAvNgHQ7dho4VklJlbPMIseZ2KLita0vZyssGAN0TbGZ6opSq3gFFIkcJP/eY+T7ImfYG1S526Df88fOndkA7b2nfMy1tB1LphcP5irDwYbD28M625uA2H+CDbgtgK0mMl7iTTIoWuPUZFLz43rxBr+iZqkZkX7YRL65rHfjZOrbB8A2zpnqRGtPFJoK3lqTx6i1BlYIPB+uRs4JOibDwd7mouZDt73lckIvATxwSXXcSvztc23KV0pc05mCIYSTzVyENc6LUsMeOGo5KS+0Z/qqGbT6RR4rLZJFbTftyIauBIwu83rxtlrqTplzQGvBcadPVqfEXa+r/X5X3n+n6Sy+jgFgniRVQd0Y0atH7q8YWKVGgVNmFJYJsIUdFKSdHjp3KWcKtA5bbhMvtgB+uZ/z27hnf/eZHdNMCM61YX0684Nl9HmLUsLOHDvtHc3/PifFRo4RdPAMA/bjh4bvP7aES84AoOuj4dbajW4cGvQzK4bf2Z0hJ7IP2WfC4X1PoYzDNXGs4zCRz5k47sVtcThKFD5SUaoI6RTvN1wP5ubDIZTjd0E0LhsOM5A2uz2d0vYOqdqcy4/J2gg80OUzD0opCKSRl/nmZmLcdsSWNn7eu0Z+OJuCucw1CebABd2anhNaO5T0TAKBOpnZFhi0DaMlWcygpFzVnhW6gRaWdVuSgoYcNKBKeQy2uX+6Rk8LpA1kUHO4v6HrXEmZC0pQHebjCR7IK8Enh6rqWT1mL0GB9U03mLKG71HB8H3XD8j/2K3X09ZCOFF772+nW7G/31HTJPi7kcDmeSMADUMcWgoFg3DcHjesPH5qPTXIGYe0gNgu1ds0uwfOEUR0UO94P6M4jR42H7UK7Ex1gTMR0vEGZgLjZZvvw/p7OibJbr8uEaVjQ2YDDRPi5cx2ct5hMaLmhqUhcg2Eocy/G9T2lIjk1yOIWLBaevIid8245yhXidR2QIRrfW0kq1N+d3vbvCAIxSVLc6gjg6+ft+TLiGiw+gewxOr7HR05Huni6bz+Mc2NZOYZnlKCowCWR9cMt0EGvJWViXqPENcpGYR65q18jRdklbjK1IP+nVy8x6IzHzvMOpECVDF0EbsHgFiULFNECnnfHzv84JeY/6yVAXffPG50wjx2wBIGfN4WTKfi2D80U3XPnvUYFx3hb4o7dsCmMEKXlarbOiLuWZR3QWY/D8YbxMOPTxy9t8VcKYYR2cNA24PJ0j23rcV2m1nEqlZtohHyYIyXKC+oO3dZhmcemeBQqQYE6pSqAUCyPz0VgGEm1N98mlCIwHmbqZlRGigrXC3VztXOveKXtyCku8yHTliBZop8Wgj8CeZfUsViIAg3A9o6EHWyPW7JonijIAjJqrPOAEE7tz13nCWuwe/I4P4iHIqBDagKOqoT71FPayqHS4EBS6VfGhwFAmAAF4JHZJ1W5+Ox6xhvpdf+wHBCzwr8AgCwQ1r4tLZVKDe4RKkEPCRiA+ecHbPNIh0wWuM1VLbtCmtSKUpVVE3Uvo7OUeFPtdkPSrcOuftOvywjPNqk1PUbrBCs9Ohvwn6sEHzUpNHm3sWUJnw069gWprIh6KFy2EdiAT3e5vaeKgeckmZJKzCGhErqHC0qUyN6gKAVIgueu10NjI/XdBss5p1W4I+qhEBVOziIGTQyjRBCgTpKUuCY10Y7qHcxIjoRVuUj5oxnr1rcFYS4SVr2DIrKG33rUVoToeRk+0VTy3iRKiYKjCbiX79wqeR9Rn82RjdJe14FUvZl8dr49XOie5N9bIbzrPKGzngs5+wTpSCZmrsfFdbjrNhiV207jzVsIEAWx2g5nCAw6UhKVJ1VlVYl3iqxhXaZibJkyWoDWTG7coLhMUZE/rJL9wIGzlU3YVJlkSpTmiV+vVARkpm6+Igv/2PWrZ2IKAZwNqY8+b4SFnwzJ5qsyC0G35SZActKSGcOM5P+9KsrlS5lEPluiRI45WJzshtN0g5Rk9WpMgJ2WffnDfNoKb3hnKfaMpdHVP/t0ujb3N8HcamRB3a+3uC0jRaxtHcbDjNEbglCiRvC7Gb4QpXW59X+rOLc0CdXCtUnji8A6D+h6x5goFa7oDZLXuF2OZH4kyfDpPcuApPaG2TVb6+alJK9nACiZgyW4KFWVIwCcDjccuLABaHTG91cnE+76XSjjE3mjA7TU0TLjbAK2RN4glPxtgRE4wtOil5dfpVAqipYZH/sVj9MNhg9W3TuyzV17JE/wTykCwVmiSr6zHHBbh5wlpnFpHW0OCte3O2zO4m0mumB1/hOClqKz6ykeLEscO4pSi0kRhZBH+VooYlZAAJTcvcgrDFY5x5Yhk/q51AV8dSEcrSMVJrtlJk8HMwCyCc4SIiskbyC1gOlWRGewvJygdEJ/mtEdFpw+vLD5GZlMlSKw3EaEYDAeZnLknOgQqKpU8vVm1pWiwh29we3lBK0TDIeESJXp8AeaC6FzHRZPsv8aJmx5b1Hv58hUuknTgV7vlVqsqj8MKS4ledG823HddyuULFgZw77rt4Zha5WaUOc9xl1j7xzj4S4Y+KTx0zLh1RscTYU7JFQprEPI+JfHtxYNR17tdC/3LFjTld7Mi+lUBD47i0sQsGzpQUI0yZAg2gE1qpoyJRuDDiAv8Gdv8NkZnAxxwEe9888jO7LeMql6laA/81cj5CkAqsYfhTIwa2JFz6AP2S2mptx79gZrUOhVxmAybJZfvaED5yp+2cjR7ttxbj4eMSoW2xDlq+sc+mGjRBFmL+RMHe57KteFu+GKedclmmYL2IohP96/tI68ZInlcsDL8wNebgd8Or8QzmiIk129To6na+OBA4RTC1FwfHhD8gbrPOB6O+KntzM+HC745puf/8zjWzP9sWSJ4PfNenU4rFa6AGB6h8NHkmULlZC9QVh6CEEL1apclZIOkn7wbVFYioThSC0A6AJRsWoyTpVGpywRi2wsg9pRVN7vQRDrqBY2JTI0iz1KEfiHleh2/7uPV9wfL8Tptuxfc6PA51IoBmydB7jnM4ZxZebP15/rfghWyuEeYEuhtifigQ+kEziwEEmKAhcNLuvIEm2PmCQCFF7cQOwS10ErSm0H0A4tyuyUePW2Fauqb6jJRNXumFSFErd5glhG/PzlAwQKDiM1GJvrqAs3Aab3jQc+fXhFXHrMX86w44budIPuaRJbnu+wLT2c6xCigX8xkDLj5C/EL1epTXUpKrw83cNaj/OnJzKr6jzcPOLpx49sJZHIa9x3sMYT3XGaMY0L3m5HbMHgdRuxRY0Lf/5WZowm4LtxpuDid3YIylu4TLa+wXdNhk4LQNmsWa2maLl6iL6yR/tdtzcLSmYcByIN+KSpo1YJN9djeaeXsCrhZMHLyIznbaDABhMgUeAyTQQnS/RXuj/IZ8Xxsr52wEqQP/+DDZi0bPYPmn1bXrxGKEAvSVV+Z4htMtqAOWr8cdFQssZHogVBdNzAVDZKTe5xGVCJGtRe7glaf+n6dTtwLt4nk+EzfXkAEdUlCiuU0G5+IzOOWkIL4C0oXKPAUScYCVxjNZ/hJWamAND7YcGh20jJJ1OzIC2FaVSDg87U7fqtQ/QGXe8aLFKtVAHClKsSMDM+WdV9RhO3W+v0lf2m5NitYVyJIsY/uxQykHILWeBU18DKQNA2NC8Uazz+5rs/IkaNl5dze/2Hw9yglTpq11/LSX6FpdJCdIDNHmbcoE2COS1Eoxwd8k8PSFfms5uAUmR730JmeE9TiRAZSu8+MavrcXM9/uF2ZOc22QQrc9R4C6pZHVS462PnWyJ5Vd7FLPHDMmJjte2odzqfVDWYgpg7Ukdiz9QCqRKu1wMc28xW219dIl5f71onLlUmL5h3ftoxk91qVbnWQIcQDNZg8bb1mIzn0GGgVwGG/cOrj3QNsz50W5PeZ4jGsKlKU4CDmhtLQyCwwdZgyIphGpavoCrDk9pyPUDOGd3cQ5pEtFdP0XLJa/jb2DDw+Tp95aHzdLmDZ5M2awnuSUnicmObgc5BBIP59UTQy3GG6R1O5wu2pcc8j3Cug0+aCrgJ7IgpG/OminDOnHRUzeQqa6OGYJD7IGHDNR2pWsjWJeCoIiYTMBjfskGBnVY4BwstM47sqX7oNtxcj8/zhFPncOpX3A0L7kfKdA1J4Z6n6ZCIPfXjOnKOrmDXPxKDae7wI4c8GF6c1gMmlj1BqGdq4S3TPZ5EjYUsDAnTvqcuOJdEaVVkg104+ITSqY5srUCHusA1GoikcAIwKnK2DElgUrGpP//S9asWcF8Eft4kvh0SelXw3bCP55o/hEHvxPU6ulQXQp/oFStR8LGrcuwdV5uMb7FLm7NYtgE/Xu/w6XDB77//I8zgYIYN7jbCLQMJInqH5MlCdV0G+GCxOLJIjV9J7dm4ydPPNezTYa1HlyS2tceyDpjGBQ8PL+gOS+Mp5ywhdII0EcPdjfDx3gOywN7dkJzB/PkBpQg8PO7Oe95ZBG+boRX5QCsSsWgy2ZIqoU+33cyKxS7BWbx+ocCK5TbicLrirndk0tV5dKcbjkU0/JOcGSVuL3dY55Fi3Uxs3ugD882tDjii4He8+AlJNltYokeZJlKoafPXoPHkDA6G4IUjy9etrHJ06lpvrofV5IsOACV3SM4iOtsOz+ocufoOLhj0nWuHdBU8xaibvzYAON/hH5he+vF4aYXbeYJW6l5By4SP0w0+kQnSYDyUzHheJsQsce5XWB1bkblsY8vK7AHcdyRe+uMy4KgT7jvX7uOZF1x9pEPMRYNOB3zqtzZJALSALJkgoepAmbzEyhDS7TbR4u5dqMG8TNh4kQkAY+fQ82ERVzqcpKQuX8rcfGIy3yulCBK1VUhNFkwVcuTPUMoEKQUOw4LBOpyHmYQogQ76N9dzSIbCljSeXMeYb0SnEr7lgkoh5ey/z4KbKv6q+yuj6DP+/fnlK7veXZJPh2fHnupako7CGt8i4K7bABcNDt2Go8wYjW/3acgKN/68aqDIk+s4jIMk9gc+mIjLTX/mEgzWqFuWL0Dw6GMXWc0ZWE1OFsKfncYaBZ49IQwnU+CSxMwFedCRu/kdjhtVBlR9XRTvJv8RIPxXLeAxF7y4gpOhrmvQGVuSePEVawQOWuFgUiPJb0lwty6YZyqRUVATFm9BQzBWFLLEuAaKj4rUyTyONxwG8nIgm1dKIxmOMxWGrYMx5EAoT1dioSxEGyMGBisfWQRjOo+JFW0AsUuenh5hjW/4a4wacu2b1wpAHXddXghRGqQhZGkZlhXaUZoyNrveoR9WOnjGDX4m//LoLNyicFAJ6AB3HVGSBOpJnWm5dXf/1rBOpRNlZ/IlZMH4+IYcJVBEE30E7nJTotc5suH9tvUI0eBtmSBEwcOR8z+jwuI7vK4jtMo4Gs88fgozcElDSdOk9tX0SID9MsCuhPxw5rxLw2XO7XOR7K1Ri/RhWHA+JJagl1aIEuPBlDSUmW7JExWzISQHMVTstPpuEK5L/wjNtLekGs2sskxWTjjf4v79VnbNZEKTkVfu+6BjC1CmdCMFIzf0XIRz3mXuShO1crq7Nv+d4CyWC9nj1iXs++J6d/eGsyhtkVuhwKrcdd4gJY3n6wlWxebdUxWvfu3aPmidBzy93FPTAoGxX2F0xOV2aDgzsFs63FyHmFVjHilZ0JWEI7/nXsXG1b64DpdgcTIeB0smYpXJUqejFOQ7RlJsSmDKKo3t0KjMlKrSXFyHyzK2v6vUBkKQOOxuJLO2Gnpcr+r383GcUWXsSyAXzqMOOHWOMeqMWzB4chqdytCi8t8FRvYBqiHMlRpIsCHwm4FToxg6qSHFF0/GWUpmbMwFf7ShBWOQsvM/LlLtn/XqpMCnQeDFA70irwEjyShGsXl5TW8O/E8dHsZmr8ik+ESY0TVSYOmoqED8tEwYVcTDsOBunPHp02fKEbQRbh6axartPC6vJ6xbj7vTBbbz7Sa8zhNiVni8e4VSsbFXanp3yYLlzBts73A4XWmLz/S+HKhTTGvfhDpSVU/n1Kw9S7bNZ+L07Ze2sJMqNd6vfBf8XOXh75WXYu2aw+DCtrDV7L8bV+rUB5oyrj9++LPvxI50QFSoqBQBlciLO7+7eWqIRMdZkzESp7cmswOgLMPq58Gjc+1YRpVaIatXzwq8UdH/XsMIEhfGLApFfC200O14zD/yQVtx8QpR5SShV0o0qgEINe9xMIEd7UhavUXNjoERt9DhxVEIgWYIxMjUmAjVz7zXEUZFbNwtHxjn3aJGztRhr9x9WZUwqgjPcXRjv+JgKe1e8XvVOmLdeqwbcL574+QbjQRwcANZPoDvN+8A5w0Gu36lyLw+nbHMIwmOZEFgqquvqlOWpGMd4JPGbT6g7zZMd9cm3MmJdg2CQw6kSi2+rhSBztLPcLEaRvH9oxNKFPhxHSFBYpfEi2mfyaGyk7nh3CfjkSHw5vqmzK1BH7U7Hy3fj7wjGeBR2Pc/JoWb61C4a9VyzzwF8FWKkhQFyltImdt3dhpW9Mbjt9YhZoXXZYQQAgf2tYlJcQIQibDg0L7DyBoGgOiBl0Aw0EFT8a56hkvQWJLEs5cwEjjb1Ap3lcyTt/huhJYKvffXoCGDxtmGFudWm4S/dP3KboQFR13aBzxHDoFVJPwYVU2s4FR6pthU8YdEXRjQh0b+1PTv78a1CSY6HXE30tLFMAYtVYIdN5zZenRbesQqIOFFzLr1SInxbUSSz0eN5fmBWB2Mt3bvuM4lEctjYHqeTBHFKsRg4KOCMMyk6TxU59/BAyTHrlt/euP8EHXMeOHft70e4eahwQhKv08lF7Bc2MfTrXXfQqWWoal6Bz1usKcbsjdI7JOek6KdwLhhYEbH5//wPS7XQ0v9rqN9yrI9RLkIrEydu7FHSvVXlqI0Gmj1vGjGTvx5n6xr0nYywtLIqP7iEn7pW+Go0FRkS4N+2GDHDSqRD4o06asDbzxfkZNsDo1tApJ1UbZ3bbUL1yo3r4xKhQSY0ol9GWtYUVj9qa9b3zw3fFK4BbPnpAJcnMjjQwhy2Dv2AUNHi/QYNd2jfAjlJDEvRPm0JgCOYvWEKA0yMXrvoME01I5zK5++PGJ2PQbjWzGvKkohCqZua5+HlKXtZ4TMACuOS6ZDMSWC6yR/bomhsuaTzp9fFe08vvvZWgDKlkYfDFk2NepkQlt81895i+TZ/cJmUKP1OHQbjOHlpbe8YB6aeKxmbA464mAcBhMw8B6h0kHlu9e4sbjndRlhVMbdMEPLhIfphlyI10/sI7qPT8wKI364bD7wo8osTCsYFWHqt8i+5oY675l54j+sGYOSGJVEEgW67F04HXJ7YT6x0ZtjHUKFo25BtR3KX7p+ZRaKgMvAmoAtFTx7YpBYKfGxN/iXU2QoRHASBfDqJdYkcDYEuZxMarhpKWh5egCxASbrMBiK3NKGll/zW48bc0X7YcMyj1i3HsZQ8nktEHXkmkYKeCiFWAxKkrGSrU5mDDNIQeY/979548T5jPg2wS8DtAk4dL4lvavecTEl4/7sDXmDa4J1lpcT+S+L3cApbR3c0mO5HDAv9JrXYPHx7pUYLky3S0ETTFBtKLn4B2cpWHkeoG0g+1CGG3JSDXagP5MBRIzTDCFy85R5z37JRcDx/2Z1hDQBZ0mLo5vffZTNuw1+itSN+CyhhG43r5UJgZNQnpxFrzI+BUvF7+WMvvM43r3hYAKm49z2EDXYolI1y0qv/+6bL5B9Qj+9AkCbOuTLCWodIG40or+uI5QgP5KYif5YU19iVghJtuXbxkX5EgyUIC8OADDM/351A0jAQyKOZ2/QyYIDd7xaZMRClqtWRhz7DZbzKmvSe11Ke0cKyS9vZ0iZ8en8QoZZOiGxyMx7i9mxqyV/DoLVwNEbHA83jMPaYuZq8a3Ly3FYqQHpKfhbSIaevEHYLBxPOsO0fqXElAxJ6BThZzrcib4nOKVI4hrpM6rpNIbtc7+tMFHZnfbeXI85GpysR68iNmggA78/XHcxVDAQGNqzpyVxxZdg8XkbvgoT9llDpQIR9nv12K8wJuC2jvCRKKExS/ywTVCi4A/MTMtc3H9aDuy/vcO5o444GHqNGQLXYOCSanmYi9AtjEFyB56KgFUFZ1aq9irhbNKfeeTX6DYKT05tAV6ZWi+uY3ptgf6LEki6fl0hjygwEnCMCnzsNEIumGNGyGgfRq8JSolFYFT0Z0IBnJeYNJ1ia6QOfU0CVtLyKGWJNRqcrMPQb7CJCm6N1spZInjT6IR1MUkGUqVhpfWhqiKbxolmF7p648+3CSdeTla6l5Aj2YF6cjAcDwukJsik5grmoHF9vkPJssEWbutYpu/IL6WQl3h3YDbFuGG5jS1Mwm0d+mlpyUMpKtzehRlIlXgUzpDK02JzHpCcgV+IsVAKORbmd6pTMzjO6ZRUaGRGTAovy4ElyKHhjwAa3igE+H+nrXqnIrQgabll3LtOLa/sk7Lw513TyX1SMElRl2k9QTs8xuckkRgLf4//vqd/5igRt0O716SJsL1DyRKjdXDRtHzLl3UkuhuP9D5pLMFg4WR5JWh8vUWJj13AqEkun5xsHe3jsFB6ut+N+ztJ//SKPFYqTLBGg797u8d9v2IwHvfHCzpmIr1PF5q6rQmGKEg7Ia0kMlMi48SCsNvribNAZbM17mygRPuHF+iOZTVZYF3GJoiqU01OEmYevjJYq0rOnCTc1uE6TzifrtDWYajFnsOuaYFLisgM0WCmo3UcbE1sLKsjbq7HjbtrKQoGQ74wAFrMGrkaCogisAYDKXQTVN08sVDuWAh2NOGrA4FcDol3f7CuqS4zHzSZaZxaZvyBu/RUJLwnP5XAk0UN4VijxpWfCS1zExXW/wb472Umzchw3cBT4/SuqroscQsKvSKqZc2VffYaT07ibDMmrZpddg2umVnA6BLVuV+6ft0OnD+Eb/oCJYGhWp+WPTx00uTapQWgURCFgC6iJTsvnI5BJxMl+0jB2XJF4I4xuM2RRN4Hg77zeHh4IejhnRmNW3uiEdqAnL+W4qYsGbcmjxHvLV5f7yh6zZJfhjGh8ZVV5yG7gK5I6N5h/nKPbe0bn1czpxeyoAQNfTkgp9KKz3hYqPCzSZNnU6YcFLrTjOnbJ9y1lB9LXbc3SFFjvk7w3rbiXsftBKBTDuPjGwASthRD+ZFNJcnFEUDzhBGchmO0QKiipn5BLpJc3fI+Tld/i5hkg1Ju3uJShT1FNKvQ+iBQLB4V7sqZNvyA9Sbg7v6Nghq6AD8PmN+OFExhIuSQoLpAncu4El7OcFTyBsvz3VfQCQAE7lxLEW1xaJWirs4Eeg9Q2OLQrG21zHjsHD52YEWexBsXoYqLfxpnMrFSGSbvXiAuS+RAEv1BUwJ9de97XgcI9lYZrWt2rc0wa5r5gMrtu6z3a4ga69ZDqdiCjQGQNP7dboES7XdorhqiYaPDwnkSRPVrtz9/fBh4R9CYkhmnA8vn1wHunTCthlIUJXAAOQRW6KGafY2VgcPsilIE3oIlBSS/n/o5n63HoDzWQFDKY7+wSE82PxOAGoTeeHxSEa/biL+/HXCyHvfd1uTx7y0Lqu2BELsBWZXre4YqLIuLvj1cGjzjosYcbPu9qZCFbb1/O0ngz64QF1Ag9glA5mzVJsBm2TJ9X70hG14+7O8MUW0l0IJqZt7tfXESIQO/HRNOfy00wjUV/Psr8C8O5Of9YOspTE5e100hBdESKzqZWYlErAigmuSTyQt1PaJhU4OO+Ga8NVmulAXWBBjLGZg8ci6XA9ZlwDgtGNhytWSB2/VA2F/QEFFhnUdoHTHdXTHY0Di6ihkOFX91zycMH18g72fYjj2qRYG5jRg/PUMfV6AyAy4jUpQwvWuBBSkqbCx3lyrRgvBygO0dusOK7nyFebg2lon74QEpaDo8UoY2tWOm7qxKoLt+o8WnoinB3IWWfehej9jejlQobUBYe0Rn21TS/E+2HrlITMNCcWf9ipwV5q2HZwy8CmVqrqmWGYOiFCUq9hIR+439Y+ppYcWjp2fI5W5YcJpuNHVw6ICd1q/47soGSB0hWIlZI+voRpI4/eYzAGB7PSI6i7eXO+pOGbeuD3RdRj4tE+GnzDgwMuPnbcDr1uHbYUOvA7SkTquOyDULcYv6z6w+N7YPPZiEB+sxR4Mb85gpVqw0fw8lM8EajMvnLPH8eoYQBfd3dOhWVfAwrhhlxoN8gbIUuUa2wArX5zu8zkQvlDLD8jRXD+Yahm34346LqNIRMRisywhjPcbjDf2gm/slQYVs6cz0zMpjPzEtcPUd1mBpAi4Cn9cJA8vT61SjZcZ5WJtK1bNC12UJF3XzQxlMZOoe7Qu+rFMThQ0lIiTVPrfJ+EZNrBTWyXiErPDmdk58rwK0oqWxEpl87UsVzdDEblVClzkbQAcScGWJriX1kKryyfXYgqamkjvmAircxJwhePCHtYOVBd+PFK133znUBKEMKuZnUXDUqf3362ZwDaJpYpQAtGLRz19LARcAlKQ3XQUOlQh/MhHHKoEGcAkKz16hV6WZu4RMsvv6pnIBLizo2bLAqQj8nt/sFixvz92fhTEonTAdb0jsBWI735gMiVkcxgRMHVHlkicZcqz0tmrXGiIxUaYVgrtr2RdAA9Pdj5jy/gnnF4W8dlg/3yOy+5vpPezpBhSJfpuxXSY8//yhLcLuZIb+8Eq5hSa3Am7ON6gutNzCexuQg8Z6OSB4g8vlCKMjDvdUBG4/PsL0Ht3DBarz0McVQmeYacX2csL1yz3cOrDvc24+MEpl5CIRkmpCkbrcrEvGlc2gtqQw6Iij8QyfZDgW7NSruhj+gSmIczSNJjXoiN5SopKsStOk/gzyUUGTxwx7tY8Pb7D3V9Q0m1JoF9Cfr+SNzfBUKaSiddFg8xYX37cHFbzgKu+ek/fU21LY3oBNmEaVSGD0bj+gJLFXzjI3FgtlYlpc2UNEApgk+f1ULndVPpYiIEpp1gXk/5LZzpWhMACQO4WwcGCCcx3mbcBhWGDMfti9vt2Rt7v1TWBWqaHGBujeozusOH37pb2PsPTwy9ACNaqAq/rvXK9MZ2QIrZp/1ei0Ktaioudw7Nd2cNL9Q51xdWkE0JwA3yJNBHdwMDLhnu17rU6ISeLH2wmGOdqU0rM3DYSra3zoV5w6xwvo0iab+l0JUYBC9/ESDX5aB1iZUe2FlcwIWVFiUJGIUfJSmqikvcrwWWJlsVoqVKsA4N6S6ObR0h5gjZoPCtEKtU/k/13vnF4WWJUxqAwlBI6alqR1glySxDX+lSwxhRCg/yPV5d/OlGA+sHcAQSeljR/3tnpICAD0BufIOXhqj1lTKuO7wXGHl9uoVYrA8+WOOMP3r83pLgRDKkwetatJkrUUYjDPZCD/8OGJDJFUhkZsB8D1eiBvEZbaQxTE24ASNIROlGE4eAhT9+xkKlRS5ekmjJ+eIW0kv+ckAZExiIIH9lrx3tIo3AWkpUe8DdDTBtkFxNuItFoqdPV05gfb2ICPnz4TtRA1LJZ+TwlEUSuZotX8dcRyOWC5TbjOEzY2saLlroOUCcdxJpYFW18KUeCCwefl0LBLJTIee8IWyVPZ4u2drLkuaN6YhfL7wxWDDq1jenH9V8WwRAXoxDRKyiENW4dt6dGPgDKxBW20P8OqxOXLPXIgdg0AxpF5d8F/h+ZubYWBywrXYOEddYQ+SZxswO/GFVpSWn1dttZx+ZIlbFIYmRJYi0gVlfXMX+51bN7XPpG96X2/YrSOnBCvJ5QiWzEMweDvXh9hZMLfDCu0Cc0ELWcF7ywubydiQb0zN3vvW0O0V/rup3GhvQ/f5xVWqE6IfhmgOzowKf2nw3obsdxI2bn4Dg/HC3r2vamL+wTgMh9a2HBIukWn/f18wKAjvhtncgftVkhFhfHABmkbe3NXNWY9GHtFpIYf5glKZvzh9AajIoxKyJoOhyp6SUXCJQ3d3Ap3k6g5WAya8mFdNCgFeF1HmqJ4OmWi5AABAABJREFUoUm8c9koj1du+AIfmJoZLGQPq9qUSP7nRGWmMHVAGeqkX72GkQW/G9fG4d6SwoWZJAvXLilIVt/xgdCrhJEXqtU7f2BR0KDkP7LC/NV54AXfjwUvnkaFe0um5TXV2YiCNVEmZj2ZrlFiiRJnm3AyCVeWam+JhAYCQC/oy1ei4OJ79Cqg04G6ULtgGFf0bPhfCkEll9sBp8MN/bC15Y1zXVsIaUWcXKFyS2zvOavQM7fUdh6KvU5y0EhbR7h00CTc0eQmJzvfcgerFzkAUhneBmKFBAqRjcFQrNW3X6DHDfq4IjuD7DUls0SF7Kj7zACb/nPYxbDRYrR3TbikbIC9v9Jh0QVShq52L+q85Omsb5Ju6tSoC3Asqa/Cjiq6AMBj60qBstGQjzIzNjqZcQ0aW5ZEvWKs03LBq7TDmMlEP2RajnlvsV3JUTBF1WAv7ywShz0AYOiHU9NFhugykElZmkRp6fUvTw8UbMzZnTWRh/jDe45nKjQWG52aT/UtmK9YCbVjdLUQFgnkPRpwjhpJJdikYSEgU8EtdLh4iw/DivO0YLQO1sQ96cgbKKla8TnZDUaTx7UxAUJlhK3D6+sdOkv7gfr5p0yK22lcMI1Ls4SoplaVaVKWodkMa50wsO8KAFqAswq3lHfpUVxIrsvU2E8xKdyYR1/9tudoviq+n/oVFKdn2+Fe6Xhb1FijaQwVz0rMjheS9WfVzNQKK72xatKofS5Sgnj5czC4bANO1rNXPXXcV9exwR3J4b9sHTKAf3284mRc87O/6xz7itPyfY0GG9vIdgwFArsq3Oc9Uu27IeGgczMsqw6ClX4KmSEzUQ0F0HzBhaCu/clpfOrTV9qIS9DwSeCQBfQvN97t+tVT6XvF0ulS/3/gs9MYdca9pdHq8I56tEmJ0DwE3pvgcN5eES167f1VH4jInVlVQlabzX7YmsAmRloWSpnRWQ8fDULSWOYJauuwsK/29OEVvYn4yBt+IWnJWZKEUBnSOHQfXiEHD3C3LQyP22tCihLJ8QOSyBpUyIy0dnj64RNxc02AYd627AJh54LEO3kjkVBdNoZ5oGXU+QYxeOjDSjmHvOBcX08wvSOIwUTIYwZyhpwCUATiZnH69ITTB5bvv7uRatZiXa42xsPaY0hb40KnLBkDte0mrwvJo4kYi8DAOGKl5el3RR7Yk05cNFBbD8Mc7nkZ0XUOR1a+aj4sS5btgPS3EdvbEeOHF+hpw/ibz6RsXbv2WbtlgPv8ASjUpW7B4mUbcPEWT842xhOxSApG7jRrU/DiLUIWOJnI/jzUedcosJqEXhkoRlIxuKS+4eZV7VkxZCUzFHLj/IL/ztOwEANFMWOB8ewPH78QjfB6wHS8YXp8JbqpKNQ4RIW4WaSo20RZdzal32PrAOwOnf3Wppx66csE3NCgm75z5CGe6KD/wPuRp8sdtkgqaJ8l5kARaR+rTTLDF0YlLL7DxXU4WI8z71LqJFeT5wG0gl454m8bKYdLXTYy9dNF3ST7gw54PC1t0Wk1ick0wxBvW48taXxi8zitKHakZ0Ea4L7ijA8psEnZ0JaYd8bjbB0uvsMcNR5swqgoPf7VKwz6nfVrIY/vet2iwoXZcw897+a4wEsoaFGYKy/eMV32WLYjBz380vWrFnApyKlr60SzX+xlwZlHkC3JdopV/2UpCo6a+JT1pEqF6IUAjewVSolZ4ss64GA07nn0j67DNC4kWjAEbyRHSeaNMnedWtRZ5YXGrJBfBTod8OHjF1qeqYQiyIaVAnbrCG9hjjPM+QY5echhL4RlEyiBf2+R6O5mQOSWR6h6Bz2RGjIHjeAsTOdbR1+eZYvXogR58q9A3e5nSYcPOBorycZgqWyT+e8/obub0ekvBK1oQPYe5riQ8Ccp+OuIMA/NS7oqSgE6DBN7utTOrBbvSt18dh2szPhmWOn7E2TxG7LcMWHGGulnohkJbUk3ibQUBcdCwRfjQI6Dlhet1cdmvZFXS/VwKYUPrXcTTYURarJNZSFoSY6ANavzsfN4chavrKpLKuNQvm59KAKOeMFVMl0NjzKH7NaxfEsKyzKxjJwoaUuiCcWGhJsnnPfcL1xEcitoVQ1bFYg5KZTa0XUJVhIE0U0LVEdLXEiKVAtrj9vliHXrcFtHpCzx8fzS5PJVqVr/nbWA5uAHc1iJ8eQM5qczXt/uMI0LHh+faT/0bom8zSOCt5A3stA92Q2pSJKTq7z7miSFjpeCU7/hN3qfPGtH/3k54NVbPHYbBh1gVIYWkZkrABTdJzXQuH6HVomvpkCtMm6+wxwMphza4rLG5WmVYWVsMJ1itWmFOSR4CmN2FYAmOHqv6iRlKYU3a5nw5DRiBiZN6EIL9MbXJmYnS4lUc1SwKmPk5mbQCbeg8OoVJk1Cxlz2DGAhCk42/MUGtV6/cgEHk9lpkbklCaiMUSYIEDOl0soq7vTsDa5B4tsh4MjLFADN8GrmTuEkKEH6oacT/mWZ0OmI+8ONFIk90fzU4KB6hzEphLcJYelpYZMThmlBxwu7mMkfvO88+mll/2myYA0r2bHi3cloZSH7000jrYJgjqioiwYQb+RjEpi6NT6+tQMFWVCajyTP7+gN8usRfhmw3EYM0wrLuBpAAp3MOKYQGe460eHCX7o9kPdLhUmEIvpkvvbUWTvTmCl0BegoWyhu9pT8UhkQOUtsW083OHOWK/5ZikCvyEYUoAfOSIIghC9AJPHDGjVeg0HIAt/0DlYmRNCDuiUFlSnsVokMbQL7l5NHzOvzGae7C7rDgsM3T1CDb2KnelV+/esPHxG8wfH+DcpUNlKCUrl5nwDAsXPQMUMEg+9VwvcjsQlcpX1lwayJ2JTB77Naq9GR4pT6LWqUmrLDtMlRh7ZwI3ZE3wKTc5YtEUiIgu/unwi+MmTd0DP7RnWBzM7ejgiOqKKnLKF7jxyGVpSJZUVNzzePXyBVxvV6wLIO6DuHlOVXIiEhCvw8wGbZYuhy0CRMu3+F2zq8vp5xPFzRjxtNqczQSkkSLp0lAIWUgFdOuFGCininY1O9piSRUtcohZXffVcLtySl6hPj1J/GmbQATWhjG0zX1J+CUuutSuRfIxU8Q2IhK7y6AT5LdjVNsJY68zUYSstR9N9ClwYPOk6cqvWlUQTF7rZ5C6pFqlXl+MQTmWJc/Ynppr1K6CVw0Amx0D7PJ4lrUA0yHnRuJm/v04nq5FaKwH70/fn1K/PASayj3jU4c5R49V2T1I+KXL1q1BBAhR2gUe0W6Y0d2X7UZfp5pVSTeOZayoTBenz48IUwbFmQVurQhCyAoPFTdQH5OiF6QynzncdvvvuR+LzsJe6XnoJ2Hy6N3lZVkqbzsHc3qMGhBI1wmWjhOG6QJtKCEmjFW3A39PR33zU8twp4mvFUZQjczTiKDLD3SQ70EG3XCdvaQxvmEDdsbS/aOWhslwnaBgwfX5C2DvOPH+iBfxeSOn54Rfd4gRrIsCu53IqC9Bmvz+e2EyhFkBLwXSfuW/eJxryodqtVpfjTMuItaDywUQ9R8vS+nc8SHsDL7YgtafwNUyDrIhIgH5j6mTYTsHc3fGH+/P33PyEnRV1jEQhLj5wVruuA2XX4eZ0gQcwHlxSuweBoAg4cwut5GXeNPR5YKUgZraT8q0tLifdSbXASy57MciyieXwoRXgtgIYDV7/p87DQvsbQonVbe/JuYQGXVGRYNhwWDIelOUgCgFt6rLeJ/d8jHr77TAtJTqaf7q5NqJOiwsDLyGrF4FbixivemajewfJ33zHNVekEqWOb/jS/zpBUey+EdRf2VNeIkpaMazB4Wwfaj7BSs4qklCzoVcBgAlykQ+HIJmAv7N1dPz/LIQuVu12xdSMzwGKhO5lx6ByursfMKUClCF5ylp3/nRnCcvTd3bxFryM+TFccOxJZXbceX1YybTOCFtmkHs6YNP27/pO5LoEP+Hq4pCxwDZQH+2A97UiKQAAFQRje8VVvlAoZm7ps5/cassTXrcrX169rJ5sFflgNnhxBKB/73RNAC8CI945e9FDcAlEEtShQTCHUBV+djhLkaNephE+Dx2ACTv2Cod/aoke+UEhD5Xz/qZRcW/J/SElRURSFQw4qLu3pRtcJGkBeO6grcbfTZlGSgvQeaaEwXT1u1F0DhG8q4o2HtUMIBl+eHpALmfFY64mqFTTW24RhJCtaiAxpIsJ1gp8HsvxUCd1hgR23ZicL7grbYrLyf6d170JNRHec2++tr8ueb5DTBjuRaZL76R55HviwkLQA5s/IO4t0OzbeN0CHhk//P+b+ZNeyLEvPxf5Zr2JX5xwr3CMiI/MmCQmQWiLApnq8b3ChR+Aj6D6D9AjsqXkBvQH5BgIENQVIIpjJjHB3MzvFLlY1SzXGmHMfC0ZENki5uAGHubudYhdrzTnmGP///ZRan7Ju1nSrIqXaZMH97gojK63fWxfAiY0xf3+44LGfYCzBx8y4QJoI1fuGCchJAUG3RQr1uU09mZsqwGkgimL0hhC8KiIZiX30nL9aAVwZVpKZQ4oCxHv18+ItANoYaiBFVZkoWWAKD7iqwUUQ02fH/dWUJRK/RxV13OuAmhyVisDoVgw9BVjnLLF5Bx8M1Ftqg3KSQKo25N4fz9jtZ+w+vWL36Y4fDnztbbcBMWq4YaFeeL9Cv5MjdvuJ1FKCBuDvB+tSZWgb4CNlrlaZrB0XIEtMrwesoYNlB3FhHGuGoY1R3fvZ7TIrAoYX3FYxK2onXbzGxZMd/8FtkKLgy9I31UcNaUlZ4hxse5/JERmhkyTOCXO/L5vDnHTjbVc3bJWJWhmRisSXeYREwdGtBOTCvaqkzSW2KL06tK2VuOfB5C0oxCIaPjnytX6yDNjK904BoRgClCDFjWMDT8fJRoF/5uQthxxTlV87Dn/p8asu4ESoEzhwj39NAk4Cn7i5H4rAyC+0UwknvtHqm/XqNekk+UIAAME32zVoxCK4CozouxVSFNwue7iNFljtKNxguw6UBs4aUccLXeRpPEA33XC6NqeisgGSjUdpcQjXAddfnqCdh+03QGwQhmVCKpO5ZO5amyVyPmatXh0n3fTdCqXpg8qJpGIlE2JUytQyC+24cPWsWh+cbsCM+fmIUgT2P35rbRHirVjkoLB8fSAjz7C1v6tVLJEMgeJJU14T4cEtmPFImm0/cz5lVvBMIiQ0QoJPusVgCe4Lp0I0Qs8VRKW11cURQAs+qFyLo1swcvpK3Cy2nz5COw83rDC7BXo3Q7sAyIzMQ8rAKADS5gdcX48kwWSswMaqiv04oYtbs/6/vyYz36C9CKx+AGzUePUWaxLYGVq4Q5bIMuOJWSd1I1gTtV5iEe2GMjLBqYglGizR8GlFY2AioQwGodzTlLwnB+QfXh8BAB82B6Mjc9jJEVulq5AF0no6NfUb0q1HWhzmlyNu111zz7puhe4DzLhAyELByN7g+nKE1gnD4xmi3IfeYe7atSa4gDGcsxmmHmGzmG4jNm8JDmbu7uqm0ng3S3IqYqjwKh5OJq6gQ6Z5RC9Ki1mrKfSjuUOppKB8S580tgpMQ4HPCr/4AaOOODC2uLa5TnZrkLQ6R7tsDqFIGEYb7Pl31BPEYDes3uJlGRq/pkP8L6zzc5LYiUqtpGvJ8jqyZebDpHvgOpLEFabNRGrBWY0/GTTHW6L+Dlr1y+KQIdDJ/F+Yxd4/ft0FHBSj9mjv4nXNffHauK/DylrlVLOFFg4zy8oIvcip1uZePWg+3vqk8e18Qm89TntK5L5d9hj3N5hhJWu7DY2zcX05wq+OuCUyYePpt9tTJay5YoEgG3zeDOLqsK3U6zKsgRYisxolYnk9IHnTXIVCUoyalbklqZQiKB6N5V0RYCaLxvW8Zxqiwen0dleKAN+1QYQstIGAlC1ZEgMkbwaXP37k57e1Qd82Dbi8HfDw8RmHv/2Zet4vO/jX/b3K50oNOWG+7L5b8B6GG2bv8Mv1QHp7HaFLamjY2qfPReDsLdmHBTkQqaogJUelz235XQSbjlAqY+W5hBAFae6xzT30G7lG3X5uw1chCw+Xcwt03p0uDfkLAP1ABp7bjZQtdXGTMjdFw9U73ALBlSqwKqPeZNS7hMz4/e5G1ngmF76tA0JWLayBeuckJ6zsFykoqX0KBtdgkSEwGo9P+wvxrnVEiBrnaUcByixp2w0zKZ/2Uxvg6o6URqrfIPrQbqrsNcLUN3JjVZBcz0fYtcPjuNCCP6zt5JKTwnYdoExsrSrdbUTX5JmK2y0c9FDavdJzTOC09tiCaUk8ex0aXbCabep1QAu2gpURvSId+M3b5kqt19cSDaQoeBomWEb3NrljolNUBZAFT+1UJQr6TPOyNSk8qnjfAPiylaLgA+vpUyZuz75baAPhtpIUBZ2xMPx51PbQeevw4i0uQaOTGUdzd09KAFkUvHi6pn7ot5b2U5Vy4K9V3CKRoqDXd2li4VPae4iffPffVgLyr1Thv+oCngpwDYCRAn2R6FVGLECIClMkfu6n7r5I065Mu101UnxdNHwW+NRRKERFzr55hUFnTsbY8DQuxDBme7HSlFC+vB7QP1zgTldaSKOEnTuULFv2petW4mu/HCFlgr0OBEY63JBWh/n5CACcY6lxez2g8wZdERTikBTWqW89dNNvjeWdk4RMBOxPweDl+QFGR5w+vEDqCK0TgaeCaWHLpdAwr7Z9auUNcNI8S6RkvWk2AxSJ8emNKu/DhOxJ1QIAPVel/nVPlEQeCkqV4KeOjuo8rLzdRuSsYNkYRZKuiE+7K1vSE9ZgmKEsWnLJxRuMOuFTt+EaNXySLb2+thcAej753bTfB91UEI+fv7WUmLA4LLcRYXXQb7uGMZgvO4TNYv94hulXdAdmiVTFEhMZt9UhJWJt+Kgo5itTlmenI05uwRQsXtaBB5HUIvCSY+NA8P/Bbi2NKWZF6TpJtXi5CluqPXLHx3inI2KmvvC3QK23zngMfG/EpBra1jD3xdhAw0lJMDRlaQgPlYEokKYOae6wvhzg5x670wWHpzcKSs6KMAJhxKHG+A0rXY/jQn3xjVg6y+ue+DcmtjZUHZA7mWHMBrufYcYF5Rt9Trt+hlW28eBrj3tvPKyK6IxHTLRB1jxQzW5QgBbMJRgsWePNO4Qi8FvO05y9xRIseg5bkIIkhy8LvVtKZow64O92VDg4HXHk95HkhQadojbWee2wZdUKQuDOra9twFLuUXdOB6zB4usyUnukCCy8Pq1CsBS6kgMzDIAPLkCA8gyUJCfuEjX+sDjyD3C8g+Iq/MFtbT2rfw6iIEtGVAPoJLUVd+8Syv7c41ddwI0Anhz1sGIGvqyaB1rgbEvqk79ulu3yGYLfxI2PJpcg4DNwMCT+qbK0o6G0aYBJY0kDHpCyoxui0GJeWRqQBSKTpE7bQLrs94+6e0bdUrtrz3D8+IYcJXLUiNsB840m10IQY1n3K05MwastjevPH+A3i65fKI1HEor2N3/3T+1XbrcB374+wZqA3f4GpSP6cYZlg4afe2yrawS+65fHtqsrnbD/8RvpvU0ETKR/VwnSEXBLH2aY4w3dhzOdJIJqfXOzn6GGDfKV1A7bK/XqCaEbmvLkulKg86Gfm84+ZzJpNIyovgP/UyHAmLBUhfukWu971BE2S1wKVV4+6ibbtN1GfVeANxeaIeRArHXJLRzPzA512UHPXUPg7h7OpPH3JBkFAKMDTv1EjA7vyB6tUnP2JX8PINB8s1U6HYDvbnpC0/bIEHhwK67B4nkacADQdQscR69RAHAPIej7945s4hXRKkQhQJohrs2Vh8Qh6BYmIlkyWZLC7T/9CN2v0MOKOHdIq4Pdz+geLw1ypthZOVblCEst43qPXctBt6G67kimqWxoxMywUD+9Dvwrc2a50QB95SSeyAlDNWMyQ0CrjMMwfUez9N5SG4SZ6XVAtybNQcPUD662ehQ6SZcicGFTzvPmoLnFWl2TSd9bLy3dPUkEoZBKwZt3WJPCD32CEqm5QQto0b5e+mY0Aqg9MwVL7TBekD91G56cwJfV4hoVjBQslS13WSm/r3U+tDMBfyczIreVSHVH7tOZr6cCcm/OSRIyhDcF6qOH79yif+nxq6fSpwJIPtukDMwReN4yTlbitwM5M1MROOiAA5MFSxEwkmyqJ1sQMjBFggs92oROZfw4zOhUxKFbkDPB9jsdYVhXqw0HEy8O2obvzAvVGFMf3pMeufbAozcQkuFAQVNQA7cZhMiEmeXhq1SZepM8cCxsiNl9fEEKGut5jxJla63ozhMGdaEqqWmBOUiWAhVIr9ufLuizxOvPHzDNAzrnG5lOCNKJS5FJx71ZTD8/UZU8UB9TOXrdqtsooHbriUm+mXZMTlGRMy/LpvtOmZClmbXOAHBZhrZwA8Debu24bEpGUanB/Kub0YgMpfCdFAwgkuSWBa5bR/IurroTc6rXaaDNV6cWsdaZBbrzONiAksnwEqNuba1q5Hp+fmQDDQc4s3zsYbghJE2nB+7h5yIwqESZht5iZ4hb0jOES76r2IQoOPVLg3U5mci4xLFsivui1YFY9cyj8RjdyrZ3LjiSaszqp/FGcrf1HmoBUKssR9E4OlJnmMOM7ocXkqwGjfnrA7apb5v6eLwSg95WA9T3PgHIgpIkYrJ8nWwIs0Ng3CzdG+QTqJ9FBaVNW0fRYzMBpx759Hfxtpl4Kst7nQd8u+3bPKCG+Xac0FSrbAFSb9wCfYaPem5/ZySpOWqLyScaIL55hy9rj4MJXP2T9jtkRY5GjrSb2GFpVYIpNLeJSeLmHbSi+UtVVU3B4Nvq0OtEfHPUYTcFE/tUWfEKShb8tt9g3ln5z4xL3ngQ62S+SwL52qmLP4UmSy4WMqt0OFA5S7x5/d/XAr4k4BYoqfnJFYwaeHQCvSI64agjRg69BcCJ0BqXoDDHOxeATxt49gqdlPjc00586OfWNqkPYwIT2Vg5oe4xZcA91b3eLNVAEVZLMrynNwhNi3UOGvmNlBhhs3h7ecA/fPmMj/sLnp5esE495Bda/HW3Ia4OKejvfn4DL0WF7UwSxuvrEVJmPH14acNLmRUQqjyQs/6ypODbIkjfLjO21X0Xf1YitYZa/3C1EMJ859irOZvbdSSZJFdFStLpgFC8FtelR2Dn23tTg9ORh5USN2/xvHVtjkGvkXGaXMGmItDpiL3bGlPcR2I+HyypD3aW/q7p1wVtKkolhGAw3caWRrMuPQpzrgFy1nb9QqqNLFp6kZQZIhMPpN5g5HDUuGwO39a+ydsqeEjLjIPMeAuGWz80OP+U7oagamKq8WyBNcf63SIfk6Kf5bYWjEv3gcDrvENMsvWBB7dBivxd6pNhyFryBut5T1iEcaFqmX0DJWjE64gwO9zeDpimoV0Lh6jhug27z88QLDlNm0F6I0VWTWwy44IcNOJqsZ73uF32TVOubGRZ6wZpIoVHBN1ScQAawn1ZLXqd8S8P5xY1V9+jXO6KH7AKZ44GHUfd1Uc10Rzseu9fo+ChJ6bLzdNgr566Y6b1QPDn5zQpTBJLHEOR+LZ2jW7aqYRdNCgKTQQxJw1bMnZWclVPz6Fjid8UdUuUqsN4wxymS1DImaSwGUDgCvsaNOQ7vIeVd714vYfq6vTBBRxNwpYJs0yvGZQBDOos1Pv4zz1+5VBjYI4FJ0vN+SoZrMtpHQ6EIqFA2k2KZZJQ4q4HB6gNA9xDIJaoYSQdFaUkF59UuXFOUlTIRaNkSReliW1xraYVw/raEgRi0LhcPsPoiM+s4NDHG7UjTET2FuHW4/T4Sokq3uLt7QRrfLNCmxqfVgQuLw8IQXOFnbCXBcgC03WHnBVCIDfi9bbH0C84fnih7+VhZ8myMSuWuce89NirDGUp/LghV5NCDoJ03+c9tI7fxYzVQSv9bIp0a4NAUZA8SdXOtz2ua4/AF1W90ar6RKsEkQt6A2w8QfdJYmVdvkTBKql3/LHbsDMeQgAzKxgkH0/pyHlfxMZ+gRvn1jZIUbV5gA8GSiVY5Zsa4v2jUv1KJkJfKQK7cW75nQDaxkHwf4/HfsbGA6s1avisMEdSzwyKwnlrxZSyaBzv+rMCs6Ov0eAfbj2ONjaVxBypPbA3HlOwTGwM0Cph75bvpJj7HSUh+Xe87JIlVb2yNAWV2c/tdJdWh7RYXH76iMv5AIADEwbSkAdPi60dFuhAi3XJguYE3JoROkPxpldi1zbgUshkVZVPxOrhVkjF0fKi9j6cwycaMr7d9vf7nttmNQbtYLdmaBLcC09FshmGFuKKjQXoxHbdOvw/Xh7QqYzf9CsySPxAUsEIKyOsTncXJwCRNI7Wo2dTYMhk+Y9JYjA0i/k8TM3pGYtqrRRSTVFLjYBT5JQcmEYYC/A3w9ZSdtakcKmtOh74hiyxgYrQzM+JVDj3AeuoKUoNUWPLXExyBU5/H9pm8+cev+oC3ingb0ZK5Kk83SwK97nBC3UhCLoknGllSJ8D3VRG0Juz1IWFQVjVHHKeRxjv2kUmZYHRAc5tTYUgJMWWpaCRo8a6UO9UqQTtPPaP1HvtbzQ0CUsHZAnVEYM5zl0zR9hxhTtMLVuwKUz4T+VCC0VO3rTABiEKhMnYnS70oUUNv1ksHMdGGZ7kwIyeLM7VDed54VKaSH3V8CHr75IFQmccPrxCqAS7n0k2qAn4VF9DmHqYYaWeeKQbNUw9sNyVJBUgVNsGRsU7npMX4L3b8Hcy4+IdXrxFx/1j864i9Ulh9aSBnaOBrgaJTMfgQUd85MEovZ7cVCb9bkK/I6en7TfobsMuvdJQ+u1A0ks+Rc0L9TR3OxpmbquDDxbP046Gr4czerfi6XDmtBZKbe8C5S5uUSOXHomrr1zqfKZgZ+h971nlsEWDJRhcgyE3KV/TShbsFAUN1IqxLnhWJTgdsB8nwslWdC4//3v6U0KKFJemdCQqJoPM0kKy2PUykhknS4y7CX5ziFERAtbdtfPbNGC9jgjBNJdnIzNWK32UzRgVoyL9f08O5FIE1uuIbXV4fT1h9o5gUfG/DNxNRcJnjduGFjz8Pr/ywa2Yo8HFOzx2C0bjeWZFbBOJgo/jBC3ofarv82g8/leHG6s4SNttWNEyBYui0DTpgU8HlcvjZG66fwCInMWaChUfSmYMJvDmw7xz62GihhS6yZbr52v5dDEn1artWIBfFhqSP1jilzhGXteTaSrgfE2Bk004mgjLLZNVFACC0neKwCUoxAKMWv33Y+QhwTtaG0TL0v5dyQp4AfcOBaZg8BYMrkG1lknHfaJOZdR05yUB16DQKYLKG+6T0ZF9xWC3xjY+nC4tTzCyOoGUJxvssLb+eOHKDwD64w1CJwQ27pB6hfTYgTcBt5thDzco6b9/zUkhR3mvpHkhbC67+vMSpad3/QrL/cToCXs7XXe4TiOraiKFANiA8Xglww940MeVWTW35CQhskCYepKiHSZAEqvB6JluXJZFJm9aMIK2AZ31CElhdCukvKfQn1kJYGTCEg3+MO2gWLVhZcIP3YJORzhFx9lSBC7etSGXroMZIeDY4v7jOEGJjNvWQcvEbRFqO80vR/z882eMw4z94Qr0G6QNkGwwzkFT9iO3XXYPF/7/NOx8XU5YvCPXZ6mfgURMwLo5TOzcmwI58joVcHIrOqXxn6YRl6BwMAmDooVjYGpjC7UuAlsiKNHvxpXRuZmP3PfYL4nCRhaay6xMvqwnxJndh3uuCG/T0Fp/kIVIk0lhfds3ZY7pyINQTx3TeQ/B1zMAQjWIAlnoz05NhIJYXTMGGRPQ1343X5tdvzYSIkAtOakjrKPAY5tUA3nVxHaSFFAlamTCaZgbuTLw8DIViZAk9jZj5PevsmGkKPiydvDcVhqNb9iDmEiUUGcpIZOiSQpPv8sREXONGt/WHteg8eA8Rn2HRx14eDnyn/XxPsFHSc16dW6l8OlriRpv/g6o2mlCRagiAO4kiCLQKVrlnCwIReAfJpasMj62wvvu61/+LiRkrxPWTPhsIhqK5qP4S49/dgEXQvxPAN4A/H0p5d/9la/7P5VS/ue/9rMIft7gyQBIejOa3OAvVIV//7Qc0wgN76RrUvh51di4t2VkwagJeF+paJ2O6HXAx9MrBbXuJrKp10GYpwFhShoxklTPGDpiGoAv5KU9hxIJn1khUn5xWDmxR5uAsFpETyzqnEnul4tAv5tJFcD85m6cW6siQVNlxZWS0gnaBEq8eTvQIJF75+8lT0qn70JpNz4p7N4NZwv3NEuREJuFVBnbhdQyLTpNksRRdRukFm1wWLLEhw/fcAwG1+sO3lvM3sEnjde1b8O4XAQe7YY1aVyCbU41IzOUKUiJjoyWB4fVik5thNwqrMB86PPaEeYzqQajErK04AOp6NQUGH2Qk+QqPTb9umC56BZI9714h5AUHvoZhjfM1d/lbz5paJnw1M84rx1+WvcNdCT5+hz4JLhzG+FgrUeMujkwj9YjZtmi1968a6oECqy9H4FTkS2yDEBTzQzd0lpTAOWvGh1hWCJaPzvIAuNS27gBYDnvsE4Dndq6rSXeA4BSCcPpSggIRcPt+bKD1hH7T89QzAmq4RlxtYjBIAYDTCMcA8XqNR2jbkNfssiTianiBWgTIImklMzqmU0L0KghH+/VGQ92g1WUYBT5esmlZmPSxknmKsL7XryFV3fV2Pv0+Rq6AJDFfdShbZwVYlWKwMynWMvzMqtjS6WvlMqTDXhwG/bct1+5FXOLCj6bVpFXWqBTFbaVYYrAD31BYdIlQAt3KdRirPdKz47WKif8aTGYosDnjvgo7wfZf+7xVxdwXrxRSvkPQoh/K4T4N6WU//Bnvu7fAPj7v/azAKq+50itlFRIMnjLEm9e4GQLfuhiczVVB+YS6caYomQ1JR1JtkTHjSmSm/PJ3cNAaSDmMbitxVWld3Kyy/mA6zRi40SY4zCh4xYLVh7EcX9aiAI7EoNDDxSEnFYHkwW1RPwdst/1a4uiqkkrKarWDqlSrhgN5mmElJQMJNjuHL3BfKHEk8DVsBAFwzjj8dM3astwtRWDaa2O6bwn6dyn13vAA6iPKVRCd5hoQHUd22BQ6gilaaBLOvCCknKrwqXK0IUUPFkniEAziY65INWFOEUDKxN+t7u231vxoF+WAb8sjlPpBX43+AYkszk1PsUSDQQKPo43HAba8KpaRpkIOywN7NXxhlbnAS3RZzdDqoTbtwdqbTnCE3x8eEWMCrd5aM9PCto8di7B6sBDWhpqeh5Crawg2BnqUfasZqrBEFpHHPsZnhfKORqSh8mMTtLQ06mILWnM6X6bVeLdxqoXw6obzX3fKhHd22trJeUk2wIaAjl69XZvkby9PGCaB3L1qtQCHKoKRKoMZUIzfI0nPqUkCZklAdcyyU57UOXu5661+2IwuJwPWNbuLu3zDktS2LigoUjDhAe3klTvXd82RLLL04JXcA0Gl6Dw22HFkWWVAqVVvxVwVdN0tEpwRWCwW3v/6gDZJ9b08wIuQQ7fo/Fw+m6lj8wv/zqRQ7fn4uF17WFVwpO+QavUAFsnrtQr34R60mRzf94cXr1Ar6gVLFgCfTQJWlZKqsCrVzAC2BmiDv68CnxwBb8dPIs17kq4181hTRJGAFYCz5sCoLDT5b9KB/6vAfwv/O//EcC/AvDdAi6E+Hv+u3/2YWXB34wF1yAgBU9aC8AOdEphzgql3HdXLWl6HDL1kNZKXxMUKXUNQJCisSpO1jfwfpO/LT1i1LDWw9nQNKxV/1tTS7799CNyEfjdp1/QjwsefiBrutmzi2t28FOP89fHe4KPjuiHhdURdMxUOvECzlVj1FiuIwUUsJxr3e4DOG0i3LBQS4eHb5n7uNQm2b4bulajxnt4Vdula8BDFo0HQvp0j1En1kVT4ITpt+8lj0khzI4qW5WgADhH7aeVw4zp54EvbnLcVas03TC+qQ9GHXGyCnEz/PkJrqAc6YP5635eBhiZ8S/0K5HreNhLSTHUqjLc17Xj0nI+AWqhlCQR5g5hs3h+fsQWDT6c3pgvQjb0l2kHrTJO/UQLAlfRi3dNDQPQzT8nBWTgaCIcw7eWqOEjVeueN/4lWPjIiT5cnY0y48Byt4oXjdm2pPZ9t+A43r7bCGrLIyWNl7cTlMw4Hc90wmKuyeYtnPXY7W/tdJKYV6JURt+tCNFg3RweHt5aGw4ghk1ZHa6vZHcxbHDTziN6g+02wDgPs1voVOY8wkLKppIlBAdMCFFguLr/28Mb9Yz5vahGnhpJdplHWG73ncYbRrdi2jrcvIORGU9OYmc9Oh1bq6VjFcnr2reYPiEK9iY0wmWVGwK1UFB36qCKSMFijRqbUhCJW0mifBeZVzXjSmYM/D559jHsuwVv84BzsBz7FvHmHbGWeEH/3EWcrHh3TdM6dmVnaO0GECabvm7LwJrI/1LR1zWJCOBM4CjxyyqwJJJKp5pH+pfX7392AT/9yX8//Zmv+Xuu0P/sDxBC/FsA/xYAjmrHUUL8y8W9Lz5HgZDvT4foZsCDpbghLTnnLyjMUWDQhPKcomgZmxLAp36C0xF7PpLW4WTHCxFAx9OuCPSsJqjBspYNAl2/QplwZ4iLjJIU2edvA55fyT059EvraxfuW023EevWMYazoHgJITKGw41wsatFzqpV53Ug2QlSGuwPV4JqRdWA/DloIFfOSkKSCqKUNjh1fMwGV74oNEQNzIDxc98CgnW3Ec2OK4t465Fm15jjykR0h4ngUOCpOC+0pMdPCFnhee1hRMbRrYhZMYuZ4FxrMM0WfeQbZKcV9gzooeuiYOPKdM+v89s8ohSBH1baRFIk8uLLywOc9Rh3Ew1u+7W1ncLUk1GF//vDx29EzauwpUVQhBqDkgDAR4N52uG8dfh5HjFwhT0FgyUp+EQDLgGSvnXcFqrH75q8DtDGNXE7hRABwHlz2BmCqh3VihPHqDkbiHHuqEKtG3AN1M5ZtlT6lDTiRicHrRLlWPL1SddBxhoM5mlA161w/YLCn9F7sxiAxk1/uT20+6sYQtDGzTaypbvdw4DnacT1NuJ4uJAjlFtUb7c9Vl8H8cDFOwR+7akI/DyPjWNErY/SFEd1oxqMb6aumv5eW2tKZI7qu4dH1zi63kTUkOXEkr+aArUzAaMGDnbDg1vbzCNye8fKCKNI/29MgDWhPZ+cJZb1LqXtdGxuWvA1MKiEP8wO5yDx5Aglu3CXgL6m4MBU0BrGUFs7VHgKzDE3dETgAahhyFtd+J2SiFng4MgvM0eg/Fcs4G8AHv/SX/6llsr7B/fN/x0A/MZ9KqWgMRO2XGAk8ENPPPA67a8A8xry8PNqcDIJg0744CKioeFlKARTV4LeICsVJW6IQukuMsOybKvraEBpTETPZpD3vaU6vKlcjVIE3n76SLv0I1VD0kS43YyPH57b97xvlYRgEKJmVYOB8gme2cCnj8/N8YYsoRaHFEzradejcpOQFWJ4yM1SRc1Dzzp89Zul466OLeMQRRLXuegWWhGCwbqQKcScA7QNcD0BkirgS3d3bGvD0SZymhKfOuNRFEp3XwbEpLCzG2IifK+WGY/9QjFjWeHiO3xbOziVONt0Q01uCVniGu/mBAlK5BlUxG/2ZxyGuQUIJG7nOJZKakNBzuE6YrtRtF1+hxooHFEmRcG4m9qwDyAlQ0wS59I3dQRA8V31euyZDJeLABhbvLLFXcmMB76mau9eCR5YAoig9BUnJaSOZBoJBjdPEsPfjVd8EFd0jjb819cTQjCtsHDdxsqrhBg1Lpc9pMz4/PnLOw63xnQzGHcTuv2Ew4dX7B/fcHs9Yr6NrZpfl57/JHPY/niF0hEPD2+kxPK2XWuah30509C3xg3uhrlV8kIUBG+xbvdTWGKZoRIFAcAvq+M2BnCyER/6GRkCt7WHVoR8HSyFHL8tI26eVCy0wNZFjMQHH0eiD95qlBrPvl7XjsOSI7ZM6U69jvg9t+8q0rgm8kiUdkJIRUKVAms9OudbrFyllV6nu5s6ZMIMO5YptvVBZZIt8z3S6wxbqMKW/Pd19hGzwDUq+ARco8DBFPzvPyVYGbijIOEh2wZ3sORGHXVqUK4q6ACA/8vLn19f/7kF/P+GexX+9wD+/Z/8/Qv3v08A/l4I8a9KKf/3v/YDM2i32pLA80aT2ycnG9y8U5nDQnkwIMmlp2RheysAeU9D6dXdzacET5O5EpWCeorGRJrmA224KETBjcNb602+GwjUU4+Yrl9b3mWl+UmVMDAvwm+2DYy21WHzFrtxwuPTa1NGNGlhETQkYltzkxlyeG+Kqkkal7XDdR7xsL+g308kEWRbeA5UvXdq5ZMBDUYVhykLWZA3kgQKSYPY049fqXfPA8qWWKMyVeSdp5MB53KWLKBMJEWOpvAIKUnXXl2qKUtIdQ/snYNp3I9eBzxYkge+bhYH5oATvErgo/N3WRa/952KGN2Kzq0t/g5AgyhVKV3tj9txhbIRYXUI3gAayKnA+xEpS0qikbkN8/ZuoSRyTyoNzYG3N28pXIGP4aRusE3fHLOEAKfR8M/yvPmfWcHy4gmlumOq5IXVS6MJGE3AwW3oTUAud4764XBtvBYpCKtQN08LoN9NTIZMtIBvtg0RNZ9YCO1ARielMuV+Jg3DG0zt+9cB8LaS6uXw+ErFDFMKAUIQp83CjQuO27k5XpWOUDZi3N9gHQ1ct83hn86PWKPGznroqPG8WUgAB8vVK59WfJZ0Wu3otJqSInZKMG2YGXM1+iSEnFrlWo1ClguqKZKTctQBB7vh83hrwRp1IZy9xZoMRukBURpetrZDvCfJ6ZU3vMOBpKtDvyAlDR80rIp4dGsbjFqZYUTGRRr2rtD4wXCxOSg0x2XIkkKMM7VWlCj4ocvodW7RggCwJIVzUDjZiEGlhm3ouUJ/3hzWTLkH71Uzf/r4qwt4KeX/KoT4P9ZFulbbQoh/X0r5H+tizW2S01/7WQA4BFUScYuL38SDSCOAUBRWdr4NirTE56Bw9jzlZjNPrdBp164/R7Dekswmy2IwWA9nPa63Ed/eTq1/NvQL+m5F5+hYG4K+p4VEDcvVXn+6kiPS+qaRLtzHljrCgvqL2+rQjwRfsuPS4FIoEuHWN8UEWfIL2d3ZNKSqNZpPDq6TGHYzfuj/2IIdSqJINlX5LJ5kidD0fXZcIDURCDPuaTtunO8LYBfgTtdmyohzB79QCHPdTHJSWK4jgjeNzDhdd+20kjNtfj5qvLGD8dAtcNxTNCqiY9Tq3WFHOYIXb/DkPHp9j7aqjwokqotb7fHmJNugMiw98k1iONzQM7dDGlrAM/sGjAn4zfEPAIDlNlJFOY3wSWPa6BRSB2FVSfHGPVkjaOioZcbvxht+wxr1VCS+LD2mqPDTPMLJjKPbkLLALyypfOQNp1ZfPWgeQzc6KZdIQqeweddeJwAcjhcGVpGi6O0bHXhrr1tqcj9ezgcyMTkP7y3S27vBaFJQKiKlnkKSFQ1cG4o2GIh4f89TMDTYdIFbg9wS0QlGJ+huw3YZ2c3KZrQsqBjhdtrRLRgNt0DEnbBH60FBZ/zdmGOpZRG42FmiwdlbfOqWZqgB7uTCKdgWblCRBIPx+F8zObBWzhurl2KS2NkNo9uw71bssba5hhCAkxmfxxucDqz1pwGyTQq7RBtl361Y1g7rPNBQljchISggmUiSvJEnCZHvUCvqtDBRUyX80IElzpLdyhRc8+odTibhQ+fRq9Q04NRpUO2fqsYD7sqVv/T4Z2WEpZT/M//rf3j3//7HP/ma1ib5aw/Fcr810c70Y099cKdyW9Aty4Cq03JvUrO1ZtCuJwWwskStU/UATHKvKk97GMgRaQ21UDS3GgLboY3zrWcYAg0Fl7VDgWjaXDN3NOCrDAiVAJWgnKfU881A6nsElttPpJ1l9QdkhD3eYN4Ze6pGWzKfokautTDid8Ahwe2cMPVYryNMR/bqmgNpnG8JLJnt+pV9koKGn/v24ZtuQ//0Xzq6iHComvxxngYa8kbd2kNGBJwvB2oPsTa30ghrJVvVCaJGjXFq+JAVStTYIJs2v/YWAy9sF+9gk8bOrXA8uJKGVR9Lh+RNg4kBwFrlkEVAmUA3G88DlL1XpylQmAOWHt+4Kq7Gkgsv3I9uxRo1JwRxPJ/dMOrQ9MBSdO17syjUTzVkka9J5pl7snWg26nY5G815DcmhWntsHqLoVuI7Q3yCgRWNL1eD6g2/No3VipjGEll8937sHRYl67hBZ6entvmB4AQC+/SlwyfQrfVUcLUahvMqsHZVou4UnjysnaIwXDrSrJjmPTcWiWkIvHLPLChhWLCToaMd72hE1C9lzy3CzUDqyqhsG70pQj8w+WEiTcMIzN+w1yYmAklOzrekJJqfXOdM6rmYdpcIw7evG0nhDpMTlmidyu0TnB2g1K0IUWvMS891mBx3brWgqk4AMHI65MNpPFmm7tnzXZtB9K69i5QvQhcgsQ/TsDBiMZ6MoJClQ0nQFUJYeaF22ea9SUOhPjvJtQ486504TXLSmBQBTtTsESJc5DYaYliUmvuVwbKiaFVPkn4XHm5tHNZWfAv9gv2OjSQUMoSMpOb01rif9edXhtKfKk9UtdtNDBbOpQiif4nCt6+PLUF0HYbHn73y93RODusC9nglcxkx98sy/NSC4x9H3FWjRLVdAMAabGtv16yQOSbjrTQqWmfFd9EfnEQIkOb3EBFkcMW6uIV5g45KQiVYGxAd6JUdxQJfx1wfT41OFTtr1dd8T5J2NVjmgcsa4fT6QxtA3bjjM0bXN8orbsGL6RM7kGKpqLF4rp1ePMOC2uEtSjoZCYlR1J46FYCNkVyML55g0En/LjnpB5ukwiVgAXf2cu7foEZ78EUdVBrOnKbzi/HhkUoWbTKfLAeEgWD2yBQ8JlvslQkVk9Gnn+4HvDTYvG3O40ntzbQUq9IATHwMfeuEy8QSkDnzHI2DrngXu7A/WPge8NISKqxrv1mIWPGMvfNcp4BzEsPoyOOhwvp/p1HCBrzbYDtNvS7GY6vpxo8XNJdalj/TEk1MNbj4ytKEbjdRnTOoJsnUrJwTitAw8vbNLRTAkG3BOalRwgGzm1w2PDHlw/UfnpXHRpR8KmfCRnM/fz3HI/a5jh2pI4aDXFfAoO8Koyq6rn3bm2QKIBnDipj7FZs3mD2DppbDLVS91EjshtzTRo20edlQV83cpu04zlQhZ9V2eXAztCYZOOYR94EnaQTUY0BXBIp6dZcXx9aMj1AEsNOFTxYiUHT94ci8GV1bWGumIaKrlWyoGfTTyzAJdwDUP7c41ddwLUoeLAJqVC/es8971EnOEk98PrCKq4RUIiZhpm0S9HPajmZifvjWcJnhSVYmEQfuisSak0QwkKttJB3/Yq1Hsd3E0y/gcA9dYCYqYViA/rDrS2ubWIdNMpKPW13mNpre98/TlHB2QBzmJszcvn5CWGzHI6coLgi3xiMpXSixJPzvkVpxcKWeUPDx5qFWQd69aGch8yS4EZZIgcQqH9YCXw0rEiLxfpKvIzxdGFuuSRres9HU0nkxVwEyrRjGRldPK5fYCwxwb23uCzEza7BtTUaSvPCVd2KtQVxY4NVr4lb0WKrOEavZwmitb4t4GCFht/uunrdeViWdQJAetYI3jR2ynC6IkWF7UbJ7Lv9DR338FOiAXMBY3C50q0Zi1YlOK68apVeWTsVzAUAv8zkDxg12a+/bh33c4FBZZysp4Si9b5wK06C2bkVgw7UAthc629+O58IfsXvIbUdPLphhRClbeoxaugUkTxt6iMXHyUpLBMtsuvWwQeNy0oL8eN45XR6WsQeHqjlUxdupSOWecDlcoA1Hof9tc1mKtzMdVsb+MVIp6Ua3BCzxNU7lII2DznPI6wmLvh7tUfKElZHPA73e6eGsjgdGkkR4Lg+btcBgFMRnY448vdW01Mt2HyWfBpUeN0croFYNEpkPO6vcJa8GgCwzD1RIPlkZhjnK0ARgYABEpB40FjtkxnALRhsWeBkIo42tW5BXYRDllizxLfVYOVDb8jAmU8plSkuC6BAA/Al1tYJ/YwPXcBeUdX/1x6/6gIOUB+7VwW69rOr1EkUErFz77tWvr3KgAXmKHELAk8uNeRs4uNqSAJ/XCwGpdHriINd8QPrgN8Dg0IwNJhhDOx03QHXHSkA1F2xUPGsy4Vv1A9vAND4yLUyFiphe9vj+nLC7uGM8dNLg13JfoNw/OllMjqQHFA0rTYAmMOMvBnMvzzS7zpe2/Otj1III9ofb9h9fG1xaLWXvc1d45kLQdW0FFQtCJUgudWidwtKoGCHcOux3fqmNhGioCQ0l2fnVgSlsW13Y1IpsjkQU5FQyG1g7JOCQIHTlNBSsx9TFvhxyN/xlilWjD6bLWq8XTnZiAfI9T0UksIHDsyLAUg+1+SSoNxPbQNhDHoP+3QGAIwbD2VZu7/MPbbN4cvtwO04sre/Z0k/uRUDV3wZbIQpd+ytTwpKUA9cgr7GFKrMBaNRa/UdssLr1mFvPHbWt/DjzvgmCRSi0BGe3//31fo4TC2EZPMOVzaVmTYrkIiraaqSEDRu84CYFfbDhP14t4wf9tTumq47Aqk9vLVTInHWI9xKvJmqcFqXDiFqakG++52bJ/dySIpEBMbDJ423jWR4nYoIWeEP1yOdWvhUTIEWJAe8eRosDoyVrRVmdcte147QvlwwbIk02lpmvt9Jkz9zqg+18ciMVdtyJCuktWCwHilJ+GAaqXHjYWZj/NRWzrtsTJ80eRY4GHmKGtdQ2fDcwuHqmdCvpoGq6mtSknwuqQDfNoEHC/ymj+hVwsCcJyEKJkWn06/ZwL/zu6j8Per6Tx+/eqjxf54UnreCQQv8DzsJlQukIMDLm5cYdcHeZJ4u0/ekLDBo6nWHIrDxYh6KaPLDvaavp2QUhRgVtH63EJa7HV1Kah+QNEtBJ5rS7x4upDPu/L3NkSXSZloajuk8pflwv5qGbol6zucd7MMVknuNJUmUxaBEBfDPC5eh9ciliZAHch6O5ktLV4neYF16WkAUhdCa3UIqE5GxPp/gF4fh4QKzn9jwQsnYkLkFGSdPCNlwHaGHFe7jG4pOEDohrhYpashEkKTC703t47vDhOQNzl8fiQbIx2WtKQasRlEBVAHlTMOnfzw/QLMLsU7xHTNGOhNg1B1S5KNmBx71FOuxu7VQTITZLej8tSEMdL9C6EwD26ApiMJ5Qh0sFon7u433LijsYmC41dE7OiW4jTIf2UKds8S09li9ZRDSXc30uvbwWWErAloI7AQN3gJjZEOW1PoBSBeZSIHyQ7d8V00qme8B1plQpLeJVDPH8dZOLRSonZGiwut0aounsysOpwud4HQCYAmdulnMa4/DeGNzGVXP+8O1Ddzfq3raKdBESuLpNhSen0y3ET+/PLFu3SOxgspzO6YmREmef2iWXUoUeq8lxSSOxtN1oOlz793aZlBCAMLTpplBC3QpwBoM97vJhbnvFiiVcRpvbS5QJaCJB+pakA2ersOC19sez5vD78apYWnr3EbHhLFboFXCbpyRksR1GmF1wjjMPKhXmJce8abaEDYUiTlI/LxYPHuJB0sii1swWET5rjMgRO0W8L0hgEET1KorAoOiBfsSNL5uBicbsdfEHCeFjYThnvicqOWc7rXcf/H4VRdwIQCngL0RMPIeatzrDMn+qimSxnvUpS3aVlEP1ciC501jzaL1wN98gZECn7qMo4lNt/k87WH4Bt33Cw6HC9087u5Qq3yS89sR6+bw+EjclPf9Z8hMWmiuYAGKLAtTj/nliOANDaBWB7wC9uVIjs/d0oadJQveFArC7IAs7+5HblGUpJBWSy0MdoXVR4oKcjMQUULqDLufYfczMaF14uqbWgmAapry7TJCqEzAoySRpg5x7rC+7eH2Ex7/5X+mTUGnFhKMItvzraeSnGWr7jxzPA7DjJxFu6E7S73DJVKfuKZ/R9bzhiLxkFf0XHkqvvGoaiNyofeWAGL5XgVV+aOygbJMdwvUbgESPc/wesB6HUgGme5ZoznW7MuEnBX86iBlwQ9Pz9/9/xjvrl+rIqQrWKYdpmBxsCvdVCbA5oSVjUd0msgYjYeSBhdOS09FwApKOgdoQXp/7QNoGZDXeaQNjPvCXdkgBGNluRWG+J6vzpubJu4LIYAJBbzn6Lz6WC8jkjfcHiyYLzsImbH/8Aqp7hmUOSmILJvKyfYbdvsbPtfWUtLougn9uDSz2rp0jeqZksaVMzjr8LBWya9rj8duxuP+AstQLgoooRCNORgmBxJjpBSBQ9mgQU5OWb73RADUMmmDckMW+/f3CQUpJxytb+HUMycAfeKot3GYefMDVJJwzKJ5P2eJUZFKKRhMzHuZk4SWBZ+6BJ8krlHheavXDTOd2IFZCu3jWmXELOCTgJZgKBqd2JQoUEliigpvXmNLElumal292xT+ytpN78k/8/f/TR8C1D7p+J6pDspSaBH/vWZ7MRt6hPg+G1OCrPWqCGyJjiWfOtFwshtXQqUIbFFjg4bgm2voF6rwbMA29/CbQ3oXstC5jQc/GqZfG+hemgh1oJ5rCQrh9YDn//QbCK6mhGTOtE7UAuD+eQ1BFoI+zcphAdCwnSUpxKlvrG+/EJS/Znh244zuMJGUzhsIJVESDUxzUtj/9gtk52EV6cHzlwcgU3UvuJrQJsE9nlGKRFod/HUgdorM0ONCw9ye2hYqS8S3Ednbllx/+PCKYbXY/miBCBhHi+60ds1OXol7BDjSMDLB6oQt0ns7J40pakyB0rkf3IpOR4yWFpgnHmq9TMR0+bgZ0vprahXpzlOc1+sBo4nQpxtgeZio3mDq6QP3BJlwGUhX/7bDtjr89I0CnjuWrUmZcVt7PM/VwHF36zkGodXHFAx8VvjYT6SscRx8XARciMwRly0yqyadpyzuZMYqU82k9tn1dE1V3odz9F7EcL8lpcp4fHyF9xa324h1cwhfPuD0+IbudIO0V0gbmYwpEKeeBt76bvySKuPht78AdTFk2aDSCWY/URtu6WiDPN6gug3D6Ypt7uBXB1PZ4xttrpfrHp7ZNQUCs3dImQBUNQfUyIDdboNVRF0E0NgoNamnqlAECqwm801vqfVXikCBwJk/m6rGqdmYr2tPSUkmYAoWN39HFTgVMTA10jL6OBdq9RgTMM0DpMzYMZ64nirO0w5GJXTWY+GfmSEw6kAJTUliUBlWZVwBbIkkgrkAn0yEfWfisawEqnJnki+j8e9J406bQMzEdFqZ7eQ49Ufz6W9N/5U0wv+WD8GoxZ53qqqdNSzNWbNEJ4h90vPAi3rdAhdvMSeJL6vCFMmBuZMFB5uhWOajRMEaNaNpS3PYjVyNpUiVWPCm9Qy3aHDaXWFNQEoaZRP/RcQaWP6XN1rY3LBQxRx141EYG2CHFeOnF5inC+QYIKoiMAP5qlCCphupCMQrDWZUf2+3OI57q2YbbQOZPXgKrjtPVbdK1LfmSkLuadA1uoC0OKxfHpCTRH+4cS+5oASBHOpzZnv98wlmdfdgYwb8ixKBKAGoVok/fXhG2Cxu0442PlDvsIfnjEnCsY5sk5ZgGzEzUTqVMOhAtDg+0hNMTDRLvQjAPpq7o9STPDJtBsoF9Lyhls0gLZTRWE8h8dZTS+UwcZDz3OSVAHDaXe+VIy+kTgf89viKyzrgvHbw0Egl8xE+tUWYFveEfbei58COlCRepj1CkvS6uIoUgqzfViXsXAA5DwhBUKtUH0iOWYrAD0/PsLwhVFVEldvV04QQpWEevrssOUDbXweEpcP15djUIilLjEwSPH16pmE2zxXseLfaq95D9Z5nI7alR21zj+t119DFl/MB3luej2zcdlINQrV627jeqUgaAGZirNcQkPoaiciX2c0pkYJseaNCFLwtNHx9GCYYRcCxmBRua49UyKJf30sJOs3dgsGcusYr+cQqoUM3t8q98pDqfwPk8lXVaFck3qYRSmb8sL80hdXOevzIiqlq69ei4HOfWNZM8L2fFtKKjyzGCFyM1tCHjamndUHuVKYpJtDWw5hpPlPXyFAEcv7+c3//+HVlhKC+jhBk5hlkuYfHZu6Hg/rYPR+PamXzh8U2auHH7s5jrkaeG8ttnpyAAe3OJOXaMHYr3ZBJY7sShzlEjd0w48ndB5iBj/Bf//gZAFUL1np8+Ns/EhOFWx5KJ0rcXh2UTlQR7SfYhyv0bqaFVfKTswqQ3JMtW51wULJ4FhBcQeVbD2kihk8vKEW24AUUOt4mL78jB1YmeZo7iI3BThv1vM9faSB6+vErpIlI3PK5fnsgvvhMVELTbUhvewg5wu7mBrZSzsMvu2byyYlONtpEfPjwDQDIgRpIP5uzgg+6yevWQKniFeRfN+pe3/vgAPC2DE07bVXCD/szju+ohvQhFCjWyidvyCSl6nxCwL/uqeXEP/P6jz8gJ0mByJlQuzXwQTO/pmJOS5HIWRAkynW4rn2zb1dlRSoCJ7fCacq6FOLORq9W8Mrk+Hl1OJqIH4cJQqDFjr1HqH7sFuzdin23sFv0zsQhqBqd1Oj5JsRIVZuUBa6fMR6JUlj12mHpyIgWNZa1awx7JSlXc/OWWCbsgwDuzszWhtEJ29x9R8Js0V+Zrr/9nlg+zSW5Uuj1vDmkLOFMaH1ppwMe9xf2XxBLSMpMs5RgcN06/MRUQAAt+MNqDrtwK5TM2DPOOSXZEA1GJgwsP7QqQkty1j50C7TKzdxTGSqaT0Mb69B3WX63gANoA+SSBbZoqJLnVp9WCX5VjaZY0+ilkC2FPmaqko82MqNFACA201aAW9Tf+VxG9rkIEDdlThI7TQTLqah3/fSC8d0i/+cev3oLJWSBf5rJEv/oJKwsGHUFwlM13lgo5T6wsPJuWU1Z4C1Qc79XxFM5sOEHACe9SCgdcNpdmx41RoV1o15o5zZ03YpuWBE4xDcl+V22pNZkJ4cobehYMg0tTc8SvTr0VIlSbYJGURnCeEAXINLCUq7UgikVdHPkynuRyIvF9T9/JuzsbiZ9d79RBeotzLjA7Gfo3QzJsViCjT+F/6yPHHUjI+rdgpq/KRXdfIpNDNaRrMyMS0s4D9fh3bCLhqEJdPzdfXylDM8bLdhK3fkvMdGCVgmFWib0xlOGYRGMyhQtRmtjpcDKuvA1UX7h2zxQoEOUEEqhBFpoNSMNIAv0YYbcB2AfoADIlw15cRTokAUEt5MS6+6JGZ6x39+QImmiATb6sHWars2CkCir8UM/Y+dWPLBcrd7s1cVX2yFKZCQhGwSrFHBsl238cyEKRhMoKZ0Hnuetb/LLS9S0ONsNxsTWSqm/NwbStJ8+PiN5jevLifjojhy6pQgMhxuUDWSFzrI5f2u/ezrvsa0Oju36tVW4rQ7WefSHG9ywEoVws5R7ySe1O+qBTjMx6kbvTKzKkaq0PNXeeHTGtyF3SoRFTdUWbwI+7i4YONWotkaEIKNTlVICwM9vJ1IH7S4Y3Ibfm2+k2smK2xAaWiWcWL2iZWqLcZ3NbNE05C8A/PFCQK/ReDbs0BA2JLoWr94REoJlpjXlJwOcsylwDQo+CxwMoIXCNd4NPSEDr57Wtw+OGS8VkcBt4Jgl1kLt41GnlkBfw5NTEXjxhsKT831A+ucev+oCrkTBo0sYtIRPwPMmoITAqHmQV4BRE4PZKoFREAUOABZmpxwL9bwFqMC9W/Kpr/Rl7dDJjKduhU2xITYDg/vfhx2ThIsX1Npnkxn7wxWm3zA8nSkMttuAIhAuYzPkSEVhCPS8BXTvYQ4ThAsQJt/f2c4CWkFsM0op8L8cUJKCky9AFoiXAaVI7P/mF2oXLLYtyGkziEuH7ukN5sOlPe9w3mG7jOgeLlC9/264mr1liSP3QPsN6rDCfj6j/+1XpFuP9eVwl0NqIi2q3kPBw7/uEZi7UZLC7ULZhofHVx7qFkgZocy9R+03S5P7pNoN45NGymSHfrCejpZcOaUsAdYMr0njLRgYUXCwno7bvFjTe8uZjJ5YHUJkGBshXIboAP1pRYkrtv/0hDh3sA/EGFm/nUgWaQNE1E2/XCvUwgvAxM47KTOO/YKn8UbpL1G39HhfE+s52akqIjJ6lMgLFwDb1bQoCnyORUIjo5TSzCY76+F0QG830h7zgvNeVljbJtrExu6mi5S/tt/QceJ8VetUWWmJEra6fgGgSHIdF9FOY+evj6TMOdya0qgbZ4wf3mCGFSVL3L4+YF265uz0m0V4txBWvXpVc1xuOyRQdVs55DlTQeSDxeotjCIsrZK5Vez1c4iJBp+hSBzYdBUyuXcJlUBuau8truudmlg/u/euVcnI3zUYvC7Dd23V/TsqZUUr0AKekNaO8i6zpSqeXcVGkrb//33Z4ZdVY6dL63kLUbDXGbkAb0HBF4GXLaNTAp+7woHYjK+tPXEAc9B48/f8zltQbYYHkGw6FRpqmr/cQfn1K/BOFhiRAA18omIIqaD5/7W8DzFrfwuSvm80ArdYmQngyrvwEJN+g8oCkHWxII51SAoh6nbRvX9UDa5SCcenV+Iic69VGtI+b99OVJEOG8nxWGtcskS4DFhfD+RkVBlyzNSg7yygNaA0ICXgFoicYU43qsKzgNAJ9rdv9EQkVePxbdds9dVEBKBV7gBgDhNUtyF7i3jroYcVkAVppb6wdp6khLJQr3OgN18dA9TmoR+vyIttqpR066GcJ0mZ84DMLWuyH2aqor8+QeuIh8/fmsolgoaGWkfsxhkv5yN+uhxxDRbXdzd7dTAKthlbHaEyGVuUzPixXzDogN89PON4uLQ2jtot1BYS5JBVPMzcfnqkdo/zULsNoitwv3uGTRLx2x5x6Yghw3K591mkHZ/o6slhCQanfsbD/tIW0+fLEQurE4xK2IIh3bgr0CC9ulIZM8eBrUk1qqJTEQPzYJTIOG89Lt5yRy3joZ+x6xYoXphmHvINfPK7TDsYFfHb3/7EpEjflDiUm+rghoU23G6DdAHp1iOuDsvLAX6mNkopAsP+1mSv1UFcovwuhi8nBbuZdrpMm0XgBKfd/gbN0WolSxjO21TsCUhR4fn5qRVBVsWGalU6oQQ+QSdyn0pBi7pSGYPaMG/EYq9tDyMzDDLhCHCHWaUskbyET2R3f14GOBWxY1TxXbhgyBUD4KfLERfvWgwbDTkzdMywMmLfkctTysyZvInlrgS92vFmLUXBbXOYgyWGCfe3JYBnr+G9bCgQKwuMLfjfPVIlfeR7N/Icb4qK08USTiZir9mSnyTOQeEWBZ4sByhrahM7mfEXSN30uv7yX/3/7nEHUKEBz5Ugnq4EBYRSGr1uyM4HF6GkwptX8Bkt1V4J2hi0KNCyYM8shi/ziIduxamfIAVFKvXdiqFf2jBrHGeWDfKHbAOpT3YLDcamrjFGlMzQwwrB1L+8OMTb0NQl/jogB4UO36BcBHwAcgbiQsBzrSD6BHVYm+YWstCgMwNl5Z5+T8HJwkQo0EVRskBebNM+C01GJPvhDcJSL7gEjTSTmYKCI6hPXJJE/LmDMAmyD8iLQXwbIW2E3i+c2amJ/x00usMEM2xAvsHG+/DJsYMtJ4qjW6ehVVhSZrh+wSmTxGsJFrO32JKm0NloMLF0DAAGTjt56BYkJk06HdAz8pcqbg1RnYK9b6eT+tqRaagsFA1dhShABuZfHhEWBzuuzSxVssS2OqSkECLxUSrQyLFbsO8IJUz8joRH7gOXItDz9fJP5weUIvD7h2dYQ8x5JTPcMiAXysb0mdowJ7fiabyhtx4/AO808QFSFmzeIheJlRfbij/uWX8tmd8RGJMQeN7y8PkrwctUos88aITrQIEWq2tkzMqUVzrhwLLQmihViqD5CUcLbnMP163oTjf4xWG+7vh9I2a44UF2jfd7L/PM3Df+cttDiYJTP5OGvNugTcSpOyMEgz1zaUieSGC0ijCYgv3OLu5AG/2uyQFLO/WkCnTLsm2yKlLAsRIZnfXQMuHjeMNDt7QZi5LU5rl5izlZcsoCzSDUm0CywWCa/LhuwksgRUrH8r/qvARIJfLG9EHDCpIPLjQVHa1jsqntblHizSsKNeYNqBQBy5iQWtmHch9g/7XHr7qAV0JXpzJXY0DKwCVIttDTZLbnnhp9Dy2uShR0suBkKZ2n9sxTAd8Y9wFoZwJ+u79g51Yc9jcY6xvkvppcCE5FOu/KH6kOzLQZCEbIVkWDkNRbJXOOQ5o7LC8HChf++EYvUGSkW48SPDI7Af2Z2i7j775AjSvyZr6TvKEIpMVh+kIUuoZSldTaSEGT23BYm6rC7CeqQPceogPymZNyTIQMhB6tQ1BpMg9VmYH5JxrrmoWoO0q7SZvlLM13X1cEUf+KgO02WjyzhNIR4/Ha3JzWeezSjO1CgQ5GZkapRsQk8fM8IhSBf3l4w85tOPXUY36dR8RMp6SwWfjrAGBAChq625p1/n1cnOw3yN5DqALogrKQysedrjDD2tQ+Ye6QPC3A3pMqQgqi5Y1dbm7dEAzyRtV55zy6buW5iGrDv8eeGNJ9t3KbgPqkH/q5MTOqRE5LClygoe1doz17h21hRjWDsADgaZhgZebAB0YfczCz0gnjwwVCpzvv5tYjLkSUrINmpSPcQOlOyGT+ESLDDCuEyvBXKjhqtJ52HsZ5DA+XpnxSJqLfTe++vyAFjem8R/CWKnJBffSUuB+eKJVIiYIdtyQAtA2e0LQ0f1qDJXkft1GEKOhZYbPwSadTlJm6d0sz3/msMAfikVeTmGfgVxGCJaVEykxSNgb5eRmQisRoFhiV8DDcELPC6zy2IOSUqdKe+ORopMWJe+iFe7RGJlwDacLrAi5FwfCuh/3eUu+TwpfVYo4S3zbxHffpwUV07OLMRSCL0pJ9UqGiyUluu4k7cvnPPX7VBVyKAqsosdngnmBxMglbpiNGJwvrLWkxvXjL2Xs1pQNQqjSR+5qIbbdlCjjuVILjgUYuArdpwA4EQSpJIiZyIKYsG/Pk+nLEtjkcjpRYv/30kQwm/QJpEtyOFpC42sYFFyZi//ufkRaHOHdNileiRAz3/EV3IlWFtJHkiWycSHzjhdnRcfXHr8hBI0w9fw3BqCyfCoRK0I5khcJECJMgOgBOQz4mIJE6Qw8rToZOCfo0Q3QF2BvAR5QrLWrUx5cQnmR4Q7dx+0U1/a/nKq6S7sbThW9curB71tHWRWaZB+RMFQNR1TRKSVCFZHiaEQmq3tw8IKqPij3NHDAh+TWkxWL++kAnCpXvQ9fbAFxGSH5v/OueVComkiPQ3XXcUmXs1xti0JjmoakLQtSUDK9Iw18Drl+uB6zB4DTMMCrizDf75+Mb7DsZ4bJ2WDjCS/JCRIoUi5gpOzRnCWdkW9TWYEl6yDiBT/rSgFu0KTDOwdIAfbdjkr/ICFOP2y9PsMMCd5ygbIAFcH0+YZ4G7A9X2GFtUX6Kh5a1PYJMEWm5CGjGEFMikMR6GXF5eYDh8IzKA+964sZLRYHbXU8nm21mPonboHVCZwK0vCcHuW5DjKrNHmJScJYwAtUE9KePiUmH/UBpOLVFJQQl269Rw8iMvaMwkTUZ9Dpg5+6u4Nk7XDeNnd1gGV2sxD2KzVmPjv8fQavoVDAFi0EH/H4Xueo2zYhWMzfv1EHFUZCl3RMAWtoUZcUqzrUEHiwRVGluR71wzycQx4u0kxQEXmWGc6L++M6kv7pI/8oLONDJjCkRorHKh4wkmpurbwjuk38lM3Qh1sklCDw5OqbcAuEaKxymF4Sm7dk4UYpoZDIAsG5D5ECCrl8JapOJMVIHRzFS31lrGgqZnm6AxLCk4TOzTvYrilcom2GCoKUWRFTojzfo3UIDzb6iYoH4tkOJEooDkqULgMroHPWqoQFEwE6WTDmbJYbJ6FEW0j3XgVUJGnlRkHOAyBHVQSBkRpEZOUpIaOTFQCJA7EEtnDFBYoP2unHB00zJPbVVBABaJ6DbKA+R+4CRueX1CJ2zosFS53lgmjHfBtymEZ0J+P3xtalTKtrz83Bjg4/G5F2L2tJ8/CUWiocaNtoQRxqodZ9f2wIkTGzSSySJ8LZD2oYWYLFdx7uMEEBcXYsNI1u4hBDU++y7DUN/zw3duB8dF8p6dN4CFjgOZMPfvKG0JbehFNn07/807dBrkg92KlD/9F3QQKjVXBEY3dqyIt+HhwC0qUVvaChX5wDDypp46ktr5wlSNqxkgWfDVTfNVKBsFudvj4hR4fhwpj46V+3RUyByZaLQ8yM5pdQRD5+/IrKruKIjclYQKbcWGkCehciKFGcDYszUz04aOjBeYiaduXGe7ekGMByUYgOAAB+IqZKSbANvBZL/VXmmRIFjq/yuDkB1wGAzHuWN15WC69rjunUYrMepn2mgXlssEtiCgRe6rSn1+zQXil2RjVdD1wOQeFzmM+VXkqHnnrQzahpOWh5Q1kq59rwFaJE/WQqpGTS3YFhpUpN3lCgQRUCVmgAlmhPTJ4n430sPvLB0xvAulAqFCNRA2DmRttKq+wuUAEcbFaxJwDPC0Sqgw32aXB9G5qb/NjrCcZ9puo0UpzYsNIixgeRSXtFxzm00HNIJ29QDGegeL1D9Btl5CBsh9+ygXIEcLJZfHiF1RPfhTBLCpBDnDvMvj9hxxQwAyAJqYOKfTlQFLXR0FpKUHQg0qCy8iErmVCADog8wg6evk0CeChAUtj8+ogQNaUkbXn+2cgGQBXm19PeBwFpiLyAsoI4L0rlHmbv2+2pLyTga/hk+Ur99e2zqAyUzjg9n5CRxOR9grcfRsjoFlJuodULysn29khmBN+xORximDlb5WO1lGm5lUC+b+elBI00d/HlH0spuYwRugXABkICOiqiFLKk0vHDXSLnb2wGJh2y5CMwbbRwLx3NV/opWCdd5vOuaORRBityiymof9jztWjp6zKoNtUqhlsi2aQw6NGwAQIlAnaNrUoiCeelxnUY8HC9QKtLpg+V5RkfKUBUF4katOz/1UDag4xNduAz0mbPb0nQeb788YV567HYT+iFSzzn2sEE1Bcv9H5olSJOg9Eazk6CRo6aKnJOhJs7krFFqu90ErSNct8EUz9WnbZXsde2bM/fYL/j0+IycRTthUFsF7fqwOtIcRUh8ctfvIutyEcjlPujVitjzP12P6FTEnvngFar1m+61zRpWPhnVVPraqtIqwYmA3UCn6s1bTFuHX1iXbnmIOViPvabnvAsWSzD4sgwADP6wCJw98OgoEUzoDCnRzpO3QNruKQJKCEghERQVm7Vdcg0al6BgFQXU7EzidS6hU8CBr5uKGfhLj/+/DDErTfCX1fLxgVQkW5ZYAcgkMfIi/v6hBNDr0pybAPtlJLFSlCi4BoOQydnVmYA9azwXbzG6FWNPYce9DZSqI0njWhJJA7UNNCQyEebhCtFFyANvEAnIV4ntjx8AALqnvrR/3bfnKHSCc55ohKqgpHfOziIQXvbIvvZ50VCuOd413e50Rfe7Z/peT9jXEhX1fG1qenLNm0K4DMjeIDLbWHcbDfdEbvIy1W+QekM+W2xfHu6Btx1J+6qrL84dsVcU6X9PH15o4doIZlUyWYEVT/BLIYXEtjoi/jGn+eYd4QyywmgCOhUwuA1Wh0Z+u0w7bMHg4h2cpEUiRU4+ug4UZcemHX8dEL6e4Pbzd5p42W+w/YZ065E9tYYAwOwWqORxAlXhtzcKpJi2Dp3xeDqcm625FIEQDawOsDrgsgytV07YAGoX7PqZ/APcF78sA1KxFKFVBB5yjUyjj1tL6sNqdnXelgFjt8IyNtcUgdfzAQUCp/2l4Y6lzI39vs3E8tbMlhcqIVxHzG97NqBlrEuP4C2kTG0wL1TCfCEkcMu15LaY1hG22+AOE3RH8tfsdVMxJW/oNFdEazPVBdh1GzR7CEoRWPnEs+uWduJqJFFLG67SdwmgD7YZoUKivNGaQv9hmOH4+qj435SoBQXQgl9P1Inlh5aDM4yi4iHnit5IUEJi9jQgnRipUZHALvGcgd2WW6ZgZiMT0xPvEk/PPX4jMw4m4PeDwOxkC16vIRYADR8rVrZTtGYZQdX+lgV8Vpii5EqcmogZgtnh1PbNAH5aOoQs8Jnvz7/0+NUX8AwwvQzYa0JJrpmq8pONWCIxAixPjevR5MlFHIxo09wsKk5WICXBPAZ6E6YicLse8cSOt6Fb8Hg8tw83RYV16pk9TfjUlCX1gFPXeCSfTYQaVuQptQUvTR18NbzITLTCqMjUUxUUSTEZsEB2BUBG/DIgbxSpproNjoepNfMQswAU6Zb1uFLvOhcI4+F/OmH5+gB3ukLvFqSZ1DH26QzpAlxP7Y7wum+4WoA01NJsML+ZmmhewsOVN8RrT5TCXaSBKLcl4twhBY3I7kU7rEBG00/b3QQFfJcMU81CxlDqeq28X5aROCjBYAkaIy/gdTg4e4clED8lKYHbMtBQ6h29UZlI6UVVjZAFQt388koYAZeh9AwVBdLUoTAZMnkDP3VY5gH/9PMPUDLjN4/fYK1HPyz8HonmKq3kvcW7RrCrBrD6qLZ2IQrSTEfgz/1G1ZOm4ZpldYtWqb3GlVGnP45XHPq5qWCO4w3WhBahFqoHINx/v+5XdI8XVufcTToAkDNJ+96rqdxuhrIBblhpI5h7xJUCi4Ugtre1xFWXLrR/1LgiTR3E1Lc2gzUBwhJHRKrM4dGOgqezwO1Km4SzHjIazJODUQn7YWI5YWw9Yh8sLvOA0a04jLdmtiGssGEkLCEOhCg4L8RCGd1KdvVEA76d2ZrBJmXBm0HPw0oPZwKe5xHXYMmUZcIdMBY1UnaNS3JdO3jOzGyLcFZYA8GtlMisZBGwkuZrRuY2qCxAo6T+w43UKidLXPknSTLAHavr6vWWC3URQhb4oY945MxYLTPO3pIeHCTqmKOCFH/Zivnr88BBA0fJmuC6oG9FIIR3DHDQgJKog6L1x0nwDnSqHjHA7iiBQQMnQ+yNp37G3q3ouxX9sGA43NpzqKqJZR4Qg6GwgtqyAGhQw2qUtFrc2JpueurJ1oDZlCmjcfj0QovguDaNt+wDvbuS/lGHBTKtVJUXgXSmqk66AMncamkDzOkGtduAUUNsESUD5vFKFnqdAEUoVQSN7csDIAr633+B2geoJxp4lRUkTQwSVa5TfEHZJC3iu42svJJe4/bt2Kz5dXE4vx2xeYtPn75CO4/d6dLkbDHqe9K94Wi4d31FyUfjp+GGUyfxtg5YgsbMeZCt6uUF58BysVrxKOfpZLBbyP1362njc75p8eV+hewz8lUhTR3UaYUYAcgFebGILCecrjtsm0PFudbnWR2FOUlqZ8wjOkOcj7FbWe7HCoiVjCaHrAAefnpviZ+SKoedept1aCa4PQRQ5XhQhDbtuBp+PJxpIxmXZmnPiUIZhCzoKuecFTiV/V4Z9XVDq6fGyk8H0JRELaCaT5s7vtcoSlAirRZh6hHmjkKs+5Xww5w+r3WC4cW7vl+V1bJu9OfAJ9qUaN5UmTgHPr2EzWJZO8xLj85t+PTwwnJO3Qw4PednAvfZlxTEMQGI0f0++acOvyunuypZaguO4FW0CVgWNDhzT+TKEJi2jls0tNme+rllpUZ208YkEQv1v5ekWouEIvKAgXvz52AYOkVF5ZuvLTtgpwV2LNaQomBOkrngBZ+7iL2mQOPIpx1y6wp2KwNL/OtL9K+6gMci8OxVY3gvIOPOoChqiLCL1FK5RolroDguJQHoDCeJRgjQAhAL8HWjt/XHnlorX1aDByfwebjBmQDn2PHGxxAhCpQGBKM2vczYlh7zTeLh4zPsuJADUxYKcBAFh9/+0mh4pdANIZIEGHOqet96snl2CJeRTBaWXXIqQ330EEZQTzsXyP307o2hlkuJDLwCAKtp8OgS2fEXkgkKl6DzDDWupESRrImeJDlAJYAoUIKE/3oi+/mwttRxaSJUvzUTT0ny3uKRGWHuaDaQiJFdK2+AqvDXV+JTU56ggL8eSGdvIjZvmE5IYCDNfe3HfkLuBNZgcObjshQFD/1MA6iaQyiJnQHgjgjIgvABkRdDR+913WzlBwqugHa0OfUR0m/Yqz8grxbD6x5xdRi+zc36771lHsw9xGM/TAjB4DYPMJocg95bRB6kaZUpFAD3FsCBIVTvre9rpBCAThHYy6qEU7+1TatuElV10xWqktepJ7yrLFDcJ89JYn3bIQWNbenaYFFbRhIspBgadjNZ6/mEWQOdI+v4h90EYyI6vuZqRbxdR1KbbCQPrN8vVcLu4YL94xnRG0IQzH3DELT7SGbkfK8OnfX43dO3pvJRPNxWMlMKEpvmvLet/SFlxoXdknWBfZ52ULLgw+7SMjXrIyWFIW7YosFtc8RO6Raq4INpTtAT0x7rZ1NZKHdZciayqcnNlh8TkSTftg5fVtfs7WuW8Inyd7UsoCgTGm7mgobweHIJuRCpUAiKRbPc166JQQB5WEohpcnIhp2YZVu8K28dEOj0vXr/c49fdQFfUsF/vAL/myP1f2qPe07UE6oxaZXoVRPolaTMzDlJ/OMkMcWCB0tM8SeH7yyqDy7CyYzXrfYtB6IM8g3jN4v98Yr+cEO3n9DtJ0yvh+Zey0FD1B64IoCVPsxAETCHGWmx2N72MC5DHm/sgqRFtHjVZIVxpou9cr87+QzZB5St4sf4pt8MIAr9XdAI5x3pfXOhXW7oIPIMZWfkq0F+c6yBDhC7AiiBcqZquw5GAdpIrj99oECI45XlYgJpsQiXoalQzG6B6ubWA3/94yecLwcaVLkNy9xjFR0sD4P3uxti1LhOI7RKeDq90usokiLA4oYQiBmyBIvJu4b6PPQLVUNchdb2xewtewKI0ZE2i7RRBFwNXK6PIUlYEFdGKNa4a5JWQgKi7kV7T++RiTAL3YzVpJKThDERSkVY55vpZWX409frETdv26ngde1ZwkrI0856yJyx7yiU4rY5glslDQmKius0pdDUyrIOPRvbWhPxcFsdhLAIDNEylsK2b28HxGBwvY0MiZIwrF4hbTUPlnXChfv7fbfSEH51NFCc6XuHpYcxAbvdBMWbkzQU6KBMhBtIGVVlh5AF/nWP9Tre1WA6oXMe89IjJYnDnoapl+v+zoZhHbsxAeOeMMgx8r3HTkyDu6kpFwFRRBtok6FHciJ9wi5YKE7/UZLMeADaBtHp2MBoVhFsjBAOsjFrHA8ia+iDlgkFAgvTE+eNBpQ/L0MLFhlUwu/HiStq0bjlPtegBVpoL8xjGjWJL54c/Y5JcKITbwAx34UCJxsxasHY2NyusXPQuAXF2b+JRARsQPrvZgHvlcC/2NOT0qJgbyocXQIQSIXDPUFtlBZinGjxXhMJ4qEFXj29sN8O1E555Bc+cvUSs8QtOHw7n/C4v2Lc32BAPTmpY+sXliLaDfz27RFKZXzi8IUWPDx3RAIcVqh+gx7JKCJtRHjb4fL/+R36xwvsp1fow0TSr0StlOnnJ0RvYHYLkBdyTkqg8DFLmAjoAtEVSLnBCnJXltdMLk2nqRduabH3r3vIuYM0Eba7AEZAHAvo0uPxfiqQY8AJf/jOgt8espBFfTOURh+Ghhd9/N0vOIVnypSMRFzMWeF8OUDKQk7WpPDttsdgPXbjDG0CjFnpSMrvZQgG0zyQcSUarIwykKIgJQUpC3zQyEW2wdISLIyPJKVjdcV2G/Dy8oD9/obDh1caYD7d6L2RQPrmEF96bC9HRH9X9lRkauV/rFxtAsxuV7RQ1gDgUgSWlYiETgc4HXBZeyxR47GbYVRuDtLNk0Z45kHt89rDiNz6rAWi5YPWqvK6dbh4R6cSlWHMBP0uhKEiZQHCHs9Lj0pQ7DWppyqcqxTR2CQpaVzmAbO3+NtPv2DYzeiHGaVI6NcEHwymtW89faMjyiDhsMI8XFo8X70uqqt3u/W4vB3QjzOMoUXfmNAq9aqZrwslAFhQRU1coQypAFUShnGGe/f61s1hYVOOVgm99bQxBpqPVGlf7YUb5qkTe1y2aLZUJCN/adHPWeLiOyxBozeUvRqSRsylBROfhhlaJhy4Qq8/79it7fRUeTapEApgiRqXoNHrhFEl9IrWqB/4QFJdk3OqFXZBRsGSJNYi8eo1M1UKQ/lyi3yr+vJUKKRGcdunhjuH/N8RD7wwuHyKCp0q+EES7OVgIrdQJFRpyxDlzAWa2kqWDGpq4+KJTszYksASgU0LKA4mpsEnJaYc+pmYxqyHVXUgJgs8J7m8vZ0wrR0eD2cYDtWFLNRiyKINHvVpBnSB0hvyIpGvHSALaY7Zhal2C9Tprpk9fT4T96QrrR8NAOi53eEksGXEnwfSf3vqda7nPbrjFe7pwtVlwfLLI+aXI3afnintZ5IQnhd6DeA4EH/FWIgYYY43lGtE+OnA2ZiBVDV9Rl4kyqqJo8EnhhQVuT6ZvxG9oQSjLDCME1Xe1x1KEfi7T79A6wTXL0hR43q5V2KtjykzRkaDBqa7Ld7i5+sRMUnMbOYYFFWqTlFFRX14ch32xxs+6gi3WwjX+3iFOEriy0gB6TdozhuVrKLISWJ+PiJxr74+n1LuxMnNW6zB4rL0GN2G0a1Ygm2ccMtyR4A2lkrBq/3YqnTQNuHAgRRATZ3XMPxaqtJCCcqHvKw9Ju+wnwd0xuPD0wtlsrIa6vX1RP3f46VV6aXIhozNWcG6DbvuSq2I1WG/oxNWZZ/U61s7j7hZyG+PyFm22LC3txOGfkH/cIXgFlphhVHyhg1rCU8/fmnZr9dvD9iWO0QKQBvyJZCSoxqRhCwt7WhbemhDLPfA7RhnPXaFMriEKIwOyMhlREyqhW5Q6LTGde0p9KJG8fG19P55VFv+zno89hO2aBASnVqUzBgs3XjnhQbgp35iD0JqlvkuURhJNZmFqLEmWnx7fTfp1AV75EzLkokLVDsKRhDX+2VTdIhWVGwK3N3oSpApKXBVfzQRI/fDlSh43mxr0fw1M/2vTiM8mhoyTDtXSKLpvwd9l9FUR5KVBUndX0IoYIH8PQg5FuCfZo1RFxzYuryzHqMlo4bh47+fOuTrCDfOBKxSGcpG7MYbjA44fXiBG1YakrkA+XQm/SzTBYUGigfS2SC87nH74weK+epXknZ9e6BAh3y9K0U+v0GOGWW9Z2RCFKhPgVjhALVgJAU31OSc2+uB8LKnG/LWcyuBI7jY0JMn2mBqr12qhSBaOQMxAktEWSTSYsnNOZKiJE+StNR70pYLnbC8HnB7PeCkMowip5/NAuvbntkqBPzqGHfajwspQgLdlAQtIjzr67zDt2Vo8sHAPOz314HTEY/9TJxoJheuiUiGNeLrvblIOQ9zvEG4DECivEbkxUBoCWHvnPS0WiBoKO7z1hMWIYXpexPrraskLUSNCVROdbxop0w3vxSF9N7v9PJbpFzHwW53iBL/rsB91IMD9t3SHMEAIENByKohkmNWWBdindRqvHMbUlKYrjtImdH1hDu+3naQslCOp/Uw/QbHzJ76PuWkEFaLFEwbXiob8OPv/0BV+9wzJ4U2hLA4mH6D2U+oDPrACVHV+ap4QHr8/Eyvjzf6GkfYDwtSvJ/QpCRXa8+igTFdsVx2eHl+oAVTJ6ybw3Xtcejm5tPIWTLfOzVe97x1CEljDqa1SQFC/27R4MU7PNoNWiachhmf9LkVEDEpeNCsAkBrv9DndM/JrJsxMdczRsf9de+wRINLMPhltXj1Entd4FQmm7soePMGGWiS5voMfSYpoVMkd/62CewN8JFBWBUvuyaFb6vFOVBBa7hd0qmEvU4Avo+M+3OPXz3Q4Rwk1kTZmB9cTej5/riQWKUC0Jsxx/t/rxylJgWhaOvjYAp6lZmnK/HTPMInhdM4QXsi0lV7sGSnZQrEPB73Nww7ysrLSWL+5ZHMEfuJRPrcx5Y2Ii0O809PkCpjeDo3bKfZLcwoIfSsGlb6oKJAPiss//kT0mbRfXijxfcfLS3k+wVCCegPK1XF3xTs8YaPwwpzmKAfJpRAnHH78Q0Hw3RBIwGtqBKdNqrstaaB5+qBrSB+GQCZ4X770oY56ebgn4+0SfUbuTo98VYOKsEvDn7qoGykKtiQpTtF4rJUFUJlqFOLRTJlLmLHyNg964JTlvAb0eYqV/l/OL2Q0Yr14PPaY/EWz+uATtH/E7ypKZtZWrlQT7tesVkASSJeBmqRvB4QV4t16ZkBzuoEbj14jgSrgzhnA7ZocPFdc+CNhsw3ThOyIPJi+58vR1yjxt+MN2JFs+KhVtgxyRZGbHXCkXv9wP2IPnuLW3DodYDTsaXYfDufKLxgmCi/9XBFKXd53jL375zChDrtGA+bokLxBteXI27TSAxvbjWUIvDp9IquW1sIhHY0e9iHiZCrc9+MX4LnPQCIO34+4jYNeHx8xXC4MV6YnJk5yYZO6HqCgL1H47Z7h/vpdj9jOF2bE3RZHWbv8LaMSNMOvQnfaZ1b9c2tkhq9Vt9TIQp8psUwgwaQ8Gi9ciUybt7h4h2sSjAy8WdDALLCrZHadpGSZhvz5vDT9YBOUwFoZCKrvgls8xetT04FJqEpXhmwl5mQ+sEldp2TS5yogjTspLaMgGI12pMLeLARBSTymKLCW9At1L2q7/7S41evwD92CT59b9DRogD8oQXudxve5ZQAnCqYI0l0Tpb+39dVIGZgZ0jiPMf7gt7rgB92Vwx2Q+dWsmdbjqSKaPCfuFmqRDaLnGkhVyyLSxFNUmePNwAG8XbHvNbQ4Hr8HJ7OsE/Mbi53LXaaOuJtDxvUsLVAhvXbiV77rYfqPcyPFwiXYT5eaCC5GcjRQ+zB/W3PkkSJcs0oV0B+KEBnAGtoIVf8cQYPxAhtFuILdBaYN6Qv9NztwxXx1tNzkPcOWwUl5aSaDrw/XRgAZlGybFS64Gkx/HY+kbtNBzgboNWdkV6VCjXJZmc2KFlaPmH9usCV8Kdhwr5j6JhOMMOGHCkPNFxHpNXBfXqlz+hBQD0E4OcMXAZ0DxdSrXwDvLctOu52Iy1x362QjEmox/tcaHEgJ7BqEXw1SMSwmenzcMND1i0n823tKbZrd4FVEcWKthisUeNlOWFnNpyGmS3eDnu34cf9mdoM3OIDiN2Rk0LnPKzjIWIGrPVtjmCtx4eP3+6fk8zwc38P1PYWpXI93qlCbvNA1ENepOpnMoxTW9A1m34IJdshbYYG0m7DXmQImRFWh8vbAT6YFgpsLG0i021ktkpqcsKSBYS+X1d0oiLYlrYBp6hgTcD5tsdt6yhaLYpGAKy97uvmoETBh921STMrwfFgV1jJuNZMG5sIBTu3QemMp/HWpIF3kqFsHoXDMDeMAEAh14bdt0sgiWiLZNQBe+Nx8Q4zSwojs1AkL9wxgznhwMnGltZTH4lbLwJ3H4yWnMwjCm7BwCeJ/3jTuISCjx25PEedv6Mf/unj163AC01up0jJFWsidPbfDCQP7FTGmiUuQTKYqmBJVIF/7BLFC4GKTQmNUIhmmCGwJuqvr4lSQnxUUMJg3ToovkBNt8GNM6I3CNwb1TphvlHUUz/MUAYwzkM5D/eBAh1EF4EoEC+0GIBbnkIUTsuZoA8z1IOnd7TGpmWQxG8VJIOrF4uN6D68EdJzdWTD33io6hXSZcD8yyP6j69w/RtKBBAFqU60grA0sCyvGTjfID4PtHhfr7SQH4/0Z++pnVIyICWU9kD0QChQ6wz78Q15ccibwfL1AdttQP9waT3w8q6XrHREDAZvb0dipx8v0Caiq+Q8JhTmLLF4h+dp16b3O+tbDqYEtyTWnkhxWcBwpF2TfC1UJatua2oZIWljReH3VgLQCupDhHo6o8ykxHFPlyaZLEEj3HoE5oMklnelJHHhXmjllrwP+uiNh9ERt7Un3kmwiEXi6BY4E/A0XpuKxieN60YBz/WGtyo1vOnDcMPDcMNt7XFZe6rATUDXeRgd8PTw2lLSc5b4+uVjGw46HfDhwzO5XlXGMvV4eTuhc1vDIqek4NyGcTe1r6ttp3rPzbcRIWq83cgxvOsW7Hc3fHi4QPcr9G5pbt86vM8vR6wL4Qa0I4eo1hHDbobUEYEj1TZPG31m45JS1MoqUdLpVSUUk5rJrWTRip7erbA6IETdtN0pS5xXGuB+GG9tw6NTDAUoVwniwJvIe6t55pZWLR7q51SVLrueMjIluzrfbpSBObitVeXvnZdaZiTe3M/B4BYUep0xVCezopCalEnvXdsgoQhceaiuBb5L1dGCPC5zsuhUhpMZL95gihKPruDRAQcT/9nqG/iVF/BQgC+LwDlkGCnwYAUGRUeFKSr8tBjsdcYPXcScJJYo2y7UydLaKCis7yzk4hQAHi2FJQMg19s6YIx8HBcZu8OVGNsu0DCIjTiZiXtaR9hhvUelsU2btNWFTC6Mc01BU8CDDdDDCn2aCI4/SZTNIHtNOm2XIDQPDxeHHATCedc0zvXCKllAvg3ESNnoQ+8+nKH3DFqyALoCjA7oe2CIdLlXqITWtEjX/7fMQIwoX9e629HPGA0QCvJZkfLFZKQXi+3lSLrVIy1MOZCbtGQB02/QzLxQRTZLdV0sunEmQNJtZAVCwWl3RW83/Hw+4esyYokGMRMnuxSB34ozeuvbcbZW4IbZI/ONTja2p8BiM6zEzLkOdMwXpKIpQVMPX2YmPd5j5qrtXLNEbh8od/J22VOggKPeaTWpBJaf1WgwxcMtmQoe+5klZdT2sP2djZLY9AHcbdqFr0+jI2JSCEm33jddj5VFUlONMm6XPfVtA6l1fvz0BUpHGEOKqaoG6tyGjjGw/bhAMnmQWhepIZIrQoE05D1ioiGgVgmH/RU9D6uFzjTLuIyYvp1g+w26X+F2cxuEahuwf3yjStYbpGCQ6imWq/5p66BVwhE3qDUhTD2UiSg2INx6LJcdpEwQKmNdOlynkQap3YaViYVSFuQs8ImlfvX90pJaKEZFaEnGm8InHkp1Mjh1Mw79TBJLbiP5pJtJqC7glu91Y0OLT5yXHv/4/IFPi4k23m5tBcgSNQca09f7JBFEaQss2eGBV6+/GzoqrjVCFhwdScq6TmZ41pavoCHmoDIGlRt1tfD31YDkv/T4VRdwJwv+fp+Y/w2MOsHJglEnDCpj1JTK83XTMLKwaYd21RcvETPI1AMablpZEKSABC3elYdSObqdjnjY3eCsRwyGFqN+bcAjUqE46G67W7arPjsLrMw8UR3R56oVvn+8EFjpMAOJMi3T6pDqn1Ghe7hQJmVtpTBcCez+Wy47OnrqBGUCemaDh7mjAeLT631T8BolaOhPJH2CDzS5PfS0eM8cGpHpPFdeA5l5imRTj4ZwARIB+ayw/fIAPa5Qu4VODuwgLUnh9k+fsE09ZSHqiN0Pz5T6wq9tmfqm3xapAN5A60jMFO6R+s1CqYTPRWDHlU0qBIgq4OM2U+ZSlrhyX7pWWx3zYS7fHgg0ZQJKoaqtMtrj3HGwL73flV/9/O0RKWk8PL40u39dALfN4ev5BKMSOuMp6fx259hsvNGkLNFnibEjy3dN7zkvI0I7chcoRTK3h25pPXAtSRYnUNriFhLRDW/eYmc3Dq/YYKynRSRLOLehEwUfP31rLsu4Wbw+PzTHa8703sWokG4jW+IFpoVSeKrdPQaDnAWOD2+QJmF/PGPMCrttakUDwHjkapDqNhx//3MrUsLS4Xreo+tXWOcx34bmlaDXTwXDYXdDzhI3hl45R4TE8y8fYJ2HHUgs0J8urXV5fHjDuJuwLh28t3i77bFFg8FuTVGSIZoRTOhKddRN+bJEg+e1x4FVJ7tuwW6c6b3JEl9uB7xuHT71QGdCY9KQFJIW8OANDd6TbjzzvaUWoBIZKWtcWJ9fuSdUbNKs7iN7Tj66gDVL/D/PFqkAP/QZRlJ+wZoFnjfK0CxRIrJR50+lgZbXrFEX7lRQ/mb4M1/7/vEr98CBna59n7uDyWcy8pCtNKNTsr3Ib6vCOQgcTUGngDmJtqtJ0JE8Q+DnRWFvJD71K7mfNEFulIpISeF62VOg73FCyRlCc7hDli3lPS9UdS5vhyZ5kipjPF6huw39p1fSS7Osj15UJsWKjTCHCeEyIt56CkqYO5jTDcIFdDohew3/ckCeO7y9HQEADw9vbBMngqDqSBkSKiArCwJRuYCyCghsRMfRAugHFOsgjAWCR/mHK0qQ5N6UBUgCwiXID5k0l5rwsg6vrQJHKIBXiLcB8dajf7xg+PhKg1udmuOxJvwcPrw2J1995KBa20OKgut1h2/nE7UKjG83/aZIM/w+vLa64wSoQtI6NVpktYhX6ac0sSlujIkwB3HnqnOrRIqCBHAPXOPl5aGF4CqR8bi/kA1+HpEKVXCD8Y23sUWD2Vtctw6nnuBK1YBTZWy1DRMSa5kZVuWjakf8qlMe+WdXSWIBwdXWzSIlif3hyuHYVIlXvrSfO2yrw7T0xLDuVxhL+NnISTrX6w635R17XofvUuCv5yOEyC1QGKCg7nF/g+48Ve+Mra3XtQwaeTPYfX5Gt5/aAL9/uNwDQILG+dsDIgdolyKaFLCCqzTPOPx8lx767W5YAtAq78FuTf1T31806afAdSU2y9Pu0k49BIuyGI3HrlsozcnQ/Q4Af/P4jI/eYmXK4+otolI4AS3kHKDEe6si/u700kK56wC10x1zTzi70jsACmMXoAUp3ioatksKvxnoOtvr1FAhgMTBZIQssCbKMwhFNFl0pzJGTRuBbjgB+hqV5F818QC/eiIPRQrdIlXg9cmFImBlJmEFL+TE3JXoVO2P0w1e+Sg1li2D3oyjIfj5qAMjZWMLVu36BbvTBUon+OsAv7hG1wPAHJQE46h/Z4cFptsQVgcpE/rHCyFbecFPt57ZIbK1WtRugzgC6rMHygsNS7l3XQotNDloCA4l+Nv/7f+Ljvjj2m6ewtyP2i/PQZFyxUTIhwmwhT4xLUiBEiOElFSFSwnxNxkiRiBrYPZIP1kIk6CGgLIU5KuCHAH5Ow2sHmVC69Mr5yFERriOyKGHfbhCykLhwFHxIBctT1HqSGlCQcPnHtvSt7aAZ5ebjxRL9rYMWKLBzmywOjWcbG1ZAGgpOZ2jNlZtC9RefFwtBTbodF9wmOVSZXRSJTx++taAXiUpPD6+3s0v3uI87aBExmm8Ndlb7Ze66BGjxrfbAYtXuK4dZulQI7k+n15gTSDjF+u9t2Dwx9uBjRp0BB+Nh7MBnQmYN4cvtwOexhv2w9QIewAtTkrHdt3Vk1kItDgLUVqP3Fgafgfuq9b3ByCdekwSoqdKdeg3aB1xve0aMAqgE44xgX6fyvdQZJU5ZGRA9hZpM1AuwD2eKcJOJaBQ7zreekhvMO4nXpApNPznt0cYVomM44zDD9/I1LWZpj6RKjUuf4wat2XAtHU4dDOMjlgZwzBwFTx717jgVkVobp2AA6eNjk0eGKJGWQSs9VRha9ro1jDAJ40Puyu1zQy1v/xmyfdgPcpGihinAw49tVh9pBD0x37mVorCT8uAn1eNHbdCrMywYDkzV+JVBh2zxPOm0amC3/S+ccB9UljzfWFOReDsbYtgO5oAKxNODNi7BPOdBPdPH7/qAr5lgX+cFAZN7kkhyE6/JuL+SqA5kq5B4hZpEuvknSUOUCWvREEpwOsGWCXwt7sNIydwkNWWFu+X6wEAsDtcMV928JujnVpHLNMAHwweHt7gHPV5Rbm7+GxYWgJODhrbf/5MUsTVwo4r+o+vFNBw2KjHDIGyFcDzYCUD8WWPtDpMX09IwUCZAGUixh+eW5p4yQJltYi3AdPXU4MYmf2M7sdnGqJqglHlq2JoUYYwZ0AzEVAD+LgDrAXWDdCCoFiS7PaiB2V1SkFSQ6shOgnBgwW1zkDIkP8UEK80rC1RovtIVvl6qqiyyYq/Ra4p6pmNMgKH/Q2H/Q2bNwjB4LL2CHwRShQ8TztkCDz2E4xKGLhPSYni5b5489QeolCVaiLSZltoQ0kS03UH72070m+syNiNRPdTOkJpUtYIUXDka/F9D3vbyC1aNchHznWs6TmVYz70C2c9Uktnvg3YNtcUHkCVl4lWzY1uxeA2GBWZ2rdBqYyVnZRVQVIXFM9DQWsIqzrsJuRMjthtdbhOI8Z+oaElKyp+8/iNKnROT1Ic8XV4eiNZ7GXHmwWdeKbrDtEbuP3c3gepEqRJgMjQuwXrtyPW6yd0e0LO1k3Sz32T474HT1WlkfeWLP3DBltDOZjxEy5DQ+Euc48dn3BIqdO3lkmBgBQZD7sbKukSIPLitHb4me9pAHjoFjzaKyynBdXPYN1IqvhxTzyV2vsWMlOPfBpauDUA7BwB7GJS8EljDQZVpSwEzTd2JuAhkxFnimR9V7JQApYg0B5QnZikUjGCLPOVq2J5tlJVKQrUM58i/bfjr60Gn3/u8asu4FqQDNBJwKoMLeiGHhThGS9BIRWBLZNB8VOXOPzhHoCsZUEBWF8JbFnBSAp90KK02CUlKOi003S0gyzYPZxbXqKQBUceJFZdeF2cauyYkBkQpQGmVLfBMHdDOQ85EId6/eWBhpn75Z70zik3TVsbSPJVSYjhRkfL7bKDkJmOtCpheDojrhbTtxP64w1dvwGLQVkL0q1HWh3shzeSGHYggNZZoiwCsp+pMs+EooUuJEn8WZMD8xFAyCjXyHFskiSGrqMqPmeYPEHPM7afH5C9pUGqyjA6ITPlrvK1640sRGkDKBou8dvICoEP4xUPw9SOvnu2nCtJIJ+3tWdzT4CzG1WF3LtWhnC3QlDbC2dKlsksE5Qqt6pL8IkrJoVl7ZqeuHJw6oIjBMkJ181h2rr7pL+qMJmWlzI58s5bB+ULPmeiPQqVIZDRjwuM803yNy89Zu/wPI/wHOBbf1+VJjrrye0r030oyO+h0gn77vpd9meVRC5zT4vy7kaJUuOMbpzxhK/tva7mmsQcnm4/wTiP7jChZAE/9TRwnogPVCtt3XmA2fHU+qOM1F13b2uALfLdkRgo9Tn6qUcO6rv3oG0INkKMCSInIPs2XCUkrWitnYeacs/CgpA0EiSsofanj4bj0Ci2rWdWt5KFuDQyISXV3iMpC/a7Cfvd1MKhl7VDqNeqIoPUtjr84flDOwV2OjbncMetIQDNrj+ohGiIZZLeqV9SEcj53hUw/N44Pt35LLFliWtQBOej2g6loAGvHuyd8+KTYjY5saD+6pr6V//2v/EjF2CKAhOATqkmC6QXXlAkoEGN/IqRDfh+F6qAqwx64wpocaefU3BwW+unpiwwbQ698e34DYD7fgHrywF+6tEdJggZSdJXBOx+hjAbNE/iq6pBfpeKQ27MzMNJwRbiOlzbbgPiZjE+XKCcx/B4RomKA2UzpM6AqLmE+T5AlQWyhiusFv752JyJ1RVXnwN6DVgNORYmFiaUKUNIat/kK/UbhU7U77YOQIAYMrV4pozyJSBNigxFfYboBcRHjW54Idfpy4CyGaYqkja7JKoIATQ9Ny2CGpvXmJce09bhvHXN3lyDamvvT/DAT4iCE6e7G03H5Po6W5ZjvX5YVVEXAAAUXiBKU2O4nlJa/OqQosY0DcjMd66Qo9GtOOyvGPoFx6Swbh1mxqP6pImBEgzevEPIEj8ME3pDR3JfMx6Dxs/PNOQeHQUgt6R7FaFkadV4yfeKPCWFGO7BE0pTzJiqvWtDktd1GhpgK2UaDpt3YRiJ48IAYJl6bJvD6/WAJViSQaqIw+1GQdOnCyTb65WO6NwGyVpysVno9d6X9nOPde6gLfXT+9OFckjfDeNLUkie3J7d4cb8Gw2bN3T9SnmYM9ETdVQtvFuoDHO63RG3KreTRUwKm++bjr0qdYzJrXUSOa3od093TXzOFJZ8XsbvtNsHu6IzAadxgtGhxeVVoFfJsp3UJQp23fJdNqpVEbetw4X77wLEKNmB7PQ0uyHz4R8WizkKLImMPJ+7hJ3OeHJEoawzPiNJuVLt8nWxrsNKAAjM63m01Ar279qMf+7x67JQQIv1yWZYWdpOZUALcseL9pbJrVSKaJFDAJozSXEv3GeBXxaaDv9uoJfSM5YyF4Gd9fh8fIOzpISo5pQ6HDPDPfG85DsoyDxcqKf9wL1mgKtaNgJNBSUopJkuCsUkNzWutACbCHskdKdkDfr2ciTDUKK2hVAJqt/I/FMEbQSLw/L1RHyI3UzqmG6jSj5l2hy8geF4LffDC+S4QuyZDeIkhAPQWYiYoLQndWEGVdydI9NPzsDqIZaIwhtPugwobxL68QY5hrv0UGbqzb8e2mKquw0ffvNLs1qXIlCiov7ty4kWQd9hjgZbluizQJISqzcoBZwDmFvvuTIptI6kfuHerHQe8TZg+Xpqska6DgrWmRjTFVua63PT96N8iBpfzg9c+ZMa4bdPX+HchmE3tZixHDUCy+02b7DvF+QsWv84FdkszSEYFC843Yekas/zDhKlJdLvHClNanZmzsSfvqwDDfyiaRvG56dv/JwJzfry01MznAhB+ALnKLChQteWucf5fGjvxbfzCWfWogtRsHc01LtNI8Q80IKpE1ngs8S6OXTunqwjuUVY+eEAEDYLvwoObpB06mFjm1SJ3Js8cC1RkcszUJ6m3yyW1wPPMlIbftaHNBHd44WBaQO+vj00pDAALJHaF7/hlhbJBYmJYlXEcZiaPLO2vZZg8IUNVkaUxjTpeMM7Hc/QhqPqZEZYOmppcTVvTUTOAhvb/F+WAVYl9OYe79apSGHLoOCJjdscJ5NwYBCoBFhBl1vbsOJipyhRVIaVAreosCaJRxvx9B70lQg78eLpxNj/1+JkhRD/E4A3AH9fSvl3f/J3JwB/z//861LK//zXfpbkBbgyb2dJPfAlSliVsdeZIOZJolMZPU94yWJKMsIHR8ODUGiRP1rqk1KmJsm1eh3w2M9w/OZrHeF4MKZMxPy2x3Qb8fDpmYwrw0oDyiKRoyTlhSyAVPXY0GTWiGQYkb1H/7e/QChqVeTJIC+WFuLVtjYNHCfW8A1Zj3kjg7I00Aw969sO3/74GcNuwvHzM/S4wDxe31X9FnmzzFlRiG8j5BwhXgirqn6k3nZtoRRfMbMWsveQ6go+urSrTShioajDAtiC+MsO/icHNayQNtL3uUAuR879TJvF9eUE123Y//CNblBRoExA161wSw8lMhwfX30iG30qJL8yIkMIIGbVerBWRex67skGjSJye++UDRChIGy2AclM53FIErdXMpwAZIR5Pp/go0YqBJB6HK8t6V3K1LC46zswUwwagSvaCjcqRaBwPmZ1/F0nmg3UjEglCyxiS2xZo26pPPT7MrzXWIOlbEW3YugpG1Nx8DZAmwIxSjRuM6lK9uMErRO7M3N7fS0ZyQQsa4c1WOy6BYd+bmobwxXrx09f2ag2IG33W71zVCl3pytJFnWGf93j+rKDdR77xzNubwdM04BuozDldeoRvcHx4wvMsELaABSJ7TxyJKFsr9mYQAXIsEIdZlYz1UBstNAT228w8z03VAjqpx+5H01DTIW3pYdWxJWvG2M1ftWW2Q+HNzzwwk5p81S53rYOazAYB8ryrJJh029wfm6tn211mOYBv1yPECjYWY8taryuPTpFaOCLd7gGw1rx0grLUChl59HSDG5mAUZts4RcMbW0bv20WBxtxA/9hkFRBmcs9PWvm8XMC7uVCQMXJH/p8VcXcF68UUr5D0KIfyuE+DellP/w7kv+D/z3/04I8a+FEP/2Txf5736ZKNiZgkuQIPc/hXoeuf8TWT4TMmAELQrXILEk0ZxM1yBhJAWECgHsNG0Mht/QXgd0bMmV4m6YqL23xGjHfpyRvMH07YTd52cK5N3N7bmWTQHXyAYYSseJP3dUHX5694ZuBWUW8F8ecPnjx2ZWqFQ4yf3z7TYgeYNhR4vU8rpHKce7S0xHlCQx7CZYy0MjbyniiuV8eaMNAiJDGjT5YriMdDoQV+Jjy4TiNcL5kU4aHy/0SQcgXyXiywH6dIN8SBB7QB09L+yAOs2QnYd/PhAv/DDT4Gd2ZKXn/v/uRNiAwDe2nzusS4dpHonGJxOKqnZ60u7u3dq4zKUIvMwjtqjxde0x6IjfPD4jBo3rTx/Q7ScMvHl0j/S7qkX7vSqiWugjQ6EO1wsis7IrL8R7i+fL8Ttrc+2LL2zCaRyPd62daesa6B9A43vXn1MTXZTIKO+4PGswlDTzrk9af66zAbbbWqTbxjp2yo7c8Nvf/NQ4Likq3K4Utqs4bm3dXEuZqslRMRClcGM+zTjMMM4TA8VEjE9swlltC4fQjIwQOkM5j+7TK+zDFXkjGmE/0HXqxgV2XBpTvn4Ot1+eUJJinTexhoSg8AgAWC8jXBHk8gzk8BR9gNxnlDWjbCRHDN7g6XDGaXdl5ouEEBkxavxyOaJA4LGfkYrE12mHg9tw2N2gVIZSEefLAV+vR1KpVLIgMk791rI1ATLrrJuDdh6m8w0bUYexxgSMw4zfaQqY9klDeQd4atVWPvwDrysAmLtEg8c6cHwP4/ORZnYjp9Gbd9efEITUnqLGi7etrdIp0oNbvs6uwfxX6cD/NYD/hf/9PwL4VwDaAv4ni/XfA/j3/9/2/ixXsi7L78R+uzm9dbfx5msiMzIyiyQkoSBQ5AQEcgZEDSE5Awk1AoE1g+IMhCpoAswXvQlgkaV6EYosMjOj+Tp3v401p9+NHtY+x65/GV1msiIjIn0DDne/97q5HbNja6/9X//mlz2YMEmSrFQpXIzkyM60PMlMiymVJhEmYsAq6dAXO8ZFpRQTBq6JbDPHJpvXAp5bR5lPbBqhbr37+g2bTZtuao9JsmqRZ0tnuaggza5DNwHuaihKYlmhgsfeXCTrMhlGxTYxQ/qc/P6ZV59/WJWYvhMPlCUxfjmmTm2FnyzHpwNTMleyxrPfiTS9qHsIiv60oXve4pylbDrypl9FK/XtUYIkoiJMlmx3TfeJzqBykf8X20eBTralvOjDhMqEtw7irCjdOriHDa4tsZseXcxku07oe20JUa8YvTKeMOUrC2ThK780U5KjPHQu4zJnHPKRLPMrDrwcV6tUPLfZTGkk9DazLnWFXgZnNkLumB639A8HbDmK1D/ZoC6v5wKHFE1PQRqgBpWKlfDGlQrYpGwMQdMBl7FkUwxsmnYNal4yO2NUWO8Thc2z253ET16Jp3l5GumHkm/OBzLtOaQTxEKJtMZDPmGtvw7oZotSxfp+5aV4jGfVKDh1giXGtuJy3vAfvvmSJpt4e/OA96IYzZIgZlmLpH6aJEmpyCfJ1xxzjDfid6JjMmsTDnkMGvO4E6y7Htb3FliN2mqOMrA1EqS8bDQxSupP8HqFYUx+TfTxU0Z/kmFm9eZRmEoBVJhX2uqaApXeqxA07VDhg2Zb9SIOS/a3IIPl3FiMEgm8UhGbRapy4D6KfH5ydrVBWIIlxnHB15NGIJ1gFtfG5US88NmHFL9X2JlTX/F+qGidpXVmhTLyVGSXol1qqU1DkKCHy2zWIt5YT5OCHpIoGqMDgzf06XGHoCi1IdMSueYTbdVERe/MemD+eetXFfDD9/5+9/N+SCn1I+Dxe9358r0/Bf4UYG82qznVsiaveZyEW7nPr1Nvlya3OkWsiIVsZHKKKVw78sbKriUG6fPqbKZVQOvkDZFoEUU5ikilmNHFJEe51MkRFNNR6FbFDx9gX8B2R9QGNY1iz9oN0Dvcu5I4N/gxx2467P0AhUrwhce4iWxuUxalmPnos6gZu8c9fVvxfN6hEJ6vzRxFOaAzL+njyQVw4dFm9YApJxkgJrn4dNrI4M4G8rujKC23IYVFpNfeksKMNQwD4Vmhsoj9QQ9jJPR6PZ7pcsKqxEK4WBlaZo787nQdgiJuhsoGim2HHzPood44mv15ZdoMvSTb7NPxdbGLzY0TFkbqwouE2wqVVDDOLJspU1p66MXoKc6W4DR509MfNzx9/Xq9T4ahZHbXJKLF72TTyGnq0tZrEAWIQdk8i0cOwM3mnIQ51+HZMBZrnJrciwacDGyzciSvBLMsmw4/Z9xenj/qHKc5wyXvkcnZNZYNSMN1vRambBlMA362XJ53hGDWk+Mf3X8n6TOlDEozK1zjrpfBZT/nVwuCBDn0CbLJPkge5G57QWt/hV/ySRSuSTC1KI2j0x81G8FrrJGbyRQTaFFoBq85vH5AZ07i+oJiOm2uFgabjt3n79fsUmXEjhmHOG72mZi1GU9ZDTw+H2jHkiKbEx1xEbSo1dogt447K1qE58uWKpvYp6Hj/oUx2rLxGvMiQjHNWEDERHbxxFmorTrKRpZLuPQ05oxjgWm3uMTZrk14kcST4SJ8Xs1rgEyM4iToFndSHbnJRfAzLNbGCNFiicrNdKCxMvvrvOhj7ouZXId1A9CWv1UH/gzc/oqfAfgXMcZ/+fO+kbr0fw3wg+J1LE2gTZPWLGVcbpLef/SGwRs6r8m1+O5qRFI/BcXoNc+zYkzTXq3gNENlDD9sFHMwfNduJb4rDQZub58oypHN4UR9eyQ7XMCIyxrNNdoLp8iTOEcVSh78+IwaJvxXMmD03a1QCd8cUTlk1bIna2F0fJDA4eiKVUm2KAUXfxFtHVXT89a+Q2lhTaxxZ1NGe9xSby9sPv+w8mgBEVE8N7iuFG46kO06SZzpC+gLMiN4eRyNGAbNRoQ8YweZQt8bkoExFApdIbxxazDOY9xI+DYKFfLcEJymfPMkjxkVcbB0X70CIN92YoRUThIenNSrw1isaS2zN6tDnwuaA2rtdpQSz5RmHsgSZHG/f2azabHpNQmzZW4rhucNWSUD52p/odh0qcAY6qnFO7tiyCBF/PzChTBGtf79el+K/7MxcGw3nIaKQ9WxqTrGVLwXMdhCB1woictSWqCvqu7XoOd+KHluN6t97sJEeb05SQJ9PmGs53Jp1uN78Jrp3OBmy4enWxSR/fZMls1s92f5fmK/jEnJuBS3zCx5i6JoXNStIIVaq7jGoLVpw3TBcLc5cfjBdzJ83/S4U8103q6B0jFI3JkMk+d0wXHVAuQ355VKS5CfW75nihl7kGIbB4sfclwr/vjBWeGFR8XlaU/fCfWydxn7ul0x6RBECAasFrGLn3iWAqdj1DJjSLBSkbzUhb6qiTEmBoumH0pcMLz/cIc1nlevPsipKkGB64D2BcR3tzlhtV9tbds5Z0ihxy4qMh1SSLvkWT5MVhxTM49JhlWD1xwnLS6FmaeyXmZ46X5agmxu85nKOgl+SNDKool5ec99f/2qAv5vuXbhPxciUUr9ixjjf5f+/I9jjP/+Fz2Yi+IL8DSpxEaJKxwSFAQ0Y5BwYx81LkTOziQJqrBNrAJl4GGM+Ah3hRinu6gwaUctrWNftZTFyDgUVE1Ptb9gqtSahhcyUB2JrRgjmWIS06gYYUqiF0DvRvRmJLMnKe536SXxDk4t4VEx/OwNp69frf4O0yxS6aocVv8FbfxqTnQ6baVo5RNKe/JqZB4j9KVwnLtChnmzdMM6d4KJD4X4jiemRgyK9ut7gjfsjEdXkqATvRb3RB0JnXzQzL0jnBTzu4MIkKoJvR3F5MoaoSS+ntBzi3kaVq9wpoQpz1YgJB1kU3J6FXc8Px/wL8yNJmcZnfhMgHQes78KVRbxi9aRIhN+b131lE0nHjKLjF9LuDRc8XY/ZTJfMAI95HqgSkrRkKCVw2TXwh6joiyvKUkLP32h8sXEMlEq0o8lVotXyuQy5kQ/NDqsUv3gBLv99t3rZEkrni79nNNNOY9Dzb4YeLU5r0HNZSGeIotfSZZJJ72kBi2vy35zToKmFGqQPEi6XpwRJ2c5NC2HlEUKqYvXVz/tRaUqDBDN2FZ4b7hL1z5PgnHrXMK09X4ibyay+xP+XOHOzUq1NdWIyhy+z4kJulBG3AaDslLAtcQL+lHj2koG3slkTJmAa0uGp92a7gNpnpHEW3e7IzfBUBZD0hNcTyhGh9VBcNn4lYqMY8HjcU9uZ+qqx5jr/RRjTO6YVtKOjKdOHilFeq0WS9fqcBJ1dRLotcftCkktG3dmHGUWOE0llzlj8IY5mU7BlRNwyDwB6JNXyhwE4lVKnFL/crLc5oZYXnHyJSqyNGa1lyUqemeZgwQpv3TK/P76pQU8xvg/KqX+b0qpfwYcFohEKfVvYoz/PH39Xyml/tv0T34pC2UK8JDSKQDOs6JVEvJQW5HQ5zquXgNGSQioi4oPQ0brFIdcjGIOubyAz5PEqrkgCfaF8cQIl5QDOKWOpb49rjeOOYxCvQPQFq0n4uw+PquEAHUNWQ5fNCnCyxDdjOpaOJ+J7x3+UjA/bxhPDfOc0WwvkkKeOoGsmMSRMN24w2mDmg1N3YkIpRmuEVhxpt5e5I192EumowkUuwvZLqVsW48fCsKUUTaP6Gpm3/xYvld5cIr5QXxUssNFiv/WJwm+RjeB7PVJJP5jxnwWzrkuJlTm0ZUcd/XWE53HfSUfSJOYKPOYr6KQRTwCcH//gaEvJeR2li5v8kK3ssladeVZ9/WaWg/QzTmVnXmVVJi6mAXHzwKZPqONZ3jccf5wwzQUDGOxDrzyF97icm8mJW4+E4OkBIWgVq71El+2ysedJcsnmrql6yv6sVz53HU+Upf9CmlokxLEx5xxKHjuauYVZtESIKA9+2KgymYUckz3k1AJpzmjLCaMcYyJ4dI0HTZzq9BomjPitLgWBoqEoe93J6EResNmd2Z7/4RJMvhFNDZeauY0NNcqUu0umHKk2LYrKyoGec+UEs2Czh2qkhOn0hqVyTzFnRumc0FZiDNndrhg5wE/5hDU9SSbe+JgmZ62K6VwHjO6DwfKw4X6D74jvzuR356TYZpmfNgzjzXtecO5bSiLkcwmiwIv6fZ11XNz87zmb4ZkVqaUvI95nKi9vDddX2GM5HAKrdSvm/Q4FBjjOdw+r0SBxXAtanlPVeZQVjanOsXXzclq46X5V5NNzEHzPGWcnV4TEp8nAchel7IxPM8ShVZo0bOcJgnf2tqIi/A42VWluRT/Y4qO3GQiXiyNp7Lxby/kWbprPh5e/vP0+58Bf/yrHmNZpYn80cZjVUwe3qnDTS9E5zWVdbwqBvHiNYF+lly6KdMYrZNZkNgsaiDXEv5QWZf8VDxKCZe0zCfu7x9oDify/QWz6dHbZCp0DsRREyeN3oPeRWIrXO84AFNA6QHKALsDBI96foS2JXwdkjy4InrxPa7ujpQ3J+HHfs8ICpDkmKGgPzeEoDm8eVg7aD9ldKfN2jHmuViF4q97ioh9JqxazKUiLPj11q/Y9+LYJz8kNrj+0SbTLQd5utZzxLcWnTswE3E2hK5gTuyT/OaMKmay+3TE7HMx27JuVUIug0K5F4TVMcw545ytmHduHO2c0/ucbTFgktGSinEN/V3YA30nGPgCP4EEOQ+Pwnne3l+7Tj9lBG949/Ubnp9uxXcl6DUUoLAzxgTyZKbV9RXWOlFvpg+uS6wOk1SGWgdyKzajISZfDeM5t4ItZ9Zd3eyymde7I+1Y8rPjgYCiSWk7+7JndBnfnA9y9FeB26Yly2bmWeClxW5WBumiJo1RkaXNIsuvm1JRDtR3RwnSTnTXZRbix5zh3DAOBUNfriZhSkWGvkzMkG7tfI31lIezDKAvtbCU5gtkQC4DcF1N5MVMDmmYHFNXP4sDpyadjpDiv5upt98QeyPCM6fJt50YsM1GNuTKy+dtzNbnInCIWzemxapYumjN0AuzJc8ntBYvG2shRr8W1szOFPn1c7aEROzKjrIYwRuUs9SJYRaCwDw2Cb8WW+c5nfJMPqMT7bgYRspiFGHaUNHOOacpJzeBg7raW78uX2DwUcz2VhiLiFHCrHueBXnIdPpdRW6KwKFwq2Xs2Ul3f8jF4Gr6JfAJ/B0oMV1QOORCz06RKVFe1iZQ2cAum5P5v7BLpmBWPEghNEMfwSffgeWti1FhVVj53/vmQpZdubYqc/hLJS5/iQ7Vvz8wXmpu/8FPhGoH4i2yPGiKLFOXk6TcfHcitBr3vJWuuh5RgNn0qz+1u9S45y22GlaHQQA/FKKsHAtCEBMouHaMeT4RrMdYGV6254Z601EfLmgrG4ayAaNneawuYzoKxay4Pa5xb9EbxmMj3h+LB0Ny9Fswc12NqMKTfd6yuor5GRzoxxnflsynGqKWD5sWbrYbci5PEuhQH0RSvUAopycpsre7Iw/HA89Dla5POpeb0rMpBtmcEpwRglmDIEASZLLMyXDZhJWjnm+7xDIJK5VtMRP7sulXheZiUxC8ZpryJJV+JRBKNlH4mSoNA31yBOyHlAiThmWKmDrCKxa6QEKXribPJEMVRF+QO0eTTgCyYXlhQGUTd5vTykFfTgoLRivXr3n/4U7w1mRaZZDTWr27YJInDgkO8UPBfG6Yxxw35pyPW06XDWUxkmfzKjwap2Lt3o3xPD+KOGyzvaBUsiooPfntSei1HwoZgu/F6dK8dsReGFaxF1bVIsTRaVAZnAEdsPlw7cBA4MnlMzlb5qedxAu6Ad+V+BTY3Z8bbDaz352un9E0j+r6Sgy9horMeN4k5aWoMQ1KiZJ12ai0jmtYtVZh9T1feOLLUlq0CtFrzsf9ev+qZGoWo/4ojm8acxkWTwKjVXbmplC0aaazRkAuA+mEGtwVkd5pftpZ5sAatxaJVEZhFUmVKTayeZLNi52sIwCXOaN1lk02/+2EPP8l1+Kn+22vmQL0LrLPFa9LAff32YxRkS75Ms9JWVal9GcfFe+GTIJCJ4WL0M6Rymq+qDV50IzOJrzMUdUdzd3ztRuwiZ6Wdru86dcCB5CA+PRkI2s4sJ7g0jF9Jenu2avTOvgMfSFBr10hlLa0oxfOEKYJNxRr4GyYhasb0zHcT5a+q4WDenMSX+uuoh9zTmeBQUw20z+J70jR9GT1QP+0Yxpz+ZBnjtPP3kBQYtCvkhlUcreTm1bYKgTFfKpx392KPHpzTVPX2wFVBMz9iLkfCUehVU4Pe/yUJbpexjQWGOOogpJYrvR/7oGhK7m0DZuqoykH2qGkTw5zPkqs1+J/IrSxch10Ztbx5vaRze68vhdx1rhzxeXbuyufOikBV/bMC+Orl7+WYemrnTBEsmQFOyRJ9fLBLotxTahvyiHhqdKdXS4Ci91sTysuDdfTxuN5JwybqFDfa5Ss8esANUbxQXHD1XdlcSUMCYefppycicPrh6s/CSn+b9TJGiBRAFMxNcZTpA0xy2eaNBcomrRRJ1y4e5RitcCI3fsbsnqg/vJd+j80TJbwtODTQo1dPICunwdF99M3BK/Z/uhrdD2JX32f0f70NW4omIcCk800ryT71ZQj7lLRf3e7Xvv5cc/j80Get72GDY+TDGqXYWs/Z5TWrba1+8NRQjkujcAgKfzCLrqPxNGOUfHN8YbJG3706js2m1b8jlRck3/gKhJb7jfvzBqc4RaihZVBdhHSCUl7XGiSslIIF+8Gof69KiK5CVTp5L3NIj5cm845KCoD2yzQO5U6cvl/LnPOEBT3haM2yQeFxFr5mwp5/ksvhZhObTMJKb56g0d6Z2idodBCYp8Sp7KxntyIIgnkiBEQcY/3UBo5jnwYcnyuebs5Cxc0n7DF9JF/SDJ6RDcTugrY2zYN/JQICy6VyIIPFyn0dSUYuJOBpqlGojO4p4YwWVwyDSIqUJHycGHuCvyUrTadthQ8fDyLJ8duf0KruFqmFhtJpXGpsJtsZnOY2RxSNNicYYtJElKUYJh53ZNVA/mmwxQzeUoAX1dSlM5tJbL+29MK6SwOiH7IGZ+3q0S62l+wm55s16KKWfjidiLnKAKi2eKnjOfTFms89aYVHHkprFbc9pYhYJbN3GxPvLJ+Nbma5uwjyl9TDoSgRKmoghS0xC9Ha/HP0JF86bKdPIfJCfd8nrNVOn86b5mSRH1RYVrj2TcXjHmhjlwLv183Ems8JZM8lzEnzxQ2OpqmExZKEsqcztuP8iWXoIBd2TG6jOe+luFY2qRiVBy7hqehYl8O1PlIU/bkmZwEjPXkhZgxCQ88rDFyl29vmaacNtnK5klPoFSUIIe6p9pdeJVdTwqLKduiLNbpBFh/IYZXmABeS05rULhTLWrbekzWEOWVg7+8XtWIyjw+hUcvYcVhuiYfhSkNt2Et0nNXYHK9zn6Wz6Eynjvr2d09c37c03b1KoxaTMbuiiM+6NXrfMkFXYbAu92Zrq059xV55mjqbmU+6QSfdVOOUgLNTGNO31VoHdjfHNHWcXj1sDZSC38dhGosBm3S5YeQUrN0WAMnTlPJ4EWECPBFHVdas1bSkec68icpUWtxHuwTdAZglMH4JX1RrfVxSHYeN8VEpiLnhD78ovUb90IRy0ShD+4zeXPnoOiCpDvf5J43pWObzRTGYxMfdqHvFDriE4/8JcFdvFGux6o5TfWjN1TuRJWKb5wtupmg0qgmeRV8rQhdITci0oGIg1rqwOcJ2pnuqy/leK+vU35TTthNvyaZRF8z9+Vq0O+GAjdlPH+4laOYN1jreF2OgMAlS8ByiMJJL+qe5v55FVdcswRfvJFB4AKiwjaS/DM87lamAIjnxOLbvag4/ZAztRW2nMh3F4oltzANic5//jlzX1Ae5Ai/UMhsJcPWL+I3gts7y9TlPD/vKYtRggms57A9rTapLxWRIapV2dinIv7l/pE8n9ikzbkfShFUOCOnJB3xQ8GY2DQAtpjIa0+5lWHb8vot/idLxuJpqJlnQ7hsJfMwF4/sohzXDYV0BHZeMihnbzEuWzeGpaObXZYCHMTfvNRy6siSHH4J9a2yGR8U57FkEyWk4H7/zNvbh1UVWJQjxjrJqZzyNSxEp2Lg0/1yPu6ZZ0s/lNRVvybLm1QETebWwWT77obu0ojNQBIF2XymOJzRxYzZiGEaOoIV75g4ZgKTIRoAZYLYJ1QzahOJF0UYLXo7owqFuTkSHfgPdbqPBKYR9klOn3jgWSXXJzCLZfhwELgxhUcs95M2nva4fSGjjyutssjF4TFP4popDXjbS5PCIiSUYrc/rZtad6lxPkdrmS18dvO4zhmAlX5Z9KLQzOuBMBuOT3uGseC53dAUA/e3j0yTQGs6wXbznAmXP8EzN2VPbjxz2wi90ImWoUjGWOLFEldKYGEdY9C4hCgsxlbbIIryTAcGn+OCBDzURuxmHfythTz/RdccFO8H6bJtlAtXihXAz5Nbl9WBXHtK67hMefI4sYxBcZxsCjBesgeFE37Ixa93cZyL5w1FITdTnvBpXcyoZiD2Ge7HBnPbobYas5cBX//j1+tkHsA8tzI+BgiQNb3kV6Yb0Wz7tClIXqa7VFJMjGe8VITTRtLPvcEYT1WOq0R5Hgr6S0PX1jJoarqVEwwikyeZAJ2/uefp4Yabuyeqm9Pa7YgHibAxooe5Fxtbn8yFtp+JT8n0tMUNBeOlFtXkpeH+1Qd2u8tKJ1z8KeJsZbjqDNOYMX4rZki2mBIFLQW1ps63SOKrvqvJ8ond4bRO8Rf58lO3oZuz1cRqX/Tp6JtUfWOJVoFt04o8PEFdEkYxUOxahuOW49M+xYhNa5FaIBRRGWq2iS99l7rmfhC/7rrqE9aZlJapGyqAppYBl0uD0GVopVVcMfGymKl1iuKbpcg/JKHHkAzUCuPIrWdbDlT5yHZzWSGtl17d2nj21bgWn+gNl8e9pPukYvfmRz95kYYU1+T48ALWCE4sYcdBaIiLlevS3RfnBm0d9c1pfSydObIkzjLVJGlPi5XCEpy9LVFNxDgPeSmnoUuPClEguL6kvH9GmbD+fUmw6tpmFSHZzJGnU7DNZXazUFCXmYXw5KVp6qeCyVm6qSAznl1z+ej+WDDtGFV6Laf1/1Uq0tQdl7ZhciXTUBGi4tC0FPnI4XCUWpDcK/2UyUA5wVG7qqMoxtVqtijHpNiVofDsMt6f9pzGYrWrBumef9JqBh+5KYyMlJTUsleFThGPYr53mg2VCWwzT6EDRXbl7L8tJ2IpTodzFFFQQGInf2sgFK2EiTIFhfMQkYlr66QIVyauclMAHxRjWIJLhSM+J1/wXMsPbrNIpoTBouaMh65ZU1E2UbHZtOKs9rxZn4cppLM0Tok/9gTRJ4fAckw0Nn9NvrEGtKL48iFdCIJ/j5Y4ZrhLJWKFIRe/EBPou4ZpLMgL6fzKqgcdGZMh/qVtJBg2SB5iUYu3dJa66ZiMo8JsscXE4fZ59Z2I6Q0t7p/FaGrICZMlb3q8dSKhTuyBxU8723YUtycISorbbBkf9/C8RelIcThjNj35/TM5rI+pTcBNGXOyjy2bLmHsgSydJuYkSbbZvE7x83IUP44po6l6ximnn4Q6J92wCDKcN7y/iPjqsJMuU05AGhCnvHwrwp28rVYF3eXdHdOcrQnji/Ku7Rq812sHXSXaoLHiarcY+U9pMDV7S5VLyvuSc1kDReJ+ay12pgDH044YFU3dkSce9zDn9POWOUpyzF4PbGsJW/DerNa6h82ZpunIvZbhZCXDRJ/sGzYpxi7bX9bNCy85rH7Mmc81U1vRnTara+N67K976t2FvOkltHvM1jnAS6tiP+T4kKEvlWC+YyY871eJLluW0F6gG4gPHn8pRGW8zdahZvHmiTzo9bm1727l9JgKq7z2gXrTyeC8mBi7kuP7v6oHPJ529FMhtgPWkaVQiCFZAuwaESO9rq8qWufErljrwDTmDGNJO5QpzUmETE3VoXqhEZ/7im4suOFEUQKIwG9OkJzRAVuMV5ve5HE/p015IRlkdqbOR3xQfNdtaNPpsrKeLxuBe62KzFGo0qNSNFYKuA0ikW+dzIIiMAeBRpYufZdJnGTrRIG+0AxfRq39vPV3gIGLp7ePcJqlVAs/UtH7q3NXQCUGgF79dGsjHbqPwpt0QQjyI1CkRjnTYTVmB3j/4Y6mrZmGgrwcKepBXNLSAC8O4tgXnRFrVx0x9w5KC9uUSzlPECKhz+TGnSQ30HXlKsU35Ui97Rg+HBgvFfXuwjZ7xlYD2gaJR5st7XHLNOVr91rVvfhrmIB3Ytu6dGZLyrgtR/KmTx361Rd88fDWlRw1s9uz2AKk5zh+OKCMl3i0eibbSqcVvcYfK+bjhv5pR39uOKgUEn0Ui4D8RoyQ8pszdsyYvxWbUN9W2MyxuZGB2IL5NhvxEnGj4LbnS7Mej7NMHOSKfPoIi17c/35w+7DKp70TLNYDYXE/TH7rhzcP62MW1SBMjNOWrq9W6t/iJPjueAPAvmrXQRdczYteCkUmlzGc8tXidllLSlARZRO+u3sU8YkOyaNbivzeZWsWZpYEOCEqdBS6WsNAkYuX93JyyHcXEco0glerwl8/jZNifr8T+OjYMLY1H97fkWci9bbFRF5dZfDZrhNb4SqI+VqmVwYVIUA/S5PSmtWDRFlPVgWBEZu9/CzAc8v801o2ER2TfsCgXmUS3zeMMDncVwXRGaqbk8x8jvJZWaIJi8RSApHhF/U1LWcRY22dIRtnPhwP9HOG0WID+/b2gaIYqdI9vxh+LY6RddVLzFw2i8o6deoxahk+JkhreQ+VirRdLYyj5Oey0EarupfQ67Fgdtm6GSzWCzGq1fVRqciu6jlNJWPig/uoeFuKJa1L9arUEkzTOkWhFUXm2dpAY1+YWQEukGiHEubuo+Imd2JslVSeTTb99qTSByRDt9JRaKdajgdWSxzR+1Enc6r0RkeN1YHSeI6zZfCa2gSsjrg03d1lgdJE3pYjlXXU+USVTew2l/UNXo6uedML+2Lbo6v5Sn8Kau0oAMw8Aw76x/WJh1YzfP1a8OiorrS8YkI3E3EQ1aTccCW7zZNwz6tRjprHDX7MeT7uccHwxWffiK9Gk24Ub5iHnO68oShHqt1lFQBNbcU45GLCZa8hv8tzU7Uc9cMxwSBpuJTvxWx/Pm7El6KaUWVEVaB3HdnnLXX4TqxynRT2+SRBAtOxEcZH4qqX25a87tdoMmE4CI97nC2XdkORjykCTIrjY7vlPBYUxlFax5ubR5qmW4/Oy5BoduId8nQsGKeM5u5ZLm22jG3N8WnPbn9i++ZhNVfKNgJnNffPRGeuj5k6wbd9Id1aKzjvAmG8VEAum+Q85qt6dpwyilzCcQXHN+RZKvZpQ10CGnb7E3lf0ibxDx7mYDh1DZnxq1AJEGuBOcPOMyFRAolaBuZL8Q4SFBJTc+AG4XjHqMQSoumpXz0J7FGPidd//XDHXiL3zGsnBbyqpTBvAyoE1DjA5IhPbvW2ZwxwPIpPfFXDoSHLr6pV6o0kNs2TzIMmBz5iXw9EN2DOHaHPU8CDXr32F2m9H/JV1LZ0s8vQcBgLzl1DlU9rDqbRgaIYyVNGq1LShTttVwrmNOUUakSXgdxMqPKacTkna+DFVlZFUTaKH4/mIeWXbhOMt/Dvh7GgyKeVsrhs5OtGbwLTLGZiyxq8pMzXCZ5qnRTu3EQmT4pFAzAp1zckjPt6XzyMGcfZ0HvNFCTbV6tInzr8XT6uuZ8/b/2dJPKEqClN5K5wq+ponzlel7ILvR/zdLGe1lkmfzWUWSa/jY0MAd4PEnz8thTe+Ogsmkg/iGG7sV6mymnarI0nPuxRJzmu6mZCVR5VJdXhlGOHVvy0R30t8ED1w3fyxuYRpeXViwPE3qyDHVuO7O6fyDa94JYudT1KGAZf/uFPZVCTOhI/XVkZ2gSqjcRgmXL86OvKBPl6Ma0D0+m08MBPH6XXRG/wfU777habz1TJwAsgnA3+2xKz7dH7CJVF5RacR4VIbb6l7LOVdRDmKwd4/QBGxdQXomqLiqIUf2ljHToTCmdZCSWvH8qVn5znwiIoDsmCdGGVvJchoTUi7DCZQ+eiACznE5tXj4znhudvXq2du7GJ5bPg4Fr4+0uHvzCQ1kKQfsYkr+yl0AevycuRhuv7HIJZj+vL8158T5SKq1WwyRzNbGm27frzi32te2EbsAQtmIVbHpbNRuGeN5BOPiFBZjpzVJ99oNCR6s2jbOSJUbL6ajtF6DPi6foR1lUq6lrJ/fuzRynSaXOIo0ZlAfWmvHbnIdFluwGe2ytsmAo1dgatid+cCOeMOBcQNObQiWK3mlE6UB4uhFnudaWDpFt5jR9zOcntLsxtxdyXazE/HI5stxfaS8PsMvIUdj0MJcNQiu1EuIqwNpsWl2wFQnqMcSzE0TPlli73Wkwn+BA1Chk4W63o5xwNK3d+SXTabi+rS+UyxMyyOWVperKsxZ+3DLNGI9DGbe7WQPbItaDPEXxqtn0UAY8L0pDeF5HPkxV9ACob1nxgSJYTL7jrz1OB5upe+f31G4dQFDJ87J3iL+ecbRb4vJLJbG48kzdoRDp/dkXKjEsye3MdHHT+hZJTsSaCV3ZeDe19GlaFIDuzmy1+ztYO4PCD78h5FlaKRbIXbSD0Gco79G1MN3PaMZfXNUh4cXwS6uF8atbU9JfJMaaQASZIN7m6tRFBiwrs/HhYfz4vJuqDmBd1j3vKXUt5/0y26yidZnze0j9vpcClNBylw8qxrT77IJ4poxgKNa+eASnoi+F+GDOmpy12zLDjwgOP8sHOFhn9jK6OWK+Z38lRvj9ucKOwTrSKNMn32c32RUcuARVDX9J38iETLHrpZqTIVcavXi7GCg1soeeVtQTi6kRni2OGG4pV3DJ1FVPqmIH1tRvSRrFYklaZSNDzLPGpE9RTlKOk2iyDv6mgKQa2m1ZYFUmlKQnqU9q0hMGRFWLTmu8vKztIZ1LI/WzF7ney6D7QtTXDnFNmE5u6E+vgD3fc3T9Q5e7K8EmeIdm+RRmPPXTSIGyTh8w0gRfPeX8pmB726xB9PtXMbUX92QPZ3VkGkIXE7MmbLaeq2CbRWOnSqTNCbonb/fWz6WboO+mynRe3ylajCw9Ko17VmP0M0whzZPrpjuj0Ctsp48GJva82fs2eXRqZ5fUa2ooy6RnOjxLGYZL8ffEtWTr2KSVoLYPyoS/ROrDbJhFZ0GsyT55PlNWwBpsvNNNz2+CC4dCIOdjdzdMKi4Sg1xPZPOZrnZhdJk6Fc/bCHCtZx2YTm3RaCAn+MEpyLwevcAE2WURbYZwvCINVvEgLCjxPGc+zWTMxPwwZnVN8UTsa63FB5PiLgPEXrd9oAc905DYPfN1rMg2vikBuZMcZohRkq4ViMwbF0wivK+GOQ0qsD2rFjSJQGqiNiH20irzrNmzzkbvdcYVQQlTkhdDzik23iljMvkcVQeCDXvjH0WkRMERF+OBQmUe9SZmB3UQ4KcavbkUV1xcrzJA1PdWbR8FvE9yhjJfU6aAYhh1zX/LN129RKvLDf/SfKPYtb149CZQwZgzPW777yefip/L5O7JNj9n0q4eEaatVPKOtkw+H9ZhRbihdLb4rMmS09nw9MSQPS1sOmN23xNGICOm4YTrXV+XocmMsj90M6GKmDkqcAZM8u0hqSmPEPvb5uKcqB5rtBZeOsEOKHDsPJVOwvN0eaapO5OjThJ+ztevxXiCGJbcUkDT6Yk4ZpXIsr11i3SR83HUl85iTd5NsDuW4qvJ80JzaDVoFNolpMvQvMiaTFalSkXHKVnHNYve6iJB+8OZbGcoVwiBxnTjrnT7cyPAuXAVE05Rz7hoy4zhszmSZI0vHdBDaY3+p2Xz5juzVCXWXSdRdZkktrRTRtoXzzPCXt0SnIV67MlMP2NctZtOTnWrs7UWK917861c8++YOHTw8PUlRRsMUid9NYEdUc4ZNRbx7JbbJRoKtwcOrDfqthRjEtG2apStPLIP8D0/gwD/lhCHHp2zYBcoiaSRiVOSbjuKup/78PfVnH/CjiISyc8Ns3cqeWfB9P2c4Z4jJiGxhDi0eOFOKUiuT42hd9mKEVg3rZrvYClTlgFZxNYRqthfQkXIc1kAO766JTHkum7b3en3+iz1EmU/J7lagnsq6tRblwJvSrTO8OdELaxu4SwLFTEt4++QN28yxzRzn2dJ6KdZayb9zQbPPJ4rUlC6Ml5+3fqMF3KjITeGw2jAFMbHaxMghE6WlUSmdBhJH3KRirnEx8nIYe5N7IpLQI/9G0ng+2x1Xfm4/iLmSUpIt+KqYyPct2c1Zuol09e6xFAn8RW6kevdOMPKl8A0TzIIxhyRXt5ue6ov3YrpUhBVq0dWEXaAHJ+5sPqW5B6+5uXmW2KmmFxpXCmbwbUkRFLuuXOOoYlDMj1sRWxQz2bZDZ16Oqt4wPqdrW/xXco8qArqS5Ixw1kSvoJWCFmeTHAjFhEg1HebQUS5iJmeYH3a4rqR9d0vwmmLTCecYoaDVmw7nDH1Xp452IMmZyFLazBIFtnEG7y271A1l2bz6YTPmHJ/3K8d6kazHqLgdM8gUavX7yFBOivbcVkxtJUO8YkJnjiJzZNWwusrF5fck4njJyMiyJf1bYBWTuSucMks47zTluFmGbAtlbTkVAZKCnvB1583KdwfpChWR2Vu6cyEWsgnT1dYxDcV6Kktn+WvRdQ7eP8i52yW4qytkI6vkWk01Yt5MxB98gSorTFkTtcFpGVpGbUCnU19eiSl8TO/f1Itf/eUZ3IzqLnB8Jv7PP0XvA7y+lefSbISN0rbrADScM+JYrSc5s+vE6iCXE5adpOmZBwlEULUYii3CH3ep1qxTpQRyMLk0WM1eNAQmwYBzCqRe1K/lElSd2C4LCcBaf/X895r23Aj7KXPrgHxxf1zgvyX4Q6mEjY+5sKiGMr23cdUJLOymQzFijBdHSJdJHmZS4PqoOE05c1RrGMMchF1yyMWYatkopqD5MGZ8Nxjui8CrYqZMsLDC0qfXdgziRjipKNYbv6Sm/kYL+OA1f3HJBfNBJKWCa8uFGh3oneU8W/HQsIHRa4YA+0y8Upa542k2iVspnfsYDLm3zF58qE99vUq4t2XPbdOlgFYlvO3+KseOTgyEytdPyb1vkmn+toAQiO/l6KnvA1oPEohAeiJesidDnxG6guHDgeHUrNhs97xlnjPKalgVldoEiBrf58wnMRSym5785szd4SIFxWmGhwOnDzdsbo5Ud8eER2sRcBhPGAoJMDAeY9T6nGIvBSAO6e1NEIYygdCWTO9LGWo2w5rpKarHsNqIuoecoSt5/iDwzP7mGYBzYhssPHB0xGYzdRruLYIkpQM2D+Rqoqh68TNPQ16TYIrb1x+I3qyWqafLJhVaub4Vt58yhuN2pdDZbOb8uMe/KJwLDex82uKDXnMTF1n0pasp8onDzbOwS2wgOIsbXySyTzIIs8mGYfng+2RFmtvp+lrqmLDReTWeilHhZrsWmKWjM8avDJ6yGiiqxMiYNerpAqYlHkmKYHk+qhIDqOafvocyh2ZDzBtiXuDLmlA2RJuDNqipR7kZ8/wBNXQwDBAD4e41sawJtQSTRJtseV8dUHOHfX6HPj4zf9hhXYupzslWOIeHC+Fo0FtQjcbcGdlwphEGx/wTYSu9nNUoE7AvwhWC14KFJ7zfztMaHrEM4UNUEFSyfRDLidNxx+yy5Pt9Tc4y1oNLw2Qd10Hz2Bar14ybMlEXh2uIMrDaCvskkV8gOGud4OtNakxSIb8OMTVaC4PIppNlpj2Z9vRTweAN5zS8bF4EEI9B8W6wwgmfDY0N3BUTr4pZ8i6NcMHnZBlS6AiEJKOPnGaDizIgNfq3hIVilHC9XUgCHIQuOHnDksUzR5WCbyNFStuJUfE8G96PlpvcU+hI5wRzOs+RQovBTKYDo8vQiNuhUYHSTmTGSdc0Z4zPW+G/XiaRHBfifhejZk7mUNl8QRUzpglQFqg/2cvRchzkKNnPhLPgw64rmS41tpwkRWa2Hxn65PWA9dPKXjh9EHrbLqkH5xQaW6ZhZgxavDA2HeXds3TqKdhhft7ghlz8R4ynfPuIKh2xF1x/GVTFxKZZ6Glqr6EP+EcRUthNR5wt0/vD+lz9KB4bphT/590X37GLeqU/jifxn9hsLxjrKDZCv5r7gqGvOJ83a7HspoLLWFDnE2U2UxXD6uSndViFHSafRQ3ptVDPlmFoeg7KemJXMp4b5r7AzZay6qluzuTpuO6GfD0CL9i28/K7SS5/AHnmVmrZPGd0bbNmTFor37t0Ne1Ysq9b6hTrBcItD0Hx9rPvsFYeT051Yh8wjYVcd19RFCObzYuIu9Q5aiNxbiaf1xNNnAzhSV7/ZZiZ/+AElYWNsD/87SuxMbY5yk2ooUM/P2DavxTutrUrd9t/rfCXSiCVLKB+fBaztT3L+RyqDP8P/09Em+E3B/jBH1HYn0IXoXXADAzECZmJbDNomissM8j121cXTG+4/KcvhZqXz6JbCAatr8lSKhVblXzdh+etJEwlQVOWOfquJni9duK39w/EqFdOdt9V62u+vKaLL40xQaxok9fMQhs8tw2jy3h1eKK0A9u94OZDJ9j3YoK1wDPOXQft/VByGmq2RZ/CpUVZurhH2qFGu7gaWS2+340VUkYAMm+4mORZoyX3oHWWUgcO+ST/NkgQ++AlxH2bSVCLVuL7FGKkTnmav2j9Rgu41ZE3pVvxHqsDLkikWmRJPJHGdmuFhXKeLOdZY5IIyAWZ6EYEU39biRy1dXblTVZ2/iiNPLPz2hG4KZM0mU23qtP8mOP7xH0uZszdJIT17UE+IADjQPyqTynvL+TEOlK8yKSsP3/PthnWTi1O0jW7S51ipGRDypseZQL5tpPiG8WhcO5LCjrsNmIPLXnzvD62yrxQF6tRPqBbRCnqpitHUwP3whzAWigb4v4GQkCHgBp6GVYNHba9EM4Z/iIfkDBHLt/dMQ0Fm5uTRG6VktBCkNeuO22kGypHmVfUA8XYU21a3JgLxvx0wzH5sS/DZKs9m7pbMUZtroq8vquIUVNUvRR1fT0Z+fEqIqrqnmIj84vcCJYZEyYexozoNbfj+/W9DguHXIWVgmnLKcElogT0abA9T9mqhl0oh8enPeMk3Z21Xo7+6bEJirIeGIeCDw97RpfRjgU7b2mahPMXkxS1lPailLBmFpsFpQP6NkBpyX+Y3rPmLdFma8HUl5MMGNuLQCzDJOnfPuKfMtxzgy4iOjfMzxuBm7R4quvNCBr8tym4eT+hjMN8+IZQb3D3X+B3d4SyQXcn9OkZzic4D3ICzdMQHwROGSb8O0scK8yuR2WB8v4Z15X0T7vVpwdEdJPlM9mml3nRpWLsSqbUCeeIcngcChnuq7j6lJeHi7CUkv/OPOWrp/vCglqKeV6MNCkGzc12hVB2ezGcm9OcBVg3lgUmGyfDOG0JUafMTcnSXDQLAHMS9QD0KSJQArpHTpPAGy7h3iDFVwNRBw65p/Oa94MMKwFmfU3hGdKgUhgsoJVha/1H4cdz0FwnUz+npv6S7/0XX1NQ/LiV7rAwkdeF0HAW7Ecj0tFNkphOQbPLPLvsOtj6ts8YvAQ7GBVpXXJz85pOGc6TfNh3QeO88D+bEspyICtHqpsTKnOSNjPKB1GpgKkm+Rpg9CQfoPfJf7oUmh1WDLG0HTD3Hfm2TX4pcYVS0KA0kv03GbECtTNZdoaFCTLbK59514m8OHk7L/CBHySRJ3QJSjBBqH1DgRkzVOawtkXhpXADvNqAsVK4tb4WAm0gBCkE1sJ2B01A7R1mnjBuhOEMc6Rs3wun3Yu4yQ8F0WkRP5Ujdjkyx4SbB0mqL9LwUU85t4dntk17xRqRrqntai5dLcHCyfZ1ySUE6BNnuxkzlJGvz20lEvoXwg7f50wpxGAZLC7sn8UUbCmycjRX4CNKXfnIJp9R6RRk85m8Vles1EqhWNz7lA7ixW3lFDWehPLHLBzlu5un9HNx9dnQKUBiHnP4XoKQrQfxH8nClbYHci8dn1GJqx1nLQ1AsmnQhcbuZhHsNAqzn9HV8/pz2eGCDepqLFUDRqH65Om9T/fD+w/oTYept+JzP3SoEIh5Aa/ewBsDwcuGYa3ANDxK9F4+w9Tj31lCn+O6cqXCLtenjF/Trxa8X2dOCARpbgGs2oLl/ljeO208OvNrhNs8W7FD2HRrGEdwlinBTdOYC4Nrd6E7bTi1NZvtRfxOWhlSLkU8zydIlhYxXpN/YlB0bcP7pxtJ4ClG5pTnuqQqKQTmbceCwWcSq6Yin1USXSdOqpZvk7f41go0vE95smMQAHhC5PIgs77KujWmzQXN7MzaeQ/e/PZg4JmKfFHPq9GLSfji5K+Wi2CobMCnXa3UH6dSfF7NzEHxMFkmrxiCyOpzEyhSRz+Ha3zSzf64ejJkZRoEFZMc0RODwFSTcFfbNI4zao0YA1I3ayThHVB5fjW6mhzg8U8Z88NORBbFTEjp9MvSubsa8gPdw37FwlWKW3spwjHFJFi39asfs65G7OEiYb+zJZxzaCMhDbqyqhNBRpeO+WlgEMcIk8J3+TXYIansVJ6ut8xhl6FfpYI6TRAcWdsSp6uJUXgWOuQ1feXq5jZ2FX0n/iejy9Y092Vt0nFUCq4mrwcJM0gOfNOYC/d3ltOUSTmYN68e1iEXSFFfQgzKekhJK9Lp9p14SV+6OlHQ5lXlmWczu8OJacw5J5c/YIV3Fq+UpdNbjJmWTUJlTgyNADfkfPjw+UcwTFWOaze5qGnbc8PpvOX+1Qe2t0dU8gKZjxvUKWC7HlTAnYTZQhTNgLJBYsumDFNO5K+e5dRVpg2xj6hGo+5KVDfAPBEvYsErG4N4mpDn6MT7jmVNTJ29cjPmZ38hBdpawc3b9jpU7TuBC29uiPWGcC9B0qprUUOP7h9XvrfOHMXhjB8zug83ZMVEfiOQRfQGjEfnE/O5wQ3Xgj71xcpEAqgPYuy1mLOFs/gE7Q/HFUf3wcp8J81ZXvL5w2wom261ewBE3TxnuPmqtzDGsbk5Yey4nshiUBSbjmZ7YehKzpctVTlwSP4oSkX6rmJKoSV+ToUf1mzLMSRTKx0IqMQuUavf0yKPz9PQdQ7XQBuQa/rfzhmnOfIPdpqt/WWlW9ZvtIDnOvBF3SfcSHOZM4yK7PLAHK40QklzEc7kEPRKkBcPlCWJJ2IUbFREq7jaMOZain9mHSYpmLTxonrcdhIzVswoE5kftrhLRZE/Q+7ReRLDlDk0DXGzle7VzVee7DASvzmtZvcxWKIr0Lkjf/OEP9XMzxuZttsgPimpW1yMsJYOyU+Wy7e3AqWkpHO0dOa6nEQksUXe4QXDdBBnD+MV+kCnjcFFsAE2BVxGpj/fCrf4dYuqI+ZmhjEQzsm03kqKDy4SH2cIEmNGnrq1PIObDSoErO2InRTuBTeP3nxkTpTlM0UxMowFw5RDfjXpB6huO6pNR7FtpcMCwmy4PO0JXq8UQqVlOIoSa9TtZx/We2jJ4jTFTDVbGVjNZi0Ci6hm6ewPhyM2mz/ySs+dIS8mSR8fCvJC3O+6tqHrK5q6JS9HHr96yzTlbJqWohw5fPYek89rNqcxbs0BXTr7PFMvrkOCd425+lRPbUXwhnJ3Qecz5z//nODNGnu2GD7l226NMtPVJCwRo+Q90elUMTm49IQPGnfa0X93y9QX1DcnTD1SuCfUZoCqlwbk6QlljZzAAMqSaDNivUGHB3g6QjsThwvqxsLNQZqXaUS//1bgm1Jwd7XPUI2n3D4SncxedFuStdWVtVTM6O0VXsz6M0Vb4i4Vc1tR7lqUDutrolSUxsAbYpQUIJ058npIFg3NKpdfNtqXopvMulXFuRRdN2drB69UTHCImJEZ48nyGWM8NptZMlKN9TR1u/qulMWIzdw6E9lXLZlxGB1p54z/fG4YvRY3QjnspfR6Ta4jt7mnNBLWMCdrkCzZBqjEIZdgZMUPm3Q6SZ7guf7Y3uH76zcspRd6zGnORACWvl7qSGUCt7l4mzzPhiZNdX3ifTc2rNjQmuYcFI+jQSv4o81IbTzbbKLJptVWst50ZEsuIDCfmiQrlzRus3g0DCk8OAuQS4K5enq4dtmjw7/PRX047kRM0Qwy6NwXiT9r0Lctmb9AJUHB+dTB5AnPkkE5vj/gBklUUTquknml4pprGL0RloNvseVwNdvKhBWmDyN6N8qJYDktWAPbO7CWaDNU05PrByn8dQ3NBn//Vl5wbVDPH+D4hEqDMJFZT3AcRF16nFH6SrVEgyoj2f6yioG0DVRvHlfTrTl1TGU1cB80WbKzXQr4gv+aOhk5dSXBG+FWz5bj446qHCi3LTpzq9/McG6wxURWDwynhqGtBaJYfKR1gkRSgQa4z9+v991LGGfxwth9Lt9fbXqjovFXWb5AMlrc7rwRe+J0OsqaHlsNq0/5oS/SyxpW/xrBb1M+pZYGwuQzccjXQqV8oLw/fmTnu0Bo2c2ZJUQhjhnu64V6GITvXwVia/BtTf/NPd3zdn2dx3ODTQZYC30yBhk4501P819/A9uS8NmX8vpMI7Gs4AdfSofddwIZPjxKEd9sE5QyE34yEPocZZQMV+fEmklD+mXFWaTvvislkedmIqZhpk/OmNX+IqeLBJUsz7N9d7tSOpflk9DL2JntTb+ecua+oBwG2q7m1G6Yjgd80NztjlTlgM1msnxa+emzs3hvOXcNRolLpdFhTUuakyOlcyK9d0EG4uJIaHHBcOwr+kQnjFHxRT2sr/0cNM+zXRXBy+xuDoqvl/tESSdudaSwgcp4mjQgXbrxBTrZZPMao/jz1m+0gEOiSyeF0YIDaUSpZFTERU3rZGhplLBQmtRlh6h4N2YMXpOpiFJCRSxN5G3Vs8lG7pqL7MT5iNaRoSvXBOz2ecfxuOPN59+y/eLd+pymkwx/liSU6u4R9rkcJbMA3RNkGvOPSshy4mZHtJaYi+kSyJFUTeO1W18ffJIBYt3DNFNtviOOhnzf4vuc4bjFRpG7oyMlXE31g2b+ZoPvC/yQY+pxpW0pHbGfdwLzaJ3k00F+aU0sK9SbVwDEvCCWFaFsQBvhB+/fJs6wTRi5R7kR0z6gpgF9eoKhF5+MyckGNFjmp63AN84SfQQRoQPC6928eRBL2+SIB6wfNluOLzpviWjzY7Zys+vkbjheqrV4mdxR7s+Ml5rTT9+uieHL4y5c4GUtnGwJttVc2hprHa9ev195394ZwrFhMQdbisVqe5romn1XMU4F+/2RopTjtsocZsHcL0bgA+NXDnrwhqmrcE4eM8vm1cvbDbm4PlYT81HUu9WulbSohF2HzCWYRRMuBZev5T00mcOWI9m+JYw5PJMwfkN5/ywU2ITVL+HF7lKtzUCYYbzUBG+ozhptRnR3EejkeIJNTXj1Vu6lupFh9zwRN1tCvUHl0qTo5gN6HIjvJ2JvGN7fAQhkElMqVBpCh1lM30TQJpi/3g7k4yVRVkWd2T3umYZCEqYWyE1f/cFBhqLDUK6uk0U5kDdiO1HFC3XbMvTVKgw7XzY8Ph/WFPolNFrYQ4H7w3MaSl9nMLOzPF+25AkDN1FjgxTudixXf5XLLPRB0a4EMp0iHXWgjIpMBwLwRX3NyRy85d1w/b8aK1GSVpEo1TO58TwMJUM6PRoV+axuKV+kFn1//cYLuAuab/rFijGmkE+AmBwHgwTvKpnufpgzxqDYZ3IMkR0tJBmrdOwaKI2jSq53MWrarqYoRrZb6XxsObF/+4H92w9kTb9mSPoxo/1wYBwK7v/wa+y2XZ3c/OvPiTYn/IkUvqiv3FLlJvTQYk6PqId3q0sbcxpmpk0z9JkMSq0ED6tM6Fl20wk9MAk0bGKyxKCTRW29qg1NOVLcCw/cD8XKuFCZpAupHHkntYZZo84n6aD6WZ7HBLpR6DfPcqIYB9ju8Ic7Qr0jVAd5stHhmzvi1qAOb1DBY07CLdbvv4VTizuJvH1qK9yY0yeLV6UlKaZoOmF/OJM8lzO6tmZ22VoIlzSiZbAHEnC7O5wkUHbbvRhkCR5MkFixhf6VF+NHwb+2mNb0l0XIQ1Arn3uBUACmruJy2q7SeZANcTpvmJ1NnZtLkMiESx7c9Wzl5y4Vbsh5+u4en7zevTdMc5a6OYHvXvqV2+QcaJtBjKgyt3qFLH4hBMV0FPit/PwDSkfKwxlbD2RvjnLi2haCTSdKIHUlg+vF2wREOQlkwxmmmfgUJZxhScXJwvrzsW5gs0Wdj+j/338S2uB+L/fIMKIy8Q5RXXsNN3GecBY1ZfXle5mFHDdEp9fiLWyYKDBKMUORTpAxEN8pxnNDluYXeSWGczFo3CTisRgVQyv0wjKdkhcGyZD8VEzu1hzUrBo4HC7i35PgmLar+ebplt5lfLY7Upc9u935GmicNmmtQ0pGCinqr+Krxzv2Vc9hc141BV1fMcw5r5PjpAuG2Wt+1m4ZvKF4AXcspVrpQJm+5qIkkRl1DX0ojSdPBX/yRjQxOtDYWZSbQXOZrlqF76/fsBeKhDUshfuQO0m0SMebpePWSuDcGJWokrzCKoOLYaXsJDtwjBJO+eCtEO3Trt1NIuOe54xD0JS7VjjVSfkYg2Z43NGfNklqnzwzZkt8alHTjHGOWDfEt38AgJkG1DSgFmrXOMD7E/NXG1A5SmdJEekIbbm6G8agmM+NUKuafh1QKR1Xt0J0JPQ54/uDCEyGXNwTt10yKZIuTQeF2cmA09xMUCgYEx5V1XLU7TtgApWi5JZ3ue/S8FXk1ebhAbPExi0c96qCPCdsdsJK0EaGX3evUU1Hod9R9M8UH54JfUH9vMUNuUA/QTG2tajjMo9KhlJZCtrIkzPkUrSH45ZxKPju/SsUkfvbRwlsqEZUPpPtLzIwTVFeh2Ja2TtLMnv0mhg02bZF526lhi5K2CklxSx+JeNFmC5LYTfZvAZrbHZnbjfdylNeitHCZzaNFBLdlphy5C5z+CljvNRr519WA5ub42qxMF5qhlZmFTmAOovXPKCCkuKmolBXjSL3R8gUvBI4rLIZ6BL0HWEZQIZwPekt+oR+gk58Stag6oXdYiPKe7nvvGH+boc5Dxj7hMqza2xgpWFw0H+QAeh2K+yl0xHePRNafQ2GaCB6R2hzCMJSWl53tdDglmFu+aKDDJDdntkWM+4sp5AldNvWkvq0nCD2L+As+V2js3mleao0DwOHG3LmviSrBmw5sn/1yNYf2e8la3bBxc0LoZG1jptti5syLs8yF9huL+hkF1xm04qday0zjNkbEaKpSJwVM5K0M3lN766MkYWkYXSkNsIoeVuNa7ZBQMQ+SskVdLNk/S55mPJSKZ6H6rcnUg2gNJ7/atvhomL0BpfSYPP0xBe/4kLL8cRHsEp21NFr3o+KycObKlLbwNbIbvY0FvigeB1PWOs55GdhIwwV1SCydNeVa2cAkNUDtpxWKfqSJahypEPxDtW1ZD/7z9cPylIArUmUvILsi4t86HLxEI+bLSYETAjSucwTxfGZ2MH83Z7gDdlO/D3MzUQM4sWiTCA/XFb2CUGvdL4Q9HrD13/wnXiW3+6gKFOh1YR6s8qoCR49iHRaTaMo8epm7dL04wcZWu1zwu2rK/xzfIKnFv2QrHTzKy2RYSR8gDhJ97UwLmLyHcnLkSoZPS2UuxjUOsQ1xbxissEb8SovJu5vHtejMoh/tC5mYeC05QpxzX1BsWuFO79iV3FlQ/jEKSZBI0pJUIVK3Z1JYqUFq12CKrTxNJuWohqw+Sy+MNWELiYJVliw3XTfmGZAJ5dGk4vHd4yKXRKD+NSpgwRn502PG3KmvqBM3bxuZtlYb0qoGsLhFmwm1E+SudRSpJ1DTa3MKS6ddDezmE0t4cPRFbhuvxZQZYL42ejA9LRdOdUmn6lqcThkSuXGWrmP9jdyv7YtPA2En03ozzPi3T38aCt+4M6tcYPKOdRLx8NJER62oqW4T0P59F/EntWy2J2aVVpflKOI6yaBMIV2O4EzjF0JQVEkX55y1+KnjP64kdOTCZhEy5xbORUtCUcyjwgcPnu/bgoAritX1sk6O0mnR7g6bhb5vEbaZZmk3E/eMrqMOh/FEz5qfBTfksoYWrcEXMcVOpmD4sFl6xAzcFWSn2bNLvvYJ/xxWtAJR4YoNuNvSwEPUYaYPio6Z1YoRSk45IHGOnbZzH05iN9AGggMHjYZZCZwiDKtzbRg4p1TFDryRe3YFeMqFAlBU5UDd3ePVFv50K/PI2Gcbijws8VMGYs3tMmcmAI1G8LuIAO/oRPs+O0PkiT5SklSbkYFn/DkZICjDXoawE2YkCrNjUbtA/mr4XrUDULv4wz9j9+IFevNafUrIYiX0PzVnu79DXlK1dHNDFZD18sx1yVV2kbMjMLhbuX3cj4RfjLI4/3wC8gLQlnhP/8D+NLg6x2hSMEV2qL7J/TUo4YWPfSo998KPOQ8zAGfLGzNpkf1Ba4rpWPNHLYayHbdtfs9b1f3QO8M9e5CVlwxUunEJd9wYeKYcqR8K5tHnC26Gqmbqz91dGlgepRsz3zTYeoxfU/TvrtnmsSD2lrHLuHCi0gk33bMl4r2aSceFanQKx3EBiBxhu1wpZeFxQLAetBBMP7Z0p03a+zW4tnRdxWPzwfqqme7vVBtW8rdhawahSNtPXGS/zPqKJTUPE+Qhsd8eJBG4eFE7MA9NuuJMTpLmO7R+YRpBtylxncFfhSfnYXumN+cUZkj9AV+LBhOG8IszonaBxGUVRPsKincm91KLxSGiwbdorNJGpK6WeckOjUkcbsXr/B/tJXN/+kJziO8T0PhKrkiNnK/rMV/nsgPZ7L2hH8u8W3J8Lxh6Cv2xpPVoFIjtf/hN/iu5Pmnb4QZ8uqRrBHjuGUtzow2NRPLe+PSa1JsOjn1ZTJMH87NqvC01tHcPaMzR3Nzonve8u23b8jzKSU06TXx3pjAvrnQlP0q6Pmm3dA7S2Wl0G6T8+XgLXPQtM5SqUiTTqBzvIY3iOYl0liRz5s057stRmo7s8nHFdL5Zes3m4kZxev7Lp+preezhQWnoEreAIs5zLIEVhFSfG09WysF/N1omYPikHvKxLldElmyzFFUvTjBVcMqLljoaa4rGU4NbjGy6cTE6stiQlej0KXsIPBBXjK/+uH1IqKTAjcN6O6EmsY0xa8J9Qbz+E5Uc8vqBil+SzJppsFF/AdJ8lZ5EFXf20d8XzB8e0e+v5Bvj8LTBrI3R+zhwvSwF/ZH5jCuh1y6uDgg+HpSj+nvvoJpIj4MhHPG8PVrsl1Hbr6W19SLDzh1ib57jT/cp+O4T/4amphX+LxC1RvZDEKA4LF/0MoH9jyjux5VzOItk0KT51O9hl4oHcmTAyRIpugSIyZWrG4VxsSorjmNd1MCESfZ3Rc4IF9SD0by6UIzXzuXOABOrRvIGtibeOXD8wZbTmTbluL2ylPWxjMnWpspJA5uPG3oHve054Z5zqjqflVWAldl6KZlGgq+++71GqatVORmf0KpsM4BXMLu5fZJA9g6itrXOeg79PkEw8T8n0oJRjjILGRh+9hNh24msvsTOjGf7PQsWLhSV0GQ1olqmMOlBecp/+HjakkbvSJOVrQAzkDoUPOEmhIEU+bCBd/via8KsJkML3/6Y2jHdb6jqrP8n/vdqpOg0JJ0pSPx6AAH34yoNznhB3+4vlc6/Aw1y/UoE9j9wbdsZivZml0BSuAnu+3RxcT+hT5CZw5TDyvziQSx6cxhm4H+3Q2+LVP8YBD1Z1ettsCLcEi+FpIi1zB14lJ5e/u0MlYWR83vr37Kmbxl8IbuxUD9gyuTYd9IaRxZgl8K7TmHnNOUrcQNpcCkuZ9RkUM+rek7ix2A0YE8uaz+ovUbF/K8Sh+CEKWLnoPi4kRSKunMhs6LuUumA1vraawUd6PEmTCQ3AijDAYEE5eLHKc8xXyJCi6cthRVTzPk16iyzFEdxMc4eE32uBdeaJqgxyeH6k9oY4llhQ1euo/TM+p8JPxEZPiqUSJlzy2qvWBOz/DwRDyyOhnGQY6NoZUIKl1N4DXDt7fymiSrVNMM6KCwm142ESAcNe55g9n06GYmvzvK8PN2QFUKdhvIctTDk5zL9gexBX34INa3Z5FV1//wW+nmz8tRTPI/KQPqq59i/+IvrpzOOpcP46mXTefVDqoaf//Z+j6qocf9bwZ0gdkNxFzUcb7P6T4cKDa9pBHVw2pIBYjVbp/TPe4JzlLfHsXbPLsWP6WCFLYysYCOJ8I7hSocqvHyepdJSLVwUWPEvd/g2lJev2Ii2yQa16YXdkQ9oHOHOUioNFb+bfSSilOmKLcwil3A8/NhldAHr5nJxNcmn6nvjkKHGzM5NejwUeDDMiCLXov178uwjUQLnH4q3YvoBUb0YURpsDetYNZvJKey/icGbEG0u/WEF+CqrF1+dy9wZq3lVyWbWNQGFTxqmlDeyYlKF9L5hyB/T1bJcQrEecJskr/4zQZV1SnhJ0NtDYRIfBgFEnk8yfu2EffL9idvxGXw1TMq8+LquayXauDKQisK5DAJG2lO8IYtJ2KC4JSOFK+eCWNG/+4GhhzVVmgrWa/iLpgG3SpS3h8pDmfClBNmA11J8Jop0XZtOWGsp749Jj/+/SoGUkqUtGSsNMAYFY/nHc9DTa6F+11lE7UZeVX2bJ0lIArKh6RF2GaaXHs2+USMMDjLHBVD0OQ60OR+xciXgJLOGzpfraZ+czCUZmZbiq/6L1q/WTMrHdlk82q/2LpFPqroENVZbQJvypEhQSgiMZV/r4Gn0TIEiVIzSowENIthjOKx3WJUoOrFxKquerpLQ3veUNW9KLV2LTaFBrBwU9MuDi/Uie8exQzoq6/lBi5zeOoYf/YanU/YXSdim71HknBL4hefwx8KFztqjRp6EcIsH7KnHpyjefMtxBeMFafQesIGnYz+Qd9G8vsLjBK87J43TMcNze1XcNgSXr0llg3xyx/J63N5Fkn0m7egDfqPIWpNSMPIl0tNg5wenh7EMjR1sCr30sHlEmQRvzqh7AmtDeRFMvSfUMVM6HOGn9yv2Y7Z4cIhFcwYxFfdLynqOq7d+ebNg2yUXrxOLk8yQNrpIDjoHEA7sA42Nbp0EJKdwYLJd8PVQoAE6ZjA8O4mWfcabD7T7DpU7sjrBIlMkp60UOwWaEEl3rmfMjY3R+rdhXkQk6qiEohoOclFr/GTdIAuMW208RTZjBtzurZZbU6LYD6CaXTuMLue5d0IfSb34E0hA+TDLTEvCHny007vmwoe3ITuLjKneDixAKr+KccnvBjA3p/F4nhS6bFtonV5KewL1LY7SFFNp0h9eBHo0EbiRaGKQf6NW+h9GqxGvUn2DJOT+6SV51LeH8Xy9otZ7qNyR2y2xLxEX44yED1diG3APe7EjdPK+z69vxHnzt1FrIOfN6h0+gBWH6FlM9TGJzpqtQ63lwSpZZAavSFExfZW+PZTV+GMp7kX6GSrA24o6NNwW5vAOBS0bU2WwkbybOaVfxYaoTe0Y8nsLHe12EUIYULeJxeXgAfDU1usG8HZGd4PhkOu2FiPS2LGQofrvRAVfWpQ5XvSjGbmt4UHvhRiFdFRketITgSzNFIKrYSpkr8A7lVUSYwoU90sKkotNos+Ju/j9IJ86BpqO1MlLHxxGCurgWLTUSTerSmm1bZ1WX6UD5NBTIAYBYOWuC4P2xzellRvk7F9LMVwQmvI81XVFvOSkJfiIJcwcDUN0gXd3ssH5nSEtiW80+uxGh1EJVr4xCRQV7rXmBgIxsvA8/mMapJSNH3YdXdJ1LDNR5imvEBeup8EC604/s0dqhG1JSC8de+gdeJI1yjxgrFWHqM9QwiYzyNmbDFHCZwgKAiiolv4ydPTlrErV5rh9ot36xGboBjf3UiWYrzmJQanCU9GgjTay5rXqW4svLoXTnteoLa9QDnp+erPSzSwaR+k2IR0s5U5DJHwLhAnSxjyNUHGjxnjuaHYtmTliK69sClWy9hUBBIOuXTS83ED3jBeJD90scgtdy3Qsn3zgBsK5iFnHgouTzvqTUveJA+ULXBooCjlw7v41izv0+WEPv9UWEHteIVIxiCDy8kQ51Iw7qGQDWu2wjGvB/HgCWKkRlT494V4oX92gSKuNhCqa+X+TPaz6/2iBWJTRYC6FJpiexJ/+SGdbF7PssmDnIAem/W9xwTZJKpa5jG80EkkiqPSkN2dsbs2eexr9l++IzhNcXe6ulEGjb9U+K6kf9qSNwPNl++EQlvNKyTkTjXzuREYrL7OTHbltL536Ei1BGUnMkO+7chq8fgJsxWxU36lnAK0x5rHdkudj2TGMbqM0VkaLcQLnfjgVgdUQgTGoHk/ZhjFajN7yCUTU2xixQMl0yGRNTQ+KL7pazovrJYsETnyFxma31+/0QIeER54aTyl8ezyuIL9Glb6zGnKKY1nk80wZwzeMKeu7m05JRMrGYYuA4AYpcC/ri9Y49Eq0I4l3x4PvN6e+GIrPtuuK2HI0aZi7mWIWR3O2MMgx1kdWKXrW5sGOmplpTAnscwwEc8vdkaXTOCrAIXC7OQDuopr+l5+X6hdiaftWwkJMM0gfNqnGjsOGN2lIhQIrXRYZteRvTkS2hz3lcbm70Rw0QqHPHzxA9BaoJ6+I34lWLyuf86bsRSFWbp7ZQGjVt+U0MscwmznhK0aES+9+WJltuAcOnXkXDpoO/xTnjZBMOVInrwylPFrAlIcNXFIQp7Z0mwvxKgZk5lVZSUQgH0pA9rBQZkLFDD0qK4VYdJmR9gc0oaUFIFTf2VJBI8akneHfQfTjFlEVhqKcKTx3yY1a5KpJ6n6yq1efkHqTh1Z+wjOs+1/9vFrahFYYQL/XDM+7GisdIYLNKb36RQRgrxu6f5Qlx7miH/Ik9FaBhiUzYRLvRuJo8afK3Q1YQ4D5jCQwRX+ssn/5o2wkxbWlBl6cJ54Rhgs/QWlWSG+cBL2TnQGe+hRd+YKYaXGRL0GfTPh/8OMb0uBRqwjnHPC2DA97sSPZ5fIAsMo2Pk0XV/D9nLF2TcZahhRkxPl56Qwu27FtOOLk5upB0w1cnghy1++J8rVGatbYQ41w0cBK3EWmmno81XUFD24XgR+IjwKZPWIHwPeWXTmKTYdU1eJEHBRWaYu26hAZWdmbxnmDB/Ff2nyZo1Yy1TkD+qBTMvPLmvyhimI13frLKXxZEqtX9tlM7uMlc2y8MJ/0fqNJ/Ls8pF2zlc/FJd+lUlOOnjDnI4RIP4pAO/HnMErqrRzGSW+u2NSRJXWURgJNtUqMsxy/CgXH+qkABTvYrHyXBLhfXICrD77IIW0U8QWSSEJSoyo8OL54DW+r0BLEIPvc/F1uD+SvTolQY1Kwp7UQbnU4ARwH6TQZG8HsJD/0ZF4UYxf3wrml/xTpqftNSvzcFk9ntEIBSymTcVaoXlpLQMnRHmppsRr9xouUTD5Rl87U2kT1g+//1Dg++Kq5rOJj9tG1Dyg7DuJ3AK5mAVzXTr3MhV8OxGeDO7USNTc/rJSDpfOUFUiB9/kX0lcXBIm+b4Q3PwW2GyId6+vN08qysuJRA29bF42kxnI5UlggPffyuteFlKEF5x33TgnyTNtxSlSZcLnjc6gtyOqmq7WBGUhHOllLQW3TtP3OsENw7Ri8TJMRozH6mEdnC9B1Fl2lvnDxkqHutkJ3PUs7BOTncEG4pub1RYBm+E2BxZfcLfAYer68X0pMsMWRJ0YXsGhpu46fHdT6oaTniF4TAiiEbh0yYK4uNJmvbu+78aik5cQSCDFAt2Ubx9TGIpPn4H0GN5BXhN2B+L9G7A5qjvL+6fPoEcZjnp1vb+DJs6I14+Ocu+riK6n9RJVGUV05Dy4iNazJOk4MVkzNzNURjxjALOYsj1Kk2Dy+cotB1AitGrqAT8Iw2mxnz3sj9RVn2T4Bqs9PmrGWTbZaZbuWavIHBUPQ0FpAn+wOVMYR50i9VwwXKacbrTCB0/020W5CXDIJ3Lj0cS1gP/WDDFBPA3OcyaG5ZBoNQqjpGg3duaQj3TeckkOYiEqPqsGrIrk2mN0JCSV0ruhQAM3hRTyOQ0VJmfYFCNv79+nrLyYBmbiz6Ezx5Iz6BNNTGkZbqk9qNyg/yjRu+aFB6xBabJ8ia+qyN1M5VqZ+uvXhN2BsDnIz4eAHlpws/Cu50kcAwHuDtLdlJKBWH7xtBaDeIT5/S45/oniUpmA2gp+qTqhKFLtE3xTEK0FJ57S8+eCiasfTWl4NYhl6OWUOte9fK1r1yKh3Yx2Tgag7bjiq9GlLu0/TqAl9VyVUfjLIV6Ll08CkkyjCo+pRsJkCZdqtRwtDmfxoMkG4nLnJTGOH3Om44YsKDL69PqmGUJ7Th2+g7uaePdq5dfrD9/JB9A7KRjTLK/j8/laVF+ulH/qTs019Ucnbvo5g6OSmDzjif7lAE7CEmLQmGpcN7noDO60X1kRKgU2q8Si8H1SzqbNOAPIjeDCmx3zqx8S6hvij1ISjB8+erpR22uhjk4KshvThuZQfpT3ePYrXMfy+6IeTsU9akMsN4SsBm2JVhgWKrj18fTcoaae7Ou/gO/eyaDcWrFU6Cb8hxrf55hmELOpQrzifVvKfVq1qEJJCIS1VxuHepdORn49RXG6QC+lSxVBlKZKoWYvg/7HnXTYOoi52z6pjqvs6po4DtANKO0x9SSkgVkTjgaOEX0rg+/YKcJo1/eiuDuuQiPf53Tvbteg8PFSc37cr9DfEq92bDc89zU3dUthZ7pYrF23j5oh/XmXCTTig8Ypg0vuqItTaoyKJpu50SGl+wSslkLduwznNbdVhzV+xdB/0fqN88DPc06fjiKVEWzIJ3aIhBvLxU7JnSs3AasivbMpqccKLzPh2yL4WTAoGST5oBmTGc0wXo9Ai+3kIm+udxeyesANRbKjtJK7+eYV4XCHu/2MaApiXq83OCAfGjetcnrdXQj1Zo25ijZHD5eVeqdCILxOLI4v0mtRC/d6ZQjcvl6522oayZMXBc6BtmAOq3ucqgS+kII1gc1QgD/cE/LqCicEyQ1V2hA2B/zhlfxfbhbo4fD6+gEPUgD08jwmOX7r0zPME+b5fPU8LxThsy9FQGTzdbgmFxbgDzOUtSJkCoFskteLXiKxyHfyc99cACc83cXqd9MLNa0sUSGg2rMMUnP5f9nu8LdvCG+rj2YModitBQlATS167q5WAA/J+6aqUTbDltV1sJsEM8q5q7GTza4sj/NR4KIiwSqeFUcmRMx0So+TNgsfVyaN6c4ScTcJhU/dKNmE29SFHl6j3BXjUsMJPXdk3/6l3B95KrLTCNMoz2WQWLPYR+KoJRYPCJ1QOZfChA6ShlPMYkXbKKhzwh/+SOY0ZbPORkLZ4LdvCbaA6kZOBQt+HQJ6FDqsOXTo7SCP9+JAptOJZtWQa02sN7jbN+iFcuucXMMkwyX/rWF+uFmzMsXbx0jIRZjYffHV9XW1CspSiAKHW3mcoSd+c6H/z2+EoXU/JG90s1pXCFXNC1Y+m3VTaH74zWq7q/uRcrbMbUX3uGfoSxHwWLGh7oeSSy/v0bYYyBImXecjhZ0l9CEYfBAo5Dxn6KhwURO8Yh5E8DN5g1GBXeqySzOTdIwJX/fUqW4VKT92cvaXFvHfaAH3UdF7s2r/h6ATB3JehwanKeP9aJPdYmQKYjG7FOEsc2hYC7rwxD27Ql7MzDhsOjaFqDh1NbUzmDTJXVJR8nxKXhWerE4dej2gGzG2j1pjLk9EmxHmTorbcky/nF5g2x2cB8y2xOwP16+fz9KdbpJY4vZePhQ//nP5/h/9iVATv/qJ3Oz7G3l+bk7D0AZ1ErtQdo38fegFL7yIwyE+0ejcI8qC+ZMZnRcyJB16+O5BbmClYFNI53o+wvuTeGrs99fnC/J7EpUsR/KYFyhr159RWZ5ODnXarDIoG/zuXr6/dH8vlnLT+viL6EkFj/osMWGeH2Ec0A+pUCMbW9jsUXmBTnQ4QhCJvzaygSbe+rLJRpMM21OIb7AFoboBN2Ju38rrPbSgDaFsVjiBvCSmQhaDT0EYy2DZoDa7j6XrC1SVXpdlo15gpY9eg21YB8RqwdIhQSMW+/wOnt+hv0lDyzwNMxf9wDk5EkYlJ6IFJpgrKVImEF0auBrpVNelF7hMsGD6gIoj+vQkYrMU1hCtxTx+R375X4iHWym6Q3vVN+QV8bMfoNyMvnlEzRNU9ZUhM42obz8QO/CPpdAH+w+oVx3qcC/38+YgmZ1pCA5g7mZ09YC6E0+X2HwBWqMexCkyfPbl+lrjZvTpWRg6mz36chQ3za2m+uPvhFZbZuibpJBexWfAHNH3Ch1GGv1OoMrGywaUKdAe21ytALR1kmVqHSZfQrDFC2V0GV3ihu+qjqycE3VZk2nPHAy7OUMpqOycms2AC4bBWUZn6bylsjNNMZIn2Nd5Q4ia81gyecONipTZzM32tLpu/rz1m8fAs/lF4RV1lIvScffOcshn7opp5Ude0hBzk08UOiT8O3CaCuZlYJAu0AfN7C2TszwPFVYHNvm47mDjlDFNOTfWY3cXSaQexXPEFDO6mcQPQmtRIX71H+WJ30hxZZqg74UDm7BOCgs3m5Qo/kGw4Ez8xNlu8a8/J5QN5vIsH4hXCdd1s2Cb//C/lk63O8mx8nySgrkUrY1ALOrhA5xH4oDc8LdbYiM2n+rp4YoLpw0Aa68sgaSIC5sdqqxQ+xsp8Is1wAI9JDMuQjq1q8RA0Vq+b836wdWXY5J4pyFml9RxL02VXrJBlqWVsEnKau0uaVuY3BpAwfmCsha3bAq3n2Hf/QT9zc8kFs451DdfwXGExsqA8+4VMS/kxDBN4rBnDeGP/mRl3ejTI/rHfwlljr65k9f7eLoOqReTqExfRTHrdYT1uE8mQ0+1FNuFk5421UVYhUZOHEHJULZKVMgA6tWN+OykzSLub1YXy2gzwg++t6GnU9XL5PlgZbAbTSGntO+vILJ3PZ7XE5aaBvTzg2wyk0/Ral+ihwu6FGzfnB7W/2sReOmhh2kk/uQDsdfoHwHp/otao17fopbhO1fUR3fnKytL64SnyzCYmw3qTl/tIBL1Nv7gj+R1SGlSMS+J2uDuv1ivIdQb1JvP181zvcsWqf9CIFjmFoVs7qbuJBru6zRsf+1QlcK+uWCnlvzVEbwmTJb5uGF43lJtWspq4Cb5zLfnDdOck6XhZBsMbkkeUnHtqidvyIGqmLDRY5NrZudtYtSFVTXeTQWztxJ2bB3DnK2/fmsKeEDRecO7oaCxnv/DzSOZ9ljjcV6muA9DzbuhpLGO2njeVB258eTJ/3lMx5XnOaN1gnsXOrLNptU3wEeNTfQel9KpjXFUpZe8u+R7sLji2U0vtMJZo/oA2uB3t4TXP5AOL2uItiBmyTo1Lp3WVTwRTQm2lDvXVigl2KU2JUpZpjAQl5+PjjifQWfo/ED0A7H7Bj0eMcevV1gm7pK390LBunOp6xSmxPTlPyBUtx9jmfDRAGv9uykh214/6K6HOcEe2go8FAVfVcGBE2zVtA/y/w+tFM5BBl3z2x/K/+EmdHfCPD9cYYWEfS5MlZWFkDaMWFapoD7L97dbwbAfRR1JLgIqv/1MXk9tidqQaS1QUHOHvv9ccHw3QfD4wxtCVqPvOyk4U4rqsjlqGjAfvpEN8//4f74Wgd0N8Q/Kdai3FIwFQ1477sVEaho/GqSuS2v5XlLgqhcMlgUDXgqM7i7gHP7tD/CbG9ztn0Bxg7ZJcq7sx7//nMK8fu/F12J06/0Vo5NffiACfsHVfdpkwwLLVaAt2m5kXBAc0V3A95inv8Aev016BiPvb3uBRqOqCMbK63M+ymMuvvLb9F7Xidr63VfEm3vc5/fE+xy9u8U8fidc9mmSpuF0QYUooRNayZBYJdZXEiS97LzVT3/Kqv6sKjmdLo3EdyfC2aA/13JfPbfSlFRD4qUvMJsQGajTZ0dPkAe0nQlnYX3pzFHdPUPUBKcZTxumrmQYC/qp4GYrQSB7YHYZj+ctSkVeb04y6xvKtRFdzl5Vso0tUvEv8ok8GXQ5ZyXtJ2iMSv7l3jK63xIIRRHxQfMwGkJEuLP5xKbqCMEwzRallsGm4XnKk3vhnOARj/OaKSbToAhPo6E08IcbJY+fsKbjWLIvBl7vn8izeXXAA7EezZt+xQrdpWI+11SZQ2XD+gEOzT2h2EP1CqVLtLbyIVk+IK6XYr58IJQFffVJITr8+AHCjKk+w+Sb9cPnluM+oEwJ9WeEbCPFOHlzy5O4bhJ67uTYnzjlsdgRi/36c7pLR89yJ487Ce1KaUvMnRTyIBsMOoPi5vpctUUp+1EhIMz4/Q/AD5jLe5QfV1jJb0UsFLPNWvw/frPtX/nauuEFL8O48ZSKaZ8glTZtnvcCfdQyN1DK4m8toXm1DvX8/gcfvebKyOa5gBcuDPK85jP4AfXqj+UtyZpUvNLrv7xfyyb281aY1/fz5/7MOhsZ/sq3PtpM0/uq3Cjv8+LHDsT0byNSgBmf/spjKzeCG2VDW+YygO4fpYN28wu46mpmtpx0os1WOCeUDfOrP5ZO3pSQbVD5jbyOpsTvHaG+EQZLcPClke798iyngpfxbCEIo2Ua1yIZ7l6nonsjEEyxQ4WnNH8RH5V1xpCeU1whnSsnHtIpJC/xmxs5zeaF4OlullPn0wNsd4S716ibe1Tw+M1eNu/PZXBqHt9BCPj7t/L1P5pkc9PmysiZRlTfYaoelT8J9BQ0IfnNuPc555NErVXlQFGKX8niRHm7PRMWozRvcJm5xvYZxyYNJs3yy/p1uK1UXKP9YpREMaUi/VD+Uj+UX1nAlVL/ArGP/1GM8V//db//8c9Ck818URl2uRTl3Lj1iGC0psombgrD01gyeCPpFSYIlpTEO7nx3BUjlbGEmK+SVKMj27InREWdTTIIWI4pyd9AEsnNR45xU1sRZkP15lGgkW++xj49oC/PSTgiR8v1CHpK3cMKQSxYY7i6FDYbYrlImTX+9i0hr9bhZ9adVltQbHZNBv/u69VgSHUXSQnf7q72rgsNy81YmxH6J9TUizf5T/9cOtqilMnSpYPREc4avYvEP/4jFjqeupwFuqgTRp+6ejWIUnSN20rdMsgHST8/Sqe18KndJB/gaZQPbL39uDt9+f6nD+Rq/LVwtdP1qMs58Y5zKURuYVh02Od36A/fCexQb1I37Am72xXPXsMsXgxmzeX5Y1ZGgiNiXq6wwkumxvI1QrjCG+l7emjXE9D3/833r1Pk7RPfX2oa04lGdAFimKYT/fEFZxrEy/v9KKyKPv84Ws2ahPHGlXkRp2TSdqkhKEn1AdqfvCF6Q3n3Trxm7jvs1mI++279v6LN1vtrpSRqLa+VNgkTH2QYPE2o7W69P1SC7wDJ0FzulwUanAbi1BO1wW9u1tfMnD6gh36dOV0LucBFujvJBrM5iC9/sYUCfHOH8iN6uGBODzKrXCA5N0PQCbapVuYLaTgf6jRDCaWczE6PL+4PoeVik/9O4pKHyeJ7iXQ7XraU+YQxjnJxwvSGccq5DBX+hSzf6IBN3fboMo7HG7Zlz35zFg/yfGIcijVsQuvAphrQOjAnhe+Cjf+i9UsLeCrOxBj/TCn1p0qpfxZj/LNf9/vfX0YFbopBprDakSWazDAK/nMZS1wqtId8ZJdPVHbGak83Fbgg0EhuPPdVxxwMTfL3FcK7J7eScbetW0LQTFN+jfSyjrwccXNGeN6R170YHFUDqklmQ70htKB0jz79Z8FH+yCv1M1GPlQPI7E3Qp2yoLJAHDPCXGBvL+jdKC6B9lnyB22G/fov5DVLmGY43Mlg5qc/hhDRyweyd8Thgj9dxFK18qjLiK6PIrUuSynqw4RNR8n45o3ceEv8W3eUm2+OMuAyATziLGiT2uN8EcOh44zSJ2Iv0/sl8DiOqWjdh49Tf6oa+g7zv/x/iWcYv769vr/VuIoxYtD4U41LR1F0xHcFMWiK+2fxN5+tsGSqkZg8oFXlsa3ADNki+thur3SxvkMdn4hfXXCPDdkPfoI5NEk4Eq+bx4JPT/N1g9VJrHMemb/apNd3Jg5WfFBmSYC3m06+l7qnkMy31CbBMknkJLmifs1YVZsrxz4OrK+h3HyS8ahqBGO/uyWWVcqaHAnf+uSVMwid7tUO8gx141GtI07hyvBI1xLbiH8uVz95d6oJUw5KvFnCmEFUDEdhPNWfv0dlTpgr2l03zHojYqeHd7KZp0075gVxIxtqKBsRFamkJZimlbXDPCXLBeCmRDUbwuFe8Pm8FEjw+V16zGqFvcxf/Cfiw4jOISn55JrfvJVinOYdJvHVs2WDzouVH78kB6njhDk+rU2Lfn1L3B0+suQlBMxXP77CYNNMfEo6jVmvMJcqAvqQRG6Dh1ONS8yUfsqpy56yECsOrSJlOWCMpx1LQRWyaU0QA5nNWe3Zlj2KyOmyYVN38h6lgt92NZO33O4k9CRG+fplqBjcLy7Tv6oD/6fA/zP9+c+Bfwz82V/j+x8trSKbfFx3J61kh8qyGRscVifP3QTcGx3o55xuzvnQ13TOsssnCu3pvHC3N9mESZ30HAz9VKxDzMI49lWPMQFre0Iw9G1NWQ3YYsInJzKQ6f7ih5LvW0mF1x2qCqg/EAn34gWi7BN81zF/LUXAboRaZZsA+1IsNJ2odxbpd9jdEvJKoAFAzS3m8oTOM+gG4nuHf665/OQHEma77ciLI2pvr8KSpyOxfxaebW5ErpzlK+2Nmzu5od8/XZOBNFIQqnQqqDdyhH3ZObsZRcJUbSaKy9R9eliP46q7EP/y3UqJW7q8RTno2pLxYU/3uOdy2tJsLxRNt9qcmuQ/o3SUf5N+zd/JJmArUcPatpXCmEUUE+y12Jfub+S4/HBBbRXZzQh1I/jrqRVWzmspjOQJihp66WzPFyng1kBlsXcX8VpPMXHj85Z8dyHbp9DrxPEmCbniiy47Jn7+kpB0fndLuW3Z/6Mfr4UaWGlqyopfibtU5D84iiXD0rU+XYgdEK7+HWiIN/eExJ+GRAkNQZ6Hm+T9aB6x+8uV1fE20RmXzXZwxDFyw08AyD5vhd5YpwHfw1GGseVJCnezFU1B6mT10EvHagrc/rpRL1bDTCPq/TtwXu5TEOZVPhHycj2xkk5H5t03ws5KIRLzf7D0372mevOI2fboL/Nk43wrcM80oLoL6rvv5LE3tWwcFzH+Wjb38Oqt2AIMvWyMy9zocrqekpcg56dn8RbqEq5sIbQ5l7/4HFsNlG+eUNmY1LIObMS1FacPN1TlwGfZzKVtuPQ127rFGk/b1bggaIE1jm0j0KXz4uPvkzunjnFtCnzQjEPB8bLlPKQTvopsncW+MCYbnKV3L2DZ761fVcAP3/v73V/z+yil/hT40/TXy//1//P/+g+/4v/8LVxf/4Kv//TnfG26h9OH698//Jyf+UVrQtCoX3ctHuff/pqP3f3Kn/obrHv+ehf5N1g/+yXf+/41/TqvxffXBfjm1/nBdK3fk3TzF7/GP53S/7P8+fQrfv5//XWez6+5Hr/39+9j9QO/4Pncw//6N3hvB+D//Wv+7NPPeR6/6PP2/fd6BM6/5Odfrudf8r0J+I/w0b38Ev76cfr1d7r+8Od98VcV8Gfg9m/xfRIu/kux8d+npZT6n2KM/+Tv+nn8ptbfp+v9+3St8Ol6fxfWL0bHZf1brl32j4B/89f8/qf1aX1an9an9b/T+qUFPMb4PwI/Ukr9M+CwDCiVUv/ml33/0/q0Pq1P69P633/9ShphjPG/S3/8sxdf++e/7Pt/z9ffG7gorb9P1/v36Vrh0/X+1i8Vv+/W9ml9Wp/Wp/Vp/U6sX4WBf1qf1qf1aX1av6XrN+4H/ru6lFIHZFD7I+Cfxhj/7+nrf0WJ+ut+7XdlKaX+1e/79Sql/jHy3i6znd/ba4W/3bX9Ll1veq7/8iXs+/t07Z868F9//TfAP3nx4f7Tl0rU9LV/9ut+7e/g+f+NVnquP0p//n2+3v82vbe3Sqkf/T5fa3qOf56e858rpf7x7+v1Lp/XZf1trvO38do/FfBfc8UY//WLHfdHiPL0n6bf4apE/XW/9lu/lFLLdS7r9/J6k9js3yqlfpTe59/39/Z/Av6H5dQRY/z3/H5f78v1t7nO37pr/1TA/5orFbXHtAsfvvftu7/G134X1o9SMVvW4Xvf/3253j9GnuOjUuq/T3DZ4Xs/8/tyrcQYn4H/HvgfgP9L+vLhez/2e3O931uH7/39r3OdP+9rf6frUwH/669/EWP8l+nPz/xVJeqv+7Xf6vULjMme+T29XuA/p8L27xDrh2d+T681Hf3/LMb4x8DzC1z39/J6v7ee+Ztf58/72t/p+jTE/GsspdS/WHjv6fj585Soh1/za7/t63ERaCFird/n6/23XD+YB+SD+uf8fl4rwD9+od/4fyDznd/X9/b7629znT/va3+n61MH/muuVMz+lVLq3yml/h1w+/OUqL/u1/7uruTXWzHGf5+e5y3ppv19vd70fA/LUCrh4L+X15rWv17sn4H/5vf5etPz/CcvBpB/4+v8bbz2T0KeT+vT+rQ+rd/R9akD/7Q+rU/r0/odXZ8K+Kf1aX1an9bv6PpUwD+tT+vT+rR+R9enAv5pfVqf1qf1O7o+FfBP69P6tD6t39H1qYB/Wp/Wp/Vp/Y6uTwX80/q0Pq1P63d0fSrgn9an9Wl9Wr+j6/8PDPeGPI6LTocAAAAASUVORK5CYII=", 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", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# comparison opus@6kb/s vs. LACE@6kb/s\n", - "for name in ['opus_6kbps', 'lace_6kbps']:\n", - " fs, y = wavfile.read(f'lace_demo/{name}.wav')\n", - " spec, freqs, t, im = plt.specgram(y, NFFT=512, cmap='inferno', noverlap=256 + 128, pad_to=4096)\n", - " spec = 10*np.log10(spec)\n", - " \n", - " make_playback_animation(f'lace_demo/{name}_spec.mp4', spec, len(y)/16)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "data = read_data('lace_demo/endoscopy/')" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "def get_adaconv_impz(data, prefix, frame_size=80, overlap_size=40):\n", - " win1 = .5 + .5 * np.cos((np.arange(overlap_size) + 0.5) * torch.pi / overlap_size)\n", - " win1 = win1[:, np.newaxis]\n", - " win2 = win1[::-1]\n", - " kernels = data[prefix + '_kernels']['data'][0] * data[prefix + '_gains']['data'][0, :, np.newaxis]\n", - " kernels = kernels[:, ::-1]\n", - " num_frames = len(kernels)\n", - " impz = np.repeat(kernels, frame_size, 0)\n", - " for i in range(num_frames - 2, 0, -1):\n", - " idx = i * frame_size\n", - " impz[idx : idx + overlap_size] = win2 * impz[idx : idx + overlap_size] + win1 * impz[idx - overlap_size : idx]\n", - " \n", - " return impz" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "def get_adacomb_impz(data, prefix, frame_size=80, overlap_size=40):\n", - " win1 = .5 + .5 * np.cos((np.arange(overlap_size) + 0.5) * torch.pi / overlap_size)\n", - " win1 = win1[:, np.newaxis]\n", - " win2 = win1[::-1]\n", - " kernels = data[prefix + '_kernels']['data'][0]\n", - " gg = data[prefix + '_global_conv_gains']['data'][0]\n", - " g = data[prefix + '_gains']['data'][0]\n", - " lags = data[prefix + '_lags']['data'][0]\n", - " \n", - " num_frames = len(kernels)\n", - " max_lag = int(lags.max())\n", - " kernel_size = kernels.shape[1]\n", - " padding = kernel_size // 2\n", - " \n", - " impz = np.zeros((num_frames, max_lag + padding + 1))\n", - " for i in range(num_frames):\n", - " p = int(lags[i])\n", - " impz[i, 0] = gg[i]\n", - " impz[i, p - padding : p - padding + kernel_size] = gg[i] * g[i] * kernels[i, ::-1]\n", - " \n", - " impz = np.repeat(impz, frame_size, 0)\n", - " for i in range(num_frames - 2, 0, -1):\n", - " idx = i * frame_size\n", - " impz[idx : idx + overlap_size] = win2 * impz[idx : idx + overlap_size] + win1 * impz[idx - overlap_size : idx]\n", - " \n", - " return impz" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "impz_comb1 = get_adacomb_impz(data, 'limited_adaptive_comb1d_1')\n", - "impz_comb2 = get_adacomb_impz(data, 'limited_adaptive_comb1d_2')\n", - "impz_conv1 = get_adaconv_impz(data, 'limited_adaptive_conv1d_1')\n", - "\n", - "phi = data['hidden_features']['data'][0, 0]" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# LACE animation\n", - "\n", - "fig, axs = plt.subplots(4, 2)\n", - "frame_duration=40\n", - "fig.set_size_inches(12, 18)\n", - "axs[0, 0].set_xlabel('Tap')\n", - "axs[0, 0].set_ylabel('Amplitude')\n", - "axs[0, 1].set_xlabel('Frequency (kHz)')\n", - "axs[0, 1].set_ylabel('Amplitude (dB)')\n", - "axs[1, 0].set_xlabel('Tap')\n", - "axs[1, 0].set_ylabel('Amplitude')\n", - "axs[1, 1].set_xlabel('Frequency (kHz)')\n", - "axs[1, 1].set_ylabel('Amplitude (dB)')\n", - "axs[2, 0].set_xlabel('Tap')\n", - "axs[2, 0].set_ylabel('Amplitude')\n", - "axs[2, 1].set_xlabel('Frequency (kHz)')\n", - "axs[2, 1].set_ylabel('Amplitude (dB)')\n", - "axs[3, 0].set_xlabel('Dim')\n", - "axs[3, 1].set_visible(False)\n", - "fig.tight_layout()\n", - "\n", - "frames = []\n", - "for i in range(12800, 14000, 4):\n", - " f = impz_comb1[i]\n", - " w, h = scipy.signal.freqz(f, fs=16000)\n", - " frame = axs[0, 0].plot(f, 'b') + axs[0, 1].plot(w/1000, 10 * np.log10(np.abs(h)), 'b')\n", - " \n", - " f = impz_comb2[i]\n", - " w, h = scipy.signal.freqz(f, fs=16000)\n", - " frame += axs[1, 0].plot(f, 'b') + axs[1, 1].plot(w/1000, 10 * np.log10(np.abs(h)), 'b')\n", - " \n", - " f = impz_conv1[i]\n", - " w, h = scipy.signal.freqz(f, fs=16000)\n", - " frame += axs[2, 0].plot(f, 'b') + axs[2, 1].plot(w/1000, 10 * np.log10(np.abs(h)), 'b')\n", - " \n", - " frame += axs[3, 0].plot(phi[i//80], 'b')\n", - " \n", - " frames.append(frame)\n", - " \n", - "ani = matplotlib.animation.ArtistAnimation(fig, frames, blit=True, interval=frame_duration)\n", - "ani.save('lace_demo/responses2.mp4', dpi=720)" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# static plot for Jean-Marc's on-site presentation\n", - "\n", - "fig, axs = plt.subplots(4, 2)\n", - "frame_duration=40\n", - "fig.set_size_inches(12, 18)\n", - "axs[0, 0].set_xlabel('Tap')\n", - "axs[0, 0].set_ylabel('Amplitude')\n", - "axs[0, 1].set_xlabel('Frequency (kHz)')\n", - "axs[0, 1].set_ylabel('Amplitude (dB)')\n", - "axs[1, 0].set_xlabel('Tap')\n", - "axs[1, 0].set_ylabel('Amplitude')\n", - "axs[1, 1].set_xlabel('Frequency (kHz)')\n", - "axs[1, 1].set_ylabel('Amplitude (dB)')\n", - "axs[2, 0].set_xlabel('Tap')\n", - "axs[2, 0].set_ylabel('Amplitude')\n", - "axs[2, 1].set_xlabel('Frequency (kHz)')\n", - "axs[2, 1].set_ylabel('Amplitude (dB)')\n", - "axs[3, 0].set_xlabel('Tap')\n", - "axs[3, 0].set_ylabel('Amplitude')\n", - "axs[3, 1].set_xlabel('Frequency (kHz)')\n", - "axs[3, 1].set_ylabel('Amplitude (dB)')\n", - "fig.tight_layout()\n", - "i=10*80\n", - "\n", - "f = impz_comb1[i]\n", - "w, h = scipy.signal.freqz(f, fs=16000)\n", - "axs[0, 0].plot(f, 'b')\n", - "axs[0, 1].plot(w/1000, 10 * np.log10(np.abs(h)), 'b')\n", - "\n", - "f = impz_comb2[i]\n", - "w, h = scipy.signal.freqz(f, fs=16000)\n", - "axs[1, 0].plot(f, 'b')\n", - "axs[1, 1].plot(w/1000, 10 * np.log10(np.abs(h)), 'b')\n", - "\n", - "f = impz_conv1[i]\n", - "w, h = scipy.signal.freqz(f, fs=16000)\n", - "axs[2, 0].plot(f, 'b')\n", - "axs[2, 1].plot(w/1000, 10 * np.log10(np.abs(h)), 'b')\n", - "\n", - "f = np.convolve(np.convolve(impz_comb1[i], impz_comb2[i], mode='full'), impz_conv1[i])\n", - "w, h = scipy.signal.freqz(f, fs=16000)\n", - "axs[3, 0].plot(f, 'b')\n", - "axs[3, 1].plot(w/1000, 10 * np.log10(np.abs(h)), 'b')\n", - "\n", - "fig.savefig('plots/lace_snapshot_unvoiced.png')" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "torch", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.12" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/android/app/src/main/cpp/third_party/opus/dnn/torch/osce/stndrd/presentation/linear_prediction.ipynb b/android/app/src/main/cpp/third_party/opus/dnn/torch/osce/stndrd/presentation/linear_prediction.ipynb deleted file mode 100644 index a465fb9..0000000 --- a/android/app/src/main/cpp/third_party/opus/dnn/torch/osce/stndrd/presentation/linear_prediction.ipynb +++ /dev/null @@ -1,320 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "import matplotlib.animation\n", - "from scipy.io import wavfile\n", - "import scipy.signal\n", - "import torch\n", - "\n", - "from playback import make_playback_animation" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "def load_lpcnet_features(feature_file, version=2):\n", - " if version == 2 or version == 3:\n", - " layout = {\n", - " 'cepstrum': [0,18],\n", - " 'periods': [18, 19],\n", - " 'pitch_corr': [19, 20],\n", - " 'lpc': [20, 36]\n", - " }\n", - " frame_length = 36\n", - "\n", - " elif version == 1:\n", - " layout = {\n", - " 'cepstrum': [0,18],\n", - " 'periods': [36, 37],\n", - " 'pitch_corr': [37, 38],\n", - " 'lpc': [39, 55],\n", - " }\n", - " frame_length = 55\n", - " else:\n", - " raise ValueError(f'unknown feature version: {version}')\n", - "\n", - "\n", - " raw_features = torch.from_numpy(np.fromfile(feature_file, dtype='float32'))\n", - " raw_features = raw_features.reshape((-1, frame_length))\n", - "\n", - " features = torch.cat(\n", - " [\n", - " raw_features[:, layout['cepstrum'][0] : layout['cepstrum'][1]],\n", - " raw_features[:, layout['pitch_corr'][0] : layout['pitch_corr'][1]]\n", - " ],\n", - " dim=1\n", - " )\n", - "\n", - " lpcs = raw_features[:, layout['lpc'][0] : layout['lpc'][1]]\n", - " if version < 3:\n", - " periods = (0.1 + 50 * raw_features[:, layout['periods'][0] : layout['periods'][1]] + 100).long()\n", - " else:\n", - " periods = torch.round(torch.clip(256./2**(raw_features[:, layout['periods'][0] : layout['periods'][1]] + 1.5), 32, 256)).long()\n", - "\n", - " return {'features' : features, 'periods' : periods, 'lpcs' : lpcs}\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "def run_lpc(signal, lpcs, frame_length=160):\n", - " num_frames, lpc_order = lpcs.shape\n", - "\n", - " prediction = np.concatenate(\n", - " [- np.convolve(signal[i * frame_length : (i + 1) * frame_length + lpc_order - 1], lpcs[i], mode='valid') for i in range(num_frames)]\n", - " )\n", - " error = signal[lpc_order :] - prediction\n", - "\n", - " return prediction, error" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "lpcnet_features = load_lpcnet_features('lp/features.f32')\n", - "\n", - "features = lpcnet_features['features'].numpy()\n", - "periods = lpcnet_features['periods'].squeeze(-1).numpy()\n", - "lpcs = lpcnet_features['lpcs'].numpy()\n", - "\n", - "x = np.fromfile('data/a3_short.pcm', dtype=np.int16).astype(np.float32) / 2**15\n", - "x = np.concatenate((np.zeros(80), x, np.zeros(320)))\n", - "x_preemph = x.copy()\n", - "x_preemph[1:] -= 0.85 * x_preemph[:-1]\n", - "\n", - "num_frames = features.shape[0]\n", - "x = x[:160 * num_frames]\n", - "x_preemph = x_preemph[:160 * num_frames]" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "# short-term prediction\n", - "pred, error = run_lpc(np.concatenate((np.zeros(16), x_preemph)), lpcs)" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "# long-term prediction\n", - "offset = 256\n", - "padded_error = np.concatenate((np.zeros(offset), error))\n", - "ltp_error = padded_error.copy()\n", - "for i, p in enumerate(list(periods)):\n", - " t0 = i * 160 + offset\n", - " t1 = t0 + 160\n", - " \n", - " past = padded_error[t0 - p : t1 - p]\n", - " current = padded_error[t0 : t1]\n", - " \n", - " gain = np.dot(past, current) / (np.dot(past, past) + 1e-6)\n", - " ltp_error[t0 : t1] -= gain * past\n", - " \n", - " \n", - "ltp_error = ltp_error[offset:]\n" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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", 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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "spec_ltp_error, freqs, t, im = plt.specgram(ltp_error, NFFT=512, cmap='inferno')" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "x_in = (x * (2 ** 15)).astype(np.int16)\n", - "x_stp = (error * (2 ** 15)).astype(np.int16)\n", - "x_ltp = (ltp_error * (2 ** 15)).astype(np.int16)" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "wavfile.write('lp/x_in.wav', 16000, x_in)\n", - "wavfile.write('lp/x_stp.wav', 16000, x_stp)\n", - "wavfile.write('lp/x_ltp.wav', 16000, x_ltp)" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "for y, name in [(x_in, 'x_in'), (x_stp, 'x_stp'), (x_ltp, 'x_ltp')]:\n", - " spec, freqs, t, im = plt.specgram(y, NFFT=512, cmap='inferno', noverlap=256 + 128, pad_to=4096)\n", - " spec = 10*np.log10(spec)\n", - " \n", - " make_playback_animation(f'lp/{name}_spec.mp4', spec, len(y)/16)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "torch", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.12" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/android/app/src/main/cpp/third_party/opus/dnn/torch/osce/stndrd/presentation/playback.py b/android/app/src/main/cpp/third_party/opus/dnn/torch/osce/stndrd/presentation/playback.py deleted file mode 100644 index 4d7e6c7..0000000 --- a/android/app/src/main/cpp/third_party/opus/dnn/torch/osce/stndrd/presentation/playback.py +++ /dev/null @@ -1,25 +0,0 @@ -import matplotlib -import matplotlib.pyplot as plt -import matplotlib.animation - -def make_playback_animation(savepath, spec, duration_ms, vmin=20, vmax=90): - fig, axs = plt.subplots() - axs.set_axis_off() - fig.set_size_inches((duration_ms / 1000 * 5, 5)) - frames = [] - frame_duration=20 - num_frames = int(duration_ms / frame_duration + .99) - - spec_height, spec_width = spec.shape - for i in range(num_frames): - xpos = (i - 1) / (num_frames - 3) * (spec_width - 1) - new_frame = axs.imshow(spec, cmap='inferno', origin='lower', aspect='auto', vmin=vmin, vmax=vmax) - if i in {0, num_frames - 1}: - frames.append([new_frame]) - else: - line = axs.plot([xpos, xpos], [0, spec_height-1], color='white', alpha=0.8)[0] - frames.append([new_frame, line]) - - - ani = matplotlib.animation.ArtistAnimation(fig, frames, blit=True, interval=frame_duration) - ani.save(savepath, dpi=720) \ No newline at end of file diff --git a/android/app/src/main/cpp/third_party/opus/dnn/torch/osce/stndrd/presentation/postfilter.ipynb b/android/app/src/main/cpp/third_party/opus/dnn/torch/osce/stndrd/presentation/postfilter.ipynb deleted file mode 100644 index 3a225ae..0000000 --- a/android/app/src/main/cpp/third_party/opus/dnn/torch/osce/stndrd/presentation/postfilter.ipynb +++ /dev/null @@ -1,275 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "import matplotlib.animation\n", - "from scipy.io import wavfile\n", - "import scipy.signal\n", - "import torch\n" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "plt.rcParams.update({\n", - " \"text.usetex\": True,\n", - " \"font.family\": \"Helvetica\",\n", - " \"font.size\": 20\n", - "})" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "def load_lpcnet_features(feature_file, version=2):\n", - " if version == 2 or version == 3:\n", - " layout = {\n", - " 'cepstrum': [0,18],\n", - " 'periods': [18, 19],\n", - " 'pitch_corr': [19, 20],\n", - " 'lpc': [20, 36]\n", - " }\n", - " frame_length = 36\n", - "\n", - " elif version == 1:\n", - " layout = {\n", - " 'cepstrum': [0,18],\n", - " 'periods': [36, 37],\n", - " 'pitch_corr': [37, 38],\n", - " 'lpc': [39, 55],\n", - " }\n", - " frame_length = 55\n", - " else:\n", - " raise ValueError(f'unknown feature version: {version}')\n", - "\n", - "\n", - " raw_features = torch.from_numpy(np.fromfile(feature_file, dtype='float32'))\n", - " raw_features = raw_features.reshape((-1, frame_length))\n", - "\n", - " features = torch.cat(\n", - " [\n", - " raw_features[:, layout['cepstrum'][0] : layout['cepstrum'][1]],\n", - " raw_features[:, layout['pitch_corr'][0] : layout['pitch_corr'][1]]\n", - " ],\n", - " dim=1\n", - " )\n", - "\n", - " lpcs = raw_features[:, layout['lpc'][0] : layout['lpc'][1]]\n", - " if version < 3:\n", - " periods = (0.1 + 50 * raw_features[:, layout['periods'][0] : layout['periods'][1]] + 100).long()\n", - " else:\n", - " periods = torch.round(torch.clip(256./2**(raw_features[:, layout['periods'][0] : layout['periods'][1]] + 1.5), 32, 256)).long()\n", - "\n", - " return {'features' : features, 'periods' : periods, 'lpcs' : lpcs}" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "lpcnet_features = load_lpcnet_features('lp/features.f32')\n", - "\n", - "features = lpcnet_features['features'].numpy()\n", - "periods = lpcnet_features['periods'].squeeze(-1).numpy()\n", - "lpcs = lpcnet_features['lpcs'].numpy()\n", - "\n", - "x = np.fromfile('data/critical_3.pcm', dtype=np.int16).astype(np.float32) / 2**15\n", - "x = np.concatenate((np.zeros(80), x, np.zeros(320)))\n", - "x_preemph = x.copy()\n", - "x_preemph[1:] -= 0.85 * x_preemph[:-1]\n", - "\n", - "num_frames = features.shape[0]\n", - "x = x[:160 * num_frames]\n", - "x_preemph = x_preemph[:160 * num_frames]" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "def stp(A, alpha=0.8, beta=0.5):\n", - " A_num = A * (beta ** np.arange(len(A)))\n", - " A_den = A * (alpha ** np.arange(len(A)))\n", - " \n", - " return A_num, A_den" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "frame_idx = 31" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "\n", - "A = np.concatenate((np.ones(1), lpcs[frame_idx]))\n", - "A_num, A_den = stp(A)\n", - "w, h = scipy.signal.freqz([1], A, fs=16000)\n", - "w, h_stp = scipy.signal.freqz(A_num, A_den, fs=16000)\n", - "plt.plot(w/1000, 10*np.log10(np.abs(h)))\n", - "plt.plot(w/1000, 10*np.log10(np.abs(h_stp)))" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "x_frame = x_preemph[frame_idx * 160 - 80: (frame_idx + 1) * 160 + 80]\n", - "\n", - "window = scipy.signal.get_window('hamming', 320)\n", - "spec = np.fft.fft(x_frame, n=1024)\n", - "\n", - "log_mag = 10*np.log10(np.abs(spec[:512]) + 1e-6)" - ] - }, - { - "cell_type": "code", - "execution_count": 61, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fig = plt.figure(figsize=(9, 6))\n", - "\n", - "plt.plot(w/1000, log_mag - log_mag.mean(), alpha=0.7, label='spectrum')\n", - "plt.plot(w/1000, 10*np.log10(np.abs(h)), \"r--\", label='LPC spectrum', linewidth=3, alpha=0.9)\n", - "plt.plot(w/1000, 10*np.log10(np.abs(h_stp)), \"k--\", label='short-term postfilter', linewidth=3, alpha=0.9)\n", - "plt.legend()\n", - "plt.xlabel('kHz')\n", - "plt.ylabel('Amplitude (dB)')\n", - "plt.show()\n", - "fig.savefig('plots/short_term_postfilter.png')" - ] - }, - { - "cell_type": "code", - "execution_count": 62, - "metadata": {}, - "outputs": [], - "source": [ - "periods[frame_idx]\n", - "\n", - "p = int(periods[frame_idx])\n", - "ltp_num = np.zeros(p+1)\n", - "ltp_den = np.zeros(p+1)\n", - "\n", - "ltp_num[0] = ltp_den[0] = 1\n", - "ltp_num[p] = 0.25\n", - "ltp_den[p] = -0.25\n", - "\n", - "w, h_ltp = scipy.signal.freqz(ltp_num, ltp_den, fs=16000)" - ] - }, - { - "cell_type": "code", - "execution_count": 63, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fig = plt.figure(figsize=(9, 6))\n", - "plt.plot(w/1000, log_mag - log_mag.mean(), alpha=0.7, label='spectrum')\n", - "plt.plot(w/1000, 10*np.log10(np.abs(h_ltp)), \"k\", alpha=1, label='long-term postfilter', linewidth=1.5)\n", - "plt.legend()\n", - "plt.xlabel('kHz')\n", - "plt.ylabel('Amplitude (dB)')\n", - "plt.show()\n", - "fig.savefig('plots/long_term_postfilter.png')" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "torch", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.12" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/android/app/src/main/cpp/third_party/opus/dnn/torch/osce/stndrd/presentation/spectrogram.ipynb b/android/app/src/main/cpp/third_party/opus/dnn/torch/osce/stndrd/presentation/spectrogram.ipynb deleted file mode 100644 index 9d3d96f..0000000 --- a/android/app/src/main/cpp/third_party/opus/dnn/torch/osce/stndrd/presentation/spectrogram.ipynb +++ /dev/null @@ -1,173 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "id": "04cba77c", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/tmp/ipykernel_252797/4199785080.py:9: WavFileWarning: Chunk (non-data) not understood, skipping it.\n", - " fs, x = wavfile.read(f'data/a3_short_opus_{br}bps.wav')\n" - ] - }, - { - "data": { - "image/png": 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", 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/eYQomhKAGEPsH9p1c/uxXZWREu/Gvb7XCgBHjsMxMBMAKlnSakXBkFIHzDnldPULEEc2HGDYxWDIZbTVdItEWjftgQkJZaSa9OZC6J6yK0tipJkZIS/dp3+5SK2N19PnqsOMaHrsnSYezqk55078lEX9DaoNbXeP8+4rTC91o0RG9hKlTKhYsJKznnz49lQXv4+4f/6fzHXZdAm68Oaq3Cffc0+GhadE/PKk0k0kpOWm3WzowpxZEx4jbb5ostkQzv9pKFH8fuN7kXBeLCTtkMKclo5UeWTQD0ObbkkbTrFmjEgaOofnsIiL7JCB22ekE3L1tHREUa4Vf8I83qWNK6ruAhGDEml1jZeSTAYU/tIT+R4xdlsRw7q5zZmdkJsR+1zDa8ZRvEwullYjLR1j2eMsu4oSqXROoGUVzmjCgom/ykR3sTjKmBFRzu0etatY6wVVnLHmlKZ7fIEVyXGmJDMD2rj+lls8kdfdPVvJqWPomUSGqFDGkok9ImYti7ruuU2RNs62c5vGL7mR/ZxMBhibFqWKB2c6r77QfXfxckTJ57WTtpD916gAHn2vSIGIx5pii6JUQl/5PJyMUsX//ON8dtw/H1GXxP6ed3afGKuXur8T4Xz1GsfyYLyd3KvvKv6RJJzUPrKmpNnuSvSRG7uHEIt54aHtyF0ilxE1FStZ8Fn1b+jCGWX2Ptf8P6N2a7wZIJJ4N1Y8I52Sa8HY77HMPmLW3WJW/TodgZgelj0hxiWIY1F/Q+3GOJ8zkAk7OqIwFiJEtSzlYpu4NouoSEHm9lAiFodTS0wQAm6FX7Fsbvfzgpon7mhlSJkdJT5Rr6Twbc4V3kTfe9V8w0Wxook5FzEJd7bSUuoQddfwZkDsEYeK6zXmIrkdc8ExVj2ZDMjMoP+tFcYMMJI9BSzyNsebXcASrPhpf+0JoM/h+TyqtG7NkKjN9lkTXK9E/fi94O0OzhbU7dlrKHs/Ofk+Kln14vGmzvG7ZPNgfO8TjjVTrCnxdojt4cdPag8ZSerQzpQY8VvZ8mfd3E1cJeFNuu0QH5L21IoZqnE7R8lkgFHDBffopGYguxzEK+yaQ9bDjxNnRxc0ukrABDpEYXfwCZ6CTAZJdNLc4yQ6PBk5nrHusjf4o15QcVMxJQ6RxWNUaKSlkhUrmTNb/Pq550wJ1N0pnSkJtmbdnr5VQ+wnhTUltdS0tJyZYxpdseju0nTzR4bCD+8oZ+GEGYnsesP/nJwrlMMJlS64s/x33zoNVKTA252ke2dy6m7+TBa8NdNtBR5j8x1el6RUIf2mJKEEn7yop3vZPfReZ0tUc7qtcOhyq/P30LdIAheE2LwyCXlTTaU2dXhAU+31faPexZuN70XCMb3XBSSkURtOtzulEGdETaxmZ8qnPMwWpaVub1KTdluZv0Sj7TM5PFY8VjyDOMKocK38OZUuGLLLSCcJ8ixNmqnEAqNCKyM66RiFERMpCBpZakEtDbfljDautmRO3xNBBcMwjrAkImokcmaOWeoZXaypwjmqEWd3t4vQuvmSprtgWO5g+kaTI6fI3qNpzxBxiBicHTJw+3SxYlF/3T/4FV2o6MIZbRh/571qwWHt+Kny+MaMKf0BpgdvjHWXmpK79X2DO2d3n/D+h5NPqQOMChgI8m1UNEk54sEkkQApQ3I7wktJ3T37XEddI+oxJtlLaAyvPf+yZop3Y6zkVO29xxb2jRLCw5V/0hXzZoizBVYuIZgkVBuWj1QOgrXjLShCsOR2ghWPy96j05rT1V8/8dhUm/6Zfapn13Mj9wcM3D4Ds4sj37aTt2oJLxyPivy+izcd34uEE+McNcPt0P9svSb0N4U1E5xN6s3xqcnjvqkSAD1v5XmE0VxGeE0VThC2wo9eMiY6TDMdIgbDUAs8Bs8IUTDm/gNkNP079BIiXaiIWm+rlIIRl3SX0txXDLgZMypZcN58/tS2QO53ybXA9pfRkgiQxmTsFz9OpFS15OrpJHI2+JgVM+6s/2a76KTF7LudYyhdj1B6Mt/DiCNqZG5mZJrhNaNkwMfD/wuddBg1GAwrZpy1X6DE7YI3NPsMdEyuBaWmOVfeQ9uTQd4dXlc4EtKsIO3kH0VZ3Vc491Iwya8xA7q4fGKiV21Qmjcm6y+SkftdCjvFSU79gFSSd4d4OyQzyS3zLKweq6ia7jZNlxL6wF+i7s62x70B6WzCmimqHd5N8abEUzBkh850VPlFD5ZJvJ7HF/VXP/8xpufYkmDyndS0sn4FxNujbfh38abje5FwIN34UVt23XVGg38BgFPHSCcYFWqp6aRjlt1jEe5ixONNmXSW4gKA0u6SyYB1vGAdzrB2+swebyYDvGZ49URRWq1o4oIze4vWNEx0h4/MId4Ivofq3mtbZlrh1GD7YanHEVWxxm9/C0AIc6buGgZDS8SoUBiLE2E/jPDxhwyLXb6q/yMhVn0LMLB5OLtQUZqSZDzgCBoJ7mes/BzBcCEnjOKUiZbkgIn7DGTEzfhvt78xacd990PzZ1VZMTY0YcaZ+WrrUmrEcUU/YjdOWFGzNEuMWEJs0jmyKVHlWlLqgDNzzKft77DiGJlLWPFJmdtk1O3ZK7Zzkjqys2O8HVJ35+gTPqeLa1baUsmMqG06Pn1zzTwjQ6wdPNUobMMBs3gG/hJpHG56NF9qCzvJGeZXqbrzlDg357FXTPd2jIh9yD1zk2wenYk2XcscyOyQzJVY9ey46wR3xGn96QOvF8Ckz+29oV4l2nDKvAmsTdJFlP4zy+x6nyC/HafRl4/7m1ARj+B7Karv0/zw9eItTjhC7q8A9LvQQObGRCKTOOVQd5IrphhU4EwNa9ZkpiS3EzIGDJiQU7AvO3gxnOiCFQuCtFTMesmNJ6NIvDukoyanYMoAj2EUf0YrHbW21FLh1FLHSFBBrGVT1KTGj8EhdCgrKpZmyXn9Zc93SaF0rOIZjayYmWMsnoN4lZEOKPDsmwG1TnqyYMbh8A/JZZTYQFqxw2UGMceQOu5g8Jo8cjpq1roglxIUrBhG5OTquTL8F5y3Xz20W/124kUQOs+vrhLHpqLqzrEmY89/QNbzjlbU3LPHnIWvmK1/s314L7pjnN0lKwbs6C6jOCX6GygxGenpiqA1ITZYU2JM9grOjgqSYLjpvU/eUX+bswRrpuR+l8yMWGEf41NZM0TEsO7OWHHykOrChmrQhjnDLD1r3g5T0taIM2N28uSg2mqVNPge6AyodlhTbhUKHgyRpD795eJ/RlFyfxlnysfadhCwZgdr8u1z/rKh2hBihTUJpehNTmYG5GbE0B8+USvxu4n79/mmkv2nFm9twhHxZHaIEc/QHwKwbI/5ZvUfOPWXWPgPKXTIXtwhonwmv2DR3GGUXWZHrlLqgF2dkotlL/NYgUE3pY5jlnrAuT1i6RecdJ9RdecPPQhGhrTdMV8v/mcAxuX/nQMZcmBzrBTcaStmcs7arrhHqrQm7RSP20KnnVoySc42Rkt8dImMJoamBw2AsG5Pt0NiazIu2/eZmoLcGDIjHLYTrpR/hMFwFK6S4zmTWWov9VDoBDruaAlcmBPW8YIDeZ8b8cM+EUGrkRU1nQQmHJBnI75o/+Jbvoovsot//s4z7QRb6vYOxgyI7joGw7k5odOaO6u/eWLi7MI5SsRjGekO1/WAOgbuyhlrWbEiKTBkdkJuR6y7M6rm5kvsOB91YBWM+f3OxEJc0HSOaLteQufhBdvZJPEzr//hsfc6u4uzQ0b+MoWMOWu/omqOt221EC9Y2IKRu7R9z8MJ2eBMycQd4STnvPuKdXtK6feY2Cu0Pbesi2ua7pT6CS1TkYyBP8CbAcArObEaGVL6S0zdVQZMtrO+loaKBWvOH/7OF4B/WzNNs+F3LbY3Gm9twlFttmZoZfY+mR0xzq5geZ8qzvhm/ZeUfo/S/CmlZnygn9BmP6KWirUsGeqIkfFYEdqY1KBaVQJKhuVQdyDCzW5OFx4dohaIOqKuyf0lIpEqduQmS06TxnM1XqGlYyVrBEOOx2KZSIEXQUxynwmqtJqqnpHZRzLDWVgS4gXWTLhU/IRMS0Y6IVPPZTNiZA0iyWzMi8FrjsH00i4OEydMwogMS2ksrSqzfte5Gw8Zyy5OHXNZoUQ66VjLitvN3xFizWH+E3bjJQ4Hf8xZ/bu+5RJ76Zc3qR79ZtoYmd8ltxN27FW85ts26rFd0+iqb5mkGOQf4CRn1d4laoeTvDefU1axI/RQ8pyCzIyINiXFOixeyidno1Iu2N7VMtu2ruIbnok9W2o/0IUTQnzya0KsEQxllhTSN0Ki3g7Z9dfJtGSiO/joKNyQmdvnovl6W5Wt6q9YN7eZFB8xNPvslH9AFc6xklO6HQBqXbCKZyzqr1CtaEzOrjnE4rhuP6ZxLb+L/z9Wzd2HWpfbhGcv4SVnbc7ognnsNzwvNlqHRiwVSyqd04T794QStklme92U7aZv097yboo12XbGqvHbMul78P7YwM4Db0fb79uNtzbh3A9L6XYZmN2+UWWYmkMG+R9RasYlO0CAi66l6ltdja6YGYMPnpKcQ1eQm9Rysiq0MdJoSJ9mHFaHdGHTQ7aEOMfZKWN/nZE9oAw5XiydKkSoY2BFRUnONdkjqrKiIxBYaCCq4vobCRLeJ6J4zSllynDwZ8k0LE7YDxNsX+EgUMVAFQMDYxlYQ6uRtcySGjSXCarMWDMzFxzGA3atp0SYMEJRqjCkVaXWQEvXO14aLPv8yN0A4CJWrGRNE1e9evGDN7q8FgHwedfyVZJZ3d5N7pEyxZMlHJ9AriUYyIf/BR01jpwROymhmD/BqBCjspSKO/Ild9d/i7NDbmQ/x2t2H36s7TMAJ0+OjUq5pv8hRLv1SHo+0ulFElKaD22Vxp/yeRt+y9N0vlJC9JRuh4wBd6u/pwsnNF363QO3z4gJOZ7duMdIJmT5gNtx3cPNG1QDy6a3PjdT9sx1ZtzlZPWLh74zARSuMs6uIGrw6tiR1Pocyr+kzltO7F3m8biXI4o4SXqGTh0jdwlvBqza7An35dPDiEscK822JFZs6ohsNhHpPPVJ+aFrrdv2VtuB+N1vVXVj8533I0B/DUX8lqj+jzXe6oST+6sUbofL8hHDMCQS6SRQas5Icgpr2c0MVsBKRhUdbZiytksmcZdLZszAGi4VhsLSg4eFKlqWredyyDniv2dtO+6aY1Yy56T5jKq9jTMlU3vEJO6yY3MKYxg4wQoMXcZeyGhiZBE6Klpu2m9odM21+CG7MsCKYCVVOUYkJaFgWVMzMxcsOaeUAbb34vQmgZszY3CSqrEqKpV2REkM+4oGNIEQ0uKQp2pKwIrQRbgTG2ayYG1WVLLkIF7hA7uTgA0GokJbZ7TaMbVHaBmTn0x30cvh9G2wByDjysZu++V3YCIZ1gy3i9erhGrDur3LHfc5GQMKRqlqkZRQcyZYkoX0gnOMWCJTHI5j+Yp5e5uxP+KT4r9LEHKFtay4t/xP2+9IVsqvk2gTEtLIHtaMn0M4fJHzmNwzn/dSMeWWX7asEw8F7lMJLuU/odQhjTQEWrpwvn3vuvmSuj1hNNzHBcvSLFnJnKAto+waUVuqXua/6K3KWypaqgfEYjeqB6n1GeKaZXvMTQ+lmaLhKrZ/8jwJxFFJyUn9D9tz1BQ/ZGQv4cjJ7ADB9POoF3PtjJrug4GOkiKIaVhuaAfZ+3RxnRLYczYVUSti7LYdjmdxj95kbDcvb7mA7puItzjhSDJI0yRm2ZqG9+IR+y7ntG34Sm6zG3a54cYMnZJboQ6WcTfhSjdiReC2zhi1Je8NSsYuMu8MVb/BHnoonTDJPE3w6PqAe8bwdfMVoKybL2n9DeZmxucxkoecD9lhLxeMpMQzryK/Mr/EiueD8BEDcVQEZlqRq6cQS0BZaE3sb1yDSbBc2QHgTJd4HNOY5jYjJww9HFdw1jbcM6ccr/8uHXQJJUkXrKOm1g8pwi6QHvllaPn37f+Tth+YT/L3qWV3C2ZoI7RR+cx+zt3mN+yY67wvnzD3M+7oL/sH85T7VYj0O+SCqBWvPtB9XZXdhPZbdSdE23GJ9xhqgfSyqm0/v4pEyp5AW0tFKw2ZDNjx17ms7/NDn85VE5VVGHOr+DGz6rcYKZLiAOtnHcQLHae+BPpMJEPwr7WjjXFONAVWxni3R9clLoxqTYgZXnMGOsJSEehwduehZBh1ySKeMJIpQGrfikGMIdBuEWQje4mCEV9W/377/k1LbOD2cZLjyLHi+xmKZaH3+I/rvwCU3F8lt2NC1yWH0XAfnr1u7xG0ZV1/3Sd8izWjF9gAWJyd4CSnlZpWGwyS5jbdGd6UTPxVOq2ZkaSsnjUfsmbUy+88ebOw0Vgz4t6osvY/pXiLE44mW1o3xbkPGOmEqcvYy4XDIuefmffJrbKfdVhRGi90Kiw6w7y1GLEIe+RG2c0DXtKCnxnpxR+F2ItABoU1SWm4yK5RNbfwbo9aezi1DEChU6UKwryNzEOHRfhT+WdYEQZF8mj5rKq5Y++Qa0GmGXMz43aTEsZR9gnjOKGVhkjEa8aUAaWx7GYOb8AKdH1iaOjbDmZI1JrT5ndb7xsrnspeJpIW0aDp910uPmEe7vKe/JSr7JNJAh+0EU7bhpbIPpeYuF189HgcnXbkdkKIDQ8/RIn89ySG+MtdytQrf9XdojEDhtkR79k/6GHqrm9gpop3Judc6G1GZp8b4QYWYaUNgUiOx2PY8xlH5YYPJbTRIMv/mrPyz/nGfM5J8yldeEUdMSkos6OeTDsn6rN0uIQiu5ZapHGd1KufeFoe9j56VsTYEHpXWO2RX2iSdbnQ2wwZ4TUjp2CYXeZifV9R3MiAgdnFRscoDsm5b5YWiCylIkqkjHmC95d/yp3ml1TtXbpwRhfOUI0M/WFKUBhGcsBhuMRYJpihpQoXzKpfUz9hJCKSJVKveNZbBFx4IZi6szvEWHF3mQjAO+UfcNn8gBE7FD7ZFQRpCVIQfEvQmnn1OETa2V3AEOKS0D3dsydprM2fA4V5+D5/Z3X9cLzFCSftvrrgcOrI1VPHyGkt7OXCThbxRhFRggrLzlJH+HoJXzcrDlzBx+PURqqC0CC0Kigwb4WzRrEChU0JaF9G+PAeF+Ybqv4hL82U3XjIT/whAyvYB+aZtQZysXgjW7l8I/Cz4Yg/Mh+z7mDRKZ0e8AP3PgC5GsQIp2HNqTlPrUHr8GazEMKyU4Iq89DR0DGNO/yh/z9tF9ZW6u0Ocj/sMc7TQbURvAi78RBnc3bDlLG3RIV5F7kIDX+j/xtVd4G3Q7wpOTA3uBqPKGPJwOxSmfM3fg03St2v0xcXDN4MGMURDkNNSytKS0MnHRPd4apeoYyO/Tzd0vPO0cbI2DkKKwwdlFZponBcKU1USmvxOmSme5zJFy98PEl+xvXtqcAgu8ah/5jGrah1QRVmT4XhSs99QcCaHCP+iYurNSOsKbdw62ft9KOuewWBxwEfpZlio9uSj11vpQGWMrtGZke9/l/GsTnmQm9zxEf8xF3Gm6RwDjBvI1WMjOOEudtPUOv66/77W1bdCW03T8TXwSd8yHV2KLmiuwSj3Bz+jIVc8NUiVTzQK0yYMSN7iUxKisGEVtdPVSV4NLpwjpGCInsvzfjsAaqRO/FT5tUXWDtg4A9RDVTdBV1c8qTsvuEcvZmW1sOfn/hF30eB2G8n3uqE490h1uR8Wf8nvti4D2rHR+G/4V/zAc5AFy1B4aSOrELAiDA1GVGVL5cJoXbaNVsHTYNwJjPuyU12uMSPwxGFFY4KzxGecv1fcGv4E0odMAkDRsaxnwuZSQlBAW+EhE+SflaTFntrwPf/4cAZAYTLkhEUZm1a6AaSQdxJMGpVrAo7GeRGiQhRhfclS3DWVri1DtQxksekWN1JL70vOYVNSbWwSexzUB2yCvu0RrnoOkwPXCjF8XP+S9Qn0MHarNmLO4ytxwRYskftFm/UHdOaKc4Oyeww+a284jMn4uhixczOcOoeVlZQx1ALBuIYWMuwn7ONvQUs2faaKN4oy074uq6oaNiVAYUxjOOY/ewjTsVsLbqf/buSdt+l4mcUjBjphDLkBCK11Mz9rJd/maO090meYjCSWjKQXCnjU3a/SjKLexG3TdXmqaACS0o2aZMiLLfw/4AzeUKexQljydnR60TewyDMuw4jQm4MUZXTUDOXFZ/Hv9qiRx+MzAyxPkcJjOQAJ4bMGKa+h/zXh6ziLuXwf+CUm6y6E9bNTSART73mRIkYLGX2PuvmKxI5tXhqMhAExGAlx5uSQocUOmBsL6F5YNXe5WL998B9C4YnVSff7pD+XdvtwXirE06IFaodIS5BI7uDTziUDziIe1w0aby9CpE2Ru7pgqVZ0tLQyJqBjtlv9uiIXJhzGmmYxdtU4aIXClwTi0BhrzD2wsRrv3AXXG6usQiR81AnKLVuqo/U/7/T1HxtbnI5XuJG5iksjJ3ijLJohTqmRd720ObMQKdQVZF5aImAxeIlmYkNnHBUBsYuMPIthQ00wdKqcFJnNNGx7AxV44koU01tuP3ccalIC+mgh/cOnaWJlipAE9m2DduoLLskQVqoA8YMvKG0Bi/CsptQ2X1eVVv3yddvkVBWdog15SuLM3bhnKV23MySZM17+qPHZjinuqLtCvbytINvQjrnbd+mLKxgPQyc8rNRSR1K/n4945i7GGMoNM0iko/R071j4L7cSxuW5G5McDcw8TAZ9EmVRFofIGAKDu8OKP0egsGKR4lkdkTUlmX9ODLpdbk8zu6SuVShjyjTvYbwI/ev+NL+HUYsA7NLoUOmpmDqLFVU2hj5FV/y2fJ/AoSrw/+CghF34m9Yt3cfI7emKiWd82vuZxzGA6aacVj0QroKXVTmWjOTBV4zLvMBa3eJhUv6bJmWtFI/UP0IO+UnRG2pwzwJ7T6ScEQyBtl1vCnZsddw6jAYKul9pkxB5qaEME9J6QGbhHfx3cVbnXBinKP9w+pswaF8wJV4AMDdpqGi5Za9jZrAbjzkIO5xz5yy0BNaKhbmHCs+PXRxTG4KKr9kFKeMdMguJYeFUFglM4ohob2sgakxTH1qJ1Qhtbru1R0r7TiTCxpZE1FSDZMerBiF81aYt8pOJhzmipWUiAAm3tDGnHUQVl1KBFXQZJncL25OFG8iVpRCIUTDpcKyDoKVnCZm2/MzdDD1IVVYRmmjcLcSzprIxBvG/v6xt1FpVYmq5L1rZmGFwqaWo6IUOqTM3qfpPX7YQEx5/sD1yREI4YLWln0741UjELXmqv0ZkzjhmhulBCKC6wERkVTJjFzPX++BdUHTJmHWwpfLiDfCxAsRqKRiofd6nKDhdP2bR+y3d7fn4Ek+Ll1cEtuWtd1lIOOtorhgcHZCiGtG+XVKu3v/PVpvpZYyM0Ll9UADTwpjxoyyawzMLvs6YuxcwhmqYqLpYeAtK86IErb3gkg69v3ugDu9ekDWzy/r7pwY53h3yG7+IcvuhGX9KUqXWmmmY2BGXPYFY2fYze9v0hJ+LSmgr2W1TcrrcIaVnKk5pIwDLg//M9bhjHV3ysX6V9vf86R2omrTywTlBFqCtJy0nz1kcWDNlP3hHxG1Y1Z/+VY7rv5Tibc04ViMGZC56bZctuJZygVfm27LJFYiRtPC2NKwFMi14DI3OJHb3K3+HiMeU/wBuZZbr5jD8hP29ZN+4U3fOGsNbYS/n1d8Zj9lL17mutkl7xeozAgHuaMOjg9dSWmPcCbNBdoo3FoLTVBGHi4VwtQru1lHFQ2312lndbXsGLrIvLOsO8NxLdyuOlbB4I2jsFDYpIpwqWg5zGuMKAMXcWKwpRBUKK2SG2XkW/ayFtsnqaBCpGDZGX55Efmr6h4Wh1eHwzCQDN8nkK0Mj4ATsBhKHfC+/+cEf/8Bb6Wm0TWLcJd59fK7bqWjC8vX1g5TrTiIe0xNxn5u8AZurSMXXUNPW2ViPO8PHUZg0SZY+dD1iykJLGBFUE0gC4sjY0BS6N6oMt8/cmA77wp2QtAab0oGdn8ruupwSA/7va8ebhn4S3SaSJdNXDG1R0zjfvpoA600zOWMSl/+nFqTEGVP9o5J6E4jNrWqMORGWIRIFQMrWVCHOdZkjOQSI3bIjZBZGPp0fi6FCT/q/jvWIfJVu6CSio+Kf0lHl4bwdHS2ZtVbpaffHpLaRVTmXaTVBKLJLQ9sCiIrmTMPd7HiGdsjvOSMwhiLZSi7GGuZV58+9rs29uwPbnrWzZcJV1gmZfdV/QWgiBSM8w/xZkDGgEZW/Zzmwc+0W8t5b4eoxmQb8hQPqXfxZuKtTDhGUjm85z9M7GGd08WaW6u/JMQLRDIyd0BuxxzYjwD4h+Z/o+mOmRQfc2R/xIgdRv1DstQzFnpC0y98VZxxbE6h2+NSV+CNcNGkauOOucdp+zmFG5Kbve2M5EHx9MzA2CuFVXZ8apsclWkha1VoozDxgUnWsivK9UGqdArbYU3kvC6Yd45IxnFlUeC8BzEMXfq+0lqGzhJUGNpIaSNlTE6XD0YTDbkNjLImpWAV1sHSxJxidUgdYB3u95Ej8E2YsdA5H4Wr7GaOaSZ8KCPmXeQ/6u+YtTcZuH1KM6XVilbXhKcoOb9IbFqirxPO7jIUz9BZxp5UkYphPxTUIVUxQyfsZelc7/SoxVkrLLtUDW6uUR2hiUJZZRQy2i6i3u48xP3owjkhrmkk37YDK8CUntyMmLFCNSYF8Fj3sPILjMlShWFHRO2IvVK5wWznKUpkrUlE9lmxIXY+WAV5N+6PzzzGbxIs3o65xo8YhJLcGSLgRMjFcCkekvt/hVfPgY7wxlBYodlWwukePCqVoIbddkIXJ9Qx/e1OW3HT3MIaRzGY0LDibP1bujBnLSta3eObdsaX4dcMZZefNj8gNyZJTOmIEDs6U7OOFxw3v8KZEu/zZB2BIZOScfEDFvXXiDi8GxNiTdsdPxPtVciEG6P/I1b91q6jkiWLcC/Zk/fnT6TA2TFGPNZkCDbpwAlIdoRq7JXLI8a4npoRe8md79Kf6B9HvJUJJ4k11jSsMGp7C+QWa8rU+3ZThu6QNq743eovQDu8O2CYf4CI5US/TvBhLE1cMat+10NV0+DUSc5OnDAwLhEhIyy7BDq4qoccmP8DUzLGLknMtH1r5mZVcy4rrsVpr1i8GUZHnEkL6rpz1FGY+o6JbylcxzRfY02kC5aIsGwzVIWxC/xwkqqfTUI7b2DdQR0MIRraaFgF03e2Hw4nkJmI77879Mlm0VkmPlKMEjJv1d2/zEbgJ+wCu9v5zqYFpaoM2SX4xLxfhHssmm9euxXxOrBQkYzCH3Hd/xFXck/poLCKF0VdgrnbLM3fvESGLiKidDH99i8Whs+aOUd2SGEtTlJ1aEW4kQ856G5wL6w5MaeMsyucPCR+qahWiBk+BG+d11+xwNyHi/dQdWeH7JY/Sj5KeAItp+tfEXXJ0t1hnX9IlJDu59gm/kl8Mvdnk2iMyQBDDIkHJX1Fpdv2nX9IBNLaKUZ876dUEjslx/cgcsXj2NdJL6cCbYysugS0UJSoafa37AQjkNv0X9nTCKZZyYfxI4IqdUhUgVn+zwkohSbU5nU35Tp/zqxr+aX9lKiRj/UjdlxG103ppKORpHLRiWfur9KamrPuK9bdKaXb4+rwz2h0xby9/URuU/JQmpK5MRN7hMWz1gs6rVPFqoEmLqjak4eqItWKECytrthUMkY29ibLh75BxCcVB409UvDViM/pe1veVU5va8LprWnPYoOI2z7Yw+yIkf0hmQwYxhFLt6CJS7qwxpoMI4ZZ9VlaJCTj0uBPkinTIKeLdZ94liiRgqSztllwB07IjWMVIlU0zGLLzWrGSEt+UJYMHfxsUiAUjL0ydIE2Crer+7wFA4x8YGDTDMaI0gXDRZ1mQU2whCjU0eBNZOg2O3UobcCIsgqWNqbZRBUTp+hOZWl7AEDqxYMq7OeGWEJmLaoJ3VZHQ9C0JGUWRiZwpYz99xtaFW6tHecNrDplGUIS9tSGloAxhlKmBFpUItG3rOC1lAJeJ1RD8lHxkUkGQ6cUNqmVVa3ZzgiA7RzCiPa/FURgIgWFNRhJFei8TRXRcZOsJBRloCMac4lVdg0l4ntn2KHZp2DEkjMW3TFtWNJ0d3hw8bAyZeAvpV2zeIK2zLvbidckBqHAiKeJKxbNrReyPd7wnx71xdl4BxlxZG7a774LojY4M9w6367iGXO9y5fNvyNqx9XBz7kSruMQxtbjjWznYEPXI/kkna8vl/A/rX5NoGOgY7xm7OiEQtxWcsmZhKxUBMFTxchZXLOIS66xz0cjzxVyPoo/ThuamOacv45nfFr9Gwb+gI+H/z25FuzHpOZ+IUess3WyTQ+Wu/Zmj1aDcfFDSrtLFS5o4hJvSkq7iyUh3IK03F391WPgAmumjxFIn16lPAhf7iVvXvOef16iERym59m9Uxr4jkN7yGSZXcZJzlSOkshl9BRkTMKIoRuxzBZ8sfj/bG8qZ/eTsKdMceoYswcGRsMDGl2Ry4hv9A6jMIZ6ggHuhhVrqcnUYzE9bz1S03LaZAyC4doAJlkkqlAFYdkZ7lZ9r78HG5Sto7DKxFuqYIncX+gXraXV9GBbUaxJO3VnEpcIoDCR3MA3q5yvVkJpYS9PSLm7a6EKyrwLrGPgvPWc1J7cwl6eYeX+jT3vF+OxtxwV6by0/XH/atbyqXyFJyPTjFxzRiSk0ZyTnqU9wEm+3Ul/d+S1RAJcco6XlGx2fMCbyNinpN/1SdxIaksGhUUnNDEh1KbOJWWINj36Td8e8mIYasYtc497+iVdqPvK2FPa5B55LX7ArpRUepW1abhw53xl/poQm94JM80iRuxwol9zvPobBEvu98ndmIEcYMTiJMfiqez51hbgdUIkcZM22mEhNogYkvPtiD15Dy8Zo+znZDgG0ZM9QCTzIuxmQm6UaRbJjW7bwTuZ4WhxlZWsOTcnXHDMl/FvaLol4+wK+9177MQp72eJXHnRJcGbhSxZywp0n4FNG56yR0/erSzrAFf1gFD8S0ZxzL6O8CJMMosV4YgMZcJ5EzjWFQfxCsPh/wgkRfZI5GvOqNu7dGa89bwas0euBTeG/zUdNY2u+jZn1RsePs0JOIVqnSqZb6UCeZ71RvfK6M3vY7zVCSfGFWoKduxVJnEXNCWhuZlz2rcRcooEiTTD7YXrwinLWBG07W2i02JahxldXDP0l7H2Gkuz5F5IlgL/oP+BdXNv65Q50SHv2TFWpFcASLufNgqfzoXf1OdMKDnKc7wRyn6XuJtFhi70bZs0U2n6BfFeLaxDgii3USisYZDax7gevnut7Jj4jpEPXCkTOu2rZUKRpdcJmbm/cKyCctpEfrFY0xK2gqEjcgbWUQWLNz0BtE2LcGGUG+EaA+sYONO31pQqOobsojYN0Tutqbv5W4Huaal6FBXbxHpSO84bs+XZbH574mWlmVxhhYk3VEG53esauZ6o60UwxjGMQ5Z2mpSntcWbAUf6IUMtmJoi7eiDYCIM4yWu2v8zYoXc2L5KTui/fd3jzuCIigUnzadU3ZrLxSeMdbcHt0Drq9fmOtXtTUQyqr7d8+iOfQWsszOG/pD35OfseLdVorjoOk5Y4ILlTpNTGMcPRm5LpPZGmXjlB8WIeTvgN9qgErGS40xk3tziPH7OKLuCa/6MrIf351KQR0erE3JrmHdCqYkuAGnZrQOEnvQ6Mxecygm5FrzfXCYzCZ5vBFaxY2EWOHXsxGl//TtaWhb1F6g2fZV4mS7W3NbfIGIpzAQvBZFA0LbnMjW0D+jHPTHEvRDfafvyV9Lck604J7AVXP2u7d1/3/GGE86bZtQGQqxodM1a8i3stJE1lc7TjEYG25vrfiThw1WdHkRnd7GmTDBK7ajDjJlYCpkwZpwUqN01cjvhQK+xo2nndto1jIzj6sBR2gQSsKIcFgbYYdlFPq3nDMn4452CnSxJ6IS+kqiCISjU0aSkE1IbKx2h0kVoQo8QckphlNIFMhvYE2XsAqeNY9YkFYU2JhWCr7s59+wxwzBip5vQkB7QQEfXPwjDeJWRNeQ2tTNaTeoKbVR2MsuRtcwa5aJL6tbpbCs7ukspA07lNst4jySb/vzrasz4DSjd2icS/YwMmXCpP29Q9RL2543hpE5k2ipERAQnKe14k2ZcTtK/MyPs5YmfdHvdUWvcKmqf2hPm4S7L5tY2uQ5H+7hwRBUd4PhGT/mSv6MwE96PPyTHU2LxfcIJKGtpuNDbtHHVz2YMjpxhHPbmFRbLx+ggCabWYU6I65dO6Ntz/YTFajMU35gQ+p6YvCEAW4RI5NzM+Ey/xpHjl39CE+1WWum8gTt1w0Jr5uaUOi4wYsntiF1/naFOWcmcv2r/F7wp+WP9z9hzGYXJUFJCX3Zp6tip4I2yn0UmXgia09aJ2iD9RGrg0hzzVrdkZubsscOPzBGrEDiWOYGuZ1wZLg3+hFl7EyVShxlNe7a952Y9hWKjaiGS4ewUIznhGe2ql21lvViyefSZedi+WrXDMNzq0SVrkPV31rr+fcUbTjjJ2tVI+caUVmOcc2f9N1hTMPCHFGZCG9esuzNCbOjiMvVJn6DXlZSKx2k3ZnLW3XlCEoUldXfGKLuKlQ/J8VwJ1xOwUwInck4tFStm7OsR18IlMpPmJmx32f1umkBHSkRetJ+hpAF+eODnh5j6497A13XFbXOH3bjHdT9COuFuldpBn+AxxUbvS7hoDL9bVwQiB65IC6cMKMI1bppb/Lb938ntmBv6h4x0xJAML5aawNfNmoNYMHQGL8pPJumAlp1SReGiC/xKPgeBTDMaabjb/pqmm7/QnOHR6/T6EXrmeEaMFqXt+RZLLvQ2TfyIJhiyntd03sDtumZgHMN+0dqg+FYhoqpYY8lIfKOxj1RBqIOjicpFKzR9K7MJi4cG+LfWf82p+4JD/zF7IUm5XDU/ZiEX/CL8BQCX7U8YhWkSCjU1x+1vH1EqsORasCvJQsOIMFOYNV+j2jHwlxC3w8X6nBeX4h8yyW9gxNLGFfGBgfrQ7TPovZAsjkwzaiKzFqRXLv+N+YLPF/8vADJ3ROn3WElg2Rku2sgytJzKgpvyW1pds6xuAXBU/hEH8QrEBG/OtWDXHpJrxr7PGThh1SlNjDSqdD3I4KJJSu27Wde3Qy0/CkUP6kjXa0NNuBHGBB2z7jdm97qWv23+30Rt2Ct/RCHj5NXT3AZxCUT0ABHVmGEi1/qU0BJ4oE0Or515CZRZEq19PYuOXr5HiqcmtKhLYljh7BAjDrFDRNxjlt3/mOINJ5wk0f40mY1XjRiTC+BFd8yLdjtFCibFR3hTMpRdIpGT+q+3hLCPyv+KgY62s4u1QhSlkhW1rJnGfT7kGpkxzBpl1ckWsnzewHkbyI3wU79LYenRUam1YxTaTlh2QhXguErSNBdaUUtLjudqPCKQeA6mJ8aV4jhvPZl1Sf8tCse1cCLnAAyDR7AMrGUkFtorhLxFsDT9OS81oWLG1uNFmHpDaVOrxBolROGbFdxpKjKxfCIfsgwtX5qbrPRsy2XYCDtur8HvCQ4anyCOKDgGJpEnpUeZORP5aGQ4LAqqQP+fct6mRWLiXA8vT7JESuJaAUwz6KJgcFTR8l53jdIPuOs+52T1l0ASvuxCcgTV7EdJNVuTH9NB9jFR05RvYS5odE2rFZkZIfkPel2zCyBQyRKLMLBJ1WEQ9tjR/5EVNb+Tv+ulZl58Yxa1worHSc7QJT7QJsGkBTay5JyT+lOsyVj7f8ZIJ7iY/JmGMuL66F/3cv5jci0YWbdtR0YgU8+eXGVuTpnF36KaRDmHWmAxfQvNMHS2rxxT0qgCLEKHF0Np0wbAmtQCjaRNVB2FdRDmrXC3imRGuD5kyy0DuLk2nLepPfxR8S9ZyZy71a84Cef3F28NOHvAwF9iz31AphleMwxmK7+TkGzjBE1/xobI2V2kN9NL87C6Vzh59mqT+6spoYXV05+P5wrf6rbF6ux+4g2acUqkGvnHZsz2Vs9wnh0bH44nXwzpEUZecrympHJp+Ke0usaIY8k5USLDWABwZk6pZJk4EworWXAWPQT4MtZYHB90O0y94W7d8rUcc7nd40d50SehRECdtX0FEZJXTkPg1KTvmuiEUjNmsuB2v1Ct4wUDs8uP448Z2DRrWXepBz5v4U7VciwJqVPGkqrLk2ioCCsqlnpGHWYs6iQ+OcpvkJsJIw4YhhFXwz6TzKf2nRFChHkXOJcVOzqgFNtLxBjiQze3Yu0gPQDiqNvfX9J5NJSOTmsKmyR8hq7DmcjCJvmfs1r4umrwYtjxrlcc2Ai3pg1CFxMRtNMk35KECNLspaIh0DGVI8rRf3OfNyOWoU7JY8FN+S0nq19Q+CN+7P4VZb+4JXhxoo6upWHmzjm3o60A5UZOJcHx09woF8tak65Z3byY58v9CH1StOT+Ms6UOJOnKyhJMWEdzmi6Y4wZgAenlk5SLW5xHIYjcjx7tsQbITfpNwyswUvGIDp8tOyyy/vFD4goM53xjb3JB+E9rpcZA5fmlYbUQu40oQRLYyms6QVThaFLbeZZm9CXv54pv4rfkGlGoQUjcnayfIu8jAi3q8DfyT+gNqbqJK5oH0w2/V1Rt/cwmeMgHDIimSQmpZA/5e7wiIvu5lNFVJOSxB5GPM4WRO0ema09WwZH+qVTsAm6Hp/0fMgLQqlTZ+ZluwrfR1HQ71XCMWZM6e/7q9fd6bb/vSGDWpNEL434pNyrM+qw6L3rRwl5pZFWKgTDuSloafi8+rd04Qxnd8ndHtZ7xlxhYB2XiylZL5DpjbKbe34Yr/bGb4m38OnivlNnZkyfCtMgexLH22Pe+OIIlpIJE3PAMA6ZOE9hpNdrS3YCTVScCId6HYCCpBRQa2ClkW/MZw+ZiAF0sSYzkYYVGFiFCfPW9yZw6aH2xnApjFGBVQg0Pe/kUdfLrjvD2im5H4PfZ9087oljzRRrSnI3Tj403TkxVm88OVnxlFYZupRwrESGzhMRLlrhr/iPHPA+/7l7j4FLwAgRmLXCeRtwD8x32v461BoIBE7NCafxK6x4ckZY8Yx0io/ZFrX4vv6Yy+UH/TWMLGTFzJzRas2YPYYxcTkmuoMRw9zdRLVLVt+xIainiYZ5aPna3GYmx1Tr1wEPBEJcJ+KkKcm4X41Wyf2lrxYjFsNAc7wkIc62R1/+Ur8AhcPukBLPsZxzKrcTYERqchlxI37EgIws7hGJ7PmMsU9cqMIm9OQ3S8OyVVYh0Kri+zUwKMyaVFUe16nldtE1CWCgGRMpyMVs7/l5m1pyq9CxL5c5MXe4s/qrnkP3+MKqWrFubrMcLPHRsmOzZJLYThmFkt+4+hkJRwlxmaSGusc/25j+fGp85Pst3u3RdsdbG+4nOdkaGfbVSewVo7+NeN1k8/tPWN+DhCM4u4ORnNLvUZgJgTaR5+x4m3A2ygNpR97vKiX1UmyvzrtqjwmxIne7jP1Rep0mBnjpD6jEbaVMck2Ch7mRh/gy948KvCiTLDWARs4Q1DBrUxuNztBGCxpYSEtLw8ycUeuCe+tfEuMcZ3f5YfHf4HFkJnFIfrtacyFzCi0YktESk6aVekbGkxtDGyOdKpf1Bu1gTR0WrNu7oDG1BLRlJDschgPGJtvaHnzezFn1C2UjK6YcMo07VFJRyePukkmW5oTa5MSnKA2EeEHUuudBeUq/l9o6T33QXy3m4S7e/ARvIpkNWIkUNsGirw48fzz/U0bGMfbpurQqtAF+tZ7xD/wl1+TH/MQli+4zXRMITCgZSE4er3KZS5zIKV+2f5nkaPxVvCm239/oiipcoMQHVAVSrz1zR1wqftLLCOWsmCUOTWw45zbRRDwZeSw4tXe4vf7rJCr5ypEWvev5nzLUEVaTIvQt8wUn9acoAe8O8XbIUEcYhJqWtSozM2PGvSSa2Vs7ByJ1b0eeyygppesYr46BOIwIZ7pkITMG3VX2QwYIhZEtnD/xnxKvZxUCF13Ai+XUJoJ0GyOhT3YAt+03/KL5Dbmd8M/X/zkD4/hC76WEZwKBlnV7/syNi0hGmR0xiiMGkpJNaRNgx4ckKfSseBZYwNtx0pyLDUK+fa01o16lYEp8QB/w0WU7Hfd9EMyzVaMVsEi/bm1AAxu33Ddv9pba5cZkv3cE6vcg4ej2pDTdMV3+AXV33pefdtt/DbFm9QjiZ5j/AG/KxCkhsmq+SnbFzZy6PSH3+wz9P8drxr77iM4lDH8TV8w54XMxjNoxrKZbkpyBvh+tyZ3TCdbc3+3N26Sp1qn2kxBhqAUdnqgRT06T32DV3mOcXcFrhutNeK0IuzajjLscy5zfmq9YxwvmzS2cLfgJ/5JpHPQtHFCJeClZ6X029apessKiZUSMgbjHOKQb+cgOCTpgHXa3Dz5ARcUqnlGHBUaGj+0o7+/knnKFtKLuznrZ/d7IW4rU+xff+7q8+o2duSN+yJ9Q9IoOhkSqDSq9ZhcUJs0TkpCqcFIrq04xCAfmBqM4RFEyY7gmCYVoexjuRQtrbYlEtE/apUzJteCMW6zD2RaO+6SwJmOgY2pZc6Y36bTGSI5K5N7yP3GvPx/7g09Yt+cJifSKA2mRgr3ypwzMLpfDpWRx0TueruMFTXd7q6Sc2xFrWSVGmaTrvZI5q3CWlJi7pKlWuv+MkU4Ys8t13UvnxUkPdEjoyDUrzvQmJQOK9T4jmzZYCUCTiNNf1Eu+sl9sJTEGOuZSd9i3gJPgzLnMuMVvGbDLz+x/RY7fElGvdfvsxx2+tF9zq/4FqpHMHZFcOOeg3UPnTbWhao4pi4ypS5u23g2BlrjdeL5KJCmb+5bdmwjxotew23QqBDEl+oTXJtHZ51f6goPefO7hFpwhc9NvAUiQULwaX0Wu6n5iTDOml7uPvwcJ58EIdD2SaAMNTTDCJ+8gNrtsZ3fTAvAgLJEk3xLoUIms9Iw6LnpIa4PxaYBYS81vu1NMZ9jVIaWxTLxlJ0uVzN9dQNT7pU8aaAtpdG/p1JCrTbu72IGBy+YHaP4hAx2xT9Kzqns1ZyBxEoLDkTMwu2R5IvjNuGDBnNvhN6zau70/UN3voB4+T/P6K66WP07E1db104aURlY01NLi1PZwXcfQJJvgqr0Hap54Pp8UG9vdzE2JsaPt7j2yKARU82d8wnM+XzIuFz/jkg7xJmKANho6NSw6y6y1fUsm9hDldC3aSLKWINJKsvjeeBflPcKwCkoVlKaHR1scO9n7yZNFB2kjIteI9grng31m7U2a7mI7TPbukIE/5LL5Aftxj5qWwgyZyT0uul/yYNL2dicN+E2OkZL4Ug/r/ZaNakUuIyyeczPbAhkEw465hhvmnFefsaw/ZQmcyS/xdo+BP2BgdlmFM9btaVK67jcps+xHjMKAhdY0tEkfTjpyLTiSCVaEnTjFyUfkmnMRGu6Fjr9tl1gcl5mSGcMdc5fT9nNG7hKHep1SSwZ9d+ErvcfMnLHUE6pwsTWC2+ieQaqQPIaduEeT/YBaFyy7Y2KkpzUkx80HI+qSlg7IML0dyMa2YuM79ErxDO2/h+5vQOMicWzwpBbay4GmlO4hcMFGfcCYLK1R8dtRIHg1+HV4rRbh9ybhbMpL4JEZweP900cjxCWRh09umV0lMyNqWacHVs1WlTqY5GD4m/bXeLvD+/nPmcQpubGU1qKazNROm8BnpN3/NO6Q4ZiajNKmBNP1CWRTFVksTh3n5oRlPGFsDlNiUcMquu3rIkpJxvvhPWay4qZ8SiRs/eKXze3nImisKQh0nJsz7knaHW3Ih41JYpxD2WESp5ybU25Xv3g22uZp32OndOGEqtksBPKYrW7UNc/rFz+NTKfa4DVH+mTSRkMVHBG4aBzHlXBSB46Z03VDjjQnM0moM6rlcphShQnRpZnCadPxt/JLKp0l/pYGmrik7e4vZMZkrP0FmRlgSXI1qsmLp3S7yeIbz5BdPBnjmNSOPUquBUPZYZDfoAsVA39AZgaJ7yI5aiK1GxNi9kTLgyedmeQSChBwdpdaF9S64LRvGZV2l0yS+OVQdmn8JWabilIjXZhz3t3h/JHzv5l7Fpqeq7WsWZgLlnrGRf0lmZ2A/XOGscBh2dEJt8xt7oXPegZ/hbdDsH/KJAzZYZfS/pxJHLFny+393MTILX7LxepzhtkRe/4DgrZ8Ib8kkwF1+BAfLJ+b33IRvumve6Rqbz/3/GTuiIFkCAkQk7Sek9XIVf2YtlzTxhVtWKY2p3b30V/a9dJDvud+tWz06pS2B4Q8f315lGOzuW6vOh/ZqA+ECN0zYNXffrwJePjD8b1JON7uJdkRcY+YQD0/24rkGHFbAUSAVf05aylosgWqkXXzZf/aglF+nbZLpk9Nd8wqn2GN4140zKNnKJ6xcwyt5Y/kOgbIbGq32a2ydGrXbAQyg4KrhXXMkWgwxtBqze/M31PImA/CRxTi2MscAyc9WVNZdhl59xNC3wKLRNryD7nobm5bhE/4xXg7ZCXzzZEAUJPk8o/CdSYkfbcOpYs7zLP3qMKMdXO7b6ltzl0SMNS4fuKNl67JcJuonN3Dmpyuf8DvV1/Pk/h4+t8bWbMKHfPO0fUcJ2BrMrcIHWf2GBcsQfPeZwi6TZXT63ilhe+Mm8v/9ZnHEiIsgeB2MOIRkodMpzVWPJkZ0VFzzk2iBs76hLKxOajjYmu/PMh/yFG8TistDQ1Bkg6aSsSaITE+zw9HH1pwunDG2brBmILc7eBMTiEjBjqmpaGVOoFj7O6Wd6JPgeYW/ogdf51xHJNz30l10d6h7Y5pu2O+Hu4wkftAnW+av+5brJYrw39BLiNabbggUmjBrk4pxeE3tutAEGEgu9R+ge+H8VY8A9lNGyizJBI5bX733Pbto9GGc1pNM82LNs1Z/yZ8yefNv8XZIUN3iEpg1X0OPFA9SNbrNG6km7otOTRVHC91GI9F2iTcfzZflRj93eqr6RtNNvA9STiJjVugGlEi1o5fapD2JG4H0CeUOd4Ot73itrv3gIVu2l2etV8wN7fxZoARx2X9gKvdfpK9kWTmFboEsS2t7RFRKflskGGtKmexYiEr7slNFt1drOTkZoQjQZ3L3iJ57DdCnclTJPYomhyPIIzZQ13EmZxlcwcAI2mXFsIK1Zp1c5ub7Qmlv8Sh/xgl0OgqLYqiWAyVdtS01FIRYttbHj8iFvkcAUPViLNjnD0kakfd3iOEC8SUvdJxihCf9vBs+uxPb2GcNJ8xdfscV0cMnaHxcStvU1p4r8yZtj8kz2SbbDZup5AG2rmBgbP8sDvAl/83lmbJOXeo42Ir6roJweFMuU3USsSIJxPPujt9zGJZpMCaIdbkWJPRdBfbB7WjxuMYaUmOZR33mdp9ln7B7ebvaPTlFjeRgtzvJ36ZPcBKEo9dy4rT7nPm9RfbnfrzIrcTBEOOJxPLRIcpaWcdt8MS1ch59Tkz+YZRdjm18kyGSIa3e9geCfrF6i9Qbcj9VcbZFUZywF63T0nOns0faJqliBq27TJvSrz9EbkWXMp/Qp29x1n9u+3M4slKzvfD2x1aOlbBbmHuS5PmZCEuqdt7D/kcffvaZamyefiZSU0+fUrRs/mNm6rr2Yv8m686fp/xvUg4yZfkUd8PB1Jg+l5wiAteHMkhiORYM2SSv5eqH+0I2nLx0HBOUW2TX7qdss81hnGIICyoiapE0mA6x2MRQkj/7yVpnm2QbW1UaloaadjhEnvmiJEO2afEIhQ+mcHNWuW8gWkmTHySht+Rkk4jpt817sUdhgy5bQwrOcbbIfv+I7zmeLKH+uLDOGQ3Dnp+wscA3AozvjA3mckxi+4u6+r2K++kNtyB5pH7X5+S5B+P51+zprvgzN/lbnWZgU0WDAC/nrXc5oJcM3I8ilI3qRWS43EIhXG9vMt9TbwP/JSgUy66A9a0fDmYclz9KolB2vTwB62pw7yXQ9rMCisSUGV/66cCsG6+ogsVXUjkvQfh5anVJdj+nlgr1FKx1otX0lQzknMp+xG5lgx0BAozOaeRdZLmf0KySQmxRMTRdWfbxaqJC5xJklFGhBwLWmBxdOEcwTIpf0RuRuxyhWEc8r75Ab63jyakPfBeeUQtFffiF1zUX9L5msxkRFWG0eON4Y/4Cep+zN245I65ybo7o2q+pgLGwyO8ZFg8uYy23CV4Nvcr91cp3R5raXq4d0cngVn3/FbctxdP2j3oM5KcJfOJ1LyRt3k2wObNVx2/z/heJJz7OwbBmBEbhVxgW/Xcf8iSW+jDLaC0K6C3TVatUK3oQkvQy0QNLJpbxPi4tLkxGW1cE7Qj8wOCScNlowaLI9cMj2PPZVgR/qE74Y58wfX4MT9wEyBVN0aEkpxOA5/Hv2JW/YaD4Z9wzf55b/XcI6xiP8fRZM9bB/r2hGGlLV3va7Ix8QqxSlBwDK5nmwfi1p1xyJAdn9SSTe/t08XAnBNm7U2q5hue9JBsZIHSv81DC9WzQ3oPka6/cq/PlA7xglur/y/HxY8YWt8zS9KQeRgLFrLirrlFpQsW3TFB674l2m0Rd94dcrn4hEKH7Md9DMJaGloaJhwwLP7F1t000LLsToi0W6h90JomLBOqy+5u/ZYigTYst6jJqA36gETOKp6xkENq9ayi46b9mq8Wf/HK50SJCJZI5MwcJ2Rd7/8ycpfIzIhVe2+rjuzdIZmd9NVr/dA1lN63FBLqcqE1MzOn0CE/Gv4PeM041B28JqKxtbKdTbYaWZA8YnLNsTiqcE7bHXMR5mgRkzqEvk8ZMsaS443pnWWH7PkPtsK6A9Jz0lFT6fyZys4PRtSWJi646T7dqi8YDKv1N/d/4yv40WzcRdOsp30qKGkTxowRzAtWThuA0eZYAm03T0rf27Xs24u0UTffWUL+niScFMaM8Hb8SJ83Yd3ToE8xZoAzQ5qH0Cz6UF9WpCBzaSa0GSg+yPLdDFP3sg9pqZg3twixYW0vwMBQpwx1yIicwyy1DOqYSGs7OiLXHzKRgk6hipG7YUktDRf2hEbXZHbETvkJOxyx6AJRLaW9z4xvovYS+pG696qpaTm1JzS62oIHUNgpPkpzhO4rgnas6i8eWFQsy+Ef8178czCJTNqpMjPnzNvbxNhhzYSo68duwKTIe441I7ydEk3zgg+Ubv2IMnewnbu1YUnb3eOVF1ptWNKQRUtQQ0S4F5fctl8z1ClH4Wqyl/ADal1woV/ThSWGARsl4Fl3i8qOsMZhMMzljFYrDHbrY9NSEbWj603/MjsiNyPWIZkCJguAxMW5WCckmjFjxsUPE/rrEbb4rP6aQblLpiU5BbUu6Ccbr3QeYpyz0jNyGeE1v9+QlIjBgoHlA59txJPbBAMPmiqXzTXYKBS0BGoN1D1fLPFwRngMQ5tadrfCjJm5wGCS346Zc9p9gRHPe/JTSi2Z+KvE2DHwBxyYG5Q6YEcHCMKZrqljw5f8HRer3yB4jMlwdkhlD4gaabWiiw/OZ58dpk8yA9lN0jYxVfcPOrcW/gjghR07N4KfG9vpqB1td/GU96XNlTNDRMxWIPjZigG6fW/SnvSJ1hEuXnBD93rxJuZTrxPfq4QT46JfOu4jR7xLQ+pNdGFNF+ZbHsiDkfDjhsJfYuKvbgmkIT682BopETE0uqI0U37o/oAcl5pmEQbWMshSmyYziVT5D+EuM3PG+9zgyA5ZxcCX7RyDkJGIeZWWIFDHBU1ccCv+irvyW0ZyiT9c/4yx2/Bx0n+dCEaFiNJKy1n4ijrMyMwQZwrauKIJyy1BTYmI5Ag5lwd/zPvxB4w02Sc0UbnZJX96NYGxO2LO7Z7f8CwI6KvtulQb6vYOIhbBE7Xide/0WhoazfCSPIA+Kad8HCcsu8icllY7lvGEoC3j7EpCmPUos524zyV28BiK3t7hotunIfAL/bfMql8DqSJwpmQ/+wGZDNDeGak1a4x4YuxYd2c9PD/9nhjntHGKsyXevt9L4p+iGoix4tbqLxlmR0zdNaIGcn+Zpr37WguMwTDUVO2vZJHuZa1o4ooHHTJVkzxM6XbwFKzshFWb2sar9pjazBj4CcRdVrLotd8caAKUzENLS8ffh/+Fav31luTqyPnA/DEb99G1rNnhMqN8n724zxUZYXuTtzbCzXDMCV/ThEUP4smwJsebMiH4yPBSoCbi3eEL8U6MODI74lq4SiEOiyRotf/XHBe3CdqmqilcbEFBT49eO1DMFjABzxbf3GyuXrQie/S9aQ2zPbjmKRJdr2SF8PbGW5BwXgY+qI+J8HVhvu2xPzpwu19ObxJKIiHW3RnH7QnSuyaKmB7K22LNBNf38VfdCeINBZ6BdVvfE0gtr8yQZFSs8KfmiDpeZqYhoaZkyYm9g9c8yZ2o4DVDMBQyBActDY2sKXTIKnaE9v55GFjL2BlmrTKT5Befa5JdiRqIGjDiKdwOqsm7BqCwU5zk7OkRZT9XajfVkjQ00lDokIIh99pfbndu6VyZRx6uJI/SdhtL4xeBiD4YCbOvPL98Fyn6a5WArY/9HcfX/IqVeY+reoOhEQYuJeaT2hJrWIaGKlwQtWPHXmWg4y2Zs9CCgU0WyGVvRFZF2+uq3V+g2+6YlrT7L+0upUwodUgti62wY9ueJlWHvj1RZldTO0sGDHTMWpbcrX9FF5apKlSY16ktC+BMSejh5K8SndYsOWMpZ0TtOF3+9QN/ffwatXFF2yeiqN0WtbZBaVWyZG4cc05ZhTOCackk61vHQistMab3eDtkNx4m7TYMgY6ZOSPQsRsP2dcBA8nwRsiMMPGphfQH9TXW4YgL03BezFjLihl3sfgt5yl93hibe2Z9G7B5yPL74Vg3X7JuvuSD8qcM8JTWptZxTJvFdDaSzNWz1pnNTC65bj68ufq2UWKqDfEZFe+DY4HvtDR5Q/EWJJzXO4lpFvPkm+LxNtEjr1VYNw+3iaKuCdFR+j123HVKHbKioQ0Rj8GJIaiimnZuq75CrUIibl7EhnOZATDSKYGOe+YWUQMdNZGw7dXmMmKsya0worQatjycAZbCCk00jNoRuRSUMiBIxz2+ZNkeb3W0EhO7QsTgXE4uI2ZyxoyztLMPycL3Q7tHVOVuWDEzc64Ofs4qntHGFav27kPy/A/GiwMy7hNgRfIeGlo/twf+0CeIf6zf7ux+atPI+0ziFEHoIixbSDlamXqDlxGu+6+JKHnwGGDGmqVJSTUCVYhcdF0v2tnSEhjZS6zt6UNtjWV9M7lzZlcJZpdFe5e6vfXQcQ3yD5i6a0w4YBzHdBKopUoLZ6wflj7RhlXzFdaMGWaXiXb4ygnnbP1rpBdVfVT/7tHz3HR3aEP+zIVz7S9TyJCWiiYscJLTSUdDQ20qAi37+Q8I+fsIhrt8RSlTDsIhOZ5pGGMQZrLi1Jyz1gFtO2BgHAPncAJ7uRDUwRrqWNJKQxdrWtbclbRZCrHdtiu7uKaLy8d+z6ORuaNEXu4BNVGhlpp1OEN6p9XwlE3M9oyFi37S+HKVxGZ2I/Iy1gdPi+dt5r7/yQbeioTzdsXGTXDZD+MvNPC77hTB8gf+X3PFTJIeV9uhQKeRipav7VfUmlA/G7joOpxhJWdsL5FJvhVYnOvd1JYxFcG2ZGaAREOuGTtSUhjD0CUPFzBc0xGrEPicBXNOk5OpycjtiLE5BKC1iSthxKJEHPnWPjo3Zsuy7xT+Qf6a0+XfMsivc8n/mIXcY9HchG0VQ7+g5b2Mx4s+iPcfCtWK3B/0C+/z5z/PWhBDnGPkClfjJQYmVZqLDu7ULRe6ZkDO2HoKY/jpcISQZmFBlayx+OjYNSWHuaUKys26Y0XNHXuTVTxDNTLKrlCFkqq5xUaSJHZLZhqJWeg3Kg/vRDds+UCXFjlJkkgn618/FWlkxFOYCblJc5VH54cvEgn0UlPmH+FMnjQCn0ra1aeeWyNDvJsyNpcYxBFe3mfHH/XK1yuMGpw6HI6SDTl0ua0eNqZykDZJK1lwzh1a2cero40d9dLjxDDsq8tF7JibOWtZbrlN63CKEtjYA2xaxM/zWEpKD8n7ptVI6A2oLI6pu0ajK1bhhHV7+uzz+YotK43rhxTWRQpE/Et7QyX7B0HfqirG9vOll/89z4p/1Alno04w8Je2M4+g7SMmWU8OYwqGZp9MBhzIn+GxjCUBBM5iy4XMk1gjDofh4/ARTvqFHeFeXHHTfZ2gnlqgRE706wfIlUsqHJU7oPR7HPEeQ7Ika2Nku2Nbdsqn4R5LWbDghE5rVu0xXbgguAafJQKnknr1s/prQuiJm2LYLT5mEP8ELwbfKw38TH9OXf4hlVSsdfWQX8hG10m1esAl8eUeBJECb3e2BEeVV2nH3Q/VhmV7h84rq9hxt05EwQklB2bISVzxO37HfneZT+xe70+UUGx7uUfw5CYpTddRKGzBsstZdkvWcpHaQ+Io2EEyk2YwXXI7zd0OpZlSm8T7kt5VUiQpPiy4Seam5HZCjG3anYfZE3+HkRJnC3zfPnQmTwCFFz4tCaVZ+ku4fv6RPqdMiLzwnIH4Q/MAYZS/R2GnlDogU08rDZHIne43W1moy8N/wZBdOkkk3lKH7OohRnuwAS2ttKkikia5jGqGS1AMIG3KjruWho5fhL/YqmQLDpGczO9ipMBJjvTGch32iWCWByPGhiYuOHb38GTUVARpOe2+YLm+ibUDSreHs+VD59ia6cNzSY29RNTLJZ5HEZgb9OvLR3hr0sz96NvhTxHtfdX4R51wUrXS0LkpmYz6Xu6LvrdLtrpaMKYgNza1YWJkJktO5DZj9tjVQ3Jj+ypiQ9iES3bAJX5EHSNLbVlKxe24ou5OMSZD1DHKr3HV/JhRHHHNjSisMPYJrVYHmLepBWRxlDoglyKhkXLHsk2kuc3sJmjbw4EfQIKpZdWdcOxPiURm3CPQsmMuU+iARho66RjKPvnwX9FSUYcZXayp2rsPPDwv8zgIaEfT3X6Mm/M60XbHVL5hRL41zRtbT2YMuRlxGH+It6nduWjhVlNT0/KeH3FYJCXuwkayHoIeFPbrq6y7q9xrAqdhzdf2K87Xv0zk2uIHZHbESA7ItSDPRtT+Out4waL5pkf4FQgWK6mqbcKCqr3N0xLrBvraampTrZq7fbvyebFJ1mmGaeUqkGaMXXjxofXDC6oyr79gZUqycgAG7uhnzOo0XN/oD7a65pz1dk4kkrFXfgJA6CE8hqS67sjTzIQ099wYsXWqfKHfcBq/wpsSyd5PYBSNGHEM/SEGy1n9O7pwsQWxPK4R+Pj5BOikw6ihYkGnNZftj5gUf8barLngHgvu8mAacHZI0917QjJL3lCIS7t7TD/nehpZ9O1LE28+3qy1wludcETSA/263iqqkTauqbrzp84pHo0QL/h68b/i7A51+aeM44R9nVAYx04ckekNOgncNidE4pYovyFdjuKIiRRYhD2bs0POUP8l66xmaZZUsmSgY3biBIvlomtZBoOqY+iF2+uOm9oPV+UeVjzXww0KPLkULPwlGlknmDSe3IwSUilb03QXjPPr7NirWPUEOroHfOFPudlzeFqiJqRe3Z6AGIbZEXk/sO3CizHWHznb3xqqZkTO1GXseIM1cNGkDcAqdKxoyKMnqieocsfcS+3H9gbzbsDYOc4zR25h4pPW1tgrAwt1NKyC71tFabc5rz7FuV3IATlgpWcsurv9edlYYkzJzPAB+ZvumTtykcSTquKst7R+Ucb749dA9X4L6lXDmiGZS3OI2M8Uh9mVh8QZN15Sm6Sn2nC2/g3eTdnJ3gdgFU7SPCbMCXFNU/6UfXa3grGG5BOEgYoFtUnt5lXzFcYM2PU3KBgRspZ1GL5QByIdf0lpd7kSkp6a5QpGhItYMZMFkUjBCCwszW1CL7LZdhcIPvHyHgIapWtf+COm/r1t16DTmmXjvhWfp39q8VYnnLTDdnh3+FB1ErUlxKofSD/9AXc2edF3cf1KvXIIdOGEm9VfM86ukPNJT4IzjDTnltzj0+rfIGK4XHzSo6Lo0TaJ2wBQhY19b8RiOdbPOVv+HaPiQ7z5E5zaxG9Qw1iHhCi0qqmPjuFQ3yOPGbumIDMGF6bsxBEJ9NkSJZVVkYjJDI1P5MVFPOmHsBXeDLgkH+A1YyEzWlPTaU1LlRZB2gSiaE97mHnEmMEb7d++aohkHA7+mD2bUVpDnjbU3GkrvrbfEGxLoyv2uMKBOWIghk/0KkGvEnt749+153zW/s0D7qqOqTdkRliHSEtkzD7HZtyjFYcIlnV3Tmfqh+Rvyux9nMlpwpJ1d9rPT57ffrSm1/TTlqZ7dXmVJi579YsJakfPhFgbGWJMQdw8Lw+8rgunCbHmIdNsO3+MPdQ5IQzTPezshBDXlD28O5OSUZzSSsNcb9OGeY/63KM0U3xIZm/nXZP2YgKlDjBisCY9y42d42xJwaj/21VGfp/bsXkhTbWmu0dlSnLjGJg0IxKE33HOZ/W/pXR7HNiP+s1VOt/pNz0YdrtObBBqgmEVEry+C9VjCujv4tXjrU44kNoAbXf8knj051u2boiiLyb+mUzNzs0ZjSZZf4tlHMf8Qf7f4tRu5WPuxjnn5oxcCpwKa1pu25sEOjItcTh2uMpwtE+pw628fI7HY5h4w16mTDPPD/U95i3crlJS6VTpQmClLWtqoiiBLvFEpGEtS75Z/QdinG8JbBsJFlFLLRWtNNxsf0Hd3KbIrrLrb4CFzh/RhjldOHmJmcLj51R6DbKXAxs8O6wZJ6Jjr1G3WTAOXE4e3t9C1QfWsZOZnseUHE4/X7bc4oycnD/gz6liw9+bvyVoy/vVj5ky4KY55lg+Z1Z9vU2wGzRj0yX/j9xfYuR/0lfLK9bd6TO4InbrGLlRvDDmvuurEU/hD1jWLyPHdD+a7oJO1rR2jWCwdkyI6yfOD4wpcLakfiKxMEmuBG2xOPbjZfa5zMosONGv6agJoUE1slN8xFB2GeiYYRzSSWAtKxppEGxqv4VTVBtmdkwlN4goa6lppeGCe9S6SPOyvtXbhRNCcITsJ9vnwKqndHt0YfncGc5G9bmmw0eLlQSL/pAjDvz/wJqaU0mJ4+nrR59Qe8SniOVi/ffbv4oU75LNG4y3PuFs4s1edNsbHr3YgK9u7xC1Jc9HGDHJvVFzBhQc2DRPSPa6kUtmzBWZbAf/QTOm4SMaDXxqf8d5vEkXK4J2TN1V9nUPj0vuomIYOhh53UKvqyCc6IJaaqLENIuRe6ziGY58i5SCZC+9kVVJ86vUOgimIJiGC59ev+tvgL+R5EDIk0fKc4lxzw/lYb+STUsUMU80s3rRSMKkkSqmaqXVNCv7PJxzS36X6ry44kA/YtR+SGGFTlNzz4gwigOmpuAwdxhK/tD8nKhw1kRWIUGZl80xITyp6ggI950/lbidmz0pvEuowWQ9cB8OHuN8a+EwLn7Y7/RHz19UnxS9l06I9VZ3TMQ/UfixC8nyW0yJwfRck/v3vUiyZEfBafqsxjTM61soYStI6imw6lnLipWdE+iodE4Xaupuo3WYZjqd1izMait+muYrs14/7WZ/jJZp+bO0yGNZyoJzvUkVLl64paZ01O0Zn5e/pWTC5e4SA8m4zSn3zDe0uqZuF3Rx/cx5UBdOCHGJZEc4cob5D1KrOSyfck+8i1eN703CeVJsVGtFDE13ur3hN4M/a8on4OPtVv4mxPUL9dEzd5h4OVxmEidMpGDs3NbsqYqRbzhhaRYM44g85kwZsOeTvlppLW0XubX+S7pwRuaOuFz8jFKHtAQMhrHx5EZ6To88VPaPKEHhC37Jqjt5KDlcH/1rAGpNA9MHFxxrhluJ9k5rLurPUY3slz9mh8tJLp/2lZQEXiRUK3qazGtF093m5nLOleI6I3L2fIY3cIkxw/jT/nqDj4ZViFx0yq/kc1Z6xofxJ1xxQ4bOMPGJKOrNfaO7Jhps9QETv8Pt7HfcXf77R77d4uyYpj3j3iNimxtE3yapmv7f3QNCs5t7NHdjcjtJREeZ0uiKtZy+9LmxZspO8RFO8i3abR0vegRmlzTfuov7qENJEjK526OwE9q4ou7J0lFbTA/hP3lgFHTSfrYFIhwO/0vG7G1nk602tBJw5OwxpjMdrshpwoJ5/TtUm0TYdOn3K5Fa1iyqOwnQImabwGfVZxjJWfmTlHRewZY8xAuOq7/H2zHGGyZxyoU9YdEe04TZC8spJcWAC4IpKdwOhTnkLH5O9y1WN/LA8vui3Zbve3yvE45qQ9Pd6TkjJWKGSTyx38XdJ3nK1tdlI8XRhTnOjjkof4rBMutu98zm+4ifInuP3E44NB/iyYhELsw5GqeELqcwjqmzDKzhAw5p4z53SS01Gy1lsL1KsTCyjj/I/zsWMuNO+A1fL/8No/wGufljoka6ECDArTZxuC/7gkuFYd4q57JgJQssnsyOWPcD3MwdsR8vY1ToJLAyC2byW1SbXi9uSmZGiQFvdrla/BinDunh0XNOOWu/eEy1eNv+eQX/jleNzcN3X+Lj4Ycv6oq79hZ13GcQdzFiGDnLQC2nXcMdc49cCwhjFEVtev9Clhx3lovOctKk6zGwpk88aaBtgEw9HU+qXMJTUWBKhyHHmAwj+WOvMzJkp/yYodlnpFMmcUwg9pydRC6uupcRRk2RSXKA3aDdLuov0068R1c96Bfl7BhnSmwPWJhVv+HRBfi4u4d3Bw8d/zD/AbkdpaE74DVL50gKgnZ00lFT0UrNqk1ouUF2HWeSS63BUFNxGr9CieznP8AVP6PRFZ3WzJtbNN1tglasmzUba/JXicxN2XHXybVImzfdxXpH5RbM7ZC6PXvqxnKzITDGUdjkL9TFRBx9UYDRq8Y/NhWBF4nvdcJJkZSkwzPbEuk1gmeQH5GZEaWZ4sgZx0nyfHeB2pS04XwrPpnbCU5yWqnp6Gh0laCgBohT6tjRtkmGfU5FIy2VrIgSaaVjHQOK0oaYVJ4l4snYczco7S4TDpnGMaU4DvMko79RLJh4Q2Zg6ITL7ZR1HDKQEa1puDH4ZxgMpWYJIiyJ67CjIwbl/zX525AWBacutQBjwW7v7PiNucM5dzivv3xskXR2n9ztELQmxo6m+/0knOcvuEqjaypZcTdkZMGxpk4ac/aYi+4bBnafsf4Ui+FquEbkapqNieE2p3ze/VWPuEocmA/MH7OvExYk5OAmEb/ccbd0T+O/iGFgdtmPl2lpODNndHQJqaULlvXNrZr1i4QxY0Qc5+1XD1ldw30ZJzRipMTaPazJGLhkHb6xT3/0uwRHkV1N97otks1CXPYW1UKVX+DNgC7WPVinIcT1Y605gDYk/hUeMlvSak0b1xgx7HCJnTBhKRUrWdC61fbeM1JiTYk1GUZ8D/CZ90CMZ+/6nd3lqvukl8fxRCInfMPp+lMyN2bHXSe4I86bL9Nc6JHZompD1BpLlu750LKov36ljdYGoBHi/CXbpN+HZPNmkuI/goTz4rFFGZkpB/EKgURWW8oFdxb/7qHXqjY0YUE0LRfaYsTipcTiWcmclZ3jyCl1QJSYILi0DNllGEd4Tae2Q5nLikCX5DzoOO2+YFF/RZXfYGQmGB1QWM/QwXteKazShEirwrIT1tpR9XpkTpPSsVPLUirOJfnab3aw5+ZkC5U2WIY64irJLM4I1Bq5G3/HxfpXT1nkI0GTM2hagIZvDRR0M2uopKIi+co00jDQMbt9pXjH3MVg2I275HjG1jOwhqle4mP739KpsgwtNR03zTfcjL+lCjPadtnPLKY91DhJljhTbs3tonZYyRM6jMiyPe79S54s3RPjnLP2K47Me0RJgphIUoNAHycOPi9Ua7q4eiIYxtlpIn/2Iq7hgbmFwdLEBXX7uPqBSM7EX2XMPp3tCL5lHu5yUf0WSACFSk+ei1Y0Zoy3YzI73BJSd/USn9if4kXoVAmifG2+4tb6r7EmZ1r+LN1nJsdg6bROAAZxBDuki+tnIvDK7H2G/pCd/lrXtHQSmHD4/2fvv5osy678TvC3tjjiSr+uwkOnBJAJFFAgiBJkkSzrnplmW1s/tFnbfMd5mI/Abhqnm03RZAkAVUAidYb2cHXlEVvMwz73unuER2QEMgEEqrhghsyMcL/3yL32WusvMMX5bNN3m4KUbJ6/T86ffI0HzYvPWUlGbrbSTK6bpzZh2JGHX6Qy/YcX1uymNuw3hIb/wSec10GbATR+wXH8nJl6ghaLpaT2V5PvVs2XKOlzo/9TenFI0TGynSQF4SxaeiT1gbtyDSVCFRx194JUtCgUOzG1qKrY4giUqseq9x4LTvjI/V8MzQF7zT9BSyIlaoGZU8zapDSQiyZGy2P1gDrON9L0XlraWKOUxnRkyFU8S5yIsEoS+9m73Ig7m3NSCPvqbUw/5+nivzx3zucvX7J9+N2+MM96hVyOKs5AknSJiYZRGKNRrKSmkiUrWXDm7tOGFR819wHPO/3/kT8Kb1EoYStL1t2FU1TBMg1jWqk5bn7xwh1p1Dv0s2ubATgkki3AwF7rYPc1PtaX7AnWVsa3zA8YhCRrlCvF3DueSEYQ/9qs9Bcdo9E7DLMb+Jh4VY1f0Lhjgk+tK6VMsvy+albX+fokfyWPjy1VV+UDhNhptn3NsWVmzNjeYsgOvThgGHpYkmqGiBBjxHfK1XQJcdGsUJLTs7so0Rwvz0VIXwWVuuYgBQINjmN1xIppJzcDbVx1StG/ucHgyyKEGRHTNWLDBRdi3UnVvGqb9DdX4fhdhfMzIHxjH50/mISTbKbT7im9WK6TJEmY/todvxAaejGexfcX2S2sKsnMASHWz+10lMqSunLsUcmSqWoYhhGjOECjKEQTgWV0tNGxlBUrWeLEJfRUrKjDHCWaPXmLMvY4kSecuq8wkjMw+/SZ0ITAwiWqXKWFX05bfiF/TyY9ttgmSEgzHOnhYo2jJqNkzC5ENuTOtTDoTfMh+2EPG9JLvwyOj/UnVMzY5gY34nuovuZo9asX9Lf972F3Fjm3nH7+BTxe/pLI+UKd2xv07R59tcNWSEm10RNENOvu+4opZ65hLoqZU7QxchpXtJJajj1GjIr3mNVfEcKcZ5Od80ecrU6vPB4QMnNt0woyugR2OuO31WaBFwTTzfLwMFUnLF6LE3Z1OyMzBxRmjFW9pN/n59TutPO8ScfbuEOU6mH1kFyVLOrLlUoIM6buEcH4zXvlL3jSrG0EvJ+9ZAHVhOCYuUcs5QireuQqKZsXsQ8RWlUTCPTiiPeLv+Rx/HSTYIwukn27Gm4qqVdZrFMiCbTmHVTXDtVik02Dn2/UDJIl828nIu6KivN1pWre7GQD355q9h9EwjF6Qml30ZJjVM6sebhJHGsJjvXfn1UfvVYWbtwZLbOu3D5/TJT0GRZ3saqklZpppyUVLvwPoIrQ4jhWp3gcvThgEra5pz7l8fKvSY6BDaAx/Zwo19BiGZj9zXd5aTnzLT4aBsZQ6Mj7Q8tB+0OOG8+v5CsUijv+FoVomuhpCUinHeAJm3nGQvLOB2TdhsuS4IhoDvwtnHgGscSiqOU6Tb5k3pgXcJZ+HzuvF3/fswmwbh9Qtw9YZrfo2Z9gY8bt+B6BwLz/Lo00NHHJ36mfJemfkHbva5DEWoXaR0duJtTti0ASLzqmuJlDPLsjN3qSFvlYoEQSnDukWd+T+pe0r0X8fIaqqMYY3Wc7exsjOVP/iIU7vNKy2ugR/ew6eQce8bF+7ueWzf0O0fU8r0irnFwPO0VkdyWAQqkeIdYs6/O/W5//lr2NkZw8lucSTdEy1zucdJYYVvUwktPPDmjDONk6+Fn3Pobnzn8dg/w2A73PIPSwGFYxcZ8Q0NqmuRWrTlHgqk/4TZ9vvfHW+m3bF3zTsGYPH6o3gsANfyAJZ40Wqf2UyodLVYr3M9DgY4GgGOR3u8Wl2bQR1nDpta976465yI+4KkJcJCRQWHG/+RSAneK7jNmjlorHssRg6MUBJmquh10EYUbFUpa0VIgYjBoyyd/CSkER+xsfkfRP3aUMzYoGHYRcG0Y2MDBwrYCzRpEv7uAjaL0+trRjO3MNh3KatKtigcNzXH9C6w6Zqa94bHeZ2Nvc9W+TodlXfUSEpXc0ePqhz3X1PsfFgCfLWZJL6QzTcrtDaSYs3dFGQfn3HxqjR2wX75PLgF5MC7qJGh00kUgj7YYMG/HM/ROW7dNkw60Mzp8jj6zuU+hxqhhDhdfDDUhibS+e6RFWldR++tyg/mJEHCIZg/zuxolSi6Wl4SysGMQcrVML9m72E2bZMY+W/+U3WrD62QGZHpBJmpUsmsdXbBiSodiasCloVFQM7LXk6XPhHYixIoT80m9bs7cxSBPRZKoPCgoz7p6RNHsBNoCCWQcHz8wB72f/nEHssy+9jQKAEjhtHVMqbMw3lAarShSaefVZl7SFIruJD3XHZ7p6AykojOQMJafUmmHcw8fIg3DKI/mMRfv4uSS6bnV+E2KyiEYkR6uCEF5XUf13G94vSY63bwYS7g8i4UCavfiwIoTmwm5SJ70rYFl/ASTv+kn+Nj62NHqeNNSa+6SL3SfTI7TKO2SR6iQt3BVDQ02meuQy4E7+/WRGFVKa+Ji/5nT1c0QKcrtLobd4nx8zIN94ogxkl34vvehZzCBCIw01qzQcpSWTHpOwtxmEpz53jpWI1RGjAi7YjkAaCd3zsvBJNudITjmUr9BY+rKFQnE7/6eEImyGo4NuzhGJzIPDE3ioHjHniFwGlLGf+DvdS52Za+R6SF/vkssAY5Mcy7L+ijch6SjJOYhvsx0GjE1GoYSZ85yEipqWpcypZcVx+wWtX6BVRmm3k9WyGqCtpWSEwWyS/73wc2bVry99T2bGZHrASB/Qi0Om6ilTsazacGmTotRwoxZtVUkhQ2b+CUfLvwLgSfdzO70f833/T1DAjbDPgi1WxRnL9ulrOUYq6TM2N8ljiY1ZSq7h2aSl0WqQkF90yK32Y6rmEKUyrB4S9dYmSa0rJqWyZGbozzZGdI30kwVy54k0LN7ntvwASLOTmooH/mc4v+B670/Zj7cYxR7bOpmw9U2ay83ayNIHPpGvuNf8FVb32cnfTUoJHbG3zG/RuDNiDN0/Xy5dNas/S21L9R5GkuEbQOsazpp7HQfnckQiRvcJ3bkmoMUaTPBqC3KMTUK3bbhOBcTXA4D8ruJNAy38QSScGCt8qDd9ZKWGjPLb6M6EKsbAlDTY8qHiuPo1RvfpmZ3UT9YjQmxZtU9Z1J9zceHs5W91AqGOGFaJkS0ZmTmXItEY8mgZS0GuFNvhn9GUf4pLoFhaHIf6CQ+pGcYJozDGYiixtASmMseJI48KKMjjHlm05Fj6Ymlj4Kmc4cQjgJaISBKYLHRgJzesnHDceKoQ+KX6mMPmI/ay7/A9/0d4wqal148DJCpaaWhpCBKYxxUaTY5N8wQMhpxFOOLIf0qIbqMnpVVGJGw8T1b+hKp5sevi7zKK7Dp9s4eJ+tIQe2g0ffpUPpB7y1yWHPEpPqywuk+pJ1T+jJP6M6zu0zdb2Ng5WqLI9YjlWrI+hkSU1MmzZu1q6Ulq3FoVHc8lWZXbTkCykAFNXDH3TwmxxZq9TmV4BQREFHVsu3o2lapG5RhdfI2qtnTcmjWR0qOxWDL6IcHcb/f/nEU8YdEeUndOtkb3UWJSW0lW1O0JIS6IvkbEEC4s5LGb2ySr4/PNSvp61Q3nu5mQX9DqBhvT95eUGPtjnHVJDy0KKxqeuohBWLi0xJyEioVUfNX837TukMaxIXqW2Z1kJBgDWpWvjO6KMZm4eRWJxI26x0Gzg89+wnH2gOPlL5/5LP9KOm2vE296W+1Nij+IhLOOUfkdemqCFovGJHRKXOKoO+VcSwyrND73p9Rt2uGcM8EzrNm+VGYbycn0gNJsAbByp0nSIjTM3RMataRUfQK9jSx+rhQ5KnWXo+UsNDysf9Y5Qqbh5W7/j/mj8GN6YukxgQh19HgC9/UDTvxX7Ki7vBdvM9CGW9k+mYKdPKAEYhSaKBzWhv8ym9KKo+xsePNYMrDXGIUJA5VuYRF38TFwKktqqeiHPgWTzXlWNPxa/R1tXNGTCUUc4KWlZo50C2LaWaZd24p07X4TuOizIZJt5Fhe8lN83Q5xL/tOqsjwLGONdkKrNCJpKF+H1EjLY867+qcEHShjSRGyxIvKEprtKN4jEhiqPfJYUsoYVX6XhTtiWX+B9zUrd0yj5lTdfGFdsVyMVXOfWgpW+hijyitbbpk5YJBdo5DhJR6OIyHIWn+5pSuSoaTcWJ/70CSZle6ZjbFiHp/iZLjhSh67Lzqrg+67I5t/r7oWklYluZ2wrD+/8Pwn1Y3CJq5OExap5agGhLBMrTQzRlCb56Bpn/DYfIJVJQO1u9FAA/jE/cdL86G16GouA7RO4J4Qnq9YVs2XvIhieU4Ifr6CsGaPTA84iyt8m7NNRqGTpt6B3yeowIl8jMTX0WH8+hApujb9N92EvfnotG87/iASjlJDSrtNT03os8UsHnHsHiURwwsL4jlEOi1e653HWl5Fmz328u+hiz/CxhyDwcZsQxar/Nlz8horDDd736UfC2o8tU+mU7U09GPBri7Z0jn/lP+B2rQ81g9YhCO2uZHaWLHmVE2T/DuJCb0VttmSbfKQKqDWB85WbZroSE7lhVxFMh1RRG6aAVUIVNHREnDiCMERJCCSALuFaHxU2GDwGIYUbNuMSMRHmHvNUxmj0IzDDr3Yoy8DhmaHGUc8Xf09MTq07hFj+FYSzfl9sQS+xhxMLC+zojZ6h0hAUNyQLXpGbZSjKx9ZupiQYNFggWE3rEcgxIjBUkbLNGoeU9P4OU4lV9cmzGn94tJG5OIuuMhu0c/f7QzT2q69W+P80cYZ9NymKgnyr2dhPbtLT01oY8VRPKMJc5bNkxf2/ROJuSUy7DYAz/u2LNpDGjWn1en5vpRsrry2qvvsZ8HN6b+t6lGoUboWYmj9YqPJ5/wqzVnWGnHuZCNueayGqE5CKh3r5dagVn16knhpecxRKGbFOxyv/v6ZqqDbLF5RKbwsUbTuKbNQ8WlhKWTE3fYdhi7f+E/da//mFS0gXm++8eoVTQIXXOW7k57z32+yEcle+s79NuIPIuEUdodST/C0TDnkcPFfr35Zu4Gj0VudhlqFIGg93ogQzv0TJuY2e2Gvax545jLjcPm33YOU+t/rBUEkT+2p6NFoDMISz1ymRAKFt3hi8nKXJWfuPsv2KUUxAvbJMWyFEY7AUpZUnapzE5fscpNd1UMBdUgv/6yN+CiMrDCS5FB55ho8kUI0PTEU4Q5tvIWPgScskq+8TrptqsOuVbQctbEb1gohRq75a3j2mKoznqiztLuPPRZy2olIejS9bsb17Q0Zwyu8oC/r1ef2BsPsOgf+FgNydgpDoWHlI7M2WUkDuBipaKilZiHzpBARa1qpyOhtZFqu8S61XvHF/N987TkqNUxDbbFJ7FRycj0ixsDSJXLiZb2uxAkzepue3eOmfJctP8KRqtun5pBVe3wJEflsSEcgTDOG9XURrNnF+yWr5ksqDLF4O5m/qZJIuBIAs65ulFz1qidDt9pPERTz5iHOH5+fS+SliushzAhcroJFCnrZTa6b79GLA3b8ECMqKZvHwA3eI+8NWIYT5s1DIKAkJ+Jp3eu2piIhzKn8GUF57umvKGOfuZxSxeklePfLYi17tTbUay/wkL5ZeER6Vzzbb0JVI4jkxK51/ruKP4iEU7XdcFNsevH18MJDLgmBo9LQM3mhnwt2RtjMJtYqtAv1CFv+BZaMMpQUscd2+QErf7KxuU2GUmmY+Mnq36FUxnv5X3AtbCePFQlJYVcUMXqWMqOKc7bNW9zUH7IdttgyWXcMGXUInHHKjGMkKjLpUYaSnlYUWtjKNFYipYlYFfBBcCENQUfG0obz3elAm82eLMROQTkEquj5WH7GrHlIboZkHRfCkEiidUjcBAkKJRotN5jEkj1uofuWZTjhePlzvv0X4pt93ji7xbV4l7ezEYUWys4P54squXROwh57DDmNKz6TX2yELCF5J60ro4SI6jHRtwEweuuFlZySPkYPsTohC6erj7iofSVi0WrY8cCeTx6tO+TMHXKt/y59bOKziKC9YprfYtaoK2DImiK7jhJDpgYbHo8PNVl3P1WmUWKwFPRJQJFZnsRAz+rPnzsfH1Kbby27clX4UONVjQ+zK8/ltSI6jMrZChN0t/EJMbKUFY1Kqh51mDNv7m+O9WXH9gpfyLL+nCUw7+gTlTt9xqPoaz6hAwEoMShlUZInlOMr0ivWquip7Xd5kyCoZGkd3SUO2cVQ0n/Oq+i3H/H3ApX+g0g4IcxpnLnE5F2X+HGzsDicnz1zQxM0tG+vkatBgsCG1EY55REZPZTsb0iVa80oHxa0btaV46liUpLQPo7IHkNuM0ZrIVeCi4bCv0sdPCeyYC7J0z5XiX9R+YCPkZkcc+bu07gZPixY9b7PdvsTQjTs5EKmu9mNF2ZOmDZw1gbuuxmBgMUkQHVILZKeWEbGJNJoTJ7xu3KHcX7QWSgUVCyZc8rcHW7ahbm9wfX8jxiEERZDGx0helz4eu2q30eUMqIIBZlKKgyuWxPfKXvcjd+hDanK0b5HDN9nqs/4ePm/EeKCXv4Wu/Y9bMzJKfA4ZvE4yf90raAYHTHWiOQoVWy4J0osbVjRhlW3G1wvCLGzf+hUBTrvoct8h7SDtDFDiZCrZPY2CDl9tYMz1RUJx29mII1ORNb1d4TYIFYzNje4G95BoTZV01yd0oYlIuaFDP0QF4QXaL6VZpuB3seWvY6Z/+A3ZpRHHJU7pbGpybjsADOz8ITGzzeVor8wu4p4jC4p7fuprefnSfH6Cr22F4emn11nqPcZ6H08bde1ePXn+aLKhrmA4nvZdyrVIzPjC+3PFpEstZFj9Rqurv844g1POKm3m5kkRAgd70ZUt4P5mrlABxZoup39RN9myChJowRNIy1n6ohFPLlS5mWn92N6asKBv0mGIYRAi8NHz9xDjqGvk6XxozhlqeY8CZ+xbJ7SFj9g6N5FdwPtTCnu+HdZ6htM7QmLeIKl4FfqU0ZuAos9ekZYukgTAg/9nK/Ux0lpQHYSoiomIO+a5ClxSBESeKEnlixqYhjSSMsw9uiLpQkDFmwxNNt86qd4v6Q023haaqlYRs1KpX59IrO9eZF2j5GVD8wd3HczaqnZiSP62m6quzUttxcHvN/7f+BxHIbPeLj6KwbZTW7LB3hxLPxTKnd2aeawJsitDehalxBgl5+xq9uMMTZY3adn98jVABdrVi7t3oME5qEFLEaSW+wk7BFNYMqvXnjOzy52IcwozXcoGGAxeAJTNWMuZzyY//9eevW6o3zubzJzgNEF2+YthmGEk11a03CoepdMyF4ezw++W3dGlSU/HI3pdOOSrE2pJ/RkgvQUJ9XHCApr0izIxxYfWzI9YGgPqMOcRfP4a/W7RsV3Geh9DsJtBr5kRUMlFb5srwR7fH14Qmwweger+127MlUo57yWhDeMsb5Au0gRY0MkKbZbvZcI46F5YQJ906DLv814wxOOJ0ZP447RnSCg1sPOO759we+kqmbN8I9hxrKT87D9khFbmJggwk1nw9yGJUbvdCZSVfcpBpGEwCnJyEVvUGZrhn+AjRDkqU675mvqXfLs+8QQ+EKeoEL30gFekoDnV8v/mFw5Mbzb/x+w0dKEgG+F+27OmTplwoSf8KON4KOIMDQKLUKkJESYtp5HfkGGYawyBMVTdcg0PGJPvYUOuwm2LY48FryX/wUexyP/EQ9Xf0NutuibHWbto2/FgO23EUoNUVHRSMussyK1aCQWPFBPWLEgqueVvtbaZ1osmRkz0LuM/RDikJGMqbOaB9nHLN1RhwSrL7UYnl8EXjzYBmjcjKgDplN6WKPPZtkJVjLakFOHjIqWWlUb2PlrXYtO88x32mGnPGFxBdfkYhi9hdVDWj97TvqpcY9ofUZtbpJL0SEgCwo1YqaGG7fSFx5PBxpIgqEtF6HbT+OXFDLiZniLDMMeuwQVCSHNTWs131xvyxAttgM/zDF6i2g9lTt7JVv4bfMWW2GbESWZaNqoktah9DB68jWyPC84N8k29vQv2ty+rPkokqFVn9wMUyIVi/Pg/5FDqN/whJPisrfN5UiJIUfrXjKTah9sHo6EwmhZPxqn1afowjKUnWTUpI44XvwCYthAruf+Cav2mJ7dZcQ+eSzSoh0dX+kvmPknDPU+4067S3W7uLf9bYwoVtFR01JJlfxCVM0ypN1uJknzarf8ABdr+mqHkR8zomS/0PQM3GGEkSF1EJoA0yZyr1lhURwUBX0DPkpSHhCDtGknnqtkqfy2u03DjU5fLdCSiKgqKkZhSCDyVVh1L3LYsMWV9Il4VDew/v21AhLKa71rVpIlQUYVNq1EJJEOlzJjGU4SejGO8TiWTGlYcrz6iBCWbPd+wNv6Jwx8n5GyiAijaAn0GYQfsjIN9+QjTqtPLx3FmtCZqT5GFcm4zM1eMlAOVFck7pP2K8QqvvRPWLSPU7uOEcv2RfbUL461KKVFAYaSEaIVK3N8STngYujOOjnTCTCxbA8vtfJibDiqP6HNq821XblTeA7RdsXxhDnhyoqv2uik3Sjv0hfLwGisCMet4yQuGbJN3ftRUgdROwiant6hjSvmzWOm1a9IbcmvgyBLd20CNQ4XI1/pL3hc/eKVzdeuikvgid8g1i3X2Wtp5v3Djzc+4QiGPDvAqpLQeZg3bobzx4jkjIv3yFQPjUVQnIihahKkNT7D/nV+yuHqF6yyGwT9DkXs8+PifwVgoRbUUnHkPsH5I5ZAMG9BpJOiEfb9DcayQx0rpqobeKKwMaeMGXk0G8h0IGC6dkImSeNpyDY6Go6pqP2cED2NWtLG23yoC4YmkuuAVZFpq5m2Qt8KNyg3WsVNgJWDupMdGBqFj7BwPlkPyMm582hng71PH1FJraAOfgOTNbrPjrpL0IGYeZq44mn9Ee73aqt7Wfk7xuRDZCRHxUSaVF2FeS18h0IplsEzjzUP9L0r20saQy0tj0La5daS0EtqrTbcETSlc9AUsRR2By2WlTvGh+qlrPc1U9+HZ3fBghLVye7XHatd4+TV0FPPhkryrRTKUABFvEUbA0U+YJo/4az+8rm5UOOe4vyCQX6TvtpBZ5aF2ORt45eJn6YMK3eaBHD9DFElud3Bh+ZrVBBevCBrNWaY32YsJaVOxncBOIpzPpdfsGyPqJp7iBQM+/+KYRiRS0ErDTYvWZnxBjSR1EKe14lbH8OR+5S5HrEjtyhjjzZWHS8v/wZos6vPbQ0mkY4Qez7r/W/xKvHGJ5x1KVzqySapHIVPcD7xbAZ6l35MD2cgcMzLCIY+qeNWv8LlNQOzz4jvpvlMjJhomORvMdMFrV/wxfzfYM0uJvsX9GJJSU4vFnwij3i8+GuUKujZXazq0eoaHS1zjqjDPDHQOyvhmXuEoNnWB4ziAK8cGBiyw0G4xlhnlDqiJbLyioWDo1p4WockZ88pguJaO8aK8CjMONZHjMIW2zHtXLsO86ZdM+URdZizZ9/jw/gWivQKWVH8SP0r6vKfsVIrljKjjTVVnNKG5Wu7T/62w4czjle/IJYf8D15j0IpCp2ohmfOc+ZbAhGDpox9tBpf4riE6EFgKqec8qBj458Lv/bsPpFAL7uZIM5+RoztBtH4KhHCAhhS2INLfy6SVAy0WLbNXbR+j4o5c/8kzRdfIwSTPI6i2nCLquhoOiJp4+c49zzizuptjE6JtI5zpvW9y22qCFr2GZkDtL2NxlDFOSt/gvsGjpe3ej9lHHYYaNPZbXjq4JmrGSF4Cr1FXqZrA7CSJbUk2ac6zGnCYoPW8/Ki9nmK2s0IMTA1BZUqKRhwrXMXXfkTmrB4ScL6+hAMWg9RkpObIVrytN7EFif/eOYv30a88QkHIFN9NAndIijG2S0qPcSoAo2hpcF3oo2texnUL3Fs+tkB2+Ytspjh8cyk4T4fsfInFHrM0BzQ6hUrVSKiuM9HGJWzw03KUJJJySC/hVEFPTXBkLMVtrHRcJ0DNIqjeMohSefshvk+loydsEUuGhV26UkS/fR42hBY+bQAdXJQG/pqT2lux21cjJzEFTUNU31KHefMuxnTeia1DiWad8IfsWv6FKLoW8XKBz51h9RSsRN3mEifU054VP9dZyJ2ynNukB3c86KW1m8rUkuvvbKKiLHhdPUxtvdnFDqhvSKRJ2HGI/UVA7YYhXFC8pkhIZQUZry5P8s4T4RbuYHNSg7dGSEucX7KdIN8fJ24DB6IdETcWD83L1jV99B6nIjLeoeT+vPX0k67+B0L/xRjcmY+udQeqWNWsuDR8q+7eYjG6B1CbJJSAJJ24iSYv6O+ciaiRJHLgDwW5LEgqMBZuNd5oLxeZOaA3fx93vZ3yURjOvO1n8uvOA6fs6ff431+zCN9n/uL/5gAQHmLFsvx4iIkX9BqRK/cQYlmJcULq5UQaxrvedihMNdK7+ck3VdPnFeh/JIFQae04B6j1Sj9rKgXwuqf+9zOkfWb+sn8occbmXDW6JCe2dnwSJRoztx9Vu0xudliYm/TxorD9mNCdEnVFvU1jGtNJGBVj22/Q/pki4+BsTrAmLxbENJD1c8ONsq/gmIlS1rV8KD6G1p3iJI+18t/zSD22dW9ZBEdAnXwqZ2lJhujMItBd/3mQ/WUx+ET+mqH3XgDGw21txgRhjZQ6IiQmPSnDdyv0ot22w7IlLDy29QxfU+DR6PodRI3bZhQS48eObrzYOkb6BvFyB507bdAHWJSWu6gwSK2k59JC6lSPZRk3eK14LeXbFKiPedMXI0EM3pIqTU9LeQ6/dy+GqLDW0m2RlacxAfnsOJuUT+RAq3KjbaeEsO13h/jaTmuPukS7etFbq9fIlK2foELiyt5DbET16zc6UZV+TeNafUrlvopg2K88X+Bi4rniQeiVbFpCeZ6hMZ2HQDPqh0+d5xrR9MZx5xI3Q3rX3WRPr9fZXaHLXubHiNWtKkdLYZSCX8aPiTmHyS1dwm04RrL8jtEwkYPMUGRT7Fmm769RqZ6DNjBq5Y2X1K50ystFJyfotVgM4cMcfEaKLuLZ2LI7H7i43Rtx0S3WFxYV+JL1pgXGa8JWvWTMOp/SzhvXqwtW9uwxIlGK4tguaV/QF/6aBTWp3nJmdlLu7zqZ93g9DyMnhCiOzd16ghe8/YxR/kOozBhR0q0CJm/Sct1Ps0th93iunLHZHrEdfV+2vl15mbrdkik7QQghTPfoBCSE03qPQ/DaIOWanEcSU0kMApjdvgpw5izl1usgp5J1c1pk2YyKqmyUGjYtok0eFAKhY6JFBoNlTcsHLgQmfvAwrd8Kj9jWn9Jz+7Tj7tcb+7ygZpgVfqsEOFBldp0CFw338NZRx3nhFRvEWPoSITfzsAzgTde9KK92O9k8/sYdvJ3GdqUPHsm/bxeCR6PjWnjMFE3OFWf4sN085lrwInzJ93MIKPMbiQ9PpUDL+ZbvIjT4sMKj7pkdPayeFb+xugJoPD+7LXblz7MkkK0KFaywMWa3N6gdWdYMybXSXTWxTQnyuhh10RGQtpEdJ+1nj0ZyaninCfLv95UEUZP0KpPiKlytnqIVhmr+t4zx3x+71bNl/jQ8BTP5zEwym/xk/ZP6WtN3yiMCHPnOWTKsXrM2epLRBSj7FZScDBbG1vvdWK2ZJjOrv15aZ4Uo+I9Cj1Gd1X+w8V/4GX3RUm/2+Bc/hml+ucyQHiUWDI9oBHDqnlxhS9SJLWC7l1/PiHFb1Uq6g853siEs2qfYPWQcXYLQ85OPCAPOdvSY5wZfEz6YEtvmDJFoejba9SdxEeMycI2cXYMvfytS7DLXA8Zhx1GsU/fakKEma+YqjOUaMb5Hab1/QQbdYccmR0GssPdcJOxsbxT/i+ETk5FibDyni/kMZUs2PMHDCkwohCS99+SBkegpcWJYyFzGllx29/lrrEIMG0SEOCzes6ROuJa2GfXFIQY8TGSKUWICYnWRqENcFRH7rVzamk5UYcEHRhxwFZxgyAJYGE7fbkYE+qtDXBPnvDA/x0Dvc+IXVpqFv4pkYCWVE2+Thvi60Kw8MLZ2isggST5nhQaCh3pm4AA3x0qbroJJ03ksKmxZN0wN98sAEb3MapM8NbumXjV+czzx5u0sdaySVctQK+iT7Xmkr1M3uZq/oymzG6QxwIbLUOGBAJb2T5t1jDjiLk7pHbHmwWuMqcUZgurelgpNw65kGZPbXQ0uk9UgTI7SOoaMRA65v26yl/HSgzEy9clJaeya83OKLI9tu1bjMMOfa3JlVD59Bx/pL7gy+W/vwC5FlZd0lu1TwhhhrI36OtdjOSJUClNqhCvrEZ1MmFja2MBnw1KTv2DzsX1+arzHOJ8rlISCUmVobmcLNZAkvP7mZ4BrZIKhQ8NrT997nuMnnQ0i3PVAVAoKTohYdO1qt8cc7TfRbyRCSeEGU1YIdkdtFimcoKgyf0Ncq/wMdLGyCEzPl79W0JccdD7KTfluwQJrO2WW6mxMWcn7JJjyYtEvOtrzTgTkhVAQn4VbUYdC0KnnGzzgia7jpGcba5ThpKRsQxtGliLnB/vVlQM3U1cjNxnxhfqPjthlwPdQZZjgYuRZbT46FFsATBUGZVPIGCtIEfY1T1Kb+kpgxGhipGz0KCDIq9yCi3UIdKEyJftjF/zXwmhJfiQevF6hKWASNcGrKl8Hy+gI/gAwzBkx7xDFtPu0ZLT17t42g5R1XYS/N9OiJhv9HkxNjypf4kvPwAgxHTxH6zgUV0RSOlsKbPNQmv1FqXd3uw6jcqJcYgz486auOZlyU6pIUb1UarzzRGLFoOLdeLcBH8liz23+5dY51cSRXEQ5bk/vxgiOck2Iy2O67lEoUY0NDhJi76KijKUDBiQS0FhB6zMDqf1l0Q8mU5V9sqdsIiHl+ZHEUeMjnn9BcP87a4SPFfrCMCsY94nvbGreUgiJrXOspvkasA217kRdslF0TcpsT6pGmZU1GqO1UOCKgihSRbuepz4S9bR+j6l2SaTEo/jKN7DhXrT9n3+mnuqOCVTPUZhTEHGKExAQ6VPqV+6mEci7qVt+OfPN913549QYnEXuHsXw/kpRm/Rs7u4WCd5rgsVWqrWrta/+33FOrmm5/brOw+/SbyRCQfoMFctTVwyd4cJxpm1LMI1WhpaaXA4bvR+AiR48kLmlLFHLw44Uo95uPqrZMWr/5JSDFvW0NOCUZBrcAFqn6RSxjojD1s8wHEaz1/KUsa8HffpZxpFsgk+bhzHcUmPnF2bdRWSS7MgKZnE2xRa0dOKOkRmviUASXjE8Fgdc8xDduNNdLNHjJGjsEIhfNAfsJsrjCRfnCooTpselYcHVcOyaTYOhwBWlWhG9JkQJDANj5j6RyhJ4OHM9DhzI0KMHElqx+ywxYfxraQ0LUkWZhkOWNDwifwtlTvlmz1siXyrVIaS5PmyaurndsavE2sipRJQErtzVxTKdBsQzyhMKLM7tD7xZVr3tKsiPNbssZ2/i5WCzKTEdRYf0YQ5q/YY78+gUz4WMRRmCy1rjpKm9nNW7pgQmkstu/NIi7LziwvE5PXPSCcO2d8M8RMs+eUttYtkwxAXuFBTMeVEpXlMG1YJKRW6Vq29wSReB4GqgxWvE67rqo+LodQQq4fsZO8yYIuVWVDHOWfNvWc8Y9IiGy/N2dTm79ZmbStSYnRlzVYcARlNB9/33RRpi33yfMCJ/4qz1d8TY48tucEobuH1XdBwqo448929aR6RBD5LlOQvJE42cclUnbGKGXN1xiKe0DzTYv+24zx5XzV3TEmp1X2UWIZ50u9bJ0zfaaslaPXVHKrfdZzfX53AQp3o6+VK7ZvFG5lw1va+K3eKUTnb9i55LFFRJW0mHI2s0Bh6cYhCUVMRJGxMx47bLzbaSIeDR4i/zkmTHvoBJUNtWfiWz/U9PC2DOMZg+ML9Fcv6c3J7g9vZjxn5MaXVZEpwISbdLmJy9owtM5cW/mVMbbM8WgyCjoKLYJVwo8hTKyykPdUwXMPHfXRnGOWBsjNH6xsYW49eJ5zacH+ZCG3b1nKgMqxKlVmhRkzan+C6YX8AWg4IKtIjo1RJ5TLI2ovHJSM4NH2jN5XVrA0cyZSFzHG+emGv/OqQDecB0kZB0GR2sgFypHvav5K8q9RwI6wZY7jSfVGrMW+Xf0HfQKYitks4fQON18ycZxVjsm2ILjHfLyhGlPlbDMw+21zfkEedJLOxdkOCBaLbHOP5cFpQavCCneh6SHx5XnTp6nQ6a0osWmUb6/NwpVijYPQ2EAjRQXSXfibiMZKnQbq0zMR1Shxn+DAnU32UTm6gY3Nzw2MJ0Sfoflh2hMoUw/w2Q73Pgb9Fj3wzfzzMJzwkJfmrz/syV+pihLhg6Y6YmyV1bKmbAoNgRbHDgGXIWUhBXwbs9O9iMOyEbTSaqSQunEKRqwE9NWHPvpcqnebT57yD1rEmmRbZLXpmh9Plp5c07c6TIy887ov3YP3siRSMineAZKPd+OmVoIWXbc5qd4JR/a7C9iyaR4S4Qncq3lrlmOwmIboEyf8adYffTXgiEDs18G8z3siEE0JChaxv7sok5r9ROZaCkhG74fqGCBiJCWRA07XSFuzatzeJ6ra/jhXFE2ZM5ZQVK+a+RKE6yf7AQi2SpH0ncOj8glZqaqmZOUcVFG0IeCK/Ur/mweI/Myzu8oP4p/TEcifrY5Qwd4GV99iu57b0gadNkjE5MH0KJXzl59xTn3LLv8P3eyPaADPfEolkKs0ofDevgVTcGhF2cqGn44V2nlAFnSwMQpUSHhaLUNEyDzUZhkJM8t3pqsKjOGfZ5AxVRl9rlsFzog5ZhTNW7vg14bBx05o5/xOomgUV50Q5/4LWQVoYEgT2RdDr3E4YxFFKNiqiJV2TkzryZbPobCZCQhH6GZF2M8jdKt5hS26QU9APfU7VCZ8u/21XPbwKFyZuvGGeDaV6na99ksYhXoVQ6lBgMSRjv5dqAMYrAAzSack5lvXnVOqIcXlAHktEHRCVZ1+/h0LRiwMGoeRMZjwJyX55qPfJpEePEVobiv6Y0/pLQqyZVh8zk6/ol2N6Ie9ItYo6zl/gipnEKo3q07irXWCTgZylkYapOuHn1c+JeA6KP2IUJtSqopWaVZwybR+QqT6F+mP6sSCLFoViFEaUZKRtVGQmSx66v/paguV6o7RVvEOIfmMnUYXpa6DWIpk5YJTfTIK+XZtJa0OwgUU4ovZzkvJzYNXcv/I6rO9dafeJ+G4GdY52c775RwkkeCMTzvqlNXrngnJvZxnAikaWrGSKiNqw+B01IXp6jOiHCYPYY9/0MErIs/TajsMEH7fSQq7YeK63AT6tNKcK9vLvsewgmYKmlor78QgdDHnMMCi2wzWK3l9iyWiCQ6IQuksppOSgJcF3jSgU6RhLnWDK7+ZD3oo/QgvEmHbq/2I3I9ORoWnJVOC0NZw2mqUTxkYjAj5CFYSjKnLmUlKzSrAi9CSjjp57+j7LeMJx9WucP8HoCXeKPwNJLSQXa+bqCEPOjXCXXA3RCNf9beZqwpR735IXSIoQFzTu61SoXwY1Ta6sJurN9bUqWXDfHWgm7YCvlp5fx/uoqLjZ++nGFVVj6Ic+eYe0EoQy9rBmTOteXTSxyG6kyim2xOg6rk0iEX9dDz7GlsYdk5ltSrON08n24tU9VyKI2myiY1gx5QmljLnmb1CQpXawWs/2Ak+k4nT5cwDy/oCMcuPKWcoIn13HxZple7gBBKyrm0jcSDGdh1BkN+mZ5AxqyKmz6yzcIc4vLtkMDPJb9PVup86h0SojRMcqnhFV4KT9ilXziNzusJd9BxtzWhqOpeIwfs7SHXHL/ogdbgGd11HsMchvsmjMS5GTCXbcJqkj2SIQaKUmqtBJ5LxEHkuVGz8cowsaP2fZPunOTdgqv0+pxmzLLXJdcKaOmPkn1Kr3PMxcDelnB2jJyVSPOsyp25NuQ/WPO97IhAPn4pmQdohKDHvyFlthTBsdNXXSMYvJavpQ7rEKZ9zgLu/lI3yEpnsBq5CK6p1cM7RCpiK5ovORiVReGNY5IQzp08fJLfqhYEvnIGxaJlolMzOhh5Lt9LuSAKeHTRqKa1FohEKg0IJoGGeaSNJFm7aBM9cwZbWRzNmSknFmMCpSeUUbhEcrw5eLQBM809CiECY+Qwt86k55LF9yy7/D+3ZIoYUiKCqv+QpP5c/IzTZWD8n0gIp5moeFOSEGRvqAQRzTI8d0atY+Jq3lnt2jVjmNO/uWynuNkuKFpM5XiWn1Kx7091n5H2JEUeiAioILUHl46Kd82vx7MjPkwHyAwVDLikDgTCX03YhdroVdxnHID81/T2sdX/FLFu6Qunl04TwvK5SXekIuA+o4p/JTVu2TZ67J1xnVJUWMun1A1oFI1nOcV5mSJYvl85ZQxNH4OWiYqj51LJiwQ6kVViKtEm67A0L/fwJgErbJMDxVxyzkjCrOaMK8G8QnAH8lCxZSdHDrgIn5hfNKLamquUfV3MOaPa4V30+qB8+YuoWYfIgCHhMNJT12stSS2g7XKEPOvrpBW7asZMksHtGwxMsAQVMwwtjkDHoW1sCFSIujpyZIpjirZi98jhr3iMY94gxNZvbQKkuwZj9/aXIXyTvn0vTPBCq5+PORafUZTbaHtqkKq+KMyj2PToNUtbdhjKg0R122h/9N/qaLNy7hCIZx+b1Lf7aWv8hjwUByllFYyQobM/YYkinFH9ldcs0l5Jly4GIiY/oYOao9p42wnSv2i0iIqcLwEU7iikP9hDImY7RAoAoejVDqRNlsQqAOASOysXbu6zUnhw1UGlKyWfNemg6c8LRtWcaGQMRiGEnBTmYotKAl4oKw8omHc9LAwnsypbiZpbK+7eDgZczY5oA+CbCgRejpTkjUWUQ01/V7XAv7yTMlhGTFYAYJNBCuMYglhaR2pBZhR1I7RilFrSpOzQOW7ug56fXn7tdLdo6J77D4xvLrSg0pGBBi2iSsjemOauFx7ail3kgMGdIG5Mw/ovbTjZjmY+nje39JHgt6sYeNhrE6ILcDHlySPfEdIkptnjtPy9IdsWq+unQtjJ5gO98cJSqJe7qnL0jSmlyP0mwGaGX2SriMhGhzgGa79wOslAzZwZIxCkMshplvWXbPqur6rZOwDUCGQRD2wg577HAiZxzqr6i79nGMjkf133Gosg10euEOL5zn5XmNDxUz/2Qz/JZLSSey6CDn2+Y6Jhr2ww0UiiEFVtTm+O5H+KL5TwAMsxup9eXPcLFGG0suBQuZ89R/mqRpVhfv0cUkLxcQfetK2ncDfU0txdc+f2uvIOdPk9L8CywElnWab871iEigMFsAz7UftRpjVdkpoVgm+dvMdb+Dm6dK+ZsIi75afHuOvd9mvHEJR6k+++ptABppcNQ8bT7G+RVFOST3GRMpeS+/gVVQ6oRcqj1dVZM87kNMlsMCFCpBM+fO00TPzCserBS5UkyytMBnGIZhxEqWzHXajUQCGsPITwgEvgx/y7J50rn3tUzKD/gz+TPKDmatBR6tkr6XloyBSS+XloSKG2pDHhQjmyotSAlJCUxbAdLcJnRqAKvosNEyyoSsI24qgf1mwNINNpWX6j4/08L33B3m3OCiYr8nkEXLjt8nSOSJ3OOzeMSWusFOu5+Oo3MxXe9yZ/WDFwxIL0eMLUr6WDN+hpH9atbSXxeC4Wbvp9z1NxnbJG4qXSty6SMnYcVCz3G+xlAgnYi/UTk+5heOZcFD90u0mM0wXYWUUMrszgbFlRabSNOe4NSKVq/Qkl9p35BAKVOGxTtsm7u0uqbJb7JypxuzuyK7Rd/sUaoxvTikVitC9HgzpmpeZ1bm6asdythnFMY00vJA30v+MdLDRJOkaUJOLTVzfYaOlq0wIccykDwJnfp8Y40wyK4TCSyahzTumLZzOI0xJE26uLpQTWiMHuHDirPV3yNiN2CIYZGM03I12PDmDuKEQKRaayEqTdGhNpuQ3qu+vYaPNZU/Q9Ds2ffYCtvkwXbKHIaFnqSZkDojhCU8AyiBSJkdUOgxuQwwks7PxZqT6uPXgh0bvYXRfUIYvgBF6JO9dyf5BKBUwbB4Pz0PHVpwbUkO0LCkDSuaTnIrN1sYtUUlttPte7Eo7DeLNy/ZwBuYcGJ0naZTTkFGJDIxezS24ZrfZWIy+kYYWchVZJKnxePhSlO3KQHt5Em+vw3SSblEai+cxYojdd4DHrkxWsYIguvsuywZes1sJuBwnKhDfAc/FVFEFESFjy1nsaGNthMaTFbR2+jkleNSW2/hPKvg+Ux/xZIT3q+/z10pKbUwthGtoMs/tB1X5uEqcqiOmMce9WLAUBt+OEkItoOOi1Z5YekVlU+7/YWLfMQ9nvIFN+W7HLBNGwMrSe27URxAhCiBNiy53/wXvnhGS2wtK3QVcEBYX5fLL2HEo1WGVhmtz1KrJjqI4RtXNxHHKEwYaEPfeDIVyXQCVewVhhAHLN2Kh6EhKE9Gho0WI2/htOOkP2HuntC4s00SuHROnTsjouACJifEiujrjmyYZgBalZfsy9fnv2ye0NM7ZNJjJ96ispOOaFozsNfoywQb87SAxhNOVz9/7esgknUt2JRQs2gpY2rR7fld+mKJgCPwSFbcW/5nRAz9/L9jEEsypciUYicMCPwRK1nyNH6ZKgpVAorCbNHTO5y1965oAflLQ+5kyWwxqtxYmUNaYAF6WlMoYZSlNrDvNlK/Wqz4tfo7NJZtuY2XlkU8SfMXWpYyx8YtemKwsY+K79BKy1E5oY5zZs3D56pI5yuc5OzqOwz8kKk6YyGnyavnla9v8Uy1BlwiuabWotEjlOSdlNGS4JedBUECvogo6vYJMbaU2W2u2+8TdKBf7OBpqcI0Qdn9YgOYucrE7h9q/JYSzm9+AUNcsJIlvVhyKy/IlWBUDyXpgY3AWRP5m+mKHZPzJ3lkZD07WYuSSBsUbRRWTnHcaGatcK+qmcqSU33MKp7RZ8JWmNCj6HxlItdNn0ifL/wpD+WzjVjoKp5xtPgZMXpu9P8ZBzrh6RUKiybrdjpLH6g8VCHQRk+NY9XZFHgcTp3Lx0xlzuPacJBn3OkH8m4RjVE4bjSrkBLX2/UBShLfpGeEQnsK7dGShuZ1yJi1iqd15D81n1PLimvhBrc4oCeGQit2xfKO7nWtvUjlAz/3Jx3bPrGmRZJNrnSVgaDA7hOiQ3etljYsLxAmL0eaUZwkpsVvgcimMWhJLQIlYCSigC2bZgxuuQv8cyCRXYNEtuIIHRVDhtT6LjM7ZRoeJXO0sOxQY8mULMTFFRtCf3kfHSt8CFeSPX2Y0saEZFsvvFvZHRSa7XhAL/Q2P9uqAxabiurslXe3WvXxOEw0DCSnio4Zx3habnKN7ez8VY71Ng/ym6wdNwOpqogxsowtczWllhWtTzMXJQalDblOfC5lNVPJU+vHzzpeUf2cgkL6uzZVjZ1nUBtWPLYaPEzciHFWkKmUcHzHd7se3sbjaKQhjwV74WAjV6UQaloecUatKmYc07DkZPVxp+m3jnXLSFOYMZkebCD4m8SsyldHgm3g9OebJRGLkjzJ7eh+IrfqCWZDAQhU/oxF86i7R+Vm7hxCQ2nSOrPRbCRwXz9gHp+yYM3hiRg9umIj8w8zfksJ55tl68PwGUop3maAvtBK+nzueejn7Ko+Hw5KBiZSmoCS2JmSpR3/yicZmFyBNzDWFuV7KC8sVEEkpF0QC6Y+wTH3VZ9CJ+VlqwraWFGFKbkM+Enx/6YgY4AlN4pCSQIEyLnm2VrlufKaNhhmznLqEyggF41F0dc3sSq1y5bBYwSKzv9mbTk2bRXHdUKufX+s8Z2MjVWpogM4qi1LrzhpFI9XkaO25ilfEGLLHbnLUFse+jmP5QF3/F0+HPTSMXrBKsU/j/+CVfnPutokuUcuZYkTh+92jrVJUvEJbLBMIIKXtMjSjlh4maXxbxoJETSi8gYIuKiJUfh0DvfqFTXtxpStlQqH44kkoEQpY/JYsIpnzJoHaJUzsNdQaI67lsarxuXkkHa8gjAs3mWkDpiGRzxd/f2mZadVwTDfJo/jzW/lsSDTg24Rf7VkI1Iwyu8wjBPKWKJE6Ivlx/wAEWGr0BR6PUeEgzznz9t/kSw3JD2g9+Ipx/I4CWFSETpvIKtKhvouZeyzlBnT+ITGn9MDcrOdPH26dlFqmyYi4Bqll+kRheQYndPTO2gsp+oICYq5y3FREsE6xOSQqyqWMuPMP+qUCT5IFulKYUV46iIncpJ+pn1A7U5fMnT3zKpPUKqPy2t6aoKnxdM+B2p46b3dIGOTyGsug7RVjDVtWDKv7yX4gq4wOrXU1n5Khd298Dm+S8RLfGyxGAy6E+5VnbJHQWbG1M0K6YRlg+S03n+N2dwffrxxLTWAs9XfUdlT3uEGMSaJ81zDyGqUDMlU0gVbeuFpZTEbfkZk3mqWPg3hezqSKdjKNLoVfh7+mqeLvya31xhnt8hlQBl7aBQuRuoQGZBz3d9OOgfSUoSCO3a4SRR1CKxRQykZprbdqpsbbWWJvFl5TeV7uAAzlxaggREy3R2PaIb2/LiNRHQU7vRarpdCGwQXhKWHU5fmQ8dN6oO33dC8byI3e8K1WHC3/VfUnQrCzLfctSN+lI0pNIxtZOmF+ytPFTy71nJgDYs2iX4+9Z778VeEkPzkFZqz+h6Ne4pgUSp7RW21+IK229eHVp2n0aZPfx5fzP93wiDw/fYDfBRKEzazr6TekDGIOS2BKjbUUnPKA1buBGdqGhngaSnMFiEG5u3jjmTacNFd9FVj7QY6sNfI6FEwwMaMkTpAlYbaT5l184O6WHWk3IRmnKszpvOPXuv6aFVS+zlP9OeIUnyJppQR74W36Ylm7gJzByvvqaKnpmWqZlgybsuEnlbsNGn+OVVTpjwliMfoVM0qFA7Ho9XfPFcRtM6AmM3MwughZb6bbJNDQ4gty+Y+y+YrBvldhvqAZThh3jxkmd/hPf9naBEWLtCEwIo2kbNJbrORwD39FTbmiWRNjiAchH1WbJGZkrk54sniJXDoTp7mdHXG6flVe+376sMZoa2xqqTQo83vN+Ec+JKQcOnn1/YdyHlFJaIo7A7CPn21QxscLY6FRGqpeOB+QdUeoVVBnh3QuLNv5NXzzeJ3Dyx4IxMOQNM+oTKOXlQYlaDMEahDIFPJjjlTqR2mJEGbYyfZ3tORmRPuLxOA4NTVtATe5we8W36fU3XGCQ8TgiT2USI84owqLtHKoKIijznjOKSUhCKzCnxU6JCOY+4DPRQDA6rj9ISYYLoLJxu9tcpHnrZpJ1vqgpwkujlznr3ckHW2AgsnxAgDG8lVmlG0nd5WzyRQxM9PE4z7g7HiZq/FSiTTHh8UVVA0XvHV0jJvLftlZJK57mcCLiiGRlMFy/0lPK4Sj6enFTux5E78PpVacswD6jDHhzrNYEgInqtDyMy1SyTA3xRGHWKdFAuQK14BTxOXaQ4AqK61NjDCjs153FZ8pb+giH2uh10GMUfLh7Sm2SgK2LiPVRlP1UPuzf/tb3SM6yjtPrkesHKnLDnaDKw1loHsYnXBSj8FoIh9LAotnT2Fv0HT/1PO3P1XFhFdK2Z0xuloNSK3E0Z6QuELaklST0/4nLPmc6weMlF3Gco2A60YZcIky1GS86Qa8GWb5hz33M8A2NU/YSuOGOT/L5Yy3/BXmrjktPkSH1YbUuUaegyQ2xsJ3t1VvovmESq3HKj3+RflX9DTimulYCTSN4omKE4WwnG8x7x+/JwvkNE7lHaboT5gJx4gKEZxCyOGwxcod6crYlhLGJ2Hf8nfvThirFg0j3Cm3rTHfLi6Cr4InQeSun2sN/d1UX8K/b/YtFnX1ihik7RREhr+9jQLr4qEIn2RbuDvHljwRiYckYxR8R5W0gLUhgT9vV5GbvU0QxvYyVqsCvRNkjF/UpUs3Dlf4aw1fFWvWFEzV2mucDde72DIQ/omEcsiSXJGLcachowTdcxUDumpCfgdYsxZuGSRu/CeNgaWsWEhFdfCmP3Ckqs0+A8R7i0S/HmgDQOjKLTw3UHRERYTYu2tAVjRQIJCuwsAh7Ol4ILatOtqD9PWb9QGCq1ognDaGIYmzXSUCmgVKJTiZg8qr1g44d7SMDCRg7JFJDKwniwoPvWKp37JRJUUnf1vGToXQ1w343C8WM14vdsdoVVGbq/hQ40PL+ZIfF3EWL3w20bFd7kW727meEkhLHa2DkLbOk7cVwz0Prdkn1wUQ0ltLHUuy4AW2HdD+v1xah/5RzR+vuHFVO3TK1s3axn/XA/TNYqB2s87vbrOh4iKJqQ2SuVPN5XClKdsMWZEQU9rvM8ZsctKnXXj9RfFWpLl4kKpsWabSf42hpyZTFnEOQtONq0fq4cM7QG3w9uMpGC3EIY2VdGKta32kKXvM1LpGt3UQwZG4WNJjDs8bmo+Uh9t2lJRctpwfOneihQbNfeYvwtAny3yWHAQt9kvU5tvaAMJjpF0+xyBlTtBKUNub6S5iElzkRF7lLFHHhJgKFVqUxppGBRv0/jFlQoIaw+bqn30zDEmWaEYw0t06wRrdjeoMhGFVSVW9aj8GXV78szs6Dya9gnGTBibNC9DgURNL38LHxq0yliGk2SR3SY+2FrGKTNjSj1BUFTNg2+B73Z1XNa/+/0j197AhKPJTJImn8YK12Y0wZApxXYujOxauDGQaU9pHVoCfdsSECpnqL1mZC3bWcHClfx6NqIKgXcHlv0iUGhPTwfqIExbzcIp2uhZypIph8zaR5TZiB0ZkCmF6sifdfBUtOQYhozpac3CQdWJYIYIS++po+easbw1WHuyp0VPSVpCXExotNoLlU+Q5oPCkalI282i1i21QKqsQjz/nEJHYoSvlpa/PrH0DNzqpSH3L88UR41jFR0VDbfMkFwZMh3pG49Vnhs9TV4PNjykTKUqJw+WLzoAwMtMptaLoPMnhG7ovG4n/Db4BbGT5Zk20BrBx3RP2o7Qe8sOmPi/7LThpFM5bnEdaTAQmUjJjdKylSmuh1u4CFO+QyWBL+Uxj8KvL8nzXAxrhpt5wKo93rDdh8X7jM1NTt1XLJsnm2NNbcEUU/eII7MFYQcbyvR7YUhUbzOVz75mUPy8MnLrDjnqVJZLs42IZl7fx4c5270f8B35Kb2QRGWtpOfLR2FooehmgD6m6zaWEoUwMIqeOd/0ZKLJpEeMCeIL4ELSAlsrIGd6wITrAEmlO9Ychs9o/JyV/T756g59o9GiMBKZtmnz1Cfjtv0RZxxy2nyJUTnvxh+yRUlP665dHqlDYBoXGzV4o8dolV/prbQGtuQ6GbrNqi8IMc2ZfKgS4OGFC3rcwP9FCpTkZFmfXAZUnG3eA6MnKMkZ5TcppWv/Rk/BgK0wQaFS21QiXjucOf++VjXMzBFtXDFrHuD9EucrKkiz0d+JdtrvP9nAG5lwPHX7gNadUZffo4yWkU0ttJFNc5nTRvH5vMfYRj7cWpKrwFGds/SKofGMsgarAqUJFBp2chARctViVGovLX2qFFZOWDpY0LBQc3pMKLMRB/4Ge4Wh1MJOHjECdcjwMef+MvKgWeF9RLcdHDSk5PL2QPMnRcbQtIxsu1E2DlGYObNpez1exY1qtQqwcJpWB6wkcUqlIkoEKx0wAshUAkgsnGHpFYOYUFpGIrVX1AEe1DUP1CNyik56J7D0eqPNZiQyyQKjzuIkRqGNMGsNs9bwqPkOR0WPk9VHVyyGCdWWNMNSmyJ0An/nvf+1Ptm3N/icVb/mEzPjRvyfKZTGSmq7/Sx8xqH/mC1zm4NwM20KpGYlSz5v/gPOH2PNLqXZ5qb6kLG7jlWQKcFKSrQAeSgwkpPZCVVz+ZwFQ9OebBatZOB2B6tK+joNi7fMbcbmJm2saOOK2s87AqSn9QueyMe0pib468mdVC1ppKGf36B2sxdYTr94gQhxhUSVEIaojXq1CxWFZBSSrpEIHNWhq+IVQ5u6BYUSXBQO4zQBHOoJfXeuK/c0znlQdYKYHQLMhRUxthhdkukBMQYex0+6hbfFx3bTSvq0+ZLH+bvsytv8efUdekZYtMlSZKgt78UbtPE6K/MhAKUYhDSDWvmED2xjAIFx8RYrd8Kq+ZIXeaU27gwxGt1tCkI8T/hKMkStZffD10jjVIn+0LW5Cj0mZLdQYij0OFEfOpmqLJYYEvepJKHWUuPe81A+ZdY8ZC//Du/4d9NGSG7gY2Bua1bZik/df37lluo/pHijEk7y/VaIWEb5XXYYMDSGvoGBScmmNIHSpCRiJdJ43SkGa1ZOdcoBOY8qy9+eeKxSfGckDEygCUKImi/mil+uZuRYtk1GGwMn6phlPEkkOnIsKdms1ZvX1UcSEzAUutfJ4kSsEm73dUfOjN1OUTbJBugqlTSXOWngy2bBUJLjZ+UjT6pIiELf6I07Z6aSu6Xu0GxbWU2mPe2yz8wpJpnjTt8RolAFxcoprmU5qjnYfGdpNEoSwOLzRaqU3hrAJHMbC4SzVjNr04IzDlsEFTiKf3vFHbraA+bZn7nizvJNd1iNe8Tc1rTesqJJoA5dk6kBy3DCJ3KU2jMyTui6sCDJyqSNQE3FWeuooueeekArNZOwRx4zHqiPOV7+PemKXU6YSZh0fU4CHZy69Qsqd4aIYid7l914A4+jlYaVXWwM30RU8kOJc87UKWccbpBsRvV/o2sxyO9iVY++2gFgaZ7SuqdkeoAPHk/S3ltrBSqB4zry1dKhOoWMR2HGz1b/XyKO+70fsRWSwGkeC2pVca34cLOL97SsOAbChi2/TgKg2e//hBF7aPOjZIYY+gwpGIllvxByHdnN0/WbO8WiTaRdaaGJnntyyEqW1HFOE5es7bBDaGnCgta9HGpv9bAjW6YEXGQ3adoTrBkzzm4R8CzaQ3xovmamke57485o9AR/wQ48RJ/aX0wJwW8SrRJ7oQpMvj21S1ybKs44kSl5zBnGAkG6KijQt3udcvjsHwUceh1vVMKJsaGXv8W7+qeM4oBta9GShuxP68h+odiOglGRoiP/zVyS2T8o0mI8bTKmrWbaCiehIg+GpUsM9Icr4bTxaAncsoPN9/oYE1xV9ShinzwWVDT8eiFMTEbPaEodqL3qqgFYuoiLkTYEIgk5JsBhlQiYt/sWkWQHPXOaGJNTZ4iwl0e2sgGnDTyqWlxMKtQK6JuCnknzBiPnLZAA9G3LIKvp24a7QbNylkVr0zKpAlo0e4VBS85eIezknkyl9qGPwsjqzczoaW0YmMBWlsr5yieAAyTey6h4j3nz4FvRU1PS+wYvlaafv8WufZtRKBLQoXtsR76k5S6SOuOomLTsAD4s0+45AZfTPW6i50SmfL76PwhhyRN7Lc1lfGCQ306OsPGqVmK88G+uSySWEJu0CMUpU9WjicuN8OXAXkNEbUifgzhiFHtMmLBdXqem4tB/3HkPvV50Z9udn2KSv4XPb7LDLTSas7jiY/cZeSz5gb7FyCbTws3vizCmx/f6/zMtzUbY857/ObPqU0Q0ShJcdzt7m0x6XCs+3HyfjpagPQm36Bmww17YpcXjxLMtPW71LH0D1wqHFjhtNY1Pz9nSJ9DMI85QotgJExTbzFlRS81CzVnFM5b+6Bmdu6tCb+YvVko0ljIbI9naKjqwDCdXKkW8KJw/YeH6CXkmOVoMRuVoLFtco6SXEqRUeFrqOE+iuM1DQmwYZDco7S1m7hE/b36G1dvcyX+CQvHIf0R9QWHiH1OygTcs4QDJcCltGGhDxAvMXZpPbHVeJlpSwkn/9CRjtISCOWoMh1Xapf7RYIBVsJ0FjIq8PUiVRxLvDLRRWLgkehlme5z5MS0Jxmg77HwbAg+XhlxrBiaSadgrIntFWsyMUlRe+GQWWXnPJDPsF4q+Wc+aIjt5iwuK+0vD3CVOQuWTzcB+bomkSkkQtnMY2cCTSvFlFdnJFZNRpKc9pW0psgbjNT4opk3O/VWBlcgkb7rWWoJog1B0hNJCe0Lnz+O6BFj5bq5Eqp72iqRzdd9/ztP615R2m73y+0np1k+p3ckrkjql+3+9YWpfbHG8fvhkMhctoRu75kp132LQMdDGQNstSiFGDJqe2I4s2n1KjDgiRSx4p/yXG8fMiGcRT1i50xcOh5+NGKvNMFZJHxcqnEptmFwlCHYCI2gG8hZbcURGcpslgo0ZXlynwWav/I6vQ1e1YcmhO0RQvG/+nGuMaaPHEWk69r4Xh+/mdGOrGFvFzAWetg25aH5oriOSnpc2BArV47C/TyYlozChkYYTHrLyZx0QYsV+8SG3wx1ucJ1Y/jQda0gpsCcZGmE3N+zmMRGau7nRGs6TKegbodCGsd8hwMaG47YusCq9GwsXOJGGT/r7LPzTSz4+l8NvFCSS4G+ONeMOAt/SdooPrxMiRWe1rjb3x4WaKIGMjH4sKGOyT1AdsBzAZT8iEDlRxyw43XCtWn/KSXyAkTxJAOkhlTvDhbVFxj9c3s2z8cYlnBgrjuQRTnbp+z1ypRgag5FEhjQqkTxPakPfBPaLCqs8J3XB0mueVJq/n9Zs24y7XcfiwUoRgetlYDtLTH2rAm1Q6d9FbfxrvlKf8bT+NbfyH/Mn9m18hHtVGlJ+d5gxsKkqGNk2cWck0fzeHaYksPKROghD49nKGqSrUlbO8EtneVIFMiVkShhnsJsn4memAkbSnEZLBDLakGDXazn+EAXnNc5rfFSMspr3hrGrnhQNKRlapZi18LPa0DMwyRLfp6dTe2nhhFkLQyNkOj3sWzYZp31n8R2G+TYfr/4tZyF5iGTmAL3pg7cXLGjheSRV7P5/LTr5zUOJpmLOA3maNMNc4mqcqVNWapEQWlQJDeROU2Vh9sjobbhWXpL5XBl7vB1vYhGWwdESuK81S46ekTI5j7V8/cUWmFYZu9l79OKQrTBmEM5125ax4XP9MS0VNmZYFA2eZWw4VWc89L/s9LXOko/OFXF5V98pQmARMVTujNwMuWt/wjAMua4GjKxm5TVLH7BhSBY/SPwyHZm7yLjjhxmlyFR+6bsKJRRK8wO1g5bdtOBHTx3TArtSS+77KT4sUKIZKIvtEJNtiHzuT1nKnEmYMJICLbLZEC69wkfhsJNemmRwsxdYOMVhJcmttcs4RdfCtkrQovHRkknJUp73LRIMxky6f9dEfNeeqqjbirp9zKsv5LrThktCrD7U1O5441abqQHb6jaWDIfjWE5ZyoxFPEFjyaRHHkv2Oiv7GCaU0iNYj+/abCfVx91zU6DEdv5J4TWO8R9GvHEJB6CKUxAwGMZ+yJ8MC3YLv4EBr1UFtESaoDoyoKM0jp3Mci3P6Jk0N3BRuh1c8qZZOaEOiTiaKRjac8JmrgqO3C6n+issGUoS7PZmL0NLJMbItFUo0Zshfs84RFIFgvbsFSuMDlzoYBBigvG+N2w5KDR1SKzr0kSGJkGeK5+QVzt5zTiv2S+X/PFEOGsz7i8LKq/YznJ8EM7qgoWzWBUwKknixCi0QfFoJTypHHW380/mZJ6xKvgnE83Aeq6XjuslCbUmEaX9Jn3sZTmh2eWjCwthuNDLfp4J/dt/Yc6qz2mzfYZ6G0HRIycXzb70sSK0pB16S2BuajyehSzwuI0u3jhssSUlgchpXBFjxHaGY5AkaYwe0rqKtfOmvrAwp9lFS2m3uaV/QB4LlE+72xKblJC7nw3R0meLVmpsSN+fdTJIbehT6IRyWl1pVX1VdBbPNBATQbFxj6gH77DFmECSLeobxU6uCNHgQt7NDVPlXOpEFF5XGK5rDYcItlPy0J1qxuPK8X/zH1FiuB7fYStM2NL/EjSIF86osBggtXNHsUceLff1l/ydu8+Hqz9nLx8gkmaeofv+UgtDG+ibQBuSsnUT4My1eCJW5UTOOwDT0HIcv6Dxi0QMjm7Tgkr6fTmZ6jM2N5OlwQVb6tfzdErXt3Xt5r/X4XwfpyvKfEyQQRIXjRYrGVZylkw5br9AiWKmj8ikxyCOKUPJ7fget+17NNJyog43sGggmdytuwdf2zb8hxNvXMKxZo9ducMgDNmVAQObdjdNEPrGca03x0dF4zVt0My7GUbfOKwK3OpXDKzlrDF8tZTOBycx9Z9UkfurwEM/5Qv5e/blLf4kv0Gu0wvrIpQh2fOWsbeZaQxM+pwEWYZpo6i86tw511bNsfsu6KuG+4sBny8yejpys1ejJSWXng58Ns+4v4TdQrGdeXxIw9R1ZMqhOmfPTHtyFToxUMvCWdouyRKShM/FKDSUWnOnMGxn6fhFEvCg30E1n1SGaSvs5JomUxtAQx1Uqry05f3e/3OzE78oZ/L7CB/OmFVnZL0/ZhT7bJus09hLA3EfI77jLt1Recdhmlz6jMpHli7y1NX8rfs3tH7GML9NoUZJVdncZqVHLFRJ6xc4f4Tzqbrbyu+QSY8spl5vTcVSzZiFQ1yo2DZvse13EiSWhEI7DJ8RYktPD8nCFj2xFErhfUkpI2qZ85sCKYoOOXXq7zOVR/zKe/DwPj/me+WITME4SzPAVLhHnlTCvWV6DhJhOql3aEnoz1zBWStMW5jFmrP6c0QMRTmkiOeV3WP/EbPqY3J7ne/IP6eIBRmmk3DJUysKISDQEaEBtrOk9G1V3Eg0zdpAFQJNt8C3Ic08Y0fsLcWwre+y0CccL3/5zLwjJo8eoN/fpc8Wu+ouQQWmZodFe0jrZ6/lqpl0BXNyM6E0E0L0hNhiVHIZtjGjjBk5hjxatuKAll0W+gZzmfLF6t/jw5RR8R0m5jZ7/oBreoAC7jIhAEufquov9OfM/COcX10y2HtTI8HRL2vp/SbxxiWc0mxzEPYZasu1QncDxzR0PCgUpW3TgFwFGmfIq5LaG47rnKVLaLV5Kzyq4G/qR+Qx54M4YWjTzq7Umu0w4X3/52Q6tbUisPIJVdZKyzKcsFTbtGGcyJw+YkW4M4iMbOCo1txfps87KCOlDhzkNYV2uKioXLqshU7ttV+cdfwLk2Yq4ywwzpI2mpZIZgKTvOlaaXBc9dAS0CpyVBV8NCu6oX7Xj8/o5kmhm0dFBjbtzjKVEuQa0dfTgZFtCcC8tVRe+Gwe+aqd8b4bUmjFrFU8XCVeS+U9bYwIikwNcKHuks3zoFSRgtzu0rQnv/Hwc41MVJ0goohKRL0r7KYHsUcpJvkC+Yh3cbOLb6Onpwx7udns1uFc2a3UqY2pJOct9VOWMmMVz5KqQqxxob5gUXBe3UknW9LGNCCeukebmcG4/JCxuUkWs6RoLI84rH+ZhBjDAsQw69+kpESiIF21Mwhj0DCTL15y3S5r0hk9oZ9d7yqdQO1mG1jt+j4szZLKD5m2kU8WDYUyvD80DE2k1BCi6mDhqaLJFF1bODVIH64Cn7pTWml4t/yXaAwDP0AhPNIPmIXEVymym/TNHkUs6JNxkOf0jPAd3gPeo9RJ7QM6HURSNW1U6kicNqmav9NX+Kjw0WyEeUOEeQNHbc2cOqG6LlQ2V0VLhcZQxh4malZqmlCCr2iVvubYKGU2xMzKn2Ekx6oEgz4KXyAoFnqXLJYESb0DQaOiopFVp7qd4NRF7BMILHzbHeMFJXJCQuS5s67qsQkA+Xva0L1KfFvH9oYlHKHUE4qo8THyYOXIleK9obCdeUY2nbRIRKuAj4qHyz6njeFvTzSPm5oda9jJE8/iu2Z/88mrCxXr0kWmzjGKhoOSTj492TXr1tCGJHGznWn6Ns1ZcuUZWd/NPHJ81JQ6UnQLvpaASGTRGlbO8OUy46NpamHsFd3LrWKHutOc1DDKYL/wNF54XOXUHYKnCZGbPcXN0uGiUOhUPc1awYXIIAoiaSC7ZdOJuaBwMSHTBiZVS5WXzeC80J7trEYkcqtvqbzpeAdCpgyVNzSBtBiEyMiN8OoGg2yH1t5lEY42vvBK+mjdo2f36KsdvL1FG1cXoLKvHusH2b+kBZKZA3bz95lQkClFrmTjiQNglFBo2x1bSpyfLirm1Jv228gY9ookXvrD5iZLH/g19zlWD1jWT2ncY67aZvpQs/RHzKpfb/7M6B1ys0Vf7WBjTiCwkiVVnF5WgI6OJ9UvoWCDflypJafxAd635HaHEMcX2PPJynnddgnR4TqTNKuHaLG0saXuYMJldgejcm7pH7ATJpgonLaOTCn2spyeFnZzz8AE9ou0uTmsLfeWCt2drYtCRyEjU7DDgEJphkbRBjgKNTWOA3+DA25QiqWfWXpasV9IB+GPQOSrhfCkbrlRJIK1j8JJkwAeIws9HXi8Mny+iIkH1UH/1zy3WQeocTEyp+axfsC9+b97qaSNNbtscUAWs3NYejhL9+EV21RrU7ymOZdogrQZyu1+twFKm5Bgk97g+lFp/JyVO97A77Uq6cmELT/uKj06LbUqHZssk8fX4hebJJoSXvIiUqJow4oQ3Av4Wa8agogFFET3xrTs3qiEk5lraCyL2CIxIV9C1KwPc+YsejlgYGu2ektiFEJM7bIfTAIfRMvCwdLRkSoVTYBHlaMKjp4yWKXoG+Ht3FLotPPyITH7ly6mB8JXKK0YZanvvZU5chXIVMCqwJ3+krf6KfEpIk3QfL7oU3VunTEKlRe2rNC3cKvnOsROIm5aiYxsWlTaICzcubDmlrGMrOKsgdMmyc7oTuamZ0AQeiYlINuh9HwUFs7QBtkQQAsdKXRq403yisI4+lmNVoHWGdqgebzs82hVdCim1FLUIh1fqU/uLUsqTtUJlUw39ynEJXhwusarRJRznSVw4jmcz3lEMrQaEkL1G1dBWmUo0dQhIFEoOmWHe3XNiUzZjWOu5Rla0gLYhLRgTdUZedjtTNdiN7+LPG0aWgJ77LLDNkf5DY7zByzaw03C3PjkEFi1x88cUUiETvfzxMY3QzLVpw0rlJQE1DmKTRmW4YQgHgRaqROPI/pN9XQekcadoVXRoawsdfsQiFStJ0SHUSXD7DoiCoVGYxmFIQNlqYJjGivGoWBUJLJ0odNzW2iP7TT6fDQ0QThr0iznsPLUIbCXW74/yqh8sgDxMdIScARyLBbV+RIlEuka/Wkk7dN7RtNzmlwnoMtYe743WpHptFHTEtgtCj7cMsxby2Ftks1EB+TZ6ZCUubaE+YBp7L90oYykRXmaH1KrQfdngbPFR6+1Iw9hSXimmk7+OAlA4P1y8+w6M2QsN7Exx5Ix08cbrtBaleCw7ZOZjJ2wzVhbApZhyKmC55fqPtP2wSXk5rrtl5v3yfUogQpU+w0TTrx0DZQavobZm3RAjG8/Sb1RCSfieVL9HY/j33Kt/CH/uvwhkyxys9cyNI6trKZnG0rbYpSnn1e8v3VC7QyHVcm8tTxaaX41qxlow16RSJQ3S4OPhi+WNffiKe/LDu8OAmvUrI9JAmTlUw9eq4w85mQqJQcfhJoOPh3hoKw4GMzwQeGCYtbkTNvkvbOVBQbWM7BwULAhbQIsvcIF4f7K8GgV2MkVd/qenTzwZ3lKrmcNLH0a6C5cZO5bnnQauHnMzzknCN/tDfjjSdwg4dSF85m2iWBqJTLIGpREZnVBjIJRHq1Sshpbx8IZztrUVjxtIisf+Tg+5iG/RotFY6n82aU7FeKSqn2KCysa9/S5B3mN7LJ6SK6HuFhTt6YTLExtBqOHGFViVGpd+NhS+ynOry4xwo3KOWm/4v+MH9MzO/y4+RPGxjDUliJMiAKP64aBNuwXmr4Rvl+OcXHMUeM4CSsUJVuZwnt4pI5YyoxBGGPJGIQhA77LcbbLw84CWIklxnBl5ZMWiHM4q2/OqEjzx2vlDzfcj9BVI9P6SyR/i7HskoeCkUxoVcOT8BmVP7302WvJ/3MDvPV3h/Rc6gEDtUMba2Y++fs8sDnTOOaG7PKdYthpzKWm3NPaJA25ot20VtP9SZsyAtzp6440meKzRcv/Vv1/MLrPB/ZfsRV7m83aAzfnXvyU6/Vd/kQmDG3sZJk8qzw9mT0DK5cQoJn2FMZRmhaj03PXcwYfhdXCkmkY2ZbSuLSB7J7f2huWiwM+VsOvheM/Xf09hd1Bi0VQ5HYfH+rOY6Z+BQCB55zwG0iLdYK+K8lQKiOGFmKgcTMO40cM7DW2OOikjNwlSP2i/oQvwoo6/5BheCcpPUSfJJa4TpmNqOwtXKxZ+RNWzaMkqaOSUvuyPcS5V589vUq8GqXhnPT826qI3qiEE0LDVvEON/kO1+KYSRYZ2jSD6BvHIKvpZQ3Oa44XgwQaaDKaDjyw9IqFgxkVKpT4oNEatvNUwt8oMwI7nYJyZOEU95eJFJdKe8XWYsKhHmAxm5ZN1knOrJUDRCLLJiMgOJ92qO8Nl7igeFxlHNW6O25PG4Sj2nRePQl0ALBfKAq91keTTWVkFAxVZGjTPsMFy9ztb+yzLxL4hhZmTie0nE7V1ziDfhD2i4adYsU4r9gdnwJQ1TkxJi2yGJNIaOU1s9ZsSLF/xS+ZuodJm0oNqMKUaXPvCgfQbragSjC7tO7sgqd8x9APc5rO3fAqbTbnSfpYqI6jYDaIONdtOEUyfGyJMbHcfWyZUqFcyY3CMsrOIbMpucdOqTsd3+2oacOAXEFp0v243txg5eHzRctJXGLR5BieUF8pDvlsGL2DVnnXZgkoydEqw6ichmWqOvQBCsVMHdKGJUt3xMfhHoXZYtvcTQZkYZ6GxlfExUVSSZ9+foOhPqDHiK3O8nyot2lpOHQf88j9DJP9JbfYT+hL09leqDSAn7WaaacmYbvWbhKdjQxNqoJOW8OsTariW8U7aLEblJ2LEYmRA93npvyQYa4Y2UjfBHbymtI4+sZxp694sMr5ZKbYyhQ7eU7Pa06qMhGUu65AoT0fjFdIV6VridDds6QAD1YUWhVfu1jGsGJV37u0SCZghcUHg/Nf5zGTYOdpIxRR0kepgtxsMTJr1Y6kuHBafUrVPEzX0RbUcQ6AqJK4OU7BhxWLeMJxXBIIzNUMh6OV5CtUyADNhNwMkoI0Ci2WgO+Ec78d9KeS/mt0Fn77iNM3KuGIGHbkFh+YHfq2G+IH4UmVYyVj5Qyjtua0yXm8SsZOTUhNidIk3H+pYSI9elpj1PnwONCZnUlqNfWNY9jpLyWhzASh/qAcc7P9V+zmilGWJG3WCLWPzgqO6shBWbJfuE0zRCTxXACG1jO06QXKVaAVtRHjrLyiicJZA3MXMCL0jOoWyvMFM/XFEyCg8mk2YyRyrXQUOtB03AaRuJGnWbud3l9a5i5dj+vaU2QNw+EcpT2Tjstz9HSH6arHSVVyb1FyWBsO69R2vMkd9uUGC1lQyYIzf28jbng5dPKEMXvUYZ7k1v2z0jexmxNdvcOMsdogjWbPtOLOn4mcLXObTHqMwhaRwIkcc4aiaG5ilO6+KV2/yq+FVNONX7cj846HJCaynaXn4WZPU/kRTyrhsPIbFNrXPKUky2nF9fJH9OIQj0tzHKYs/FOMKugzwUvL8fJvLv126w7xeYsWiw9f394wekJuEhx8EY6IKlBKD41hGIbJV0Z/j9qsOAgTSpMG748qwYhwUAaMBL5cGp5Ugd1ccaMMmw2Qlki/48z0TXpufzgR3nN/3mmwJXWMpGieEG0jGyh0y1aWLDLyrl02tA0BYek14yxjbCND21Jox2mTU3nNSZ1x2ibljq0svUON1yhJLWetAmeN4f7Scz8ev+DZuxxX7cZbN0sLd6z5+oX08nMbYsV28T36MmEcdhKEvTModFlNE8ZkXQuvr3bYze/gcDz1n9KEBU17gvOnzNvHPM6SIvdaJseSo2OOl5ZWahb+iGn1KSKW0u53fKJvVt2sRUi/zcT1bcUblHA0pd2mFwdkOsmRbFSWnaJVEdtBoI/qjMPa0NORu4MVuQosnaGN6QWzyl5YvJMFdd61kIwKaR6jPY3XaIlMW81/OlI8cQuu2x77hWIrSy9ioQOjrEEB7w+Fm72OLKouyJ1EofEJXryuNNbticBahTeZr2mVVACsUqxc5KhOQ96bPUXPRHZzx8D4ZJUdhKgCPZPUEXbyhp52l1qBqR2oOaozKq8SrLWNPFxZtIy5EzS7W6cY2xI7zs6iLjitekxby8xpejryJzsKF3IeVwULF/m/3BPuLf/zlZWJYNB6uNEJAyjMFjX6N+47v6jtEcIMJZpR2OK2mnRS90lDrI2R+ytHTyv6RlHHpOrdBnjc1KxoWJufXdcj3h1YeiZudOQKFbHimepkYZ3H4qXHKFKkobDdx6pzvkUtNbVUjNhlT90iEmg6zox6piUkGKr2qCPQvqzVo1FS4PwU509Zi/TU2QF5NiCPBVtxTEHGbTWmUEKWJV5L5SOnjafQihu9VIX88aTCqMDCmVTphHO0WlJDh0cry+OVsFtE3h2k+7pwmiYIXywMp03i9BQ6WSdMmwTUmM5LfBSulQ3bWcOd/pwPJw25aZkMZigVaFtLjMLxYsjRqpdmmSothrqbAU2btKl8sBL+mo+YxSed4sLrt3fOHWjV1/3oc7FVfsD1+A4SkzndUpY89h/hY8uB+YCRbCVn4KBxXe3jxGP19/DaMbWHG47Ng+V/ufQOKTWktPvYrpUco8fqLYBOIHWt57dOFK+vRPCyTd7vO96ghOOp3CnzfMrSTWhV8o9MQ/KkmmxVTC9RWfHB1inAhvB4WOWcNIY2Jt+ZpYPHK+hbxX7RspU13eDUJ0mYZa8DHaRvv14qBu2AlQ/cXzqWzgCGkQ3slytK07JbLtAqVQk+SAfHLjr1ab0BH9RBX3LyvNmr0kLvDHVIrO5CCbURSmfO2xuSzsfFRE6tfFJIyNR5ggsI0zpj5XUHWjhPPAAHZZLdGdm0i62cYTbvY4zDdGZtT+ZDHq96ZCrwwXiO72SBKq+ZuwwXhFWc4sNZp46cjLa0mI3B1NoUrFYnWDOkb/ZQ1uLC4krXzt80RDLa7uUZ6OQvVJrzynUNgbYq4jsX2MqDbtKc68AM2LKK0kCuI0sn/HqaQAQ3e4qRDbTdYGMrjNju/Yjaz2n8FB+qS8lCqz6l3eZAfwcbM1Ysmeszvpr/76xbjO/0/0cMBoejkRUhzC+dT2o1vrhFpFSyQlCSE2J9wfzOY8wePbPDMIyopeJn4d8RY+Av5F+zX1ganxCYSmCSJdHbSVYzti1905Jpz5NVj9Mmbc6mbaoGdzIwKvC90ZLvb6WLEbuNzGmrmbWKe0vHk7CgDQlKb1XycqqD8MU8GRLebpIY7d3BiuujMwrb0O8tUTpQVznOa3ptTesVLmgqb9ASKE2bJHZaQyORnoGd1TW0tizN09cWuFz74KxdSlv39Dnlhmd9hpQUbJXv0VMTdsN1tmKPlmS/rsXgQp1AGxj6nRCnQThWT/mk+j9QYrlWfEgZ+6z8CYvm0ZWtwBBmLOqr779SQ7Qq2Co/oFRjztx9Vs2DVxDMfeUrw++b8PMGJRywuk8ZexuHTSMpeaQSPjK2SU2g0A6rPI1PYIHG604ZOeDaBOcc2sjNXiDvdNMarzmpcyp/TnRMNtQBJennlQhNJbQx4kJawNqQdl6N15Sm3SB9QjcLKbQnqtAJdaZWV4AuQZ6/vKmFkQijn84VX1UVLY6aFo1mQE6uNDdKw8AYch037TxIAp5nTcZcIk8qy2krSU1ax01LL5FLExhiO2sZ2YYY4ddPr2Ek0LMNWiUQwSBrNkrb8zbjsM5YOcXcQR0it8J76H5nSoUiEpJQ4TMLqA9n+ObsXExSDKjeK4p+JvfKzX+JQjqJ+cQqdwiKo+pXqEJz10+A5ABLp6htVaT2CXG13q33DfzT7QwtGX0TKFRLpgO9DtF3s2eS7l4Nh5XitIksvOeBvs/xPLXArNmjtPssm1SJaDWmn11Di+WUxxuUGBFELDE2ad4kLZ6WWThk1Z5w8QVfJxNQhNhcuSDFsMKLIYi7xH1Sasgwu0FPTdDBMApj/on677AodrLEY8k1DGyaB64JlyunO8fNltK2XJMFA9uycIb7ywIfhX6n4NHTjtK2nNYFj6uC40bz1yeRma+YmIx3zYidQtjOfUryEsmisFNo+l6zcvC5U2S64OaqpHEG0ylxPD3bYtlmPFwMeFKlanzmFD0d+WA8p+yMFAvtuVZ4vt9s86Dqc6g/Tppjr7BOpgq0T4wO75colZGZMVrd2FQPVvexqkzISl8Ru5kJJO6NCxXG5BivceJpaVhKUgeP0dFIQ4PbVM5ZzLhT/Amt1Jy5+xzFJGWTmwkuFB1wYfUK6DDBdiCaTPXQYpMihQ3fikHbs+jR31e8UQmn9Qu0Nhti442y3SzakPLzyiUoZeY9p03Oz0/7LB3sFSkpnTbCvaVjkpmu9IfTrvQ/bjQrL+wXntu9NKwNXTJwkdSW8ZqlV9Qh8tUy6UUtXEmuzgfRh5XiaR2YZIp3BulBeFyZjZmaldjBqT11EE4bjQtw2gqVjxw3SSDUdZghj2dOTRUURd2jDQqrUg9+vXgYgUmeKqezRlj5xG3QJg2GB90Maw3dTguMwweh8oaFy/jldISP8FZ/xSSvaLymDppHq4L/8BSWvqHB4/DcV59y2nxJpvv09M5GMt69QAgxvRSe19tBxa7doBkW76DEptlGbDcmWoIBMZ37ZLqfTQDXPRbrWU2hz91bAXxIBO5pq5ii2M8dN8olWgX2S/BB8dWy3w3SNU3QSDwHIAgaq0r2ej9EYzluPtvwkNax3/8TrsW7XC/+VzQ6ed3EJQs546z6/Ll25BruvG3uAjALCVBQuxk+rPDhbKNBd9GpUdD0swP6ageFYi5TtBhC6GMxHDeKlU9V8/qZl65NNnOKNlpy7TE6oIgMsxqrPLXXtCH97sprRrbFdSoeAEMT+Mm2ArJOVipsNlRNlzCakLgzPrCZmVqJtEFDC49PtwlROF6VLJzlF2c9fjl1bGfCzV7aJD2pCozKSI6kydvpHHG5ft5e3FrazCwIl+D3wS+6eYggkncw586/yp1tfITWsUZGTvmYVf+ntHFF5c/I4oB3in9GHgskKGppeCSfcVJ/Rt9e4534Q5x4luoE792Gs7uWRpKoCME+ZwSX2PttdwyRun1ADazaJ+RmQuzWh28DMfZ6Uj+/vXizEo47ZJqfAtubB05I8i5GIqVxG7FJSLuhdwc1TVgT5WA7V4Bh2kb+z6cNgrCbZfS0cKcfuFG2SVSwm/ksnO7mILJxPDQiLL1n4Vr6YhlZi5JIIWkG42NSsC51+l6jIjfK9FD4TjtKS+K1xNi1e6Jw1kROXcsDecqRus+N+C7fz7c7PlA6p4VLC+qijVQhUihhnKXd+9KlK+K7tmHfRHZytxEjTdcgtRgL49gqlqiOJNt6zV5RUnnDR9Mh//V4wF7h2S9qMh348P/P3p/9WnbdeZ7YZ017OOMdI25MZHDQSClLOXVmVVeVu5B2t2G4AT/aD4b/Or8bsGE/GW647XYZVZVZqZRSA0mJjPFGxB3PtKc1+OG3zrkRZJBSSpmVFOAFEKR0zz33nL3XXr/pO8wd133J36wWnOszajWnKr/PKE0ZxzG9GrjW52Bh/ZaY8+ZD8cXS/TeV8oFV+2uUKgWCmuJNC0VZbo1+xJ30HjOnd9dqi7R6vUN/NSh+tZJM+f2JY/baa0JSLIcCpyOToscaQVeNrWGIFU3QvNc84KL6DonInn2ASyUtKzzda3pyimn1IaWZcTu9yyjVTFTJSBvaGFlEh1WWTfWQLgiYIiZPZfcY6X1KNeEgHOJVIGhPr0qanbW1tHa+KOGS8Kz7Uwo9oVAjsQ9IGpedm6bWMrEyI6yMOGuuvABuloMIbDpV0ngrYBnXc9WX/GJR0UeRnaltBBx91AxR00VFqRPfmyyprHQUFIlXjXC3ll7zvBF/pZGVe1IaQYPWNkgLeSg4a+tdN2H3fKTEs7bj47anwHJsKypjmTlFZRATtpRIJLEH0FWeecWv3Ec3TqtvC0o3MOf+t4AHF/YYpypqNee2eh+bLKPs1jrgGZRwz0LYEGxH1Ik6lfyQP0FpxblaslILBtUJHSCtWHRPiElDFr5VGZX5NoBAjEuabGGgvllH9O+9vlHfRmHRiEGTZK5uB2F2KvHOeM2k7vBB02RNsbU3u4fj9U3dhsQLfUHA88xriqHA6luicJtkPrL2iqebSBcTbfTElDgqCm5VmtMm8Xm6IMY9Si3eMVMnumbH5cCfHIAivGGyBnDeOdZe4zIkFeRw7KPiybDiqXnErXCXf6F+xNSa3BaS39VKCJhKiXbc69bUooUm12G7rBbpnJQ0Q7KEBCH/TmFC5j5ECoTjsK3QFoPmRSOq0lMrW+CwCBhlGNaeNZccxXvspRkGjUHRJsOCmwrg61eisCdUdr5zpWz8xddCjsXozL82s9j+wDNmn0mqKbQkFU83QkocW02hpQJc+8jYKn64Z6lM4qAYKHTMVgyaNiqeNpUE56ZCq5uKdYgqz9EME24RGKjTGINlzSVtuGbsblOU72EpqRCE0lN+iU8tt/SHHIcjDIYah04TOn2f1q1Z+Of0cS3ioFm+Z6FvKh+RUrHyX6rCmBGEN31SFBajK/q4wpqSWdyjTiVHZkSlNe9P4bC4Cfhrr6HVWA0ntWeclcun7qatU5nASe0ZomLqRMR1EzQXvaPQkbnzlCawVwpxMyRNiG/uoR+vrzFo/sVkyqRIrLJ7bm0tSiVqO/DutCcmReMdfTB8b6a4V1su+pInG1HXWHrPOkBIlpGRLsAqRBoGrC4JqSSEDemrgs1rdhH/kGX0HJX9k5SyjNwxTtfUak6dxmzUkufxKVaXHKi7KDRLLujZ0PkVWouNwetLK8VhmnKQJgxEOgZ6NbCp7kuPQA3EzNNqwwLQX+9C+g1RCPjHWt+sgKNK1lzxpO1xSlM1mpFVfG8ukE2A3ovx2KIv0Spxb9QQgZdNxTpzYqYO+qg5aPYwaO5X5c6983owhHSDgLtTy2C+CwbBj8k/D8aGh+o+McHSw8obbleKiQt5wK52RDW4ybSVej3wqZ2ywNhG/ng65cPhI86T5zxuKOKY0kgb6JOlNNgejkV0s48ix3M9RB75a2pKfjQbsVek3d9tveK610QEnWQV3M0HTOMtn1zv78h3IjAad+g3mfuwI9wZJY6mx0yJ6V320oSRtixjzystcuyX/jE+dRg9/4196cGfUbuDPGwdiGl4i8fLlv/w1e+jX7cEUFsNMKnytte8DYlrP5Byhm6UDMErE6itQG8XmdneBs1VL78vckM3bTixApaW3nl6AiAViX1IkQpcKsSADMtAzxXgU8er+Gsu1bMdSdYoR5XGVGmMtQ9JBOKuPRIYVNgdOj62xAyPjmlN9Fu5k8PMI2lIyTP4Vwz+FY254KC6wzSNuFsbpjZxq/TMimE3K4xYtNJYBbeqjoOyZVIIamzTF1y2tbRtVSK7bBMRr6Q2yGxw5gaUSrxs5Pqn3Lp8uql4vLlBrGkUKy/zzzt14KgcOCg75pVwbLRK9N7y2XLOWeeYusBh2WOUw0dLGxW2tfgE01zh+KRIPjHgaf0VQ7YdePsyWLOH0SWV3cPpmkqJ4OigxGm1iysW3eek2KB0nedocsdFJ1Deu8GQ7BxtDElFLofHWTdPcWU+A+IbVuqHoz9ipPchwkY1fMLfMcQNJ+bbHMYDzvUFZ+kRpZpwJ0ob9Yn+hDZcU5t9jtx7tHZFE45o/fXvgPD85wcB/EPXNyrgxLTmrPslf1cOjNQ+d/wJ+7ECFJUJDEmz6CvO2orHm4Kpi3x3tqC0Xn4eNdd9wUXvcFoDI5yGk0q4Ak3Q9AG6KDOQyohOGkjGNsRtyX9TUYQEycvDJjLwmpet4VWbMErt3Dmdltvvk8LHGyHC7c+Mgr0icVgm7kVDYpr1pAIKeGcsh+BVD2edPMTXQ2QZBq70JV0acdWP0EoOVOH5wKSImSSXdlyKrddPSIomGM57Rx8U5720QO7UgYfjsPPe8dneoNCRH84t34pHO1fTLpr83SNDbBjCjYSHUhWz6n0UYtkQU6CPK1KKO8jnuvsVX2fn+5sQOInIdTplomaEVFMa+NZMMvKrwbDxiqlTQEFp5P6tveLHlyUJ+Gg+8O5kyazouDcSBOF1X+TqWNNFzadLzWfthrVqxXQLQxMuCbHnOn6WJWXGVHZOZebcTR8wSiO+l1soCVGeXLLhWp8TiXjl0Ukzi3sUye2kYTyJlp5eCUy9Vxt6uyb5+AaIIMSlTMR2wdhQFXeYu/tM4gSHZtFL22zizE6tfGtOeG8UMskSumAZJQkgfbAsveOyK/jptTz+P9gb2CsHDooeqyOrwfF4U9N4zdNGnotblZCwP14YftbKoavRYvfQB9qgGRlNqS21CfiMolQq4aPJ+1X2aEgKo8Wew0TonDwzQ9y2AyOb6DnVj38LIm62KTBjSj3BUjJKUyZxileBkZrSmA2NucCjBVCQmjcqImv2MbompUibBTWDHnC6ZlZ9h8a/jQ8Uue4+IxQDt7lLTcF7fERUEZ8CK7XBpWLXknNYmdumjj6usbrC4AgMhCQusv/w9YcVbOAbFnBA1AZ86iipeL+aSLUSIq+6IsN/ZZj5/fkam/k0IC24rbaaRh7Eiz5QasWdWmDHTgdw8NnK8qt1y9wWedAqSDidiZEAl73m44UoL39nLoTLNkh1UOjE1Kk3Ak1pxFfnrIGrPnAalzzRn2TmcKTWc/5r831u18KVWfvEzCluVxqbkXSFTpgS9grxx2mDYeMNR91DYiJDtmHpA03yvFNVfDBRea4ktdnSC4luz0kb5aIvWAya5aB4tPb4lBhbaRGus7p2GzTnnQSWyiRmJvFsk3jkrxnUQMgPSucvSbFB6ykKTeWOcPlB9akTfTAMKIgp5raV+72UZmNc0oRLOtvuyJ1jEymMOLaCZmzhbk2u5iJ9UHy21qx9zAZgmtqKPXdImr1StOSuOyEjtqHCx5rP+sDz9idiK+COsKZkkQ+8Pi5Ftwvo6w1zc8L98IBaWQotBn4HqSSyTxsDp1wwqJvvHYl4FNdqyVOE02GzaVvvr4lxmflNc3w4/9I1q4t7HBUfsh+P2Vc1tTGMnVQjZ53mvCsodcLqbUtZZYRmQR81Oks0ARyWLVM7MHMSlPeLjsp6kWpKmqW3fLoUMdc+S2OImrrmoIQ/tfu8aiP/Of1KqrdwhzoWXF9rzLXiB/PRrn23CZb0ml/VZVdy2hZsvOayVzuVaIDHTcu5WuxUqN0X2lVfu09S5KL9mBh7ZtV7KP0BAz0tK1b+5VsDV2FPcGbMvnuAQtOlFZFAwYhCiUp0IlCYCeevi7Lmn5T2gIk5Yh5rNIomeQEAqSUrriSYMFCoEZFbOAoepo/AwKfpP/Ns8z9h9ITKHVG6fUIcZ4TbFKNK+rhiCGsGf8YfYnB52/pGBRytp+xV7/OA77Ifx1RGqoRfraSF9ZPwlEfD3/Aj8+/4X96qmdi4ay99shhz0esdJ6OPYqvrlMq+Njq3ZBIHZeKPTEUC2ij/bAPNyIiN9GEZeDgWyY9ZIRvt6aamCZomSK/a5ZlCAs66jIzKHAj6KTp+m4qCW66k0IqZU4JHyVVUG6SaCUmxGuTvfzCF/cIztTf6axMrM4jzNtHFxNQaphicUlzn30uQKySBCX9rpikyyfWdUZ9JsYJKgsRFb7LTqDS5nJYgfdkrhiij6jkj+hRokiPqSGHn+FAIpDd1tMMZnb/6yh506e6icCR+H2lzw8geMoszZk6SjW02P85qDFuTr0pH9oohqzCUNEFxWPQ4HfBRc9GMaLzjZVvhE8ydp9CR25XHacO4mRLW/3M61eGVJ+CZjG/RpRXL/nlueSiW3WMac0EoPeM034KMmMYpB2pETEkqUSxlKiiwdAys1IbP4493dslaT3Hmhhi6nWMJWfTNAXjaNW9lTrD1tRE7iq2ETUIB173meSPOskeVokqKz1Zj2jBl6gJ7btgBZgAK7RiSzsHXsPaiQRiS8GtCSuwni1PgbGJsxdK9bkdEIhNKRsZyUhkOSiEvd9FkQrR5TdJG5ktXveG0Sfy0f0mZSj4oZlRGca+qOEklfUxsQuQ4nPCqfJj9iZZfgvUqVWHNlIPiPQpVg3sPhSEwsOaaTbxk0T36Cua+QimNUeLjA9CGa4bY4NwDylTlyqRgxj6T8RGejoV/TheWxDTQDmf09pCRNjitGGeVex/2QMNT//esu08FLj/6Eyo1YR4Psckw1ocM5QN8aGn6U4wZcVR+m0LVDEnkb0T6aPlPJqT5det3Jdz+pvWNCjiFnWNVSUyRDT3PG0OhNVMn6rTv+NvU9r9mriqeN4aZ04ytxerIYekZW82zxvJkDWdDzyf615Sp5nvDfcZW1KCHBHMH+8XNMB7k3yIJInOTe7Xhu/Owgxib3A7wUXPVizxHiIohyQGtUDv5j5ASd2rLh3aeDaekHbcYBGm2CYkmBJw2lEaqtiZDS68HhU92h8JKKcu4m8RhIYCCNmwlfeR/Ow21iXkGZTIHI0m1gyDrUlJU+T2fNyISOi9EqmSI0oZae/jr9pRrfc7D8D63XSXmZqnkME6Y2BkLd8Wv1v834KstBbZKBG+gzd7ymtdRaYkIKb6hxyYrsOyfs3EPcXoKwGVnZYaVr4OCTHaE+26gNJ5btajxbrzjoqsyw160wj5dyj36aM9wXMpDNXOBxiuO9JhNLFmlhqgSd9MtRtrSFZHOBTa0nOrHooeVxmi0VIB4zswrXhEYUscmXkKCe+o72DShomCUSnrzXTbmjJQ8hZ2jlcPr+Q6iu21XCmenYGvlXZm9HST617TUfkS7nlEbw0d7iduVDOcjMLGaWSGKGMdlLyhPHfHZUl3mNZrFYGij4pdLIY0WGcCyVYsuDXx/rqlNEuK0voFFV8YS0l2GmHaag1vwy9obnm9qRjZwVLUYdRMs22DyzNEw76bo7XOTxHPKabV7hpZ6yab5DJCZljU13fBSOE9YnNnDmgqV70ETrwlpeK0y+CpZHOF/iQq5QzxxPav+GTEuGcKShT3IHJ1IYSacpG8xS3vcUnfRVue2aWAaRxirsgKDvPc4FAxpgtlxynoWwzNaMyEYT5FqqXz0hC57SUW/ZmFmVGYmiLsUMunzn8cj56sAGr/v+kYFnJQiQ2pY6CtSnLFPjVbSYhrbxN3aAvtZlFDkarY8GpXbKVqJ7hNAmbWxnvolzhv2dUVtDF0Qs6naaE7qlGcskoG1QbPx8hCcd5bWCrmz0GGH9NrK1Wwl3nWez2gFzzeKqyHilNkFDZXbGS+a7MNjxShs7OR7aYRToyHLuLMjYQ5JeA5Ww61KMnqQgfC2FReR1oQCTuqAzsHm6cYJci3JvOpW5al0ZL9QVFrxolX8bCEAjZGR7GyURuiouVZLLv01e2nGkRWfehctNSNG5cOdPIt4bQgQQGGpirtYXe48Pb7yXiNku9rdJWTocDec8TZYazc845H5MfC/wOi0G5Vu0WVOCxy3zK3FYTt3SoqfXE35+MZZgT5GzkKDRrMYRoyt3t3LTVAM2ddkzgiNYmIthVa4qDFBYVONiQ9J3FhUL9WGVm24SM+4bD7JiKcjCjOhjgKbdkq8icp4m1nxv2KjNjxJP6P1V2+VD4px+VpNA6teU1aCjgsM7KkTZm6fvULmmwp2hOZN0Fx0hsok7tWJqes5rETvbNWXXPUlCgmyRZTXrnxi2XqWqeO2HfHBVGWztrR7NnSG32/nMHuFoQ2K806QnpWXTD8mSEnEQk+bAqtFbWPqBsZbA0VV4GONz9V+SPDrpuWVvshoLo9XnvuT/0ZUxNMKH1vafABvrQl6L/w9q+uv9GJ6HVos+y4RU0ehJ9ljSCqKSXE3zynXNP1pthBINCimE/HW6nKCapPFYLlSC17ElzgK7vtjCq1pGBjosbraVQpN/4gGaN0VtT2g0CNGep+Verr7bJv+MZ2eopVDKU1hj7Ji+e9jU/C7rn8agug3KuD0/oxueMEFf8u0+hYP7V9RGWlfdUExLwTtpRCnwk3QPGukNVYbEbI87+Bi6LFK8yF3JRPOpeGtynJcpTx0B03cQZfXXuMjnLaBz+IrZusJL9qauTP0sabQidVg6KJk1m2QrHo7wwF5cIaU6KLoIY2MqPFWmbtzb2Q5CI4hD0ive1gPYvX7YByotGi3OR1ZG8vSy2HwtAlYpbDKMLJidHXeew4Ly92Ryp468hn2CxEnbcMNMGLu5NBICfosJDokQe29U9/0ySMwClN8muJTYoiRwghSUHmNjoqoxAsmxiVVcZ8D9x4gLZ8+bbjuPqMdfrsHJKWeTf8Sa8aUZgoO2v7JF15l2Ku/x0f8OXsuMHGBuRMu1VVfsPZysF0Nik2wdHGM0wKesErcWN8da5Ze8aqNjIzmj8cTKi17KSTFi9bwbBO58i2/Np8DMI+HlKlg8AWlMpxyyTM+wdPR+iuMLnjP/CmzNKFVG654gY8dpdtHYdBKCIZLvSTGiMuD45aeM/MKT4eNgqzq/cVvBesdUsNUH3Mr3MnKFLLzVoPZoS5BIPVbfbSlt0Qq9oqOsetZDgWP1hWFSdyqOvZV4laldvJGXZSW1toLYu3jhSRwd+qCmUscFIH9ssepxK0qSJUZNC4k5gUclGHnQttFxWVviEGRNhWFLncCuhpR99juXeGvCb34ihcshmdYXTI1Jyg0MTudftXSyuHs8VvkiOYAGVX5WosytVw1PwEUe/VHaGWyHM2aSfUee/aBIBKTw1FwFA4waK7UilZtiCpiXisCBnoeqRcCHtEXdGn1JeFWEDUNnakCRjmOq+/Tp83OmmNrBvfm6/8his/f7KVS+urSSSn7zzKp0nrKfvUh/9r8JVYpXg0dHQNHZsTciYHazKXs+yKtKLNFicWb8nzs5GB/1Uof+t7IcFBIW63LqsJFDganjaIJ0vd2WmWJ+y1aTR6Iv100PNPPeS/d591RkUmIAhZY+xuorVEwdZE9F3Zw0y0ZNCV41Vmuevhs0/I3/H/YM3f57+rvcFDKAbmdpwjKTHHV37DpjRIkW5PLOPmMKpMhYa8QxYWZ80ytx2TjOJFyKXcAgbW/EXp02d9+G0CBXVB0+qbN8rKNbELgc/WcJRc7dFpIMhzt44Zl95gY1791/1frqahOu9v0YcWy+/xLh++/rP8PvF9N+De3miyDP6BV4vF6wlUvZmJ9VDtghFKKDyZiESFK4LI/nNrC1m/eOyV42miebAY2yXOuL+louYiPiWngjvkuB3GPgUCnOgwWlyRPa1WLVzffc6kuuRg+o/fL3Vxr+/1G9pCxPmQTL7nO1tClPUAr/Yab6NetUfmQqT3hTnoPmwwVBRpFxyBeK6rmoLD0MXEx9Iy05Uf7hv3Cc6tumRV91vszNN5y2pakBLfrjpHxDEkTouZVV/DJ0rHxcN57hhSZGlHuOCwVx5VUVFtC81UvigObTFo+LhP3x8NrsHXNz64tF33goDDMC1h5OM02vFNrMFqe4wi86jue6zPW6prz/ld5hvPlOeF2BnZYfIBTJX1qGGi5bD55S9VoUKgv7UulKu6N/5Ii1Rl1F7lKYsY3dsccqPuUqWI/zXForBIH0zYFOgaRp1Ida73iWfu3+LBE6wqt3BcqkzcpAFqNcXYuvj2pR6uCcXEbq0qx6sBwOXz+lgTsm7/SFtb7lvWNqnC2653Rv+IoHFMbkeo4KCsUYg3wqvMoHLcq8dWYWmkjPN4olkPkpNa8WwloYOMVbYIXXiCvs+GImZNg04gBI4OWQb5VUBtBPFUGRlbcMn1SLAZN7xUr1bDgJZV+l3fGksnt+EFZo81HlW2hpVpZesPna7nMD8ZBXt/JQX/gCv5l+DcYJMtrgggqNiFyp7LcGYmeW0wSDASTJcFoFWKGwW4dQaUN6Lz4++w5GGdp+GnZEpJm6npab+njhCYYNl5kdhKSXZZKc39kqQwiDx8W3DMz3psIQfV2pemCZtMcobTmif9J5in8LstQ2GNqd5Cv39aq981go/VUGPUGitdcV7fXfOU1IQoc3Si4UwsB9qxLvGwVV0NgFQfuFBXvTaD1mkfryJASJ5VhbKE28HDsuO4toZ+xyDa/PjYMRg6IfTViZmdifxzEWfMX/f9I70+Z19/nRH+LIbUM4YZHA5EQ17T9UxSaUbHPVN/ipHyfQfWchl/S+i+3075qKQw+dSzUFaWqKKMDlHg3IVyYlGSeOLcFhZZKfDEYIhWLvhBDNBNYesujtd1B9/cKvYPSi/qG/M1Sa2JI/E36mIv2cz7o/4zvdvuUWhK6LsDP1xsW6kZj7/3hiNraXeJT28D9sWa/NLxsEz9feNZp4IV+yShN+A6HVEoRlSBwBuIu2LytTWb0HKNrqjwDS0SG1DHQ4mP31halmIq9ubQaUxcnHMU7uGQZlJdKJQMNuuEZF/wErUfsVe9TqglH6S6jVPOJ+jvONz+mdLd5t/hT6jTinfJP8cqzSmf0YcXIHWHy5/OxfYNrE9OabripWiKw7HrRflMlRlkUGmePGfwF/1waaEpVwNY08fevP75xAad0dzkKx0woZZjuE1dRpgS3K813ZpbaBqZWhBivB4Mi8WAEoIhIO2nlZWbSxiiHaSoAqVRKI4q0Q4TlIMHnZ801F/qcb/fv8GAkzO9Ca1LaCn3CR+UBH4S/4P0pHJcdhY7U9iZjSklx3pVZ8j9yWLYclvDeRDLpxVDsXEOHCFOreWes2Xj4eNXRE7hb1ByWbid1M3HSutjyK7RK3B8L63vLlg9R+vZd0PxqBYtBdOYuestJ3TEpOozKki5a/HQuusRxpfjeXPJQ4Q0pmiAB7VZlmIY9hgiP1hGnFXuFGMRVyjKOY36/Fej9KXGnlfV2xrXRFSsa2lDuVLFlZof4sejEs8Zxukl0AdYhsImeX+ifsgnn/LH6t3xnXGfraanU+igB56rXNAGZr2hoY2KhVkQV+Y7+C5y2zFLFyBqGGLnyAyFF1vQMynOn+gFtesiQGp6Hn1OZOQ/KP6NXDZeDVEh71ftUaso4zZmFGYlEr0RJD9gx3X+b1fkL0RazCGxXQx1rHro5e4XM9cqM7ByiJCCfriJDto+eOGmvHpaeNsiQOyTRGFwMhiH/3uN14j/GX6AwHIdbaBRDvk+X+hWfdiKpM+kcGpjrgj11yNSKmndI8OlSbNS/Oxd1DjF5S8RkMrnWcje+i9OwX8jztRgUjU9UyrIfj5m6fTon3BbhqwycdR/nfdOImnbyXMUV7GjbsCVEKuyOOHzTUrs5uJ2dU5oJOonGSZEqoOJW9QMuhl/vbKNjXLHsn9HomrKYYKKlVBNG5QMqM8fjcangPe5RKM0mPqBXgYaOtVqTiHS2oTX7vPiaeUyMS9r+y9I7CouzJwJuCJsvabL9Uy6hNXT8Y8Gyv1EtNaUK9uvv8TB9hMNS41jR8R+b/yOQ+NHof8df7e0zcyIv00bNk7VjSGqnKC3zFakUThvxal+mjkjinhuzV+jdkHK7YpJDuouRUuvcotLMC0GVfbwR174fTCbcqsTJc+48Q9RsgkYDo2xetc3Aq6y8u+UHbbzjby5mnHeKvUJMrLby7okbSPXYChBg6Q2LQZBl+6Vk9sdlj9ORNmzhzbJC2qLlFC8ay8oL0KCLiQ+mif/q6IIy62FFFNdtRePdTqakD4arwbH2hp9cai4Hz9zdyIy0UdpQtdEkpMrcpGEn2zGonkZt6NOG8/7Tr3T4/F3X7fFf8kfpj/hf3xPF5yLbLPxyOeJle4NS23h41Q30ScRQvQrcVTMOS5sH4KLe8OtGlLqPTE1ldIa2Jz4brvnP3f8ZrQp+UP637KURhTI4rTkLG07NMwKeIbVoDDUzLJZe9Xg6ilQzThPWasWz4e8IseN29RHzeEidakocr/QZn3X/gZgGSruHUZYuiHjnl+G7X8UkNxg94bD+DiO1z0e8x+3KcNFFLnzP1Dju1WJAuLXfOCgSExdovIhurj083XiGlBibm2G/7KdEHyM+JdZJpFgmqqTQmjYG2uR38vwKhVXyzByXlr2C3Yy1MCK54/TN9OW0cZx1cs9et5gAsVVvg/ztLkYCiSFFBjzn+oJGrVmEU1p/lRGQ3Vshz1pPGRcn+NgxhCWgd9fa5yC1XU7XTM3JzuBvm5gCrPSKc54SkminKTQP1PeYpQnCuIkYNCUOg2JkLFYpVsHTpcCpfsFz/3Nqu8fD+D0chiF751zqV6zi+Wtw+xupnbd9p61AaeWOMMrS+uud4Otvt35XVYJ/+O/9wbTUUuq52PwtaqQp1YT9dAuD5S/q/z0Oy4d1nbN8aKPYJN+ufe4ji2/HyAojujKKqdW00fCisfRRtvx6SNRWsZ/31Xazn9QarfTuoXvZwo8XDbWyfHc82qkRX+Z+dRtuKgOjwEQZhF4PFh8Ve4XnIGmsiqLSqxJzF0gYrnrF041AvQ9L+b3KCsDgVjkwdQNXfcHYWmF7b4xAqLUImJ53BatB2NtbMzafh8a1FQb3zAUmVuCwi75EDcVOnsSoiM0QV4HHOn69KugyNPZI28wTSlRGsV/KvOdVG9jEwKf6Ecv4knt8mxPmhCRadGvVsrbndEpjk5DYrKko9IQ+rmj6099B88rgVIVK0kKzr2nJja0odm95OEOETSbf3TYTaftYveNz9VGCcCSh0VRG7sHGJwGBoHFGoNfn+gWblKu4BBuzZBMvd8NdhSY4OaiKVDNiisdzrk7xdOJpY6QtElVkqZZc4WnVmsrORR4/NnKghitS6na8EjlMe0gxZ7MJoydYM5ZDJixRyjLjmFmYo83NvbqjSyqjMjdHrpHLQ3yrowScQWadtTHUwLwQAun2eVCorNQBfXS7tptWMERDH2WvXA2BPkaecU5Hi+kfUBlDoW84alsi8tZq48kGftKdSeBWPVUa8UDv45TiV+GCM/UMp0qcKuV+UOzajxKYtzMP8QyyZn93QCtVoFWN0TUhDbTD6W5mElNPafep7T5O1ThVYbBEIl1aEdLAmAkhIw8VSkQ79T4Wy17aR6FYqAVP9TPqNGIcx0REgqch8nl6ykDHSE0pETO/iT2mUhMikYhmQolBU4eCRh1zXu3zstNoZZm5uxjcDhyxbRGu+qf4cElILZ23FHYqDrvpv0SV849bc3yjAs52nW/+Bq2n+Pp7zDjmX1RHHJeJiYuMbeBla/mbC5nv3BdCML9YJM58w4Oy5sH4Jvv3EZ4Na1Zqw0MO2as0tRFGfUiC6IkJ2nwYbzPBmVM8GFVYnZjaQXzhvaYPmi6yY+bDVrk47WyMjRWF5rW3OyMrkOHsvVHiOlcTpRara5OVsMWwLXvr7Hx1zE5e5mowIswIjG3gajC8alX+DFvvILGnLnWiNoHLvuAX1/UOtLB9rQbmhecgEyVFfwxqpBnxqo1c+4GZFYhm4xMXQcAb0zijViPKWNAS2NCJ/I5qMNFRmtkOULBd8Q3J/ZslEu3S6pDWwRFKafrhJQnPXv09DuJtamPoY0QHs5PiWQyaxaCojZAfiwr2inpXwW7Ri5VJN+ABpbCdYiDlgxm2CtwjSibutsxJwinXKey4GUbPqdzRG0TX7XxBqQpn9uj9K77Ya7eqxFjHMr5k7V9R6DEze4eYAqvwMsNwRQU5pZbBf3VAPijek76+KilTzUm8hUFxFhqeh8A9O+VOLftjExTk+Y2YrEUmTqrouROhzVddQCnFtyrNYeEzL038lj7NnZ3aSut2O9Bvg5AyrVJMjKZTCh8lkJ6FDWlTc6e23BsJbWHiRGW7DQYfBfBz2EpQTylhlJCztYJjZszimJVqWKkbLLtCMzHisnq++RvZT7u9c8NTSaknpJ4Qr/HhzZbvVoG57Z+hdI3VY0o73f3c6RE6VyvbgKOTQr3eSSBwyXPWwzla6bzHHaWeSNssW62/ypSAbbu0MGOijSJMnC6JyXOk3+EwHnA/POC+fcBatXweRYvt0L3PJM0Z6OlMw2Bvql8fzgnh+ndoqf2z4L++tL6BAUeh1YjKHXKcHrCf5uwXsF9GKi2SJoelkCaHCCsvmW1MEYdkuwLpFPJaGwON6kULLKVsv5tog2QRJvMjxlZLVoY8dG3SrLz0uY9LkdAptGFIikfrgqebxMgqblVyoE2ttHp8FDXqIQeZPmgue1HPfWcsA1sftVQohch9OHUj0XOeSYrLHFxEQUCC4qs2Hx5lYGoj4xRpnclXTdoY4luSmLmBWdFRW89hKYf0anAMUdFGLQdsts+urd+Zcv34suCql2tVakMXI6cZ5RdzC6FKFSToVM9KSdvh1eY/v/Hwb82wxJ/ki2TO1++2w9kDRu4IIA9YO7r8gMQ05MozsfaGNiRCsnQRfnotvvfv6EPenwjvZEvkvB7Eg2hsU7b9FovxLmqOy5IhKz1sybpDTBgUh+o+MRMVNRpVf3eHXkpEruwLXq2/+MBHMQl7/XtlO+qJucU4ThgzAStggY6GSEArh33N7+ltV+fm/SwLf8qBe5d3w7s4pZlYiwLmOBIwczpXIVsri5sKvg0a1WyVOCS5uFPbPEMZmDqfK+ZEGzSzwrLoE/+peUmjNtyOtxgrt2tzjbRlbA2VUtwPt2jjAZHIeWw4jtO8D8NuxiltYMXaJ87VkjIV7Kkak4MNwJAiK9UQ8DgKdBK1Bp00RosZQ128Q+dzOM1ZSgABAABJREFUyylFBO7y5cN3CyPWaizGSClmImdLisudVNEbawT31Lc5SnP2nWMTHDYa1qrlsf6EITWQpA23rTq2GneFHnPHfJc6jehMy0CPwWKTKOCbmAVf0zOa4QJVaDrTMIlzjtMeLk2w/AhvAilKPRRVzBy14Y2P+YesIP0NCziGUfmAW+47HIRDHto5YyttMpDyPA1ywDqVCEqxHrZtEqkGViHiG00bI+swoFC8a/cwSg6Vqz5wHhtOzTNGacrdeESphNS3FX50OTMXiHFknH14NGCi5rj0IuWfQQcpwdh6Zlmra0tG3baw7o2EnCmyIRJ8Jk4MrTTQR8NlL7pef3/teNkGVsGzomOmKu5XBWMnopsjE3cHw8wlxib74LAljkZsVukNSeN0YOx6fM7UfNQUGY00dYO4mG6FPr3lvBN02rl5yZpLxmqfeTwEYNASuFu1ZkhdbicVjNQ+J6M/Z6Bl488Jsc8in8vfyJSOaU3v16QckELYvBGgVv0zhuoHLMPAs6ai0CKYqkmcVJa5PyYCTzcxK0+I1t1BEXbXRZjxwnrXJN4Zi0HYs80og07k+197OEuPiCkwMUdywKURLhV5TtUyYp93J39FItAnCRxdXDHE5g3EXkotPrRs4iVzfbQ7PM94yovVvwckQ5fXvu0abatD+R7OjDlw78ow3TjaGPhpfEQk8sfmPe6OVFZBZ6dzppPm27zD2BiaEHZ0gUILknETA05p9gqbybNC7HQ6cVwmxlbRrY7YhMCSjrO0oqZkpBwhJV50chCGnOxNTcnIaI6qtAs0a2/xUfOiKVh6xWEJ/7PyiKsePm9adIKRKYRYrDR1KrjQG855smsthTTQ9QtpQb6mK6ZUgdI1hZmilCZEcdvcJjlaS+Iak9/Bi42e4+yUEDtCbN9wpq3UDJVkTtnFREwJh8YlKwRRGhbdky/s60hl9nZVzkBPmSrGaSwVs7YMKbFKHSip1gAump9xnlrq4h2wf46jwCaDTfBUf87Kv8rV9JVUW+qmYtsGzj/E9Q0LOIGYvBheqURtxO9k4+Ww/ngZeBTPucUe744KNj7x625JIHLbTDhwlpd9z6PtIE3BKNXctSWj177ptJ8wG96n0Jq9wuSsMOXqCGLSHFeB9yYbnBIQgE+aR+uKF61m6lIW+8w2u0bQakYndArEpPDesfY2M9+Fzf6ys2x8bl+pRMpCnT4prnrL2ptd22dJy1P9a+7Ed3lXF1h1AwteDCK42WU5ecnes1JCZ3NLSXrIx6XnvcmakDSrweXfkQPWqZuDwanIzA386aHh28Me590+yyHhU6JTUbLP2BGIWGWl/x5HlKmgUz2tKtioJcv4nBAbxsXJbnbTh/VvJDd+lQxJjEteqcd0qiVuTpgbx0d7mpkL3KnTTrbHJ9AEjE70QfPpyrIcbtoI74wN86KnzAEYwKpIGyxTWzGyFqNKLtvvs9BXnA4/w4eWkTui1nPW4YzNcIY1NRN3G2AXXL8Oabbqn7Osj7CqpEyiEFy6uzKLiG1GAH15aT1Cq0JeQ6C2+0zSnCI5hhiptOFPzbtYrTgopUW8rezbGPGqw6oSh7Se54XFKBGNvR4kGfvMPMEmy+3NQ8Dt/vZiEIRnQtCJ+9oyTQK9NkoUCNY+8dJv0Ghuuyq3JyUZmLmwS3C6nMwIylK4UFYnCq0wSvZolWOrUY42WLTXBO0ZdEeTFhJw/CVxh0a7WSkNDP4apSyH9XeYcMigOhn009HFFX24gWxvg5h/TYxzr/4BVpds4iWPuOTS3GIW93EUjPMspmJCUvELSEr54NfNz1G6xlcfUqrJrpV8FO8wYkaTPI/N5/RpQ4wDRhf0uXXa9I+4Lt4HoE1LhthwvX7TWTbBG/MaEc/dqmlsvnRNvsnrGxZwhGn+pH/CqTlkL/1vmNsC0OLXbjQP0oGYqEUYWcVfVNKH3bZHjigo/QEhJYYUqLTdSeNspWZi0rzsIy4prBJSYJ0hM02A5ZAYWb1z0tyizfZLT8Ry2WuebiSbPqkjVVJcdiVFntkAPGtKHq1NJoHKhlgM0v7bfh6rEyHKIPxO3RGSYuYK1l5zt5/zqv0XxAQXXaQNiuNKU2qxE+iy0ObLVsiqh6V+TYoE9grh/BiVWAwFXdRc9DbzhGSLXvSWIY121z7lWc7YJs47RROCWDAYzcXg+Tn/kSFumNt7AgtNE0ocNmlsMiituTZjetgx7Y0qKbMz6m+Wmv/yUlj6uMHpgrtlycwp9px4/mzFKtsohMUEGOQ+36oie4XaXY+5C5lj4jjvKmJSu6pVK6i0zHTK5KgZMbUndFq4QYvwOePihPv1nxEY2MRLhrjZgSC2w+q3rZQ85/2viMkT04AzY/aKdwgMXLWfEeLwlvPCUNp9rC538ivnzS+4iH+L0XPujP6YeTzkW+oWVgvhUuR5ZFBfas0d/wCNoshtti0q7HqI/CI9plYjvpseUmpNG0S9og0p84wGLtMag+FQi8nbB1PFYXnT/mu85lU3YYhkKD28aAWCfae2WOWIKNZ+iwKU1UWxBzltFD9rLylx/GAyYWzhoFT4qNBdBcMtlmrDWl1KSym92XpUWGbVhzhdM1FH2GSp0wiD5VH6ey6bnwEao2tAU7q7u9+V+UopmmyqoPEXGYQge7bR1wQ1sAynLNtPMHrG3dGfUqcZ0+pbNMPFropSyoh9NZrV8IJGX3LsRNW7TgUBcS4tUp1vgsyL9twDrCrxqdsFmnX/ghh/c+USfwvX0m/q+sYFnO2KqWdBQ/QwRIvTmsNSc7u2lCZRaVEN3qoFP2ssfVZwHhuTtcbczosmJCiR+cztKjF1JYsBfr3uCCT2M1Gu0IqxVYxtyMTJgXEpWeg975jagpRqLjp535GJ1DYysuKQ6LRk2WtvcVoCTqmFLzPOrcEmKK6GXGXkDL0Nwi25W7fMii635YSM13jLEDVt1DulgPMOLnvPaVwyo2ZsJRMLSe001R6M1nSZVW4zj2dQKlsvS67Xhzezc6MSaLENnlqDT+BTolCGD9KPaGzDFS9ZxpdYZbHJ4FWgUy0btWTTvyTEFSE2GF3TDc/5fTIwMcwyTOKEd8YCltgG0k9XJWetorYS1GuT3phFhKxAMOQZWB8MS2/5ZFnQhhvlhu161UYe6U8ZaKnUlMKMKPSIPm6YmlvM4h6D6klaeuvbim07rH7bCvGapr+Brg7+FVaVKGWwRoLUl/lHIQMSDHVxj8JMbtpL8Zonq/+B53qOr/8bZu0sS/mb3JaSL3TXTmTv5eqhykCZu7XBte+wCTL/UkHzoTpgbERhAxS3dcHUuRuysYK93ALeJmFrbXcVtlhjCNnaJ0H8XefWt/C7pFUtIrGJtY8UWvO9al8QpyHzo0LKc9ZIgaFIDq1EJqgqjt9ojSUCVpUUjHaeQ16JCGfMEjbbtqZc4y/eGUPp7uLDepcIjcsPGNtDIoEeEX+1Zg9nprhUUlJxbD4k2IHPV/93BOgRKKzMH7fvc6Ecyhq6NKKLtVh8qDfnMAqNweHpRHcwJyOYMXvuR9TMaFnRpRVtWLDpH4v9R66e/rlEPX/f9Y0MOKPyIRN7Cx11HtXKqg0cZxKkU0l4OBvLEEV+5laVslilYjmILppGoJxWKYYkFsxHleKkDoyMxqiSmMjSMHIoFxltdtbUzArD8XSBcwOF8XTeUZl9xrZmr/A8yJ7v06pBq8imL+m8485os5OU2Rp9nTZ6p2/mtCCJzju7U3QuVGK/bLk1XaBVxOjIxXrKzy4Pdi0KpRJnXeLX7RqH5VBNGBlpC27hqxLkpDKbqI7SekLSLLuS1lt+uZzwsrXcrgIndUfIVt0hyQEdMqJLKWh84FXcUOJ4YOcYNaeLtwkkzuOGS33BhgXLcEpMkUl5D4AuLAjx9yeMWT3mHb7PAzfhsPRMbGBsPUol3p/ASbUVSRVn07H1aBKz3CFaDAWbIKrZm2BwKvHH+xsi8GRdcT2ISOp6EP7H7fSOoINoiURqxqBhw5JH6afSEmNCqScYPf8SD+Lr5zIAhi4sUUpncdOvAw0E2uGU3teQoswscKC0QKTxdKrnUI2YWpOTK0WfkWTkdtoWprw1AZxaRcTxrXhrB0Kpzc2BuBoMrzrpIvj88ZZOlANKzU5/cGQjpVZYJRX3RR845Zpy2OceQh942Yr003ElyuSlUTht6EPiapAooPNVeBFWXOlL9uI+c1UzTRXfjj8QHo49p1MNL6LPw/6UEWuGNP5LCl1zHU7FhluV7NffY9W/+Brhy0Dvz9CqZlQ+xChBVwLCjckoSQAfFoTi2+g4olQVpIp5/T0W7ScoxJVVq5J5/X2sKmnCJU/bvxZNPW2F66NOcKpknztYLAt1ySKeikZcCig0hZnIv1WNSwWOA6bqgKW9oA8LUooYXWauzpoY+z84jbVvZMDZsw8o1IhOdaikuG0qJlbtVJR9ujGZuuhk84+tVAuOrfyLJqUEWSes0DDT8h5VVt0rTOZxsBXzZCc8CIKsqYLBWk9dtYxHG5ROjIqOg2rGpOw43rsUZE9X4b1h0dactzWnbcmjtWVsEw/HPUZ5xtbgo+JqMFz3KuuTiU/Oj/Yb5kXPremC+WQpml8mYEx8Tf/KEaJi7gr+LBQsBsN5J+rXi0H669+eBfYK8Xk5b2vGdqC0HqsDR+OV2BRYz3IoaLxlNTiuB8PTjd4FGZDrqxU0MfDSPBe7ZH+EUwafokipoBilCYPuiCnS+SvWOVtXqqCwRxT2hJiGrwQQiDDhV6PYanfALabsF5qp7XdovhCl+huSYj1I5VeZyGFhpA2aYeYTN+yguRqZ+Yxdj0Ig6Y23fLqq+fXw5tG/9Z9fpXP6uHkDlTSvv4NRTvyBYvEV8yfzFd8pMITljkeiv8Jk7PXA9Xr1lOhRyYKZchgP2dc1J5Vl6m68mU594sd8gkqGj9r3mDvNVS9qF1bL82AVzAvpEOwXnpEJbILMBl+0mv9x8wydNA/1MZXWgFQzp23gabxmlkbcr0QF22rZK/dqyz2OsrOtWJfvFVJx7xWeMlMFNkGSsIvO7J49gJM4J6Q5n21a/l79PTN1xHucoHGEuM9Sv3lcWbOPM1NMboVpJSoGhZlQqSnBDW+Fqt9czEhIKzbdNaCI5ftUZsYQlrtgY80+WpVchWcs1StGah+LtNCc2VoYiGPnsv0cgMLtU9sDQm6jxhRp04JAzWG8xYgKFwtGaspaX7Pwz9kMZ7tKd6kfM68e4lRNoUas/MvdHotpitFV3iP2D2l8A3xDA47JHhULfUmXapa+JmEgZ01b0clCi9OjUvB0k1h60ce6XYlVwNQalJLKqNSJeREZmchlb3m8NvS5xI8pc1MUFNpkW+rIrVq84EMweG8pqw7nBu7cPeWeFlnxlDTDYFmtx6y6iuebMU83JYel59/euqYwgUk2cLvuKtpgOL+Y8LQJpCTV21Y+3aoogIMgZLmUFN4bhmgISVMZDwZCkvnSEAXZM2STue2Da1XitC256jXHZeC9/Lt79UbEPE1gFD1PNzW/XFjakFgM8oDVuR1ZGGkvzqzjxN8lkoS9T2ClF3SqwaUSi+UqPPuSptp2puHD8muzsO3P6uIdYvL0/iJbHsjnGetDnJL5lNGC6tuiANugaTIvqci2BVKpGZ5d1fQRfrDX8cHsGsUNcm89FCINRGJsB+7VhkI7rnpLtd6jjZHL2NCojqv0jFX3lJT8Lggs2k/yvX+bvtS2RfnVLQ+rx1LhvIW8VxX389xrIlyd/inhtQMQYFp9QG32CVGsmBsv7bTtJzFK8W54VwjFTjglU3cjLLslz4YIPWpX3cocSD7/JE6waCotcjWlkfbcyAhpMSIoNY3aUQvK3JIeWZXJzIH7ZcfWRyokxUXvOO8MiwGeNR6nFO+MRQV9S7p2uuJ2/y927dwhRhyGcRyzV77DtdLZQymgMh/GpZKH6SNK5Vinlg0rVmi+ToPsTXhxYtM/pVVnTIq7jM0W6FGz5JyXm78mpZ61u0tl92iGC4ZwgVIlzkwz61/2cu814+qYiZpQMcFicakQnTg1cM3AuTplGU6Fh+WXb1TKIV5z2fwMhUMpm/eIAcSGXGY4Ztde+0Na37iAI6KH4mWzH/cB+JV+io+eHw3vc1Qpprlt1AcZbg9Znmf6GpdGIYdmHxK/XIqa7vdnBffqRG0jJ1oGlMuM7DlthEU/MZYq64alpGgGy+cXR1gdqXKlMC5bJqMN1gZc0RO8kaF1UlgtM52ZG7g9XkmloiWQ1MGgSXww7ZgXjj5qloNm6iLzQrL3R1cH+Isj9quG/dGaTV+yHopsxCaD8t21UtKeq23k3iihkXZgFzUjE6mquBMXDUmx6koSiufrCVe946w1ND5xOXh+lX05DodDahzHRcnUKWqjOEojhhiFxZ8COs0Z1IhPw39gk5WPZSms2ds9JD6sf+uSvzAyHB+7Y3xsWbS/RARntkFGKk6MaHMZLd9NWoyW0ybbLYw9Vif2ClFSqEzIAUoM81ZDwafLEW1U7BdCTtyEm0RGZFUCA+L4uWfuMqr3f0vlXoUIHX690KIoChfoPMt5fcXoMabEKRky93aOVyUhinbYpHzAu/qPiCnyTEtGHfoP2QslW5mqz9MZP23+T2g94t+p/y33rVgL7BVx5/2z9orHazHtuzeyTGzCZxHUxZDoVA+pkCBjhbcDMHOKd9WYPiZWeTAixE2RuUlJOEAvO5tVDmIGrghC8snG8mwjpOJn+hV1HDHu9mnC1kcHVl58o1ap44V+SVADwchs5rL79RsV5eBf0Q7nAn+232OWxKhulCYc6AdQwxAbmuHlGxDot62UWkJqWfWRg9HDnePnHrcZj/6KqESVYKCl01ZiWYo7OPZOBiZFrrtHlHaPZE8IDLTxmpA8fVgQY78z3Pvqz9KT6L/mJeE37rNv4vrGBZwYW152P2fsjpkwpabkQ+5TKhG6PCxFyqYPimXUfNJs6PF8q5qKsZiRfn4bFE+ajpaBhVqh0TwYbtGWiqmNTKqBELWoJgfNVa9ZBZ9bbopCi/xLRHHe1lko1NIGzdQFZm5g7nruzCQzuW5q+mgpdeRW1TF2g8iABMN1VxGSEoho1Jx1jsvesF8EfrS/prae2+Ml1gSKrqQPFkVi3VW0mccQARXZBZytblttxV9k6vwbqke36w2HozWF9VSuJ0bNqqvovLQfItBGxbX3tCkwYoJGM1ElZYb5hghLH3kRVqKZpjOcOIlkyxaaqVTFpHwgrYPhjBC/6P/+1UupAqOnufUgra8hNrtvcjl8zsZ+n5XXnHdO9LCi2rW/toALkApVc9MaDSkb66mE0wPGyX/XtkJlaHgb4NHa8PlaNNi2wpyX+hVD6nCUlGpCoce0X9kmg20L7bf53il1xEycSl9AX/X+FSE2WF0S07AL6NYc8s7oz5nEOUdpjogkWWG4M3ARIgaDRlFScnv859RqzrEV0c7tc/G8M/xi2TMxlg8misJAzIFq5SXYAHy/OKTUitt1wqrE443iqg9MnWbmFF1QDFv+m5YKZ78QSR2TkYFtECoByLxyiLAe5D5ZpSlTRSTyebdGd5ortaBRG87j5yzaX33JUvqrVoxLNt2SM1XS2zuADOWX4SXXuRq1ZorSY4Zw8bXzNWv2mJX3AVhyThMuUcrgdI1Ost81hkKPSfaIbnhODDckU2fnWF1jdcm6f87zr1FTr4t30MrtNN664ZJEpHKHOD2i9VcM/voPbk7zdesbFnDk4PjiQ2iUDCYrc2MHoB2MrSExonsNcZSSQESdhttlwRAL9oNseq1g0Ws2XnM9GCoTOSgGJi7xZ4cOHw1GRYwSRM7Wkne/bDE68dAOOB1YDwWNd9RuyGgoTeMdjXecNhVXg+GksjgdGKLJfWvDJ8uC615g0VOXmLrAftVS24Fx2WJ0JESNUtB5y2oo6YNhE8Qfvs3GVWM7MKl6ZlFxlGVvdEZlLYaCIWqcjoyKjrpqmU5ldjNpSwZvCVFRmBETG7g/smgsRs13jG+QoXMfBG59Msx3gSxEuBrEF8fqv+Bi8iE6SYBqWEgm+Q9A0KTU48M5yx1S682Mv9BjrlOD6RS1EUb9q1ZIjMelZWxfQziZbH+sE/fKdmdjcN7WTF3PXtWwVzX8eSlAiWVX0kWD1TVj6zhtLD/tF/SqxySHUoar8JQuLL7STXK3c5X7rTNOpUqUsiIi+YW9LhWiFujtawxzZ14n/sk+qFOJyLEqfA7BCY3BcJTuUceaUaV2M8khKvaLyJ/sO0KGJ/sE90YDUxvYSsdtraoFfCNzx5Ma9guDzQhApxU+iUvs7rNnBYMbXtiWoAmHpfzeWeu46DWhget0jsIwDXcpMEzSmDKVHHCIqv6UVrWcqWc08Tqbpb1+DQtm1Ycy7FfbYfsInTTPhr+j7Z+gVMG0fA+nR8zVCRZLo9b417hPm3jJsv2cRKCwBxhd0PhLGi53UHajS5yWFvHan2d/niUxbVCq3P1eZeaCnFMCzy6rye71vX/BF8sVq0tKM2OPE1G1sBqN3j1P63LFprhk5V++1knYttIk4ftDC0bfsICTdszzJQHcRwBch551ULwbS8pceVQmYFSBUTajb2Q+c94ZXrYy01j6yDIM/Jj/L11Y8H76c+5yyMgYaqM4LC0HxbDTQduiZbY6ZiBCl05HCuM52btgMl6jtDw8MWj6vsDngNAHy6ToaQbL2A1MsvJAbQca71gMBqsMaw+XPThl+WAqm3DTy/B43UuQab2lDXJ7KhPog+FpU9IHzXsTmBQ9zibGqqfxjpfNiE0wfLosWAzsxEX3/Zq6atEmUpQ9xgS6YLnqC0Ym8GDU7Kqv7d/SSPuqj5o2GOZe43MbZkiKiKbQCoYJOmgxoFIrAkMm5339klmI/goi6JuHdkieC3OOiZoDL4jEl2HDgOc2++zlwXepIz4J72PtRcVbdMEsl73i3XHgjw6g0B6XxVRL6zExclDIddZYzvt9mujZ0NLohic7sMBvs75eWVerMVpXFHaarYOvibwJl32bRQMIQfBS1yz1Ky70hELV3Ap3KHF4QhaRFE26ioITfYzTMrDfBMVV5oDtF3BSexqvuW5EVmZiA4dVK5B+lbjsKj5f13RBc9FLq/FZ41nGnrtFzb2RJHQHpXznLVhhPcCrFu7UmltVx8iEXbLWDJYhaR6tCz5e9UyN5b+rv4PT4j0l/DiZka29YjXA87bmcfwZXVjg7HFuRUlHIaWekZaWe5dWorfGHqM0Yc/8W6gRCwVVEklcx4ZOdbyIn7Lun1PaAyb2mLE+ZG98l4DnrP9kV2XIN9N5RnTD9QlGpJZ8WOZ7HZm42yQii+4RMfaMy7uMzCGFGjGxhyzsS84yeEH2gLRTh9iglaMzjdz7HAhrNcNRUKaKkjsoq+mGy2zlkWWXVInWBdH//wPO77FuWhYx9jS6IaZEp9oc9W8zcQMjOzApemaF3KA+VyIgPWjQtFGhlcaqgtvxAzZmiU+eF+ma7+lDfrgXGFuRrQHotKi5xigSIavBsPaGPee5P79iXLaURU9Kiug1KWm8N7RdSTsUPLreZzm47Needtm11YF5NTBNUsn0wdAGS5+11i67iqHRXPROfOKdl7kD0i4aoqbp5UE8LPzue160NT4p+iBIn23A+OFeg1GJpbf8+HKfB13FqBCwg8vf9cHeBUfjFe1QsB4KFn3By1YC3sx5rEo8awoue70jDPYRrvokytBBtNbO04pz83KnMyZmYm8euNuWmQ9Xu3ubUkDr8jWfDdHPU8oK8oa4O+Q7f8XKnjNTe5Raht/H1TQDPNhpwh1VHV0wvGjlWsk9SNytPSeVeOdoEtddxaerCW1QHBRhp68GMCTFIol1gcPi4pTj6vtc+6e0w/nvTbiLaU0M7Y5gmJKHr1X8fT2AKbqwpPEXXMceZ6cY5xinCVWqZMCPo9aZgc4NTL4yIkpbGLFnT0lg+Idl2ilYyOcTLlhMEoi6CD9ftlwpsedIKmJ6zRCLbMAmwWarSDFzmv1CMXdiI1EZUXVQCnzQhKCl+tFih7D9ZrWRhGG7EoYmKEqt2eMevd6nZ0NMni6s8LGhsns7dFqJzP9mYb6TkxlbkxU3BO7eM7BWK643PwMSPgihdKsSoTDM3X1cISrSCrOTW5LPFOjUIPwZVTIu7zLEBqcFSdanDSGuSamnGS6ATHrWEwDm9Xfks+oJBrcTty1SzThKhfa6Zl8k8kJ9xlX7mbyXrtEUxNjvAmKMf3hcnG9YwIHCnjApbhNT4HP/Nzg94ofpLzmyJUeVkDFrOzCtGsZJMSk6+mB5tpqyGFzOOYR7cFKJwvPpao5Vlnscsu8st2uFzdwNoyIhyVxl5TWFTjtodG0DI+txJqB14mo5YwiGTV/SeEehPdOqlTZVkgfotClZec1RWdAGi9URp0NueWkiokpw0W/JmDJvepz5RHdrzcQFrBLOTR80V8ONA6NC7KtH5uYBLXTkVtXu5hWaxK16Q2FEbmfRjPBrw2VbE6JinDlFSsHY9TsYuY96B40FaU3FrKQsMHR5eJsYGIhElXCpxCtPnzZiRlU+JCa/yw6dHuF0TRcWrLrHOciELxzeUtnKCfRmleDDORdtZFTvU5iDXZAZIrxoEk0IfHtmOSx7DsqO7+6fY42ACV4HWAzB0HnHoi/5bGVoQkJPDPulvGbbBpqpik0auNBXmYsTqMycIay/NuC8rjggZl+vCZliQVmUcmhV7NpjW/2vL1c1itLdQStLiD2JwODPMrFQyKBO1wyqY6kij8PP6OOK7+p/xf1yn0ILr6w0icPCZ/fZQKEjL9uSxxtLE8TiWYRrJUhvgqHxBpXng1MHD+qK41ByWCpGJvGsgcddg0MzjwVNCPyd+inrcMZ3/H/FAzdBGP4lUxtFq0+LhFAbDGMbuVcbFkPi41VLpS0g9tVNEB+nrU18SAmLxStDikK23aIhu+EZXbGg0GPG5minURZJXMaW8z4RiPRqyHyllqgit8d/wZAatLIYHCv/aveeTtcUZkSZaspUsdQLFvGUkAaGuMmK55GUIlpbtLIUesKYPQpdszQH+LhGK0dIA6vu89f2gSRVbvRdajXjOpzS+AumxR1m7IlSx3akkEOOT6/vjS9Xz+qbd3z/xvWN+8RDuGLRdShlsbrG6Zp3i5p7o8Rh2WNUpPOW54s5Sgm3IqJwWqqVq8Gw8QLhrCwcmsS/dhNCmmaDJ5GvedFYDsvAnVHAaM/7E6kSztqSq8EwdQMPxitK4wlRs+lKni/mLPqSqevFRVMnfBBgwHJwLLPlQFV6VoPhP2ymJOSAlEAhhNXKSMAY2cC8kAP//SlvHJBSCRkuouOik167qCcIqAEjAUmkWXJ1FzWPNzVrr3lv0nBvvOS8GfG8qemDGG8N8Saz/HDa8K29CwojaC6/Y+iLqVxlNC9bw6N13A2WI/BSXbLQUoGIvW9LGxcZ5htRaGq7T6kmO2jpxowYXEM/XP6GvvOXW1I+XPL5+v+Bqj9kbBN7hbR+0kQe0P1yw2HVUJiANUGqTB3ZftOEku+kEvOy5d/cEhvwUosVxNpbmbN5xRP1igv1nBfrf7/7+0pVb7T/lKre2j8XZJFDbbk1KaL1GKUsRpc7+RSjLCH5nZ7a266BD2usGWON/K1tkOr9kqZ/TKscXVhidc2Be5eJ/hZHaUKht75NUGjxfTIq5cQnslfI3DElxbenoFRkmueiL1vH80b8g2ZO9ulfHrXZJVag03dqxQ9jwZAUG6/oo+N296NMU5BWWwJetoaN1cxcJcHOBCrjs+Ot4qhU3BuVWVYoopTMmIasjlArxcZr1vGKTbhkM7wifOFa9cMlg1oyWGlNLfQpRjmu289egxnLzEOrWjyEyvuM9SFNumYTzjHKMq+/T0qBVf+cRfocq+W621ji9Eg8jZInRuHbpOQxjFDZM6hOI0ZpQlX+BQM9r8InbPo3Z5lKlWhd5erGMMSG3p+xUpqzYkqfNpxtfkoi8M74X3MS7vJQ/ZBu/C1e8Yiz9X962y55y975L7m+DkTz9vUNCzghl/sHVHaP2/oD5nFvpwLQBsNl5rIsepHesFtuBtKCWmc3Q5+zZYVkepDyHCIytpqJE8XmSdFT2oHDkcweDjcTrrtSEGpJ0UfLqqtICdoglYzRkfI1a+mQNFeD5arXu8H7Zyv4++EUg2WcKiocH05KZg4iWuRiTKTUAWsiVWZ6v9hMpDWX4dROJ/YKaX3cqobXfHPSjo8CEqAisOc8cwcj44n5ZxI8Yg7Y0u5TKnFrtGZWi4THwXhF5x2fXx2wzDI6i0Hsr31WGZ46URletrOsAiES6gM9hRnRpRUXw8fiXhkbseQ1M7yei8lV7N5oU8gy27v3lbtCqzEPR/+WSTbXO6nEdmHseirrd8TYIRperqaIGdsNTF2rRONlbgU3gTqYkBGNUtX1ETpaAgNaT4lxk/fkmwed0WO0cvRv6Z/fVGp5b+SD70ZaxaDzISaf5e3kvRBXxNTslAVm5bvsmXv0dsMmXoqhXfeEXmk+MH/G++aAsRX7gZVPPGpbSmWoTMHcGbESyL5AbVTC94oiqinAmYH3JqLA/foeSXmeufRil1FkNQ5LojZb6LlU4PuF3wWviKhdzIoep8PuPtSDZ+QNGyQBKrSQRCsTOKkkRTjrHGedcIsEDSm+M0ltoRGyrJlidLEb6G+D0uuVqDV7WFMzsodoZVkNL1iER2/M5bQa87oCs1gXyM9EaPXLbqzRr/GqIBFp3F00mo1aMqSWZfsJ25vq7LHo59kHGCVeO4kgxmxmxsaf83T1/0Kpkmn1LqWeMIlz+ezJoFPNxByxdHelBZhbfV9UVf/nWf/wv/0NCzgAgdoesGfu8X66S2U1170YRjW+YFFaxjawX/b0wXDeu2wprekCHFeRPz0caLzmopd+tlGiUnt/HBHAYxSDrqQo7UDlBCIdoqb1luXgMnigotRxJ2v/sq1YDobCBPaqZveJrY48GDUcFJbTpuBqkG21l2YYNBNtKbTeoegerTUXfeBeXQrzP0uygMi5b2dSDlGjfjCSQCEPb2Q9ODbeMit69suGkDSdt/ikqUwQEU6d6KNlZAc+nHf5gJHvuAmW3lvhBlXykIWoMb20QIaocRmMcW8U+dY00UXxF7oeFJ+bX3Pa/B21O6A2+3IgJEMkyBwGw+BfMQANio29LQ9JbN8ys/hNm9ZwMvoT7se7O1XhxeBogyElMiFWABog93RL7txWno23DJkoetUbfnod6WPk4bhkr5BDeIhSif5QP2QV7vPL0ZxNvMSnboc+S8Q3+vILXWTE0tsH/V9cW9a60UUmuZ59CdFnzT7qNSFJyagjIXU06RqDY25OaPWK3svBWiaR9w8p0QTF9RB5qV9JZr44ZmQsI2MotIBp1iHQRc+lWjNOFf+9LZgVPfcnS0ZFhw+GZnAs+oq/v5pyPWgue2mpntSae7UkS2Mr1T+IkaFG5mBj65m54cZMMGquuoo+apRK7Jc9oS2yQ6/KluwyS+uDYeU1TzeRTYjcje/SqRPOii1a7ebgj2kg+IYmPsrX7jDbEcyzrBK7FphT9W52Ii1fS0LIvM7OiWl4q/mdVpao3nZMKmmfRs9Z/JyQutwybl/73bEEiCT8HR87rhuZIYHC6BkxdSQ8KXmum5+hlEONNHCPtV6xSZcs+mc7nbbCnlCYGQOGEO0/qpX7f4n1jxpwXu8p/j7lXqFH2GQZUkRHgXqqKPMYo7eOl542ak4bkxWYE/tF2vnF1DZynEU7t0Ph43JgVvSiXaYik6JnVHRolVi2Nc3g+NVyzKcry1GZeH/SUuVWgFKJO3XDcSmcmM+u9/OD4tkEy99djVkMKkuwi/LwVBeUWnO0U3Imt/9E+Xrt4e+uip3IYqET74x7jqpWUGI58156szMQ0yRedQXnneVeUuyVgi6q64GUFJdtTRsMn63GnHeWvSJwt25vWmUZ5j0qemrX03YlPs+lhgxmkGDLbm4kGe+2nQcPwruY2nLpH3PRfozRNaWZZta0+JUoVUgbQVk5GH4nl0LQquIwnTDSliGBHzSP1pJR36pKZi5wq+q4M1pR2EDteqlMgxA9QQ7BUkem1jMylrUvaIJh7RNXPWxCYB0GnDKMjaHShsN4m4mayyhXaXrVM9DTsuLKP5ZDTNcUZsKybX8jPFXrKfPyIX1cselfykHzFvi4D5coLKPyXeBGEHLZXrNSj6ncLY6KD3GqYlyITUKBWAesvYhfvoxLHoW/Eaiw/QvmYUqpS1zWPZtYS2kstyvH2Ea+Pb9mr2oYly111bLcjLloRlz3Bb9aaS57j0+JBOwXDqcTIxO4VTcyL4yaIRp+dj3l5wvLYem4W7s8B5IW3tJb+qCZOs/Yeu6NWg5Lw9btFthRCi46xRDFEsOrgFdeJGv0KFeey3ytrlBK7B6MLqjNPlaVBAZ87IhpyAlDZOlPURisLnHFjXK0ynI4Cs2o+iEulTgKioyYA1jqBU/bv75p6aVITGtCvCbEa3r/IlcePr+nWIWHsKH3pyhVoZVDK03p7uxaqSGucPaAefkROtMBDI6DdMIojXiRPuN8/begLNYcCmJOaYaw/g18om/u+setcJRGqxprxnKz/eU/+JAxek4ksOAVf6deUaoJH8VvsecMiz6x6OHOyHBYaMbW88M9yT635f/Hy4pPzzRzp7k/jqSkuOhl8D13OlcK3U7mBcBngqYctqK3lpAhKkiQMIg8RxsM513BeW+Y2Mjtqs9Ckt2uhQUwtg6j7Q4lpNjCP9POpE3adJI5ng+SkRlV7sRRvijrqJWQ6g6KgVHOMFtvc/vD0wfDT69mnHWaW1XgwagnJMV5V3I9GH58KcPY//aO49vzS1axYtVVXLY1v1qOiSimNuB0xGrx/FkMmicbyba3A/u5riDcY6XP8WEBaOriHZweMYSloGey5EjMFc1X7QOF3fW3X0ey7b6zrqhSRSTxqhUwSG1EnmU5iI/QaWP55WLEXhF4b7KmzIAIoxMj47M4asQpSUZuVYY+qjc+BRRc9ZpH68AmeC7NK5q0oNDCqVjFc9b+1RszqMEcUruD3Nr46v28X3/IWO0ziXOu7Tl9WDN4/5VNxIRn032OUuJwaXXJsv0VKbU0/WMWZsJYH/J9/pwKh1OaTYg8TRc84xOSElOwWs95wAF7peGkgnkRmLrAnpPW7K3RCmc987H82wdDCJZuEHDFcrCsfGQVPQOeSOLRRrHylnsjy2HZUrthx0f7dlIclyV9FFi6T1AYeeaqrBCx9pbzzmFza86pRJUURsWd0nehpQX4stU8Hla7VpW4pNodaFkph9Fjxu54J/XfxRVdWDCEJc5MmboTQhpY9E+IscfoGqOlFRaj3+n8KVUyGv054zRhkkaMVLGbW87ChKIs6FTDVXhGFxa0/esJhpi23dzzmllxH6VEEVruqXzqkTlEYxhoSSky0vvcDncpcRzZ6g318snwx1yOvs1KLXgVf00fVr+RD/ZNX/+oAed1T/HfdYV4zVX7GePiNu+qHzKNI6aFodQiQ19kYtnzpmDmAh/MFpTG00chNC4HSxtcHpxKG+u4lAP0ejBc9RP2ipr9ZkRlPPOyJSTNx9d7XA9SSXww8QxJlJzXxlDkgesmB6VXneGzFRyUhlI7KhPZK6TttTWc2i8VhTFcdppfLGWzfXemmRYxCyYKKmc5iCx8H7cbUzg0Wz5Qymz5LRwahPgp9r0DY9cTkmYzOELUvD/Z8M5YcVC2TMuOTV9w1ZdUxvFgXGWSZOTFZsLE9cLn0YGJC7RBc9o6+iDw8gScd4knbYtBMbcuq1InSix3+ZDRZJ8y1YzShE61tHZBHxaZGPeb1aLL4mTXo4+p/xISLCVPInGZGi43DXuq5k8PLHsuZFQenHeWF60chG2Y5gxcgsuzxnHWiRX4w7Eg+d4dN6gvADSkjVlw1oqy9xYltPIv8aHdacIpVTGtvgXApn/Jqv31138/t88h96njiDoVmGiJRWBtz7lu/v4tv/EasU9Zxu6YWs0pRhPasKDzV1w3f89STylGIs0/TSOKZOh0yxA2WF0xMUeM05yxNYytorbiXhui4rIvZMbiBkorlfHgLcvNmGVb8WIz4ecL8Un6kwMx3GuDAAVOG8Vp66mM5XooiCisbnEqMsrv1QaDUZYhKp6sKyLk+0GesQr82ihRnK5MwNrIQdlxUMLLtqINhcjo9HfYxCNe6JdsWOz0yiBRuiMKPWaqj1EYLuNj+rASHyEjit4zjvHak4ooHkbDGUN/9kYCVNgTnBmzCKes1Tljc8goTbFYDJaNWvFk+Ft8aDIvx0hinBqMnjIp7mT9x20C29KESwo9odAj2rTgKisnGF1n6L8sVWoU9zCIjbpCWqNbQm2dCs70+iv2yj/VUpnI/I9fQX0DZzhSKg9xSqEdBk0ft4zlrYSJosrsaatjJvINRK2YFQOHXloufRTQgMoVy2pQbILiRatJOPaLxHdnliLbDx+WMmwOSfFoXfG3V5GJ0UDF2AZcPvjvjQYOS9Erm7mBNhj++mLC2gujeuriTohwVkR+tC/97ZkTmPLPrws+Ww/MneWkFgWElGUztj4/rxrH80Z4J/dqydJPm61KrAy9D0shlSpEmj/qiM1w6XkpLZJp2XALRTs4btfjHQ+oi5oJUBjPrJTKqfWWUtdsgtg+nLfyJW4XgroKSax3f6F/xaV/zD39fb4dPyCkmGU9PSF1u9aDmFx9dfav1TibYUmrwOox/Vugx41quJX2+XBSMrWJe3VPbT0uA0Zq45g4Sx8VV73wL0ojHKInG3jVDXTBvVE9KgW3q57aiDGbTwqtEmOnAcs7/bss1TFPzM9ZhjXj8i5Tc0IiijZWuH4jsRIk25cDbGXmlKlCJ1EDuNbnnK7/msSANfsyn3ntfYo8ZJ7bu2hlaNOSdTwnpoDTNbYoiekAheYqPGWlz3gvfZ+xcuzHfZT5LgvOeNH+lMLMeCfdptCZr6JTrl7luj1d7Ek71g4YnXi0nPF0UzF1gY/2Vl8A48ieWfuC01YUJxaDw2e/pi34YshyUUuv2RoBbttmRiWO8vfsg/DcnE75Xgq5eUiaq97ydCMAn00a6BjwymcPoptD0KiS2uxzPzygUhaj3kU7xZCkHdfFgZVaE9nQhmt6v3wribd2B9KKSwMxBQG4MNCxkqrWX38FJF6084xy8g+OSOCq/xUxLmnVmFgOrLonu6rYh5bCnuycZMfsoVE09FzFFQHPQl/Ss6EwI1wSJ9J/vGVyMPk6o7f0T9au+8YFnNd1uX4S/gecrnkv/Al7fsJBLJg7mYdURhBcPg/6Q9KEqGi8ZZ1tnGuTMolT2PGHZeC+DTRe5iKVkRJ/SIqpHShsoPEykJ64wL3a4na8D8WtkZijuYwsS0kxBE0frbTgvGHIqrtbHk+pRZgTbobdToPTwtavTMIpuFVJBjjLxM+TWlqA553h359J1iPAA1HuNUrxwVQzdX1WpO6IKF6tx4Kmi6J84HTAWY/RkePxihA155sxyzxUv+5qNIlCe7BywAxRCH0RyUKLPJBuQyKlxP34gGN1AhGuaejVwEataNVaOArKonSVkVxfbS0tqhJfb5Eb4jWv1GP20oz9QoK5qD4rmr7YXe+tworLqtFTFzOaz3BUOkKCl62mDXDRi5rCe5OKvSKy9qKisBikmgs5U52mEd/hR+B+xECgTwOdarniBVo5lCp2D+bXPcA2mUzrU1RpzKS8T8itxpiGNwzarKlwusapUtjv6//MTZtRURcPeN/+OWUqmVBjsmK4zVlVwFOqCcfld3GqpAmey15xN+Z5iQnMio61d/z0akZK8MF0w7zsuDde8t7sSj4Xik1f8MlizibzsjSJh+OB780C4DPkX7Hxbx4jMYnvVEACi7RnB6auZxNsfkYNLztLpRN7hSRb513F0hsue7MjbJ7pCzZqyVn/Cb1/s3Oy7j6lDwuc+2gnums0PGs9p/olSy447z5FKU1pprhi9KV5mwz2tbTdus+JccO4fMi+e0DrF18h2LrlxAiB9HxzCRiq4g5GlczKdzHK4ZPMkZyd0w8diYTNnjuFHjHlgEjklX7FoDoW4ZSUArXap1IT2rRiGV+y+A1V9D9s/fOKfv6TBBylqjck5n/bVRX32Xfvcju9Q8CzzqKb41jhlGav0BxXImP+orVMXWS/LBgZn7kX0iueOeEMbEUelRLr4ZGRwfHUwhECF3Y6MkTNzxdT1l6xV0RmzmevGRn+i1WxDKIb78CKcvOQJNj4oKkzxPbppuSi1xxXgb2cTaYkD7DMieThLbUADMY20gfNp0vJzD/aS0ytZ88N7DkodMHSO2IS1V6j5FB1Skh9tRt25NUhaM67iqvB8rItMSoxsoGJG2SAW3QoEptMwhuSYhNutoDPRmx9VlsAdvJADT3n+oxeNaw4u2FZ6xFdXNH0mV2tC4yd0/slQ1xmAcavXqPyXWlRZAHD3i8zY/vmAF/7c9amxceaPihW3qAxPG0ciyErZpu0E+MUm2wBYcwLz7yQNuXKa7ogBmAhinrCeaf5+0XHT/WPs8eJMM3LVGOw1LHGYFjoa87TE9HZ6p78VntbqYJKzfAEklBlCXgqMyekgSZcEt5gixsqMxdEVdZyG5cP6cPi5nrZQ8pU4rBZ0VrTpwAJzvU5Z/FzJuaIB/E9UUtAM6Qb6f8+alZZb++g2A65E30wTIqOWb2hGQoWbc16cLzqLIvhhpB5u07cG8n88LBqMSpm6LTi2XrMWSfVlNVpx7FxWki4Q9SctRWnrd0lhFWuyENSO4mpkUmMrOJqgOfh56z707e26RUWZ8YYJdWsTwI/f67P+Kz7D/h4Q9btfSXEW12gqd7gRcU0kIhYPSbpisrMsJTM7AlaSTAKsReOWbZG+LLPThD9Nixai3yNQLQ3b7xuW2G9ChfE0Q93/3/nFyzbXwEBVX+fwrxm/c7XP0N/SOufBKX226q8fnH5sMYWJbfSFKsUhT6SmUHug1QZyaUB4yJjI4NglTkpKUrpf9lvVV0TPh9CCTDKsvKa21XPndEa81pAqLMZ1zpnXx8v4P/a/l+YuhP+3eLPmBeWz1by/e6NAvdGLV0wXA92p16dBYA5LCOVlgOw85brvmATNL9cGBZD5HYF35sj1YdKYCL7Ocurss7XdokNtPx7moPNeadYDgmjHO9MNGiIIeGj2QENVAYYDFGz6Av6qDm9mBEivDMe2Ct6TpuKp43dMfe35FSjRK7kYhBv+5fm2Y7cmVLA6ora7okNc6ox2uFNRzucfY3D4peX0XP27ANGzJikGZFIazf0qufl8At6f82kuMeeucdhnMj9z4eUVYn9QlNozWWvOG3EH+mglLbr52tDiIYuJoYo//9JJfdlYoVIu2W23yoK3PAjrlPLM/OEgMfjiUQu9HO6uOJq/VP+oW5XKfVcDp8zcjN0buYNqiNEYa73/poQm9d+I+JjS1RhpyRtlByq3XBJiCsaM6YzHSu15DT8kiE2O2uHI97hj/gLTFDU2uC0ztcIZk7ULi56x2VficDpqKPKemdVdkpddRWLtubpesJlb/nlInHtB0KKRGATSppQclRapm6gMOw4ZAoJHAJoEWmbo4xku+wqrvqS09byOBcYOiPm5s4ycQNjNzCyni4qit5m2wOH0fVbA86ofJeZPSFEgblvl02WSXGbLqzovCWlmFGBA8ZMKc1UQAPZU6f1a5TSu9aaUppNuqRQI+6Y79KoNWf9J/jUEOP28P/y+WbNIaXdy23l7mvPwZQG+rDC6RG1nhO1373Wx46gB7q4og1XXxJ4/UNe/7iggd+T+erDJet4TuJDCq05zHDikUkYLcPqxqtM7IPWafYKx5BuDtWtl/oWyqti4rpXdDHx+dqzSZ4/mtVCHLQDxzvPmsQQNBftCDc4PtrTjFb/PWsfedSv8X3kWI+ojWFkDSMjsEmNyPx/vFQsfcRlM6rbteVeLZn1s0ZaOSsfGWLk1+vAL1eBu1XJd2YxqzwLR6cPOhNOBRF33VvOetmIU2cwCo7KxEkFR9VARBGjQIDbYHneFLxqFXdrxVE1sM4tChCpGmNvdN6cTkwzcm6baa69oYvwouv52HxCm5Z0foFRJXvmLiY5WlbCSUH8QqKaMLaH8qD1Xz4Y3raMnuPslGU4JZlISYVGY7DYJH1xpSwjvc9xuMVB4XYEwbEVUuval7xohHdy5XvGxrFXSKX4ZDOwTgNSCykSDqd1PhTln4tOpHFqY7hdOerBMPgTvMoeQnjO0opV/5xp9SETc4surVh0jzI/5uv64LKsLhlUxyzuM0sTOgYqPaa1azbD2RcqwLTzAbpZwtfYHrhN94TF5H3qNOZD9acYYwhIRnVLTTkZWfooEOmtblpI0AXNcpBZy0nlqU3I8keBkesxOrLsKhZdyaP1mP90YRiiuMgeOSeWHUYMCx+vI10wHJUVtRHLApNJnltVgZEVUdxR0QtXp5NgJNp8ikQiRKlKhuzeulUzWA2G5QBdjBR6RG+qHRHz9dX5K1ZK88Ts4XgdxnwJkcyRkopiXH5AbfcoGEmrMS1Y+3OMclhbopVjYo6wlNhkJUFI4vx6Pvzqa9Fh1uxjzZiJu42jYqAlJk/jL2mHl1+xTxKr/pmYt9kBoxy3x38pT4AqMxzfEKN/C1n6D3f9k7XU3jZA/W1W4y85tZdM/IjECKvUzqu9yq6DPrdDEuIg6LRlMQhn5bwT2+mpU9wbRSqXOM56WRe9ZeUdM5d4vqnZK0Td2ejIdVvRBqlwhqg5LgfujbYZh81BIOGT+PG0UVNlqZBx1LTjgrU3OwJhnW2sTVYKSAluo4hYrnpYDpZJtsUemciP9oXr46PmVSfvtRpE5XcTPE5pjDKUenuNs6wNWS4++5CIwKfhTt1zXL2Jxtrqub1qa161Jftlz/uzRbZ0EATeaVOxGAwrOs76T1BorC6FUZ0W0u/OJX6BMLzLVGE5pihGnIbmN3JuFGLQ1vZPaIElH3NVvLNrV4TY7yqlpd1jrL7NKMvv1CZQ6IBScLceGNutnYGj1Impk4f7OzPDEAueNZbzToAZ60Fg4Ve9TGkOCsO8EA27ISbOfMvfh/8nRpV8T/9Lpky5q/4VqpSd7GOkZeBFdZeWFRfDr7/k1vjFte5PiSminGEWJngV2KilkErfShhNvI5UM7pG0hqZGxgzZ5SmzOKMd9yUkc3cLy2v0AqWg+JVEH+ksdVYRDE6YnAqYbdq50ntZno+aV40NeedzQoA8leXWSLhqLIcltv2MGgiF70jJkcTRsQEh6UoDQwZqfa6IKfNtt+JgrWPQluYSMu30KJY8LJzXPWGp5vEZ/2CV+YlZ+uffiXHKcaWZrjgafw7AHyWnRF4vex7radyH7rPWHcwqz5kbu/Rhmua/jS/psDomqm5xThOmKQxNY4GAR1M7cnOHkBhsXYfZ8YUekIfV3TDJT6s6fSCQTV0YYH/LZ6DGJd0cblzfhWKQWRcvs/YHtKGa3z8en7XH9r6Jwk4v03m91Wr6R/xs7CmsFOm8UQIUUGkwh/E9zixY2K2Zp5HzZ1aNFYlc0o7i9uxFShmoW88dK4GCQgARc7oN4MTNdvsQRHzELpD7YAF90aSCaa0tTfWWVE37YQvdUbpPN1YLnpp6xQ6UevIcTkQk+JqsLRBcdlplt4zL5y01JBg2EfF041iMcQMKJDh/e2yoDKKk8ozc5LhFyYwdT2z1xQPYtQ8nGjaYKntQJVfN8os/CEYYpRstPUWo9Mu2KwHUat2OjG2kUM9Yl+/S53bXduMDyTzB/AZmbbiisvhc2FwK40x8zcOU6PnGYlmdxLzX7RW/qoMMqbIK33Fgb+FT2KPoLMHkJAEBekkQIzAQSmgju1cobY1B6XdOV0OUVEb2fZ7hVR2L1vNYkisVSsupcrjjVQNlZa23ToENinQqJ41okAwsodEs8eq119pYRDjitK8j012N8Pp0ormK15vzf6OSAvQhWVuu22RmpqDuMco210PMaGzwKrMWYT8WWSC9NUQ0ANoJWCVsU0cFgMJeLKe7FBoMYl8zSaISOf358L8f7KxdIGdp47RCQv4KPs4ckMcOess171lExRrT3ZgFUuRLfoTBAwzK2DuAkV+35SUAA2SfKdOiWipNVMxIHwLSiymFqNG7Lt3KZTMPBKBVTxn48/RyuF0TUgd6+4ZKXWEJNdfK0fpDmn7Z/hcCT2PHZvyHfb1XfbiPo1qWOsVJIFOx9RhzRitLJWZUykBAHTZO2erEv22RGJryaFzy660BzIrUiWVEuWKgLiaprxTQtzSBFRO4n+3UcU3aX3jUGogN8yHczbdZyhV8cHorziOR9x1Yw5KxV4hbQGjwq4qeN5sh/zC1amMqP9uOTQgCgBOqxyE5MB7vhFRxK3pVJ/thn0GHaQETTYtM/qL1YKiTYbGW36xqFjlh+y4StleVwLjKnvGN17TRbg3Srw7MRgVdiirrW7Z503LC33G1E+ZULJnC96fKAkCpfBvNt6yyAGidsPu83fe8vFyynlnGBlRW5g7z93xGk3aEV1rOzBy4tHTDJY+m8QJwEIO4Xt1Qdh8hFHCExpS4jq2rFXLL4f/N93wjKq4z8zd5aL9+CsPXGsOmZX3iSkwxAavmrdUBAqtJ2hVMC5uY5Qjpq01dsevw39ikv4tjZeKasub+Z9eRX6ifsoP0vf4kwObX6+wrxFwQ76+0j4U5Np4Ku993jsar3nVRn4Wn7BSVzgzpTATbDLC/wk9KSSemMc8H37yxiC6dHcpdsZobxcynFXf5rZ6n0mcUOHokiOq8CWTwd3nDUtCWNINz+XKqBJxVpUKx5kxGk2XAp8PDZHIiAqH5kxdc6aescctPlS30Upx6XsCkYMw2oWGxDZx2iZGcl0OS5FOEvizxYbExEq1UxjZ81sldaMSJ3VO9PK+erqpOOs0Vm3FP9NrMPSEQZKwMrMbl97sEi6TiaD7CrFd34wI0eOM6Jy9HZYc8P6S/eIWszihxGKU5iX7nLsJXVqx8XL418UJAE1/yrr7dKcG8HoF4sM5y96xUi94mrkySukMpz7Pr5F9voFMdq45rL9DSAPL/nn2yfnySimgiBizhzNj9t0DDuJtBnq61KLRjJjuzNcAdGG4UpYhvB3O/Ye4vpEBZ7u2VgXvp3scV46TWoT+yoxGG6JmMVj6bDFcGcHuvwqRJhhKbSlNZK8QZnphEntOfFK23IHCbNtmUrW86iwvW6mQ9gp5+D5b18SkWA2KLgoIoc9mVg8nfQYKBPYKtRPW7KLmWeN2FQNIVWW1ZHE+KtbZ1Gw7tA8Jpsbh4i2c1pRaM3WaykgbZEsqvertzmBsv7RolVCIanZlRJzU5eB4PViur+akdEPmBDlwTqqek9EaFwV4IdfT0QfN7Soxto6XbeJx19DQ8cqc0qdNhj4Lh6YJl7icjW/9QF5fW6JeSB2dv8geHiLYqdUIrStKu8fc3sWpcidcuDLX9KlhFV7S+yVdOQA1GkEIOh351rRmtPkBh6XGqMgQFS+aOn9vcaqsbWTPhZ00kFJbtQfZMz4Jv8jjGTFj6v6YIhWUyeFJPDaPuArPIEBl51h9m1JN6NOG883f0P0GANHYHHEQ96iUpdKGTRCnT4FVf7n1/OUWTATlIGnIWe/CrChTSUWBJbtzkrinDvi2OUarG/fbuRHU2H4pLa82aB5vhFfl8uzyVik2HD63wl51BZ8u5WgYWQHrLAbFRWdysJLq8P1Jl80JQ0Z8CuDkoAzcqwWQMCs6lEqs+oI+Gl51lpVPLAZ4mZUITmpJAodc4SyGyIW+4pynrLtf8XWteWPmTNKYiSp3YOWFvuRl85OvnbOl1JJSLfs4t+y2BFCdVTIKM2GijujsirPul1kw86bF1flLOi5ZpSf53gW+rA8iq3S3sbqmNKKgrtBs1Io1Vyz8KVpparMvvj4cUaaKTbyk6R9/6ftrPZVq53dAAv9zr290wKndATN9wkgZSsOuSlgDIVmWg+Lj5cAydbzQL1lzyYP4IfftFKMEKBDR0MvXfLoxXPeJeyO947scVg1KwaoXqOheYUgYrnrFjy+lNXF3JCS28w42XqDSVZZXCVGhddpBTK8GQbldD4rzLjG2mnsjspaXHOynmf3eZSBBTNIGMUrx/sSwX2yDkAxX2wBD0iQETvp0Y7jsEk5b3suePtvVZCb3rSpwUAxc9Y7njXz/wpABAolKR0bWZ7FOg086a8yl3aHsshXCSDlI4JLoVL1ud9yH9RsilNYcvvFzq+Vw+6KwIWwNydaEcI3O3jnblHgZX9GFFRN7zG3zbR6oeda1E5i3VZGP5op3xoazTvFkraktnEi82SUHV4MhJsNBkbhdi7/QaSsttsoIdPfeyDDu36MJiUvfE0W9D4BJnKOMYRb3OGDCkCIX6oqVvua3kews1QSHximNU4q5qniYPmJt1zxTOvvWv/rS71mzD4APC1K6yZpDbOhUS696zpRczyIVuFQQ4xynFJXRzP5/7P3nr2RZlt6N/bY7Jvy1edOW7Wo/08MZamgkiq9IGUCQ9EGAAP2pAoQXAkHxpQyHM5zp6e7q7qrqykp3fdhjttOHdSIyq81wwOaQ1YAOkEhUVt6bcSPO2WvvtZ7n9ziF1fDMapwS9qBG7lmj8mCSHcCbUVMO8d0pK143mp9tGzSKM1fiNKx8pElRfg6th2ybQigbTky4CxcOhIc9gLYwAWvSQbr/ftKMjKg29wF/KUMTFF9sM9e+Z0dH0ok2Lvm7is3+hF0nh1NyCg85cRee/50nAmfPDrHRXVgfqN+1O2ZqzilyfaAMuFSQmTErT2WOOURUN2kps5p3+IG/7aqLZxRmwszICavLG0LuWPZf4cMSa6aM3Ckxd9w0P5OWdP3HlLliYR6hao1PDV2Qdp0zU5TSh2yezr/hH7LNJllOcp/81zCDfqMLDkCgYxsjpld8tQu0OTI1juNCs/KJn+nPaPOGMUccI5G7PmfetJ7Pm0CtLJ9MHVMnLYDHo8yDqufBaPe1Yfk+Rwbk2D9zDLA/mNohJyZrKqOojXDR3LBT7oPiTWvpkwxvncpsPNz2gSYatNJURnNUJEqd2ATFfT8kgirFyGmejSUjZ1G0lCYezHFdUjRxXwzkPWkjLEOkHVhvSsn8wujMw7pj5uzhFDC2kdNKHYp1Hmi+UxuYlx3jspM/cz3t0KoTrH9mBOQCtHKsvaXvT1npgpv0S/pwhTUzCjNnVDxFK8HTlHqCpWSExOR2tLRsKOzxAUT561cmsG5/DsDGyoO5Fw104Zbz8l9jtPwM8Z122WgwtGqg0obaSnSB07LAKSXt0D7poU1keN1Y/m/rr9ipNX/EJzyoZK6nFdyFnr/M/xalDB+oP2aSax5zguOMnkRDoMPT0RJy9zWQ5N91/3YEuhRYJmhUz0rf07Ci8dfDoPjt5ezZIAUuiKn/jYWztMdcpHMMCk9Co5gaR2nUYe639pm/3mxwWL49qZjafLhXNkGx7DNWKxaF3K8JoXLsZ1x9gpGyRDL3wWNQPKwcsyGKe6/0e9WIbKUyshHao4acktN8pe0hpmDseuxwGpo5mQ/d94JyOauU5FplCVAbUTJKJaX+EV9U0Kctvb8jD2Tlw3ulaxSaFQ0+lYyVY2Qs34//hNvxd8T42f+SELeHNm5hL5iVj3FUQhcwnq54SEbQN7f+C3zcEuMOa6bU7niIFbAyTx7qitUlVp/RxdXbFM5BZv3uxiqmHq927NSdtOfihpiDMAfzlpA0mWOMKplX74tIhxKvekZ5ykhPecXP2Q73QUr9wB38+xHKf98rE/5LtF+/8/pGFpy6eEZtjzhVzyhTRZcjqyBKtamW3Uwf5fj+cfyANgc+17/gOn6G1X/Eqaq4zR2fqv/ERJ/wOHybsYVHdcfpENRV2cDWF9x1Jfe9499ewst8z3tmwXklMuZtSNRGM7FI62J/sw1zDqOkN61QTGzGZw6udZ8zIyMP+VWbBqOnSKbF9KYJKdMmUQtJ3LWosJxOBJ1wWhOz7ABRDPOozAcTeFQbnoy6t4miOhGjKNyuO8tREQ8gzsejhpg062DJWTFxnlKLHHvdVof3PSSDgsPsSSv5eXYhsw2RG31Ny4aJe0BlF4TUEVIztApm9HHD5fb/DcC8/h4P9EcklWToasZ/Z/tpn4oZU3NgUoH0zH/S/Y+k8l/y/f58aLtI0fnZquKyzZxVioe1KKQWpQSGGSV+JqcleVUFS0ia4zLyT/0T+iR+nNJwCOZzSnNs3kdnTZ2kXbXOHSEmfqV/xtXuLw6A2pzDf5YQDdDmNZ5Ipzo61dKoLav4mjbc/9ZdeIw7lJkysicYHLmQQI2ROqLOI87SMR/UFaV565mCt+q0lZegvrmuRGXmM7uguOo8d7mhxDFSlqm1TJ1srvacvqtW86ZN1Ebx4cThE9x0iZgz80JaZfvC4pNkT7VR8attYhcjC2cZW8XMwZkVuvtP3shp95NZ4KgIh03D/r42SpSWVsOzseZRrrlqMy/7BpsNx/Z9yVnKiRC3A1vPo3A0/po23LM2ryn0hI/zjzhOAt4s0ylouM4/RylLPaggjXL0cUNQHVqJBLnWc9q8Pmx69lcftoeNj1LVgYOmMAcc09dJBAZrZoTYvvM9XtMHQ6NeDwhgeQim5Qcc2aeUuWaSphgM9UCovspCVxjlKeM05lg/ZWevSbkj5fAOT+4P7/oGFhzDkXuPCQvO09mhR93lyEhZrFKHPPUuKnZRo3H8C/dDRrXQhAsN7zHhz9U/Ezq0i5SDCfKmqxjbQB4Q9qKeEdRMHQq6lLjtRIq9KKSV1kZ5QD5de65Y875Z8Gws/WyfjPx7Y3/gcsUhCG4dDH2EdVCDRDVSmcRpCQunDoIBUbjp4SE2QztLBA8pywwHRMnjE7xpDW+ajNUF700VYNBZfAwTGwlJcdlaPu0tiwIe1v5tdr3KIjhICqPLw8zJDoPjWdGzIHPZ1tz29hCnUGjNIhyx1QW7fDcQc18AmQ6I+Ykg3sv3Adl9brjnqv3pOy2j350QKDupQPyNnvswa1HhEB7meyEvLHshIdBqumg5Lg1jW1GYePB0PN9VXLYy+C6N7KKnjqHI52HWpQ/hfTprkkpsVUuD5pX6nE24Ytc+P+z24t8DDbJ3nNdqTlCRUa45yjN2dGij2ajyt574BPezxbtj0AwuIs1xPOFcT5kXhol7uxkAud+tyjRRWr5awclwGskICLI2BmLNyJhhxgf3PTitOC3jAKDVZPRAs5DvOx0I63v4JjDcs4IPclpR6eE+5q3vxw807qNS/vtNa7nqpAvgkxTCmz4MfjuDZf/64fN+xY/jv0ErR60XdHH1G+9VpifFYfZiZhR6xC03bNlS5gqHeGmOyveH6aZkA6/9a/qwFOKAchRmTGkn1GrOrPo2fdoSYiNJrMMsR6m36jI1/Fnbv/wt85NIiHfUxTOm7oI+7ejTBoU5dADG+oSKCY/SQ05VycgJyQTE7hEznPUPadMDtsmzpadhdSh8Wo0xuv5aUftDur6BBSfKLoSel/oVjkKGrjgWznBcCrhzbMX5fDEsLG1k8FPIr8rA5NAKE2jny0ay3M9KcVkblQ8S4+/Pa56FyUHJUw5zjpgV6yBtBhAuFsgps4uwDRyCwZxOGDikJPpBQt2KcVzyWcwgGAiyeC578RadllAOCwdq+LtalDxf7aTXfVGL+q42mQe1tMYyoqbzydAGy5u24KbTNBH6lLls4bK1FFrxaAjOigPyZ+Ei02H2tAdYbgbxxC5qwqDuOioUY6sZ+QVdXHCWTtjYhsbt6FTLJl9z336BVo6RO8XqEoMj4n8tFvgtE+zd/JC/65pWH/Mt9Y95pueMrODs9+HRpYGp1ZxVIhm3eiimfcHznWMXJMPH6X2suPhwNkH+3acjx9xxkMqHnNmoG7q44U38yUHmDWDNfAiQS4ddqvDbfntaaSYQ05Ib/xmlrYEZZXYHaXT8zyB/dv6KnNOhdXLvvgT7L0l+wrxwdBE+b1o6PDNVUWmZPz4eqeEELrO/n64imxT4cFTx3Vruu/tefuYmSGvtospMbWRiI09Hitet49OVGiCo0lnYx0ff95l7H1k4w4cTOe18MIWUDXe9tPMmg3gF5H5Ng/HUZ/hik/ks3nKSp3w4LinfeU53QXHXBxyGZ+5P8PRsuMGo8jdivr/+XkcpAmzoVUNkSsWIcRpzwvfY0fKT+G/pwt2hBRqHz7wPFYWWQX5tjqjNEff5S2Jao6lxdooPa/r4lqChVMGv06aNLg4kaa00bVxSmAnTwSx8331Jyp6dukJhmBQzHuqKysjGZ/+auqS48h2v9SWlkmgOM4TxyT3XEWP3d9473+TrG1hwoM87CjWiRki7E1UOOzNFbaSNEIdFfV9sfrbuuFbSp1VoFnnCo7KkMpqpk7nK3CXOqnwg21YmMnbikTmtLHUwrLwM3XOUXdsuwBdbTyTz/qjgR+UMpyTArTUKhhNAysKpWntRkO13eSmLVForWZraKN9/HRSvm8jP8guO0hH/opwcEPJGZZpg2HpNznBRDdJRJTugsRXe1NwF7NAai0OrSVRxmWfjwHHhJZtkkGX7wW9RGZlvjAfOWhcN2744eFUSikonHtT98HrNUHzkvV4Hi88Oj6VVUfD51fdk3hZu6OKKsTsT9I2d0/a/PufIv7PYqF+7Jef2MUdpzNRpauOFy8WgBRrB8cCxK3QefnaJIX42FoHAOmi2QXb0Y6dYe3jNEq96pv1DjJLNhM+ZUmuexU9oVMOVey5eG31EkWvu80shDOS3SjylhM/1d81xYupZcUWnGza5Zs0Nd+0XA4vrd1+HGULxDK0cR+4pIcUBGQljB/+orNCUhKHFOLHiOVND22sOnFaKnC2FFiJ0EzRjK3OrNkp7txhYZ2ngmUmMtKaNmds+EnOm0hqnFXfec6e2dN2YmSuxWrH1ovZ7NMp8MJFW7lHZDaZkKczb4PBJc1RYvudPhvtIzq+lSZQ683SsOCkt192YqnW81jd80fyEnBqMmQPj3zq7sEqMyV3ekHPiiDMeqQWJjB/k5z5uf+vnlHPLsvsSZ8aHqGofluTci2GX+W94xpQqGRVPmdgzDI5CjdBZ8KyJxJvwKY1/Te3OqdyUJt79BvLprrpiGxasA3y1k3vvYS0z4zNXUoWH9DnSEajyGGfPiKk9qNP+PtHs/zCXmJL/SwUE38iCk4cskiXXFHrEIo7wSfGqSbwadqldjpTKcFE5jIKHVcF5OjvIXLXa71yFOmA0nFeeo7Kj1JHSBiSKWBDr++IA8hDcdnDZeUbG8J2ZY2IzT0Yts6IfYoyF23Zl3YCmMay8GWTJYjwEOeVURpHzu8Fuivs+c1Ia/g/lU0Ym83jUUuhEP0ifxQkuuTj7iUYeZjkXVc+s6Cl1pHuH1CsnNjlFzF1g4jxOC2okZs2yK/HDLEf8RvogElAqo5WYXduomDnFKCtue8ObRop7ymIs3GbPTjW85jOWzReU9phT9wFkcXun7CkYMU8nUMDazFgNAWK/61KqGnrjVqIKzBijSkZ5Ovxse9mtFBKhFGuaqLhqDdsgJ50fLAKlTjjnSQ5W6xH3vbSHxhZmTvG+PhGj4wClvO4jt7E5DPQBHuT3JAslb+hVTxc2h5nLft5U2GOMLui8JqZ3h/9vF4HOv+TSv8LoGaU7+nuDP7WWfr7VJUaVFLmmyI6k9tgaIXgbpbioJSyv1HKCve8tX241WslmpTQJlSElQRfddnJvusHLe9MLqcMMpO0maiojm5ubtKNTPWdpxmTwpuw9SpsgP+d1J3jSRVFwUohsfey8PBd9eZi5OZ04KXuOCzUEscn308NGyqnMyIIbjNMuF4yLC0Lq2Aem/ZY7h7E5pWZG1HJqnMcpc2doU6YNMj/bFyqlKip3fpgTKjRO12QSO395CA80ao41Y2p7RG2PCMNJvTCTw+nd54ZduqEN91hd89B9n3Ge8KH5RyST8KqnYYtR5eHVHo/+mJm+YJwmvM5rtnrLnXrFIj9gGp4ysYrjUnFcOr7YwpfqJb1qmBVPDq855ci6e/7fKV7696NNfyMLDiBOW1rI4En4nHnNHXf6CqtKylwxSzPOshVzolIYA5s+ch87FAqLYqQt55U9DEf3sl+tMl0QBtseOFiZxNSpIURK8TRYKpN4WO8GT0FPoYP4XpQYEI+KYmibySmiS5qQtAgTBt+HRkxzkkOS+WjytqjsX1Np5GHa+oIuve2br7zh+U5UQI9H8VBQtsGyTPJ3S504KjqUgqmVBfe2dzzflZyUgfcn68Gz00qh3EzZRU2XpHWWsxTGbdD8dCl06PfqitNKQuJA3N+3fTjsGKtcMTEneLejNkcSWJWPOHcy5HbJiq4nWSo9xo1qbrZ/9RuLrbNnSO58IJOIaQOkQY1TsLH3bPIRbXQDTVgCu2LSQh1QmqkdKBMm0QRLq97OvKzOLAo1ELZlN72XsF91bpBPa9bRcqvueekFkzJzj7CU3Plffa0VA2/nTT4syWbKpHxMpb+Lzw1tXNL6m1/bTWdiWqI4pXDn9OH2P+uhyFloquJeT3jbsDQz5uqCrn2IRVEoAXQuCsPISGt1FzU3neZ5IzvQXXBUxgzBhTK3uew81bCjdlpOOj0y5N8FER3YQRL/USkFf+rECrANJVsvxTAmsFrx8dRhFUMLz/JsrFkMkRyVCSTUwecWB8XgTWf58VIK3KORvL79fOdNF3iuxfj6xPyAxu74cvfvfsdJMlOqCSfxlCNVU2qNc+qwSTMogvr6+7xwT4WhRskmX3Oz+/HX/FCaMcaMianhrvkJRk95WP8xLpcHyoRPO0LuDhTpkBpu03M2ekShRofvvenfkHPCmiO0KpnqM8ZJyAKd6pimKY84o9aGahiSbYNEgeyyBwUGeyApKDRZJVb86nfeO/91r7276b/O9Y0rOEoVTDhhkmdcMBfzYyXZMY85R6vzA8l4Yi0XFYehegKBa/Y16xC5TGu65LjuxjRR41PJZfe2Hzo2iYej5iCjBbjrKtbB0iXDTacYW8NZqYkqczMoupzeU6oZkDfpEHF9ZCJGifyzMBGjE6WVnVdMmpQ0182Yu64cfl4pODJs1fz1/YTLVuYtD+ue2iYe1hG7d4KrTBMNG+8OX5uyYh3kpPbFtmTZK47LzMJFmUMljUcdAKfdMJsxQzGMvG3/FVpTJMOr1vOyhWNnOa8lmbTNkQ0Nr/SvaNKS0/we31f/BD3gvFdqw4/7/zshrjkf/YjT/JiNXrLNd6y6F791gd3H/pbumJg9fRD8zd6jc939nKPiHJ8mEkdsIuWAGbrrBRUkFIWvizD2c6qxTcyHdFCn3wWxwNRGSq3okqVPBY2fDWRhT592JC3FXauCqnyfiT2niXds2s/JhLcD/ngP1YfDvTGiV8vfsP8V9oK5fYRRDleUROVZxteDx+KelNrhdCd06Jl7hFMVozzF5T0oVg+onxvqPOKxnjMyMuSXdq1sEF7uEn+jpHB+3H2XsRJwqRiUDX96bAeZvbzK/clmHQz3PjK1momTTKdnYxEL7E2Zbpjn7AGogpSSU4rT8jvA2jv20d77FFCfNJet46bXrD00MWCVYtlLy7MJEvAH8ChdsBvMxsv4+ne2La054jSeMaGUuWpK7FIm5YxVmlobTtMxXxbP6PwNs+oDClVLoisdG//mt/rDjsofHgQbhRpxGs9wGDqOSCrTmRZPT1BBzNDDdA5kJNCzY9W9GFpphmn1IUaV3MeX3POS99QPeaqPmBjNUSnP3jbAOolC716teMHPWO4+xegps1I2cl3ckIkYXQnX8B/8lPNft2X3jSs4hT2lpCISeK6uKFPFe2EBFiZWMbLi8P+Wlh3vxIVhRyZ+jKk1HJeSannRHw3OZ/HMXHeKFztz+LfOKsN53VIyLEYqs4uGy1YelvNKRAM/XUmhWbg0qHkC86JH54xPe9GCObTZACbOsyhbumC52oo0dOy8cMyiGR7AQWU3KMg0cFF7HtUyPLzth+iDJE5spyQdcT/gd8MiKoRdR8hwWgZOSzgrexZlO7DhpED9ZFmzCSLhFtS/4PqLISCrT5qcBXB62SaWwR9C2KYOPh5X7EKJ7z3XGuo8wmGJJLoh76W2x/RaDKJLfcPr5q/+ThNeTJKcaU31WxMZQ1xT55rSSHEvdMSqRFJSYHYB9kq2eZE5Kz1OpwO65aa3XLeGeZG4qAPbYPl0VdBGWBQyv2iHDsGYgrPyExSas3hBmRzGGDDgCXTZszMb3tSObf/q8Fpzblm1n2F0faAufP2Se67LGwyOQEfMni6uBqLxMJcYnu2Ue4riY8o8zBTU2355Ih127Ls4BizLXlA/d51sxHyOPOVjEom12rFF8y1zxEUt94xG2rqvBwzfR1OJL3hYK44KPVAZJLV2E/bqSYnJaGNmFyObGHitbyhzxR9Xx8wKeN1kbnxHGyumVsLxLlt78DkBPN8mXoU1Q1AzNRY7FKvLELlLLVNV8qAoedVn3vQ/pfPXv/XecfaMafEIlw1aKQotQXSKYWaa5S2tsmXmHtGaCXNzwSTN2eqNRBDoMb2eQk6Dcq3kpPyI43xxiJRQWQtBPMOEmhLDm5y4V5dYSuacorOmzEJ5vtd3tOpd6XKkC2tKC2fmYyZ5xlN9xIPKUBnBAPkBRmyjYq4LTFow4R/R1N9npzbc85qQJNAt5UCI69+LWfnf6/rGFRwfltwXl0IOJmF1yThUNNFx3yuUUiyc4UElFIE3rR7c4pGZe7uDlohpgXlOrJgqz6uBrDws1EZlnBKwYBvt0FqLPBuJCmSfLLnPvGmjzDhOSs9JtcNocfm3wfKL1YKNl3TDvQQ5JpkRfbkdEzNMnCSAPt+VvG70EMA2tH8Gb7sg+BM3neOrnbQ1Tsqv7zJqK5SEw3uWNJ1OmKwkrldlFmXLtOy4bUZ8uR2Rs3hVANpBCr5w++HuIIKIlqU3ByBji8cnRxqK28wpxlbxrXTKRTjiJm/40nyFzZY6jw4Lo0JTMGKUp5xV32MVXpMHLIsPy1/zryisOaYyc4xybH6NsHtUf8I0V0ysOqDvjc6EKGKAuz4PSh8pHouiP7RxUlb4XOOT3ANTKyy678ykoL9qHHe9tIo+mChOfcW8/S5dSqzp6QejZ1ARr3rhXqlmkMp+/dHJuSXE9rcUV4PWI/rwmpu9tFVPxcT4OwavKe249b/C6pI0qNm0clhVMtEnnMeHACzZsY4a3U8YJ81XfcPn+hcY7ShzjaPgJB1RK8uiUCxcGjYr8u84vW+X7uc2ibnLA75JPDZXraKNmaves8kdb8wbbsJnTM0Fn6RPmBrHxAkd/cMpvJ9LnM6sgyHnIWAwKT5dZW5Cx6kt+f54ilUcaB3FwFxbFIaYxuyiogkwNwWP9A9p3YY2rwipZecvD1RlH65Ypp7noyPGec5xFDWrVkqMsTnRiyaQx/lDksqkITdnmmaMmRDMQ7rR+4AQG8ywJCaVuOYFt90v5b0yY5we8Z76IYs8wWKYqmNU1gcw61ZviQTueU3j778mFFBKU+gJz9Ijjl3BotDMnLQRL1s1SMXF8wQw0RY9aAIyiVZP8VrC4kLu0EMk9h8azPMbV3BS3nLV/i2z8hk/yH9GpQzO7J32YpyMOXM1DD5Tlt3MfnCcDrtE6TFnNeA8dOZ161h7xaM68Gws27t2SLz0WR1c+Edly3Vb88W2ojaJb0031C4czISzqmE+5Oholen6gpAMu+AYu57Kyt+1JjKrFA/GG9pg+en9MZetDOlPSpGujq1wqPwwWG2iZuMNiyJyUScqnZgOgE7Jxym47x3boFFKiqJ5xxS6b2OUJjAqOoxK1NZ/TSDxYidRvmPztvHz9WYT9CmxUhuq4LjrS5xWB5XcLiY2uePGXHLtP2dsTxjxAS4XVGaO1RWFGmGyZcYptZ0RlafJK3p7zKZ/QUxbtBIj3aR4yKl6RjCBenxElzYsm789vJaZFdHGyHpKEzA6E7XitAx00Q3ssDww55L8/EMrB2raqLA6cTbeYnXkg+H/rbuKPhrWvmDrHa+agjcttEmMxNt0IybBd0LSFAZrJDHy68Lm39XrTuTUoNX4IAQA/Ttk0QpnTwlxyXZY6Kw5YeRO8WlHl1dMihPOzfhgJFbvfG4dnnUUE+RIiWnwcVExcYqplc3Tdad41cRDy88pxcwJMWCV5D7cy/3bmHntd/QEZqri1IzQ8QJrLIt0xFkh2JuNlzC7hctMnMzZfl01OLYl21AdWnM+iY9KKXVIst0/u02ApZdQtZoRVlkqNSFaT3cAWSqUKohpw1eb/wcgMNXaHlMomaPsQwNDGjh+OVDYOYUeC81BOUomnKQLggp8Ff+GLqyZFA8Y65MBPyNqQiFPj8S8mwu2esuGe7QytMric8et/5wQG/nMfw3OOnZnTPQJRJmHrr2YZ+/7yKu4OWQwAUxyjRsICvf6Dq86+ryjSxtW7S+AOPi8Rv9Z0sU37frGFRwQ4GNILShJBZShu6JPIh7YX4XWHA9JmT9ZeXbZ83414v1JYlHkoWUilOV9y2rmZCe39g6fFashlOr98ZZJ0Q+KNTOgUBQpy+nH6cTJdMO4bKnKjnJAwnjvqMqOJ0c3hGhYNmO2vsCohAmJygZm9Y4ZDLJUx7IrWQ6QzG2Uk83ISET2wqUBSSMJiH003PclCQ4ns9rqw8xI0CHSEpNFd1iQs2bbVZTW82C6HBbYmi5Y7ocE0l3UXDbVYcFWStRyj4GHtWMbzrlsNc938jCUw45YA1NV8l58jwv9CJvM2wek/xIfb1npr7BGWkIKPTikO5TSjNy5cNOGa6rPOYpHdMqzVXeHXT2ATzuqYWBdW09pA8UQwHZRtweJ9F74EbOiDZZtEJr2+5M1f3zSonXCDafCNBT3keuprTqcdNukOS0LoJBY6/4V4+IhC/eUGQLhDCQ61dOpltdmJm2xsCalLVqPKex84GvtZc97CbhlUjwm5o423KOU+y0tkYwPVyhVMa+/h9M1tZrjcomjoMgFJ3HGcS1BfJVhMMBmdjFxrscc5/+BXQ7cqDt65Q/C2XVQbAJsghhBK6M5r/TQ0pECvfSGNsDKZz7vNqShgBZYLsqC4xKepgkxTyg0zF0mZLhsxZNW15mT0jMaBDaajDWSoROSptKWzzaWL7b+MA80GE70CKPUIco8DxORlV7yJn4KwIn9kCqP31nIM0ZPD94XEKHFzl9Tlx9xmh7SqZa1qdhwfUDDtP2GYBbDKSlKbMDoz6mY8MB8AgZGeUIZC47sGRu3wquOLm9kbSKwUzvuecPKv6QwE8b6BICJe0AwHffN3xw+0XH5EaWZMNMXFLngSt1zmyzjWFFiqbTlPTcbmIniLZw6Q6EVp6mgT3NWwfNcv2KnS7ZmQUxrcu7Jf2DFBr6hBSfnni6uuXMrulQzUSVOacbGcGTUAJVk+F0GnueVJWdLyHDfa8Y286AKFDpJsdmbPLXIge+9xanMcSHFaOw8hYms+pJVX7DydpBVK77cVVS6JGTNUdVQtz2V69n1JXeNLJy19QcIaBvtMKSWBW6cpEBMypbS+kP2zDZqLlsznNJEqfOdWcNJ1RyQNZIrX0hRnGyprafQkV20B3n2nnqds+L5bkQT9WFo/qju+e7x9QEhb5Tk0XdJHaTT8p7LImz13rAnNG6oue/10HaJw+KeSWRh2g25LDFnqmz5I/ev2ZZbftn9T5LxrgqMnvJ216fp4hqfGk6KDzlODyhjQY1DZ8VYHRGNZ/8oKfVubLac3pwRUsRsnyY5XJUJHNcNWmUmwRKzPrQ8m+DY+II2Gm57R85wVEhrcp8Jk7NEX5RG8y93/0saFZlYczhJWMUQTZ3oUmbKlK3bsimW7JKwsgyONq1Y/rrPJgf6tCGm/p2s+9+8tBbv0sI8wuWSlBNJJVq2tGpLGUvaKIKTmy7RpcQr7rjXNzxKT7iwY4605dvu0WFukjL8auu5ZI0ePufTPOGTQpBKbthsWMVQyBQnuqYymg8milLDJkj7Z2zzoQ0MUKrMnxyLMjJmdUirvetKCp04Mu2QNyUw1RdN5K/Uf+KUx3zLnL9FPmk4ToaYS7YhswqeSao5U6fip4kBrzxa75esr5/IjS545v6E47RgTsXcSbz4Op6y4z2+GM9p0vKdz6AhZ5H47tIdndrQxhUpez60/5gTvUAzQqkj8gDXDTmxpqVTnZy4Bq/ZcXpAJnGjXhP4uinTaml9XfqfYZTj2L7HKE+ZMOe8dAexxdbDF2mNV55P8hljC2UWf51PRope2hDiLf/tvTf/9a5vZMEp3SO0snwW/wKjLBN9TsWE78RnnFVWQqRKWcwlgVDAlD7DF+vEL8ItD5jz3ZmjMpm5k9lKZRJWyWngQR2obGBRyYO/amuWXcWrXc2rxtIm6SNvgctGFrVfbuZUZs5pGTkr3+7CjU5MrQSa3fcF22CHtkJiGxx9smjyAaVz3Vbc91bw+YORdZ+f0yXNZTOi0InKBJSCb03X8m+UHVZHQtKSYRMNS2/lhDO8lj3k86zumQ1FtPGOnS/46WqGT4rvzNZ8OL+jdJ7SerrguN2ND6e7lJW06FQaUhsNTYw8z7c0asda3RByx0X6kPN8RE9krUSps18HZGFQWDNn7B6QsqcNS6EdD5iSV2HJunzM1JxznB7IZ0/FWB3x7pj43oeDCi8mLf6krLhrK141JYsicFx2TIuOk/EarRN+KDgvlwuu2npIoZTP0ahMRvHFtqSJash4YTDjisKpNoqxtTilBghoZhXlhN2lSBweepst1/4XNP2XWHPEefUDKj2jL54RUkMId2QCStfknCjMmGnxkC6ufoPdBYZJ8QiAK/+L4bU6FPrQAnK65NbXdDnyhfmMVq0PpyAQWoJS6mA23jPLHlSOeTwaBulCMXdKZPozNwT6DWmdlYlM98rKLBL+N23J5uAzS6yD4VUjJ61FIRu7feHep8wm4HKIi6hM5KKO/KOjikftn7LqM6/6jpG2Quu2+fD1S6+570tCymyio0+JL9Ubdmo9zM9kE1O7Y3xqaPsXKAyl/aecmpqZM0ydAElN7yDAMryg6V/+lrlZpBikzOUQhFamkkiWwD3k7xfswaUKlx3zdMJITSFDp9phUuTRygiJIPdoPaXUkyFd9CUKxwPzCYs0Y1FY5kOHVZR+imdpjs9yAr3uxGulFexSoNGrIbTvD7fYwDe04MBg/kwNAaHFamU502cs/ASthlTOqHnZSPRzynvUCHxgjqmNOMhlOJmpjQzIx65nWrbM6h05K7rg2HYV/+HmmNeNZl5kZkVCec0uKAoFJ6XIiCcDC008O2+jdGM0bBkWsmHArthLVQ19U5MQcrFPiuc7x5smUwzk6akTdVVlIttg2Q2Kt4T4ba5bR20Tf3Z2xajocCZyFHdcbqfswnhopclOfRtL1l5zVmaOqx0xa3a+kDlF0IPSyNIGS1X01FVLmTsq1+Oj5WozpQv2kIB6Unr+6EjwOHf3IxSao3yMRdPhuVR3bPWG+/jiLbMqpwNnLaaWUk/wuQG+nvQZ05Jls2TJ33JdPKE0M07Ue5jsqAcZa86RT81nzHaf0HgnxOEkcdxT1w/+IcunqwkPqoKzyYrCBqpSdpql9Tz0b/PuU1aEaAjJcNOMaILlpiu46S1Xbebfx58Ss+c8PKHOBQ6LU5pP9S/5avvvRFtlRhhdUpkFhRqxcE85KUQWrdDs4t0hwbQqnjB3T5gOUn+AmANbs2Grf13uGw/mw3czWko7pQm3xNSgKs2jfEGpDD/I3xYd1XDfjwvD1KkDZw7kVAiiyKsM3HSKl03Ep8zPk2ZsLT86ikydZzySE3RCEaJsPu66Cp+kDS3zQZHTn5U974/ffpYJWPcFTTDMXOCsauiS4XqwEpxXDfUQCXJRC338SVeih82WQhRxbRSD9xde5L4Gg1eBG76i8fekHDB6ijW1oJTsiGP3AU6VFNGxjB6jFKWRU7lVAmUd2ZMhx8mhhwF+recUueY4nWDQJDKZzEovuVKv0WhMdvI7lkjghf9rOn9D6U4YuzO2/urwWRs9x+iawp6ilWVkT5hwQmknKGUwynGUjpmokqUPrINiag3n1ZC4GgO73LNRO3rVM0szFqrmSt1z0/yM9AcM7dxf38iC04dbtCqHPHcIcYtSGl8EFKLoWHm5ObdB+tfP0x0rfc+T+JjHRXVQ4ADDaSEydj2ToqNyHmMi3jv6YPFRM7WRUKqv4Wfu+zSoxARU+IOje84nK0Zlx6huSEmRksYHy6u7Y5rgGA2nhpAMIepDdEBC0QdDzJqpm3BSFoe9SqHfpnFWJlIg+SUj65k6w3TgvbnBHDqpd7hhNnQxqWl8wevthC5piaA2iYkTlEkIii6JDHvlBU3zui0HJL0lJo0zkcJ6tJJh+r7YCP4mDAUtARO2YcKvtp5lbvHK06gdOmsW5jFd3nDV/pSUWpw9PSzKTlWE3JF/i6JGqQJr5pRmhlGOLXeCio9rUm5Zt79krT7nrLqgS4Y6afpghdI9FNqxDTweiTgiZU3blYdTkFHpcIrbdBVGJyZliyLjTMBHS2HGGF2RsmO7/IRdjtywYaU2BwnyKE/5ZPS/ZcU1b3Z/SYhLck543TCxZ4zzAq86mrz62gzqyL3Hw/QeIypGyrLNnjf6kl41VO4EH6uv5eGEeIfCMi4/wupyON0YJuacQtWM8pRIwufAig0azXfcGSelGsydmTYKLw0YMExv3+9Cw8IZ2ijBelVv+WT2bry6YecdV23Nxjt+tnJsg+Q2dSkxt5Z5Ia3Hj6bNcJKPWAUnZUcs5IRUO8+InqOyISPfNw5AT4WcvNZ+r5KTGczzbebWd2QyM2ST5gmQLQ/5kN72bNWSNq/p4+YQ6bxVVyiluVafY5TlafwB7zWnqGH+G3NmrI/QTv6dRMQhPhaAXsnndavf0OY1BSMMjkmac5IXeAI3+havJFbc6RqlDCF1h3s45/4g8d9fPm6p6zmjPOUZf47J4uxJZMw7LcH9OlAqTcqSChtUoFM9NzlilOXj+l8e2HJ92rFqP/970cq/adc/SMFRqvq90uhy9sTsiWklqiB7hNW1EGCV+AHuDmqaiM+ZB8x5kOZMnaG2cup5sUtDtntBbR2v25LKJE7KjvN6K7HM0dINSjWrM01U7IIcZc8rzcRmPpy0zFzP2XjNdLRlMtkymg0qtWF2cv7wDTkavLekaLi6PuH5/TGVDRyNNlgTCdGQsxrmSfWBTAAMrSsO2JmRiUzLji5YfNLCuk0aHy0mJLROhGiISbPzjs82I0JSPKx7FqWnHFpvu+C468qBhybtlY03GFVw1xV8vp4ytYFH4zXOJGZVw0LtuNmNWfuCfvAMNUFz34ty6UHleIjD5wkxnbGJieu4w3PCtDomkXAU2GxZ6xWbfE0bV0N7KWP0HK0LpsUjxvoEl0vqPGKrN7zpfzJ4U2TIOyo/4on9AR+7GUat5IQytP1e7ca8bh1jm5jayKwwTApZBN5spzTBMivkVHvf1bxpapxOHJfdYb4GcorcbzIu445E5lxNKbVGq3d1YLAMZxTjmjZvWPqv6MI9MXds1BUxd4NKiYOz/DhfMEfmfJHMrb7ns+bfkLM/bKjevaw5QSsn3ys09OH2ay54Z8/4oPwnuFzgKHDZSgbTACi96UTGfB9kER1pg1FqkN1Lm3jlIzNn+FdHFWMbeVB1pKx4sxsLwcIbrjvD2sPPdzu2qiUNk7TL3qB7zQMzQauayqQDxcMO4pNCJ7ZeDNZGi+3gVVOx9m/nbTed4rNth1WapyM30Lxhnh1zp5kXMi9b9mJvuO5L2uwpVEGjxrR2ixtaYQ/yM8rscFgsirkpmFhhwa1C4D43vGz/6ncGpT0c/3MUmqv2p8S449n4f85H6ZkEI5JoVM/r+Cl9EkyNUZaxPmHKMdfqy8MJR7gG6rDuxbRlE6+J2lOpERrNSt3jVcd78X0e2AqnRXCREVn4NBmc17Sp5kvzFW/6n1DbIx7mjykocKog6kCuEtv+DSHe84ckjf6HOeH8ntGnCoUxc46rjyjVhGfxPUbKcVJYKiNo89tWMCtLdhgMn1QzTsq3BrM+KZwy7GLm/3m/YacaDBadFX8yPuKfnmnpVQ8y5kUhYMjL1h0AmScDSubhaEtppY3TdaWADqPGFp6ybslRs1lNCcFSjxrKquPBg0uOF/ekZAjB0PuC5zfH3Pcli6LjwV5WjSjK2iGvpY1GFD0mDCIDIyy0pNj0Jau+IiZ1GPInFNdtxVc7kYQ/rMVgmlCs+5IvtyP+4sayiYnLIexrZGfMnMKZRD2cYDa+xIREYSSyehcc7VAgQdoe35mFoc8u7/Fu8GqsvaHsxiL3jDWBjEUG7S46MNCrzTsP4pKYYJl6pqMzcdCrhM4aq8sBcVOQs6e2C47TgpF9u+xbLcigD2f3PJm8vYXHrudktsToxHy0JUTDz2/O+cVqxlHZ8+FsyaYv+Jv7KW1UnJWR2kZuOsdNJy2YT+rpQZ4Lb93zPkEfM3PreC+8x46OzmzYpYDTNU6P2IWOEO/ResRF/SMqJuisWbIbPquMp2dWvkefNr+Vq5b2YNCsB4KFk6/MEYik1PMm/ZKxPuGT9AkjbYmJgQCdh6jyTKUNGhhbjVF7yT1MnNAGCiPKSA3cdCVKZdZerAV7gsDIwvvViJBqdsP8ymmBuNZGKOhtlMIU0lsydzGIeUotkfAgHYm1V7xuEm/CjhLL1DgKrYeMHbl3nVbDz6EGXL+ot57rV6y4YsIJdZYC7lXLlGPeMwtqoxhZke7v7xSrFBlD4x0h7OcfCq0nGF3hzJjKzJlxSpELHrv3sU7joj201zQKmw2FmaCV41S/xyTP0GkwrqrvcDR+hEYfiBBbvTnQByIeoxwxh0NZ0Gh+Zb7gs+iZx1PO0jFvSzGsaOhUh86ak+JDDBY/3Bedauhz8zXbwB/S9Q9ScPLvOdjKBEK853r3Ywp3xDPzHmNjDkPQmDkwvS70dOBJCS13E9QhV+Pei8flo2qCU1MmTh6Ex6Oek6rFKFGC+STDfJ8UR0XgqIDRQFLWCI+qj4bSegoT0DphC4+1e+GCJkaDD5bb1xc0vmBRb1nM5Xjtg6XrHXddxU0nooK7rmLiPCf1jpgUTRAI6NPJitp56qKnLltUV7LtSmJ+e0vGvFeh7aGgmYvB1HpStszLt3JbkaSOaaPmtp8RMzysZaE9KTtOqh1tdFw39YHvto+aLofdaRrk2BPniVnxuikPC1POspC98js6PCu9JJGY5cUhUGqaZjTmiK9nW0JMK/rc0LIhk2jiHev2M97dsV1v/5JdeUO5/Vd00eC0YdNL0W+DxSeDVeK9USpTlT3WecaTLUon6qrlg92YEA1dcDgd+VMbxJQbHF3SjIyhtUJ0AFFjvW4DXYqDLF+xy561ksKhleTRr5pfkZL4Np7xPawxlLUjENnlhkSiUy1btSEqj88dgbdu8d+O+lmTgBCFymv0GKWr4XQIMW2EcmA7VvopIVUse1mM5rpgURic0pRm/ywNnrTBlza1mYd1oE2aL7aWlOGskrlkGHJujovIw1FzCGZ7F02zf7KvO8enK/lHHo8yI5vohwgCnxQrL8KFE6Sl/dFE7snt1LIJNV3ULL3ETOz3Ene9nM66CG2K+JTY5UAm84gLHnFBQ0+rWhwFY46YpCnjQnA7W5+/ZpswShRw55T8Gf9XdmpHUIFIwGCx2dKpljtegYKRPsJmy1X6nG3/iuPqW3w//RFHasQpf0ZSmbvU0KqWnW4HE7BkURksozyiwHIej3BK43OixdOonhv95q33KsMyvKT111zrmpdDquhYn6DRNHlFyB33zWeHGV9VPEErO6CXAgr7e23q/3td/zAtNRT594a+RYlgjQVb2zKKjusQ8AQcllIZ9DvNjj5xQJTsSbfai1fnYQ1TF3kyajkuW2rrGZcdXXBc78Z00QzuezWEpMWDz8WayMVkReU8o6rB2YC1gRQNygWq2ZbkLV1bisKmqXm1m9AFi7WRrne8Wc/xSfNovObpNLPuSta+EGz70LbaD1cfjDeczKRQKZVZdzV/c3uCVvDxbMmsEE+JVpk+WqFFt9LvJiuMlhz5+WjLZLzjUe/4sKnZ9SW/vD8+xDI4nQYsjkiHH4y29NHwYjdmG8yww1Tc9JoXu8TMab47D5RaSNyVEdOrAgqjGdsJTYCX7YQmB1pk+LlVG7bcsYs3/Lo5si6eCuVXefrcoJXjN9sDkT6s8UU6GG33114WvvElu2g4CY6jzYaq7BhPtqLsm24Yj3e8ePOAn9wdU5nIB7MlVkd26/khernUeaBTDIteivRE0rCxudX33PDVMD4uMcrx/ep/h8uOjo6d2jBiwjRXGDQxJxKJRZ7h0IRhx7xWO94YAS/+NjCJUhVyounJOZIGXIrg+WHkTjm27zNOE06YfA3nMrKK2rwtmvIeySzu8ShxXLx9b4UsLp/l41ro4yPrqa3HGpl77UU1e2luzPpwup0XjkUh93xtZeH75brmslHMCyF/9FHxt8sCreD7846TsqW2gfnQYvvlehCmVGoIOszsghQNnxJaKWbKYZQaguAUXzWKllZSWRG00j5FVw231zIElrmhomBuCmLOxOG+2hOXt2pJk5ds/dUhtdPoOc5Oh5OlHajQ0oVwyHx3qe9ZciVOoRwP8QT721rk8xqnNdvoudX33POGy+1fkLMfTqyaUfGY0/q7cgrKnlJNeBAf4TBs1BGdaunchm0nBUdEOBqlnPiOlEVl/uCKzj9MS03ZQa30X5aZsL/q4hljd4bPPTd5w625YZOvJZ0vHR1ghiZa/G7B1JrDsX5s4U9PoNKRRSEKsOOqYex62mC53EzRKlMNw/mj4VRgtaBT3uzG/GI1ZlFEns7vGFUNs9mactRgC4+xEVP22HFD6gu4kgVwMdpR2CjCgWCwJvLe6aWANodd+Go7Yd3W+KgP86PyXVSNdxiTUCoxLRt+cHxDHy1rXxxacvVgCm28O4SpqcH7k7OiLHomszUT4DhLSubRZEPXO1btiMY77vuS182ckYmcVOJfGRk5FV62jqXXWCXRv12E/3Qn84DvzCMXVX9Q0rlg8MngtCJkRxMN/z7+nMv2b/5OjprT9TCHKKgYY4xjo1+Q0vbwIJ2O/5Qfpj/hu7OSsb1l5DzzeofVkVUzogmOWaEOJ8Dn9yc4HTnabimtZzrZUtYND0+vmI83hGBZNSNCMoytx6nE1jgqYzHecN/LzzG3jpgtXUpEMk0e4VRFzYxn6RFOGZyS/s1fq9e8av4jk+IxXn0br6TtlXPkff0jTvKUkTJYpUgpoZX5jfdCqYLCnjItBFujMfRpx33zExJrQKHVCK0cD+MFJRajFTlnltHjSSxyicLQxMSl7yiV4TvTgkWROCsDM+dpo2EbDG3U3HSyUH88TUxdz6xqmJTtQWDRBMeXmwlNMEOYIExtYl70VCby8ez+kKOThxH8SemEum4DPisWhcxyxkNR2s8Ejco8Hry/tRWxyn2viDlxWhhOKz0YkmVm+6strIPkUo1yjcVQYBgZS22knXffCwXjihVX+iumHKPjuRi+qUm55p4NO7WhzWtW/VfkHA7RGFoXpBQOSJpb5ejsR/isuMueTnU8939F619TuQtm7hGFGg2kdHmOI5G73BNS5Av+mvvt37z7MR/WxNZfU5oJI33EcX5AkRxzVaNRkt6bK97oPeC34Kj+LlaVWOTP7sNzOn/33ymi4L/8+odpqWU/IDd+v1NO039JF+4Yj44w6QSbLZWaMktHnOUFgcxqgOQVWotBbyDXVkakxrWJPBhtKGxk3ZW82U6460uuO8eiCHw8u6d2nnHZYo0sYru+wKnESRmobaDxBarJzGZrbOEpxg12QOPkrEElZqd3jPoN9jqyWk+wNmJMwNnAdL6W5NFgDq03HwXZ46KAJsdObsS9uKD1BT4afLRsfYFWmdN6h9MRa+RXu51w35f0Q5yB0xJzUBW9pHZuRmiTMMPDXpUtbvBXlNYNr1+EC79czTA6c1J2jK1nHQzrIPHWe3/GqDey2xsYc3vy9OvG8tOVxyrFWWmZWs08nrJ2p7TKHrwov351cc3WLilyTZ1HEkkxPEB7pMuT/AnHrhh24wM+XolowtlwUP+FpIfgMXk/73ZjFBkfLePeYXSicJ5dW/MXlxdsguai7g6Jr0rJ3KAyUgz2CKV17lipDS/yz7jf/Q3j8iO+795j6ob7DUjNtzgpxUeks6LONUf8GVElvlSf8ot4yRP7Q97PD3FYzvITjHWs+Nk7z0xP7y+5Tx2lXfDYfg+lNWbk6NOGPm6JqaHQI6okvLBdCkQEQdSoHas04rqt8QQ2es0szSm1iCpA5i1t0rRJTir7YMJywAGlpA+erOebKWtv+XJn2Xp41XrWueORG/No5DgqEu+PDbUNLMoGayIPRluOSkMfDbsoM8mlF6ZaoS0JeNmUvBnMzsBAOpCT81GhhuycfQotdEOr+9b3XKslnW7pVc8iHTNmilPqUJhOSsU8WxbhhG04EqbaQGPYxkiXIyt9z318QRvvCXEvSU9Du7JFoQ/CjXnxhJQynsCleUmb1/i4JWfhAnZpg1cNndpIjMDgmZpyTJ1HFHqEVmMy8bAmaj1GKUuMS253f8XKHOGrb+N0xTrPpXApYbnZXGLNEaU9ZqEeHWgTAEte/kHKpP+BZNF7W9zvzrD/e185cZTOuFAzKn1MqRWz+i3QMmVBs0ytkITbJIPs0TucMKMzVkdpF+hE7QIPakVlA8fjLXrAtPtguWtGvN6N6ZOmjZpdLLi7PKcyiT9XmQdZoe5nKJ2IwdLsapTK1KMGPZxSiqI/tH9S0mzXE2LUrDYTuiCL314GvQ8zKgf3duML1l3Fui/ZeMfE+UNLbz5ZY22g7wt8cHTesekLfF9y2UlmzrOJEAhe353QeMek6DiZLWm6ik9vzmij4dlkxWK0k/jsgXqQ4OBClxZMw3klbcZ9URI5qxqQQIZfbS2vmsjMwY+OREGoEdzJn+SHfNCf81KveFn+kj7t6OLqa9iXzr/iMiemxUNqPuQkX3A0+j+TVGLJNT076lRQGUXKmVVf0gbLpi9kplR01K4n55ImSExBRFqEI9eLH6fosYOSMA2ZLEJxUIM67e1p46qzfLr2NDkcWjCBRJlLPuZHUP+ICSWLwhxUVUbBs5HlUT4mDsmhXYQr37GjY+1f0/TPWdvHwEMqZThjRhkLXpuTryVYyuzyRogF1jJOYx7wj3Ba5mjyOGQimTZ77vWKjpYVl3RxRW2OmKgTZnnBt9RjJoXGapmp3CdDypY+iQpz6hJ/crSlMoF6gN4uu4puN6GLhjDMBo+KxMQqxs7RRkfKcpJQaNalxWc1FPvMXVey8vK1YZiH7UkY+8uqTKHfkpz36rY9qb17Zw4ksRPS5rRK47LjJ93/eHjPRuX7HOv3+efd94QX58RisNIKpw1rn3jlRXWoBzlyn3fE3BFTJ+mZ76xPSjm0Lhi5s4MKrabAExjnOUZZ1voVMSp8vGeVtmhV4MwYq0tG5oRCldRpxCTXfJC/w6P6IzrVsuSaTKJUE2y2bLmnyxtW3Ve82f77ry151hwxLh6ybH4GREK8o3dPsVk6RxpNaSZ0Zv4HRx74B/bh/H7F5uH4n7PgnPf1gkWhOask48UoQeuDOOv3BGitMnpwmFst8kyA26YWVYqOg6fEUxTijbnfjQ5srYzitqvYBsOs8DybCGKmMBGtMpuuYvfVEza9zGBWfcHrtmBkEn96fsms2uGjnF5kvqCFtza0zj5dzrntzZBFI56JYkCCrLyoij6e7gRto0RB5nQkJENMkbLqKKsOf+cIwUihc4E+GdRw1DZKiuuDxS2Fe+sH0Trx8fE1IRrZzQ4/sx6wOCcmCs6/K4dETVHAmeG97ZPkloSsWBSSLvmgSswLLWggK8ysbTDDQiK4+FW652r3lyhVUtojnBmjMKTcyYM0UJTL8YQRM07SCSlnbvUrurhipTesfM3IGu77AqUkfjshEQwT57EqYU06iECsSUyrhmIoNsYk2q5g19b0wfJ0vDl4P0CyWzZeQsSm1qACvGZLp1pmaSZIemUohkyZtZcd8SaKUvJ0oP/ur4zsihOJ992f4t0PeZQuOCosPotvrM4FJ9W32MXj4fTSYXSJ1TVWlyy5ZmfWnMRzysF8CDJLusxfEHJHCCJA8HFNTA2mKqnVUya55qw0A/5pf7qRjdhJGfl42grQVuVDEmzKiquu4Kazh1mWUATiQOvIBxPpngJ9Ucsp/64rhZDROlZeM7WZoyJQmMSi6NGI9LwJFqszR4XcazHL8yt0asNXO8NNlxhbiYUPSQRAfcw0ORBUxBmJmS7dIz4wf8pRnDOuFKWBtZdneLcPMIuRMMxsSiRy5CRfUNgR2UYikmWzCZcATOw5hRpxlM6Yp+nhc1ypDZ93/y/CUGCsPSLnQHrHxJyyKNJC7rhUX3GtDaM8ZZQneHqWQegaj+33mKQpZ5zisNyX3+FKfYWnZRduxNiq9pTwd9rsuaVT9iBPD7Hlv0/E9O93fSONnyAxvlaVkOE+9PTJUhnL2Cp8hjbJYHuv7T+rIiOT+Pna8dnGc1paPp7KonLTyQI6c0IUXhSB46I/xC0LGr2nNJGL0ZYPZvdsfcGyq4aC04gkelBFgUiPp67nyUQW7Fm1k3bY8P+7IK2wNlqu2wqlMh9N1/zAee7amvu+EDXSkNh5VvY4nZiXHYUJjIpeAKBDgFtZeKq6xdUtRzYwD5btesJoNeXYF0xcj1aZx4tbppMto/GWcrKj39Wsl1NitNztxrTBiolUR0bO863F3eHU0CXLXWdZB81dB+uQDq3KqYP3x/4Qgx0y1CpRZHFJN0Haa28aRZfyIf44Kj8MwHu6AFpZjK4xqn5nvrOXeCciMmyXoayovDYxkLJh4jwjE6gGh/vz7YQXTTkEswkr79F4Q8qKr5bH5AyTomdUdNzuxnw1zO3GA817TwCHtwvzLgqRoWyOaFOiMhqnFcsQeJGWuGw5UkNOTZbX+5fxC+53L1moRzxJj9jR8XP+gpQ9P0j/jIduhLOKwihan7lMazZ6zTq8pg9rnBlTuDGNv6XzL1FYfPkEp0coY4Z249thdx83ItF1H1DmmpmZU1FQK0ul5fX2KQ8ZMZmpizwdtYydRyNqxlVf8vO1xFa8N2mYOc8JUJnEm6bgi00eZqEinx9bQTWNbKA0kklU2nA43ewj1I2SVNVF4aWNnDRN0vzlXc1lmzivNCelRBZsg/DpCjNkPRmB6x4Xgq5qo+am13RacZJKRtEyM/+atu6ZUnHqCkZGcVTIBm5foHwS2nltDMdmSsyCR+pyHFRqg88PzzbcsOu+AKDQE0ozYTu0KAExYOYNKXsUmnHxgErP2IQrunBL7Y55aL4zWC5E3v8q/Zytf8O0eMQD9eGQE7UAOMQfVDgmxuLSjDJ9iFeBnZU48/v4gi6uqIonAx3B0qYVTb6jDUtS9sS0HfxZf1jXN7fgqJImL+nZ8YqfQ4Kw+WcU2tFE8eKUWnFayYMh2HxNoeGsdFRGsOkJ2UmBKJH2Ec1r7yhM5KJqqGzgfLKmcv0Be3LbjPhiOyJlsOsxVgs886hqGJctk/otfNF7x+12Sh8NtfU4KwWjch5nIt+vWrSOuMHL03YVXe8IydB5hx7c7wA32yk77xgpT132GC0xB1pHYjCofkj6dB5jwmFo2wT5cx8tIQxzCBuxZU89ltdaDNHMMWti1IcBrk+aJlj6IbYZoLZg9dvMnlILPqgfWpYhi4zYJzW8r4kqQ8bQRsWLXeLOe044h8m/xueONq/waXdIzHT2jLF7gFaGLm/IKrHVlfhxKKntgkmeMTKWqYPjsv2aaODhbCmRxd7RBosbTjYArXekpLlra15upkId0ImQNF9uaxKKk8IzsmGQxMu84aaTHbVRipEx1EZRasXYFlzk46FtKKbElde0KdGw4r79jFC01HpEmUt+wJ9j1HAq8T2VtlRJs4mBRjfc8vKw0IV4w7j8iFn5hFJ9B4XGKREAlFnej40WVVXNnPf1jxjlmid6wsjus4A4UL/3gWnl4LUZ23DIR+qCpfHS1t17ZE7KlknRM0tiqh2ZEX4Ifyt0JmTFj5cFqz7zZFzybNwf0lcTsOodbdJctpqrNg9tyvLQYlUKno0Dz8aiJG2iIiYpTnbwoimVeTqSE/me/g1vIwtmTlMbxbrruDZXEM+YRAtomiiFa2Tl1y7ATWqY5JJCO5nvFJaYLU0/odMtgY4m3uHj2znI1r+hT5tDRLjRc8bFBUoZxsUDFJpj/ZQ6j3hoPsBpi0bhkqWl5wv9E7q4YmouOC3fE1ETiag8IXZkEmtzh9e9pOTGih09G70hDVioIheUevI1YUnKUYzAqcPH+68Zgf/Qrm9kwdF6Su3OeZDfp841p3pMpTVTpw87GK1EmTIyGaszXZRd992Q2X5WWp6OhdG091c8HnUcld2hnTQrWp6eXuFswAzzl8vbE662U4xOfH9xTxcty76QeNym5rqteH+6ZDraiix46E+Pig4XjZg1B45XHha5suzQ78yUYhzMoHummQ0URS+Fqm4OXwuw2Y14fi/488V6RmEDpfVYG4hRHPdaZcZOyMn3uxG7TuSqSme0jlRjaS9pnel6x/VywaYv8ckdyNV+8OCcV37wXaghe17zyzWAwiqL1YpHIykw26BZeVg4WBQZBxypSJ8UrxtNT8RgOEpnRAKNmtHZhlB0hNQwL58x45wtd6zDa6L2zPQpGk1EvCo2m2HXng8Ebqsj1kZCL6SFquiZ1TusjYzqBqXS4I3S2Psj9G6MH/A+eYCq7kGRu2D5clfyYieGwz4JCfpN3NArzzSMKDCUWl7H3tsRUuY2dqzVjoiXnBUzwWSLwTBSIsp4zR0rfUeVx5SxwquejVqS07szRpE8d3FDyxKrSmpzBMBdek7KnhP9IR+k71FTMDXugGzZBaiNOphTt0nRR8mTGVlFX8uMpYuWIkT6aOj2p3Qb0MjpTivpb2kVOKkavq0yy77gP905NiEP8RCKlYdPV5KDM3PpkMckmxEhim+DZRv0wYStgXkhwoCNNyx7OYmsfMIqxSYILaFP8jOMrGxydkFx1eYDbLQyiqkqadIxW73lr7miCAUnq2NKLI+qkokVmvypGWGHHmDOUBiFydJacxREVRONwHhT+RiNoVYzXC4x1g73YKBTzeF0GbPnOn6GTw0L95RTHr1tcalAreYUZsSIGY6CW15x2/2Swsw4cx+LCTi9ZpVe0dnHdOmESR7xAeckBNLpCezMmibeSds0tjgzZmLP0Mag3HtkEnf+V3T++g8u9fMbWXBy7g48KoWohfqU+Lzp2KiGCzXj/bFlZDK1TUOPWDLdZw6mzjFzMu9JwH1vyRlOq4bH83tGVcNkssXYgHOCul8vp3R9wc12wqvtmJENzIqOapijxKy574shSno/m7H0Xt5CZwLOiKFw19TyfXYTKhN5OBCM9wj8PkqxmRQdF/N7ANqupO3KYeYQKZyXIuQCk9GOtiv58u6EtS8OrmSjZL4Eb2cx1oiC6+XtKV9cPeBotOXh+SUpatabMV1wIkrwBTkrRjbIgDjK7KWNoiCqbWJMkl1h1MQku3pZ2BS7AXVz00XAcBJFIeiHU48GychRLdfq5fBZSvCYHeSelZqKnySfMjM/JObENvXs1E6wMf1rVuNnWCXQy11wpIHLlTP8aj3nqisY28jMeeau58nijsJ6RnWDG7h2I+cJSZhURmfqQam3xwbJqa4YGGSKJmQuoyIS2KqWLht2SWOToEusV3gSL8xLmrwUH4WZUakpRXIUB2+I4mk+hnzMVdpxaV7hs2Dm1/2rw/0e05I+FkNswQatJ7hK6MUX+luUueI4Tpgahxtw/ruQ+Sze4lXPB7tzFoXBJ6ENxJzpklC+ZXYjwX1NsNx7x2XrmNrIh1PZNH2xmbINhrGNjEwUekAQnJHR4u85KWVusycA2IHbpgdk697AObZCapbZj2CkAC6binsvcujTEt60irvQE0m89vKc60Fa/agqmTk4LhKPRzIbvOkUu6hY547n6qcYHCY7VnnDL7t/g9YF/5z/I0/r6tDRACnKPsOdDyK2oMdmS8uG++5LanfMM/V9qlyxUDVOKbqc6HLkTi255SUGx5QToOYu/YrO33APePd2sa/UlA/jR1TK0OdIQAp4LD05J1bpNTF7tv0rUuqpzREui7y9NmZ4nZouZ15s/91BPi0Gz4i2FxSqZpLmcnJykZ2ufwtx/Jt9fUMLTk/Tf8nPU0/tjnmPHzJNI1Zqw1ZtIEPcTFk4O+x8ElOXGFt40xquWkFYXFSIx2TaUejE2XhDXbbUVUtVt6So6dqStqn48VfPuGprFkXH4/GG+77klytZ6MyQpFnbwNx5xq6ncJ6UpNWVsyIES4iGm+2EVV/RRZGHxqz4aiPfZ4+tCYOy59l4w8XxjcyH+pIYNZu2pguOUdFRFx19cNxux2iVeThb8lglbrcT7juBKz7fjXBDAJsbBAiFEUXe0ahlVHakKEXwi9tTtsHhVBqC3Dy184SoaaPQFG7ainbgu+39PVOb6JLirpdi5Abzp/Ts37LG5GRjaAJc+54VDXf6ivv+OX14CzbcJzMepTPOWeC0ptKabYws1T1rbun612QCa25p0xPWXnPbldCVEu2QFRd1x8fTDSvvuGxF1ReyprKBk25DaT1V2fF0vGXX1NztJvioaYKcPgodKG3gPBmcytz1ll2wbIHX5gXbdMMf5T/jcVXJgjCgVvok+9rv5PdJOfOCG97oL7CUJBJb1fKK15DhvfSYI+d4pCec548lEtl8SWtW9O8oxXNOg2pKVstSTaiYUOYKmw0v9SU71hyHBzxNR1il+NAcE3Pmi3TDX/sbPo4f8uG4HNhqZjBDJjZBc9NV+Cyf50eTBqcTViVC1myDYeXlV8qwCYKgUSgmQ7tOeHWJl43lxS4zL6SVl1DcdIY+MSChJPnzouoYO8/ZaIPRifO6wCfDqi/ZBkttS/pU0cbMOkQBbBrJHjqvFEeFxyrJZ4pZYb3BJHhSjJn4f8wueVZqg0LzrfKPKXE8rathw6lI2bDymZdthx8+k0Si0xIl0MQ7+vCGmBpe1TVjfcQ4vkehHSLRUJDnlPnbh8/IKzl1p7wlpikgheYoneGSG1RwwwYQ2bD0cUPIHX1Yk3M4PAP3/ZdQQMNDinBGBna5Z6eag3IVwNlTanfMmAUmW5JKBAI37c/+To/bN/X6Rhac/dWH1/ThNavJUxZxwofmlLE9O+y2nYabXmOV5riMVDqzcHloe2S2QXPTWb7c1vic+RfnBd+Khk0zolpPRSo7uKl30ZKBlS+GDBstpkadWTiP0ekw89ifcKyN1KOG4C2vb05pB7/MomyIA61YWg6iClv1FX3ShxPKvGoPEuq93PmnVxe82NU8qlseTVa0wXLbjihMZDHaURY9F+6e87Rk1Yy43kqGh5hK5fvGpFmMN5yfX0lbzQaKsucD72h9wVfLI+77QgqT64lGY2OiC5ZXuxHbYXfbJWlZTpyc8O46KUKPR4MMfaR5UInM2Cj5f9XQej51jlGcUUZH4WpuzXPuG3nYOv+KPtwyH19wEqUYp5gltpqv2IWbg2/n3j/npfmQup8SsqIcElyVykytpxoG18EFFLDyjiZYCh2IhaYqO6pa6AzWRkIwrNuaPlqumxHbYFn1jpU3fLnV/E/dlyg03+dbTN13OKsUE/sW1uSTZhuEu9ZGKUImWEHVKGkh6pxxQzCdVQLNLDRUKNpkiITDKU8+uzm1OyakMTH1lHZ6cK9/mX9MF1e07Wty7ocYh/+BTJZZBvBBfson+oyTWnFWyQK9C4OYI76NoVbIyfW0bsgZ2mDph9PiPjkVRFxzVEgrahdlDnNSBhbOc1T2fGsqJ6e73tJGuGxFFXZcamZO2mdHVUtlBNGkVTqktGqVybnGKPmamDNzawYyuTogee56CTEcGWntfrmFlQ+H2VoTAlu1oaTiAXOqYd5olKwNQqNOfGG+wmbLh/kxI2MOFIOZmvN6fE6XN2z9FZ1eMdNH+DjhhXnJbfyCmX3IeXxEUvmQeTMvntDbY2LuDl/XmjWZxDZckXPipPiIOae8CH97iAqf19+j1BMKJREf23TDyr8ku0RJhafnSj+nS5shDVZipFP2dHHFrfoKq0q5LwDeoa/9IWFuvtEFR6sxhTviQXzA3DgejzQPqreSypwVPssw+6o1xCyzhId1IA+msS4puQETrL3lthnx/GbML9cFU5f5aCIKtDdNyS7qw/e+qALfmS8pbWBe79Aq4aPg/OfjjcxETMIN0uNiaKfN647CBVa7EbfNGK0i47IjRMOb3YSVdzyoG05HW47Ha0aTnZxAtmMA3l/ccj4qRZlmPc4KTDNmzYvlAoD3jm44Pr7j6OieD0wkBkvbVCIIiJYYNZOx5P3EoEmdtM+mkw11kO931hc03vF6PRN46ECeNipT6cRPt4bP2g0fVGO+O5dT5PcWsvNqhnjqNsqsx2lFE2VRk0UCWqtIaOa5gnTORM2ox3N26Y5l87fk3HLtP2dmjg7ego1esfM3wyBXPFy1lcRFoxRjG5g6z/EgGrhrRuJDSho7RFAclS218zxY3FKVHav1lC9fPWRaNSzmS3l/koZe5LyXreOq09x0mTd9y436ikKNMOohhVYH67K0CsWD8mm7IpM512MqoxnnilP9jFGaMKFkTcvz8FekHDg2/4oZ9tDqumLFi+2//1rvPeeA0yNG5oRazdFZo7MmqEAb7+nDEmvmWF0zc4/IKVNR8CP1IZVRPJrCxMkQv9SZTdCsvbAHJzYNra2e+WAujkmx8QVfbGv6OFgITKLS+SDVr4f5zt4v5gf00z5hVSspXlYrTpKmT2o4Hcn90XiZT143Y5TKlDpidGLVl6y9G1h8UsbnhToIf3wSevJ9r4bnW+6ryihG1h6K/I6WF/1fMXZnPOUMpyV6wye4bAOXacuEkj8vPkKpfVAjgqjRGu9HdPmcRk0IpjuYNkHYf9j3B7HGmkTCqw6fOzbhEh+3A5nAErM/qAaP3HtYVeJyKbw2JcvrPoitVBMW6QSXHbUe0xQnZBKXfEXPjmX3pRSs0feZqFOavKRNK3KOh/leTD05J+I7OUp/KMUGvuEFZ1w+YmGf8lCNOas0p2ViUQSsynLUTpp12O+2DMs+c9lmQPLavzvvKHXik5nMeSojzvTzqmXhPPfe8df3o6FQSeJmH2R3GLIUkdIKQ82aSO8LQjDkrNnuRhJ37ALGBJ4+fYG2gRw1OWtOguFx907wV9JMq0YAkiYI52uyoZzsyElhbCCGAXPTChjUWc9yO+GmHUmrbGiDxKQJ3mGdl2GvDVR1SwyG7U4W1PVmzK6pWTc1N7sJTkcuZvdYG7jZTrhra3GeD20/P0RShyQKNK1gokqMUvRRoZQiZ/BZ8flaeuLr3LFV7YFPVeaSB2ZCqRW3vmNLz0qvWOlrClVzlp6Q1SNWk0f0uaFJSz6Lf0EX7gnxBq3GHI++g3NP2cZrfGqY6QvK6CiNRDjsRQNGJyZFT2nCwO2TmO/j6RpnPZPZGmsjRdVxHA1F2VOMGlI0lHVD35Z0wbIoxpx0Jbe95WFfcbz9J4OPSE4wrxrZ0Pichj57OqQ/rpJnkxRHuuKReUqfEpsYqCj42Pw5mcSIgj4ldimyyz2dbpmUTwmpQymNwlDbBQUjtumGm/7nGF0ycQ+wlDxyP8Q5iXrQaCZpxLGpKbXiqJBd/ayI1EZONpug2QxiDqfhvBJ1pghBJNxvH8fQRiGMSzYr3PaiMJy6xEUlmwc9mGVfNQVLr0UYEIQ6/agOjExmPN5DbAfUkcpcDX62FzvpHDyoIlMXWXnD2mtKDX96LF+XEI7dTWfoA4cOxjYkXoUtGsUH1ZjxwIqrDDyOM4L6X2CzISrxNlVGNj+F1kxSSaHMYMTN/CK/olVbHsXHzHUlnDyV0Ghm5gKANu/wquckHfGME/JwZ6ec6dMwDzaP6GwrrLO8oVQTZmkhX8+OlCWaQ2fNqfmQ8fgUoxxFrtFZy9epljW37OLdIe/IqpIPyn+CQbw2OSd6pAhu/ZuvGYT/kK9vdMFpwz2dOSKSD+2BlZfUzD6Jx+Cs9Eyt7Oy6JP3km076znul19yJvPiyGbH2lvO65elsyYNgOK9q+mhYeUeXZEdbacVx4VmMZCedkmHXF/z48iFv2pK5C8xcTzGEulXO8/TBa6pRSwiGFN/KkgGMiaSsGPkG6yO7rmTVjg7tNOUihc5Ebwk3x2y6iqlqqMrI8WzJ8WyJ1hnrpJW0Xk+4W84EcTNga3LWdL3jF7dn3HUl53XD0SARnhQdOStutyIPjknL/OadGOG9NPony5p7r/iq6bhUd9CeSF97wM7vSd0ZODU1T8z4oO4KZLbRs4yZK33DVi1F6klNInGr3wCI5JkZSmt6XTIbBqIul8zSnKQyX6i3Mx837Dy7pFHB4bdTNFlI1ig23rH0lkXh+ZbKlM7jg8OYwGy+Yjxfo01Eu4BJEW1EufctnXjaViy3E+52Y+7amuNyxDZoXjcymN+ESJsjE22ZWwMYEg6fMlehZUPLJgFJevyN3rHlnsvmb4SAXf8LHsRzidzAME1TTuyH5MFxlEm0ec023QgFOt4RoqIwY7JOoI6G09+SNm+ApzxQItd/vvNopchYjgrFXa+5aSVJdlHIwp8Gu8BlW9NGWHrF3Z7siZwcn45kHtNGxSZAyJqYi0F5Jif+qzYfsqe6FBn1lo231Fbo45VJ+GHj0g9G4ZAVUyfzz5GRuPInI+Ea7qM4fNKsvKFPmtdN5lXf4gl0yjPLIz6qJhil2IbE2qdD1EKhNd+zJ0P7fJBYD12mbYz80nzOJM+58CdopXjGA8hCjtilwJfmV7zc/QehOgzx3yFtIScej/+c9+L7ZDKBiMVQKYtDMctjYq54aV6wDC+Y28fM8mMAvOpJ9Ad69CwvmLGADDYbgorc61v6vBMEjjk/nO4TiWW+IuLZBTnl/y4k1B/y9Y0uOD5ccRmu+WL0Ht3uiJAcPhm2Ae77zNRpxlYzMomZC0PrSdhiknyoD8mQpQm8P2Sl1K6nLqTNVTsvaqXNjBQsZ2XL1PXUztN6hzNyF8ekGVvPcaEHR34lsuhmxNh5atczahq8dwc3v1IZHyzrrpY++GxJWXS8WS54uZlideS8e4Mre2zVYauOB49ec9zecXNzwhdvLqicZ1bL7GY83WCcJw4+G8naefsRhmS4bCteNTKDaoKVFMesDpEDIK7vPmlOq5bzekvMbz05L3bwqm+ZGsf3zANShptOHvSJFSXazGlmTvOrXcdP8isepHM+rEc4LTwpgPfCE/r0mFvvh/TMe96ET8lEKjPHqZrT/JijPKdUhomWDBKvZUG7MeeHk9OGjq0XUUATrNCsszpEZtdWZNoxafpoD1ghrSy7piZmLafVspM/Hz4bkM3AqJSCrMisvaOxhpgsrVOcJEfGHfxIMcM2KLqo8LnERc1/Uv9fbnd/hVIVpTslxObtcJjX1GrELE8YK4fNipCOadSOV/GndHGND0tS3qH1hKp4QqHHHNmnuFwedssGh+Ft26fUivPKYbVk0Vy2Q6SyVgeZcpsVN2tDlzJfdS03+k7adWjx8bgJRsFVpwTkqeWk86bJ/NWqocDwwbgcFnQ5Odz6hq/Mc6o85rZZUOG47wtG1nBSZmZOog720R5ueK+nrsfqt1Lwy2bEZxtRlO3l0xe14qSsWfnMbR+pjEihY4I771nT0iuPVz2LNOPcjhhbOKukuK6DnMAKrVmkE0a5ZmQsfUr8Uj2nZcNFfsqMmkU6oa+/S5tWrNrPIAdQGtB0ecNGNTL0V6Jqq3N9iKEGmKQ5yn2bMld4AhrFSRqk7GrJSt+zydds/RVO18ztYwyOOo+Y5hllLilxrNWOO31FyOIL6tP2QK9+91KqQOFIueUPKXDt169vdMGRK/Nl/jH35oKR/zYPaoW2UGgxHIK0efxecjz02i9bxecbzcxZcZbrTDVEyXZDtLTRiXHZMi6hchILvc+8ebla8B9fPWJqI39y/ppR0fFwtuR0MIaGqLlsxvxkOaEyjvNaZjBl4aldSxwk00YnjsdrjImMRzuMjTw7e8PZdElZ9GyXU4wNVGOZJeWhSHa9466tKXxB6x2jomc82WLLnvF8zXi6efsOZU0MBu8d/zNgPUQdwNtMe/l78vumL2mjwBRfbacHxdrUef5XDxv6aLjtpR+/C5JtsguZT5sNicxjN2ZsNddqyVf+r0juhzxNH+C07JSdztRGQIxTV3Dqj1iGGaWtaNSO+/iCXbphYyeYZBnnChPVoNTx9ARcLhnpIzKRlV6yDmN2UWZJlUm4rPh0Zfiq6fh4UvLdeWBW9Dw8uqEqe9xwery8OuVuK6eXl001zOiE0/VggHc6LXENN23Ni0bk0e0wk6iMmBNDlvchpMFvEjIv0pJ7c8siP2IxfsRtes6y+SlKlYch8UX+gFke4wlc524wfb6iiXesWoF3Gj1nXn1XoJA5YHUlfpB33Osv/V/T9l9xVzxjkf/XTI1jjijRBt4olXlrlASZZ9z2kTYlamV5ks9YOMtZJT9bGxlmnVJId32iTZErVvyKv2ZhHvEh32ZiM0eFnFSO2xHz3UfsUuBWbWjo0b2iCkaw+WgqnZi9gxzaGzu1yvjh2Vn2BS92+2RdIUXsC2WhFRMriJ02SuJni9CagxJkkEFMubVRB3NpZcQE/t7YcOrPD/f/2mt0FG/XSi/xAzXgYf4Ar3pW4wsyiSLXWCyzNGdEKaU9gydxrW/pVEuTl4Tc4dOOEDsqM8frR18zeb5o/oNEhauKSfkUpQx9FvP1TfyMmD0X9rs8TBfM8pjjOCEBnfJ4E7iZXLLNd6y6F/ThtVBXzByFbIRS7v6b+W8kPvutevL3vf4ACo4EIx2lMxa1Zu6CeESGpEo3+FteNxXboPl0lfmb+Jx5WvDYyg5ur+d4sZ2yDfbAkToqOz4+fUNVdhwv7tEmsd2OaNqKadnxwWSL00l2zT6zmKypyrc4iWfe8f0BWzMZ7zAmvE3iTIay0DjnD8VhebdguxvRdqVAOu+PuWxGVCbybH5H7Xqc82ideLOe83w3Gn5eT8qKtpFC4govyrO6o5juQCWUTeSgmSxWhK7g9asHXK7mOB0PJGwxRWaW6ym7vmTdVdx1FW003HTl4T1ZFD19GtNEzdRlpk4WgvfSWMCdQ9T0U3XM3PxvONElx6X83Fet7LhDfou30QoqrXkSz+hSpNAFjZLB60atuNVvaNKSNq4Oqh6j54LA0YWQe/UCn8aUWlEPu+dPZpqLuuC89JxXLeejrURIVC2mCCidWAwm2JQVt30xDL5l1vD5pqYf2qhWyeK2GeZ3bczEJIuxz5nLuOUr/RljFjxJF1Ks9eds/BWn7gPO4gUztcCPvk1SCY8M6DWaDk8kiqyWhFMVxpxzNHmKwlDkAoVmp6S15lR9ONls9YZAh9M1fjCI/kz/mIoJ95unjJTjo4njpEwDV1BOYJetnIXen0ggW2UkQj0MgFGf3u5ETgwYDS93hssu8UgteKr/BVOreTaOTGyiHjKUZs5xXhle7CpetdcALMwR80Lxqgn87bblB5MJZ5WhV5q2f5saCxx8T11SvD9JA69N2kY/vi941QTckAK6V9e1KfHSvGCdriiZYFWJZ35or7URVFKHeO3jEp6M08D/kyTQR/6CHUf0yhMJzLLMwrbR02WRTC/SESVyQo8k7tSGOy1KwP0MJuSOPokc2+kRehAG7FjxcvvvgEhdPOO8+gERTyZhcCJrxnJsJH5iyTU/5v/D3FzwKD7GYZkpeb7L6PBc8KKsuCailcPqmkwkxPVQAP7bXL9vxMyvX9/YgqOw1OUTRvaE7+XvceocZ1U+5KfvL6cSKGFAiQ/AMvdSbP7oSBhp56Mt46LjYirBW1onzID4H4130vrqHW1T8fz6jOeDbwb2qBB9UNkIddgQo0huT8+u32aCJE3wVqKmxztGs7enEN9UfHV9zpvtRIaxKvNqN+LTdTEw3gKzssMoaUH8cj3jr+4sz8aWp5MVKWm+upZdW2EC1sh85+ThJbbqKWYblEsYF4jBYEyUqIYBjeOcp6w6wfo7L2y15QyrI7ftiOc7aRFOnafUkYf1jvNKs4uGVe/wWbHxkmz3povcpB0fFlO+O5btdc4yQ+uTRAKvo6fB89q85Np/jtM1C/NYsj5UQ8yehiUpB1btlwdPgdFztBaxRSailMZpyQmJWYrB/ip0ojaKk7Ln6eye2WhHWbVoF4m9JWeNdZ7ZRLwghYl0wbLqBTapKdhFzS/Whk+bNSMKjl1xaPGkDNehY6nWrMwdXRIM/a2aYLPhmIfM7RkpJ+71HaM84SQvaHLPr/TP6NOO1pxS5PqQ+OlUyTQfDd9fZjiBQFJJVElxiTKaOtc4LC4VRALReCigj1uudn+N1hVttWHMEYvuA5zWFDpTalh5xWfNDoPmW7pi6hQhZ0iKdVDc94Lv2cSEU4rvzRUnZeBxnQ9cNLnigTEXs8JHQ2ESZzqzcIqPpg8PGB2fYOYMyVc4zSHo76RsD+msfTIsveW6s8xc4qNJI+T04XkujxO7ILHwzQCQFQGB4SJ8mz59m+dNyxt9S43AVp2G+SD4qYxEGdz18NPlPrZA5Nf3akOrdpynM2baYbQYcyttOUrHZDIlDoNmQ0OjG+655Kb9JdZUHLv3MVhCEsRMF+5IaY2zZ0yqU6b5mB+N/i/oLLM6nRQvzVe8af8WZ8bUVoL4JmmCRdOYHTvu8EP8xf6EBOB1TyAw45Tj4oKVvuOy++nQev3DiyR49/rGFpxMoOm+wsctG/d9psmyC5qt1bxqLFedACW/P28pzZ4CnXk8ylRmxlGReVB11AM/rAuOwobDglsWHqUSMcgc5Or+iF1fctWM2QYrMMOqYRccv1yPxUltBOWxbEasu5JF3fBkcPqvNhN8tOz6kj4aziYrHqqMNgltA0olPnj4gsd9wd16xv1uxNgWVFoCq+pBBn2zG7MLjouq4f/0dMesaHl4dHtI+Xz3Kis5bcXO0d4sSFFz9+aMXVNztZpz19YsSjiarAnB8tmLJ+SsOJmsGNWivBsVPcuuYuVFsXRUOFJWVCZQmSCqpiiR1gCFgR8uDClP5fSoGBz60vrYhkiXxTRY4vC5Y9c/R6mS3m3IOdH611/bOTl7xsfj/z1lrhjlERrFSq/paHnlf8x98ze8mJzSp+9RJsWqd+gB1giiPpyNdpRFR86adlvw+fOnrNr6ECp234y52o3pkmYb7KG1ZlXm/XHivJrgk0hrY4aVlxNaqQyzPGHFHZv+Da1ZkmzE6erQgilzNZxIFM3AxtqGG0JqxG+joM87Qu7ITKgYEwnc8xqf9iijeJhZGeWocylIneyIJO51OVAaaopyTKEnLHhAnUfceyGGVwOFoDDwjxcj2gi/3Hg2ueOhG3FUaF40gV/wFY6Ck3TERDsSQh4vhqK8DZa73g5R0TIbuuukQJ2VhrMqUw95UwkBsrZRsw2Jl/mecXPCm6pgUQSB0epAaeQUIxLpkjZqfrEeUZjEw1rUpLWJ1CZSBIv2lj4qllH6E3Mn934fS4w/Ze4shYGpyzyquwPXDeCLzQgwhwiEPmVe8Cmb/g0Uf4pO5yQEmdMT6JTci6NcYlDc6ztu0q8kTiOuSbljpV7JSUOVOFfT9M8BmTOv0yVRH3GcFpRYApIwWuaa0k5RaG7Tc6wusepDRrkeAKKdzIu0GIb3EdRaGRSaNm8IqiMm+dyzSfTh/19w/sGuTMCHK1blmnkU06RPCqvhrMxYDZsgw+5NEC+AU5mLSvhqm2DYBMOrRkx2p1XP1HqMFiZXYSLjoiMkw09vT7j3lpPC87De4bPmpq0pTOTPTq8PKZ4pK45GGx7M70lJsWtqtl3Ff7y8YOMN356veDhbsmzGfPmT7wn6f7zBmcDRfEk9agZJdebJZMXD8Ybaei6Ob3DOcxbuyFnhnMcVwkwrRi3aRMxgeGzuprRNTVEOvopgib3De8v9asq2qzif3/HRk+ekqPHe0WVNFyx9NKT1nGI3pvGOLloSim9NW9poeNUUfB5LRka8GWOTeFx3JIYAr6j5dOW46dIg2pCWxnpYoAFKpamscMcm4Xt8UH1MT2ClV7Rqy+sc6MPtoQ9d2QUX6QG1sozsMJAIsFH6YJA0WJySE+6s8BiVORkEIaejHW7gyymVKIqex2eXnPUFL29P+dn1A+66ktetowmKN62w0PbzrZkTNVWGw9zgqu/oCFQ4RkOsb0hb+nB1gG5WxRMqs2BiThnnOVqJf+Yyf3FoDXb+JVXxhIl7wLF6gqOgTjVeee5I5CwtE6Ml0/4onWEH1lmLZN40aset/xVN/xJnjlmUzwC4zl8C8EqVmOR4FN7jiZ0yRXNSCv/sg7GjT44mZO76RKU13+cZPiWW9HQp4gfyehstube8aCz/adXQ4WlVS1CBey7p8oYPdz/kkzRjYjVG2SHfJuNU5Dtzw9NwilWwCZqMZd6VlMZSmoBS/P/Y+8/n2LY0vRP7LbddWiABHADHXVe+y7XnkOJwhiNyKI0U+qCP85dOhBTkKCjabrar7rLXHgubyMxtl9OHtTPPLbqZ7qoib8doR1ScuvcCOInMvdda7/s+z+8ZIz0MrxvBn7f3VBT8wVHJ3ARan0QggXczpps+HV5Oi2SeVTKSj2ZaHxkxTI6JsRiZDn9GBM5LM6J8FEsjeXj4Xe51zcfhz/hF/78yzR+zkgmwqdDoqOmx6PRPFHKOkSVBr9AiZ8oKjaYRW2zs0Oo4ZRehcbGnCfd8Ji1KGKZxQRlKbsJnB/SMFBOUqpgUR4ggOfXnPBKXDKSWnsdhR5n8PCzJY8bP+Hes279AiglH5dcp1JxGZvgwHFJJ/7ZdX9ENR6DVklXxDSYc8YF4xCKTzE1SI22sYuuSHNIFATKV4I1LUMDGwSKDR0Ua+ELqba8Hw2YwLDPLSZmG4209xUWZnPwkGvLrtsLF9LPnxvN09kCV9UTSLCChbBIPre4LQhS8P9sASYIMUNuMN22FkYHO6yRDLjqU9sxnO6qyTcmefU5mHNWkSbOZ8aFBpurItjk3r8+QMnBy+RYzaZmc3TPhHmkc0jiCV/gmJ3hFVvT4wZBVHabqaO4WPLye47xiXqS/8/PNkrshO2QJ7X1NJbDKGNHxKW45KxKjLY5y6igjyyyiRFIEbWzkdTfwlzJF6R5zQRlKzpkx08kHUY8u7UTdUuRqdmiVKZFzJt+nihotxGH204qeVjQs9GPKyRHP/fOEsZdxHPSHg6z2dT3ltq04qWo+yixZPjA7GqkGQ4b1komxrHLN1hmULGmcGFE1cNUF/nLXM5eG8zKBJBc6xTU0wdHFnojHqBkOeVCgSaHRMkeMRDE5qpnO5Ht0eQqbm5hTtMjZ2FfcDH9ClT/l2/LvUY1fZ9WAFT0ex539jC/6f4bRxzzPfx+J5JP+X2LdHcvyW3y7/L+l5TCocaFMqqk38iVNuKci57JKseCQFmM9Stm3znMdGi71lOcTiRYCJRMRWgl4GDQPVrCz0PhIKTQyChqaREYQaePfiAd+1gnmomAIGUqkKkKQEkSXJpKrpFSrtGdqBpSMtFZjozyQNlZ55AfyCCUAkln1ZQPrwTPTinmWJjjnpRxVeIHOp7iI2UgmSCbQlNmkRn9aQNB4zXpIYvqjzFFIxXObsRwMwX2XqzK1NFs2ZKIkDwUSyVo+4HG8dj+mGb5gmj/nffnbZMFgUAQiSmp6OmTxTZpwT4w+tdnoGUQygOayZMKEZ+I7PJp8eICARkL6nOVbFBoVDfO45Fl8xPg2jPeVAAE6/A53kw/ZcMN195eE8K4roOQCIfTfOn/OV3TDifiQPqAgknIm9ynQTAKrzHFexINh0Y2VTaUT5O+LrmMIOatMYHRklacM9lXRUpmBMhso8w5rDffNlM5pavfLb8XCOBbZwDzrOFvek+c92ji0djinU5JnXdENGT7scTfjAi49p5Mt86xLHhcvMSpF+DqbwtN8kLxdH/OXdyuW2cDvaktZtXRtgXWa6aSmmu9wTtP16WG/enGBMZbji2uK1ZroFXZXIo0jP3kAGciPNxBE8gIFQRkEj0LaLNyQkP2LqqazGb01tNawszmvm4TMOCs7jEiw9BhTIuiLEee/92V8uoNb2/Eoy1kVAiNypsMPCJAIvaNKowuRT8RLPm7/1zTojA4hcmbFcxbmCYWYkseSIlbU0dLGniv1CktHHHNqxCgJ9oSkNiKyDOnPm67gwSpm2rPMLNWQ4axO87l8QBnHxeVrVie32D6jbiqsNXywmDA4RWMz+qDYDBlbp7ntFb/YxjTvIEmBG3rW8p4u7tCypNBLJvJ7SKFQaASKY79iSkk7Gl0bscWFlhgDlTxiEVZc6g8xasTZo1MMtHigjzsGn1IoAcrsMaU+oowVJhq+b/4xGLhR17wOP6cQc444SxR1VaWKxXyLTKZ5Ri4jXRB8XkPjIp/6e1rR8C15ye9N5wmxoyIbK/jJw4CNgUdZTqUEWxfYOU+pFKe5RgnDN9T5SFxPijY1+rG6kfbch6TWa0XDcb1kKnI+nBp+eNwz05aj0TTd6gw/Ql21zFgPmp1T6PH1SEgxG8FzqjSPK3/Iu3JA4wO7YAkYCimpdJLpV/pdm1nJgCYdSL6cVBtIsyAfI0dxQh7fpxEtW7k5EB0sA1/0/xbnH5jl7/N88t8ljl1Q9KLnrbw9VDI2NFR6xZF6mjaa2KQkp+gQSKzoqWWNjDLJpsVAT2qd7u+ZE3/KjIJcKiol6XzgF7zBMnAZLphLg2PMg3JX/wE3TUQN4iu6fP9nrq/sK46x46r+1wBsqx+y8OeY5n1mI6zTjEbPz2uNDRziZaVgTGfcM74ig09pmpOs52x5P3K1HE1bQpMMovXo70jKt8jUWJ4t7yjMcIgXCEEyjKmTUgbyzDItWjqbsdkVDF6hZTjIpgevaZzhqivQY/tnPjT0zmBdQn8ss4GpGQ5+ocXRw0GBpsuO6viBo8dvCV4x1CXBS6RxRK/o7uZs75bkRc9cv0GIQHN1jG0LhPJjbo5jcrImOkXfFASnkTJQWcPtw5K7tmIzZLxq0xB2biyVSRkxHkHrFW+7FB+9pwUPIWAJKAlTHZkbeDJJkvSrMYBtD7p8Es7Ji3/EvbjiTfPHRCytvWOQO67dPSHsmOQf8J76AVYMtCHRl0u5QAlzOAkOOD7bwTzTTI1hYeDJpOb9McVVyRQr3fc5zmnapvql+2mfEZRSUgcylSCfPkpmZqC2hqnOgZzWJxOi85Fa1my45ogLviG/gyewiTscDj+a8gyaXCiuxY7P7B+NPpwtIBligxUzqlCxFCU2emoGHuSaL3b/jL2n4ss8rME98Kz4NiU5Dk8gHphfXlsyVRFioPY5PmpWuWaRRdaD4E2bDgN/JX+EwvCN+A3eU0seV5LjLJkzO5/k3VOlCTEyMyKFn2USJSSViiwyl8LRvjTUlyLSj9Hrt71mPUAXPPfymjZuOOGYpdHMTeLclSblP4Uo2A3ZAZq6zAYGLwGFAOZmT5YWgMBHO4pDJJFEHXgIHWu54T4mifPUJZXZEBTvT9PPrXRiHm6s4c3YRtcisnWKt71jGwYWMuNYTfFxgo8rPJE2eDyRafbf04uOSZiQ+wTj3Ce37mdwShiUWmBIirLUgMtRwnAUT9FR0caWno4gQ6qMY84qPEJGgRPp8z6RE1aZHgGlqbNiu0f00bMRDbek3JyIJ5NTlFwQ916hGJJ4IP7tM4V+ZTccSA+hlBNKuWASpmQqbSKlTtjzZRQsTTIxflpn3PSCowyeTRRzY7ksuwMNIFOewlgGmyFEYLCGpitTKqfT5DKATg/DzAxUIzZmcIYXV49wQfG2nlI7zePJjovF/UHNI8aSHhRXbYWrk4u6dpJSB07zHi0DzZDRO819V7K1hqO85/3l3Zhv4wleslmn9tfq5I5s1hCswjYFQkaKxRapAzEIfG8wVcdRkaIHupsF3mk2t0u6PmcYMqzXTMqGo6M1XVvwi1dPaJ3hfPbApGxYtxUv6imldvzh6R1hNLS+aitap+iDYJk5fv9kg4+C2hpar7jqDKHPMSIN2Y2MmNHtXWrQYzyxFIKZK1jaMz53Oa9iko1av8aHnBhaDnTkmDONE844JRK55oaGLXksqeKUCRl6ZJttrMYFCWPldzcorjvJeRn43SHHSM/bZkrz71WtyRgs2I2bz0x7ii/FO4QoEt4oCBofqL3lUpzwhNP9DYmNY2pj3DvLw8EMeB5WrNQ/Iqo0jLbC8ZpPeON/ilMfkIcLJIIJGTIccTL5Aa1bj8PkBGbMxZQyVsxD2jDv5JpGbNnYNwzujlzNDhXVZZFTqDTT7H3KwRlCYKkz/qH+bTIpDspOG1Ll07jUNutDpPV7A2Hy8yTiekLk1E4hRaSQYhz0J3XonhrgM89lpel8xrH9OjZEFrmk1DAz/sAvS/Hogs/rKbe95jR3HOX9GD+dDog+Cgaf5NIpbl1y16dny4xm1GNVknvNhpad3DKPU2ZaYiTcdBk7a5gaSz62Wvfhb0qkg+hxpshdcQjUe9FZPhEvyCk4ZpliJ6ICilTRsmPBCY/CMTbmWDEwyHctrW24Ym2/wIcB57dk5ogn6ilHssDFKZ7IJ7zg8+7fYtSEI/McI3OqOEuheiQpfj4KHzqfYuat8/xF/7+MrTJBkT0mxoCSJT68MxT/bb2+0htOxBFCzUm44PHoQbjqFFpEisqhVeC0rIkkh3LnFZnyZKOiy8iElNkOOT4KPn5Y0nnFRCeTICRfgCRyVjZoGTiuamZVzTBk7PqC3hlu2orBK7ZOM3hJpZKYQEtPph3OK1pnaLw6eD4erOJlkzJmfhQzCgXfnCePiw0yBbMpR5UlqfKuTnG/bqRR5xuLzoc0OxkMQnn0qEqzdYntsgO3zVrNbjM7gBUBlAoIkU58PiS6tZEeP2p+vdc8PrrjyfEN/ZBR9wXbvuB1m7MeFHMTmGjPwlgupxvimCraWsODXRyG+z6C9wnemUl4Wjn0OFOzQSBF+vMpC/Lyf8bi2cotloGae2xsqeQRLviEAEElDA6v2A1vmZtLELCMFcssqeI+rxNiZpkltVSlAl+beWYmmTiFgHzcSNZDxtYlxZILgrtB8m83W3ph+XZ+NJ6q09X7lKtUu8DP4ksateU4PGISC+7kmlteIIUiExUheh7cK1xoabP3aeI5c6Ycq5IYI7sgkVEghUpmzrEKkiQ5LgFKsSCqgBiTQTNRMg/LNE8Q7yT1Jub40eznYk8uDIVIKP9sJA040uJ1XiomGs4KN4a0gYuCT3aCV13PTBmOM4WRgpNc4wPc9YHbHo6yRJDYWHjTpe/fz0tuB0cTHR+UFR/N0nt7Vnh8EDzoFLkheJeXdN/nZCpx/TLl+cHJFVp5tl3BZijovOSzXZqtprlZ5PMd3FlHIaHYEz72gE8jWZqCrFeooFjIjMk4rPrFTiOAy1Ix0YFSe86KHhckjVMUKvBsohi84FULb3vHQ2zpxA4reqRIbbWb+Dk2NMz1BbN4TBnKxBfEs2ONi30Cu6LZ2bf09hVarfhW+T8yj1OeZRNKnfKUkpnW4vwtPtTI7ANMzNExLbmlUsxH/NA+fK71nhZLZU7YhY7MHFHpFWFUMPZ+gx/+/xvOb+QSaIw+odALJuOJYD141gPkMg2LhYg49gufGNlaaRNJ/y4Fpn1eZ7T+HU12lWecFxnLzPLBYn0gM+uxDeVcwqNM845p3rEsa1xQvNwsWY/mwdu2Yp51HI9igTIbGJzmzW7OfZ9z0yv+dLdlIQq+vTDMTWCZDcxHUUEYR82vN0sgtfWEiMzyjsJY+sGwW8+RMpDlA0p7zKJGVR162hAGzebzc168uEzEhKrBGIf3aXOp5lvyqk2ybOXJsoGTrsBacwgn6/ucfshohpwvtgtiFHzvaD1upgmUKWVCy/vx/dYycF4MFEqPrzstGvvF7cEq3DuCCZ/uHP+OvyAXU044RSFZhgWBiJEZjUxo9xvxCiUNVZwlb0roEaT5TR4LKqlZmBS4N9PpUDExlkwGCu0olEWrQDW+v7m2hCA5tqmq3NqM+/Gzm4uCNmrMmOXzWR34xN+NSqEBEzOec4Hhks+44WP5E/qwo/cbKr3iJF6kzcQo2vBAE+7ZxStm6hzn30Mi6EeT54QluZlShSk+BjoCNR1evKMp7OcCTvTsRJoVtPYOJTOemR+yCiuO1H8HJUQirRgIMWI6SaUkT6t3XpRcxrHijrgg2Q6K1gm2NrChpYhJTmwkTHSqHnZO0nv4vLH8q+4aHUetVlTshhwjJLmQnKiCwUc+q9XBFAuMERyCsyJynHmmxjMzlkI5FkWHlp4q69HjIcCo9JkdZ2OM9Whr+NZcEYDBJ3TRbW/4yUPyXhkhxhauAlcy04pKpwV7oiNKxlHGrVjbtCY0TnDbp/cMkohiYz1dcOQYHof3xtA3gYuBTq3oRBKBeBxrec+duCWPBR+Fr6MQdNh0aDLX9PY1uV6yjHMmwhwOACZLrcEfdl/jrLygEx0b7ulEzZY05D/3cwQaMXqFBAIbU7X8gfxtfPm9hL+JBoenF5bWNHwGDPaeOApZ/lMEgN9cZEGiAu5be3/dv+MrueHsXeZGTTCywsUEDXxWac7LRBnYOoMfctYjzDOdqNMAMpORQgUWJpGlT3OfYgpCQuCcFZbHVUOpLZPRmNY7M3poknRYiYAZF7DLR28xxvLYvSEEefDu7HlcfkT7O6/SQ5YJfnfl+M5CU6iek/KeTI0PnfRsuopNnzPE1G4YQnL6A3x3dcNyUmNGz5DWDpVZdGYRMkAUyGJAFgPTixuem3EGICJ+0Hz++VPW7YQLf8sqs3R1RtuUGGM5u3iLVIFmM6Vri0NlY70mkwEXkhQc4KxsKMuB63rKy2aSkkvHWG0jE16mUqkl5cYU061TXHWC2sWR3Av3oWMT3mBkCSqZOdf+FS70FGJOTgoam8clPqaMEyt6er/B+i15NuU8HnGaaxaZPfy9+/aOj4KHPucmlMkJn/WHmQ6kxctFmdpDynNWRP7eqcLHnH0CjIuSrFsd7r89mHOIjikTpvHraBRGSQyKiUqV69yVtFj+Kv4rtt3PaNUNsQgJoWJfIYXmu/Lvc6EnDCLQE+ijoxE7GrHlTfuneL89MLy0mlHqYzI15VH+ewDc8oI3/Izn4rs8lUd0wfMQ2/F1BqwQZEowMz7JxkcfzVWXIgA+2QV23jJTmm8WS6ZacJzHL6WppNZOkNBHz1pc0cR76uE1Rs14z/wO0zBNUQxScGMtf2HvkEhMTFFlQFLMxRmgKHVgkY/t7PH5qvsCGxS7IeVNVcrxfP6AEuHgMdtX4rshZzPkGBHxMWMIgrseWh/51D7wmfgrHtuvI8SK41zwYZkCFq/7jNpJOiepHQdOW4hJBm9j5E3YcquuWIQVqzhLMxyGFDfhEwXgWl1xH18dEDaVXnHJKZXS6CBxMfBe/A7LySVlrFBR4kn8NyMFyyzZBSotmLiMhoaX7b8DAh8Wf5/TsGJVaHKV1o/OJ4n3kTHkXvFzXnAfX3EuPmBBSR8FDS0SyaPsm1jTs/NX9H5Lb1/9R9fQtBGIwz/9+q6ENCUm7M1f90d/JTecTC8wsmSiTshEifeJ8BpJoUw3veanG3PIz4ikIXWI8KhMMQatl7Q+G4nSQ2Jl9TlbJ6nGuQ7AzW6W+sZDjg2SQiV/zklZc3lyTZYPlNMaqQLKJeWXswZrE5ngfjtn1xd8ullQf2kTOis6vn50ix5DzoSIWK9xQY3KsIxMeibGspAdz+brRA+YbSirvRkwJYnGRmDbnL4p0uvQLv1Z9MyeviVYzfAwQcjAyfEd82E3ho6lzciOswypArroqYC8apn7zWHzfNqlauev3jxmY1P6567PyZTn2XSXmFsueXYy5VmOi5uR4VBRFjGZcaUQh4CyR7riv/H/EBs9rbfcy5SHA9CIgqPyGwyioRb3GFEwi0eUoUJl38LRswzHhJGVlY/Jq9d9Roz7YbMfkftjzMKuGmcP6ZT/eWO4aiOlTvHjkdQ6SyKI5MxfmsjSpPlM59Oc48EpHJ4jWTJR+5lAOtVvvSPEdCqtyHguv8d6+pg2PnBvPyOTEz7Uf8A0TviwmHKUpdaiErC1Ga/bglvf8lb8iIhDyxWZnlHpFcdcYmLGPKZgvSADnaopfY4WgpnSLMQszRxEgnVCZPCSTRA0LlXzt32a24QYqaTivJSs8kTj8BFuB8FPdh2ewPOiZKoFC2V44j8gxzAtDEYIjjNFplIoW6UiT7yhdudsbOTzLvmoVjqnkOmAUTvB1qaNRQi4a9MBRogvzclE4G7I+cuHOYUKvDetKVQSqtig2FnDxhoexlA8GyIxRlyEVrZY33Cn3/KLPsOGGb+/chwX7WEDq21GbTN6r1gPiSbxyU6ztUngIVB0ouFqrGh3rJFCcRweUcacC3/OYy7TfE5GdEh4oCEEPhOv2Yq7Q6yAIUNFQS4kc6PIVVLxhZgC4F7JKwA+KP4bFJrLeEKlUnWW5pEpXdXHQB8CnkjFFES6D/roeRBbXvJTQvBomSfnkCwQQv0nN5x0/To3mv/IT/8bYG++khtOZ6+wcoIUhkE0PPAGJQwn/R9yUQqMSM5jLVN2OsDLRrOzcJSllo8c2wpqXBiViDyZ1GmQuM/SsRmf1xVDEIdskO8udzyepzabHeXPYWxTXd8fs+3KlMmiPLs+kQm21vDJLqPxIj2YOjDRmt5pWmu4qaeEKFAyEiO8rKdcdQYt0/xhnlm+tbqmyvoDM+1+O+e6npFJxzRP7Ygq75HS0/U51mtOj+45eZpuuGA1waskvfYKZzWDysjygYunr9IMZlcRN1PKWU1edaiiR5UD0WrsrqTfldRdyaYtsUExBM3EDMyLhsFr1uMsq/OpVdn1GcM+kIu9gomDQi3E1Hs/LVKE9XowTEOOm/wTau4Psug7XnHb/QSjZhTmuxiRMcSGgQbPaWp7jK2HTHnOxnCwQrl0OBiNvJ3TrIec3ivedtnh5Dgxgt7DyyZSe88nvMHjeB4vWWhNNjr093DTADzKM3zM8DHJpDfW8kBDL3p2MvXRj8IpJmpMNCzDilyUKGOwseVnwz9HyYxz93/lokyb20R7jjLJMlN0fsZF8/+k8Z6H2NGIlhte8Wn/b1KsuvguecyYhwVzUgvyyjUcyYKLUtP6yM+6LR6PFkf4Uo2bZdpQIEEwZ0ahRRpMpw1PcDNuRjOVvCpzI5joSO8VDBOWmeJplczTuXQokYQhSkTOxmfKRsFvOYOP71rVn+wiP286hlBykuepLTtinErtUCJVxvtDWW00MYqDJL/x8vAs1i7Fj3w4S3PY+yHdW0/qU6T8LXKfk6HJ1d74OXCyWFPkA84pnFfcbefwcISwhlIlcvqTMGXlS96ELS/lx2Si4iRcokOKD2hEyyLOmMqkfAsxNeS66GkZ+GT4l1h3fViriuwJp+ofUZI2kS8DVCslOR/OyIVilekD+00mmw2dT1aDiZZ0PnLvBzoGGrmjE/WheuxFh4jygFxKP0MfOG5/m66v6CsOxOgIMTl3kzEwqVKU8BwXjrOiPRADAN6baHxMyY/7rPZhLNG7MZ9mfxkZyGQgl57vLNdo6ZnmfULfiICSgevtnI9fPSVXnmezDVp6tn1B5zW3XcF1n5A0y8yOD9S78rh2ikxmzHRJ4zUvm0QpPs7cmErZ82S6S67rNsmp6z7HB4mxSXbd2gxJpMoGLk+v0MYdZjK7+zlNW2KyAT+MsEGriV4e2GkPmznNTc68ajg9Sw+IG0wiajcFwSqMlzAq7VQ+UGjP8/CCvsv5/PUFr7fzhNzJLCakMLreGTqfVGK3veb1mBuzdhZFZKqTf2X/0A0hTbQ7H7gODR7PnCVT5vR0WAam4oRJeYSKhipOkznOfUI/vEFNDPMwxQdDMVZU+8+z1IxVlkerQGsNb9uETenC3qOVFoBlltJgbVA8HZ4w+NE4GCOtj2yspw2ee1HjRykswFFcUAnNlo4rNW7u0aOEOXzNnbplG66wvqFzDwgh0aqgUAsEqe3nQhIt+P37LWCVS2ZBonuBDopazFEyw4aGl+qnCPkulmDBCfMwQ4hUSRRK8Hfy+fgew12flIG5gkJG5tnIQBtBpbWTbG3inj3YkKolLcmlYJUnddpZsW9TxkML+k2XbAdDSF6WR2XkcTWMRI9EoLjtMzqfTvidTwKZPshkBs16lNyrOGE95NTWIEU8tEYL5VMM++j9Ag5Mt0Cq9B+swQXJUaZ5bzhnM0TeDmlet6dRp+8LGJPixJehxnrNtM/ZWcWDVQcS+GM155n4ITZGHuKAxWGxOOHw0eOjZhd77uSaPBY8EgsKSr6Z/QM2xX3C1sSCIhaHzemLJs0z8hE+aoTgSZEjeJdo+razdNFzanKWmRjVgcnX9Gfxn9PYG3K9JFdTCjEhjwYdFxiR4YQbo67tO5m2XPytUq595TYcIQpm+XOEUChhiARe+x8DcN7/9zwq1ShdVeQitb9CFLzpSu57zVlhOSk6eq/YWpMQKEXqJy+LhtIM+ChH9YxjNqkPME+A6/tj3m4X9E4fCLmDVwxecd8X1E5z0xtuesXSJHBkrh3vTZKc9LrP2NqU0VOa5IhntITMRvzGfsZgQ8qFb1VgUs/IlD8kcM60ZT7mtzink2ggH1B5ypVfRsFQF2xujg7vXQiSh82c3hlybVlMdkwnNfkYNx28xHvF0Gd0bcHm5SUPzYQq7zlePGCygWq+I580fK3oed5nbLdTbjeLxNoaw94mY6x2rHPWgyeTkqdldji5RRLqpvOB137HK/kZW//mgPkosifkaj5SkQtElONnr8hDhiTnmfkhbVZz6s8pRAr6mozejlPpfokrF6JImJ6QFFVKRkRIEufjfKyMxmoyAMvsnfy285K7HtZ2HCwTGMTAPa8JeM75bS5Lw6O44KMwp3aBL+IDPR2daNiJDVf2J7T9i18aoObmEqk1XQhsbDqx3w2aN23gT2LC3jz2l2QotnS0osWKRIXeM7sAPBYXe4I4HgGmkdpxIENLAaWCaqz4c5kqk0IFXBCsnaLz71hoNgS66Kmk5ijTZDKZeaNNPDUJPFjJ2zZybx1/If6CLm5Yikumcc6Tfsn9kDM3kcvSHTw6RiaquI/JW3Pba5RIEnbxpc/qqtPc9okSsieHfDirKUb6uxAxqSGdpg+SnTUIEVmapLjsQ8kQUlU6zwqWWaTxGtWNuUe7JHBJh0/PrGgxyvHEGY5sel8XVrG1ybhqQzhk3wQCMkoMmkJKCkpOKAkiHZw8kcfimKfxGDFS0PsQuA8tO1Hzc/svGNzdwR+TZ+ccm/dZcMIH4lF6b2NHLyyPRH6gsLuYDgrH+j1KdcRZfMo0VlRkVCrROpqQs4str+OP6dwDWhWHe+Rv0/WV23Bi7LChxYcB69coOeEb+T9gFZcsjaJ2gojCx5JKBc5G2eXTqubZ5B3pdhcM171hoj3fmW5YTrcslg+H3BkhA3GsgIKXdHWFHTLumwmfbOcHisFMe86nWzLlCFEwcZpHZZPC1wRk0uGC4k1b0QdJJgMLk4yjEzOgc8+TLLmErU+l/m0z4a6tcEFymruktFIps6cbBQhfNBW7hxnLzPNsN2OW9zzvcibTGlP06MyCjOgsPYj5NOVtaOOwQ4b3khAkOrOYSYuQEV0MRKfothP6Lj+khW67ktt6mjbO81dU00TQzoue7XZKY7MDNgTAeYkSkfcmPXNjKKRjZtLP6nzy7/xkY6hdZCMfuB1+weBuDp+x8y2ZnDD4HVa0qHGBlSga0SSzJ5oqzijJKaSiUFDoFNWwV6J9sj7mZoyHkHCIN7YIXreCjQ0cZZKZESPIMS2GZuyx71ya5/y82/KJ/DE9Ozq7ptBLvs/f4VhnB8PkXk00BMH79pjOw4vGsQuOR/IMW3oGYccTqMOKHhXTybf3qWqII336KvwE5zveyB+NmPsSLQp0zJnrC6o44zycYYTEx7RJvhFX/FT8GUdcYrtLuuj4TH0CwN/T3+bZJG02hQrUTvFZrbCBcf6RNqdkmlbMTJp/9j5VLj/bRLqQyNFGCl7aLX8Z/wU2tITgkFLzTH+bSzHnNrT8uLnmMlzww1CSjb4lH1Nw29pZLovs4P/JVBhnX6NoZ8yrsiHNeyZascxyWpfir13YHz5S665U/nC4iFGMXQV/aLv5kGCdhSp4XLXMs57eafqgDjlPUkRWRcs8G3jTZdRjou3P1M+xIknNfbS07o4QLe/nf0gekoBAkDaJv4r/Chd73le/w2lcplBAWaPRVJQpOvrfm6d0wwvWQuO15dQfU0nN+/mUQgnOy8hRZmm9ZDNIjBDI8eBloqEYYxKa4LmPNZ+KH+FCh5YF0+xd3lVhThh8hvfN3wqS9FduwwFoh8+RcsZx+S0qecSZP2KhDctMsMwChUr98NZJ/unbGQC/t2q5qGqysa+fjfTYSnkKM2C0Iyt6skmLbQr6XSo7YhR4r2jqin7IOJlsmRctmXKURXcY3PsgaW024nX20tOkZPMxEYg3VvFXa8mLoeX3lhNWRZcYamM2St3ntM7w482cX2wTdffDacfUWJ6M8uz9a6r74hCUdjfkPNg0KK/uB1bzBybTmrxqWTy6Teo1EYleUQxNkjx3Odamm9btyQMyggwIkaTSUgaMdlTKU+YdMUpe35ziryTzsjkQpZ+dXFG3FT+/PT14ndIiIhm8oFIwNwNCQBlSeuhJrglR86qd4kOPwBBJv1+IA439D3EdAJviGxhZjdG7ihkztChGz3mSYe8l5JlMisXb3vC20yNnLX3dUZZ645/Xjj9pN+iYmlPAwajZj9koQQSOuEjJnLqiEIpHhaFUSfUYADn6WeJImC6U4KLU+KgZRrJCMlem1lEI+5O/YOcCIe5Z0LAyH9DrHQ/DC6zf0sUriI55+XXOxftMYnGYr2x9wOKYhhm5+IhpqKiUokCR+Y8AyLPEEEyHsVSd/Kn/FIDLcE4hFAujqZSg0u+SS1OkxrvEzSvfsI4P9Kojj3OUyBnEDoFCxlS+vp9P+WE2YwjwqtkDW5OBM5OCyyLjaRV5b9KiRhm9C5KfbibcDZJCRd6bRmonue9Tm+l+0NRScdMlRuJpHjkrUhT1adHig+RlMznMeHwQ3PWCT2vLkdFclKm1/WiyY5a3h4pqT28XIpIpN6b2zim1ZEvHy/pfomTJJLtI4mghEVHRiC23YcatuuJq+CmZmnKhvskkTFjEilIqsqhYxBKFQEtBGc7ZTv6Qxt/iQj9yA9MG0oUNn6sXzMKcc3XGWRGTATYbsFFwlClWuUJsP6B26T31ET7xd3wef8QQdnTDNQjJJDtHiUQ2EELSDm/+i2w0QoyRIb9iGNtXcsMRIsOoGQtxTh4L7kXNzmlO45yZfofaEGP/M0S4Gww2zLioaib5lilwMZo/pYg4r+ibMvls+oy+yw+oGkhRw9NJg5QeqZKbP0ZB3VR8cntK7cwBFmlkQMuU0T7NBnwQh0C4by01z1zJUea4aktymfPQpxPJ/vtbl7I+lIBC+XH47ciMw+hEhN5HVGd7hRnJa+CD5EevnrIZMj5c3vHR88+QapT3OsXbt2fsxvZCiIJlWXPqNMGndpsP8kCtnkwatPKJSF30uMFwv5uyG3LWXYW/FSyKjqNqhxSB8+kW5yW1zcY2o2RtJUpGVnvCsVf0QR4kqdNYsizeo3VrmuELYrSEsCUAmT6nMicHLL8LPa27ow5vDn3pcjrnfTnHRcFdXzDxmlynPKC9MOSsGHhU9hgZmI0bn/OpIrvtCtZ2TuclOyu4GwR/0t6xEevxfZWch0c80gWt99zGFh8DW5uMgjd9PMyjpICdc7zmPgV2xTkZCikECsFdbHgpU9WRjWDMXUi+mqW6ZOXPuFO3vG7/FAiU5oRCL3GhJ0RLiJYX4sdM1IrCf4AWkmuxppY78liQxYypyLko03vd+mRoVSPbbP86cwXvu8cYIbiYaAqVqh8lI71Pi3XvkwEyxMhxpplpTeUmLFyBEZJKf0iIyYyYKsdkWF3lgmcTiwuC7V4ZOFY4W5uSUIWITE1Shuba4YPg3BpKrceWX+B1m3E3BDIpeVyl1zfPApmSnBSWR2VSanYuzWazfSy4U3RRcF4GzktFLj3zLG1OWnqMdhR5TzYmvgoZCV7StCVdn7MwjrnRPDdztvIfv2ulIZnzPYzSvORzfhL+FcFbfBjo4ppX6i/J5JTvxd9hbpJx1kiobZolOSLPwteI4iN63R/4bFb0vxSylyI8xCEAL5dJ5j/REiGgdZLrPsm6j92UB3UOCpT+Fh07Xtf/Iikb1YpJ9ggpM4Lfbzj7hvav/4rRk3oIv9rP/0puOFot0LJMPe2YccaMRaaZ6HQiarw6KJD2ipydVQxBMM8MbhwqG+kPi/QwpLlFCJKyaji6fKB7mHH3xRKAJ09eUcxqvNWHFlu9m/BQT/nFdsbGqrHHnJAoE+1xQdIHSe8Vn9Y5jRMUKklt14PifijpfRrUQlLWZWpvPk0tmrshKb6UjGTSUZpkQK37nN2Qj9BMkVhhWU8+8qKOXOptv3x9gdif/hFsuxLrJY+Pb1mt7tK/j4Le52zaChcUk7IlK3ryoqea7Yhe0o+x1CeLNQtruN6mPJ3dkBIza5vx2XaGDYLToidX6T04ydOJ/7rLGYLgVatpPXzRWO5jw7V6Q+82SQAiR0q0mqBFzon6gOOQ/C8CwU7veCH+gtbeHe6FB/+GIL/O/QB/sZ6wzDzzrGOhHRezBy5Isu9hfA/NmLsSxhZgpjxLm8K/aqd5FiTvTxf4uBzvH8F2pCS3Hq7ldcLT+3NsVMRRunZQNcmKR/4ShWTAMeDIo8GQkPkTlljR08ZkCD7nA6ZMKL2hkJrS51D+LkNsqMPtlzYbR4iGEPeonbTAz8OU3OfkGAyaiU7tRSMjJ2ML/25ISrFMjrOAHL61SKdrG9Is52dbeDW0TIRhYTQ2RPrgkSKhXiYGFlmiSHdBsB4ivYc2pNdzOclZ5ZFV5jge4yGejxTxrU0BfRurqZ3kOPOJXzgq1Iz2fG15nwgcfcF6yFhljq/Nkgn1cdlTascTGVAipBjqILkbMn6+zQkRLsuU9JtUbO9Ui7VTbHfl6L1Ln/26nhIQzPKWk+O7A6cQ4FWb81cPASUE39OP8XGUjwNeJhn5fZyxlTm5XDFlxSBarvufYkPLoPxBzSiAe+v4M/Fv8dEykSuMyA+MvTN/yTO9GF+rwIbIZ13LTzvLEBaYcfMtlGdnDb/YJvWhEante5rlFO5DuuDY0NLKgr76LkPY0dobNu1PR1OrgPH+S+vAb8Lw6f+3v+R/x/WV23AEmll2mai44ZRJLDgpNBMt+KKO/Ogh8riUfHOe3tQ+pOVWjeykGAUuKKyXtM6kvn/RUZUty7NbiuMNMreoqqOyt8wur4he4bqM6BVDU9A2JU1bsmkmaOX5Hz/6aRpmdgl1s+0LNqNHZZoNxAhnRU4/Gjh3VnHd6yTDdY4XcY1E8oFeMjeS4yxylPmkAmoNPhr+fF0iBTwu0zxkazVbJ1nljg9nG3LtGMbWnlb+0H6DRMndR1MfLx5QylHNdxTzmmA1tssIUVBlPf0IUPROM3QJdLmtJ3x+v0KIyNl0S64t07wj15ZmyLhpKoSA57NtMuqND+/GGiCFb90NCW/z8c6xCxaLS57kaFAip/V3B5S6Dx2FWRHEu6C2SiRT3bWaE2LA+wcijlxO2ThHoQxHmWeZOcworc1GheLb7ZyfbmYHbp0SATsKQ+6HjK1Liik9Hhimem/4TJuxEglaWWrNyn2AJCngpEhzDheg7ybciQXzsOS5mSc1mEoqo8Gn2Ob1oLDRIYNgynNyoXhWGRYmHgb866Fi1rzHfWj5M/8ZvbvnqPiII3F5yGWpfMVU63EZKXAxp4seSwrEu+1lQtiTxAPzLMmeJzow0+Ew0/JRcO00Wyv4M/eCn/X/b87LH/AD9236GLgRDwgkx35FpgSWtPH3PpGmhxDosAQiW5uhRXKZC2HIZWT67z2760Fy1QkaZ7BhQqECR3mPEQEzVih+JLuflR0XVUSSjMT7anTr1DiL8tROsRlSJ8ONs9lSJal1GPNwGpfmdUoIvjbTKUF0NP0a7RAyHroVkA6LZ4VhZyNb59kFx5vRK3MZziikZhWOyUVBiIlWHwioMYXW4elCRMl0KDjJNd/vfo8Oy1uusPSc+yfMKDgyhlWeMn7uBk8fAm5cuJNPTRC8ZmM1rXtnKch0Ws9q5/ki3mBExiQW2Gi5bf6E/3iV4X/Drptfz/UV23AERXbJXJ4zCVNWcUYp1QFBUmkBaE6LyLNJUrZMx9K5tQYf5ZiLIWhsxqu2YmEs3yhbFqd3lKf36GUNQUAUBKvxXY7rMm5ePaJuKu6baUrzLFrOj2/JsoHp0QapfJIeR4HrM4Y+I4QUbua84qGe0tmMq67gutf8dOP4I/6UBad83zxlosUYihVYmtQG2D8EfZDcdIYAzIxjbiwT7ViN7bvGGRpnDp6Xy+mWoypl6kxnibnlxtabMRapAiYfkMrTrae8fXOOlJ5HZ9co5dluZtzfL0fqddqkp1mPj5JNVwLlIcahMJbjSY2WnrLoECLStCWDTx6jbjRddkEx0ZHfO5ZAztaVDAFuuxk39gm7rOeVepHc23TEGJiFOVPyA6I9upxCzLCyPZzSbtq/4sdVRWa/zklumZsUgtcNqa3no8QGyTJzPFjNP7+eYcdhSQRu+8CDG8iEolTqwPtSQrDMklrqto/c9JZKKY5zeUgyhZT3s3UeIyTfju9hlCRX+xZTOlv2IVK7wC44GrlDCIkKChkzCmVYZO/yYZo8of8bP+NJ/T/RhRSKZiSj/DhgY+TeJanunVzTyRobezyWJY/w/RkKcaiCSp34aUowqrwkbzuNi3tPFDyLj8iL/wuP4oLTQhOAY39KiKkVd9sHrmzPtbyljBXLWNHjeKVeMsSWnT2nHEoMihzNTBmeTVIejht/hg2M0uw0Yy2UZzIGHjqflGOQDkiJrfAuBG/vjzMysfcanxSmPzxOrbmjrB/N2wUPVh9azEvjeVolf9azxT2LyY4iHzDZcBAEOadpu4LWZiOVOvKmC/wRf4qUipI5huywCrViYC1vD+BYHTWZ/i6BwJoHNmHLk/4UIwyZFHwwyYnkfOCnRCLC/DJ4VIz3m5GSymd4kpBjz/iLpM2n88nzdSQFUx35cKZ5Fs6TynAIBPert7T+a19fqQ1HoPBhYBuu2HLFSyxTccJ/G7/DSo/5NzJh8k+qhsIMLCYpr16OJ6iuL+iGLBkXvaRXEqU9KrOEIcOtobtd8nB9zNBn7OpJarn5tJkcVTsujm7TjGOxBSBYhesz7Jgns3fnO6/o+3wMYsvpvMGPgVDvTzVn7neoNJzk6YS3f8C2TnE7JAn3R/MHMuX5nbxLc5vxhmqG1FLbDRkv6gojI+/PNkyznirvDy2CEAXeaXa7CSEohEjvw3y+ZRIF0ngenb85BLoBHOUDR0EgVECqFLP9qCmxg+H6/phmyGlsxrorWRYtJ4v14T32XvF6s+S+LzAicFk29CEtojZINlbTh8SJc0EgBInFFQ3zcEQtd9wPn+F8zVnxHuW4EcyMIJOC9/oPaLjkFyU09prHxW/znn/C5VQxNQ2Z8mz7/HAirp1iniUp/ETrNOgOks6nPnkmJXNXsLGBG9fh8LRi9G/YBNg0aAyaxjnWTqSqS6a2bB88lpD86UJy5Wp+7v4EKRS/FX+XI53xhXvgjfyCmTzmMpzhiGxEjY+eISQPlpJJcZWNWCAbBfORdL61aeh/3Xk+C/fsxIa34af42OOGmhgDj8rv8Sx8jbkoOM0MnY987O/w0XHqz1HjRlM7yZtW8MftDZHAY3FMpSTvTww/yFb4kIgKtYNXnaUfK6d95PIszKhlzc/lawCKOCGn5E68oWfHXJ5z7Ff03tFtc1wMfCZf0rHjaXiflSxZ5Yqzsk3R30VLjILbZkLrNLnyzM3AQ5/zRTMhl5Hn0y2FdpwrT0Dwtql41eYsjOfZdEuu3pmfs92MYgw9DMBEO54diOspDmFXV9iHZA94GOeZ9UjPeNMZ7gdBF1JMeBVnPI8XGCEIMhk9J7GA8GjkgKfh/yocI5EHOng2wkU7H9m6tIEeZwolBesh0PjAZLyvYxyr4SBoPHgCrzrL3ZBMwOdlpFCR81IQx9ypnRMssshZEaidHEkVFRfy77K2X+BCSwgDPmz427QJfaU2nIhjcG+4HeNT35v+Y079GZMSSh15Mkkn3NOi5ahKJ/tmZH8V2YBSaehutKXIBo6rmlxbikkDQdLcLghWcfX2jM9vT5PhcuQ6ff+9j5mv1piyQ2UW1xa0D1N22yk/ffWEzZBz2+fUTqJlRIvUztvZBBzcCxmUgLkJQFr0xPjf9uyvANwPkqs2EqLhosyQoscod4AbAjxsl3y8mTM1lq8vHsiUoxqruU/vV9x0BY8nO54f3yDlnukmuW/mDE4xq6dM7o5SK/HkjuA0t9crnFdMpzV50ZOXPfmiJgZB5Te4scWWNRVvtwu2NiNTnr7PDxuc8wlbsrOGRZZiHwAGodgGyZ/cK9bWcxu2NKI5tCQ6VbP2rwjOIoUm18fIKLFEspj60EYKHpmUJBnC99nlG56HS55WGRdl4Gjkc8UINijuhykvW83FiCQSIoXtAbiQRBM2CtwYTd6HnAj4MMFGkbhvNinOMpUG7+vBJ+NjTMoyS8DjMRgKqfA+Z6keE/H03rJxggUVR/Gb2Bjox8U7jxk5Jo1xI3RessZQO8WbVh3ikfdV2NZ5PhOv+XT4NyiZMTGnKAxZVqEwfC085+nEkMmkMotRcOZOiKST9Hp04nejofURC5QQnBeaSqf5DqSF7LZPJ+kQIwbJQlUYIVi7gbVoWIY5T8UpRkoWWZLs5uoZRu6rQ9hY+KTusQROwymeFZ/IH/Pv+s8Rm/8Hf+c0UBrLvExy/Ye+RPh0aOycZqIt31qm9udkRD+92c3Y2FRpnBcDE2OZZgNGOaZFi9bJfzXLU7Dh4NQh+l2IyKaZYJ3mbT3lqtsjddJ7ZUeO4m0vWQ+RW3bc2U8J+imZTAKLG9fT0PFC/px1/znL/Bnvh29i0JQiwV7nJlG6Ox95sGGcASUjbSAtqBMtx3braPKVMFMpPuIXruZWvmUejih9ibQzLkpBLiNHlR+hqwIXk4y8dvKQTisQPA7vszRntKLB0XM7fEw7fP5rWoF/89dXasNJV4qXnmQXabMh46oLbKxACc1Mu5HzVVAYy6RsfmmGcb9eclvPiBF8lCmUa1SnmWJATRselz2nZzcIlcyU0jiyRY00Dt8UuC6jfZhydXXK1WbBP79acT8INkOkC5Eb13Ej7zgJx3xQVhQKZjoFKTG2qK46wY/ae07ElJnRaOGZZ8kIGiNkUo1x2cnkth1ytAgclw25sSgROBoX9Hcu6jSE3VnDVWeYmaS0izEeYg2clwxB09mMGAVSRmyXM/QZb+5WdE6zaCYUZmA+2zJtc4QK6LHFd/zohqWXLB8eaLuCuq242iZHu1EB6yX3Q86D1eMmGg/RwSnuV2CEYClKJjFjTcOtfDsmW+4I0SWZtJBszD2Vr9g5yZ3TycyISGy8MWsmFwmEONOeRdEeqN6Q2FzPujKZcbuCnZNcdQmsOdUpSG+mU0WhdWBC2vD3aZAznRahu15xP6SF9LxUY4bKHvyYNoW92762knZ4RC8sG7llTWAWZhQxo8eykRs8joYNRuQ86r9OoSRbl+T0Lxv4//q/wkdLxRyFphYPdGLLzr5lcG8x+oSL+AFVLJmLIi10WXpd+3ZfIAkE9q0sH6F2iZoggKVJ76cN8DC88wE9OMdNqGlFy7X8gkxU/LfmOzyuIoXKyKU+oGxCDHRje3J/aNrPV/og+XCqCGhCTJvEEH4HG36Xx9VAqW3qQMw36MyyXD4QvOTqbsXb7ZwwAl8zlWYtaqR/B8TB9Nl5xX1XUihHaQakjHTWsBty7vuCN21BLiMXbUWmPIu8JTf2MC/SIpKPwoZ0f0oKleTb3zBHPFf/BC1Scmi6cmzMKP132GbvMw8TTnSOkYKJFny51XrVeT6LN8zjlAuTNuz0HsAwEiz27dtCCeY5zHTkD/UxNhxjZPosI2nznmg4Lx258rQuVb6vWs3rNsnthxBpvOdGXdPGBybxiEVcccPPf4Nr8a//+gpuOBEfalp7x8v8Uwqm3Pk5E1dwaScHl76SgUxbjlf3qHFuAaC0pypTGb8fsCvlE/bFOFTRk5+smc9akOMJyAv8psK3OdvXJ9zfHvFQT3m9WbAecnxMjKT3jjwTHQhofHg0+j6aMSU0/f37Kui8FGTyiJmBi7KnGL0rkFqCQqSF7/O6GOXRgXysVBaxo3caH9PD13lFpRzHkx2TsuUPJjv+AFKLIRto2pLr2xN8lMyLlmO9YzapqaY1WnuUsSjteXJ2hbWafkjzp5fXZzQvn5Bpz7xIEunppEGpBAedTmvarmBncySRqeiJeyVMTBDNt11C/g+jzPPpJOKj5NOd5M7GlOced3T+gd4m0UBuVslRj6QXA7diw0N8gxAyZcQQ2LjXDKHmUl2gRPLY5NpS5D35mGV05AxaBrYPSz6rDbWDN51nCIEmpniAM10xHxff/eB+vxCUKgkJapcWif1nksk0hM9k+p8WiRM3BMFgYGpmdB6+6Dp2tLSiZS3uWYYjPuQcABcjEsEiE2NGzEi2do4b+3NcaNGjdLodviAtPYoie0ylVyg0gch9bCDCrs8pBsVUK44yiQtw3TsCcFlq5oaRa5YWu61NkmdPqqwXOuXHrDLNI7HAxQWdf4SR8O2FY5VbSp3QS51X1NbQBcmml/ReHLRQq3w/m3Gs8v4wS4EkiU+k9chNVxERPNWerOiRaiRc72eeXcmLXZUI3tMNVd5zAsydprOG1hlKbTmZbtHaI2XaONZdxat6giDFwPdB8mf3c5SAPzh1zMuG91fXvDeKhzpraIaMn2+WPFjFJ7sURbGMU450houRtU13daEEEyE5LypyVY35SPvfLUUwpGRhWOUaNZzShzT7AjBItJDMdKqCfEjzrc5HrroRdTNJGKG9n6gLgtqlZydFggSkdpTjWlEpReMTS650gr5/QjPGfAAcmee8Hl7z61KR/aavX/uGI9AoNSOE4W9sSIpxYHBveeO3FNkp/yj7JzydSD6adTyqdizKltOje6T02MFgB4N3KfzM5ANHqzucNfRdkvT24wk/RoluCioZ0Sc10UrcusI3BevPLmjqCbu6ou4LmiFPFZJyfO9ol3q0RXOQLRu1jwaOuKBYtxWtNVz3Ez7dSR5Xkd9bvTOh+SB53RsaJ3ndCq57xypTPJ0k41zrJFZGXJSjUc1TjiQASaQYvSdKeSZlh8lsghQOBq0883GT3aeHDtZg14ukaBsjsiezHTEIXry6PJAFZkWHD5KHdoIPghf3Saa8muyYVTVl0fGdyy9wXlF3Jd2QIWA8iaY2zl4xlB7a5Fu5GQa+kK/pRYuIkkxNmeZPsKGlHT6nBWRpWHGGFvpAGlCjw/pIPwVA+5RpclooIoIYEx0iRsHrzYKXzYQYBc8nSbX3zfm48IacwUv+zZ3j34SEZ2ncLUrkzPU5mShZ+TMqClY6Z2kSQPFtZ5M02GtyJcYTMTSOg7zdyIT0XyhDHhSbqJFICjImOg2C6zFNs/dQj5WJErDKNL8d/iF3Zs1Pmv/ll54Ro485yT7Cxo6fun9BjOGAunkmvsMTscSGyHpIRsu5UUk0MMqk9xDSAsEMeThdK/GuCrooEyHCyMBEW4QAPc79Oq9pneZuyHhRG7oguO0iNgYymVqetVO87UomOnKSuwPUU5LmKaV2dF6xHTKMCNjBYDKLzhIHUI0Kw0XRMss7zAilVcpR5VCYgeUkolSqehL1PByoGFXZ8mGfU7cVt/UUGxRnhUaKyDTviVFQFh153jMMGbotD+KcCGghKGOOJ4lJlJAYIVFCkI/m1j2FQQoOlIrhSxlPkXRA2XmHFpKVzsfWearOP3ZrrvwLTuIl5yzQo7gjGwURqSX87pA20Ylu7oNkAGqXIlcCMDV+JJ2nr5+bgtYXvOmS7aAND/xt2Wzg17zhSDFByoJMz+jt/d9glqWo8qecmW+MKrUlldScFcmhXOnkUwHY7tKwv+4Leqf5eLNkPWi+v7rjG08+x1pD2xVIGcnMgNYenQ8Uyy1CecImx21Lti/PaHcTPnnxhIe+YJoNVFnPomxYTbZpDuCTHLkZMrZ9QZWNNGr5jnxrg6IPqe1R6bQAwEgyiCkcrnYJx3GUwVmRPB6dT4vaZdVTKs9szMyZ5d1BOTaMvqLXD0eIhyXHk5qqaBmGjNamALW3TXo/joqOTDoam9H5RHs+KhsKY1kt79HG8ej0mken10jlE9PNGtomGeM+vjljazNskDy0JZlO8Qp70sLgU8tKEjnK3EjATiFZrZd8XisaF1loQxme8Gf8hLf1v0SKCfPifSZmytI8RQnDuX/CkahYhIpTTsY7YPSPjLOQFsvPO8sim9FZg1HusICsqvowQ5IiIohjjPQ7c+oiK/hB/91xWC4Oi0lkL3lOxr0fhxskMnlwQsEkzNBC8OBT/MVN73hDMqOmKGJwIiUxHjFnJUoylRRuD9bxz/3/i8FveM7vc9qdcGEqzkuZNiqZQVgyzZ/Q2Bsqc0KhFlTiiHlY4nEUZkogYGKORLIKM+aZGiM5khPdC4CI68TBBH1eCoaxjaYFnBdJMbbM3GFDmJp0X/UuCT2u+5IuSN60hqtu316CIUQenMfGhM8HyIQil/uAwySkWGaKUsF3lvBe0ZJLRS4TS7BpS7xXTINA5wOzxQOT2Q47GIY+xznFrqlwfU4z5PROMy9aFpMdbVdwtT5CycCT8zcUk4YiCrRx9H1O5zQ2qBGOKnm5WaBk5GyyZTV/oO+T96y2qQoXwGkhMHJ6eB9zKZiOqrLWRzof+VF/zyvxC56Hb/C9yZwhwBdthyPyOCuYZ4KJETzX+XjvpdZm51MIXB4NlZhTxpxcyVQFhZrcGy59xUQnEraRkcZJNlbQCIEQ2QGf5GPiFOy9f7kMOAQ7F9nawDq2rOU9u+71X3eR/a96/do2nFTZVMQYGNwDIfxNqhuP8x2tfmDClEdZTjUGeYWY2l9aeW7rKS/rKUYGVkUq1RunaLyktSadbLRndXJLDBI7mBTF3OWITUAPhugV7e2CV188ZnCGKk/qryIbyMyAdZquz+md4a6d0I1zgtoplpllMbZ19rOi7diC+LyWfNb02JAz0eYgJpDARZk2y5vOcDcINjZy3VvmWjM36aO47crDPEfJwLov+LQucUGgx9PkyXbB1FhskONAXNJ4iQuCP7mf0Hk4LwOrPH2NEpFtX3C1naNk4Hy+ZjatMVnaiLN8IC96gpdMJw39YHh1d8JNW+E7McIQE3W785KfbQ0PQ+TDGTybdEkpN1YdapSAzkxykXyt/YhsUrKOr7hr/gyA3FyQqxkn8oJMSgokUhhsCDyE4eBVkIix5pGHobcPEus0MUpu6ilv2oqJdiNh4F17Z0+RfiYDl2Uy6LZjm7J2KRL5YZA0XvByiLwNv6CUC064PEAy7X6wH8FIydxPsDh2Izp+FecUMpkxK52o2bdDOon/XfUPkVokcrCCUqWAwD5E2uCpRUdr73B+TSskkYBShsicaZzwkThDj2To1AYcB9DiHQtuCCnK+I/4GXfuM77R/wG/VS2oNHwwTbOrk2KgGv1ipbb4IPBRshsyPtlN6LzkwUqGAD/Z9vxcfcwqPOKpTKKDfPx8UqBeailNdcQFaHx6fedFMmUujE2bfNHx/mSL84qrh6Pk77KGsugShNZYhj7n7mGB9YpmyNNcU6WDVu8MX9yekmvHsqwxxuKspt1N2O0mNF3Jri8SZupLNJF9Uq0LipuHJbsh56qtuO8z/vWt5sE6TnPFMtvTzNMBZD93+XTYHT7b8/g+S5EaW6WCb88LQoT1ELnqEvSzjwFFeo+kEKmFGSN2lPT3wrL1lkJqvpEvKFRqSVajSjHdi4KPd3b8GeoQIJnJyM4Kdi5VqimqPcWf9yGgkQmmWv4ua/+Sxt4wjGKrr/L1a9twIu6XciL+ppcPLR5LEAElYGLg24uO47znrNpRZT2TvOPZ6gbvJdZrBqepbUahshRpq306Od2k9lCeDSiVZhmm7BHKE73CW03dFzivOJ5tyIyl7Qo29ZSmz7nvSlpnUoRAEPRBjotPSshMkta0iO3hhLUblU2ji712ktdtMh5+beZYZpajTJAriRKKuyEtbjedSvG4Q0Hr4HEVeVz1NC4RFJSInOaWTHmuupxP65ypDixMUrYsMztuaokwnclEuk5JowPOS267aqy2FMV2zuA1ndfMs47L41syYynKjrJqMNpxYQ132zmvt3NqZ9jY5LnZuUQe3jlF48fTrE6CiPcmKdfkL9bw0m2ZkvMt8Rwbn/JQfh9PYDvGSi9DRS7FeNoMtMFzLW/oRXtAwnyfv8O35jkXZYIwHkLpoqAPaQO87kvWwwQtEskhDbg5BLFlKtA6yYNNFcKXKRU+wrOi4MT9n0avRNpgGh/ofPLEBNI8ZioNTRDcig6FZqEylkaOJtC0QbZejB4fdfBhQFrUtjbypu/5c/HH2NBQ6mNKfYyRFUoYSubkMceMj6WPke3g8cQxlyWFvhmREib7kUrwAe/xdfkBlVIMITIXgieTjlwG8hEBtBsyHvqc2mkerCaObDglI8f5GP0scy77bzEzsMpTZPU+yO606McWXNrU77uCX+zKwz1XasfbtuTjXcV7k4xZ0SJFOKhJuz6n63NcSABb5xXd6B1TI3Zn8IrWGV7XU162OefFwO9Ntom80UxwTvHnby/5+bai8YKNhbmB31rWTLSjMpZMOT59OOLzpkgxDcYdGHYRuB0cayvGNte72AwrHK1ssAxc+59T96+4m/wex/bbTIxgplIbc2PTfK4NnoZhJEDIsaWYTkX3fWoL/5X9Z/T2FVLO+L/L/5lKy7Thy/3hIbIwkeej+vCsSKmt+7Vk36pufSJbQ2oJLoxmGhU+Vmin2IqrX3nd/S91feVEAz48cMwlq3DEMksGKD1G0O4RFSEkSaRRjtPjW6QKXJ5eEYLCWk3b5bR9wV09QcnI6eyBIu8xxUB2tCU6SfQKZRyTvKOzGXfbOT5KHrqC2qXcmD1S5tm0JkTBZjQ6Vgf+mTgodq66nAC8P4VnsWBmPHPjWBp4WoWE0PeKh3GD0iLh3R9XqbSvXTp1fW2WTJ/7q1CBVZZ65SdFR6EdZyNnCtLimCqMdDPfDzl9kDytas6m6WHV0if6QkhD3au2pHazcQYjmZoJ675kYgYezR7SSXQEey4nNUoGtl1B7Y6wUXDdB16Iaxb9BaeFokbBOHvZo4YSrDLgSLLRXEo+NFMEAh/nxAg7H3hwDjkuy1pI3osXxAjreEEvBnIUd31klSkGn5zkccT95NKzHGOnZ1rSBcl6kGy95KaLdAEKKdFScz84XsRbGrHjLr7AhY5MTTEUXITnXKg5NsaDKkuLZKzMYBz6p2F8LhSXIQkD2uixITDTin6UVn8402mGM5Ko3/QDu9hj0GgEjsjz+C0QoFB4PJ/xV9z3n9CbHUEF8lhg3QKL40fun9Lb1yzL73Ahv8YyzDnWE3IFc5NhRpl0JlPrbKb9SEl2IzRzSu0kLxvB7eAO3pD9TMlIOC/Tqfu8GCi1O9z7clR5aRGY5QmrVBhLpi1Po+C7Y2tTjoKZx31BZzN8ENzW06QcK1Ms+a4rGbwi145Mu0MgoZKBeZHo6ze7GbXNOMo7zsqGTDnq8WcqkT73ibFclAONT8N5LVNwXIyCXDlihOUon1cikivHKiimuqBxih9vNK/btAmlnBrJigoJGLlEAHfhknW5Q3vF575m4QomWlOoyFmRYJs/2wbeyFec+0s+yIuRDJAgrjmaZTiiz7/JtZAcmedJjTZErjuISJ5NJOeFY248qzylmW5sshZEUgWrRIrX6H16nfuqdl+dKQELUXLOB6jccPV/pArnP7wEUo4xuWHHX2egY2KWTosuLaaf7EoKlROiINOOZsh56AoWRcfJ6Q152SUSMvD25Tkv71ZpAY6SjHc9/xgE0ckDYWBzc8S/ffWUnTWU4zxj/7VzM3A6SW3BfmR1reuK+9GwOdEWF1IrbesUP35QbKynCY4+ej6qSmbLBBr1Y8bNba/pvjTfW+Wex1WLEQEl44FCLUWkH0PO+iCxMXHTfBQ4n6TehXY89AXXXTm2shIPyu4jF8ZFubeGOhSHPB8fUsT2TCf5qFbvpqG90/zrV0/pvOSs7Jhnw4HvJkXkKO8pteLvnuZs7QVSwNZKbnrBT+uWXCi+OcsoVGRuJFIsaL2ni57Be7qgDif+CHwmXnPD57iQIikm5pTvhh9SSsWpqAhUhBi5G1wK4PKSQaR8FRcUr9qKl02WElZVcvPPJ+nzflzJsZpLaJqJ1tAsacOMUlS0quG1+zFrd4ctOtr4DBOz1HfHcKQTyiWSHvC0GTlqBq7VFR5LFWfoqFkOcybCUCrFVCdA480wMOBZiw2tbJjHJctYYZCciGmaIUWPJ7AQ55DBEHa86v6U0hwz47epKPj75n9CZYJKSbJRnrvKU5//qkv3/EkeeFQMZGNFu1c31k7z8VZxN3jufM9G7PggnnBephiHcgThTvXekJoo67ddwSd1mk9Mdap0ll1JpR1HecfRSGWvyvaX5phSJipzM+Rs+wI7Cmq09BzPtkjpDxSApq54e3+M0Y6jxQMmsywXD4Sg2Gyn3DdTrJfcNtOkkMyS2XlrDXeDRovI3PikXlQeLSLrvmDdF2TKk8v0u5QmtRFDFJRKc5ynqn5PZPbx3bqUZiuCSuc8CnnyY4WIEYLNAI1MtO1cRr69MHwjfmNUmKZZ7JsuUHuLQCS0TfiI75qvYaSkVOlefOl2NKJB1GeAZqLTLLR2ih8/CLYjHHYrN3wYnvHBJKN2kdfdQCRiRJofNtHS0qNQLJkDT9nlV3T2lhC2/7vX2v/S129kwzH6lBgdzm/46yoohCiQSFoGPm8FldIYqZCM8wqvmBcNl8c3CBHp2xLbZ1TTBp0PzOdbnsvArql4tVkewtZ8kAxNidpYolMEn8rfs7Jlpi2dT7iP88mOo6oewYNhRJwncsFUp9/FB8HrtqLz4l2K4Hi0P8+zEQHP4b/d9uIgrU48rWQa+xqaj+b+EMpmg+RH6wVXnRqrny7NqfIeGyRv2xIX4XHVshINW5tx26eBaIhJqPDepGae94kj1las+4I3Xc7GKn78kDxcD8UAAILySURBVHrM/8OF4+uLeyZ5z6xscS710Xd9Tr2dsbaKLlRUXYEbW4NGRGbG4aOgcXJsHb1TXz0yBVIw8qAEqxweFYK7wXDdSxrveMsDnWh4439K77fkaoaRFZGADwMuJCOhGREoe15Y+v8jIkSG8YScZLkxCt50hl9s0+D6URHIVOQ4s2QyHBIjzwrF04li8Ia1PaP38DA8p/GBK9/wVr1lEiY8MSmvZGbS6b/3SRDhg8RFTSTnmZ8RSQFzjkiN5SbusN7Sh448FqyYUwnDUp4mPwxJonwfGz4Vf0WIDi3HkLVoiQQqtWKpn7IMKx6rGaVK6qRcQalSdPl+o/AhmRABFiZ5tW77jM1oRN4PnT+apzaNjTk+5hxlltO8SwQI+U5wAVDbjM2Q8brN+PN1EgvskZDHWU6lC07yCaf5gplxXFSp+t1XQ0dVzaRok5DFmbQ4jwSAvSnzydkV85N75if3nD9/mf7+MUup31a4PmO9mXHTJG/NUdEe0FVD0Hy2K/nZNm28C5OYZln/TvYO7+C6hfLMxzC22iUF3hc1/Lx/oCJnKjW74PhMfQbA0+4ZU2mYqDSTe9M5fio/xoScJ+05hdQsR+PnMksHxjAaNDMZeVwqhqD4oh24ZYeMAhUVk5BRqpxKC76bzVFizkzHgz1iGCX3WqRZYe5z+vHQHUZKwZMybYCdTxulc4GAYScaXstrfLRkckrUgXb4P9iGkzabNX8T5EKMHW/FZ+RyyjKsqHzJsZ0hhaAeNfouKFqb4YJi2+cIAV8/f8ni6IEQUszyti/50XqJGfv+8yFluwQvUdqhsuQ1mZiBQlmOJzvybDhIj/dctWbI+HQ3o3WK2knsOOirvwRkFcBxnsCGMxOpVGTrJC8aydZGPh02aCS/NZ2yzCKPy+TnmZuOyagA29qM1mmuOsXrNlBpxWpE/e+sIpOR96Y7KjMcWhE+SDbjpraxSZV0mhsmxlJbQzPKXCF5f765kEgElfK0zqBVILeJPr2Y7JgWLdO8Z/CadVtSO8N1l/O6S20iLfTIhUppjWbs50vkuxZSl/xG708VR3lgogXnpWRrc142htov6HXLTqYZzRB2HOvnPBfPEwJHSGyM/DvxRzz0n/Nd83/mB5MjVvm4MY/+K0itkxgTJl+icJEUl+BgY5NirFRpeO5Hhdq+ntMSFplgjuIszvgOM/SoMAxj33xwCWLZ+YiR6fRrhGCSp0998Om10oOLnnksqcQxSqaKRIwS2xBBjo70ImZMxBE191zXf0zEYfQplTllJk+59I+pRMZUJ0Xbxkb8kOS5pVNUOsFWMplaYHL0CNVO0/gkgqhU5HTMk3k63RyUnQCD1/RO0zjDy2aCCymWXcKYNyMJwNdmOi1sgQPNQJDSQa96TTfSHfTYFRDiXes7047H8zVV1vPo0RU6H3ha9gj97vA51CW7+zlSBeYn90jjcH2W1JI2w0ZJhic3Fh8k1+2EB2vwMW3COxd52aZ7rVSKXMFFCTMTqL2k9pIQM/wuDf73h7JCCZ6Z2cHQa6IkjyW9aPlz/iXO9Xwj/AHvs2SuNb8vvo4E9Dh32S/6970Y4aKRF0OLQnKZ5+QyzdjKkHEr77gKn1DJI/r+A6Yi52llKBRMTWSmPbWX3PaK2sGdTaihY1FxxoRVkQj5kX38RLr/EtnE0AfNK17yYvdPAZByNubVfHWv38iGE8LwK33/dfNnGLWkyS6YqhXP/NfZxzfDAkiKtUJ5LiY7ilH2HPzIOfOKWd7y/eM7jPRcHt9SlS2z4wfyaYNQHqkDpur4pnYpYM0pQlA0bclDPaUZctZtiR1bUGkwLsdEQj1GEaTIAS3TzSMFfNEYPq8Fd4Pnja/RSOaioJKKoyzBBpeZZWoskshm5IK5kPw3H84GnlaSmUky1rkIPK48hXacTjdkxrFpKpohY5l3fEeGUbSQBperokVLz6NJj/nSLGgv63ZBcdVWaRNdL2h8Sh397uqa3CR3eJX1lGZgcJpFXjLPyuT6HjQ+CDKV2n/7RMeUyKnoA9z2aqzm0rA5UyBCkuw+rtKCthy+ziAib1zNvbpn6mfJC0HyKqgYWcZzYhaYxvLd4N2rd1lBJFXSPOuZ9Dm5Ugfsig3wi21g6989fJlI7a7OBz7hDZaBMlaYaFiJKcdGk6n9opJgiTYkr8WA506uueM1M1Z8xJMUQaySqXOqFSZUaJE2JfUl/8bHdc+9qLkUc85LzcQqGJ6xESvq7Jre3mLdNQ/uGl3lHMkPMXLMTQmRn4Q3PHDDb9lv8cHUJHn3nugwGgUhtVI/mDYcFS2ltiyqGqUCYSQlb7uSZsi46yretAW1l7xq0me1GOOp97ODmQl8c94x0Y7L6RajUqqtH0Uzh3tq7B5shpzBKxpriLHChjT8P5tseX+5JVvsUFWH0J7uzYrmbkG9nXB1t8IoR5YPmKJPfrkh42Sx5uzoDus022ZCPwp3bgdF7wVaMjr/NaWC96aOQqUwPiMiL9uC16065AMx/l4+wk+aHS/UFxyFU1YxVaqreEwgMhdHOO04Z/ZLc649acLHNLzvQqBSikkUND4wkOaQ10Py9ORScqEqpi5nygwX0/2zixmT/gQf5Jiumnw56VkSnOUGH/XBO9V5eNkkDFEz+roymSIkYDSSuqcw/R+4tj+n6T/9ldbd/xKXiPE/XYUIof/6JQqpLRZj9zd+UbPia1RqxdPwEQpJL5Jr/CN9zHkpOcoC54Vlng18dHxDYQbKokNrR9fnh1A1gCwbWJ3cYcqOYplu/NAbfG/oHma8/uIx/WDoXXJA33clO5sdpL6Z8gfp9U8fltx0hrnxzEzCUMzG0+MwViN/ua74onmX/lhIySqXoyQyfEkSuW9HvUOGqHET3c9jAIwMVCrNUd47fUtZtThrDhHS1hlCEIQg6Z3h0/UxD0PGRVVzVDZM847FPGWz7F3er+5W3HclXzQVn9eGs8Lzw9UdUkRe7GZ0XvG4qjkumzFoTXPfF/z5ekLt0mzNxUQVuCgHdlbxttOH3ym97qQUe9HAZ12bBrSjE3uiUoCVj/Fw0oR3BIAQofGppbPQhplJRtrvH23QItCP3ovuy5HE48+Q46zrejTZfl7D2z7NU2rRoaOiIicQ+YX8CRv3hhPzPpf+MQZNJTUhRobocSPQEhKW3hNwwmPHBNcylijkIUFUIzFocqFYZXr0eKTN9x2DTPBF7dl5x8fqU+p4n6K2Q8uj7Ft8LTxPUvHR61L7xHY7LwyneWRqIqvcUinPoyoR0+dFk0LHjE3BYyOS31rDq5tTdkPOm7ZiMxg+qQ1/uelS3PFUU40GzkoF5tnAzAzM8p7T+TqlwU4atHaYMsWaCxFBBvxg6HcV1hqausJ5RYySEARF3lNWLUXZsnjvNTIfk2yD4PWffoOffv4sVddjXPjjZy/IJi0qs0gdcF2GbQp26zmfvnxM6wybPmcIirddzv2gMSKSqUilAk8m9YGxF6KgduZQ2fuYuGTrQTMEwctGsh4CH/s7fu7/1XivVlTyiN+X3+MklyyzyMwEtlayHgQuwM6le6FUchRqQKVToN3OvdvQUnUcEkHcWza0KBT5mJjkxyf7kSlY5fJgIoakZExBdqmKunYdr+UbjsOK52aOFmOkBpGXtuZe3mNihiHjSrzgVf3/+Ruvub/qJURGbs4QSJr+Y/Gf+rrfjGgg/s0DgLQ6YqmfchROeWZmiUNkklx1piOl9iyN47xsmGY9i8mOLBvwPhGchyE7SJ33qpjJpEGIQHO7QK6neKvx1nB7c8y/fvGM1o1cMBGZGsvUDMzznuNJ6oV2NhtNj5KtExxlkbOiJdeORdHivOJtPcVHxad15I/Dx3wzvsd3l4mXtsr6NKMZN7FNZ1hbwdKkKF0zylcBbvuM2iWpcYrSTh4TLd8N9/OyRWtP1xSEViCExHmFkoFHkx2rIhkfX2yWnJSJFpBlA8vjNUJ55vMtzmp+6PToaUlS43bI+Xw7p/GS666k8fqQZaJE5LxwDCMEMUSRFFGjMW1f9r9uHTZGLgvDPEu+gY3YcSve8Kb5YwCOyq8zjSu+Ez/kSaVT/so4AwsxbRrpFCfZesfaBZQoeFblKBnYDMnz9KO15E3f891Fwe+uthgRKMaq7jhPasAPpvukVoONKaZ7Lzf97f4HI/o98hAcRkqmSqIko4cKWpcEAwlgmVptbztLFz1v5Q296HjkH3EkS/rgU35MjLQ+VQ9T88sMrlJFnk4UW6u46Y6woqeJtwzugbV6ySsxxYSMua8ObDkpksdnLZPBc2Ekg4g89AW9c4eoCqU9SjvcYOi6gq7PuWsrHmzGTZdxNyTRxntVwTJ7Jyd+NNlSZT1V2R4iKJT2hCCpdxNCFJzoG+SkRZcdetpCFFSDTllSTUGwmvs3J2x3s+SVGrJEL7ibozKLmbYI4zh+/JbvT5MYR6qA0o58uUUaR/SKGPboJEFRdjy/eIW1hl09obUZ3K8YgjxQtyvlKXViD26GgmH0W3UjYidTHsI7o+pV5/gs3hBE4Kn+PnHkP5exYmkkM7OvzlOll8vUpuxHll7rA61PlUY2ysYXI1bowYrxa8aYifGAEogpohvYiRorBl6GAdf2zOIRq3CEQTJVaTl+6Tfs5JbHPOLv5h8c2H7AOE8UHPkSGQSOgBW/icC1v94V40Cu5ihh/rNf95uZ4fwKiXMhDNzZz8BA45cokfwMpUrtm31FsB9WbpsJps+ZVA150WOMZRZqmrbkbjtDK493ir4reHg7p+5KMuXIx4Hz71y8OMhAAaZVQ1F2hxwN6/RoTBPMjcNHWOUD06xHj5gOOaqDiih5fyoQuw95XAmeVP14Eku9ZhNT/3WjFMIqMhWYjwow4ECIdUEw0ZG5sYeKxwXJ6/sVah2Yl+lEW7clm646vH4fUqLiEBQTbTkq2gNh2ntFW5cIGZPXZlajtEMal0La2py+K7BepYC5IXk2rJDosTdxNJKY9yZPH9Pmo0RkaQKlEkCqdE6LmD4zKXhkz7gfTvlJeUknGhqxHaN900J8UkSWWSBE6H2ibP/lBu5iQy1rWtkw698HUiT38TQpli7KnNZpbBy4aguUjOTyS/fIWDHupb5ufN3DSJKe6TBmr6QZyF5xlJRb4UAaDvEd9NNHwXsTRUDR+ccjRy61XBqnWVtNISWPK0GuIpkMB08QpEPTXtXk4ylbu8LrbxN0ikIYoicQaEmqpF70Kc6aRyxNxMg0awkxoWQCgmbIEdsZ3Uid2A0Zr5vEG7uY1DzOtuQysMwS4ftspKjPqxSprr8EiLXWMFhD3e1pXqBEYBgyMqvpujnxdgmAHMGYrssIQTFZbJkdP6S2dp+6DN3DDCECelcdeIcmH/BO4/qM4CWZT0KHzctHbB9m1E3Fpq2Y5B0X52/Jix6lPOWQsW4raqcTd2xcB1pnkMQvATo9U2NHqGuGjYLOJ+PkWaGZuEcMIdJ6jxaCQiVphI2R606wHauYBGtNytGJFlQI3rSOTbAICrSUY5Bamu30Poly1m6gZqATHbXYYcjIYxoFJEZeYO0/YdN/xrL4gCU/wCApRiXiWXGEkUeHlmBiBLoUtjfGW5/mmlOmvOksn4iXdGPC7H/Nqx7eIv9rbDi/yqVUxVyfozC8imvKISfEKZVOw/rOR87Lgo9sUqZwtyJTgd86vmU13SSOUtEzbGZ8sV2QSc+sbJlQs1hsODm9pa1LdvUUKT2zaY1SjnLSIrXD28Rma5uS64clzZBx3U6S6ZPENpIi0jpDsAIfq0S+HeXI31o0fHOeNq8A7KzhRw9F0uerkaeVe7696BAk3IqBg1M+lwE7Dm4hpWq+bMo0wB03xePMMTOWo7zjZLpNi6jT9FHzuq24GxSX5cAZyb1tXQJ2/uL2lCEoPji6ZTV/oKxaymmNkBFpPLls+fDZ5ykFdDul6UrWTcVVm7JMrvtsFChIep9Ud7mKNC6JFpLrPJKrgB7Nly4oOglzI/gGK2w4xsZIjJGLUjMzaa51UqTNbBhpBmtryPsZWqRWwrMprIouUYjH3BMpIo0yfLar+NlWJxTLaJCbaTk+mJF5ljYWG/btkQSjLNSX25pp8/Mxna4bn+jOnU9CETvOTrSIFIpxEUpKRuBwYEktweTEh9RaNCK1wU6KNiGRRkXiRZmPIpSUmrqzGWsrqG3KqrHRs6BEjgTt615ylMEqC2Qq3Sc4PSJd5myGjPVgUvR3kQQpz06uKIuOr6svbaBeEoKi69NGsW5TS2zdVqyHnGXWcz5fk2dJ9ZnlA/mkQRrH5uaUt9enh98X0nxQisizxy+ZX1yjxkRdosC3OcFq+s2EoS6QJlVhwUv6LkdpRd5lKOMSF9FpsmzgLO+xzvDFq0ukiEzKBJddFC0upAp+PWS/RPKwIxGjUJ6JsSN6ByDFXAcSa24IgutO0ndhlCyP7UsXsDGQ+zScb3zgwVsKocilQUuYaYUJSdBRW1jbwGd+jY6KJzp1ZB7JnEBO4yds/Zwey1o+IJFcxhMKoTjlmF3+PRax4qLIyGRSvkkijU+HmM7HJFoJgYeQRhRuyDFjyzWtJ4JpnNPLFfe/4ijjV732ib7/uesrteEIURwIujrqQ+JnCucCJeRhoHY/vEtvLEJCnYdx0RciMilbni/uiTGlQzqnWK3uknS66Fmc3gEQx+9JJ/wJm82Mh3rKQ1cmPlkUhxu58Xv4njgs/vuTfuMVNgjuB03tBCe553HVoWVgZpIhLMYk3UzcJUUfEgBzajwXk+1IBEgxAx8/LPnFrmCqA+dlTy4D87E1tx5yamuYmjHhlCT9FAK+fXT7S6a6ad5RFj1SBo6KlsGnqu1+O+fV3QnNkJEpx7xsD6meWvk0DzAWKUJC9ww5d0NKVX3ZBO7swGWRc16kRXui02L9WS0AxSpPPe43reCLdiCMs8JIPGTGqK5iCApQLDN5yA5KlR+APAxu5ya1TvZxDTEKbruSu8GwzBz/4FFHHyTrweDCnpoM60HwdqPYucCNa6lFx1vxOZaOOWeUsaKKFROyVMUQMEhWJsfI5EzfxI636i1X9ifM9DnfDr+Vkmhlgj4WKokFMsloVOYgYZ4bfzhxl9qmDdgn8UX6DRnvo9Qu6X2aF7zhPrV9xAkTpai958YODD5nmSmWMnI53jOlGTDaIWVEiJA2iuUGqR2mSAeZflcyDBlDl9N2Bdtmwk9vT/FRcFY25NqxHnKu2iIt2GVLnvcUZXtQdAIUk4YTd4cbDzFCxBQvMIb5DbuKjAY9rxEqoJYNBDCbGt9n+CbHNgXBadqmJMsGlHHoScvqvVccOYnvcoY2p36YcbOdp5gNEdDK0zuDDYrOJ5SVFnuMURINaJG6DZlybEhtxBTdnpJD/+iu5LPaUip1kDgfZ0le7YPARcnGJhNnJiVHIiPEyHXvEEIwVZK5SZ9agJHI7dDjPTwzqRtjZGRrFQ+DYusyGPUrx5lhZgRPpcLIgts+8nnXUQnNRCcFG7ybSe6vfUReHS3CC74INZ1vKGLFLFbYcESZnTO4B5y//zWuyr/e6yu14WT6mM5e8Xr4nDJ7xjeypyxMykMpVByljYJlFrgs+1FFs6HIBo5mG4qyww4ZTV1RtyWdNYmG65Ne/vpmhbo74uhozdHlFcFquqakawp+9uIpt13FbZ+zsZpCBiY6nZQ+nG5RInDXVtTOHAxywHgK1tT1hNopfrQO/Dy+5e+4Sz6YBhZlw0fzNQCtM3Re88e3M/7FTeTDqeYPT7ZMs4HjyQ49ViM+So7ynjOrKdRoyJOBaTagZSIHzIxmWaSeOyTKrpTvKLvGWLRxSOXR2lN6SVW2eK9Zb2Y0Q+rvv24SIv5oKJBfkrEflw2TvCMiWI4Lko+CnTXUriTrck5ywVHuGXyqPhsvuO4tQ/RIURBJlYERkl20XMlrGrHluv8pzte8X/49PugeIzFMdSrFWy9HNVCSJCd1UKRSSSFopE8GWRm4mOxYFSoZ/ca2mR2jjPfx0+sxLG7nJPdDxRAqtvZ4bIGkn33jet7KO5ZhzrNsghD7zPmEpK9ixjfje3w/+wCAIFLrZB8Dba3HCc9GrLkLX3Asn/KPJh+wygPLLCH/e6943Uy57zN+tjW4A5Hh3bD5ure85JYylnygT8ZTb9rIIpoQNVLAbS9QQrGa7DhabFie3pLPd6jcHgb0QKos7ub0TcHHn77Hp+vjX4oTKLUjV473Tt8yndas7o64aKbMi4ZqklrU0/M75ChEACgvbljlFr8rqV+e4sfhfAyCzd0Rb9+cURYdszdb8mnD/MOXCOOIQUIQhwNedfzA4nkCT0arcU2BrZOnjiAIQVFUHd98/2OsNdzeHrPrSjZ9ztYamrESlgIYN779YfD5tOaoqnk/73k2XxOCZDMUdE5zUeaA4bYPfNG3nOiCZ5MEx6ydog8RENiQhB6ZTPfw1lssgS9CQ+86lmHBXBQoIfhAnVDIZMitVHp/BekAcTOkdeJcTw6bSOcjS5M6Hatc8KQqDjDZzifLRZLiwzKTtE5gbYGP4SBk6UXHjjVCyJRQCsSYgh8F+lcaa/zNLoGS8//Nr/pKbThKZlxUv8d5eEpFQT5+2K/bMD6YKWHvm3PFyVHLoui4PLkmL/pDvOzt7TEvHo6preG2z6mU51snV0yKdjwFpkGlbQqGpuDm5oS6K3jbTNlaw+Oq5ntVTWkGplVzwOnEKJiNFcLgNNZrOqfZDHnCsbs0ezgtJIW94NnEs8i7lHpYjKmH7QRjA2eFZ+s0Z0WS9e7d2UoGMuXQMhnekqlMjkNvCW2FkR4bFHFs/WTGYoylnCQlXVuXqQ8/ZLRdQQgK5xTGWBaLTXqvjMU7xdnynm/6JAcfbPqd3mzntM7ws/UxjVfMRnd5jEm+WWrHk8qyyiWDTyiZVOHEcaCtGYJimcFRFjBCUOn/X3t/9mRZdt1pYt+eznwnn2PMyAFIgABBsthV1lXVLVnrQS/9qnf9mzLJTE+t7jaWmiyCIEEiEznE6OHjHc+4Bz3sc69HkiBYHIRimeWGhXkiwqd77jl77bXWb30/zeA158Mzeu95p5/SmIa5n8KovNmTtH+1GWhC1IQBBwXYvJ/QWj3OtsQT5qaPdtKJizNJrdPctvHhm5gBPZ56C+VYeMl59gAhjT4lEXhau4zGPY6ol1EEEUtsMVvzQVFoTz72LPb9nJ1N6V06glg9rZuy5hEVZhyCFbypUy5FchhKXPaSd42j9Z5t6NiTvvZ4nykVlujZkljD53JGlj4MftY2KqfcASvjSMqGZL4FERDa4XYZzfUCZzV9ndF3yYgm0gcCcaIc87SlTDounr6jenzNk6pBTnqEBoyI8qksASlBa1AakjkhSdHeM+93YC10LVjHyepX+J0kdAa3y7B1xvWf/yD2Px9dY6qGZLEhPV7huoRhU+AHTbOq6PuEq5sTVk1xAIxO0pYnp1dI6UnTLjISnUKKQO9KtlYeSqJGwPnIe0u1pR0StIxzPHakrUerZ89EC84y+GMVP2c/p7WxarQlj8KMVEVfpHhwiWXx5ZDSe0+iHspalRYjdQM2Q7SGzlUYy7BReLBIYl8okfuKgD+U+1IZ+WnN6Nh62Q6sQ0tBQqU0nfe0oxArQaGF5EfyMYl8wsY6bqlpRE033P6TLWH++Svg/Oof/Kx/VQHHuhanh2jMxcCrYWAQltfiV/Ruyw/Fv+W5mZCo2ITdn8hDEPRjk1JKzzRpmSYt5+WWVA+cLu5J0o6srDFZz+Z2zutvnqG1pSx2TKoN58c3AGhjMckQMTghSkvX6yldb7jaTg+o84iqSflyk4yzC/Gm/7iKyq2TLNrn7i0UhAhkpiczPX+oLZ/PY6NTS8e6z/jfrs7oveDHsy1n+Q4XJJm2OC9QwtM6zS9XExon+OGkieWUNAZaqXycQfKS++WcbZcdTOr216ezhm1TIERgUuxIk4E07Sj0KKoY4YoAnZf85Srnm60jkYZMFUwMvCjj9viLpYolBqK17iLRvKiilPSn830fIwaMaRIbrp44NOiD5Imd4cIsokV8BLRqEa/hT2aGwRtu2ggIvXE11/Kap0OFC3EORI19k30jftUnvK0LahcdP+OAX4oSEUOiRol2Ij3Wx43lQwl36wS1jZyqzeDJlOR5GbPqVHq0/O50wH6SQBLtg8+y6EcvUQghx5JrFFQPYezTCdAjkv4oiaKDRJpD38iPsz+XNm4YZchI0d8BgA5+P/cTyFRki1mr6bYFQnpcl2B7Q72uePv+PB6Sih1aOS7mS06qDdPJlunRPSbrSecbVN5hnm1hkkFeEYqS4D3CDjGY7HbQ99A38RedpIjZjKANfjoHqQ7XRfQdwg7I22vk+xXDr3N+/sUP6Jzm3wTB/NE1MhlQ6cCwKVi+O8U7iZDxUPf08Vs+0jYKCsY+4i9ffYRHcDFZkYxur4WJw8rtSLuY6BgTISoQ39clG6uZmoFHxXaEvnLI7Dzx8JSph8Pk4AWXjWDZ+3EeKTBP1EicCBwl8etKrbDhwX01WkGE8RASRi6DwJtYZv7B5IEx6ALcdjEQtalknoho+T0iiva/39xopqHibuh55VeYoClJvuPdkysRCeVO0IoWLzyJWWBd8n1J7b90De6Ojc14bwr6ULMe3gIwMRfM9BMWvmA69i1246yMuD2JJmM63oR3u4jFKM3oFigdbRd7OGnWIpSjWqxIiygjNUWUgbrexEbmtqDZ5XRdyrYu4gxKXbIbDD9fTrlsYs33OI3QzkzGE/HcOBIZDifP7WB4V1d0TnHTGbQIfDbdMksjGHOWNuwtoaPoQIyInDg0d9vmXHcJ2ahkM9Lzx8d3aOmohzjAl2jHbMxm1nYSDea0Y2E2B2JC1ydsm4LeajZdFvtNfUqqB4xySOHxQdLb6PkzTTtKE+vdn00S1oPmulWjM2Z8qp8W8KTQB4z6Hl/Ue8FlI0fVVkRwRHtcTyLjA6ZEFE8YGdU3NjyAClMZOMvi9yq1YWcVVVsxHTLOc0FpegoTZzcA1n1GYzVfbDL+Ymk5SSU/m8dM911j2AywtfHUaIQgUYpl7/iT8Kd0fssT8TnzEDlolVZsrOOleE8+FJhmQaklSsTX3FjYWI8YG8/WB971HTUdj1TFcaooNFSasSkd7+kXlWdu3Cg8iIeY7VhPK7N4mt7PX5xnknMmI44+fr0e//t1DXe95yLT/HBqmRsb7ZZ3Je6dQl0dc7uac7mZUiU957MlRg8YY1HK8uSHX5M9ukXmA2JCjJZSjDu1jlgFasTQw3qHv4suuMuvXtDVeTQ5dIrZYkl1cYfKtuijdwi1j74BcZ4RFseQJIhJR3Z+z2eP39K0KbY3LN+dkmQdJu3Z3M159e4RqR74+LOvY0kwj3bvdpsz7HJMMtD3Ce2QcLmZ0Qz6kClCtEXwiLEEKlgNhtpqCm15VowcRBe3uJi1RqKCJERzuU4fhratFzQ2sHOOiVZMjcKFwDfbMdEbTyercfBzomNvedl7Xg1bEjSf5AW5ij24qzawtB3XYsk8TPksL0hUFM9AlE7fb4HRkVVJyMf+9H4A18iEwmpa71iFBgIUIcUIyUzIQyb2pD/iyhne+A7n/vVibeD/T4Of/5yl1YKz7KcYkZKFEh00hgQd1OHBnid7Dw7Hk3H4bVHsyExPOyS0fcJuSLhpCoz0PJ0uqdKWR48vqU7vUEWHrmp8k9LezA+nwqE3fPv+Ea/WM+77hLeNplCBTyc1iXRsrKF3iky5gxXvZj/HEsB6wa+3msvG8qLSfD6N0uvWRYpAoeJMS+djOeo063g2WcUy34gFMWMPZlWX3Ld5nNHRlkRZzhd3pGlH16UM9uGs4Jyit/EklYzsqsViSXm8pF1NuL9djFPiAucUt2OmZp2kGwnSm7Hcskg7UukOKjA/Sok3Q8LX25zWR+tcj+Ais5xkHTurR7GE5JttRPvv3ECH5Vv5FVftL9CqZGoek4qKp+4ZhUg4MrGBepwGnuTDodQTguBVnbIZRi8QAk8Ky+8f32CUxSiH85Jf3Z1w2aY0Ns5HzY3n46rBjPSFPaG7dYqtlax6Seejom7wey7Vwy0ep/ejAOAiD6QS7vpYU9/ZSLbe+58MIfAq3LCTW567p5ya9PB9bAjc2g6J4A9nGY9zO0r5AzuruG4VrYun3dZ7Xvl77uU1H7uP+bzKDoq5KC6JGdXxqLYzIpItZsbyeydXFEkcBVDS044mZrNyy8XTd5iiJTtZIrMetejjkXu/Oou/V/gmpX5zSldnJHmHzjqGOmO3mtDsCr55f0HvNM+Pb5hO1zR1zq7JSYylKqP1gLUaKQNPfv9vSH+4giKFsoLdFvfrgNtl7C6P6ev84L6rlEObSBmoTu+QyUPvqb2fsrmbE8aB5rrJ+dPXH3HdJaQy2rlvBs11F/1jLkaMTzkCaW2IpdNVn/CmTlEiWn0k0nPdJWwHxXUnedd45kby2dSRyQgpFSKwHaIgYdlLrto9nTmuvT9RdEB96MH1PvC2jSzAE51Racl9b7nyOyZkfFal5CrO9hgZWPeSrY37RpRegxlPGXe9o3b2QC24cx1fyl+SiIKf8UNmJpIKtIymcZvBc+c6fin+lNre0vav/yW35H/0CsH+jgc//xnLunu27oqpfsS5PzkgT6SMevkQHt5kLQKLrImmaSrWONdNzrvdJGJfzt6RpR1HR/fotKc4XmGm8eQTBk2/qrj89gltl9L0Kb3VNENkkU1Mz0fVOJvgFVp4fnr6niqvD5uxdYpuxMXc7io6q1kkORurmOiWedJ950Rm/Z5gLEeFixhLbQOzaoP5gHmltSU1A0ZZyrzBGMtksUQnA95F+Ki3mr5L4ueWDVI5pLFx1uZkiZrXVB6OrSJ0hmFV4TrDo/tJnEtaTbldz+idYjokbIeEP72dsBwEH1eOs6xjogemaYeSnideUjvFm9rQ2piVlNqO/vax33SRpbROcN1lbAbwu4+5V99iZI4mulduRU0XBlI7wchYN4eo+mmdoneSq1Zy1wWOUjEq1uLaS8D3QMbNIEeJcywzTUxPqi2ZtijhoyOkF/Re0wzx62oXMS17bljrok1D9H+JBOLKDOiRU7Y3uWtdDFirPkqlPw3RpqAcZ2veNZIvti0CwYlOo92ziuWWzkmc3UNcYyb8fmi5lfcsxRW1u6fmMT5kB6MuAQf3RyUCzgvM+DqTsVyqlOfo6J58tkUaG6f10yHe58pHfpkEv1GElcBtc4ZViRtiKQ4vcGN2+/XXL3i9WlCannle01vNbVtgvSRdzWn7hCJrODm+o2tTbu6PEAQWszVJ2mHrDP2uQZ20MeCUFerzFtW36KNvcJuc5v0R9d0M5yV+P+g5lka75YShSbm7Oebd/TFF0nE0XZGYgWfTFYs+5e2u5LozEZZZxOdlL12fJx2TtOOuyVn1CZtBU7uYRQ7j8Oeq11x3sfB1nsUeTO8EEsE0iYFrouP3S2TKso8lQy1ilhTCA4/PB8YeTiyfTk2GC5EzWCiPR+LClN7BcogZ/V5lK0Tk+TUDbKyjC55btgz0DKLHS8+pPyEXBUcq5Y/DH0CAIXhuurHsR+A+7LhWl6Cg5Bht0v/qAee3rX91AQdg031NwNOJ57igeC+v6ETDj4bPmBjDzHielw2Ziil1NxjaPsEj6J0m1xYtI+lZWxcnmo2lW5d06xKpPEI5+l2OHBVd3ku0dDw9e0813RBC5LL5IBi6JFoA6HjyH/qEtovKmGwcIE11hAyq1QIzZiZCQKYtx2MduRuirPj9rmLoon/OdrTV7a2O9enR3/12PePtdkppek6sITc96Qc+OADZZMf08TVCOVTegQjIdEBohzAekgAW8ALSgeR4RbCKZLbFD5p5nfG4SbFtwnY9YRgMnyxmdGO2FIKIwoixtzM1PZWJ8M/OS8pRubZfSgQWSc8wst1A8pNywpP+f0YgDggPr0Z12NDzulvyiT06OJ42Lpae+tFT/q4L3HYBKTSfDAmJjAeL3mte1YYvN4GTVHKexVNi5xV2kNx3GSGIOCAo/aERPfi9gu2Dxjt7FE8YqdTh0FzfexP1TpHIaK89T+LXaRHVSHsSw/MS/t3x/nqM7KtxcPe2S7lpDZLYV9IGfjrJ6X2O4DEA8yRwlFi0jBsWMCqnRgk+sdl8kTeUpmeSN6RJh077GGiMjSo1L+jvJvhBs7k8iXLoLsG5aOd8u6swyjPLdyTGMp8vSbOWs6NbyqzBWh2b7srxuFqPPQ7Fsi7Ikv4wGN0OBiU9ad6QlQ276yPW706ZP39HPtmA93FHtSCUQE0aqskbKvkGrCDYuJkL7Qhe4nuDs5qjsxsWx/e0Tcbd3YLOGuo+oRn0wXU2V5EWvidhCBHItB0NEqPAI5GeyUiiTsbPeVp0nOcPIw2tk9z1ChcUJ+N7rpQjAVzIuOx6Mqn4wURRqJhdShEOxHQf4lyXFtFXSAm4bBSXzZ6YbmmFPMx+ubESUqjY81wkgos8ClMGf/Qd3PFfrwf+xP+co3DBZ+oMGwLvwh2NqJn6GQVRIGNCSi8aNu6Kzv3XHwD9betfXUlt/Mmk5hGfm/+RSSi4MDmFFuSjs+J5Zvm4iqW0KukPJ9kQoEyjI+i2zbnaTihMz+99/DXFdEuzLRi6BJP2JAeLaIkQnqRs4wOrHEJFVtReyfbm7SN6pynTljTpWe0qlnWBkqO9rfTkJn6/+7pk26e0TlOPZm3n5Rb9QZmq7hMaGyXbH3rgiNE/xCPIR85UZw3bPsVIx6PZPVnacbVcsGwLPjm95KMffxlttJsYFHTWI/WICQmCdl1yf32M1o7JYokaib0hiPhaRYgBOYt9kTBmT922oG9T+jZlV5d0veFmN6F3it2QxI17PPm7sT8BMUjYAH+zNlyORlc+RFRNPgI2xzGGw1DbTCsW6V4dFAP13pVz8LHscJY5fjjZRo8TPeCC5M9uj3hdK6YmkgrMOMMzeMHXW8POxob+UeIOFIfWC65bQe8Dd72j9XFDioJSMEJRKMUPp4JSe3aj+nDPfIvig3BQGumxxCUZFVCjOi5TFiUDiYwfG2sOyslfrdM4xW5juabQ8bXvh2D3WfHgBV9tFKvB88Op4EXZUWjLNOnQ0lMlHYm2HE1XZHlLvSvY1sXhKXJB0o7WGts+oXWaTMVnJjc9p/N79AeA13KyI5vsGLqEvs4i7qiOfktp2qG0o5huyWabg0xaKIeZ1ghjx2wqgIxBOwwSt82jHcigCV6gyxZZdPgmob+fII0lPb9HpA8kab9NGZYVu6sjvvziU7ZdxqvNlJ3VzJOeyvTcdxlXbRIp6Is7EmXZdNH36cOS2tsmiVDbNDrSLvtI1d77XqXqwUtokbUk0rIdIsHiuk35ZhcRR/PEHw4kcTgzypVteOAiHqUx4Cz7GIymJnrdNKOYZT8MDJFKXqg4JL137c3G57FzChsEN63hfStZ9YFf92sEgmemIldx/kjJKFi4HaKs/2/q/+d/RZXaw/pvqqQWVyAETxKifSswPpgwT1yc2i5qUj1QjWDN6CioMSp6ppdBcBIEuenJJzvS6ZbJ80tk1kOIp6vgJG7U/bvOQBB0m5JhrDM3dU7bpazbIhqY1SXAQ+AYNOs+mszl2hKd6eO1vmozfr015CrwpEvjXI8ZMMJz22WsB8WjvOOHx9cRYpg3SOkPw6tyPJH1g6FoikjlbQpWdYkQgaM8qo/6XU7wEtdrnNXcvD2n61OsVVivkMIfrAxeXkXl0mzkqxltY/1fO9KiQSp/gChW57cIGfCDxrYJ3bYgvzyjbnN+fXdC49RYSmtZDQnvmlhOiMh+xodU8XW74wv5CxIKjm0sQa1lVNH8d/KH/OFCkSlPrjzpWMqCB6Zc4yC4WALxRN+VbJS3/+xoyaejCkgS5avdmME8KSS9GxvKY0Bsx+xJicgo2/ieO7nkaTjlcWZQI0gxyldjVvRqB5ddS8NALRqqUBxmddZDoAueuY69qLhSEglHaSBXnhdVzSJt40boJetB87r2rO3AlbhnED2n3QkTmYxunHpEOcXA/a4d2ISOx7YaES0KP6oQ90iXN+v5ATbrgjj05KL1eI8R/nBfHhc7Pnr8Fq0t2gwxQIyHBZ31MdtoxIHcfLueMVgdhzul59xJTN6isx4z2yITizAWdBjBnoHhesru7Qk66ymeRvvj7a+eU68r0ix+7f6gQxCEQR8CFQDKo6c7Shn41Atsl/BsNWWwOgJovSLbVfR+ejBO1MrxZH6H3tPfvaQdEh43JdZJdtYweEk+etA0NjLS9rYDUsTDY6IdysZnL1Geo8RTO8HLXcTY7JeR8U/vopBACYENKrLVEs9F9uDF1DrJdRvofTgoDjfOsg0dT03Fj2ax9Drlg6xZBOaJi95OqSRvZoSxhBd7SPFzozNtSjKcUpf/no2/5q7+Of8Ua5jfxfoHAs5e8viPM1H7l1jONxgUmdRUWpCPwWaRWBZpxzSrMdqSjWZj9XrGssvR0jEtY5lsApGiWzbookVmPSKzMAZgP2iGTcFQZ1y/PWdbF9R9SjOYw6R+qgcuFrdIEbhdz9h2KdnYX+kGgw+C1mne1QWdFweUiRCB50Vkry17jRSBiVMHV8ZH2jJP2wOLrZxECKk0Dqkctk3o+wS3nrDrMpwXaOUPUvB9OUiIgEoG0skO26a8efuIm92EZ8fXPDm7Rmc9SVUTnKJdl3ir8eMJbxgMfZdQNznvr06Jos74uk9Obinna1QyxA1CBk7cLV0bM6lNm9OOTqgz01Moe+irdF7ifELvBJVImXNBFaY8FlOUEChxghKCFxUcJ5Z5MnCWx1mlYZyneV0b3tbhkNnu+xjxcxRynMWJ5azomOq8QDgd/24Mshur2I2lGyNjOes08yOLrGA95JxlgtNsLMFod8DPCBE4yzRDkDQ2Z2erAwnBBXi10+zsvs8UqQp/U29J0Ty2KZVWzExsWq/7qPhzAT6uJLVNSesTam+ZKEM2qviO0/gaow+NYKI1xktyFdAisBo0q16TKs+nkx2pdFw1OetBMU/sQdFYjF41x6NdQTUOCWcfSOnlaNymjD3QCLb3M+7v53x9e8p9l/Gf7wtaBz+Z9zzKG7rB0HVpHCYu4ntmR+HM/OyW9GiFKlvmf/AVaBAJYKE4vUcZy+Z2ztX7s4iZ+vRVhHZahe8My6+esF1OI2l6sos9HieRylOUNc4q+j6JtgXtg8/NXhBjzECW9khZI5WnbcbAPCSs1gm9lxynHZlybAbDejCHGDeMuJx+hP6GIEilpzIOLSW9Vwz+AeYaxsymdYGVHQgE7gaBFpK50ZRajUaMkZsnicFhj6MpdULA0LrA/34bxSgLk5AoQakfYJ37gdDex0Hlb5soTLgwOUepODgIayGo/AwhFXVySz/c/6vIdv72+ldZUtNqwTR9zk/Df8dMGT6uFDPjOcsG5kkfQZnKkupIytWjsgk43HxaOZIk2hacfvwaXbTYOsO1aYQL9oZmW/Lu6oze6ljKCoJFsaUqasrJlulZxN/sywFDlxCcIikaTNHiBo1t0+hSeDen7VLeLI+4aQou25TXtabSgRdldwB4Alx3Cates0gsT8uaadLy8fklSdLT9wnWKYyOCp79EiKQ5U3EhRgbJd1Vg1msY3lDecKg2L28oN9lJGWLzlvS4zX6oxa0ihJY67Dfaob7KSrrkHmH7xKGdYHrDav3JyPp1x9OoXvPeqUczilWqylNl3G9nXDX5lSm52gMGJ3VDF7x9WbKbRcxM0OIZaiJHr1bQixRFaM52iLtOC+3WCdZdjmdU/xqXXLXC941cTbl47Ti35/2ZMpRqAh5f73LuesVR0lksS17w683BjdmwxHuGOdYUhmb7WIcFIRYS98jiiCeGJPDrAZjKSYi++Pfie9kEvtyYu8UvY+9gMtGHnD2RsK/Oxl4Mm7M8f6MB5QhCJa9OQTSfR/JjCy9jVX0TnDXR5n5i9LyKO/oR7M1LX18FqRnksT7a2+lvBdIJMrxaLoiMz1+dL11owR+v5T0HE3WJEnPejNh3RS82cz4+bLC+Vi2UaPsP9duZOQ9bAsuCLY2btD/4fEbPn3xLeXpHcWnl/EC2qhyjLVVgV1WDJsC26a0mzKipl68ReY9w+2UYZdz++qCV+8vDoKa/XPtgzhI+W+2E15tp2jpuSh2KOEPfLfz6Yqjo7EHtJ6xaTN+cXfMzkmeFy3HWUPrYj9ovwYveddkNFbS+YeZp33VorYxs9hbjOzvo52N7/nOwlftlrXYspK3NH7Fx+EnfJ7NMDLej/HnxGrNeghsbLSkvxdr0pByIkvS0Y7ayEgI39tT7IehOwa8CKTBYNC09Gzkhp1YcdX+Ndbv/qtZTEtRIoTGutv/NkpqSs5Y5J/x3H9OETJOTBL9xIeIPAHDEARvasNfrz2nqeL/+mjFLH0A1q26jGUfT+FSBE6yhv/haEWZjk1V7emv56xuFyRpzw9++CU668iO1si8Q1UdohjTexWn8kINwUlCq+GDDSpYhWtiEEqrGtsbppMN213F3WbC892Exmou2xQl9MH++SSvIyVA2QPN+W4dlTuv1nNWfcJ5XnNWbWK5QDrSpCcra5LROTH2mkb0unSIJP4pn19SDDqKB4xFThxMp2ASQpaDNqhnIKUEKQnaoO2AaRtE2zB9/58JTcBeVwzrkmGXHybB7+4WWBftqK0fT4Ej4qce4uDt/qF/nNecpjFbaVwM5vuKxGTctEptyZQdN0s5+u6ktF6O/w7HqURQkinBVWtQwoz9rmitfJwOXHcJv1jGk/hdF6XON7ElNTLOBFoItJSHGQfY0wTgbeN459Y8UlP+YK5IVZSuBmBn9YEuvQ807WhPvt+AWxctgjeD5LJ1pFLwtJBUOnCU9Ad8vlGe1mp8LzBBYNKoYNuLGHov2fUxGK37+DOOkjh4msjAzmqmSc+zak3nNC+3E1wQfJ7EjD+MATEifh4206ZP+dXtKW+aiC9Sgu+MFFin0MqxbArWfYoQgT9YbA4YHEk4ZNfLPmXZJ6Pl+EDnJa/qlNoK7uuSelug0550WWDrjOXXURBx/KNv0KdbtN6gqoYwaNI6QxqLPtkhMpCTW1IL6dGao6trujpjeXPEri748vaUZZ/wtNpyXGwJo+vp3oAOOLz+/aFTKU+V12jpeNGn1E6TjAH9ssn5eptSas/jvI9cRuJ96YI8ZA4BcZj+9wSkSJBJZPtNk4GJdhwlktpJJruKna0Y/DkuxPGAV22LEZLJaD2wc44hOBoGOjEwp+AnyekoyR8Bsj7G57W1vB1RRyeyBAyt1wzBcyeX1GLDEDp6arb9e3p7+U/Zdv+RS411kL+LzplmHx9s0/++9a8q4MyyF5zynDM5QYmYskYVUBQL6FFJdJQ4Pp8opokjN7EpG0/Wkjd1wdc7w6Pc8QdHdxyXW0zeRqno+Rox0+TcceK/jKf+Ih+RHTlBTxF9FyerdzvC7RAbn5ucMGiGbY7rDXg5uotGxPqeSgCRVDCbrTB6IEt62j5hmsTez0W1pkgiSFMpF+0PnKbpE95sZgxecpLXPCo3DF6xbvPopGgNhR5i6a2sUdqitcNkHUmdIZQ7NHFtmxx8RbxT5Edr0ubmgJ/fD+iJssJP54SsjEW6ag7eI07OEd5j1veYtoXbK6org10XFG/O2K0m/PrLH/JyV7IcFOtBHCCq+49KBB7nllnSsx4M6yFKk+uxhxLnYKKB21k2HPpbMQjHmnzvJEJIUgmLRFAozyK1hyxRiMBkDFhvm5SXO0/nPVs/YJA8z9MDwcDI8B0fnNrGUsU++DzOFU9YMBmbvEo+nOJf1Qn3/V7A8GAxDFFgIMRD03/ZB2pnCUGNMmsOPSUXDK4TXLU5f7VKRhlt3Pxbt5/HiPTh/T2fSMGTguiwKuIBKpGeWd4gROCk3B5gtb3TWBf7dr1TbPoULTwn5Tb2NWWUUp+Ps19Z0nM0XSFl4H41pRkSPrt4y/zkDr0vo36Qyfghls36JgpJuibnbjWj7tIR86OZjCViMQYBM91x+gdfELzAbgvs12cxq04HhlXF9mqBSXsmVYOUA/TxORLKoYsWty+FmoHPjq8ZnGIYFXSCwCJro+iGwDA62XZOMUnbsTdpMUmP3HqWVynL3nCc9sjRHn1q4u9515ux3xXnpX69zdjaiKzZlzgvcs3gBd9sPb9cD3w+SfnhNGamkRzu+P25jZYpNr7/X28Vb9oVtdiy9O8BOOUZFSVPTcVFHvuJRgYaB692jiEETtPoZFooRW5zOtHxS3FJGnI+Eo+YqYR5OMeFM2pv2dLQmOfcmafY0GFDhw8Dm/aLf7G9+WG539gdknKCFGrkuf39619VwAHwRF7ayre8Ut+iQ8q/6T7hJJWkMjZBnxQDs/Fm2zfar/uSzWC46jTvm0Cpx0FHL+l3eexFTHeI3MG8jBPReyyHd4h6h2hquNsQNlB/fc7rv/6UbjR02yPYAbpxALR3inYsT8S5j8A8r8lMP0o5U0LgcMItko407aKNdV1y1xS83FWk0vOs2pBpS5F0kSbQpzhvRu3/+Dr6KFWF6M8zP1qSHy8JTjHsctyg2S6ndG16KKFMVhNmdQpe0qwrghdMzm8xkxp99BX66Ncxi2uJDK1ZClkKaUYoSoTWqGmN2q7Qk4bJJud/zDo26wnXywXXu4rbLuPrbX5oqkoBd71iNeQj5yrg2JMU4LaLLDEtDK1LKFSgMvrw8LogeF1rVj3Mk2iBnGvP3AxkynKUNyRjzwii9Ph5mWJ9FA1E3lk/NmyjRPs4HXic12jlD2ic3ZBgveSqzXjfagodsf8QxQ+xXCRY94FpIjjOY2YViMOvGxtZf3MTG8VHieQsSw9BN/aMoincuktZ9imti7MfnYOXu3Fg0G64l3d8xlN+MovDjNNRztuM/kCxbNgyS1vKrMU5yc12Su8Uqy6jdYrzYsdZtWbT5VzVJbmOw8LT+Zqnj9/GA0i1ozhexcyiahDa8Xzc2JH+4BYarIr2AqOoZr9M2ZD1BtsmaDPQdwlZEu3I5+UO5zS2N9g6i3NhY8n26m9esFzOOD29YXJ2hzSW6dP3CBlw64LhXrF5c8puNTngpTarCV+/j0KTx4t7ZknHu9sTbpsHJR5ACLHn+sOjG8rR4dZkcVxhr7p8XG6oTEbnFOs+QYrAaTqM71NUoE1MH3t/QVBpM2a0cRZqOR46PqkEuTYk0tE7yc5KbvuoGvuoHMiVY2YsMwOQkKkjLpsZ/6t/TcCzCDNOdMp5LjhO/dgvhDlwloEPEhf8eICV6G7K+6HllfslWqY8CecI1MF+423ruZVRmDEJxyBgJ5b0oeZ3sQQapWZYt+Su/vN/+PP/NfVwhEg4Ln6fH/k/AGAYodyd6JFI/uPkiN+fN6OJVJxyHryis5o/u1tw2USJ7Mw45onlcbmjSjqenlyR5S3FdIspWoon16hPBfSWcG8Jg6R/v8C1SWxUWsXy6piX7x4BUKUtUnqqskabgbbJqJvYtPQjp+zlasHOajLlMMIfpK2FHmKWpSyJsQjheXl7yrtR8RZr955Z2pFIy7yoKdKOdV2w6TJSbZnnO9K04/TiiiTvsCOGpzhekT2/JvQKt80PIgjXmfg5VpMUDdlR1Ob7QROcpNuUuF6TVg1mssO1Ke26jI3XNOJF0vkGPWkQ6YDIY44fOknoNbtvHtFuSlb3M9bbik2X835XsR0MX2wyWicodGTLpWOfJuI7wiEI2CCYmNiPEWOJbWc1L3cJm0HwZ9s1l+oNn7lPeJ6nHKeB5+VAZQY+mq5I9cCmzehGiGrtdGzyJv131IJuzD5TbamSaNxmxjLgMFpaRMnyyOIjUDvNN9uCxsmDV07cmAK3neLnS4sA/vsTxUXWM086pmmUKicjKUIrhxqR+kp56i6l7RNaqw8meXd9dJGtx5Lc1DiOkgfigvOSb3Y520GQ6zib87xs+P3Hrw4g2j0hXMhAMdmSTXdxADTrkIlFVU2cydIhHijGFTyHGS1fJ4fysB80tslo1yW2i2XUYTARnjn2RdOso+8TttuSps346u6EjTUUY2/18WzJR89eo9OeZBxIjj9T0txP2K4n5EVDdXofy9xjcN28O6HZlgdRi9IuEuAHzVcvn7Pqco7yHdOiph/GTEcEUh37PNuxIvBoccfJ6S3eSfouibTp1ZxuMAfi+36lKoJyP+QOvtxMed+kh3moD8Ge930st51lsae8n9fqveRtY+i94FE+ME+GgzFi9DyKYxt7Cf0+D7A+HoiMjIeMEAR/sUy4al20oUhg2cNXTQwgpzpCjcVYDr7pLK/ENRAtXTyee3FFF7bc1n/2T9uI/5FLiIQQHvrN/83IokPoqe0tWgqMUEyFicNOtDSiJXCEGDem19uIwt6fij+b7PhsApmKU+aptkzShiztmMw2sfxUNhEcuKxwP7f4oaTfFAcLWmBspLfMpSfLW4TwmLxDGkvx6BY5aQ93i28S7LrEtQmf3ixom4w378+5rStK0zPNatJkYDZbIUSgqXMGqzmfrJjndTzdjM6l/R6R4xSbJud6V/GuLnlabfn06Svyakd1cYsqYnlQJBaRe0QpQQYUNXhP1twReghdVP7IdECUH6gMe0H48jF1NyNZbMg+u4sqoib2qOrXZwx1Rve6IDgV7YHzNpq0KYcbNO9fPma9reisoRs36+jvI8cZm4iqmSc9qXKkKpYa9pYBmyH24jLlSD+0zpaeeeLJlODfySmtG99jAbkOHI+ctz0Hbjuk3LYZG6tY9ZqpcXwk/XeI2sM47KmtYdXFQbm9XLUbs8DTrOEk343/JihspHM3Vh82hmWvuWrj4/KzeZQuz4wd5bQePc5iFWNQ25dYAZyL2bHR9uBbpKXnKOkPG5Lzcbp9NWiMDJTEjezFSAFfD+bAsgth36NwKO2Yn92SzLaYxQZ12scx9kTFwcvOE+x4WGgEfiQ5+z6hvp3hekOzy3FOs95UbNr8UJYbvOK+zQ6urhKYpzHL2vYpl02kEOyFF4/GDGtabZH7ubPRVXY/q6OLlrRqqJcTfv2Lz8mzlmc//RVmvuX4dIkwnuDGDMtHybTvDEnS0zb54bqu11Pu64oi6Tg9uf0OpWMYDPW2iMinwdB2Ke/WM1qnmaUts6zltil43+Rkyh08dKqkiwrRUbQxNe4wuLsY1bBGZmwGSTYy5DLlOM4anJckMqf3e/aePgwQp9IzL/pDNiUJbIaEndWjEDgqI/NRDPNJJTnP1IGULhB8RkHnA9f9wJ31TJShUJL70PA2/A2prLjgYxSSnprWrZGi/J0o1YRICWHgv0SK/S8acP52pPunfP2RecEEE5UZ3qGF5A+zM0oNn016jkeBQCybiEPDtR1xJY3UqD4wTTry0UWzPF6SLDbo+RaRe/zG4NYFumrIPrmNKPZsRM6OQMPcbpj2b+Ivlpj491qDrEZ6rkVsWoJVSGOplKd0ktnpHW4YgYHS453C9Ya2yfji3ROu25yn1YbTyYos7SjKKCCwg6bvE/7s1Qte7nKelg0/Ob5mVuzI8jgjM+zyhyzMizgLMd1FVtbZEBEmjSQ0ht23Fyyvjpke3zP5wWtkaiO0sRRU//6KSt7AZIKffhZfo5RIa5ncXkHbEVYDoZH013O2b08Io0tk3yW8uz3hrimYZS2T0UZBi2gF8SjvkcBFXjNJO+zYw2hsdC7tvOC+V/Q+OldOjTv0TKyXI8gzqoEASu2otOMo7Xg2u48lNx+VVmflhrMyOp7up+F7FzPePT36OBmYjlmPkoFln/Dz+4KdjX0SJeDjKg4LxvuK8edaCmV5Uxfc95qNFaz7uI/PYzLE+1YjRRzwPbIGLT2pdHRecT/2Nkr9IIpwQXA3KukCD6j6bDTser2TfNnUTGTCp1XBxASeFx0TMzA1A5WB0vT0g8aPijTjLEiPyjtEYhkHj/B3Ad9kbF+eMzQpq/s5dZNHHNN4ULgbS1OLrMEoy31TsOwyGqcOfY2ZsaTKcZbXlKZn8A9w2b9Z5dgQJeWZ9HSjHftMbCiOV6iiIxmDyEjNxITt4Xk/Ggxp1pEcbZDTLkqolYBNwO9MFMdMWtS04Xi+JVhFe3lEu5rQ1DmNNXF2ZnQkjfTrnmBl7GM6xVBHx9HTu1uGPuFmOWfd5LytC/70znCUwh8sPBMGprIhMwM/mN/xtDJ8u5nyf9wWVDrw03lNri0vyho39s3isG8cpB2cpLAG6RT1iGfaWclykJQ68GxkublRrXrVply16pCpZPKB93acDswTy18sM/5iU1MKw3lmotmfVCgvyGS0o35mKub2f6DzjluxZCtX3Gz/j+/uq2ikLAnB/osGICVnVOkTpuoCFwZ6/uEy3r9owAnhnzuvI9GkJKPPxIXSJDIiI3LlOcvaWDIR4UCMvmzyUZqo2dmHRvDzMo99FenxgwYvYkawSFGPc2Q1ASnxSVRViL6LTfPtBpoG+gEaH69QNqIw3m8JOxXNpJxiWE/ZXR5HxLoICBnGhmvH0GRjzyQicpzTHJdbcjNw2xR8tZrz0WTFj198gzEDSdqT+4af2jd81ERKwl/envKoKcizliTpEct44u+7OIswmWyZntyjsg5Txze7fhchiV9/+5wv7074eH7Hj4N4mKdRDl01yHRAXdwikvSB7Q6EyQxROsSkRzhLdrIkOV+OJTVDsIrjj95GH/shDptu7mYkI+Rx2ye4IFH7DcgaGqvZ2AhbHPyDGGSeOM7z5nBqHbwkV2qUDGt6L5BwkP6eLu6R0tH3yWH6fQ+ANHmLdwo7ksE/X03p+yTOYSV9FGhYTdulXORzamt41+RsBnUom2wHw/s2wcjA87KO2bKKRnxnmacyA+vB8PU2Ge+xgUpHisGyT3jTGP56FcnYn0yih81qiP2Rm05x3YaDw2TtBH+yXrOSSz7jMaepZjl4PJ7OO25axc4KyrGXkIzZoBqHjtOkZ3F8j8lbkkkNImCXFeFG0lwvePPNM+zYX3Re8tc357zcFZTaMTXDwUE21Zaz2T1F3jDZTFi0OYm2ZKaPgM2ROl5Ntpi8Y3s/ZbWecjFd8pMzeeAI9k5hpKcZzAEsG6zErWNQc11CsDKW+4qWvGjJH93EoDLtQIK9LLDbnM2bU67en1EWNacv3qCy/sFBNO9JXc38aMnzPnIEvZP0dU5f5wjhmTy5Jn1+Cx7SUfTTrCu6Nj0oQz+f33OS5Yf7fvCSeuQiJsqyGLNPH+KwsRx7i0dZTW4Geqfpxte5GxIkgaMs3stfb6ash6h2fFEO0Yiti4eMvd8SwEnmuO8Vb2rIlWSeaJxy1FbR+6hauzD5AeTqQlRgOiKhI/aYwiiwiqW0rb36O7tqwP5Wr5p/TKKwD15FckYiKyp5zNQvxn+T/8BX/4uX1P6ZASdYHMPoKyGZm0Ci4uYUgqAZ699qTIH3b7gRgdPMMfeCb7eSr5uWicnI9YCWjs3djL5JmXiB6dbIEwfVJPp33N9CPxBuO0InYx27S/FDiWsSpLEkRxuCF2y/+ZhmXX3Hz30/YFhvS6xTpMmAUpY3V+f86u6ERDnOix2JsuRJz2ws3WjhKZI+YmRcStdFIUCadKRJR5F0HBc7Em3pBxMJAYsVUlucVbRdFqGdRyt8n7B7e4JtE66vTqnbnG2XUZme3mkuXz3Ge8lqF3/3Z+eXVIs1xe096c0lvjMM91MQHjPffsc1UuQeeRQl1CQCvENv3hNaaF+esH1/TJa3PDu/ZBgMm1156GnddumIfonlo0+nK/SejD2SAXwQ3DQFL3cFRgQWaU8GNDZmrkaGA86mmm7QaY8eMTz9Lsd2ySGY7rFEALPz25j11Fm8XrsCuy1Jk54n8zuclzyx5iChDUFwVVdcthGFcpLXzPKaT0+vMCMnb29m98d9ih0RP7F/OOevVoFCw6M8Bpi7Lhq4XWRxePDTyvGTmftOTX89TNgMJWeZYmYCjwrBH6l8nKWJ/aSJjgqzndXc2Fi+WhS7aCO+dJhdTr2u0CPnTwjP8m7Bl1cXKBn49OwdRd7wH+ZL/n0QJFlHVjZRwVU2B/GATOyBvrF354TIOdsbuwkZmFnFYyfjsKZTMai0Kd5JmuWUts5ij2eXI+qMbjnBO8XqZkHbpaTJQJJ2FNMt1dOrSLPYJAQnufnLT7i5OaEqt5w/vmRoU779ZczAj47uMXl3UMGtl1Ne3x+jpGfbFGjpSIxFSkc63ZFyCxpk4kE2TM7uSIoWfWcxI/6ntZqdNdy0e8fMaIFxXFiKtItlLjNEUsGQjMOekm6Ex+5VfIowioLiYfiR0+Q6OZTQdoNh1WuGAJ2LTLVHueU07TlN4bNJBPtubcwsr1rFzsYM+EmxFy5ENWPrLVs6fhG+ZN2/xcicRFVIqdCkaJn9F221Ss7QqsT5Du9bAv/llakQLNvuFQBNckGjz8jFlJk/Rv4DQedfVQ9HyJw+1Fy7lo3T0aFQwNbGaubHZcHjIkUQh/QyFTjJWsyoOvIIZibnPE95UkT5sQ8iBoPekJQNQgZU2yA3r/Bjyai/P+bnf/5TrnYTHk1WHE9X5EVDOdugjD3IjDd3c7bbkiJvKKodXZtxv5oyDIamTxmcwoyyXusUx1kTN0vTo5UjS3qkdAyjp31ueqTy2EFzu55hneJosqYYIZ3eSzLT8+jZ24iaP71HpgOTVYWrU8xshzld43cJflAIEaiqHUZbnj97TTbb4HozGnQFLp6+Q6roECmNY/vuhFe/+GHEeCTxdymmW5S2rK7j/ENZ1FSLNdlsQ/mTS0hl7Ak4yeXffMyffPUDFmnD48U9YsToGOVoRsuCJ3nHs8k6YohG2SzEh/tmO2HZ5az7JGYzI67jw2n+aB0so++JcihjMUUMOPdvz7hfzknTjiztMMlAOY8CiXpdYXsTB1eV4+r6hL+6vsBIz2mxQ0nPsslpnea83HI2u+dosuEHJ/IAZd0PvO4zsIgcEnGQUsvRgTSMogdB6wI7G0ik4CyLuJJ54qjMEKna+81rbLD/bJ7QeYEW9jtU8T3gcX+thIj9qyT3lOMQ6q7LeLOKthP7GZmL6ZKT4zsm0w3/p8f/X1QykJ2sUHkXS1a5iKUtParSrIuZ6y4QnIQ2ADpm5Z2BICPNGWJjf89PEx6hPXoc9hVbTxg0Oruh8gKVDqi8I1h1GCUwyYC1mu2uoF3O0dcn6G+eUeQNjz77Fl02LD55zeKT1/hB43uDaXucUziruLtbRNvptCXNOm5Wc77ZTii1ZZ7X0S3X9Ggd7xFk7Fu5XYJv0sPh5PZ+wc1uwm5I2AyGbrSu0B9IwJshwe/ikOk0q9l1GV+v5+M8WewBHqc9R2n7t2jwKmY6ec0ia9gNCds+SuC7fcBwsQqz7BVKGArlmSY9Oy951xhqG/E1U7PnLIIB0tEvx4eMxiX09iOC8WhSlDCYkDINczweWWoau2Rwu793EDTg8WHA+c0/qg0SsITwMIPT9NGzzGtHxWx09/n7128NODHVcvyu0DZKZmztNV9oTyIKJn5B6Uue6gmFFly1npd14CLTvKgcGYFSD2RjnfzhAc2Yj6cNKQJ50ZCkPZubBcvL08McjDYD+WKDd7EEZKSL2IxdVJDtJ/1TY1F5x5MXf47MH+rRWAitiE3OPirA2svjg+ILIgyzG0+Azmmck6R6YDCRJiCVo0h7Pj+Jmvl6XdF3Kau65Ov1nI+BF8mAmdSYix1MDErXoPdGWRKRWIpP4tDXTH8d4Ynp2Dg+mGwRm8g+EDaW0Mk4v6M8Q5uyWU+iW+rJPTpvmXFH0W3p25TVzRG2S8jO7xH5QGgTwqCppht+dHrJps351dUFSnhOyy2Jtvz07JLfG8UQnTW0g2E3DuTuZcnLLmc7GKZJz/PJehz+VNRO8+Um4bLxfFwpnpdQ9wldHUsgcYDXcvLiDSchyn39oONpPevj3EdvDn4+AIvZmt+XnnVT8Iu7kwPuRgLVOHwrpSUxUZLadjGLudlO2A5plMA7RaIcExPLLevR4vosGzhOBY2VrAaFFjBLhgMbLpUe6yW3bYYdLR5aJ3lTa1oXvW4mxnPVKl7tPEYKJiYOrEo0mX14iD+qBJ+cXaKU49kI3qybaCt+dnbN/Pk7dNmiz7exJ5Lq8R5IH0x2rIPe4ZcCv8u4/+IZu9Xk8DPWmwnvVotDT0wSeDa/YzbZHHhmRd6wOLsdzQs1IUiy2QZTNujpDn0WxTW62YAVZEdrXGciEbpL6JuU3WqC0i6WQusM22TY3mDSHpVFb575o+uY3XcG7xS71YS2ySjTlp+NfkBPn74lKRrS2S5+3dEmBtc+YLcFrk5ptgXNrmDZlNy2OYn0HKUt123OmzpCZU8yRa4Fmy5l06UUpmc+UiIWaUc5wmpDEBHnZAY6qw+D5s2oftsNhnYsMSajQk+OvbpPKkuuHPe95qbTnKWWC2XJlONnytF7yctdxnKI738iAste8LKO7/V5qjlOJbk64QfumN57au/Yho436iUuDBzxGKNecK+v2Plbdv27v+MC6v3mX4RIEEJP3X1D073G5h1KmN/6+b814MRg89sHef4lVwiepr9k1/2aRF+wSP5HzMhzE8RTY6Elp6nloyrW2GdZixL+ICLYk4tbp9iOWP04HOcZuoSuSw+BqShrCrUkO1rzh//hP42/RDzVCeVQI+p/Ly0VCwFZBmkWP3qPGP/sfd2rszeULbhlQX8/oV1OuHx3Tj+Wb/Z4jiLpDv0FofxhWM57ibVqbLTrqADblCA9yW6J1APkgNaETaB7dxxVQGfr+DsmjKdY8cEGM4wlsVHMUHpE4knFHWa+JXjByRBPtn7QBBtr4tv1BJP0TGYr0qoBFeXR/c2cYZsjjePi0Xtmu5z8vseFqPoaRhYVEAPN6LXzpo6Kp/h+wu/N1vz07BJBQKs4e/R6Nad16uCyGH1uHB7BajXF7AqybXlQggUvmJ/fUj6/RBiHzCOe38y2sVS4KbFtwuTknousxw+aH93NIt5oVAZmeUta1TSripubE7resOuyKLkfHSPv+oQ3dcLEeD6t4oR7pCQIZiKg9yigD3hvEGewjHQsu5y2T1gNmje1ofdxUDQ6fcLURBfPH00DNgia0ZbgdR17OWeZ4DiN5bWirMnyhuriLtIkVKQ0S2OjjF27eBjqBe5SR++n+0ksc424ImfVIQu8X86p+5RNl7HuIul8PRgy5fhosjrgZepRJRaC4G4949urC4x0PDu/pKjGbEeGqCxbxV5nGNQHNgSefeVGKk9eNkgdPXyEDNxfnnC3nB/unaqoOXl0hdL28Hf1rjhUA3qnyMxAWtUk0y3pkzvkZNyvXLzLpHJ4FTPvJO15fHTDSbWmHRK2XcaZgH93Es/lz6YriqQjUXGwuu0TNm3Oqs34q2WkOvzR0ZKzakORdORZi3WKsy6j6RO+Wi1oRhrEsYnvexwo7Xg0OpDmekDJwOAiZig+MwIjPMejg6keS6itkzROIoVECo0P0UPHerjsel6LK/JQUIaMrdhx038ZocdpThoylIhkDiH+uYUshRQZAQfB/kbKQMCyan+F4J8RcH7X0E7nN2g1o9JPydUC4w0Dji/cFd57/g2Pucg9uY4BpneKu6bAB3FAoky05VlRH5Dwe59woTyLi5v4UI51YDFi+ffDbgB2l2PbBzyDdxLbRllScbLEVA1qukNOluMnAFbgG0NwEjGiHWydYUfnxNf3x/ROcZTXpB/IN6M6zcAA7a7AWcWbq3OWTcGi2PE/fforrFO8evWE7KrjI+VJR0+b4AV6kpJ/dkPowa0zfJdQvztmaFLS6Q5TdAzbnHo5QSpPeXKPNA5vJQSBbVOGNkEnA6aI6r9+l8dN6HbBalexmKxjmS3to9ItC2QfXZE6SegVYVD4LuF0ZLE1yyl9l/D2/Tl3dUmVdByVW9ZtcWCPVeMA59PZPYvZOgbYIcF5yXWXsR40vY+Qw6tWsB4KPrWap7O72MQexSD1LtomFE1K3iUI62LvQQb0vAYdSPMVFEksI5kEQkPV38ZA3A9gA6ELhEaSXs9xXmJ7w8JFJZgxw6HsOQwGOcrllXIkRYvScQYmkpJ99FqS/mAq5q0mWEWzrthtKpo2PfTS1NjPKtLu0CcaBsOqLvmruxOMl/zRYiBRjotix6LYMSu35NUOk/Ux2BgbRTGDYFiV2DbFDZqhTWnrjK/fPWHbp+R6IFEROpuZnqZP+fr+GA88rdYUaUcZ4iyR9ZLJ6HWTKIdRjtOTW8rZhnaX0+wKvJeUWUMI0ZGzaTPybYFJogAmKRqGJuX66vQgXgBYLJZMjpYRJzXffGcA9Vx8w3GTcff6nDdX54gmJ7ufHq6/c4ov3j/m682EuRk4yet4zdoEaTLUzQS5+cA59GrB1TdPASgnW/IiUjpsb3BOs7AK6xSP2uwArt3DdIXwEWQrPJO04w+OlnHYeqQ87KskUnoSM1A6RZ70DE4dfIr6wbBr8wO5ep9tu4OVR+CuTflml5PIwLMienzNTM/M9LypS246iRLwKHe4ILjtIlvv1/IlL3f/C1X6lEfqRwQ858mPxi3Js5S3dGGLCwPiAGH+py8pE3z4bjntby+tZsh/Tobzu15KlkyTp1zwCSpoJJJGNHzR/38Y3B2Pw/+dTyeaZa9ZDwojApWJMsMhPOBllAyx+VduScahMNsltOODskfDBC8OzoNDn+Cc5H4zZVkXBy5TotwBECov49TzdLqmnG3wg6JrswPF1joV8elOMa22EROiLS9O3x9UUtYr3qzmXDVFPPms1oeNR4pAkXRMi/rgR++DwPr4ff2gYzliVTI0GWW4RZ11YDkAOF9//RE3mylaxo1iXm04e/oO2xte/80nDNZQFjtMMjD0hr5P6PqEVV3igsSOnLAi6UelUjy5By/xjUEEizAemfhD7ivpSeaeYBWm6PCDIssbHtUFTZ2zrQumWc0fFdvvvN9F3qDNgBw9haZe8Lza0lrNj6bx5+4hi0/KDScnt2RlTfX4BpV1HAdB8BKZ9si8j66m97E0ZObbeNpfKJjN4g/0HqyFto/s/3EJDeSe5HjFMcTMaJfHMug4LBuyeF2EiAeVvZXD3vRsP8Ao8+4Bte85lFqz7p7ZELMN10URyB4KG8bXYbtolFaMm5odsyQhAlXakiV9vCesZmhB3E9iBp4MB0PBzd2czabiq+tztPS8OH3Px3lzoCwvFktmT2Kz/mcikgVkGr/+MN+lQjTv+2CJxUig2F/HtoPNQGgFw/UU16YH5WLfpGzu5ngnD7YG27qgd5qszkmzDpO3mLKB/WHPKlTej0Og56zaKOGuuhhUXt6cselTpknLH52+Pyjp8qxFqnjvdctJ7KdlXVSznSz56EkcitxbkgyriqFO2d3OubtbRERSuYv2H7uKbjDxUCN97J8e34wvOVYn+j4ejIwZMCOOyVp9yGwP94gITKcbzs6vCGFEYXnJejWlG63AO2tYpC1q7FPedimQHubXTrKW47SLM2HjnNt2MAxS8BM+5lF2gcXjvGMQA51oGELHzt8y+BofLM53DPb6N+63Wi1I9RG5nsfXGCyDr38DEsf9nZJcfJ0ZIew5lgIt839ewCnTT/FhoOlf/tZv8i+zFImeoYRhoGcntjRhhcLwU/N/oTI5n08SSh1r47m2GBE/auEPcxSrPuGbbcnjXHE+WaGUY72Om9C6LmnHiXJgBB3Gj42Nm8BRXnM2WXNfV7zdTij1wMX8jrKoOXp0TbLYEKyEEDMfsfFxQ/HRBfH6+oRdl5EYS7sr0NpyenGF7RL+6qtPuWkKjrOan03WbNqMl+v5YchQicCPj685zZYkSU+SdUjp8F6SmCFmGcZSPn8fS0c6HLDG5jhCET+yX3KxeUB/eKvZLadobXn82bdI5ajvp3R1HukLVU3XpkgZcO6hVzC42HtZrid0vSFLO2a7HGXsgXTwnTVmFg8Gb/GhXPYzbndVlPKOpZFtH2dUZnUbT9Z5zXy+Ii9rJtUW5xTrbUU7JAezumnekJU16aTGLDbIMnLu9nJtu4y+RCrvCF7SXc/j+9Lfotprwi7glgXDquLqyz9iGAx5WcfJ+ZG44HsTKQxW0dU5dtDc3B+xafPoWZR09E4fzPeeHl+TF+OJdz+FX8UgpEalX3M3xXbJg7pu5PB5p2i2RbR3tgrnFFKGKCqxOrLRvEQJjxCC6+0E6yUn5ZYk7ciTgeLZ+wibPU+gLMl3b1hsviL08ONGgQ7IIyBLIC8gSQhJii9OQCqCTr7zFu75cr9pBT1uJB/goGTfRtVi32KsjaVbPyDaNTT12CuyMATsVYbbZQgd3Xb35U4ho+AA8VC6L2cbPrt4+53h2eNyyzRrIrhVBKq8ZjZfRXWejBUPQTSES47XqMc2ZrWJgbbHvVLjgc1g21hWz7MuCl3Gw6f3klQbbrYT7tuco7zhkRkOpVIpAmVRR5n4dHPo/7o+kj3kjacfDNZquj5hCmRljVLRNiFaKFj6LmG5nLPclez2A6DASdaOyjZ3qMwArPuEZZPSeYkaydOPc8WRq9gMnqXt8SGncgUWz0rO6VTD0r2l8Xd/7wCoViW5njPnAissNfdY2r/zeX93CbSaI0VKqiekaorCoP6Bchr8AwHH+gYfht/2KR8shZTFP9CIEkhREBh+gxjB0dsVjTAkusCFgdavSWXFmZxwnGjK30Da2Z8s9h+j22Kc/BYikJiBo/MbTN4ecC2+1wfTpz348jsnU4hY9XGAcy8LlRcCqnEz9x76FZNNR2ihe3uEq1OSouW0zigXa/KL23j6LWLh+uT3vsJ3hn5d0e9yVjdHqPcXh3quEv4BozNfU53d4zpDuaqiK2fRxd83sYdg43cSWXr45BypDZPf75j4NvaU2g52A+5+HKI7AbSi3LzDN/LQ83mYRDdsvnpMX+cHvIi1+lBO+uJXUaI6LbdkaUc1X1McrXBtSldnBKdo2pTgJUnao9Oeqqw5soZhpEz3TnHbxolsLT2pGXBOjxbIUcVknTpgSm6akusuRUvPxx9sQFjBcDVl2JQ0q4rtcko13bD48TfR2MvHDKK/ncJN7Mvty6bHzy7xTjI0GX5Q7O6nDFfHEXsznlo/3Ow+lMFnpuej0208pLQZ27qIUl/Ts3KK/vUDNTuESFBurCHXA5kZxjKVxXrF9W5CazXdeHottWViehJlmY5KxdtdRTeq4SDK8NOsjUDavEdUgXB+gT15TEhyvCkQ3iFcBz4+X8I7gk4I8qG0Imwf/3iHaHcIOyCshb5DeBeHmwHcWEKxNt7zWkfYrZTxv/fPgveEakIoKvz8mJBkD/82/jztHXK7Rmw3hG9WrF+fI5WnenSNTCz1uxO6bcHlu3O+vTshVRH1JEU49AXr0bMGIM9aVNGQLdaovEcVLSKxsY+TpDHgrRtCF3C7EltnNKvqgM8BDiVS7yRtG11O133Gm7qg9yqWPJOOk/kSk/SUsw066zBFh65q7C7D9SZmNPN1/D5NRt8ntF3G8puPKLKG04s4H3N/u2DX5Oy6jHpIKE3P751sD++tdYpf3p7yvk2YGMdEW267hK+20UtpD8ntPQw+8Gt3w192/w9SveBz/R/RQXEbXlMPt1jf4Hz79w579nZFLTRSK5wfaN2awX33c4VIkCLH6AlKpKSqwgfHpnvF4G7IzRFHPMbv/yd+e8//twacbnj7W7/4u8v9F6gewm+ddJUiJVVTJmFBI2p24QYbOjahxwyCtFMM3jB4aF2CEqPviYwolcoM3HYJt10EQT63hlLUTJ69x5yuEU8qwuwMkaSEJI3N/v1DuR/8bBsY+nGz7uLkc5WDKQhFCVrHzxnZRkgQGWQvondOMV5vkY9fJw2oHILHPOrB9qS7JaEWzOuEx8uK4FSkPHsRewDKjb2iGpwkWWwOGcS+NCCMR0wE4ngOWuOL6jtqNAHx/xc56mz8+/3mMZkgddw0glRx0+l7cJb5s1fQO0IXoBe4dU5/P8HWGeXdFmdjcLBWUyzWFD+4jCUpE60c3E2K7x5OOgvzLS/SYRyWld9hdnXLCX0dwapJ2dBtCtabiMxJzXDod2XKcj5ZUZ4s0UUbg0mnqa+O2NzNY6BIBtKqQV+0MMlQuYonXGvBW0JREqopYbxGwntEvUXYAa5vCPcWe1dSvz0FIB37C8+qBpHZg5tlcDEwB6uw25zgFLqqkUUHTkZ0TJOyuzrCO8kPpztUMsTTdW9iGXc0wfuoN4eSbgjikOkYM1At1lHMcbegaVO6PmFwmsVkTTrdoYyl+fYcmQzki1vk9AgvFUKliKFG1WtEu0O+eRlLiFkCSRIDSdtD5w/vVXs7i66uu4K2Hof4ZMAOMcPdW1EAB7uMJOmpqh1KW5KsQycD00/foB718b6fTEe0TgtC4i6e4KsFIckR0wUqu+K0+BuCBSHjoUfdzghBUOQN55M1WroYVMbD494Bdxhixq2Njf28UYatLgaYlrDeEd73UT3aJQ8zQ15w9e6c13cn8TUSmOUNH2WR+3b6OBKdHz95GzPP8b3R2lIu1qi0Jz1ZRSBpk2LrjNWrC/7TX/8YJQI/ePSGvGho2oymjeZviR6iX9f4Hg8j5eHVZsbrXc5HVc3vP469SSk9zimedSmlLrjtUl7uMlaD5L6PG8tERzTS27bnmjW96Hle/AeSkGC8RiI4Ec/pkjO6sKULWzb9299YVvN+Q9M1tP31mATEQ4YQGVKkGD3ByIfh2ERWnIkXaDRd/jEejyHBhIRW1Gy4/W+HFi1ExiJ9wYxTpn6CEQlWP8IHx2v5ivchpa2fcJGZg12BIPZ+9/4mEjhKIjjvNGsos4Y06+LJpxQPJ7P9sgNyu46b0mYTT0SNJ/TQv5tz/9UPSIqW+e99jZzUCLkECe7WMNxOCa7ADwqZDGSf3CByQbgTuE2OntcI3UNjGd5E4oCeDtG73UcptUgHktMlYdCEsQSULDbIoovN+DrFNSndcoLtDe0uPxB/k7wjv7jFyGV87a+vCBbs+wq7GyWm7ZRssSZ7foNvDNtfP4mePR+9Q5/eIfTYv/Cwl+ILHS+kkEAWULImyzt8ZzBVEwUHxA0pe3qNOE5j2aIcS1pPPWo88cZfLIsn4f3feY8eevA9uR2vufdgA5MhsPjJ14TO0N3GvkC7iR4r5WxzQNvLSY/QkM238Xos1qSnS2TVwfHo/VNNCVpH+wVt8FmFz+NmGqRGeIvoa4TrkGdrZLtD11tm1+8++N1lVCOa5PC7i+Djpu0duh/r2ntzO0D5DuNbsiZOugcLHzS7HpaPs0zxcyI3jPCQhcXPkczc26j2+uC67zNzVbYRG9Nq1OuvUbcr/N34Q3QgNIbbXz2nHW3IQxDfkec3bYb3km2XMbgPTNlENN0LQdAM5tBQ38vMXYhsOHV3TKIc83IbaRhVTVFdwq5G3NYPgFBAXS5RGjieEmaLeD0n46FPSoT3VD99Q9G843iT8WRVgfCH0qR38RqtXl1wdxt9me7v55htibOapGhZVF8ipkCRIrKA2HW4q3idXZ0y1FkspemB0/k9J+fXJHlHNhLX7795TNtknH38mvzZ1eGQ5DtDezPHO4XMO+TC4TuP3eYIGTirNuMeFvBeslgsOVb+O7Ylw1gtWRzdMfcSoxwTM2OR12Rpj1KWdCRcn7UZmba0bsHbJhIKMiVQQnCeRwzSZjBsh5RW1Kz9JVJoGrXDhJRzf0aGYcDTMbBKn3NlvmbwDc1wBcGTJadkaoYf6TD7fwuhI4QWF1oKeca5+iF5KKhCcai5dgx85f6Upr/ktPh9noXPmIczPldPkB8OJv2G9a8m4BTJE0oWmJAcoHUKjRKaIkwwIUGJ6BR523mubM2RyvnjI8HMOLJR7y5EQI7N/iJvyMbSRGgC9j8P2O0OM9+iT1p8I+mupthdzu2rFzR1TpbFwTKVDOSzKBm++4tP4007ymiHwWAHw2SxZPH5t3Ey30PYBNa/fMHd5RlnL15TVW8ItcDuMggyKuMaz7AusE2GzluSo008Ed/Mcb2h8hLT7+iWE5rl9DA9jgyUs3hjX71+xO16xtPrS879rwFwTYJrUy6/esZ2VzKbriknO5R2qOuRLzUOEzZXC9T9hHZd0WweZoaECCRpxN/IUSywLx94K+m3sZyYzbeoLA72hdsO6IBtDFT5B3LsSMmMb/B+0/UjqfiDvxMZcV4kAZVaGIaYJQUBmzJe7y7B1rEHoBcGJhPy4y25XeG+htUXz0inWwoZnSbdTY3vDKpqUPmAPlaER5H+LfouHjJWa+gsLKpoVwFRYLDb4r5wuCY9eCjtM6BYfo0Zm1tP8IN+AKpqF+GTIc637LOg/YyQGk3xVNkeJMP4uKHtN9T9kPF++SFmQyoZDlRlggTh8Z1BaI+qO4TZUH/7mLs354eekjGW8uSe4njF9vKI3SaWZtMkPhNFFasNF6MtgTIDOomqPDXOoIURRrkvPQvlojKuTxi2+cG/RsiAa1PWv3hxGNBVRUf6NGb+zRdn9JuC/PQec/oSP86uCe2RRzbK+DXI0tO/z1i9PiOb7Jh88hZholAFYCYD+XTL+mbB2/fnDGPDPmtTZtsU1XbxAKAVfiNZ/uo5APliTXa05nn2FU8HfYCdisTGkjRw8fQ2vtZSRGXjyE9U3mPa0dwsiygoVW9Jmi26avjZ42sQHl2236EyuF3GsM3xVtPvYqaQVjVCOdKi4XRdxftpVLypJEr6tY5IoY+mKx6V28ivG6LV+W4w9F4yTAxH/YTWTdjZp9TOc+Xi/lCJiAe7cw1rucbjmagzOrmls3f40CNQSKGZyFMqP2PQPVv9mDasuat/DgSkMKQhIw0pBk3HwK28ZRAduZwfsp9rdcnMH1OE+T+oh/sHAo4ad4WHgcoqfYb1HYPb4HzzgUrhwyX42+1HITKUzFEyp7c3f2e6tXdr1vqKXk7oQsuSS262/xnwZMkTMjVnEv4tx2gSKThRORMT+x42wNsmxXrBo7znUbFjmjUUVU1aNbHnIcG1KcOmjA/s/gJMGlTec6J8NFcbl9SRbCtkOOA99NEOUfBgFTmtcI8+BTsgvv0amoH5//kV8+Qd1B1hB2IhyP4wlnLk+y1sG8Q7R7hWECTD2OyePLoheEG/rujendJsSpo6p6h2LJ6+R1cNybM15Jp583XMSMZTsjCeJI+b1CcfXeKbNG5aQaCPt4gXRwCk3XuwDv/aYtcl6dk9i726a10SBk273E/ou7EXIg8bC8T5CZV1cTZJesIQS0x2k4+B+xFdm47qtrF+LwLWatqR1rz/+zC+D8fHtyw+eRO/76wBG3lgzaqKJZZqh3OK+9cXlPM1yc8u8fOjeDoG1O4r8ts1MukJjSIMmuWXT2m2JVneYPKOdLYjeXtJGBTt5TG2LWnWTxgGw/HztxSffhufhlQQmkDz9kmUl29zVNYxbMrDQK+3UV22n+ORo6ppX4L5cHVtinMKpRzGDIcNRozKRICmzg9qwXZIDrYG+1kjIQLTyYaiqqMiskvIy4bj3/8SOesRj6aEyRnV5zuq7q/HG1iC0oTpjJCkJEnCQkPQKUFPomhg7OkElYLUo5AgJch96guH0BcswlvwjmA75LAjb2NJUvQNwnvk3XWsFEDMYIoc9+gnoBOyH16Rt2sYHPQJou0JzbgHBGAIuOsEt83ZvDrj8vKcWb0+ADkj6cBH64yjFVM4XJsk7WPZsjPI6x1i4WAxRz72HKVfEHaK+tsL+k0Ry29ZF0ub2xyhHb6xUXk4CnHCBsIy2nKImYkBbDaN13S1gtYiEjDnq4es1UdCe7DRKsSNhwFTNVG2vt9vqiaKf7Qnm+64f3vGX339MbkZ+Pjpa9Ki4fziknOg3pbs6gJrNb011F3KX98f0zrJPHHMTFTnDj6yB4dNyRA8hVIkUrBzGh00g+ip/T0BT5k8IgRH73esupeQQkVUcUqhkOiR/txiZI4KGoHAE3B4OtFgQ4chw8iM2t+zcq+5ESmvVYUIH6bxf3f9o+ZwQmjJ1YJUVxRhQsCz4Y4ubP+W+U4ABEX6EUYWZHKKEfGXa+z9b0QpDPaarUwZVIOXx0gU0yw2qXO1IBEFpTcUGtIgKJSiNLF/k8rAcdmQjVPgRRKNlJZ3c6xVTNRLxCIle6FJKkPIZvjsU4JOYplFKqSP8mq8jU3T/X0kFSGbEqRmMBWoHKGy+EdolK4IgP3jljDq1IO3BN+CbUDnSF0RvIX1F4h2jbn+muLmPey2hOs2ZgZl5JXlTZyriaIFeRAyyNzjf/QZbn5KSPK4ScBYs+GwQcSNwcXNRGi6pMCX5/HfpQE/QL9E2C6+u1KBd7HE5B1ltx4VSA3CDsjtCrG8i32tdRMVRzcZrs5IPtkQPnqO8J6070i7HeX7PyW0D3NJMh2i6d34QMa7bpxR2SX4LolZyPFAqKP4IlhJfnpP8eiG+t0Jm9s5adFQHK1Ij9f4R08Zzj46bJSJlOTq11Ck+EcfI6Tk5N+sob+JPYR+gNkMd/ZjBFDYAbxjVt/FbEdK4Azu77FfK4TyVP/xJp5m6wZ6S1q0VGXs89HUMYPbl9L2Jbh9efBw8wToLKEfS5R8kN19+JTdRQWXTKLEen9KRofYAFfjtu9jpu6WGTLvkZ9UhMk53Uc/xS0+RiQLlJnH20GOAWO8L6TQSPHdYLiH7QoRT7sPv7YlBIcPFu/igdL79nBfB9tE7qFt4/1muxiMnhADUlIQTAUqQ2anCKEfnovuGtEtMddfon/9V/F6mQScJVxahk2ByTseP3lHUjToKmZj3e00InKKDpn0qLQnr3YxY5vsQIQ43Ho/ofjhO3hSIpIEkdSIbU26W6LrjH5V0WwK/KgOFCLE7HEvc1fuUH5UyRADRN6hTu7Aw+pPX7C+WWDMcPD7yc9iaXVYxyHj99885WY158n5e85+8C3DLufu9TneS4qqRpmBbLrDTGryomaWN+RJx+R4ic5bunUVGYE6VmnWmwnvRgv6p+V2dHiN9hE3bcabJonGhmxxwnIqTpkYgRIpM3fGla3ZylsUhnOeo1G8Nd+y87fkYoYJCQTQIiUXkkfFv43BiTkOS0nFRZriQsZiKOiC416saURNLqd45XEMdB+QwP++9Y8uqSWiYOoXPOYYAezCCQ0dTXL/t+TTAed7ElmRixmlrxhES83t3/u99ylaF7bU9nb8fooXxf+Nx8w5TiM9OjoqQqk9R8lAphzJKCXcDYZ1nzJLDZnpyXpDGGKN2E/n2JOnuPKUUD0FqZE6ZhjBxYCBbcC1MfC4Np72VBZPgCqPHyE+PKHFdTcIlWGyC5RMDw+uDxbrdjHQQMywJh/ji5ZBp7jpCcL2yI9344a/N9kZN4G2Qda7+EAOPaQZbn6KK48Pp1HZ3KO29wSp8FkJUo2nzTHgSBWrVsl2fA0GdLzGwdiHICVNfJ3e4v0Qg5iPf8SwQ7ZrZLdB37yFvkOfrqDvCOfPozpKJ/h0EsUHP+7G1xNfhx1/j++8tn1A9w7po2Wt9R7RN2TL21juGgJYz+yTl8yabyCXcHxEqC7onv0+Pj+OrwnoPi1QZ88PQTZIBfuTulTxo6nARHn8fgPcv9eiWyHbNWKokT/eEYC+mBKkIpjy4eSvfgMY8TcMwu0zAQBhu79ziPnwa4V38T7we5My96AmkypeP3hQlPUtsq1BSmwxjf2pYhFfd7B4P9p3WBunwvv7w3sJIFyUMgvbIbvRmM8Uh/sab5HdBrW+QdoB1Taxd2VHtWpTQ93GYLufy/kwmnpPWBwzPP6UoFJ8cYNXGeSn8VlLFgSV059rfFYh+ga1XYId0OoG3dzBpsNvJEIFRBVACMzT0a/oPmYQso8ZuCla8mdXiNzFfmKWEmYf4adHyOUNYr0FGzAXO7TfkXT38TA3Akr3ZVB0eLBHiH7kB/WmSD0cR1HO9A+/odq8OQhg7DZn8/ICZSzl80uysqd8/p6PnURNW8RMknHP5N+8gSHQfnOE3ebkz65QjwPZ5/ecujErVQI8ZDe3+DrFbguGOqVY1WhtDxl1CIJ1U7AbEt7WBa92kRr9qV6MWCVBqgDi2MeZLsjsz+ItJQQOz4wTpFKkIcdhKULBE3+MFoJMSZSIaJ107JUrEUfXfNDYoFB2xhAmTKWhVIraeZa+42E67zevf3TAKcKEIhQUWsV024EJktKc0g5X3ymxKZmgZUoaMgwJkt/ueS2QFHLBlBPqZME1HiVSypBh5B5hH+15WxeYJZLHYylpZyPG/bpLuO0UP5gYPjq5QmlHfzvD2C3Kfklyd0OoJrij8/FB6mNZoN7G027XxqbwtosNWB1ijffDTDE34+m3JdxbRAH+937MMD+L8lOVYq6/xrz5dmxgTwjaoMdZBvnmW8J1/aBk8x62EUhJlUY1EcS/X+9wlwaRLNHZS+R0jay3USn38ormizNk0pM8Wsbr837GUGeHklpytCb9tHuYSYCo4mlC/PnF+PPyUe7dtnHjSDPC3rpAqlg23G6ga7FfwLCckP/wW/TY9N3Paew3ppCkD7Mbf9/7/Z1sIGYH8WcqxNDHjGGIIg6RhjGbcMjdDaKvkd0mym1ffwWvbyAVsJhF7E+SHtR7vpjGn7EvI32wieMdso7XdL+xBqlQyR1BSkIxwSfZQ9CE78qLPwieH5ap9v+2v78egsjDjXTYxL9zDRzS+zjT8uG1sgNY+3CPth3iOs41mad/TpgtIvn8PmbMB9XgVVSi2V2G72MvbOiSQ59Hqlj2EcbGHpIXNOuK1fURzhYMw0n0mWozOmvITR/N0LqU69EEcZpFy+9ptSUvGhZP3lP+3v87vqRGRnTTsylhMgNtCFJGUvt2LL/t5dVDD97jd9GBV6YD2m2jIrPcg0cDQnn0ZMekiqZuInXx7GRdvEZtg9Cb+LM+6NsJ7xH7jPfwPuxZczJmWlLGoNpbRKIRaQZJEqXeUiLOPcI75HoJuy16e0fyKGY4cuKjyZIOY3l2TqgmDwHbe7LTFmwN+YQw2iMI774jXFKzAZkPY+8qGiEmYz+361Ksj9b2jTWsBsVVN6o5pSRIAYhoKzQE3vcdDo/F4fA0NFhpGejRxL05CxkOxxtuSX3Kx2pOoQTTJFpsrAfBbTeOnQhIleAsSzESWhf348ELxEMB9u9dvzXgaHUM+MOUaZG+YObnJGhq5/AEVqGhEx2lWCCLn3Hb/M3BeyFTMzIxIQRPJ1pSUXGUfMxOTdh0X/+d0poLlpwpj/0pUpzxIx1LakaISJDxgc4JEhWjuJGB686wGvQh0znLOuZGcZbFVLzeFXz7Zz9GSs/pk0uykxXDxtEu16jEkk63BKe4efmIusnZ1CXbLmOe7zg5uqPvE97cnmKd5GK+pMgbpkf3lKdLVCExxxtCG5C/+CWJ/yXDZYndlITZlnBU45YZy19FA7Pq0TW6bNlenh5wM0I5XG+4vT0iBMnZ+VVscJ4s0adb8CCy8RT912+Q8s1BSdZ9O2f1+izetN88AcCOEtDb2yNuthOeHN/wYvvXCBnoV7E/c/f+hO1upF6XNf7g2aPo+ih5HpxmcIqT2ZKTx+9RCZgiQSgV+0nHa4a3JeHbNcOmYHtdMfSGpo79nuOLa7LZLd7qw0T9Xv4LxP8/yoNN3pFNt7GObizBC4bNAtsb+jqjbTImRyvmn3+LSG4w5tt4Pcbmevv2iM27z9HJQH68IjjJ3ctjuibn5MklxZOvGdYFze08bgxjkzuZxA2r3RQHzhikcYhzsosDisP2gKdxe+DnqEDay5vxghDkd91RR7Bmu5pguyQyw7SLm7yxBwdVRDhQCkKIajTfG9p1hR/UQaiSlpCU0aV26BYobcmO1rGB/04T3MDu+jnruwXOSQarMdqO793A1bdPuF4uuK9L3tUlU9Pz6fE1edZS5A1KOzabis1ofa6Ep3eaq+2E1mkaqxm85CyvOas2vFnP+V+uZigB//3JitNyd1BppbczzJsdMu1R8xgA7V/2+GYdS6zpgCgHOB0VjPuA0/awswxXczZvTlGJJa+zKLQYEVT1m/jspFX9MH/jO4IPiM5C7+C+RvRX8UB19AAlpe9p/mTK6vUZxdGK4tFtlKvXWWzkP75DVAG/kvhdhixb5FE8rMnN+mH2SOmH4LgULH/+CVI5qhfvkHlP8+0J9f2U6dP3pL/3bvzZLvap7uIBQM2XyMndIZPymwcVqdL2wLuzvcH1BrW36Q4CMRh6r1n3CS93kj8Tf0IpFvw4fEpCdN01MjBPBJKUr/oNfz78vwCYp89jeyLMSH1GHlJyDGsa3otvIn9t+AHVkPKUFJXGZ6xQgiEEahuQASot0KM2qHWBlR24lO+x4u9H38A/EHCq5BEBTwhnBDwz/YTBW0IIEMDiuFaXNGHNun9LP9yTmAUL8xkKQyLiAOdX7f+KdUsSfUpujhBCMUk/xobuoAEvkmdU+pSUh7LFXnqw9gOBQAgJHgVEcyK8oLaSQQaM0CPGPq7OKzZ1iZaxoSulZ3l1TLYp4xtqHDoZRtnlwPR4SdHV6DuHlo7eab599/iAFTHKcnp2TT7dRu+PuymmTQ5MNsaP+mgX038TJbU6aSmXd7g2pb5Z4N5G+9u6ydk2Bbe7itwMvLh4S5bHgT6I9eA9xub+7RnO6cNU9Oknr8meXaPyntnTK1wXLQj8oAg+EhNOTm45P79Cpz3DLs665I9uEDJgipb5pojpuVMMQ5ytkTJwdHSP0i4qj0TAZH00+IIoiR4Uwy6P7oujZPluOeeL2zNc2FsRBxbXZwfooxSBZVPw5XqGDTDR8T1Z94adk1xkPS+mK6yXLEdhwXm5jXYGI99qGGK2FLxgt5oQgmBxcUN6tCY4RT6L9WPXJtg25eb6hHVTYMZ6PxBr/Twor1xnEMPIpUuGw/cHorvqoPnqy094vVxwlNccT1c0XcbL5VF0Fh1r/efFjqMRzuiDoLOG97soeHg+u2da1KzrgmVTHKbWBYFEx3vteLqiLOtIIBg9d5rRoyUZ+5F3dwuGwTCdbFmcX6Oz/sAgG94vGHY599fHvLk9ZZI2nJ3ekGYd2WKNLluen6x47gW2zujWJVL5SD74AMnC6wu6LkVKjzEDqYuzKNaraLHxwbO0yBoe5yVaBh5N1hxN1mRpjzYDyIDd5sjOsLdvt3VGGHTkv0kfxR0vB0TeIj4pCEUFWcz2E/+Gos6QOgpUAHxnInm9zqhHmrvOophALAQUeYTqag2bNWIzVlqWm0PvKwxRAGOt5ubVY5ovPiZNeuYndyT56E6KQ048clLj7g3NLxb40UEUOCgNdTGKDBDMfviSMGiadyfxnpKBJO/wfYL9djystCl4EQOu9Gx/9eRBhTrO6XRtGq1Klgu2XUaiLIm2lHl9GBxttiVDnxDuj1kPhkTCc/85FTnnuT4A4mPGAYmKrqA5/zMABRoCtN4yjJnPlg6F4nMfy24WR8vAzhoSKWlcYDN4ts7ytXqF9JI/7F5wkkpCiFlPy8Cb4S9w/rdbHfzWgDNX8dQsR+VB5aeUISNBMR3LJZX7GBc8S/0ptdmiiMqIndhyY7+icxusi32b3l4yuOV3ym5STjDyCB8GtvYaNHjpWfKeq/pPgdjEWoQz5mMzbPDwvgkUWvC8DCQy0HvBENRYfo0GV2/qnEx5Huc1hekZrKZo49Tv7JM3MWXN7FjTFQybkvWrp/zy+px52nI+WZOZnsViSZL2VGe3UXUyGk/pso0qsAy4OCJkOaGa4rIS2e4Q2zWifYdtMtp1yXY5pWkzfnX5mC82E46SgafVhlQP5GVDUjSRiyUC2/fHrO5npFkXXT2TgWS2Q6U9al4jqoAiZnGsC/qro+gEOpiIwkniG58lA/n5HTLvUWcWEs3ko7dMbIAhENoHrxchAiIbywFGPvimxDePsAK3Tbn5i89YLWcHU7LEWH5y8RbnJLsuo3ea97uK9Trhy03KX60dp6nmDxcDE+0ozYARnqflhiqNplpqnPvI9cDgFd+s5mysZqItlRmYbKestpPDMKAZswKVdcjpDpRnuJ9w88VzvJc8+/jlQdItk572ds795QlZ3nL02as4w5IPCOPxO3OomW+v4qxONt2hs46Pnr/k/PT6wITbdhn3I4X8k9mSSdaQ6MjViqRvTZF0zEejvb2njpKeVFuslwcvlp/flzQO/qfzKZ+fXcZNPhnQ2lGOWPwkjdYJ25tjbrbRQuJoDwbtDKFJ2FyesFrO8EFwNtpw7zPNo/k2WgVMUihLEt9R0MWyqbWxDLXqCK1AFy2T4+UhGwV4uh83GFErQ5tgu4SqrCmSaFL2+Px9xA5VNTrr6TcF96/P0dqRljXSWPKze+SIJBIZB2sPkURsTtAaPz2KpWcpKWeX0Fr8WhAaw/rXT+KQsBlYnF/jBk19NyN4QcqYfXwwS+d3kuFmyvpl5B/mi2hF/r/9pz/mz2+P+Gy65YdnlyRpT3m6jMO7Jx6mVexRdZbQGXbXc6TyZPMNQga2Vwu6Oj8o47LZhuLj95APZP6e4KLlvDA2UrObNNovbPOoTMy7uO+M17NtMna7WM422uKc4td3J7ypc15UO57P7w4UlT0k1nvBm13FX64MzsOPswXrwfG/d68JeF6ER0y05nrouJZ3eDxOWopQcaKOyJSgdZLBB4YQ6LwjEBjG47ohDpC2znPfw8thwxf8afy3kJOLGZ0P9P4BSejHnr0P3W8LKYgQwm/9hO/X9+v79f36fn2//iXWbxdNf7++X9+v79f36/v1L7S+Dzjfr+/X9+v79f36nazvA8736/v1/fp+fb9+J+v7gPP9+n59v75f36/fyfo+4Hy/vl/fr+/X9+t3sr4PON+v79f36/v1/fqdrP8fqBHqRiA820cAAAAASUVORK5CYII=", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "from scipy.io import wavfile\n", - "import scipy.signal\n", - "from playback import make_playback_animation\n", - "\n", - "# coding noise demonstration\n", - "for br in [\"inf\", 24000, 15000, 12000, 9000, 8000, 7000, 6000]:\n", - " fs, x = wavfile.read(f'data/a3_short_opus_{br}bps.wav')\n", - " spec, freqs, t, im = plt.specgram(x, NFFT=512, cmap='inferno', noverlap=256 + 128, pad_to=4096)\n", - " spec = 10*np.log10(spec)\n", - " \n", - " make_playback_animation(f'animations/opus_{br}bps.mp4', spec, len(x)/16)\n", - " " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b8408ca1", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.12" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/android/app/src/main/cpp/third_party/opus/dnn/torch/osce/test_model.py b/android/app/src/main/cpp/third_party/opus/dnn/torch/osce/test_model.py deleted file mode 100644 index 616a0ec..0000000 --- a/android/app/src/main/cpp/third_party/opus/dnn/torch/osce/test_model.py +++ /dev/null @@ -1,96 +0,0 @@ -""" -/* Copyright (c) 2023 Amazon - Written by Jan Buethe */ -/* - Redistribution and use in source and binary forms, with or without - modification, are permitted provided that the following conditions - are met: - - - Redistributions of source code must retain the above copyright - notice, this list of conditions and the following disclaimer. - - - Redistributions in binary form must reproduce the above copyright - notice, this list of conditions and the following disclaimer in the - documentation and/or other materials provided with the distribution. - - THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS - ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT - LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR - A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER - OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, - EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, - PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR - PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF - LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING - NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS - SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. -*/ -""" - -import argparse - -import torch - -from scipy.io import wavfile - - -from models import model_dict -from utils.silk_features import load_inference_data -from utils import endoscopy - -debug = False -if debug: - args = type('dummy', (object,), - { - 'input' : 'testitems/all_0_orig.se', - 'checkpoint' : 'testout/checkpoints/checkpoint_epoch_5.pth', - 'output' : 'out.wav', - })() -else: - parser = argparse.ArgumentParser() - - parser.add_argument('input', type=str, help='path to folder with features and signals') - parser.add_argument('checkpoint', type=str, help='checkpoint file') - parser.add_argument('output', type=str, help='output file') - parser.add_argument('--debug', action='store_true', help='enables debug output') - - - args = parser.parse_args() - - -torch.set_num_threads(2) - -input_folder = args.input -checkpoint_file = args.checkpoint - - -output_file = args.output -if not output_file.endswith('.wav'): - output_file += '.wav' - -checkpoint = torch.load(checkpoint_file, map_location="cpu") - -# check model -if not 'name' in checkpoint['setup']['model']: - print(f'warning: did not find model name entry in setup, using pitchpostfilter per default') - model_name = 'pitchpostfilter' -else: - model_name = checkpoint['setup']['model']['name'] - -model = model_dict[model_name](*checkpoint['setup']['model']['args'], **checkpoint['setup']['model']['kwargs']) - -model.load_state_dict(checkpoint['state_dict']) - -# generate model input -setup = checkpoint['setup'] -signal, features, periods, numbits = load_inference_data(input_folder, **setup['data']) - -if args.debug: - endoscopy.init() - -output = model.process(signal, features, periods, numbits, debug=args.debug) - -wavfile.write(output_file, 16000, output.cpu().numpy()) - -if args.debug: - endoscopy.close() diff --git a/android/app/src/main/cpp/third_party/opus/dnn/torch/osce/test_vocoder.py b/android/app/src/main/cpp/third_party/opus/dnn/torch/osce/test_vocoder.py deleted file mode 100644 index e71a5c3..0000000 --- a/android/app/src/main/cpp/third_party/opus/dnn/torch/osce/test_vocoder.py +++ /dev/null @@ -1,103 +0,0 @@ -""" -/* Copyright (c) 2023 Amazon - Written by Jan Buethe */ -/* - Redistribution and use in source and binary forms, with or without - modification, are permitted provided that the following conditions - are met: - - - Redistributions of source code must retain the above copyright - notice, this list of conditions and the following disclaimer. - - - Redistributions in binary form must reproduce the above copyright - notice, this list of conditions and the following disclaimer in the - documentation and/or other materials provided with the distribution. - - THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS - ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT - LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR - A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER - OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, - EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, - PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR - PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF - LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING - NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS - SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. -*/ -""" - -import argparse - -import torch - -from scipy.io import wavfile - -from time import time - - -from models import model_dict -from utils.lpcnet_features import load_lpcnet_features -from utils import endoscopy - -debug = False -if debug: - args = type('dummy', (object,), - { - 'input' : 'testitems/all_0_orig.se', - 'checkpoint' : 'testout/checkpoints/checkpoint_epoch_5.pth', - 'output' : 'out.wav', - })() -else: - parser = argparse.ArgumentParser() - - parser.add_argument('input', type=str, help='path to input features') - parser.add_argument('checkpoint', type=str, help='checkpoint file') - parser.add_argument('output', type=str, help='output file') - parser.add_argument('--debug', action='store_true', help='enables debug output') - - - args = parser.parse_args() - - -torch.set_num_threads(2) - -input_folder = args.input -checkpoint_file = args.checkpoint - - -output_file = args.output -if not output_file.endswith('.wav'): - output_file += '.wav' - -checkpoint = torch.load(checkpoint_file, map_location="cpu") - -# check model -if not 'name' in checkpoint['setup']['model']: - print(f'warning: did not find model name entry in setup, using pitchpostfilter per default') - model_name = 'pitchpostfilter' -else: - model_name = checkpoint['setup']['model']['name'] - -model = model_dict[model_name](*checkpoint['setup']['model']['args'], **checkpoint['setup']['model']['kwargs']) - -model.load_state_dict(checkpoint['state_dict']) - -# generate model input -setup = checkpoint['setup'] -testdata = load_lpcnet_features(input_folder) -features = testdata['features'] -periods = testdata['periods'] - -if args.debug: - endoscopy.init() - -start = time() -output = model.process(features, periods, debug=args.debug) -elapsed = time() - start -print(f"[timing] inference took {elapsed * 1000} ms") - -wavfile.write(output_file, 16000, output.cpu().numpy()) - -if args.debug: - endoscopy.close() diff --git a/android/app/src/main/cpp/third_party/opus/dnn/torch/osce/train_model.py b/android/app/src/main/cpp/third_party/opus/dnn/torch/osce/train_model.py deleted file mode 100644 index e8f94dc..0000000 --- a/android/app/src/main/cpp/third_party/opus/dnn/torch/osce/train_model.py +++ /dev/null @@ -1,307 +0,0 @@ -""" -/* Copyright (c) 2023 Amazon - Written by Jan Buethe */ -/* - Redistribution and use in source and binary forms, with or without - modification, are permitted provided that the following conditions - are met: - - - Redistributions of source code must retain the above copyright - notice, this list of conditions and the following disclaimer. - - - Redistributions in binary form must reproduce the above copyright - notice, this list of conditions and the following disclaimer in the - documentation and/or other materials provided with the distribution. - - THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS - ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT - LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR - A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER - OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, - EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, - PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR - PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF - LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING - NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS - SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. -*/ -""" - -seed=1888 - -import os -import argparse -import sys -import random -random.seed(seed) - -import yaml - -try: - import git - has_git = True -except: - has_git = False - -import torch -torch.manual_seed(seed) -torch.backends.cudnn.benchmark = False -from torch.optim.lr_scheduler import LambdaLR - -import numpy as np -np.random.seed(seed) - -from scipy.io import wavfile - -import pesq - -from data import SilkEnhancementSet -from models import model_dict -from engine.engine import train_one_epoch, evaluate - - -from utils.silk_features import load_inference_data -from utils.misc import count_parameters, count_nonzero_parameters - -from losses.stft_loss import MRSTFTLoss, MRLogMelLoss - - -parser = argparse.ArgumentParser() - -parser.add_argument('setup', type=str, help='setup yaml file') -parser.add_argument('output', type=str, help='output path') -parser.add_argument('--device', type=str, help='compute device', default=None) -parser.add_argument('--initial-checkpoint', type=str, help='initial checkpoint', default=None) -parser.add_argument('--testdata', type=str, help='path to features and signal for testing', default=None) -parser.add_argument('--no-redirect', action='store_true', help='disables re-direction of stdout') - -args = parser.parse_args() - - - -torch.set_num_threads(4) - -with open(args.setup, 'r') as f: - setup = yaml.load(f.read(), yaml.FullLoader) - -checkpoint_prefix = 'checkpoint' -output_prefix = 'output' -setup_name = 'setup.yml' -output_file='out.txt' - - -# check model -if not 'name' in setup['model']: - print(f'warning: did not find model entry in setup, using default PitchPostFilter') - model_name = 'pitchpostfilter' -else: - model_name = setup['model']['name'] - -# prepare output folder -if os.path.exists(args.output): - print("warning: output folder exists") - - reply = input('continue? (y/n): ') - while reply not in {'y', 'n'}: - reply = input('continue? (y/n): ') - - if reply == 'n': - os._exit(0) -else: - os.makedirs(args.output, exist_ok=True) - -checkpoint_dir = os.path.join(args.output, 'checkpoints') -os.makedirs(checkpoint_dir, exist_ok=True) - -# add repo info to setup -if has_git: - working_dir = os.path.split(__file__)[0] - try: - repo = git.Repo(working_dir, search_parent_directories=True) - setup['repo'] = dict() - hash = repo.head.object.hexsha - urls = list(repo.remote().urls) - is_dirty = repo.is_dirty() - - if is_dirty: - print("warning: repo is dirty") - with open(os.path.join(args.output, 'repo.diff'), "w") as f: - f.write(repo.git.execute(["git", "diff"])) - - setup['repo']['hash'] = hash - setup['repo']['urls'] = urls - setup['repo']['dirty'] = is_dirty - except: - has_git = False - -# dump setup -with open(os.path.join(args.output, setup_name), 'w') as f: - yaml.dump(setup, f) - -ref = None -if args.testdata is not None: - - testsignal, features, periods, numbits = load_inference_data(args.testdata, **setup['data']) - - inference_test = True - inference_folder = os.path.join(args.output, 'inference_test') - os.makedirs(os.path.join(args.output, 'inference_test'), exist_ok=True) - - try: - ref = np.fromfile(os.path.join(args.testdata, 'clean.s16'), dtype=np.int16) - except: - pass -else: - inference_test = False - -# training parameters -batch_size = setup['training']['batch_size'] -epochs = setup['training']['epochs'] -lr = setup['training']['lr'] -lr_decay_factor = setup['training']['lr_decay_factor'] - -# load training dataset -data_config = setup['data'] -data = SilkEnhancementSet(setup['dataset'], **data_config) - -# load validation dataset if given -if 'validation_dataset' in setup: - validation_data = SilkEnhancementSet(setup['validation_dataset'], **data_config) - - validation_dataloader = torch.utils.data.DataLoader(validation_data, batch_size=batch_size, drop_last=True, num_workers=8) - - run_validation = True -else: - run_validation = False - -# create model -model = model_dict[model_name](*setup['model']['args'], **setup['model']['kwargs']) - -if args.initial_checkpoint is not None: - print(f"loading state dict from {args.initial_checkpoint}...") - chkpt = torch.load(args.initial_checkpoint, map_location='cpu') - model.load_state_dict(chkpt['state_dict']) - -# set compute device -if type(args.device) == type(None): - device = torch.device("cuda" if torch.cuda.is_available() else "cpu") -else: - device = torch.device(args.device) - -# push model to device -model.to(device) - -# dataloader -dataloader = torch.utils.data.DataLoader(data, batch_size=batch_size, drop_last=True, shuffle=True, num_workers=8) - -# optimizer is introduced to trainable parameters -parameters = [p for p in model.parameters() if p.requires_grad] -optimizer = torch.optim.Adam(parameters, lr=lr) - -# learning rate scheduler -scheduler = LambdaLR(optimizer=optimizer, lr_lambda=lambda x : 1 / (1 + lr_decay_factor * x)) - -# loss -w_l1 = setup['training']['loss']['w_l1'] -w_lm = setup['training']['loss']['w_lm'] -w_slm = setup['training']['loss']['w_slm'] -w_sc = setup['training']['loss']['w_sc'] -w_logmel = setup['training']['loss']['w_logmel'] -w_wsc = setup['training']['loss']['w_wsc'] -w_xcorr = setup['training']['loss']['w_xcorr'] -w_sxcorr = setup['training']['loss']['w_sxcorr'] -w_l2 = setup['training']['loss']['w_l2'] - -w_sum = w_l1 + w_lm + w_sc + w_logmel + w_wsc + w_slm + w_xcorr + w_sxcorr + w_l2 - -stftloss = MRSTFTLoss(sc_weight=w_sc, log_mag_weight=w_lm, wsc_weight=w_wsc, smooth_log_mag_weight=w_slm, sxcorr_weight=w_sxcorr).to(device) -logmelloss = MRLogMelLoss().to(device) - -def xcorr_loss(y_true, y_pred): - dims = list(range(1, len(y_true.shape))) - - loss = 1 - torch.sum(y_true * y_pred, dim=dims) / torch.sqrt(torch.sum(y_true ** 2, dim=dims) * torch.sum(y_pred ** 2, dim=dims) + 1e-9) - - return torch.mean(loss) - -def td_l2_norm(y_true, y_pred): - dims = list(range(1, len(y_true.shape))) - - loss = torch.mean((y_true - y_pred) ** 2, dim=dims) / (torch.mean(y_pred ** 2, dim=dims) ** .5 + 1e-6) - - return loss.mean() - -def td_l1(y_true, y_pred, pow=0): - dims = list(range(1, len(y_true.shape))) - tmp = torch.mean(torch.abs(y_true - y_pred), dim=dims) / ((torch.mean(torch.abs(y_pred), dim=dims) + 1e-9) ** pow) - - return torch.mean(tmp) - -def criterion(x, y): - - return (w_l1 * td_l1(x, y, pow=1) + stftloss(x, y) + w_logmel * logmelloss(x, y) - + w_xcorr * xcorr_loss(x, y) + w_l2 * td_l2_norm(x, y)) / w_sum - - - -# model checkpoint -checkpoint = { - 'setup' : setup, - 'state_dict' : model.state_dict(), - 'loss' : -1 -} - - - - -if not args.no_redirect: - print(f"re-directing output to {os.path.join(args.output, output_file)}") - sys.stdout = open(os.path.join(args.output, output_file), "w") - -print("summary:") - -print(f"{count_parameters(model.cpu()) / 1e6:5.3f} M parameters") -if hasattr(model, 'flop_count'): - print(f"{model.flop_count(16000) / 1e6:5.3f} MFLOPS") - -if ref is not None: - noisy = np.fromfile(os.path.join(args.testdata, 'noisy.s16'), dtype=np.int16) - initial_mos = pesq.pesq(16000, ref, noisy, mode='wb') - print(f"initial MOS (PESQ): {initial_mos}") - -best_loss = 1e9 - -for ep in range(1, epochs + 1): - print(f"training epoch {ep}...") - new_loss = train_one_epoch(model, criterion, optimizer, dataloader, device, scheduler) - - - # save checkpoint - checkpoint['state_dict'] = model.state_dict() - checkpoint['loss'] = new_loss - - if run_validation: - print("running validation...") - validation_loss = evaluate(model, criterion, validation_dataloader, device) - checkpoint['validation_loss'] = validation_loss - - if validation_loss < best_loss: - torch.save(checkpoint, os.path.join(checkpoint_dir, checkpoint_prefix + f'_best.pth')) - best_loss = validation_loss - - if inference_test: - print("running inference test...") - out = model.process(testsignal, features, periods, numbits).cpu().numpy() - wavfile.write(os.path.join(inference_folder, f'{model_name}_epoch_{ep}.wav'), 16000, out) - if ref is not None: - mos = pesq.pesq(16000, ref, out, mode='wb') - print(f"MOS (PESQ): {mos}") - - - torch.save(checkpoint, os.path.join(checkpoint_dir, checkpoint_prefix + f'_epoch_{ep}.pth')) - torch.save(checkpoint, os.path.join(checkpoint_dir, checkpoint_prefix + f'_last.pth')) - - - print(f"non-zero parameters: {count_nonzero_parameters(model)}\n") - -print('Done') diff --git a/android/app/src/main/cpp/third_party/opus/dnn/torch/osce/train_vocoder.py b/android/app/src/main/cpp/third_party/opus/dnn/torch/osce/train_vocoder.py deleted file mode 100644 index 590e6d1..0000000 --- a/android/app/src/main/cpp/third_party/opus/dnn/torch/osce/train_vocoder.py +++ /dev/null @@ -1,287 +0,0 @@ -""" -/* Copyright (c) 2023 Amazon - Written by Jan Buethe */ -/* - Redistribution and use in source and binary forms, with or without - modification, are permitted provided that the following conditions - are met: - - - Redistributions of source code must retain the above copyright - notice, this list of conditions and the following disclaimer. - - - Redistributions in binary form must reproduce the above copyright - notice, this list of conditions and the following disclaimer in the - documentation and/or other materials provided with the distribution. - - THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS - ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT - LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR - A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER - OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, - EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, - PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR - PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF - LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING - NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS - SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. -*/ -""" - -import os -import argparse -import sys - -import yaml - -try: - import git - has_git = True -except: - has_git = False - -import torch -from torch.optim.lr_scheduler import LambdaLR - -from scipy.io import wavfile - -import pesq - -from data import LPCNetVocodingDataset -from models import model_dict -from engine.vocoder_engine import train_one_epoch, evaluate - - -from utils.lpcnet_features import load_lpcnet_features -from utils.misc import count_parameters - -from losses.stft_loss import MRSTFTLoss, MRLogMelLoss - - -parser = argparse.ArgumentParser() - -parser.add_argument('setup', type=str, help='setup yaml file') -parser.add_argument('output', type=str, help='output path') -parser.add_argument('--device', type=str, help='compute device', default=None) -parser.add_argument('--initial-checkpoint', type=str, help='initial checkpoint', default=None) -parser.add_argument('--test-features', type=str, help='path to features for testing', default=None) -parser.add_argument('--no-redirect', action='store_true', help='disables re-direction of stdout') - -args = parser.parse_args() - - -torch.set_num_threads(4) - -with open(args.setup, 'r') as f: - setup = yaml.load(f.read(), yaml.FullLoader) - -checkpoint_prefix = 'checkpoint' -output_prefix = 'output' -setup_name = 'setup.yml' -output_file='out.txt' - - -# check model -if not 'name' in setup['model']: - print(f'warning: did not find model entry in setup, using default PitchPostFilter') - model_name = 'pitchpostfilter' -else: - model_name = setup['model']['name'] - -# prepare output folder -if os.path.exists(args.output): - print("warning: output folder exists") - - reply = input('continue? (y/n): ') - while reply not in {'y', 'n'}: - reply = input('continue? (y/n): ') - - if reply == 'n': - os._exit() -else: - os.makedirs(args.output, exist_ok=True) - -checkpoint_dir = os.path.join(args.output, 'checkpoints') -os.makedirs(checkpoint_dir, exist_ok=True) - -# add repo info to setup -if has_git: - working_dir = os.path.split(__file__)[0] - try: - repo = git.Repo(working_dir, search_parent_directories=True) - setup['repo'] = dict() - hash = repo.head.object.hexsha - urls = list(repo.remote().urls) - is_dirty = repo.is_dirty() - - if is_dirty: - print("warning: repo is dirty") - - setup['repo']['hash'] = hash - setup['repo']['urls'] = urls - setup['repo']['dirty'] = is_dirty - except: - has_git = False - -# dump setup -with open(os.path.join(args.output, setup_name), 'w') as f: - yaml.dump(setup, f) - -ref = None -# prepare inference test if wanted -inference_test = False -if type(args.test_features) != type(None): - test_features = load_lpcnet_features(args.test_features) - features = test_features['features'] - periods = test_features['periods'] - inference_folder = os.path.join(args.output, 'inference_test') - os.makedirs(inference_folder, exist_ok=True) - inference_test = True - - -# training parameters -batch_size = setup['training']['batch_size'] -epochs = setup['training']['epochs'] -lr = setup['training']['lr'] -lr_decay_factor = setup['training']['lr_decay_factor'] - -# load training dataset -data_config = setup['data'] -data = LPCNetVocodingDataset(setup['dataset'], **data_config) - -# load validation dataset if given -if 'validation_dataset' in setup: - validation_data = LPCNetVocodingDataset(setup['validation_dataset'], **data_config) - - validation_dataloader = torch.utils.data.DataLoader(validation_data, batch_size=batch_size, drop_last=True, num_workers=8) - - run_validation = True -else: - run_validation = False - -# create model -model = model_dict[model_name](*setup['model']['args'], **setup['model']['kwargs']) - -if args.initial_checkpoint is not None: - print(f"loading state dict from {args.initial_checkpoint}...") - chkpt = torch.load(args.initial_checkpoint, map_location='cpu') - model.load_state_dict(chkpt['state_dict']) - -# set compute device -if type(args.device) == type(None): - device = torch.device("cuda" if torch.cuda.is_available() else "cpu") -else: - device = torch.device(args.device) - -# push model to device -model.to(device) - -# dataloader -dataloader = torch.utils.data.DataLoader(data, batch_size=batch_size, drop_last=True, shuffle=True, num_workers=8) - -# optimizer is introduced to trainable parameters -parameters = [p for p in model.parameters() if p.requires_grad] -optimizer = torch.optim.Adam(parameters, lr=lr) - -# learning rate scheduler -scheduler = LambdaLR(optimizer=optimizer, lr_lambda=lambda x : 1 / (1 + lr_decay_factor * x)) - -# loss -w_l1 = setup['training']['loss']['w_l1'] -w_lm = setup['training']['loss']['w_lm'] -w_slm = setup['training']['loss']['w_slm'] -w_sc = setup['training']['loss']['w_sc'] -w_logmel = setup['training']['loss']['w_logmel'] -w_wsc = setup['training']['loss']['w_wsc'] -w_xcorr = setup['training']['loss']['w_xcorr'] -w_sxcorr = setup['training']['loss']['w_sxcorr'] -w_l2 = setup['training']['loss']['w_l2'] - -w_sum = w_l1 + w_lm + w_sc + w_logmel + w_wsc + w_slm + w_xcorr + w_sxcorr + w_l2 - -stftloss = MRSTFTLoss(sc_weight=w_sc, log_mag_weight=w_lm, wsc_weight=w_wsc, smooth_log_mag_weight=w_slm, sxcorr_weight=w_sxcorr).to(device) -logmelloss = MRLogMelLoss().to(device) - -def xcorr_loss(y_true, y_pred): - dims = list(range(1, len(y_true.shape))) - - loss = 1 - torch.sum(y_true * y_pred, dim=dims) / torch.sqrt(torch.sum(y_true ** 2, dim=dims) * torch.sum(y_pred ** 2, dim=dims) + 1e-9) - - return torch.mean(loss) - -def td_l2_norm(y_true, y_pred): - dims = list(range(1, len(y_true.shape))) - - loss = torch.mean((y_true - y_pred) ** 2, dim=dims) / (torch.mean(y_pred ** 2, dim=dims) ** .5 + 1e-6) - - return loss.mean() - -def td_l1(y_true, y_pred, pow=0): - dims = list(range(1, len(y_true.shape))) - tmp = torch.mean(torch.abs(y_true - y_pred), dim=dims) / ((torch.mean(torch.abs(y_pred), dim=dims) + 1e-9) ** pow) - - return torch.mean(tmp) - -def criterion(x, y): - - return (w_l1 * td_l1(x, y, pow=1) + stftloss(x, y) + w_logmel * logmelloss(x, y) - + w_xcorr * xcorr_loss(x, y) + w_l2 * td_l2_norm(x, y)) / w_sum - - - -# model checkpoint -checkpoint = { - 'setup' : setup, - 'state_dict' : model.state_dict(), - 'loss' : -1 -} - - -if not args.no_redirect: - print(f"re-directing output to {os.path.join(args.output, output_file)}") - sys.stdout = open(os.path.join(args.output, output_file), "w") - -print("summary:") - -print(f"{count_parameters(model.cpu()) / 1e6:5.3f} M parameters") -if hasattr(model, 'flop_count'): - print(f"{model.flop_count(16000) / 1e6:5.3f} MFLOPS") - -if ref is not None: - pass - -best_loss = 1e9 - -for ep in range(1, epochs + 1): - print(f"training epoch {ep}...") - new_loss = train_one_epoch(model, criterion, optimizer, dataloader, device, scheduler) - - - # save checkpoint - checkpoint['state_dict'] = model.state_dict() - checkpoint['loss'] = new_loss - - if run_validation: - print("running validation...") - validation_loss = evaluate(model, criterion, validation_dataloader, device) - checkpoint['validation_loss'] = validation_loss - - if validation_loss < best_loss: - torch.save(checkpoint, os.path.join(checkpoint_dir, checkpoint_prefix + f'_best.pth')) - best_loss = validation_loss - - if inference_test: - print("running inference test...") - out = model.process(features, periods).cpu().numpy() - wavfile.write(os.path.join(inference_folder, f'{model_name}_epoch_{ep}.wav'), 16000, out) - if ref is not None: - mos = pesq.pesq(16000, ref, out, mode='wb') - print(f"MOS (PESQ): {mos}") - - - torch.save(checkpoint, os.path.join(checkpoint_dir, checkpoint_prefix + f'_epoch_{ep}.pth')) - torch.save(checkpoint, os.path.join(checkpoint_dir, checkpoint_prefix + f'_last.pth')) - - - print() - -print('Done') diff --git a/android/app/src/main/cpp/third_party/opus/dnn/torch/osce/utils/ada_conv.py b/android/app/src/main/cpp/third_party/opus/dnn/torch/osce/utils/ada_conv.py deleted file mode 100644 index b5b93f8..0000000 --- a/android/app/src/main/cpp/third_party/opus/dnn/torch/osce/utils/ada_conv.py +++ /dev/null @@ -1,71 +0,0 @@ -""" -/* Copyright (c) 2023 Amazon - Written by Jean-Marc Valin */ -/* - Redistribution and use in source and binary forms, with or without - modification, are permitted provided that the following conditions - are met: - - - Redistributions of source code must retain the above copyright - notice, this list of conditions and the following disclaimer. - - - Redistributions in binary form must reproduce the above copyright - notice, this list of conditions and the following disclaimer in the - documentation and/or other materials provided with the distribution. - - THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS - ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT - LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR - A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER - OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, - EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, - PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR - PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF - LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING - NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS - SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. -*/ -""" - -import torch -from torch import nn -import torch.nn.functional as F - -# x is (batch, nb_in_channels, nb_frames*frame_size) -# kernels is (batch, nb_out_channels, nb_in_channels, nb_frames, coeffs) -def adaconv_kernel(x, kernels, half_window, fft_size=256): - device=x.device - overlap_size=half_window.size(-1) - nb_frames=kernels.size(3) - nb_batches=kernels.size(0) - nb_out_channels=kernels.size(1) - nb_in_channels=kernels.size(2) - kernel_size = kernels.size(-1) - x = x.reshape(nb_batches, 1, nb_in_channels, nb_frames, -1) - frame_size = x.size(-1) - # build window: [zeros, rising window, ones, falling window, zeros] - window = torch.cat( - [ - torch.zeros(frame_size, device=device), - half_window, - torch.ones(frame_size - overlap_size, device=device), - 1 - half_window, - torch.zeros(fft_size - 2 * frame_size - overlap_size,device=device) - ]) - x_prev = torch.cat([torch.zeros_like(x[:, :, :, :1, :]), x[:, :, :, :-1, :]], dim=-2) - x_next = torch.cat([x[:, :, :, 1:, :overlap_size], torch.zeros_like(x[:, :, :, -1:, :overlap_size])], dim=-2) - x_padded = torch.cat([x_prev, x, x_next, torch.zeros(nb_batches, 1, nb_in_channels, nb_frames, fft_size - 2 * frame_size - overlap_size, device=device)], -1) - k_padded = torch.cat([torch.flip(kernels, [-1]), torch.zeros(nb_batches, nb_out_channels, nb_in_channels, nb_frames, fft_size-kernel_size, device=device)], dim=-1) - - # compute convolution - X = torch.fft.rfft(x_padded, dim=-1) - K = torch.fft.rfft(k_padded, dim=-1) - - out = torch.fft.irfft(X * K, dim=-1) - # combine in channels - out = torch.sum(out, dim=2) - # apply the cross-fading - out = window.reshape(1, 1, 1, -1)*out - crossfaded = out[:,:,:,frame_size:2*frame_size] + torch.cat([torch.zeros(nb_batches, nb_out_channels, 1, frame_size, device=device), out[:, :, :-1, 2*frame_size:3*frame_size]], dim=-2) - - return crossfaded.reshape(nb_batches, nb_out_channels, -1) \ No newline at end of file diff --git a/android/app/src/main/cpp/third_party/opus/dnn/torch/osce/utils/complexity.py b/android/app/src/main/cpp/third_party/opus/dnn/torch/osce/utils/complexity.py deleted file mode 100644 index 4ee6e3f..0000000 --- a/android/app/src/main/cpp/third_party/opus/dnn/torch/osce/utils/complexity.py +++ /dev/null @@ -1,8 +0,0 @@ - - -def _conv1d_flop_count(layer, rate): - return 2 * ((layer.in_channels + 1) * layer.out_channels * rate / layer.stride[0] ) * layer.kernel_size[0] - - -def _dense_flop_count(layer, rate): - return 2 * ((layer.in_features + 1) * layer.out_features * rate ) \ No newline at end of file diff --git a/android/app/src/main/cpp/third_party/opus/dnn/torch/osce/utils/endoscopy.py b/android/app/src/main/cpp/third_party/opus/dnn/torch/osce/utils/endoscopy.py deleted file mode 100644 index 05dd475..0000000 --- a/android/app/src/main/cpp/third_party/opus/dnn/torch/osce/utils/endoscopy.py +++ /dev/null @@ -1,205 +0,0 @@ -""" module for inspecting models during inference """ - -import os - -import yaml -import matplotlib.pyplot as plt -import matplotlib.animation as animation - -import torch -import numpy as np - -# stores entries {key : {'fid' : fid, 'fs' : fs, 'dim' : dim, 'dtype' : dtype}} -_state = dict() -_folder = 'endoscopy' - -def get_gru_gates(gru, input, state): - hidden_size = gru.hidden_size - - direct = torch.matmul(gru.weight_ih_l0, input.squeeze()) - recurrent = torch.matmul(gru.weight_hh_l0, state.squeeze()) - - # reset gate - start, stop = 0 * hidden_size, 1 * hidden_size - reset_gate = torch.sigmoid(direct[start : stop] + gru.bias_ih_l0[start : stop] + recurrent[start : stop] + gru.bias_hh_l0[start : stop]) - - # update gate - start, stop = 1 * hidden_size, 2 * hidden_size - update_gate = torch.sigmoid(direct[start : stop] + gru.bias_ih_l0[start : stop] + recurrent[start : stop] + gru.bias_hh_l0[start : stop]) - - # new gate - start, stop = 2 * hidden_size, 3 * hidden_size - new_gate = torch.tanh(direct[start : stop] + gru.bias_ih_l0[start : stop] + reset_gate * (recurrent[start : stop] + gru.bias_hh_l0[start : stop])) - - return {'reset_gate' : reset_gate, 'update_gate' : update_gate, 'new_gate' : new_gate} - - -def init(folder='endoscopy'): - """ sets up output folder for endoscopy data """ - - global _folder - _folder = folder - - if not os.path.exists(folder): - os.makedirs(folder) - else: - print(f"warning: endoscopy folder {folder} exists. Content may be lost or inconsistent results may occur.") - -def write_data(key, data, fs): - """ appends data to previous data written under key """ - - global _state - - # convert to numpy if torch.Tensor is given - if isinstance(data, torch.Tensor): - data = data.detach().numpy() - - if not key in _state: - _state[key] = { - 'fid' : open(os.path.join(_folder, key + '.bin'), 'wb'), - 'fs' : fs, - 'dim' : tuple(data.shape), - 'dtype' : str(data.dtype) - } - - with open(os.path.join(_folder, key + '.yml'), 'w') as f: - f.write(yaml.dump({'fs' : fs, 'dim' : tuple(data.shape), 'dtype' : str(data.dtype).split('.')[-1]})) - else: - if _state[key]['fs'] != fs: - raise ValueError(f"fs changed for key {key}: {_state[key]['fs']} vs. {fs}") - if _state[key]['dtype'] != str(data.dtype): - raise ValueError(f"dtype changed for key {key}: {_state[key]['dtype']} vs. {str(data.dtype)}") - if _state[key]['dim'] != tuple(data.shape): - raise ValueError(f"dim changed for key {key}: {_state[key]['dim']} vs. {tuple(data.shape)}") - - _state[key]['fid'].write(data.tobytes()) - -def close(folder='endoscopy'): - """ clean up """ - for key in _state.keys(): - _state[key]['fid'].close() - - -def read_data(folder='endoscopy'): - """ retrieves written data as numpy arrays """ - - - keys = [name[:-4] for name in os.listdir(folder) if name.endswith('.yml')] - - return_dict = dict() - - for key in keys: - with open(os.path.join(folder, key + '.yml'), 'r') as f: - value = yaml.load(f.read(), yaml.FullLoader) - - with open(os.path.join(folder, key + '.bin'), 'rb') as f: - data = np.frombuffer(f.read(), dtype=value['dtype']) - - value['data'] = data.reshape((-1,) + value['dim']) - - return_dict[key] = value - - return return_dict - -def get_best_reshape(shape, target_ratio=1): - """ calculated the best 2d reshape of shape given the target ratio (rows/cols)""" - - if len(shape) > 1: - pixel_count = 1 - for s in shape: - pixel_count *= s - else: - pixel_count = shape[0] - - if pixel_count == 1: - return (1,) - - num_columns = int((pixel_count / target_ratio)**.5) - - while (pixel_count % num_columns): - num_columns -= 1 - - num_rows = pixel_count // num_columns - - return (num_rows, num_columns) - -def get_type_and_shape(shape): - - # can happen if data is one dimensional - if len(shape) == 0: - shape = (1,) - - # calculate pixel count - if len(shape) > 1: - pixel_count = 1 - for s in shape: - pixel_count *= s - else: - pixel_count = shape[0] - - if pixel_count == 1: - return 'plot', (1, ) - - # stay with shape if already 2-dimensional - if len(shape) == 2: - if (shape[0] != pixel_count) or (shape[1] != pixel_count): - return 'image', shape - - return 'image', get_best_reshape(shape) - -def make_animation(data, filename, start_index=80, stop_index=-80, interval=20, half_signal_window_length=80): - - # determine plot setup - num_keys = len(data.keys()) - - num_rows = int((num_keys * 3/4) ** .5) - - num_cols = (num_keys + num_rows - 1) // num_rows - - fig, axs = plt.subplots(num_rows, num_cols) - fig.set_size_inches(num_cols * 5, num_rows * 5) - - display = dict() - - fs_max = max([val['fs'] for val in data.values()]) - - num_samples = max([val['data'].shape[0] for val in data.values()]) - - keys = sorted(data.keys()) - - # inspect data - for i, key in enumerate(keys): - axs[i // num_cols, i % num_cols].title.set_text(key) - - display[key] = dict() - - display[key]['type'], display[key]['shape'] = get_type_and_shape(data[key]['dim']) - display[key]['down_factor'] = data[key]['fs'] / fs_max - - start_index = max(start_index, half_signal_window_length) - while stop_index < 0: - stop_index += num_samples - - stop_index = min(stop_index, num_samples - half_signal_window_length) - - # actual plotting - frames = [] - for index in range(start_index, stop_index): - ims = [] - for i, key in enumerate(keys): - feature_index = int(round(index * display[key]['down_factor'])) - - if display[key]['type'] == 'plot': - ims.append(axs[i // num_cols, i % num_cols].plot(data[key]['data'][index - half_signal_window_length : index + half_signal_window_length], marker='P', markevery=[half_signal_window_length], animated=True, color='blue')[0]) - - elif display[key]['type'] == 'image': - ims.append(axs[i // num_cols, i % num_cols].imshow(data[key]['data'][index].reshape(display[key]['shape']), animated=True)) - - frames.append(ims) - - ani = animation.ArtistAnimation(fig, frames, interval=interval, blit=True, repeat_delay=1000) - - if not filename.endswith('.mp4'): - filename += '.mp4' - - ani.save(filename) \ No newline at end of file diff --git a/android/app/src/main/cpp/third_party/opus/dnn/torch/osce/utils/layers/limited_adaptive_comb1d.py b/android/app/src/main/cpp/third_party/opus/dnn/torch/osce/utils/layers/limited_adaptive_comb1d.py deleted file mode 100644 index 0d87ca1..0000000 --- a/android/app/src/main/cpp/third_party/opus/dnn/torch/osce/utils/layers/limited_adaptive_comb1d.py +++ /dev/null @@ -1,230 +0,0 @@ -""" -/* Copyright (c) 2023 Amazon - Written by Jan Buethe */ -/* - Redistribution and use in source and binary forms, with or without - modification, are permitted provided that the following conditions - are met: - - - Redistributions of source code must retain the above copyright - notice, this list of conditions and the following disclaimer. - - - Redistributions in binary form must reproduce the above copyright - notice, this list of conditions and the following disclaimer in the - documentation and/or other materials provided with the distribution. - - THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS - ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT - LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR - A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER - OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, - EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, - PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR - PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF - LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING - NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS - SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. -*/ -""" - -import torch -from torch import nn -import torch.nn.functional as F - -from utils.endoscopy import write_data -from utils.softquant import soft_quant - -class LimitedAdaptiveComb1d(nn.Module): - COUNTER = 1 - - def __init__(self, - kernel_size, - feature_dim, - frame_size=160, - overlap_size=40, - padding=None, - max_lag=256, - name=None, - gain_limit_db=10, - global_gain_limits_db=[-6, 6], - norm_p=2, - softquant=False, - apply_weight_norm=False, - **kwargs): - """ - - Parameters: - ----------- - - feature_dim : int - dimension of features from which kernels, biases and gains are computed - - frame_size : int, optional - frame size, defaults to 160 - - overlap_size : int, optional - overlap size for filter cross-fade. Cross-fade is done on the first overlap_size samples of every frame, defaults to 40 - - use_bias : bool, optional - if true, biases will be added to output channels. Defaults to True - - padding : List[int, int], optional - left and right padding. Defaults to [(kernel_size - 1) // 2, kernel_size - 1 - (kernel_size - 1) // 2] - - max_lag : int, optional - maximal pitch lag, defaults to 256 - - have_a0 : bool, optional - If true, the filter coefficient a0 will be learned as a positive gain (requires in_channels == out_channels). Otherwise, a0 is set to 0. Defaults to False - - name: str or None, optional - specifies a name attribute for the module. If None the name is auto generated as comb_1d_COUNT, where COUNT is an instance counter for LimitedAdaptiveComb1d - - """ - - super(LimitedAdaptiveComb1d, self).__init__() - - self.in_channels = 1 - self.out_channels = 1 - self.feature_dim = feature_dim - self.kernel_size = kernel_size - self.frame_size = frame_size - self.overlap_size = overlap_size - self.max_lag = max_lag - self.limit_db = gain_limit_db - self.norm_p = norm_p - - if name is None: - self.name = "limited_adaptive_comb1d_" + str(LimitedAdaptiveComb1d.COUNTER) - LimitedAdaptiveComb1d.COUNTER += 1 - else: - self.name = name - - norm = torch.nn.utils.weight_norm if apply_weight_norm else lambda x, name=None: x - - # network for generating convolution weights - self.conv_kernel = norm(nn.Linear(feature_dim, kernel_size)) - - if softquant: - self.conv_kernel = soft_quant(self.conv_kernel) - - - # comb filter gain - self.filter_gain = norm(nn.Linear(feature_dim, 1)) - self.log_gain_limit = gain_limit_db * 0.11512925464970229 - with torch.no_grad(): - self.filter_gain.bias[:] = max(0.1, 4 + self.log_gain_limit) - - self.global_filter_gain = norm(nn.Linear(feature_dim, 1)) - log_min, log_max = global_gain_limits_db[0] * 0.11512925464970229, global_gain_limits_db[1] * 0.11512925464970229 - self.filter_gain_a = (log_max - log_min) / 2 - self.filter_gain_b = (log_max + log_min) / 2 - - if type(padding) == type(None): - self.padding = [kernel_size // 2, kernel_size - 1 - kernel_size // 2] - else: - self.padding = padding - - self.overlap_win = nn.Parameter(.5 + .5 * torch.cos((torch.arange(self.overlap_size) + 0.5) * torch.pi / overlap_size), requires_grad=False) - - def forward(self, x, features, lags, debug=False): - """ adaptive 1d convolution - - - Parameters: - ----------- - x : torch.tensor - input signal of shape (batch_size, in_channels, num_samples) - - feathres : torch.tensor - frame-wise features of shape (batch_size, num_frames, feature_dim) - - lags: torch.LongTensor - frame-wise lags for comb-filtering - - """ - - batch_size = x.size(0) - num_frames = features.size(1) - num_samples = x.size(2) - frame_size = self.frame_size - overlap_size = self.overlap_size - kernel_size = self.kernel_size - win1 = torch.flip(self.overlap_win, [0]) - win2 = self.overlap_win - - if num_samples // self.frame_size != num_frames: - raise ValueError('non matching sizes in AdaptiveConv1d.forward') - - conv_kernels = self.conv_kernel(features).reshape((batch_size, num_frames, self.out_channels, self.in_channels, self.kernel_size)) - conv_kernels = conv_kernels / (1e-6 + torch.norm(conv_kernels, p=self.norm_p, dim=-1, keepdim=True)) - - conv_gains = torch.exp(- torch.relu(self.filter_gain(features).permute(0, 2, 1)) + self.log_gain_limit) - # calculate gains - global_conv_gains = torch.exp(self.filter_gain_a * torch.tanh(self.global_filter_gain(features).permute(0, 2, 1)) + self.filter_gain_b) - - if debug and batch_size == 1: - key = self.name + "_gains" - write_data(key, conv_gains.detach().squeeze().cpu().numpy(), 16000 // self.frame_size) - key = self.name + "_kernels" - write_data(key, conv_kernels.detach().squeeze().cpu().numpy(), 16000 // self.frame_size) - key = self.name + "_lags" - write_data(key, lags.detach().squeeze().cpu().numpy(), 16000 // self.frame_size) - key = self.name + "_global_conv_gains" - write_data(key, global_conv_gains.detach().squeeze().cpu().numpy(), 16000 // self.frame_size) - - - # frame-wise convolution with overlap-add - output_frames = [] - overlap_mem = torch.zeros((batch_size, self.out_channels, self.overlap_size), device=x.device) - x = F.pad(x, self.padding) - x = F.pad(x, [self.max_lag, self.overlap_size]) - - idx = torch.arange(frame_size + kernel_size - 1 + overlap_size).to(x.device).view(1, 1, -1) - idx = torch.repeat_interleave(idx, batch_size, 0) - idx = torch.repeat_interleave(idx, self.in_channels, 1) - - - for i in range(num_frames): - - cidx = idx + i * frame_size + self.max_lag - lags[..., i].view(batch_size, 1, 1) - xx = torch.gather(x, -1, cidx).reshape((1, batch_size * self.in_channels, -1)) - - new_chunk = torch.conv1d(xx, conv_kernels[:, i, ...].reshape((batch_size * self.out_channels, self.in_channels, self.kernel_size)), groups=batch_size).reshape(batch_size, self.out_channels, -1) - - offset = self.max_lag + self.padding[0] - new_chunk = global_conv_gains[:, :, i : i + 1] * (new_chunk * conv_gains[:, :, i : i + 1] + x[..., offset + i * frame_size : offset + (i + 1) * frame_size + overlap_size]) - - # overlapping part - output_frames.append(new_chunk[:, :, : overlap_size] * win1 + overlap_mem * win2) - - # non-overlapping part - output_frames.append(new_chunk[:, :, overlap_size : frame_size]) - - # mem for next frame - overlap_mem = new_chunk[:, :, frame_size :] - - # concatenate chunks - output = torch.cat(output_frames, dim=-1) - - return output - - def flop_count(self, rate): - frame_rate = rate / self.frame_size - overlap = self.overlap_size - overhead = overlap / self.frame_size - - count = 0 - - # kernel computation and filtering - count += 2 * (frame_rate * self.feature_dim * self.kernel_size) - count += 2 * (self.in_channels * self.out_channels * self.kernel_size * (1 + overhead) * rate) - count += 2 * (frame_rate * self.feature_dim * self.out_channels) + rate * (1 + overhead) * self.out_channels - - # a0 computation - count += 2 * (frame_rate * self.feature_dim * self.out_channels) + rate * (1 + overhead) * self.out_channels - - # windowing - count += overlap * frame_rate * 3 * self.out_channels - - return count diff --git a/android/app/src/main/cpp/third_party/opus/dnn/torch/osce/utils/layers/limited_adaptive_conv1d.py b/android/app/src/main/cpp/third_party/opus/dnn/torch/osce/utils/layers/limited_adaptive_conv1d.py deleted file mode 100644 index 55df8c1..0000000 --- a/android/app/src/main/cpp/third_party/opus/dnn/torch/osce/utils/layers/limited_adaptive_conv1d.py +++ /dev/null @@ -1,200 +0,0 @@ -""" -/* Copyright (c) 2023 Amazon - Written by Jan Buethe */ -/* - Redistribution and use in source and binary forms, with or without - modification, are permitted provided that the following conditions - are met: - - - Redistributions of source code must retain the above copyright - notice, this list of conditions and the following disclaimer. - - - Redistributions in binary form must reproduce the above copyright - notice, this list of conditions and the following disclaimer in the - documentation and/or other materials provided with the distribution. - - THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS - ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT - LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR - A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER - OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, - EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, - PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR - PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF - LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING - NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS - SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. -*/ -""" - -import torch -from torch import nn -import torch.nn.functional as F - -from utils.endoscopy import write_data - -from utils.ada_conv import adaconv_kernel -from utils.softquant import soft_quant - -class LimitedAdaptiveConv1d(nn.Module): - COUNTER = 1 - - def __init__(self, - in_channels, - out_channels, - kernel_size, - feature_dim, - frame_size=160, - overlap_size=40, - padding=None, - name=None, - gain_limits_db=[-6, 6], - shape_gain_db=0, - norm_p=2, - softquant=False, - apply_weight_norm=False, - **kwargs): - """ - - Parameters: - ----------- - - in_channels : int - number of input channels - - out_channels : int - number of output channels - - feature_dim : int - dimension of features from which kernels, biases and gains are computed - - frame_size : int - frame size - - overlap_size : int - overlap size for filter cross-fade. Cross-fade is done on the first overlap_size samples of every frame - - use_bias : bool - if true, biases will be added to output channels - - - padding : List[int, int] - - """ - - super(LimitedAdaptiveConv1d, self).__init__() - - - - self.in_channels = in_channels - self.out_channels = out_channels - self.feature_dim = feature_dim - self.kernel_size = kernel_size - self.frame_size = frame_size - self.overlap_size = overlap_size - self.gain_limits_db = gain_limits_db - self.shape_gain_db = shape_gain_db - self.norm_p = norm_p - - if name is None: - self.name = "limited_adaptive_conv1d_" + str(LimitedAdaptiveConv1d.COUNTER) - LimitedAdaptiveConv1d.COUNTER += 1 - else: - self.name = name - - norm = torch.nn.utils.weight_norm if apply_weight_norm else lambda x, name=None: x - - # network for generating convolution weights - self.conv_kernel = norm(nn.Linear(feature_dim, in_channels * out_channels * kernel_size)) - if softquant: - self.conv_kernel = soft_quant(self.conv_kernel) - - self.shape_gain = min(1, 10**(shape_gain_db / 20)) - - self.filter_gain = norm(nn.Linear(feature_dim, out_channels)) - log_min, log_max = gain_limits_db[0] * 0.11512925464970229, gain_limits_db[1] * 0.11512925464970229 - self.filter_gain_a = (log_max - log_min) / 2 - self.filter_gain_b = (log_max + log_min) / 2 - - if type(padding) == type(None): - self.padding = [kernel_size // 2, kernel_size - 1 - kernel_size // 2] - else: - self.padding = padding - - self.overlap_win = nn.Parameter(.5 + .5 * torch.cos((torch.arange(self.overlap_size) + 0.5) * torch.pi / overlap_size), requires_grad=False) - - - def flop_count(self, rate): - frame_rate = rate / self.frame_size - overlap = self.overlap_size - overhead = overlap / self.frame_size - - count = 0 - - # kernel computation and filtering - count += 2 * (frame_rate * self.feature_dim * self.kernel_size) - count += 2 * (self.in_channels * self.out_channels * self.kernel_size * (1 + overhead) * rate) - - # gain computation - - count += 2 * (frame_rate * self.feature_dim * self.out_channels) + rate * (1 + overhead) * self.out_channels - - # windowing - count += 3 * overlap * frame_rate * self.out_channels - - return count - - def forward(self, x, features, debug=False): - """ adaptive 1d convolution - - - Parameters: - ----------- - x : torch.tensor - input signal of shape (batch_size, in_channels, num_samples) - - feathres : torch.tensor - frame-wise features of shape (batch_size, num_frames, feature_dim) - - """ - - batch_size = x.size(0) - num_frames = features.size(1) - num_samples = x.size(2) - frame_size = self.frame_size - overlap_size = self.overlap_size - kernel_size = self.kernel_size - win1 = torch.flip(self.overlap_win, [0]) - win2 = self.overlap_win - - if num_samples // self.frame_size != num_frames: - raise ValueError('non matching sizes in AdaptiveConv1d.forward') - - conv_kernels = self.conv_kernel(features).reshape((batch_size, num_frames, self.out_channels, self.in_channels, self.kernel_size)) - - # normalize kernels (TODO: switch to L1 and normalize over kernel and input channel dimension) - conv_kernels = conv_kernels / (1e-6 + torch.norm(conv_kernels, p=self.norm_p, dim=[-2, -1], keepdim=True)) - - # limit shape - id_kernels = torch.zeros_like(conv_kernels) - id_kernels[..., self.padding[1]] = 1 - - conv_kernels = self.shape_gain * conv_kernels + (1 - self.shape_gain) * id_kernels - - # calculate gains - conv_gains = torch.exp(self.filter_gain_a * torch.tanh(self.filter_gain(features)) + self.filter_gain_b) - if debug and batch_size == 1: - key = self.name + "_gains" - write_data(key, conv_gains.permute(0, 2, 1).detach().squeeze().cpu().numpy(), 16000 // self.frame_size) - key = self.name + "_kernels" - write_data(key, conv_kernels.detach().squeeze().cpu().numpy(), 16000 // self.frame_size) - - - conv_kernels = conv_kernels * conv_gains.view(batch_size, num_frames, self.out_channels, 1, 1) - - conv_kernels = conv_kernels.permute(0, 2, 3, 1, 4) - - output = adaconv_kernel(x, conv_kernels, win1, fft_size=256) - - - return output \ No newline at end of file diff --git a/android/app/src/main/cpp/third_party/opus/dnn/torch/osce/utils/layers/noise_shaper.py b/android/app/src/main/cpp/third_party/opus/dnn/torch/osce/utils/layers/noise_shaper.py deleted file mode 100644 index ba8a3af..0000000 --- a/android/app/src/main/cpp/third_party/opus/dnn/torch/osce/utils/layers/noise_shaper.py +++ /dev/null @@ -1,100 +0,0 @@ -""" -/* Copyright (c) 2023 Amazon - Written by Jan Buethe */ -/* - Redistribution and use in source and binary forms, with or without - modification, are permitted provided that the following conditions - are met: - - - Redistributions of source code must retain the above copyright - notice, this list of conditions and the following disclaimer. - - - Redistributions in binary form must reproduce the above copyright - notice, this list of conditions and the following disclaimer in the - documentation and/or other materials provided with the distribution. - - THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS - ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT - LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR - A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER - OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, - EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, - PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR - PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF - LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING - NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS - SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. -*/ -""" - -import torch -from torch import nn -import torch.nn.functional as F - -from utils.complexity import _conv1d_flop_count - -class NoiseShaper(nn.Module): - - def __init__(self, - feature_dim, - frame_size=160 - ): - """ - - Parameters: - ----------- - - feature_dim : int - dimension of input features - - frame_size : int - frame size - - """ - - super().__init__() - - self.feature_dim = feature_dim - self.frame_size = frame_size - - # feature transform - self.feature_alpha1 = nn.Conv1d(self.feature_dim, frame_size, 2) - self.feature_alpha2 = nn.Conv1d(frame_size, frame_size, 2) - - - def flop_count(self, rate): - - frame_rate = rate / self.frame_size - - shape_flops = sum([_conv1d_flop_count(x, frame_rate) for x in (self.feature_alpha1, self.feature_alpha2)]) + 11 * frame_rate * self.frame_size - - return shape_flops - - - def forward(self, features): - """ creates temporally shaped noise - - - Parameters: - ----------- - features : torch.tensor - frame-wise features of shape (batch_size, num_frames, feature_dim) - - """ - - batch_size = features.size(0) - num_frames = features.size(1) - frame_size = self.frame_size - num_samples = num_frames * frame_size - - # feature path - f = F.pad(features.permute(0, 2, 1), [1, 0]) - alpha = F.leaky_relu(self.feature_alpha1(f), 0.2) - alpha = torch.exp(self.feature_alpha2(F.pad(alpha, [1, 0]))) - alpha = alpha.permute(0, 2, 1) - - # signal generation - y = torch.randn((batch_size, num_frames, frame_size), dtype=features.dtype, device=features.device) - y = alpha * y - - return y.reshape(batch_size, 1, num_samples) diff --git a/android/app/src/main/cpp/third_party/opus/dnn/torch/osce/utils/layers/pitch_auto_correlator.py b/android/app/src/main/cpp/third_party/opus/dnn/torch/osce/utils/layers/pitch_auto_correlator.py deleted file mode 100644 index ef58ae8..0000000 --- a/android/app/src/main/cpp/third_party/opus/dnn/torch/osce/utils/layers/pitch_auto_correlator.py +++ /dev/null @@ -1,84 +0,0 @@ -""" -/* Copyright (c) 2023 Amazon - Written by Jan Buethe */ -/* - Redistribution and use in source and binary forms, with or without - modification, are permitted provided that the following conditions - are met: - - - Redistributions of source code must retain the above copyright - notice, this list of conditions and the following disclaimer. - - - Redistributions in binary form must reproduce the above copyright - notice, this list of conditions and the following disclaimer in the - documentation and/or other materials provided with the distribution. - - THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS - ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT - LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR - A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER - OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, - EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, - PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR - PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF - LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING - NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS - SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. -*/ -""" - -import torch -from torch import nn -import torch.nn.functional as F - - -class PitchAutoCorrelator(nn.Module): - def __init__(self, - frame_size=80, - pitch_min=32, - pitch_max=300, - radius=2): - - super().__init__() - - self.frame_size = frame_size - self.pitch_min = pitch_min - self.pitch_max = pitch_max - self.radius = radius - - - def forward(self, x, periods): - # x of shape (batch_size, channels, num_samples) - # periods of shape (batch_size, num_frames) - - num_frames = periods.size(1) - batch_size = periods.size(0) - num_samples = self.frame_size * num_frames - channels = x.size(1) - - assert num_samples == x.size(-1) - - range = torch.arange(-self.radius, self.radius + 1, device=x.device) - idx = torch.arange(self.frame_size * num_frames, device=x.device) - p_up = torch.repeat_interleave(periods, self.frame_size, 1) - lookup = idx + self.pitch_max - p_up - lookup = lookup.unsqueeze(-1) + range - lookup = lookup.unsqueeze(1) - - # padding - x_pad = F.pad(x, [self.pitch_max, 0]) - x_ext = torch.repeat_interleave(x_pad.unsqueeze(-1), 2 * self.radius + 1, -1) - - # framing - x_select = torch.gather(x_ext, 2, lookup) - x_frames = x_pad[..., self.pitch_max : ].reshape(batch_size, channels, num_frames, self.frame_size, 1) - lag_frames = x_select.reshape(batch_size, 1, num_frames, self.frame_size, -1) - - # calculate auto-correlation - dotp = torch.sum(x_frames * lag_frames, dim=-2) - frame_nrg = torch.sum(x_frames * x_frames, dim=-2) - lag_frame_nrg = torch.sum(lag_frames * lag_frames, dim=-2) - - acorr = dotp / torch.sqrt(frame_nrg * lag_frame_nrg + 1e-9) - - return acorr diff --git a/android/app/src/main/cpp/third_party/opus/dnn/torch/osce/utils/layers/silk_upsampler.py b/android/app/src/main/cpp/third_party/opus/dnn/torch/osce/utils/layers/silk_upsampler.py deleted file mode 100644 index 0d20b8a..0000000 --- a/android/app/src/main/cpp/third_party/opus/dnn/torch/osce/utils/layers/silk_upsampler.py +++ /dev/null @@ -1,167 +0,0 @@ -""" -/* Copyright (c) 2023 Amazon - Written by Jan Buethe */ -/* - Redistribution and use in source and binary forms, with or without - modification, are permitted provided that the following conditions - are met: - - - Redistributions of source code must retain the above copyright - notice, this list of conditions and the following disclaimer. - - - Redistributions in binary form must reproduce the above copyright - notice, this list of conditions and the following disclaimer in the - documentation and/or other materials provided with the distribution. - - THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS - ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT - LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR - A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER - OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, - EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, - PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR - PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF - LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING - NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS - SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. -*/ -""" - -""" This module implements the SILK upsampler from 16kHz to 24 or 48 kHz """ - -import torch -from torch import nn -import torch.nn.functional as F - -import numpy as np - -frac_fir = np.array( - [ - [189, -600, 617, 30567, 2996, -1375, 425, -46], - [117, -159, -1070, 29704, 5784, -2143, 611, -71], - [52, 221, -2392, 28276, 8798, -2865, 773, -91], - [-4, 529, -3350, 26341, 11950, -3487, 896, -103], - [-48, 758, -3956, 23973, 15143, -3957, 967, -107], - [-80, 905, -4235, 21254, 18278, -4222, 972, -99], - [-99, 972, -4222, 18278, 21254, -4235, 905, -80], - [-107, 967, -3957, 15143, 23973, -3956, 758, -48], - [-103, 896, -3487, 11950, 26341, -3350, 529, -4], - [-91, 773, -2865, 8798, 28276, -2392, 221, 52], - [-71, 611, -2143, 5784, 29704, -1070, -159, 117], - [-46, 425, -1375, 2996, 30567, 617, -600, 189] - ], - dtype=np.float32 -) / 2**15 - - -hq_2x_up_c_even = [x / 2**16 for x in [1746, 14986, 39083 - 65536]] -hq_2x_up_c_odd = [x / 2**16 for x in [6854, 25769, 55542 - 65536]] - - -def get_impz(coeffs, n): - s = 3*[0] - y = np.zeros(n) - x = 1 - - for i in range(n): - Y = x - s[0] - X = Y * coeffs[0] - tmp1 = s[0] + X - s[0] = x + X - - Y = tmp1 - s[1] - X = Y * coeffs[1] - tmp2 = s[1] + X - s[1] = tmp1 + X - - Y = tmp2 - s[2] - X = Y * (1 + coeffs[2]) - tmp3 = s[2] + X - s[2] = tmp2 + X - - y[i] = tmp3 - x = 0 - - return y - - - -class SilkUpsampler(nn.Module): - SUPPORTED_TARGET_RATES = {24000, 48000} - SUPPORTED_SOURCE_RATES = {16000} - def __init__(self, - fs_in=16000, - fs_out=48000): - - super().__init__() - self.fs_in = fs_in - self.fs_out = fs_out - - if fs_in not in self.SUPPORTED_SOURCE_RATES: - raise ValueError(f'SilkUpsampler currently only supports upsampling from {self.SUPPORTED_SOURCE_RATES} Hz') - - - if fs_out not in self.SUPPORTED_TARGET_RATES: - raise ValueError(f'SilkUpsampler currently only supports upsampling to {self.SUPPORTED_TARGET_RATES} Hz') - - - # hq 2x upsampler as FIR approximation - hq_2x_up_even = get_impz(hq_2x_up_c_even, 128)[::-1].copy() - hq_2x_up_odd = get_impz(hq_2x_up_c_odd , 128)[::-1].copy() - - self.hq_2x_up_even = nn.Parameter(torch.from_numpy(hq_2x_up_even).float().view(1, 1, -1), requires_grad=False) - self.hq_2x_up_odd = nn.Parameter(torch.from_numpy(hq_2x_up_odd ).float().view(1, 1, -1), requires_grad=False) - self.hq_2x_up_padding = [127, 0] - - # interpolation filters - frac_01_24 = frac_fir[0] - frac_17_24 = frac_fir[8] - frac_09_24 = frac_fir[4] - - self.frac_01_24 = nn.Parameter(torch.from_numpy(frac_01_24).view(1, 1, -1), requires_grad=False) - self.frac_17_24 = nn.Parameter(torch.from_numpy(frac_17_24).view(1, 1, -1), requires_grad=False) - self.frac_09_24 = nn.Parameter(torch.from_numpy(frac_09_24).view(1, 1, -1), requires_grad=False) - - self.stride = 1 if fs_out == 48000 else 2 - - def hq_2x_up(self, x): - - num_channels = x.size(1) - - weight_even = torch.repeat_interleave(self.hq_2x_up_even, num_channels, 0) - weight_odd = torch.repeat_interleave(self.hq_2x_up_odd , num_channels, 0) - - x_pad = F.pad(x, self.hq_2x_up_padding) - y_even = F.conv1d(x_pad, weight_even, groups=num_channels) - y_odd = F.conv1d(x_pad, weight_odd , groups=num_channels) - - y = torch.cat((y_even.unsqueeze(-1), y_odd.unsqueeze(-1)), dim=-1).flatten(2) - - return y - - def interpolate_3_2(self, x): - - num_channels = x.size(1) - - weight_01_24 = torch.repeat_interleave(self.frac_01_24, num_channels, 0) - weight_17_24 = torch.repeat_interleave(self.frac_17_24, num_channels, 0) - weight_09_24 = torch.repeat_interleave(self.frac_09_24, num_channels, 0) - - x_pad = F.pad(x, [8, 0]) - y_01_24 = F.conv1d(x_pad, weight_01_24, stride=2, groups=num_channels) - y_17_24 = F.conv1d(x_pad, weight_17_24, stride=2, groups=num_channels) - y_09_24_sh1 = F.conv1d(torch.roll(x_pad, -1, -1), weight_09_24, stride=2, groups=num_channels) - - - y = torch.cat( - (y_01_24.unsqueeze(-1), y_17_24.unsqueeze(-1), y_09_24_sh1.unsqueeze(-1)), - dim=-1).flatten(2) - - return y[..., :-3] - - def forward(self, x): - - y_2x = self.hq_2x_up(x) - y_3x = self.interpolate_3_2(y_2x) - - return y_3x[:, :, ::self.stride] diff --git a/android/app/src/main/cpp/third_party/opus/dnn/torch/osce/utils/layers/td_shaper.py b/android/app/src/main/cpp/third_party/opus/dnn/torch/osce/utils/layers/td_shaper.py deleted file mode 100644 index fa7bf34..0000000 --- a/android/app/src/main/cpp/third_party/opus/dnn/torch/osce/utils/layers/td_shaper.py +++ /dev/null @@ -1,145 +0,0 @@ -import torch -from torch import nn -import torch.nn.functional as F - -from utils.complexity import _conv1d_flop_count -from utils.softquant import soft_quant - -class TDShaper(nn.Module): - COUNTER = 1 - - def __init__(self, - feature_dim, - frame_size=160, - avg_pool_k=4, - innovate=False, - pool_after=False, - softquant=False, - apply_weight_norm=False - ): - """ - - Parameters: - ----------- - - - feature_dim : int - dimension of input features - - frame_size : int - frame size - - avg_pool_k : int, optional - kernel size and stride for avg pooling - - padding : List[int, int] - - """ - - super().__init__() - - - self.feature_dim = feature_dim - self.frame_size = frame_size - self.avg_pool_k = avg_pool_k - self.innovate = innovate - self.pool_after = pool_after - - assert frame_size % avg_pool_k == 0 - self.env_dim = frame_size // avg_pool_k + 1 - - norm = torch.nn.utils.weight_norm if apply_weight_norm else lambda x, name=None: x - - # feature transform - self.feature_alpha1_f = norm(nn.Conv1d(self.feature_dim, frame_size, 2)) - self.feature_alpha1_t = norm(nn.Conv1d(self.env_dim, frame_size, 2)) - self.feature_alpha2 = norm(nn.Conv1d(frame_size, frame_size, 2)) - - if softquant: - self.feature_alpha1_f = soft_quant(self.feature_alpha1_f) - - if self.innovate: - self.feature_alpha1b = norm(nn.Conv1d(self.feature_dim + self.env_dim, frame_size, 2)) - self.feature_alpha1c = norm(nn.Conv1d(self.feature_dim + self.env_dim, frame_size, 2)) - - self.feature_alpha2b = norm(nn.Conv1d(frame_size, frame_size, 2)) - self.feature_alpha2c = norm(nn.Conv1d(frame_size, frame_size, 2)) - - - def flop_count(self, rate): - - frame_rate = rate / self.frame_size - - shape_flops = sum([_conv1d_flop_count(x, frame_rate) for x in (self.feature_alpha1_f, self.feature_alpha1_t, self.feature_alpha2)]) + 11 * frame_rate * self.frame_size - - if self.innovate: - inno_flops = sum([_conv1d_flop_count(x, frame_rate) for x in (self.feature_alpha1b, self.feature_alpha2b, self.feature_alpha1c, self.feature_alpha2c)]) + 22 * frame_rate * self.frame_size - else: - inno_flops = 0 - - return shape_flops + inno_flops - - def envelope_transform(self, x): - - x = torch.abs(x) - if self.pool_after: - x = torch.log(x + .5**16) - x = F.avg_pool1d(x, self.avg_pool_k, self.avg_pool_k) - else: - x = F.avg_pool1d(x, self.avg_pool_k, self.avg_pool_k) - x = torch.log(x + .5**16) - - x = x.reshape(x.size(0), -1, self.env_dim - 1) - avg_x = torch.mean(x, -1, keepdim=True) - - x = torch.cat((x - avg_x, avg_x), dim=-1) - - return x - - def forward(self, x, features, debug=False): - """ innovate signal parts with temporal shaping - - - Parameters: - ----------- - x : torch.tensor - input signal of shape (batch_size, 1, num_samples) - - features : torch.tensor - frame-wise features of shape (batch_size, num_frames, feature_dim) - - """ - - batch_size = x.size(0) - num_frames = features.size(1) - num_samples = x.size(2) - frame_size = self.frame_size - - # generate temporal envelope - tenv = self.envelope_transform(x) - - # feature path - f = F.pad(features.permute(0, 2, 1), [1, 0]) - t = F.pad(tenv.permute(0, 2, 1), [1, 0]) - alpha = self.feature_alpha1_f(f) + self.feature_alpha1_t(t) - alpha = F.leaky_relu(alpha, 0.2) - alpha = torch.exp(self.feature_alpha2(F.pad(alpha, [1, 0]))) - alpha = alpha.permute(0, 2, 1) - - if self.innovate: - inno_alpha = F.leaky_relu(self.feature_alpha1b(f), 0.2) - inno_alpha = torch.exp(self.feature_alpha2b(F.pad(inno_alpha, [1, 0]))) - inno_alpha = inno_alpha.permute(0, 2, 1) - - inno_x = F.leaky_relu(self.feature_alpha1c(f), 0.2) - inno_x = torch.tanh(self.feature_alpha2c(F.pad(inno_x, [1, 0]))) - inno_x = inno_x.permute(0, 2, 1) - - # signal path - y = x.reshape(batch_size, num_frames, -1) - y = alpha * y - - if self.innovate: - y = y + inno_alpha * inno_x - - return y.reshape(batch_size, 1, num_samples) diff --git a/android/app/src/main/cpp/third_party/opus/dnn/torch/osce/utils/lpcnet_features.py b/android/app/src/main/cpp/third_party/opus/dnn/torch/osce/utils/lpcnet_features.py deleted file mode 100644 index 3d109fd..0000000 --- a/android/app/src/main/cpp/third_party/opus/dnn/torch/osce/utils/lpcnet_features.py +++ /dev/null @@ -1,112 +0,0 @@ -import os - -import torch -import numpy as np - -def load_lpcnet_features(feature_file, version=2): - if version == 2: - layout = { - 'cepstrum': [0,18], - 'periods': [18, 19], - 'pitch_corr': [19, 20], - 'lpc': [20, 36] - } - frame_length = 36 - - elif version == 1: - layout = { - 'cepstrum': [0,18], - 'periods': [36, 37], - 'pitch_corr': [37, 38], - 'lpc': [39, 55], - } - frame_length = 55 - else: - raise ValueError(f'unknown feature version: {version}') - - - raw_features = torch.from_numpy(np.fromfile(feature_file, dtype='float32')) - raw_features = raw_features.reshape((-1, frame_length)) - - features = torch.cat( - [ - raw_features[:, layout['cepstrum'][0] : layout['cepstrum'][1]], - raw_features[:, layout['pitch_corr'][0] : layout['pitch_corr'][1]] - ], - dim=1 - ) - - lpcs = raw_features[:, layout['lpc'][0] : layout['lpc'][1]] - periods = (0.1 + 50 * raw_features[:, layout['periods'][0] : layout['periods'][1]] + 100).long() - - return {'features' : features, 'periods' : periods, 'lpcs' : lpcs} - - - -def create_new_data(signal_path, reference_data_path, new_data_path, offset=320, preemph_factor=0.85): - ref_data = np.memmap(reference_data_path, dtype=np.int16) - signal = np.memmap(signal_path, dtype=np.int16) - - signal_preemph_path = os.path.splitext(signal_path)[0] + '_preemph.raw' - signal_preemph = np.memmap(signal_preemph_path, dtype=np.int16, mode='write', shape=signal.shape) - - - assert len(signal) % 160 == 0 - num_frames = len(signal) // 160 - mem = np.zeros(1) - for fr in range(len(signal)//160): - signal_preemph[fr * 160 : (fr + 1) * 160] = np.convolve(np.concatenate((mem, signal[fr * 160 : (fr + 1) * 160])), [1, -preemph_factor], mode='valid') - mem = signal[(fr + 1) * 160 - 1 : (fr + 1) * 160] - - new_data = np.memmap(new_data_path, dtype=np.int16, mode='write', shape=ref_data.shape) - - new_data[:] = 0 - N = len(signal) - offset - new_data[1 : 2*N + 1: 2] = signal_preemph[offset:] - new_data[2 : 2*N + 2: 2] = signal_preemph[offset:] - - -def parse_warpq_scores(output_file): - """ extracts warpq scores from output file """ - - with open(output_file, "r") as f: - lines = f.readlines() - - scores = [float(line.split("WARP-Q score:")[-1]) for line in lines if line.startswith("WARP-Q score:")] - - return scores - - -def parse_stats_file(file): - - with open(file, "r") as f: - lines = f.readlines() - - mean = float(lines[0].split(":")[-1]) - bt_mean = float(lines[1].split(":")[-1]) - top_mean = float(lines[2].split(":")[-1]) - - return mean, bt_mean, top_mean - -def collect_test_stats(test_folder): - """ collects statistics for all discovered metrics from test folder """ - - metrics = {'pesq', 'warpq', 'pitch_error', 'voicing_error'} - - results = dict() - - content = os.listdir(test_folder) - - stats_files = [file for file in content if file.startswith('stats_')] - - for file in stats_files: - metric = file[len("stats_") : -len(".txt")] - - if metric not in metrics: - print(f"warning: unknown metric {metric}") - - mean, bt_mean, top_mean = parse_stats_file(os.path.join(test_folder, file)) - - results[metric] = [mean, bt_mean, top_mean] - - return results diff --git a/android/app/src/main/cpp/third_party/opus/dnn/torch/osce/utils/misc.py b/android/app/src/main/cpp/third_party/opus/dnn/torch/osce/utils/misc.py deleted file mode 100644 index 68ee4bf..0000000 --- a/android/app/src/main/cpp/third_party/opus/dnn/torch/osce/utils/misc.py +++ /dev/null @@ -1,95 +0,0 @@ -""" -/* Copyright (c) 2023 Amazon - Written by Jan Buethe */ -/* - Redistribution and use in source and binary forms, with or without - modification, are permitted provided that the following conditions - are met: - - - Redistributions of source code must retain the above copyright - notice, this list of conditions and the following disclaimer. - - - Redistributions in binary form must reproduce the above copyright - notice, this list of conditions and the following disclaimer in the - documentation and/or other materials provided with the distribution. - - THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS - ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT - LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR - A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER - OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, - EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, - PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR - PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF - LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING - NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS - SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. -*/ -""" - -import torch -from torch.nn.utils import remove_weight_norm - -def count_parameters(model, verbose=False): - total = 0 - for name, p in model.named_parameters(): - count = torch.ones_like(p).sum().item() - - if verbose: - print(f"{name}: {count} parameters") - - total += count - - return total - -def count_nonzero_parameters(model, verbose=False): - total = 0 - for name, p in model.named_parameters(): - count = torch.count_nonzero(p).item() - - if verbose: - print(f"{name}: {count} non-zero parameters") - - total += count - - return total -def retain_grads(module): - for p in module.parameters(): - if p.requires_grad: - p.retain_grad() - -def get_grad_norm(module, p=2): - norm = 0 - for param in module.parameters(): - if param.requires_grad: - norm = norm + (torch.abs(param.grad) ** p).sum() - - return norm ** (1/p) - -def create_weights(s_real, s_gen, alpha): - weights = [] - with torch.no_grad(): - for sr, sg in zip(s_real, s_gen): - weight = torch.exp(alpha * (sr[-1] - sg[-1])) - weights.append(weight) - - return weights - - -def _get_candidates(module: torch.nn.Module): - candidates = [] - for key in module.__dict__.keys(): - if hasattr(module, key + '_v'): - candidates.append(key) - return candidates - -def remove_all_weight_norm(model : torch.nn.Module, verbose=False): - for name, m in model.named_modules(): - candidates = _get_candidates(m) - - for candidate in candidates: - try: - remove_weight_norm(m, name=candidate) - if verbose: print(f'removed weight norm on weight {name}.{candidate}') - except: - pass diff --git a/android/app/src/main/cpp/third_party/opus/dnn/torch/osce/utils/moc.py b/android/app/src/main/cpp/third_party/opus/dnn/torch/osce/utils/moc.py deleted file mode 100644 index 3dffcfd..0000000 --- a/android/app/src/main/cpp/third_party/opus/dnn/torch/osce/utils/moc.py +++ /dev/null @@ -1,153 +0,0 @@ -import numpy as np -import scipy.signal - -def compute_vad_mask(x, fs, stop_db=-70): - - frame_length = (fs + 49) // 50 - x = x[: frame_length * (len(x) // frame_length)] - - frames = x.reshape(-1, frame_length) - frame_energy = np.sum(frames ** 2, axis=1) - frame_energy_smooth = np.convolve(frame_energy, np.ones(5) / 5, mode='same') - - max_threshold = frame_energy.max() * 10 ** (stop_db/20) - vactive = np.ones_like(frames) - vactive[frame_energy_smooth < max_threshold, :] = 0 - vactive = vactive.reshape(-1) - - filter = np.sin(np.arange(frame_length) * np.pi / (frame_length - 1)) - filter = filter / filter.sum() - - mask = np.convolve(vactive, filter, mode='same') - - return x, mask - -def convert_mask(mask, num_frames, frame_size=160, hop_size=40): - num_samples = frame_size + (num_frames - 1) * hop_size - if len(mask) < num_samples: - mask = np.concatenate((mask, np.zeros(num_samples - len(mask))), dtype=mask.dtype) - else: - mask = mask[:num_samples] - - new_mask = np.array([np.mean(mask[i*hop_size : i*hop_size + frame_size]) for i in range(num_frames)]) - - return new_mask - -def power_spectrum(x, window_size=160, hop_size=40, window='hamming'): - num_spectra = (len(x) - window_size - hop_size) // hop_size - window = scipy.signal.get_window(window, window_size) - N = window_size // 2 - - frames = np.concatenate([x[np.newaxis, i * hop_size : i * hop_size + window_size] for i in range(num_spectra)]) * window - psd = np.abs(np.fft.fft(frames, axis=1)[:, :N + 1]) ** 2 - - return psd - - -def frequency_mask(num_bands, up_factor, down_factor): - - up_mask = np.zeros((num_bands, num_bands)) - down_mask = np.zeros((num_bands, num_bands)) - - for i in range(num_bands): - up_mask[i, : i + 1] = up_factor ** np.arange(i, -1, -1) - down_mask[i, i :] = down_factor ** np.arange(num_bands - i) - - return down_mask @ up_mask - - -def rect_fb(band_limits, num_bins=None): - num_bands = len(band_limits) - 1 - if num_bins is None: - num_bins = band_limits[-1] - - fb = np.zeros((num_bands, num_bins)) - for i in range(num_bands): - fb[i, band_limits[i]:band_limits[i+1]] = 1 - - return fb - - -def compare(x, y, apply_vad=False): - """ Modified version of opus_compare for 16 kHz mono signals - - Args: - x (np.ndarray): reference input signal scaled to [-1, 1] - y (np.ndarray): test signal scaled to [-1, 1] - - Returns: - float: perceptually weighted error - """ - # filter bank: bark scale with minimum-2-bin bands and cutoff at 7.5 kHz - band_limits = [0, 2, 4, 6, 7, 9, 11, 13, 15, 18, 22, 26, 31, 36, 43, 51, 60, 75] - num_bands = len(band_limits) - 1 - fb = rect_fb(band_limits, num_bins=81) - - # trim samples to same size - num_samples = min(len(x), len(y)) - x = x[:num_samples] * 2**15 - y = y[:num_samples] * 2**15 - - psd_x = power_spectrum(x) + 100000 - psd_y = power_spectrum(y) + 100000 - - num_frames = psd_x.shape[0] - - # average band energies - be_x = (psd_x @ fb.T) / np.sum(fb, axis=1) - - # frequecy masking - f_mask = frequency_mask(num_bands, 0.1, 0.03) - mask_x = be_x @ f_mask.T - - # temporal masking - for i in range(1, num_frames): - mask_x[i, :] += 0.5 * mask_x[i-1, :] - - # apply mask - masked_psd_x = psd_x + 0.1 * (mask_x @ fb) - masked_psd_y = psd_y + 0.1 * (mask_x @ fb) - - # 2-frame average - masked_psd_x = masked_psd_x[1:] + masked_psd_x[:-1] - masked_psd_y = masked_psd_y[1:] + masked_psd_y[:-1] - - # distortion metric - re = masked_psd_y / masked_psd_x - im = np.log(re) ** 2 - Eb = ((im @ fb.T) / np.sum(fb, axis=1)) - Ef = np.mean(Eb , axis=1) - - if apply_vad: - _, mask = compute_vad_mask(x, 16000) - mask = convert_mask(mask, Ef.shape[0]) - else: - mask = np.ones_like(Ef) - - err = np.mean(np.abs(Ef[mask > 1e-6]) ** 3) ** (1/6) - - return float(err) - -if __name__ == "__main__": - import argparse - from scipy.io import wavfile - - parser = argparse.ArgumentParser() - parser.add_argument('ref', type=str, help='reference wav file') - parser.add_argument('deg', type=str, help='degraded wav file') - parser.add_argument('--apply-vad', action='store_true') - args = parser.parse_args() - - - fs1, x = wavfile.read(args.ref) - fs2, y = wavfile.read(args.deg) - - if max(fs1, fs2) != 16000: - raise ValueError('error: encountered sampling frequency diffrent from 16kHz') - - x = x.astype(np.float32) / 2**15 - y = y.astype(np.float32) / 2**15 - - err = compare(x, y, apply_vad=args.apply_vad) - - print(f"MOC: {err}") diff --git a/android/app/src/main/cpp/third_party/opus/dnn/torch/osce/utils/pitch.py b/android/app/src/main/cpp/third_party/opus/dnn/torch/osce/utils/pitch.py deleted file mode 100644 index 2a23381..0000000 --- a/android/app/src/main/cpp/third_party/opus/dnn/torch/osce/utils/pitch.py +++ /dev/null @@ -1,122 +0,0 @@ -""" -/* Copyright (c) 2023 Amazon - Written by Jan Buethe */ -/* - Redistribution and use in source and binary forms, with or without - modification, are permitted provided that the following conditions - are met: - - - Redistributions of source code must retain the above copyright - notice, this list of conditions and the following disclaimer. - - - Redistributions in binary form must reproduce the above copyright - notice, this list of conditions and the following disclaimer in the - documentation and/or other materials provided with the distribution. - - THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS - ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT - LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR - A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER - OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, - EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, - PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR - PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF - LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING - NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS - SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. -*/ -""" - -import numpy as np - -def hangover(lags, num_frames=10): - lags = lags.copy() - count = 0 - last_lag = 0 - - for i in range(len(lags)): - lag = lags[i] - - if lag == 0: - if count < num_frames: - lags[i] = last_lag - count += 1 - else: - count = 0 - last_lag = lag - - return lags - - -def smooth_pitch_lags(lags, d=2): - - assert d < 4 - - num_silk_frames = len(lags) // 4 - - smoothed_lags = lags.copy() - - tmp = np.arange(1, d+1) - kernel = np.concatenate((tmp, [d+1], tmp[::-1]), dtype=np.float32) - kernel = kernel / np.sum(kernel) - - last = lags[0:d][::-1] - for i in range(num_silk_frames): - frame = lags[i * 4: (i+1) * 4] - - if np.max(np.abs(frame)) == 0: - last = frame[4-d:] - continue - - if i == num_silk_frames - 1: - next = frame[4-d:][::-1] - else: - next = lags[(i+1) * 4 : (i+1) * 4 + d] - - if np.max(np.abs(next)) == 0: - next = frame[4-d:][::-1] - - if np.max(np.abs(last)) == 0: - last = frame[0:d][::-1] - - smoothed_frame = np.convolve(np.concatenate((last, frame, next), dtype=np.float32), kernel, mode='valid') - - smoothed_lags[i * 4: (i+1) * 4] = np.round(smoothed_frame) - - last = frame[4-d:] - - return smoothed_lags - -def calculate_acorr_window(x, frame_size, lags, history=None, max_lag=300, radius=2, add_double_lag_acorr=False, no_pitch_threshold=32): - eps = 1e-9 - - lag_multiplier = 2 if add_double_lag_acorr else 1 - - if history is None: - history = np.zeros(lag_multiplier * max_lag + radius, dtype=x.dtype) - - offset = len(history) - - assert offset >= max_lag + radius - assert len(x) % frame_size == 0 - - num_frames = len(x) // frame_size - lags = lags.copy() - - x_ext = np.concatenate((history, x), dtype=x.dtype) - - d = radius - num_acorrs = 2 * d + 1 - acorrs = np.zeros((num_frames, lag_multiplier * num_acorrs), dtype=x.dtype) - - for idx in range(num_frames): - lag = lags[idx].item() - frame = x_ext[offset + idx * frame_size : offset + (idx + 1) * frame_size] - - for k in range(lag_multiplier): - lag1 = (k + 1) * lag if lag >= no_pitch_threshold else lag - for j in range(num_acorrs): - past = x_ext[offset + idx * frame_size - lag1 + j - d : offset + (idx + 1) * frame_size - lag1 + j - d] - acorrs[idx, j + k * num_acorrs] = np.dot(frame, past) / np.sqrt(np.dot(frame, frame) * np.dot(past, past) + eps) - - return acorrs, lags \ No newline at end of file diff --git a/android/app/src/main/cpp/third_party/opus/dnn/torch/osce/utils/silk_features.py b/android/app/src/main/cpp/third_party/opus/dnn/torch/osce/utils/silk_features.py deleted file mode 100644 index 8c5dbf0..0000000 --- a/android/app/src/main/cpp/third_party/opus/dnn/torch/osce/utils/silk_features.py +++ /dev/null @@ -1,144 +0,0 @@ -""" -/* Copyright (c) 2023 Amazon - Written by Jan Buethe */ -/* - Redistribution and use in source and binary forms, with or without - modification, are permitted provided that the following conditions - are met: - - - Redistributions of source code must retain the above copyright - notice, this list of conditions and the following disclaimer. - - - Redistributions in binary form must reproduce the above copyright - notice, this list of conditions and the following disclaimer in the - documentation and/or other materials provided with the distribution. - - THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS - ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT - LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR - A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER - OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, - EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, - PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR - PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF - LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING - NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS - SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. -*/ -""" - -import os - -import numpy as np -import torch - -import scipy -import scipy.signal - -from utils.pitch import hangover, calculate_acorr_window -from utils.spec import create_filter_bank, cepstrum, log_spectrum, log_spectrum_from_lpc - -def spec_from_lpc(a, n_fft=128, eps=1e-9): - order = a.shape[-1] - assert order + 1 < n_fft - - x = np.zeros((*a.shape[:-1], n_fft )) - x[..., 0] = 1 - x[..., 1:1 + order] = -a - - X = np.fft.fft(x, axis=-1) - X = np.abs(X[..., :n_fft//2 + 1]) ** 2 - - S = 1 / (X + eps) - - return S - -def silk_feature_factory(no_pitch_value=256, - acorr_radius=2, - pitch_hangover=8, - num_bands_clean_spec=64, - num_bands_noisy_spec=18, - noisy_spec_scale='opus', - noisy_apply_dct=True, - add_double_lag_acorr=False - ): - - w = scipy.signal.windows.cosine(320) - fb_clean_spec = create_filter_bank(num_bands_clean_spec, 320, scale='erb', round_center_bins=True, normalize=True) - fb_noisy_spec = create_filter_bank(num_bands_noisy_spec, 320, scale=noisy_spec_scale, round_center_bins=True, normalize=True) - - def create_features(noisy, noisy_history, lpcs, gains, ltps, periods): - - periods = periods.copy() - - if pitch_hangover > 0: - periods = hangover(periods, num_frames=pitch_hangover) - - periods[periods == 0] = no_pitch_value - - clean_spectrum = 0.3 * log_spectrum_from_lpc(lpcs, fb=fb_clean_spec, n_fft=320) - - if noisy_apply_dct: - noisy_cepstrum = np.repeat( - cepstrum(np.concatenate((noisy_history[-160:], noisy), dtype=np.float32), 320, fb_noisy_spec, w), 2, 0) - else: - noisy_cepstrum = np.repeat( - log_spectrum(np.concatenate((noisy_history[-160:], noisy), dtype=np.float32), 320, fb_noisy_spec, w), 2, 0) - - log_gains = np.log(gains + 1e-9).reshape(-1, 1) - - acorr, _ = calculate_acorr_window(noisy, 80, periods, noisy_history, radius=acorr_radius, add_double_lag_acorr=add_double_lag_acorr) - - features = np.concatenate((clean_spectrum, noisy_cepstrum, acorr, ltps, log_gains), axis=-1, dtype=np.float32) - - return features, periods.astype(np.int64) - - return create_features - - - -def load_inference_data(path, - no_pitch_value=256, - skip=92, - preemph=0.85, - acorr_radius=2, - pitch_hangover=8, - num_bands_clean_spec=64, - num_bands_noisy_spec=18, - noisy_spec_scale='opus', - noisy_apply_dct=True, - add_double_lag_acorr=False, - **kwargs): - - print(f"[load_inference_data]: ignoring keyword arguments {kwargs.keys()}...") - - lpcs = np.fromfile(os.path.join(path, 'features_lpc.f32'), dtype=np.float32).reshape(-1, 16) - ltps = np.fromfile(os.path.join(path, 'features_ltp.f32'), dtype=np.float32).reshape(-1, 5) - gains = np.fromfile(os.path.join(path, 'features_gain.f32'), dtype=np.float32) - periods = np.fromfile(os.path.join(path, 'features_period.s16'), dtype=np.int16) - num_bits = np.fromfile(os.path.join(path, 'features_num_bits.s32'), dtype=np.int32).astype(np.float32).reshape(-1, 1) - num_bits_smooth = np.fromfile(os.path.join(path, 'features_num_bits_smooth.f32'), dtype=np.float32).reshape(-1, 1) - - # load signal, add back delay and pre-emphasize - signal = np.fromfile(os.path.join(path, 'noisy.s16'), dtype=np.int16).astype(np.float32) / (2 ** 15) - signal = np.concatenate((np.zeros(skip, dtype=np.float32), signal), dtype=np.float32) - - create_features = silk_feature_factory(no_pitch_value, acorr_radius, pitch_hangover, num_bands_clean_spec, num_bands_noisy_spec, noisy_spec_scale, noisy_apply_dct, add_double_lag_acorr) - - num_frames = min((len(signal) // 320) * 4, len(lpcs)) - signal = signal[: num_frames * 80] - lpcs = lpcs[: num_frames] - ltps = ltps[: num_frames] - gains = gains[: num_frames] - periods = periods[: num_frames] - num_bits = num_bits[: num_frames // 4] - num_bits_smooth = num_bits[: num_frames // 4] - - numbits = np.repeat(np.concatenate((num_bits, num_bits_smooth), axis=-1, dtype=np.float32), 4, axis=0) - - features, periods = create_features(signal, np.zeros(350, dtype=signal.dtype), lpcs, gains, ltps, periods) - - if preemph > 0: - signal[1:] -= preemph * signal[:-1] - - return torch.from_numpy(signal), torch.from_numpy(features), torch.from_numpy(periods), torch.from_numpy(numbits) diff --git a/android/app/src/main/cpp/third_party/opus/dnn/torch/osce/utils/softquant.py b/android/app/src/main/cpp/third_party/opus/dnn/torch/osce/utils/softquant.py deleted file mode 100644 index 5fca5b2..0000000 --- a/android/app/src/main/cpp/third_party/opus/dnn/torch/osce/utils/softquant.py +++ /dev/null @@ -1,110 +0,0 @@ -import torch - -@torch.no_grad() -def compute_optimal_scale(weight): - with torch.no_grad(): - n_out, n_in = weight.shape - assert n_in % 4 == 0 - if n_out % 8: - # add padding - pad = n_out - n_out % 8 - weight = torch.cat((weight, torch.zeros((pad, n_in), dtype=weight.dtype, device=weight.device)), dim=0) - - weight_max_abs, _ = torch.max(torch.abs(weight), dim=1) - weight_max_sum, _ = torch.max(torch.abs(weight[:, : n_in : 2] + weight[:, 1 : n_in : 2]), dim=1) - scale_max = weight_max_abs / 127 - scale_sum = weight_max_sum / 129 - - scale = torch.maximum(scale_max, scale_sum) - - return scale[:n_out] - -@torch.no_grad() -def q_scaled_noise(module, weight): - if isinstance(module, torch.nn.Conv1d): - w = weight.permute(0, 2, 1).flatten(1) - noise = torch.rand_like(w) - 0.5 - scale = compute_optimal_scale(w) - noise = noise * scale.unsqueeze(-1) - noise = noise.reshape(weight.size(0), weight.size(2), weight.size(1)).permute(0, 2, 1) - elif isinstance(module, torch.nn.ConvTranspose1d): - i, o, k = weight.shape - w = weight.permute(2, 1, 0).reshape(k * o, i) - noise = torch.rand_like(w) - 0.5 - scale = compute_optimal_scale(w) - noise = noise * scale.unsqueeze(-1) - noise = noise.reshape(k, o, i).permute(2, 1, 0) - elif len(weight.shape) == 2: - noise = torch.rand_like(weight) - 0.5 - scale = compute_optimal_scale(weight) - noise = noise * scale.unsqueeze(-1) - else: - raise ValueError('unknown quantization setting') - - return noise - -class SoftQuant: - name: str - - def __init__(self, names: str, scale: float) -> None: - self.names = names - self.quantization_noise = None - self.scale = scale - - def __call__(self, module, inputs, *args, before=True): - if not module.training: return - - if before: - self.quantization_noise = dict() - for name in self.names: - weight = getattr(module, name) - if self.scale is None: - self.quantization_noise[name] = q_scaled_noise(module, weight) - else: - self.quantization_noise[name] = \ - self.scale * weight.abs().max() * (torch.rand_like(weight) - 0.5) - with torch.no_grad(): - weight.data[:] = weight + self.quantization_noise[name] - else: - for name in self.names: - weight = getattr(module, name) - with torch.no_grad(): - weight.data[:] = weight - self.quantization_noise[name] - self.quantization_noise = None - - def apply(module, names=['weight'], scale=None): - fn = SoftQuant(names, scale) - - for name in names: - if not hasattr(module, name): - raise ValueError("") - - fn_before = lambda *x : fn(*x, before=True) - fn_after = lambda *x : fn(*x, before=False) - setattr(fn_before, 'sqm', fn) - setattr(fn_after, 'sqm', fn) - - - module.register_forward_pre_hook(fn_before) - module.register_forward_hook(fn_after) - - module - - return fn - - -def soft_quant(module, names=['weight'], scale=None): - fn = SoftQuant.apply(module, names, scale) - return module - -def remove_soft_quant(module, names=['weight']): - for k, hook in module._forward_pre_hooks.items(): - if hasattr(hook, 'sqm'): - if isinstance(hook.sqm, SoftQuant) and hook.sqm.names == names: - del module._forward_pre_hooks[k] - for k, hook in module._forward_hooks.items(): - if hasattr(hook, 'sqm'): - if isinstance(hook.sqm, SoftQuant) and hook.sqm.names == names: - del module._forward_hooks[k] - - return module \ No newline at end of file diff --git a/android/app/src/main/cpp/third_party/opus/dnn/torch/osce/utils/spec.py b/android/app/src/main/cpp/third_party/opus/dnn/torch/osce/utils/spec.py deleted file mode 100644 index 59f5353..0000000 --- a/android/app/src/main/cpp/third_party/opus/dnn/torch/osce/utils/spec.py +++ /dev/null @@ -1,210 +0,0 @@ -""" -/* Copyright (c) 2023 Amazon - Written by Jan Buethe */ -/* - Redistribution and use in source and binary forms, with or without - modification, are permitted provided that the following conditions - are met: - - - Redistributions of source code must retain the above copyright - notice, this list of conditions and the following disclaimer. - - - Redistributions in binary form must reproduce the above copyright - notice, this list of conditions and the following disclaimer in the - documentation and/or other materials provided with the distribution. - - THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS - ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT - LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR - A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER - OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, - EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, - PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR - PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF - LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING - NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS - SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. -*/ -""" - -import math as m -import numpy as np -import scipy -import scipy.fftpack -import torch - -def erb(f): - return 24.7 * (4.37 * f + 1) - -def inv_erb(e): - return (e / 24.7 - 1) / 4.37 - -def bark(f): - return 6 * m.asinh(f/600) - -def inv_bark(b): - return 600 * m.sinh(b / 6) - - -scale_dict = { - 'bark': [bark, inv_bark], - 'erb': [erb, inv_erb] -} - -def gen_filterbank(N, Fs=16000, keep_size=False): - in_freq = (np.arange(N+1, dtype='float32')/N*Fs/2)[None,:] - M = N + 1 if keep_size else N - out_freq = (np.arange(M, dtype='float32')/N*Fs/2)[:,None] - #ERB from B.C.J Moore, An Introduction to the Psychology of Hearing, 5th Ed., page 73. - ERB_N = 24.7 + .108*in_freq - delta = np.abs(in_freq-out_freq)/ERB_N - center = (delta<.5).astype('float32') - R = -12*center*delta**2 + (1-center)*(3-12*delta) - RE = 10.**(R/10.) - norm = np.sum(RE, axis=1) - RE = RE/norm[:, np.newaxis] - return torch.from_numpy(RE) - -def create_filter_bank(num_bands, n_fft=320, fs=16000, scale='bark', round_center_bins=False, return_upper=False, normalize=False): - - f0 = 0 - num_bins = n_fft // 2 + 1 - f1 = fs / n_fft * (num_bins - 1) - fstep = fs / n_fft - - if scale == 'opus': - bins_5ms = [0, 1, 2, 3, 4, 5, 6, 7, 8, 10, 12, 14, 16, 20, 24, 28, 34, 40] - fac = 1000 * n_fft / fs / 5 - if num_bands != 18: - print("warning: requested Opus filter bank with num_bands != 18. Adjusting num_bands.") - num_bands = 18 - center_bins = np.array([fac * bin for bin in bins_5ms]) - else: - to_scale, from_scale = scale_dict[scale] - - s0 = to_scale(f0) - s1 = to_scale(f1) - - center_freqs = np.array([f0] + [from_scale(s0 + i * (s1 - s0) / (num_bands)) for i in range(1, num_bands - 1)] + [f1]) - center_bins = (center_freqs - f0) / fstep - - if round_center_bins: - center_bins = np.round(center_bins) - - filter_bank = np.zeros((num_bands, num_bins)) - - band = 0 - for bin in range(num_bins): - # update band index - if bin > center_bins[band + 1]: - band += 1 - - # calculate filter coefficients - frac = (center_bins[band + 1] - bin) / (center_bins[band + 1] - center_bins[band]) - filter_bank[band][bin] = frac - filter_bank[band + 1][bin] = 1 - frac - - if return_upper: - extend = n_fft - num_bins - filter_bank = np.concatenate((filter_bank, np.fliplr(filter_bank[:, 1:extend+1])), axis=1) - - if normalize: - filter_bank = filter_bank / np.sum(filter_bank, axis=1).reshape(-1, 1) - - return filter_bank - - -def compressed_log_spec(pspec): - - lpspec = np.zeros_like(pspec) - num_bands = pspec.shape[-1] - - log_max = -2 - follow = -2 - - for i in range(num_bands): - tmp = np.log10(pspec[i] + 1e-9) - tmp = max(log_max, max(follow - 2.5, tmp)) - lpspec[i] = tmp - log_max = max(log_max, tmp) - follow = max(follow - 2.5, tmp) - - return lpspec - -def log_spectrum_from_lpc(a, fb=None, n_fft=320, eps=1e-9, gamma=1, compress=False, power=1): - """ calculates cepstrum from SILK lpcs """ - order = a.shape[-1] - assert order + 1 < n_fft - - a = a * (gamma ** (1 + np.arange(order))).astype(np.float32) - - x = np.zeros((*a.shape[:-1], n_fft )) - x[..., 0] = 1 - x[..., 1:1 + order] = -a - - X = np.fft.fft(x, axis=-1) - X = np.abs(X[..., :n_fft//2 + 1]) ** power - - S = 1 / (X + eps) - - if fb is None: - Sf = S - else: - Sf = np.matmul(S, fb.T) - - if compress: - Sf = np.apply_along_axis(compressed_log_spec, -1, Sf) - else: - Sf = np.log(Sf + eps) - - return Sf - -def cepstrum_from_lpc(a, fb=None, n_fft=320, eps=1e-9, gamma=1, compress=False): - """ calculates cepstrum from SILK lpcs """ - - Sf = log_spectrum_from_lpc(a, fb, n_fft, eps, gamma, compress) - - cepstrum = scipy.fftpack.dct(Sf, 2, norm='ortho') - - return cepstrum - - - -def log_spectrum(x, frame_size, fb=None, window=None, power=1): - """ calculate cepstrum on 50% overlapping frames """ - - assert(2*len(x)) % frame_size == 0 - assert frame_size % 2 == 0 - - n = len(x) - num_even = n // frame_size - num_odd = (n - frame_size // 2) // frame_size - num_bins = frame_size // 2 + 1 - - x_even = x[:num_even * frame_size].reshape(-1, frame_size) - x_odd = x[frame_size//2 : frame_size//2 + frame_size * num_odd].reshape(-1, frame_size) - - x_unfold = np.empty((x_even.size + x_odd.size), dtype=x.dtype).reshape((-1, frame_size)) - x_unfold[::2, :] = x_even - x_unfold[1::2, :] = x_odd - - if window is not None: - x_unfold *= window.reshape(1, -1) - - X = np.abs(np.fft.fft(x_unfold, n=frame_size, axis=-1))[:, :num_bins] ** power - - if fb is not None: - X = np.matmul(X, fb.T) - - - return np.log(X + 1e-9) - - -def cepstrum(x, frame_size, fb=None, window=None): - """ calculate cepstrum on 50% overlapping frames """ - - X = log_spectrum(x, frame_size, fb, window) - - cepstrum = scipy.fftpack.dct(X, 2, norm='ortho') - - return cepstrum \ No newline at end of file diff --git a/android/app/src/main/cpp/third_party/opus/dnn/torch/osce/utils/templates.py b/android/app/src/main/cpp/third_party/opus/dnn/torch/osce/utils/templates.py deleted file mode 100644 index 5fc84ef..0000000 --- a/android/app/src/main/cpp/third_party/opus/dnn/torch/osce/utils/templates.py +++ /dev/null @@ -1,347 +0,0 @@ -""" -/* Copyright (c) 2023 Amazon - Written by Jan Buethe */ -/* - Redistribution and use in source and binary forms, with or without - modification, are permitted provided that the following conditions - are met: - - - Redistributions of source code must retain the above copyright - notice, this list of conditions and the following disclaimer. - - - Redistributions in binary form must reproduce the above copyright - notice, this list of conditions and the following disclaimer in the - documentation and/or other materials provided with the distribution. - - THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS - ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT - LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR - A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER - OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, - EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, - PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR - PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF - LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING - NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS - SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. -*/ -""" - - -setup_dict = dict() - -lace_setup = { - 'dataset': '/local/datasets/silk_enhancement_v2_full_6to64kbps/training', - 'validation_dataset': '/local/datasets/silk_enhancement_v2_full_6to64kbps/validation', - 'model': { - 'name': 'lace', - 'args': [], - 'kwargs': { - 'comb_gain_limit_db': 10, - 'cond_dim': 128, - 'conv_gain_limits_db': [-12, 12], - 'global_gain_limits_db': [-6, 6], - 'hidden_feature_dim': 96, - 'kernel_size': 15, - 'num_features': 93, - 'numbits_embedding_dim': 8, - 'numbits_range': [50, 650], - 'partial_lookahead': True, - 'pitch_embedding_dim': 64, - 'pitch_max': 300, - 'preemph': 0.85, - 'skip': 91, - 'softquant': True, - 'sparsify': False, - 'sparsification_density': 0.4, - 'sparsification_schedule': [10000, 40000, 200] - } - }, - 'data': { - 'frames_per_sample': 100, - 'no_pitch_value': 7, - 'preemph': 0.85, - 'skip': 91, - 'pitch_hangover': 8, - 'acorr_radius': 2, - 'num_bands_clean_spec': 64, - 'num_bands_noisy_spec': 18, - 'noisy_spec_scale': 'opus', - 'pitch_hangover': 0, - }, - 'training': { - 'batch_size': 256, - 'lr': 5.e-4, - 'lr_decay_factor': 2.5e-5, - 'epochs': 50, - 'loss': { - 'w_l1': 0, - 'w_lm': 0, - 'w_logmel': 0, - 'w_sc': 0, - 'w_wsc': 0, - 'w_xcorr': 0, - 'w_sxcorr': 1, - 'w_l2': 10, - 'w_slm': 2 - } - } -} - - -nolace_setup = { - 'dataset': '/local/datasets/silk_enhancement_v2_full_6to64kbps/training', - 'validation_dataset': '/local/datasets/silk_enhancement_v2_full_6to64kbps/validation', - 'model': { - 'name': 'nolace', - 'args': [], - 'kwargs': { - 'avg_pool_k': 4, - 'comb_gain_limit_db': 10, - 'cond_dim': 256, - 'conv_gain_limits_db': [-12, 12], - 'global_gain_limits_db': [-6, 6], - 'hidden_feature_dim': 96, - 'kernel_size': 15, - 'num_features': 93, - 'numbits_embedding_dim': 8, - 'numbits_range': [50, 650], - 'partial_lookahead': True, - 'pitch_embedding_dim': 64, - 'pitch_max': 300, - 'preemph': 0.85, - 'skip': 91, - 'softquant': True, - 'sparsify': False, - 'sparsification_density': 0.4, - 'sparsification_schedule': [10000, 40000, 200] - } - }, - 'data': { - 'frames_per_sample': 100, - 'no_pitch_value': 7, - 'preemph': 0.85, - 'skip': 91, - 'pitch_hangover': 8, - 'acorr_radius': 2, - 'num_bands_clean_spec': 64, - 'num_bands_noisy_spec': 18, - 'noisy_spec_scale': 'opus', - 'pitch_hangover': 0, - }, - 'training': { - 'batch_size': 256, - 'lr': 5.e-4, - 'lr_decay_factor': 2.5e-5, - 'epochs': 50, - 'loss': { - 'w_l1': 0, - 'w_lm': 0, - 'w_logmel': 0, - 'w_sc': 0, - 'w_wsc': 0, - 'w_xcorr': 0, - 'w_sxcorr': 1, - 'w_l2': 10, - 'w_slm': 2 - } - } -} - -nolace_setup_adv = { - 'dataset': '/local/datasets/silk_enhancement_v2_full_6to64kbps/training', - 'model': { - 'name': 'nolace', - 'args': [], - 'kwargs': { - 'avg_pool_k': 4, - 'comb_gain_limit_db': 10, - 'cond_dim': 256, - 'conv_gain_limits_db': [-12, 12], - 'global_gain_limits_db': [-6, 6], - 'hidden_feature_dim': 96, - 'kernel_size': 15, - 'num_features': 93, - 'numbits_embedding_dim': 8, - 'numbits_range': [50, 650], - 'partial_lookahead': True, - 'pitch_embedding_dim': 64, - 'pitch_max': 300, - 'preemph': 0.85, - 'skip': 91, - 'softquant': True, - 'sparsify': False, - 'sparsification_density': 0.4, - 'sparsification_schedule': [0, 0, 200] - } - }, - 'data': { - 'frames_per_sample': 100, - 'no_pitch_value': 7, - 'preemph': 0.85, - 'skip': 91, - 'pitch_hangover': 8, - 'acorr_radius': 2, - 'num_bands_clean_spec': 64, - 'num_bands_noisy_spec': 18, - 'noisy_spec_scale': 'opus', - 'pitch_hangover': 0, - }, - 'discriminator': { - 'args': [], - 'kwargs': { - 'architecture': 'free', - 'design': 'f_down', - 'fft_sizes_16k': [ - 64, - 128, - 256, - 512, - 1024, - 2048, - ], - 'freq_roi': [0, 7400], - 'fs': 16000, - 'max_channels': 256, - 'noise_gain': 0.0, - }, - 'name': 'fdmresdisc', - }, - 'training': { - 'adv_target': 'target_orig', - 'batch_size': 64, - 'epochs': 50, - 'gen_lr_reduction': 1, - 'lambda_feat': 1.0, - 'lambda_reg': 0.6, - 'loss': { - 'w_l1': 0, - 'w_l2': 10, - 'w_lm': 0, - 'w_logmel': 0, - 'w_sc': 0, - 'w_slm': 20, - 'w_sxcorr': 1, - 'w_wsc': 0, - 'w_xcorr': 0, - }, - 'lr': 0.0001, - 'lr_decay_factor': 2.5e-09, - } -} - - -lavoce_setup = { - 'data': { - 'frames_per_sample': 100, - 'target': 'signal' - }, - 'dataset': '/local/datasets/lpcnet_large/training', - 'model': { - 'args': [], - 'kwargs': { - 'comb_gain_limit_db': 10, - 'cond_dim': 256, - 'conv_gain_limits_db': [-12, 12], - 'global_gain_limits_db': [-6, 6], - 'kernel_size': 15, - 'num_features': 19, - 'pitch_embedding_dim': 64, - 'pitch_max': 300, - 'preemph': 0.85, - 'pulses': True - }, - 'name': 'lavoce' - }, - 'training': { - 'batch_size': 256, - 'epochs': 50, - 'loss': { - 'w_l1': 0, - 'w_l2': 0, - 'w_lm': 0, - 'w_logmel': 0, - 'w_sc': 0, - 'w_slm': 2, - 'w_sxcorr': 1, - 'w_wsc': 0, - 'w_xcorr': 0 - }, - 'lr': 0.0005, - 'lr_decay_factor': 2.5e-05 - }, - 'validation_dataset': '/local/datasets/lpcnet_large/validation' -} - -lavoce_setup_adv = { - 'data': { - 'frames_per_sample': 100, - 'target': 'signal' - }, - 'dataset': '/local/datasets/lpcnet_large/training', - 'discriminator': { - 'args': [], - 'kwargs': { - 'architecture': 'free', - 'design': 'f_down', - 'fft_sizes_16k': [ - 64, - 128, - 256, - 512, - 1024, - 2048, - ], - 'freq_roi': [0, 7400], - 'fs': 16000, - 'max_channels': 256, - 'noise_gain': 0.0, - }, - 'name': 'fdmresdisc', - }, - 'model': { - 'args': [], - 'kwargs': { - 'comb_gain_limit_db': 10, - 'cond_dim': 256, - 'conv_gain_limits_db': [-12, 12], - 'global_gain_limits_db': [-6, 6], - 'kernel_size': 15, - 'num_features': 19, - 'pitch_embedding_dim': 64, - 'pitch_max': 300, - 'preemph': 0.85, - 'pulses': True - }, - 'name': 'lavoce' - }, - 'training': { - 'batch_size': 64, - 'epochs': 50, - 'gen_lr_reduction': 1, - 'lambda_feat': 1.0, - 'lambda_reg': 0.6, - 'loss': { - 'w_l1': 0, - 'w_l2': 0, - 'w_lm': 0, - 'w_logmel': 0, - 'w_sc': 0, - 'w_slm': 2, - 'w_sxcorr': 1, - 'w_wsc': 0, - 'w_xcorr': 0 - }, - 'lr': 0.0001, - 'lr_decay_factor': 2.5e-09 - }, -} - - -setup_dict = { - 'lace': lace_setup, - 'nolace': nolace_setup, - 'nolace_adv': nolace_setup_adv, - 'lavoce': lavoce_setup, - 'lavoce_adv': lavoce_setup_adv -} diff --git a/android/app/src/main/cpp/third_party/opus/dnn/torch/plc/export_plc.py b/android/app/src/main/cpp/third_party/opus/dnn/torch/plc/export_plc.py deleted file mode 100644 index 7f153c4..0000000 --- a/android/app/src/main/cpp/third_party/opus/dnn/torch/plc/export_plc.py +++ /dev/null @@ -1,100 +0,0 @@ -""" -/* Copyright (c) 2022 Amazon - Written by Jan Buethe */ -/* - Redistribution and use in source and binary forms, with or without - modification, are permitted provided that the following conditions - are met: - - - Redistributions of source code must retain the above copyright - notice, this list of conditions and the following disclaimer. - - - Redistributions in binary form must reproduce the above copyright - notice, this list of conditions and the following disclaimer in the - documentation and/or other materials provided with the distribution. - - THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS - ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT - LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR - A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER - OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, - EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, - PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR - PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF - LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING - NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS - SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. -*/ -""" - -import os -import argparse -import sys - -sys.path.append(os.path.join(os.path.dirname(__file__), '../weight-exchange')) - - -parser = argparse.ArgumentParser() - -parser.add_argument('checkpoint', type=str, help='model checkpoint') -parser.add_argument('output_dir', type=str, help='output folder') - -args = parser.parse_args() - -import torch -import numpy as np - -import plc -from wexchange.torch import dump_torch_weights -from wexchange.c_export import CWriter, print_vector - -def c_export(args, model): - - message = f"Auto generated from checkpoint {os.path.basename(args.checkpoint)}" - - writer = CWriter(os.path.join(args.output_dir, "plc_data"), message=message, model_struct_name='PLCModel') - writer.header.write( -f""" -#include "opus_types.h" -""" - ) - - dense_layers = [ - ('dense_in', "plc_dense_in"), - ('dense_out', "plc_dense_out") - ] - - - for name, export_name in dense_layers: - layer = model.get_submodule(name) - dump_torch_weights(writer, layer, name=export_name, verbose=True, quantize=False, scale=None) - - - gru_layers = [ - ("gru1", "plc_gru1"), - ("gru2", "plc_gru2"), - ] - - max_rnn_units = max([dump_torch_weights(writer, model.get_submodule(name), export_name, verbose=True, input_sparse=False, quantize=True, scale=None, recurrent_scale=None) - for name, export_name in gru_layers]) - - writer.header.write( -f""" - -#define PLC_MAX_RNN_UNITS {max_rnn_units} - -""" - ) - - writer.close() - - -if __name__ == "__main__": - - os.makedirs(args.output_dir, exist_ok=True) - checkpoint = torch.load(args.checkpoint, map_location='cpu') - model = plc.PLC(*checkpoint['model_args'], **checkpoint['model_kwargs']) - model.load_state_dict(checkpoint['state_dict'], strict=False) - #checkpoint = torch.load(args.checkpoint, map_location='cpu') - #model.load_state_dict(checkpoint['state_dict']) - c_export(args, model) diff --git a/android/app/src/main/cpp/third_party/opus/dnn/torch/plc/plc.py b/android/app/src/main/cpp/third_party/opus/dnn/torch/plc/plc.py deleted file mode 100644 index f08e564..0000000 --- a/android/app/src/main/cpp/third_party/opus/dnn/torch/plc/plc.py +++ /dev/null @@ -1,144 +0,0 @@ -import numpy as np -import torch -from torch import nn -import torch.nn.functional as F -from torch.nn.utils import weight_norm -import math - -fid_dict = {} -def dump_signal(x, filename): - return - if filename in fid_dict: - fid = fid_dict[filename] - else: - fid = open(filename, "w") - fid_dict[filename] = fid - x = x.detach().numpy().astype('float32') - x.tofile(fid) - - -class IDCT(nn.Module): - def __init__(self, N, device=None): - super(IDCT, self).__init__() - - self.N = N - n = torch.arange(N, device=device) - k = torch.arange(N, device=device) - self.table = torch.cos(torch.pi/N * (n[:,None]+.5) * k[None,:]) - self.table[:,0] = self.table[:,0] * math.sqrt(.5) - self.table = self.table / math.sqrt(N/2) - - def forward(self, x): - return F.linear(x, self.table, None) - -def plc_loss(N, device=None, alpha=1.0, bias=1.): - idct = IDCT(18, device=device) - def loss(y_true,y_pred): - mask = y_true[:,:,-1:] - y_true = y_true[:,:,:-1] - e = (y_pred - y_true)*mask - e_bands = idct(e[:,:,:-2]) - bias_mask = torch.clamp(4*y_true[:,:,-1:], min=0., max=1.) - l1_loss = torch.mean(torch.abs(e)) - ceps_loss = torch.mean(torch.abs(e[:,:,:-2])) - band_loss = torch.mean(torch.abs(e_bands)) - biased_loss = torch.mean(bias_mask*torch.clamp(e_bands, min=0.)) - pitch_loss1 = torch.mean(torch.clamp(torch.abs(e[:,:,18:19]),max=1.)) - pitch_loss = torch.mean(torch.clamp(torch.abs(e[:,:,18:19]),max=.4)) - voice_bias = torch.mean(torch.clamp(-e[:,:,-1:], min=0.)) - tot = l1_loss + 0.1*voice_bias + alpha*(band_loss + bias*biased_loss) + pitch_loss1 + 8*pitch_loss - return tot, l1_loss, ceps_loss, band_loss, pitch_loss - return loss - - -# weight initialization and clipping -def init_weights(module): - if isinstance(module, nn.GRU): - for p in module.named_parameters(): - if p[0].startswith('weight_hh_'): - nn.init.orthogonal_(p[1]) - - -class GLU(nn.Module): - def __init__(self, feat_size): - super(GLU, self).__init__() - - torch.manual_seed(5) - - self.gate = weight_norm(nn.Linear(feat_size, feat_size, bias=False)) - - self.init_weights() - - def init_weights(self): - - for m in self.modules(): - if isinstance(m, nn.Conv1d) or isinstance(m, nn.ConvTranspose1d)\ - or isinstance(m, nn.Linear) or isinstance(m, nn.Embedding): - nn.init.orthogonal_(m.weight.data) - - def forward(self, x): - - out = x * torch.sigmoid(self.gate(x)) - - return out - -class FWConv(nn.Module): - def __init__(self, in_size, out_size, kernel_size=2): - super(FWConv, self).__init__() - - torch.manual_seed(5) - - self.in_size = in_size - self.kernel_size = kernel_size - self.conv = weight_norm(nn.Linear(in_size*self.kernel_size, out_size, bias=False)) - self.glu = GLU(out_size) - - self.init_weights() - - def init_weights(self): - - for m in self.modules(): - if isinstance(m, nn.Conv1d) or isinstance(m, nn.ConvTranspose1d)\ - or isinstance(m, nn.Linear) or isinstance(m, nn.Embedding): - nn.init.orthogonal_(m.weight.data) - - def forward(self, x, state): - xcat = torch.cat((state, x), -1) - out = self.glu(torch.tanh(self.conv(xcat))) - return out, xcat[:,self.in_size:] - -def n(x): - return torch.clamp(x + (1./127.)*(torch.rand_like(x)-.5), min=-1., max=1.) - -class PLC(nn.Module): - def __init__(self, features_in=57, features_out=20, cond_size=128, gru_size=128): - super(PLC, self).__init__() - - self.features_in = features_in - self.features_out = features_out - self.cond_size = cond_size - self.gru_size = gru_size - - self.dense_in = nn.Linear(self.features_in, self.cond_size) - self.gru1 = nn.GRU(self.cond_size, self.gru_size, batch_first=True) - self.gru2 = nn.GRU(self.gru_size, self.gru_size, batch_first=True) - self.dense_out = nn.Linear(self.gru_size, features_out) - - self.apply(init_weights) - nb_params = sum(p.numel() for p in self.parameters()) - print(f"plc model: {nb_params} weights") - - def forward(self, features, lost, states=None): - device = features.device - batch_size = features.size(0) - if states is None: - gru1_state = torch.zeros((1, batch_size, self.gru_size), device=device) - gru2_state = torch.zeros((1, batch_size, self.gru_size), device=device) - else: - gru1_state = states[0] - gru2_state = states[1] - x = torch.cat([features, lost], dim=-1) - x = torch.tanh(self.dense_in(x)) - gru1_out, gru1_state = self.gru1(x, gru1_state) - gru2_out, gru2_state = self.gru2(gru1_out, gru2_state) - return self.dense_out(gru2_out), [gru1_state, gru2_state] diff --git a/android/app/src/main/cpp/third_party/opus/dnn/torch/plc/plc_dataset.py b/android/app/src/main/cpp/third_party/opus/dnn/torch/plc/plc_dataset.py deleted file mode 100644 index 2dfaaaf..0000000 --- a/android/app/src/main/cpp/third_party/opus/dnn/torch/plc/plc_dataset.py +++ /dev/null @@ -1,60 +0,0 @@ -import torch -import numpy as np - -class PLCDataset(torch.utils.data.Dataset): - def __init__(self, - feature_file, - loss_file, - sequence_length=1000, - nb_features=20, - nb_burg_features=36, - lpc_order=16): - - self.features_in = nb_features + nb_burg_features - self.nb_burg_features = nb_burg_features - total_features = self.features_in + lpc_order - self.sequence_length = sequence_length - self.nb_features = nb_features - - self.features = np.memmap(feature_file, dtype='float32', mode='r') - self.lost = np.memmap(loss_file, dtype='int8', mode='r') - self.lost = self.lost.astype('float32') - - self.nb_sequences = self.features.shape[0]//self.sequence_length//total_features - - self.features = self.features[:self.nb_sequences*self.sequence_length*total_features] - self.features = self.features.reshape((self.nb_sequences, self.sequence_length, total_features)) - self.features = self.features[:,:,:self.features_in] - - #self.lost = self.lost[:(len(self.lost)//features.shape[1]-1)*features.shape[1]] - #self.lost = self.lost.reshape((-1, self.sequence_length)) - - def __len__(self): - return self.nb_sequences - - def __getitem__(self, index): - features = self.features[index, :, :] - burg_lost = (np.random.rand(features.shape[0]) > .1).astype('float32') - burg_lost = np.reshape(burg_lost, (features.shape[0], 1)) - burg_mask = np.tile(burg_lost, (1,self.nb_burg_features)) - - lost_offset = np.random.randint(0, high=self.lost.shape[0]-self.sequence_length) - lost = self.lost[lost_offset:lost_offset+self.sequence_length] - #randomly add a few 10-ms losses so that the model learns to handle them - lost = lost * (np.random.rand(lost.shape[-1]) > .02).astype('float32') - #randomly break long consecutive losses so we don't try too hard to predict them - lost = 1 - ((1-lost) * (np.random.rand(lost.shape[-1]) > .1).astype('float32')) - lost = np.reshape(lost, (features.shape[0], 1)) - lost_mask = np.tile(lost, (1,features.shape[-1])) - in_features = features*lost_mask - in_features[:,:self.nb_burg_features] = in_features[:,:self.nb_burg_features]*burg_mask - - #For the first frame after a loss, we don't have valid features, but the Burg estimate is valid. - #in_features[:,1:,self.nb_burg_features:] = in_features[:,1:,self.nb_burg_features:]*lost_mask[:,:-1,self.nb_burg_features:] - out_lost = np.copy(lost) - #out_lost[:,1:,:] = out_lost[:,1:,:]*out_lost[:,:-1,:] - - out_features = np.concatenate([features[:,self.nb_burg_features:], 1.-out_lost], axis=-1) - burg_sign = 2*burg_lost - 1 - # last dim is 1 for received packet, 0 for lost packet, and -1 when just the Burg info is missing - return in_features*lost_mask, lost*burg_sign, out_features diff --git a/android/app/src/main/cpp/third_party/opus/dnn/torch/plc/train_plc.py b/android/app/src/main/cpp/third_party/opus/dnn/torch/plc/train_plc.py deleted file mode 100644 index 12b31c4..0000000 --- a/android/app/src/main/cpp/third_party/opus/dnn/torch/plc/train_plc.py +++ /dev/null @@ -1,145 +0,0 @@ -import os -import argparse -import random -import numpy as np - -import torch -from torch import nn -import torch.nn.functional as F -import tqdm - -import plc -from plc_dataset import PLCDataset - -parser = argparse.ArgumentParser() - -parser.add_argument('features', type=str, help='path to feature file in .f32 format') -parser.add_argument('loss', type=str, help='path to signal file in .s8 format') -parser.add_argument('output', type=str, help='path to output folder') - -parser.add_argument('--suffix', type=str, help="model name suffix", default="") -parser.add_argument('--cuda-visible-devices', type=str, help="comma separates list of cuda visible device indices, default: CUDA_VISIBLE_DEVICES", default=None) - - -model_group = parser.add_argument_group(title="model parameters") -model_group.add_argument('--cond-size', type=int, help="first conditioning size, default: 128", default=128) -model_group.add_argument('--gru-size', type=int, help="GRU size, default: 128", default=128) - -training_group = parser.add_argument_group(title="training parameters") -training_group.add_argument('--batch-size', type=int, help="batch size, default: 512", default=512) -training_group.add_argument('--lr', type=float, help='learning rate, default: 1e-3', default=1e-3) -training_group.add_argument('--epochs', type=int, help='number of training epochs, default: 20', default=20) -training_group.add_argument('--sequence-length', type=int, help='sequence length, default: 15', default=15) -training_group.add_argument('--lr-decay', type=float, help='learning rate decay factor, default: 1e-4', default=1e-4) -training_group.add_argument('--initial-checkpoint', type=str, help='initial checkpoint to start training from, default: None', default=None) - -args = parser.parse_args() - -if args.cuda_visible_devices != None: - os.environ['CUDA_VISIBLE_DEVICES'] = args.cuda_visible_devices - -# checkpoints -checkpoint_dir = os.path.join(args.output, 'checkpoints') -checkpoint = dict() -os.makedirs(checkpoint_dir, exist_ok=True) - - -# training parameters -batch_size = args.batch_size -lr = args.lr -epochs = args.epochs -sequence_length = args.sequence_length -lr_decay = args.lr_decay - -adam_betas = [0.8, 0.95] -adam_eps = 1e-8 -features_file = args.features -loss_file = args.loss - -# model parameters -cond_size = args.cond_size - - -checkpoint['batch_size'] = batch_size -checkpoint['lr'] = lr -checkpoint['lr_decay'] = lr_decay -checkpoint['epochs'] = epochs -checkpoint['sequence_length'] = sequence_length -checkpoint['adam_betas'] = adam_betas - - -device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu") - -checkpoint['model_args'] = () -checkpoint['model_kwargs'] = {'cond_size': cond_size, 'gru_size': args.gru_size} -print(checkpoint['model_kwargs']) -model = plc.PLC(*checkpoint['model_args'], **checkpoint['model_kwargs']) - -if type(args.initial_checkpoint) != type(None): - checkpoint = torch.load(args.initial_checkpoint, map_location='cpu') - model.load_state_dict(checkpoint['state_dict'], strict=False) - -checkpoint['state_dict'] = model.state_dict() - - -dataset = PLCDataset(features_file, loss_file, sequence_length=sequence_length) -dataloader = torch.utils.data.DataLoader(dataset, batch_size=batch_size, shuffle=True, drop_last=True, num_workers=4) - - -optimizer = torch.optim.AdamW(model.parameters(), lr=lr, betas=adam_betas, eps=adam_eps) - - -# learning rate scheduler -scheduler = torch.optim.lr_scheduler.LambdaLR(optimizer=optimizer, lr_lambda=lambda x : 1 / (1 + lr_decay * x)) - -states = None - -plc_loss = plc.plc_loss(18, device=device) -if __name__ == '__main__': - model.to(device) - - for epoch in range(1, epochs + 1): - - running_loss = 0 - running_l1_loss = 0 - running_ceps_loss = 0 - running_band_loss = 0 - running_pitch_loss = 0 - - print(f"training epoch {epoch}...") - with tqdm.tqdm(dataloader, unit='batch') as tepoch: - for i, (features, lost, target) in enumerate(tepoch): - optimizer.zero_grad() - features = features.to(device) - lost = lost.to(device) - target = target.to(device) - - out, states = model(features, lost) - - loss, l1_loss, ceps_loss, band_loss, pitch_loss = plc_loss(target, out) - - loss.backward() - optimizer.step() - - #model.clip_weights() - - scheduler.step() - - running_loss += loss.detach().cpu().item() - running_l1_loss += l1_loss.detach().cpu().item() - running_ceps_loss += ceps_loss.detach().cpu().item() - running_band_loss += band_loss.detach().cpu().item() - running_pitch_loss += pitch_loss.detach().cpu().item() - tepoch.set_postfix(loss=f"{running_loss/(i+1):8.5f}", - l1_loss=f"{running_l1_loss/(i+1):8.5f}", - ceps_loss=f"{running_ceps_loss/(i+1):8.5f}", - band_loss=f"{running_band_loss/(i+1):8.5f}", - pitch_loss=f"{running_pitch_loss/(i+1):8.5f}", - ) - - # save checkpoint - checkpoint_path = os.path.join(checkpoint_dir, f'plc{args.suffix}_{epoch}.pth') - checkpoint['state_dict'] = model.state_dict() - checkpoint['loss'] = running_loss / len(dataloader) - checkpoint['epoch'] = epoch - torch.save(checkpoint, checkpoint_path) diff --git a/android/app/src/main/cpp/third_party/opus/dnn/torch/rdovae/README.md b/android/app/src/main/cpp/third_party/opus/dnn/torch/rdovae/README.md deleted file mode 100644 index d1d3f49..0000000 --- a/android/app/src/main/cpp/third_party/opus/dnn/torch/rdovae/README.md +++ /dev/null @@ -1,48 +0,0 @@ -# Deep REDundancy (DRED) with RDO-VAE - -This is a rate-distortion-optimized variational autoencoder (RDO-VAE) designed -to coding redundancy information. Pre-trained models are provided as C code -in the dnn/ directory with the corresponding model in dnn/models/ directory -(name starts with rdovae_). If you don't want to train a new DRED model, you can -skip straight to the Inference section. - -## Data preparation - -For data preparation you need to build Opus as detailed in the top-level README. -You will need to use the --enable-dred configure option. -The build will produce an executable named "dump_data". -To prepare the training data, run: -``` -./dump_data -train in_speech.pcm out_features.f32 out_speech.pcm -``` -Where the in_speech.pcm speech file is a raw 16-bit PCM file sampled at 16 kHz. -The speech data used for training the model can be found at: -https://media.xiph.org/lpcnet/speech/tts_speech_negative_16k.sw -The out_speech.pcm file isn't needed for DRED, but it is needed to train -the FARGAN vocoder (see dnn/torch/fargan/ for details). - -## Training - -To perform training, run the following command: -``` -python ./train_rdovae.py --cuda-visible-devices 0 --sequence-length 400 --split-mode random_split --state-dim 80 --batch-size 512 --epochs 400 --lambda-max 0.04 --lr 0.003 --lr-decay-factor 0.0001 out_features.f32 output_dir -``` -The final model will be in output_dir/checkpoints/chechpoint_400.pth. - -The model can be converted to C using: -``` -python export_rdovae_weights.py output_dir/checkpoints/chechpoint_400.pth dred_c_dir -``` -which will create a number of C source and header files in the fargan_c_dir directory. -Copy these files to the opus/dnn/ directory (replacing the existing ones) and recompile Opus. - -## Inference - -DRED is integrated within the Opus codec and can be evaluated using the opus_demo -executable. For example: -``` -./opus_demo voip 16000 1 64000 -loss 50 -dred 100 -sim_loss 50 input.pcm output.pcm -``` -Will tell the encoder to encode a 16 kHz raw audio file at 64 kb/s using up to 1 second -of redundancy (units are based on 10-ms) and then simulate 50% loss. Refer to `opus_demo --help` -for more details. diff --git a/android/app/src/main/cpp/third_party/opus/dnn/torch/rdovae/export_rdovae_weights.py b/android/app/src/main/cpp/third_party/opus/dnn/torch/rdovae/export_rdovae_weights.py deleted file mode 100644 index 3ef9fab..0000000 --- a/android/app/src/main/cpp/third_party/opus/dnn/torch/rdovae/export_rdovae_weights.py +++ /dev/null @@ -1,365 +0,0 @@ -""" -/* Copyright (c) 2022 Amazon - Written by Jan Buethe */ -/* - Redistribution and use in source and binary forms, with or without - modification, are permitted provided that the following conditions - are met: - - - Redistributions of source code must retain the above copyright - notice, this list of conditions and the following disclaimer. - - - Redistributions in binary form must reproduce the above copyright - notice, this list of conditions and the following disclaimer in the - documentation and/or other materials provided with the distribution. - - THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS - ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT - LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR - A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER - OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, - EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, - PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR - PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF - LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING - NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS - SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. -*/ -""" - -import os -import argparse -import sys - -sys.path.append(os.path.join(os.path.dirname(__file__), '../weight-exchange')) - -parser = argparse.ArgumentParser() - -parser.add_argument('checkpoint', type=str, help='rdovae model checkpoint') -parser.add_argument('output_dir', type=str, help='output folder') -parser.add_argument('--format', choices=['C', 'numpy'], help='output format, default: C', default='C') - -args = parser.parse_args() - -import torch -import numpy as np - -from rdovae import RDOVAE -from wexchange.torch import dump_torch_weights -from wexchange.c_export import CWriter, print_vector - -def print_xml(xmlout, val, param, anchor, name): - xmlout.write( -f""" - - {param} values for {name} - - - - -""") - for k in range(val.shape[1]): - xmlout.write(f" ") - for j in range(val.shape[0]): - xmlout.write(f"") - xmlout.write("\n") - xmlout.write( -f""" - -
kQ0Q1Q2Q3Q4Q5Q6Q7Q8Q9Q10Q11Q12Q13Q14Q15
{k}{val[j][k]}
-""") -def dump_statistical_model(writer, w, name, xmlout): - levels = w.shape[0] - - print("printing statistical model") - quant_scales = torch.nn.functional.softplus(w[:, 0, :]).numpy() - dead_zone = 0.05 * torch.nn.functional.softplus(w[:, 1, :]).numpy() - r = torch.sigmoid(w[:, 5 , :]).numpy() - p0 = torch.sigmoid(w[:, 4 , :]).numpy() - p0 = 1 - r ** (0.5 + 0.5 * p0) - - scales_norm = 255./256./(1e-15+np.max(quant_scales,axis=0)) - quant_scales = quant_scales*scales_norm - quant_scales_q8 = np.round(quant_scales * 2**8).astype(np.uint16) - dead_zone_q8 = np.clip(np.round(dead_zone * 2**8), 0, 255).astype(np.uint16) - r_q8 = np.clip(np.round(r * 2**8), 0, 255).astype(np.uint8) - p0_q8 = np.clip(np.round(p0 * 2**8), 0, 255).astype(np.uint16) - - mask = (np.max(r_q8,axis=0) > 0) * (np.min(p0_q8,axis=0) < 255) - quant_scales_q8 = quant_scales_q8[:, mask] - dead_zone_q8 = dead_zone_q8[:, mask] - r_q8 = r_q8[:, mask] - p0_q8 = p0_q8[:, mask] - N = r_q8.shape[-1] - - print_vector(writer.source, quant_scales_q8, f'dred_{name}_quant_scales_q8', dtype='opus_uint8', static=False) - print_vector(writer.source, dead_zone_q8, f'dred_{name}_dead_zone_q8', dtype='opus_uint8', static=False) - print_vector(writer.source, r_q8, f'dred_{name}_r_q8', dtype='opus_uint8', static=False) - print_vector(writer.source, p0_q8, f'dred_{name}_p0_q8', dtype='opus_uint8', static=False) - - print_xml(xmlout, quant_scales_q8, "Scale", "scale", name) - print_xml(xmlout, dead_zone_q8, "Dead zone", "deadzone", name) - print_xml(xmlout, r_q8, "Decay (r)", "decay", name) - print_xml(xmlout, p0_q8, "P(0)", "p0", name) - - writer.header.write( -f""" -extern const opus_uint8 dred_{name}_quant_scales_q8[{levels * N}]; -extern const opus_uint8 dred_{name}_dead_zone_q8[{levels * N}]; -extern const opus_uint8 dred_{name}_r_q8[{levels * N}]; -extern const opus_uint8 dred_{name}_p0_q8[{levels * N}]; - -""" - ) - return N, mask, torch.tensor(scales_norm[mask]) - - -def c_export(args, model): - - message = f"Auto generated from checkpoint {os.path.basename(args.checkpoint)}" - - enc_writer = CWriter(os.path.join(args.output_dir, "dred_rdovae_enc_data"), message=message, model_struct_name='RDOVAEEnc') - dec_writer = CWriter(os.path.join(args.output_dir, "dred_rdovae_dec_data"), message=message, model_struct_name='RDOVAEDec') - stats_writer = CWriter(os.path.join(args.output_dir, "dred_rdovae_stats_data"), message=message, enable_binary_blob=False) - constants_writer = CWriter(os.path.join(args.output_dir, "dred_rdovae_constants"), message=message, header_only=True, enable_binary_blob=False) - xmlout = open("stats.xml", "w") - - # some custom includes - for writer in [enc_writer, dec_writer]: - writer.header.write( -f""" -#include "opus_types.h" - -#include "dred_rdovae.h" - -#include "dred_rdovae_constants.h" - -""" - ) - - stats_writer.header.write( -f""" -#include "opus_types.h" - -#include "dred_rdovae_constants.h" - -""" - ) - - latent_out = model.get_submodule('core_encoder.module.z_dense') - state_out = model.get_submodule('core_encoder.module.state_dense_2') - orig_latent_dim = latent_out.weight.shape[0] - orig_state_dim = state_out.weight.shape[0] - # statistical model - qembedding = model.statistical_model.quant_embedding.weight.detach() - levels = qembedding.shape[0] - qembedding = torch.reshape(qembedding, (levels, 6, -1)) - - latent_dim, latent_mask, latent_scale = dump_statistical_model(stats_writer, qembedding[:, :, :orig_latent_dim], 'latent', xmlout) - state_dim, state_mask, state_scale = dump_statistical_model(stats_writer, qembedding[:, :, orig_latent_dim:], 'state', xmlout) - - padded_latent_dim = (latent_dim+7)//8*8 - latent_pad = padded_latent_dim - latent_dim; - w = latent_out.weight[latent_mask,:] - w = w/latent_scale[:, None] - w = torch.cat([w, torch.zeros(latent_pad, w.shape[1])], dim=0) - b = latent_out.bias[latent_mask] - b = b/latent_scale - b = torch.cat([b, torch.zeros(latent_pad)], dim=0) - latent_out.weight = torch.nn.Parameter(w) - latent_out.bias = torch.nn.Parameter(b) - - padded_state_dim = (state_dim+7)//8*8 - state_pad = padded_state_dim - state_dim; - w = state_out.weight[state_mask,:] - w = w/state_scale[:, None] - w = torch.cat([w, torch.zeros(state_pad, w.shape[1])], dim=0) - b = state_out.bias[state_mask] - b = b/state_scale - b = torch.cat([b, torch.zeros(state_pad)], dim=0) - state_out.weight = torch.nn.Parameter(w) - state_out.bias = torch.nn.Parameter(b) - - latent_in = model.get_submodule('core_decoder.module.dense_1') - state_in = model.get_submodule('core_decoder.module.hidden_init') - latent_in.weight = torch.nn.Parameter(latent_in.weight[:,latent_mask]*latent_scale) - state_in.weight = torch.nn.Parameter(state_in.weight[:,state_mask]*state_scale) - - # encoder - encoder_dense_layers = [ - ('core_encoder.module.dense_1' , 'enc_dense1', 'TANH', False,), - ('core_encoder.module.z_dense' , 'enc_zdense', 'LINEAR', True,), - ('core_encoder.module.state_dense_1' , 'gdense1' , 'TANH', True,), - ('core_encoder.module.state_dense_2' , 'gdense2' , 'TANH', True) - ] - - for name, export_name, _, quantize in encoder_dense_layers: - layer = model.get_submodule(name) - dump_torch_weights(enc_writer, layer, name=export_name, verbose=True, quantize=quantize, scale=None) - - - encoder_gru_layers = [ - ('core_encoder.module.gru1' , 'enc_gru1', 'TANH', True), - ('core_encoder.module.gru2' , 'enc_gru2', 'TANH', True), - ('core_encoder.module.gru3' , 'enc_gru3', 'TANH', True), - ('core_encoder.module.gru4' , 'enc_gru4', 'TANH', True), - ('core_encoder.module.gru5' , 'enc_gru5', 'TANH', True), - ] - - enc_max_rnn_units = max([dump_torch_weights(enc_writer, model.get_submodule(name), export_name, verbose=True, input_sparse=True, quantize=quantize, scale=None, recurrent_scale=None) - for name, export_name, _, quantize in encoder_gru_layers]) - - - encoder_conv_layers = [ - ('core_encoder.module.conv1.conv' , 'enc_conv1', 'TANH', True), - ('core_encoder.module.conv2.conv' , 'enc_conv2', 'TANH', True), - ('core_encoder.module.conv3.conv' , 'enc_conv3', 'TANH', True), - ('core_encoder.module.conv4.conv' , 'enc_conv4', 'TANH', True), - ('core_encoder.module.conv5.conv' , 'enc_conv5', 'TANH', True), - ] - - enc_max_conv_inputs = max([dump_torch_weights(enc_writer, model.get_submodule(name), export_name, verbose=True, quantize=quantize, scale=None) for name, export_name, _, quantize in encoder_conv_layers]) - - - del enc_writer - - # decoder - decoder_dense_layers = [ - ('core_decoder.module.dense_1' , 'dec_dense1', 'TANH', False), - ('core_decoder.module.glu1.gate' , 'dec_glu1', 'TANH', True), - ('core_decoder.module.glu2.gate' , 'dec_glu2', 'TANH', True), - ('core_decoder.module.glu3.gate' , 'dec_glu3', 'TANH', True), - ('core_decoder.module.glu4.gate' , 'dec_glu4', 'TANH', True), - ('core_decoder.module.glu5.gate' , 'dec_glu5', 'TANH', True), - ('core_decoder.module.output' , 'dec_output', 'LINEAR', True), - ('core_decoder.module.hidden_init' , 'dec_hidden_init', 'TANH', False), - ('core_decoder.module.gru_init' , 'dec_gru_init','TANH', True), - ] - - for name, export_name, _, quantize in decoder_dense_layers: - layer = model.get_submodule(name) - dump_torch_weights(dec_writer, layer, name=export_name, verbose=True, quantize=quantize, scale=None) - - - decoder_gru_layers = [ - ('core_decoder.module.gru1' , 'dec_gru1', 'TANH', True), - ('core_decoder.module.gru2' , 'dec_gru2', 'TANH', True), - ('core_decoder.module.gru3' , 'dec_gru3', 'TANH', True), - ('core_decoder.module.gru4' , 'dec_gru4', 'TANH', True), - ('core_decoder.module.gru5' , 'dec_gru5', 'TANH', True), - ] - - dec_max_rnn_units = max([dump_torch_weights(dec_writer, model.get_submodule(name), export_name, verbose=True, input_sparse=True, quantize=quantize, scale=None, recurrent_scale=None) - for name, export_name, _, quantize in decoder_gru_layers]) - - decoder_conv_layers = [ - ('core_decoder.module.conv1.conv' , 'dec_conv1', 'TANH', True), - ('core_decoder.module.conv2.conv' , 'dec_conv2', 'TANH', True), - ('core_decoder.module.conv3.conv' , 'dec_conv3', 'TANH', True), - ('core_decoder.module.conv4.conv' , 'dec_conv4', 'TANH', True), - ('core_decoder.module.conv5.conv' , 'dec_conv5', 'TANH', True), - ] - - dec_max_conv_inputs = max([dump_torch_weights(dec_writer, model.get_submodule(name), export_name, verbose=True, quantize=quantize, scale=None) for name, export_name, _, quantize in decoder_conv_layers]) - - del dec_writer - - del stats_writer - - # constants - constants_writer.header.write( -f""" -#define DRED_NUM_FEATURES {model.feature_dim} - -#define DRED_LATENT_DIM {latent_dim} - -#define DRED_STATE_DIM {state_dim} - -#define DRED_PADDED_LATENT_DIM {padded_latent_dim} - -#define DRED_PADDED_STATE_DIM {padded_state_dim} - -#define DRED_NUM_QUANTIZATION_LEVELS {model.quant_levels} - -#define DRED_MAX_RNN_NEURONS {max(enc_max_rnn_units, dec_max_rnn_units)} - -#define DRED_MAX_CONV_INPUTS {max(enc_max_conv_inputs, dec_max_conv_inputs)} - -#define DRED_ENC_MAX_RNN_NEURONS {enc_max_conv_inputs} - -#define DRED_ENC_MAX_CONV_INPUTS {enc_max_conv_inputs} - -#define DRED_DEC_MAX_RNN_NEURONS {dec_max_rnn_units} - -""" - ) - - del constants_writer - - -def numpy_export(args, model): - - exchange_name_to_name = { - 'encoder_stack_layer1_dense' : 'core_encoder.module.dense_1', - 'encoder_stack_layer3_dense' : 'core_encoder.module.dense_2', - 'encoder_stack_layer5_dense' : 'core_encoder.module.dense_3', - 'encoder_stack_layer7_dense' : 'core_encoder.module.dense_4', - 'encoder_stack_layer8_dense' : 'core_encoder.module.dense_5', - 'encoder_state_layer1_dense' : 'core_encoder.module.state_dense_1', - 'encoder_state_layer2_dense' : 'core_encoder.module.state_dense_2', - 'encoder_stack_layer2_gru' : 'core_encoder.module.gru_1', - 'encoder_stack_layer4_gru' : 'core_encoder.module.gru_2', - 'encoder_stack_layer6_gru' : 'core_encoder.module.gru_3', - 'encoder_stack_layer9_conv' : 'core_encoder.module.conv1', - 'statistical_model_embedding' : 'statistical_model.quant_embedding', - 'decoder_state1_dense' : 'core_decoder.module.gru_1_init', - 'decoder_state2_dense' : 'core_decoder.module.gru_2_init', - 'decoder_state3_dense' : 'core_decoder.module.gru_3_init', - 'decoder_stack_layer1_dense' : 'core_decoder.module.dense_1', - 'decoder_stack_layer3_dense' : 'core_decoder.module.dense_2', - 'decoder_stack_layer5_dense' : 'core_decoder.module.dense_3', - 'decoder_stack_layer7_dense' : 'core_decoder.module.dense_4', - 'decoder_stack_layer8_dense' : 'core_decoder.module.dense_5', - 'decoder_stack_layer9_dense' : 'core_decoder.module.output', - 'decoder_stack_layer2_gru' : 'core_decoder.module.gru_1', - 'decoder_stack_layer4_gru' : 'core_decoder.module.gru_2', - 'decoder_stack_layer6_gru' : 'core_decoder.module.gru_3' - } - - name_to_exchange_name = {value : key for key, value in exchange_name_to_name.items()} - - for name, exchange_name in name_to_exchange_name.items(): - print(f"printing layer {name}...") - dump_torch_weights(os.path.join(args.output_dir, exchange_name), model.get_submodule(name)) - - -if __name__ == "__main__": - - - os.makedirs(args.output_dir, exist_ok=True) - - - # load model from checkpoint - checkpoint = torch.load(args.checkpoint, map_location='cpu') - model = RDOVAE(*checkpoint['model_args'], **checkpoint['model_kwargs']) - missing_keys, unmatched_keys = model.load_state_dict(checkpoint['state_dict'], strict=False) - def _remove_weight_norm(m): - try: - torch.nn.utils.remove_weight_norm(m) - except ValueError: # this module didn't have weight norm - return - model.apply(_remove_weight_norm) - - - if len(missing_keys) > 0: - raise ValueError(f"error: missing keys in state dict") - - if len(unmatched_keys) > 0: - print(f"warning: the following keys were unmatched {unmatched_keys}") - - if args.format == 'C': - c_export(args, model) - elif args.format == 'numpy': - numpy_export(args, model) - else: - raise ValueError(f'error: unknown export format {args.format}') diff --git a/android/app/src/main/cpp/third_party/opus/dnn/torch/rdovae/fec_encoder.py b/android/app/src/main/cpp/third_party/opus/dnn/torch/rdovae/fec_encoder.py deleted file mode 100644 index 20ab4ac..0000000 --- a/android/app/src/main/cpp/third_party/opus/dnn/torch/rdovae/fec_encoder.py +++ /dev/null @@ -1,212 +0,0 @@ -""" -/* Copyright (c) 2022 Amazon - Written by Jan Buethe and Jean-Marc Valin */ -/* - Redistribution and use in source and binary forms, with or without - modification, are permitted provided that the following conditions - are met: - - - Redistributions of source code must retain the above copyright - notice, this list of conditions and the following disclaimer. - - - Redistributions in binary form must reproduce the above copyright - notice, this list of conditions and the following disclaimer in the - documentation and/or other materials provided with the distribution. - - THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS - ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT - LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR - A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER - OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, - EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, - PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR - PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF - LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING - NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS - SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. -*/ -""" - -import os -import subprocess -import argparse - -os.environ['CUDA_VISIBLE_DEVICES'] = "" - -parser = argparse.ArgumentParser(description='Encode redundancy for Opus neural FEC. Designed for use with voip application and 20ms frames') - -parser.add_argument('input', metavar='', help='audio input (.wav or .raw or .pcm as int16)') -parser.add_argument('checkpoint', metavar='', help='model checkpoint') -parser.add_argument('q0', metavar='', type=int, help='quantization level for most recent frame') -parser.add_argument('q1', metavar='', type=int, help='quantization level for oldest frame') -parser.add_argument('output', type=str, help='output file (will be extended with .fec)') - -parser.add_argument('--dump-data', type=str, default='./dump_data', help='path to dump data executable (default ./dump_data)') -parser.add_argument('--num-redundancy-frames', default=52, type=int, help='number of redundancy frames per packet (default 52)') -parser.add_argument('--extra-delay', default=0, type=int, help="last features in packet are calculated with the decoder aligned samples, use this option to add extra delay (in samples at 16kHz)") -parser.add_argument('--lossfile', type=str, help='file containing loss trace (0 for frame received, 1 for lost)') -parser.add_argument('--debug-output', action='store_true', help='if set, differently assembled features are written to disk') - -args = parser.parse_args() - -import numpy as np -from scipy.io import wavfile -import torch - -from rdovae import RDOVAE -from packets import write_fec_packets - -torch.set_num_threads(4) - -checkpoint = torch.load(args.checkpoint, map_location="cpu") -model = RDOVAE(*checkpoint['model_args'], **checkpoint['model_kwargs']) -model.load_state_dict(checkpoint['state_dict'], strict=False) -model.to("cpu") - -lpc_order = 16 - -## prepare input signal -# SILK frame size is 20ms and LPCNet subframes are 10ms -subframe_size = 160 -frame_size = 2 * subframe_size - -# 91 samples delay to align with SILK decoded frames -silk_delay = 91 - -# prepend zeros to have enough history to produce the first package -zero_history = (args.num_redundancy_frames - 1) * frame_size - -# dump data has a (feature) delay of 10ms -dump_data_delay = 160 - -total_delay = silk_delay + zero_history + args.extra_delay - dump_data_delay - -# load signal -if args.input.endswith('.raw') or args.input.endswith('.pcm'): - signal = np.fromfile(args.input, dtype='int16') - -elif args.input.endswith('.wav'): - fs, signal = wavfile.read(args.input) -else: - raise ValueError(f'unknown input signal format: {args.input}') - -# fill up last frame with zeros -padded_signal_length = len(signal) + total_delay -tail = padded_signal_length % frame_size -right_padding = (frame_size - tail) % frame_size - -signal = np.concatenate((np.zeros(total_delay, dtype=np.int16), signal, np.zeros(right_padding, dtype=np.int16))) - -padded_signal_file = os.path.splitext(args.input)[0] + '_padded.raw' -signal.tofile(padded_signal_file) - -# write signal and call dump_data to create features - -feature_file = os.path.splitext(args.input)[0] + '_features.f32' -command = f"{args.dump_data} -test {padded_signal_file} {feature_file}" -r = subprocess.run(command, shell=True) -if r.returncode != 0: - raise RuntimeError(f"command '{command}' failed with exit code {r.returncode}") - -# load features -nb_features = model.feature_dim + lpc_order -nb_used_features = model.feature_dim - -# load features -features = np.fromfile(feature_file, dtype='float32') -num_subframes = len(features) // nb_features -num_subframes = 2 * (num_subframes // 2) -num_frames = num_subframes // 2 - -features = np.reshape(features, (1, -1, nb_features)) -features = features[:, :, :nb_used_features] -features = features[:, :num_subframes, :] - -# quant_ids in reverse decoding order -quant_ids = torch.round((args.q1 + (args.q0 - args.q1) * torch.arange(args.num_redundancy_frames // 2) / (args.num_redundancy_frames // 2 - 1))).long() - -print(f"using quantization levels {quant_ids}...") - -# convert input to torch tensors -features = torch.from_numpy(features) - - -# run encoder -print("running fec encoder...") -with torch.no_grad(): - - # encoding - z, states, state_size = model.encode(features) - - - # decoder on packet chunks - input_length = args.num_redundancy_frames // 2 - offset = args.num_redundancy_frames - 1 - - packets = [] - packet_sizes = [] - - for i in range(offset, num_frames): - print(f"processing frame {i - offset}...") - # quantize / unquantize latent vectors - zi = torch.clone(z[:, i - 2 * input_length + 2: i + 1 : 2, :]) - zi, rates = model.quantize(zi, quant_ids) - zi = model.unquantize(zi, quant_ids) - - features = model.decode(zi, states[:, i : i + 1, :]) - packets.append(features.squeeze(0).numpy()) - packet_size = 8 * int((torch.sum(rates) + 7 + state_size) / 8) - packet_sizes.append(packet_size) - - -# write packets -packet_file = args.output + '.fec' if not args.output.endswith('.fec') else args.output -write_fec_packets(packet_file, packets, packet_sizes) - - -print(f"average redundancy rate: {int(round(sum(packet_sizes) / len(packet_sizes) * 50 / 1000))} kbps") - -# assemble features according to loss file -if args.lossfile != None: - num_packets = len(packets) - loss = np.loadtxt(args.lossfile, dtype='int16') - fec_out = np.zeros((num_packets * 2, packets[0].shape[-1]), dtype='float32') - foffset = -2 - ptr = 0 - count = 2 - for i in range(num_packets): - if (loss[i] == 0) or (i == num_packets - 1): - - fec_out[ptr:ptr+count,:] = packets[i][foffset:, :] - - ptr += count - foffset = -2 - count = 2 - else: - count += 2 - foffset -= 2 - - fec_out_full = np.zeros((fec_out.shape[0], 36), dtype=np.float32) - fec_out_full[:, : fec_out.shape[-1]] = fec_out - - fec_out_full.tofile(packet_file[:-4] + f'_fec.f32') - - -if args.debug_output: - import itertools - - batches = [4] - offsets = [0, 2 * args.num_redundancy_frames - 4] - - # sanity checks - # 1. concatenate features at offset 0 - for batch, offset in itertools.product(batches, offsets): - - stop = packets[0].shape[1] - offset - test_features = np.concatenate([packet[stop - batch: stop, :] for packet in packets[::batch//2]], axis=0) - - test_features_full = np.zeros((test_features.shape[0], nb_features), dtype=np.float32) - test_features_full[:, :nb_used_features] = test_features[:, :] - - print(f"writing debug output {packet_file[:-4] + f'_torch_batch{batch}_offset{offset}.f32'}") - test_features_full.tofile(packet_file[:-4] + f'_torch_batch{batch}_offset{offset}.f32') diff --git a/android/app/src/main/cpp/third_party/opus/dnn/torch/rdovae/import_rdovae_weights.py b/android/app/src/main/cpp/third_party/opus/dnn/torch/rdovae/import_rdovae_weights.py deleted file mode 100644 index c824986..0000000 --- a/android/app/src/main/cpp/third_party/opus/dnn/torch/rdovae/import_rdovae_weights.py +++ /dev/null @@ -1,143 +0,0 @@ -""" -/* Copyright (c) 2022 Amazon - Written by Jan Buethe */ -/* - Redistribution and use in source and binary forms, with or without - modification, are permitted provided that the following conditions - are met: - - - Redistributions of source code must retain the above copyright - notice, this list of conditions and the following disclaimer. - - - Redistributions in binary form must reproduce the above copyright - notice, this list of conditions and the following disclaimer in the - documentation and/or other materials provided with the distribution. - - THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS - ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT - LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR - A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER - OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, - EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, - PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR - PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF - LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING - NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS - SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. -*/ -""" - -import os -os.environ['CUDA_VISIBLE_DEVICES'] = "" - -import argparse - - - -parser = argparse.ArgumentParser() - -parser.add_argument('exchange_folder', type=str, help='exchange folder path') -parser.add_argument('output', type=str, help='path to output model checkpoint') - -model_group = parser.add_argument_group(title="model parameters") -model_group.add_argument('--num-features', type=int, help="number of features, default: 20", default=20) -model_group.add_argument('--latent-dim', type=int, help="number of symbols produces by encoder, default: 80", default=80) -model_group.add_argument('--cond-size', type=int, help="first conditioning size, default: 256", default=256) -model_group.add_argument('--cond-size2', type=int, help="second conditioning size, default: 256", default=256) -model_group.add_argument('--state-dim', type=int, help="dimensionality of transfered state, default: 24", default=24) -model_group.add_argument('--quant-levels', type=int, help="number of quantization levels, default: 40", default=40) - -args = parser.parse_args() - -import torch -from rdovae import RDOVAE -from wexchange.torch import load_torch_weights - -exchange_name_to_name = { - 'encoder_stack_layer1_dense' : 'core_encoder.module.dense_1', - 'encoder_stack_layer3_dense' : 'core_encoder.module.dense_2', - 'encoder_stack_layer5_dense' : 'core_encoder.module.dense_3', - 'encoder_stack_layer7_dense' : 'core_encoder.module.dense_4', - 'encoder_stack_layer8_dense' : 'core_encoder.module.dense_5', - 'encoder_state_layer1_dense' : 'core_encoder.module.state_dense_1', - 'encoder_state_layer2_dense' : 'core_encoder.module.state_dense_2', - 'encoder_stack_layer2_gru' : 'core_encoder.module.gru_1', - 'encoder_stack_layer4_gru' : 'core_encoder.module.gru_2', - 'encoder_stack_layer6_gru' : 'core_encoder.module.gru_3', - 'encoder_stack_layer9_conv' : 'core_encoder.module.conv1', - 'statistical_model_embedding' : 'statistical_model.quant_embedding', - 'decoder_state1_dense' : 'core_decoder.module.gru_1_init', - 'decoder_state2_dense' : 'core_decoder.module.gru_2_init', - 'decoder_state3_dense' : 'core_decoder.module.gru_3_init', - 'decoder_stack_layer1_dense' : 'core_decoder.module.dense_1', - 'decoder_stack_layer3_dense' : 'core_decoder.module.dense_2', - 'decoder_stack_layer5_dense' : 'core_decoder.module.dense_3', - 'decoder_stack_layer7_dense' : 'core_decoder.module.dense_4', - 'decoder_stack_layer8_dense' : 'core_decoder.module.dense_5', - 'decoder_stack_layer9_dense' : 'core_decoder.module.output', - 'decoder_stack_layer2_gru' : 'core_decoder.module.gru_1', - 'decoder_stack_layer4_gru' : 'core_decoder.module.gru_2', - 'decoder_stack_layer6_gru' : 'core_decoder.module.gru_3' -} - -if __name__ == "__main__": - checkpoint = dict() - - # parameters - num_features = args.num_features - latent_dim = args.latent_dim - quant_levels = args.quant_levels - cond_size = args.cond_size - cond_size2 = args.cond_size2 - state_dim = args.state_dim - - - # model - checkpoint['model_args'] = (num_features, latent_dim, quant_levels, cond_size, cond_size2) - checkpoint['model_kwargs'] = {'state_dim': state_dim} - model = RDOVAE(*checkpoint['model_args'], **checkpoint['model_kwargs']) - - dense_layer_names = [ - 'encoder_stack_layer1_dense', - 'encoder_stack_layer3_dense', - 'encoder_stack_layer5_dense', - 'encoder_stack_layer7_dense', - 'encoder_stack_layer8_dense', - 'encoder_state_layer1_dense', - 'encoder_state_layer2_dense', - 'decoder_state1_dense', - 'decoder_state2_dense', - 'decoder_state3_dense', - 'decoder_stack_layer1_dense', - 'decoder_stack_layer3_dense', - 'decoder_stack_layer5_dense', - 'decoder_stack_layer7_dense', - 'decoder_stack_layer8_dense', - 'decoder_stack_layer9_dense' - ] - - gru_layer_names = [ - 'encoder_stack_layer2_gru', - 'encoder_stack_layer4_gru', - 'encoder_stack_layer6_gru', - 'decoder_stack_layer2_gru', - 'decoder_stack_layer4_gru', - 'decoder_stack_layer6_gru' - ] - - conv1d_layer_names = [ - 'encoder_stack_layer9_conv' - ] - - embedding_layer_names = [ - 'statistical_model_embedding' - ] - - for name in dense_layer_names + gru_layer_names + conv1d_layer_names + embedding_layer_names: - print(f"loading weights for layer {exchange_name_to_name[name]}") - layer = model.get_submodule(exchange_name_to_name[name]) - load_torch_weights(os.path.join(args.exchange_folder, name), layer) - - checkpoint['state_dict'] = model.state_dict() - - torch.save(checkpoint, args.output) \ No newline at end of file diff --git a/android/app/src/main/cpp/third_party/opus/dnn/torch/rdovae/packets/__init__.py b/android/app/src/main/cpp/third_party/opus/dnn/torch/rdovae/packets/__init__.py deleted file mode 100644 index fb71ab3..0000000 --- a/android/app/src/main/cpp/third_party/opus/dnn/torch/rdovae/packets/__init__.py +++ /dev/null @@ -1 +0,0 @@ -from .fec_packets import write_fec_packets, read_fec_packets \ No newline at end of file diff --git a/android/app/src/main/cpp/third_party/opus/dnn/torch/rdovae/packets/fec_packets.c b/android/app/src/main/cpp/third_party/opus/dnn/torch/rdovae/packets/fec_packets.c deleted file mode 100644 index ee08ba9..0000000 --- a/android/app/src/main/cpp/third_party/opus/dnn/torch/rdovae/packets/fec_packets.c +++ /dev/null @@ -1,142 +0,0 @@ -/* Copyright (c) 2022 Amazon - Written by Jan Buethe */ -/* - Redistribution and use in source and binary forms, with or without - modification, are permitted provided that the following conditions - are met: - - - Redistributions of source code must retain the above copyright - notice, this list of conditions and the following disclaimer. - - - Redistributions in binary form must reproduce the above copyright - notice, this list of conditions and the following disclaimer in the - documentation and/or other materials provided with the distribution. - - THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS - ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT - LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR - A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER - OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, - EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, - PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR - PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF - LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING - NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS - SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. -*/ - -#include -#include - -#include "fec_packets.h" - -int get_fec_frame(const char * const filename, float *features, int packet_index, int subframe_index) -{ - - int16_t version; - int16_t header_size; - int16_t num_packets; - int16_t packet_size; - int16_t subframe_size; - int16_t subframes_per_packet; - int16_t num_features; - long offset; - - FILE *fid = fopen(filename, "rb"); - - /* read header */ - if (fread(&version, sizeof(version), 1, fid) != 1) goto error; - if (fread(&header_size, sizeof(header_size), 1, fid) != 1) goto error; - if (fread(&num_packets, sizeof(num_packets), 1, fid) != 1) goto error; - if (fread(&packet_size, sizeof(packet_size), 1, fid) != 1) goto error; - if (fread(&subframe_size, sizeof(subframe_size), 1, fid) != 1) goto error; - if (fread(&subframes_per_packet, sizeof(subframes_per_packet), 1, fid) != 1) goto error; - if (fread(&num_features, sizeof(num_features), 1, fid) != 1) goto error; - - /* check if indices are valid */ - if (packet_index >= num_packets || subframe_index >= subframes_per_packet) - { - fprintf(stderr, "get_fec_frame: index out of bounds\n"); - goto error; - } - - /* calculate offset in file (+ 2 is for rate) */ - offset = header_size + packet_index * packet_size + 2 + subframe_index * subframe_size; - fseek(fid, offset, SEEK_SET); - - /* read features */ - if (fread(features, sizeof(*features), num_features, fid) != num_features) goto error; - - fclose(fid); - return 0; - -error: - fclose(fid); - return 1; -} - -int get_fec_rate(const char * const filename, int packet_index) -{ - int16_t version; - int16_t header_size; - int16_t num_packets; - int16_t packet_size; - int16_t subframe_size; - int16_t subframes_per_packet; - int16_t num_features; - long offset; - int16_t rate; - - FILE *fid = fopen(filename, "rb"); - - /* read header */ - if (fread(&version, sizeof(version), 1, fid) != 1) goto error; - if (fread(&header_size, sizeof(header_size), 1, fid) != 1) goto error; - if (fread(&num_packets, sizeof(num_packets), 1, fid) != 1) goto error; - if (fread(&packet_size, sizeof(packet_size), 1, fid) != 1) goto error; - if (fread(&subframe_size, sizeof(subframe_size), 1, fid) != 1) goto error; - if (fread(&subframes_per_packet, sizeof(subframes_per_packet), 1, fid) != 1) goto error; - if (fread(&num_features, sizeof(num_features), 1, fid) != 1) goto error; - - /* check if indices are valid */ - if (packet_index >= num_packets) - { - fprintf(stderr, "get_fec_rate: index out of bounds\n"); - goto error; - } - - /* calculate offset in file (+ 2 is for rate) */ - offset = header_size + packet_index * packet_size; - fseek(fid, offset, SEEK_SET); - - /* read rate */ - if (fread(&rate, sizeof(rate), 1, fid) != 1) goto error; - - fclose(fid); - return (int) rate; - -error: - fclose(fid); - return -1; -} - -#if 0 -int main() -{ - float features[20]; - int i; - - if (get_fec_frame("../test.fec", &features[0], 0, 127)) - { - return 1; - } - - for (i = 0; i < 20; i ++) - { - printf("%d %f\n", i, features[i]); - } - - printf("rate: %d\n", get_fec_rate("../test.fec", 0)); - -} -#endif \ No newline at end of file diff --git a/android/app/src/main/cpp/third_party/opus/dnn/torch/rdovae/packets/fec_packets.h b/android/app/src/main/cpp/third_party/opus/dnn/torch/rdovae/packets/fec_packets.h deleted file mode 100644 index 35d3554..0000000 --- a/android/app/src/main/cpp/third_party/opus/dnn/torch/rdovae/packets/fec_packets.h +++ /dev/null @@ -1,34 +0,0 @@ -/* Copyright (c) 2022 Amazon - Written by Jan Buethe */ -/* - Redistribution and use in source and binary forms, with or without - modification, are permitted provided that the following conditions - are met: - - - Redistributions of source code must retain the above copyright - notice, this list of conditions and the following disclaimer. - - - Redistributions in binary form must reproduce the above copyright - notice, this list of conditions and the following disclaimer in the - documentation and/or other materials provided with the distribution. - - THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS - ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT - LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR - A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER - OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, - EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, - PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR - PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF - LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING - NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS - SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. -*/ - -#ifndef _FEC_PACKETS_H -#define _FEC_PACKETS_H - -int get_fec_frame(const char * const filename, float *features, int packet_index, int subframe_index); -int get_fec_rate(const char * const filename, int packet_index); - -#endif \ No newline at end of file diff --git a/android/app/src/main/cpp/third_party/opus/dnn/torch/rdovae/packets/fec_packets.py b/android/app/src/main/cpp/third_party/opus/dnn/torch/rdovae/packets/fec_packets.py deleted file mode 100644 index f44c1a9..0000000 --- a/android/app/src/main/cpp/third_party/opus/dnn/torch/rdovae/packets/fec_packets.py +++ /dev/null @@ -1,108 +0,0 @@ -""" -/* Copyright (c) 2022 Amazon - Written by Jan Buethe */ -/* - Redistribution and use in source and binary forms, with or without - modification, are permitted provided that the following conditions - are met: - - - Redistributions of source code must retain the above copyright - notice, this list of conditions and the following disclaimer. - - - Redistributions in binary form must reproduce the above copyright - notice, this list of conditions and the following disclaimer in the - documentation and/or other materials provided with the distribution. - - THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS - ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT - LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR - A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER - OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, - EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, - PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR - PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF - LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING - NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS - SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. -*/ -""" - -import numpy as np - - - -def write_fec_packets(filename, packets, rates=None): - """ writes packets in binary format """ - - assert np.dtype(np.float32).itemsize == 4 - assert np.dtype(np.int16).itemsize == 2 - - # derive some sizes - num_packets = len(packets) - subframes_per_packet = packets[0].shape[-2] - num_features = packets[0].shape[-1] - - # size of float is 4 - subframe_size = num_features * 4 - packet_size = subframe_size * subframes_per_packet + 2 # two bytes for rate - - version = 1 - # header size (version, header_size, num_packets, packet_size, subframe_size, subrames_per_packet, num_features) - header_size = 14 - - with open(filename, 'wb') as f: - - # header - f.write(np.int16(version).tobytes()) - f.write(np.int16(header_size).tobytes()) - f.write(np.int16(num_packets).tobytes()) - f.write(np.int16(packet_size).tobytes()) - f.write(np.int16(subframe_size).tobytes()) - f.write(np.int16(subframes_per_packet).tobytes()) - f.write(np.int16(num_features).tobytes()) - - # packets - for i, packet in enumerate(packets): - if type(rates) == type(None): - rate = 0 - else: - rate = rates[i] - - f.write(np.int16(rate).tobytes()) - - features = np.flip(packet, axis=-2) - f.write(features.astype(np.float32).tobytes()) - - -def read_fec_packets(filename): - """ reads packets from binary format """ - - assert np.dtype(np.float32).itemsize == 4 - assert np.dtype(np.int16).itemsize == 2 - - with open(filename, 'rb') as f: - - # header - version = np.frombuffer(f.read(2), dtype=np.int16).item() - header_size = np.frombuffer(f.read(2), dtype=np.int16).item() - num_packets = np.frombuffer(f.read(2), dtype=np.int16).item() - packet_size = np.frombuffer(f.read(2), dtype=np.int16).item() - subframe_size = np.frombuffer(f.read(2), dtype=np.int16).item() - subframes_per_packet = np.frombuffer(f.read(2), dtype=np.int16).item() - num_features = np.frombuffer(f.read(2), dtype=np.int16).item() - - dummy_features = np.zeros((subframes_per_packet, num_features), dtype=np.float32) - - # packets - rates = [] - packets = [] - for i in range(num_packets): - - rate = np.frombuffer(f.read(2), dtype=np.int16).item - rates.append(rate) - - features = np.reshape(np.frombuffer(f.read(subframe_size * subframes_per_packet), dtype=np.float32), dummy_features.shape) - packet = np.flip(features, axis=-2) - packets.append(packet) - - return packets \ No newline at end of file diff --git a/android/app/src/main/cpp/third_party/opus/dnn/torch/rdovae/rdovae/__init__.py b/android/app/src/main/cpp/third_party/opus/dnn/torch/rdovae/rdovae/__init__.py deleted file mode 100644 index b945add..0000000 --- a/android/app/src/main/cpp/third_party/opus/dnn/torch/rdovae/rdovae/__init__.py +++ /dev/null @@ -1,2 +0,0 @@ -from .rdovae import RDOVAE, distortion_loss, hard_rate_estimate, soft_rate_estimate -from .dataset import RDOVAEDataset diff --git a/android/app/src/main/cpp/third_party/opus/dnn/torch/rdovae/rdovae/dataset.py b/android/app/src/main/cpp/third_party/opus/dnn/torch/rdovae/rdovae/dataset.py deleted file mode 100644 index cfb32b0..0000000 --- a/android/app/src/main/cpp/third_party/opus/dnn/torch/rdovae/rdovae/dataset.py +++ /dev/null @@ -1,67 +0,0 @@ -""" -/* Copyright (c) 2022 Amazon - Written by Jan Buethe */ -/* - Redistribution and use in source and binary forms, with or without - modification, are permitted provided that the following conditions - are met: - - - Redistributions of source code must retain the above copyright - notice, this list of conditions and the following disclaimer. - - - Redistributions in binary form must reproduce the above copyright - notice, this list of conditions and the following disclaimer in the - documentation and/or other materials provided with the distribution. - - THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS - ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT - LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR - A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER - OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, - EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, - PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR - PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF - LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING - NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS - SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. -*/ -""" - -import torch -import numpy as np - -class RDOVAEDataset(torch.utils.data.Dataset): - def __init__(self, - feature_file, - sequence_length, - num_used_features=20, - num_features=36, - lambda_min=0.0002, - lambda_max=0.0135, - quant_levels=16, - enc_stride=2): - - self.sequence_length = sequence_length - self.lambda_min = lambda_min - self.lambda_max = lambda_max - self.enc_stride = enc_stride - self.quant_levels = quant_levels - self.denominator = (quant_levels - 1) / np.log(lambda_max / lambda_min) - - if sequence_length % enc_stride: - raise ValueError(f"RDOVAEDataset.__init__: enc_stride {enc_stride} does not divide sequence length {sequence_length}") - - self.features = np.reshape(np.fromfile(feature_file, dtype=np.float32), (-1, num_features)) - self.features = self.features[:, :num_used_features] - self.num_sequences = self.features.shape[0] // sequence_length - - def __len__(self): - return self.num_sequences - - def __getitem__(self, index): - features = self.features[index * self.sequence_length: (index + 1) * self.sequence_length, :] - q_ids = np.random.randint(0, self.quant_levels, (1)).astype(np.int64) - q_ids = np.repeat(q_ids, self.sequence_length // self.enc_stride, axis=0) - rate_lambda = self.lambda_min * np.exp(q_ids.astype(np.float32) / self.denominator).astype(np.float32) - - return features, rate_lambda, q_ids diff --git a/android/app/src/main/cpp/third_party/opus/dnn/torch/rdovae/rdovae/rdovae.py b/android/app/src/main/cpp/third_party/opus/dnn/torch/rdovae/rdovae/rdovae.py deleted file mode 100644 index cdb07b4..0000000 --- a/android/app/src/main/cpp/third_party/opus/dnn/torch/rdovae/rdovae/rdovae.py +++ /dev/null @@ -1,719 +0,0 @@ -""" -/* Copyright (c) 2022 Amazon - Written by Jan Buethe */ -/* - Redistribution and use in source and binary forms, with or without - modification, are permitted provided that the following conditions - are met: - - - Redistributions of source code must retain the above copyright - notice, this list of conditions and the following disclaimer. - - - Redistributions in binary form must reproduce the above copyright - notice, this list of conditions and the following disclaimer in the - documentation and/or other materials provided with the distribution. - - THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS - ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT - LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR - A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER - OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, - EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, - PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR - PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF - LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING - NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS - SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. -*/ -""" - -""" Pytorch implementations of rate distortion optimized variational autoencoder """ - -import math as m - -import torch -from torch import nn -import torch.nn.functional as F -import sys -import os -source_dir = os.path.split(os.path.abspath(__file__))[0] -sys.path.append(os.path.join(source_dir, "../../lpcnet/")) -from utils.sparsification import GRUSparsifier -from torch.nn.utils import weight_norm - -# Quantization and rate related utily functions - -def soft_pvq(x, k): - """ soft pyramid vector quantizer """ - - # L2 normalization - x_norm2 = x / (1e-15 + torch.norm(x, dim=-1, keepdim=True)) - - - with torch.no_grad(): - # quantization loop, no need to track gradients here - x_norm1 = x / torch.sum(torch.abs(x), dim=-1, keepdim=True) - - # set initial scaling factor to k - scale_factor = k - x_scaled = scale_factor * x_norm1 - x_quant = torch.round(x_scaled) - - # we aim for ||x_quant||_L1 = k - for _ in range(10): - # remove signs and calculate L1 norm - abs_x_quant = torch.abs(x_quant) - abs_x_scaled = torch.abs(x_scaled) - l1_x_quant = torch.sum(abs_x_quant, axis=-1) - - # increase, where target is too small and decrease, where target is too large - plus = 1.0001 * torch.min((abs_x_quant + 0.5) / (abs_x_scaled + 1e-15), dim=-1).values - minus = 0.9999 * torch.max((abs_x_quant - 0.5) / (abs_x_scaled + 1e-15), dim=-1).values - factor = torch.where(l1_x_quant > k, minus, plus) - factor = torch.where(l1_x_quant == k, torch.ones_like(factor), factor) - scale_factor = scale_factor * factor.unsqueeze(-1) - - # update x - x_scaled = scale_factor * x_norm1 - x_quant = torch.round(x_quant) - - # L2 normalization of quantized x - x_quant_norm2 = x_quant / (1e-15 + torch.norm(x_quant, dim=-1, keepdim=True)) - quantization_error = x_quant_norm2 - x_norm2 - - return x_norm2 + quantization_error.detach() - -def cache_parameters(func): - cache = dict() - def cached_func(*args): - if args in cache: - return cache[args] - else: - cache[args] = func(*args) - - return cache[args] - return cached_func - -@cache_parameters -def pvq_codebook_size(n, k): - - if k == 0: - return 1 - - if n == 0: - return 0 - - return pvq_codebook_size(n - 1, k) + pvq_codebook_size(n, k - 1) + pvq_codebook_size(n - 1, k - 1) - - -def soft_rate_estimate(z, r, reduce=True): - """ rate approximation with dependent theta Eq. (7)""" - - rate = torch.sum( - - torch.log2((1 - r)/(1 + r) * r ** torch.abs(z) + 1e-6), - dim=-1 - ) - - if reduce: - rate = torch.mean(rate) - - return rate - - -def hard_rate_estimate(z, r, theta, reduce=True): - """ hard rate approximation """ - - z_q = torch.round(z) - p0 = 1 - r ** (0.5 + 0.5 * theta) - alpha = torch.relu(1 - torch.abs(z_q)) ** 2 - rate = - torch.sum( - (alpha * torch.log2(p0 * r ** torch.abs(z_q) + 1e-6) - + (1 - alpha) * torch.log2(0.5 * (1 - p0) * (1 - r) * r ** (torch.abs(z_q) - 1) + 1e-6)), - dim=-1 - ) - - if reduce: - rate = torch.mean(rate) - - return rate - - - -def soft_dead_zone(x, dead_zone): - """ approximates application of a dead zone to x """ - d = dead_zone * 0.05 - return x - d * torch.tanh(x / (0.1 + d)) - - -def hard_quantize(x): - """ round with copy gradient trick """ - return x + (torch.round(x) - x).detach() - - -def noise_quantize(x): - """ simulates quantization with addition of random uniform noise """ - return x + (torch.rand_like(x) - 0.5) - - -# loss functions - - -def distortion_loss(y_true, y_pred, rate_lambda=None): - """ custom distortion loss for LPCNet features """ - - if y_true.size(-1) != 20: - raise ValueError('distortion loss is designed to work with 20 features') - - ceps_error = y_pred[..., :18] - y_true[..., :18] - pitch_error = 2*(y_pred[..., 18:19] - y_true[..., 18:19]) - corr_error = y_pred[..., 19:] - y_true[..., 19:] - pitch_weight = torch.relu(y_true[..., 19:] + 0.5) ** 2 - - loss = torch.mean(ceps_error ** 2 + (10/18) * torch.abs(pitch_error) * pitch_weight + (1/18) * corr_error ** 2, dim=-1) - - if type(rate_lambda) != type(None): - loss = loss / torch.sqrt(rate_lambda) - - loss = torch.mean(loss) - - return loss - - -# sampling functions - -import random - - -def random_split(start, stop, num_splits=3, min_len=3): - get_min_len = lambda x : min([x[i+1] - x[i] for i in range(len(x) - 1)]) - candidate = [start] + sorted([random.randint(start, stop-1) for i in range(num_splits)]) + [stop] - - while get_min_len(candidate) < min_len: - candidate = [start] + sorted([random.randint(start, stop-1) for i in range(num_splits)]) + [stop] - - return candidate - - - -# weight initialization and clipping -def init_weights(module): - - if isinstance(module, nn.GRU): - for p in module.named_parameters(): - if p[0].startswith('weight_hh_'): - nn.init.orthogonal_(p[1]) - - -def weight_clip_factory(max_value): - """ weight clipping function concerning sum of abs values of adjecent weights """ - def clip_weight_(w): - stop = w.size(1) - # omit last column if stop is odd - if stop % 2: - stop -= 1 - max_values = max_value * torch.ones_like(w[:, :stop]) - factor = max_value / torch.maximum(max_values, - torch.repeat_interleave( - torch.abs(w[:, :stop:2]) + torch.abs(w[:, 1:stop:2]), - 2, - 1)) - with torch.no_grad(): - w[:, :stop] *= factor - - def clip_weights(module): - if isinstance(module, nn.GRU) or isinstance(module, nn.Linear): - for name, w in module.named_parameters(): - if name.startswith('weight'): - clip_weight_(w) - - return clip_weights - -def n(x): - return torch.clamp(x + (1./127.)*(torch.rand_like(x)-.5), min=-1., max=1.) - -# RDOVAE module and submodules - -sparsify_start = 12000 -sparsify_stop = 24000 -sparsify_interval = 100 -sparsify_exponent = 3 -#sparsify_start = 0 -#sparsify_stop = 0 - -sparse_params1 = { -# 'W_hr' : (1.0, [8, 4], True), -# 'W_hz' : (1.0, [8, 4], True), -# 'W_hn' : (1.0, [8, 4], True), - 'W_ir' : (0.6, [8, 4], False), - 'W_iz' : (0.4, [8, 4], False), - 'W_in' : (0.8, [8, 4], False) - } - -sparse_params2 = { -# 'W_hr' : (1.0, [8, 4], True), -# 'W_hz' : (1.0, [8, 4], True), -# 'W_hn' : (1.0, [8, 4], True), - 'W_ir' : (0.3, [8, 4], False), - 'W_iz' : (0.2, [8, 4], False), - 'W_in' : (0.4, [8, 4], False) - } - - -class MyConv(nn.Module): - def __init__(self, input_dim, output_dim, dilation=1): - super(MyConv, self).__init__() - self.input_dim = input_dim - self.output_dim = output_dim - self.dilation=dilation - self.conv = nn.Conv1d(input_dim, output_dim, kernel_size=2, padding='valid', dilation=dilation) - def forward(self, x, state=None): - device = x.device - conv_in = torch.cat([torch.zeros_like(x[:,0:self.dilation,:], device=device), x], -2).permute(0, 2, 1) - return torch.tanh(self.conv(conv_in)).permute(0, 2, 1) - -class GLU(nn.Module): - def __init__(self, feat_size): - super(GLU, self).__init__() - - torch.manual_seed(5) - - self.gate = weight_norm(nn.Linear(feat_size, feat_size, bias=False)) - - self.init_weights() - - def init_weights(self): - - for m in self.modules(): - if isinstance(m, nn.Conv1d) or isinstance(m, nn.ConvTranspose1d)\ - or isinstance(m, nn.Linear) or isinstance(m, nn.Embedding): - nn.init.orthogonal_(m.weight.data) - - def forward(self, x): - - out = x * torch.sigmoid(self.gate(x)) - - return out - -class CoreEncoder(nn.Module): - STATE_HIDDEN = 128 - FRAMES_PER_STEP = 2 - CONV_KERNEL_SIZE = 4 - - def __init__(self, feature_dim, output_dim, cond_size, cond_size2, state_size=24): - """ core encoder for RDOVAE - - Computes latents, initial states, and rate estimates from features and lambda parameter - - """ - - super(CoreEncoder, self).__init__() - - # hyper parameters - self.feature_dim = feature_dim - self.output_dim = output_dim - self.cond_size = cond_size - self.cond_size2 = cond_size2 - self.state_size = state_size - - # derived parameters - self.input_dim = self.FRAMES_PER_STEP * self.feature_dim - - # layers - self.dense_1 = nn.Linear(self.input_dim, 64) - self.gru1 = nn.GRU(64, 64, batch_first=True) - self.conv1 = MyConv(128, 96) - self.gru2 = nn.GRU(224, 64, batch_first=True) - self.conv2 = MyConv(288, 96, dilation=2) - self.gru3 = nn.GRU(384, 64, batch_first=True) - self.conv3 = MyConv(448, 96, dilation=2) - self.gru4 = nn.GRU(544, 64, batch_first=True) - self.conv4 = MyConv(608, 96, dilation=2) - self.gru5 = nn.GRU(704, 64, batch_first=True) - self.conv5 = MyConv(768, 96, dilation=2) - - self.z_dense = nn.Linear(864, self.output_dim) - - - self.state_dense_1 = nn.Linear(864, self.STATE_HIDDEN) - - self.state_dense_2 = nn.Linear(self.STATE_HIDDEN, self.state_size) - nb_params = sum(p.numel() for p in self.parameters()) - print(f"encoder: {nb_params} weights") - - # initialize weights - self.apply(init_weights) - - - def forward(self, features): - - # reshape features - x = torch.reshape(features, (features.size(0), features.size(1) // self.FRAMES_PER_STEP, self.FRAMES_PER_STEP * features.size(2))) - - batch = x.size(0) - device = x.device - - # run encoding layer stack - x = n(torch.tanh(self.dense_1(x))) - x = torch.cat([x, n(self.gru1(x)[0])], -1) - x = torch.cat([x, n(self.conv1(x))], -1) - x = torch.cat([x, n(self.gru2(x)[0])], -1) - x = torch.cat([x, n(self.conv2(x))], -1) - x = torch.cat([x, n(self.gru3(x)[0])], -1) - x = torch.cat([x, n(self.conv3(x))], -1) - x = torch.cat([x, n(self.gru4(x)[0])], -1) - x = torch.cat([x, n(self.conv4(x))], -1) - x = torch.cat([x, n(self.gru5(x)[0])], -1) - x = torch.cat([x, n(self.conv5(x))], -1) - z = self.z_dense(x) - - # init state for decoder - states = torch.tanh(self.state_dense_1(x)) - states = self.state_dense_2(states) - - return z, states - - - - -class CoreDecoder(nn.Module): - - FRAMES_PER_STEP = 4 - - def __init__(self, input_dim, output_dim, cond_size, cond_size2, state_size=24): - """ core decoder for RDOVAE - - Computes features from latents, initial state, and quantization index - - """ - - super(CoreDecoder, self).__init__() - - # hyper parameters - self.input_dim = input_dim - self.output_dim = output_dim - self.cond_size = cond_size - self.cond_size2 = cond_size2 - self.state_size = state_size - - self.input_size = self.input_dim - - # layers - self.dense_1 = nn.Linear(self.input_size, 96) - self.gru1 = nn.GRU(96, 96, batch_first=True) - self.conv1 = MyConv(192, 32) - self.gru2 = nn.GRU(224, 96, batch_first=True) - self.conv2 = MyConv(320, 32) - self.gru3 = nn.GRU(352, 96, batch_first=True) - self.conv3 = MyConv(448, 32) - self.gru4 = nn.GRU(480, 96, batch_first=True) - self.conv4 = MyConv(576, 32) - self.gru5 = nn.GRU(608, 96, batch_first=True) - self.conv5 = MyConv(704, 32) - self.output = nn.Linear(736, self.FRAMES_PER_STEP * self.output_dim) - self.glu1 = GLU(96) - self.glu2 = GLU(96) - self.glu3 = GLU(96) - self.glu4 = GLU(96) - self.glu5 = GLU(96) - self.hidden_init = nn.Linear(self.state_size, 128) - self.gru_init = nn.Linear(128, 480) - - nb_params = sum(p.numel() for p in self.parameters()) - print(f"decoder: {nb_params} weights") - # initialize weights - self.apply(init_weights) - self.sparsifier = [] - self.sparsifier.append(GRUSparsifier([(self.gru1, sparse_params1)], sparsify_start, sparsify_stop, sparsify_interval, sparsify_exponent)) - self.sparsifier.append(GRUSparsifier([(self.gru2, sparse_params1)], sparsify_start, sparsify_stop, sparsify_interval, sparsify_exponent)) - self.sparsifier.append(GRUSparsifier([(self.gru3, sparse_params1)], sparsify_start, sparsify_stop, sparsify_interval, sparsify_exponent)) - self.sparsifier.append(GRUSparsifier([(self.gru4, sparse_params2)], sparsify_start, sparsify_stop, sparsify_interval, sparsify_exponent)) - self.sparsifier.append(GRUSparsifier([(self.gru5, sparse_params2)], sparsify_start, sparsify_stop, sparsify_interval, sparsify_exponent)) - - def sparsify(self): - for sparsifier in self.sparsifier: - sparsifier.step() - - def forward(self, z, initial_state): - - hidden = torch.tanh(self.hidden_init(initial_state)) - gru_state = torch.tanh(self.gru_init(hidden).permute(1, 0, 2)) - h1_state = gru_state[:,:,:96].contiguous() - h2_state = gru_state[:,:,96:192].contiguous() - h3_state = gru_state[:,:,192:288].contiguous() - h4_state = gru_state[:,:,288:384].contiguous() - h5_state = gru_state[:,:,384:].contiguous() - - # run decoding layer stack - x = n(torch.tanh(self.dense_1(z))) - - x = torch.cat([x, n(self.glu1(n(self.gru1(x, h1_state)[0])))], -1) - x = torch.cat([x, n(self.conv1(x))], -1) - x = torch.cat([x, n(self.glu2(n(self.gru2(x, h2_state)[0])))], -1) - x = torch.cat([x, n(self.conv2(x))], -1) - x = torch.cat([x, n(self.glu3(n(self.gru3(x, h3_state)[0])))], -1) - x = torch.cat([x, n(self.conv3(x))], -1) - x = torch.cat([x, n(self.glu4(n(self.gru4(x, h4_state)[0])))], -1) - x = torch.cat([x, n(self.conv4(x))], -1) - x = torch.cat([x, n(self.glu5(n(self.gru5(x, h5_state)[0])))], -1) - x = torch.cat([x, n(self.conv5(x))], -1) - - # output layer and reshaping - x10 = self.output(x) - features = torch.reshape(x10, (x10.size(0), x10.size(1) * self.FRAMES_PER_STEP, x10.size(2) // self.FRAMES_PER_STEP)) - - return features - - -class StatisticalModel(nn.Module): - def __init__(self, quant_levels, latent_dim, state_dim): - """ Statistical model for latent space - - Computes scaling, deadzone, r, and theta - - """ - - super(StatisticalModel, self).__init__() - - # copy parameters - self.latent_dim = latent_dim - self.state_dim = state_dim - self.total_dim = latent_dim + state_dim - self.quant_levels = quant_levels - self.embedding_dim = 6 * self.total_dim - - # quantization embedding - self.quant_embedding = nn.Embedding(quant_levels, self.embedding_dim) - - # initialize embedding to 0 - with torch.no_grad(): - self.quant_embedding.weight[:] = 0 - - - def forward(self, quant_ids): - """ takes quant_ids and returns statistical model parameters""" - - x = self.quant_embedding(quant_ids) - - # CAVE: theta_soft is not used anymore. Kick it out? - quant_scale = F.softplus(x[..., 0 * self.total_dim : 1 * self.total_dim]) - dead_zone = F.softplus(x[..., 1 * self.total_dim : 2 * self.total_dim]) - theta_soft = torch.sigmoid(x[..., 2 * self.total_dim : 3 * self.total_dim]) - r_soft = torch.sigmoid(x[..., 3 * self.total_dim : 4 * self.total_dim]) - theta_hard = torch.sigmoid(x[..., 4 * self.total_dim : 5 * self.total_dim]) - r_hard = torch.sigmoid(x[..., 5 * self.total_dim : 6 * self.total_dim]) - - - return { - 'quant_embedding' : x, - 'quant_scale' : quant_scale, - 'dead_zone' : dead_zone, - 'r_hard' : r_hard, - 'theta_hard' : theta_hard, - 'r_soft' : r_soft, - 'theta_soft' : theta_soft - } - - -class RDOVAE(nn.Module): - def __init__(self, - feature_dim, - latent_dim, - quant_levels, - cond_size, - cond_size2, - state_dim=24, - split_mode='split', - clip_weights=False, - pvq_num_pulses=82, - state_dropout_rate=0): - - super(RDOVAE, self).__init__() - - self.feature_dim = feature_dim - self.latent_dim = latent_dim - self.quant_levels = quant_levels - self.cond_size = cond_size - self.cond_size2 = cond_size2 - self.split_mode = split_mode - self.state_dim = state_dim - self.pvq_num_pulses = pvq_num_pulses - self.state_dropout_rate = state_dropout_rate - - # submodules encoder and decoder share the statistical model - self.statistical_model = StatisticalModel(quant_levels, latent_dim, state_dim) - self.core_encoder = nn.DataParallel(CoreEncoder(feature_dim, latent_dim, cond_size, cond_size2, state_size=state_dim)) - self.core_decoder = nn.DataParallel(CoreDecoder(latent_dim, feature_dim, cond_size, cond_size2, state_size=state_dim)) - - self.enc_stride = CoreEncoder.FRAMES_PER_STEP - self.dec_stride = CoreDecoder.FRAMES_PER_STEP - - if clip_weights: - self.weight_clip_fn = weight_clip_factory(0.496) - else: - self.weight_clip_fn = None - - if self.dec_stride % self.enc_stride != 0: - raise ValueError(f"get_decoder_chunks_generic: encoder stride does not divide decoder stride") - - def clip_weights(self): - if not type(self.weight_clip_fn) == type(None): - self.apply(self.weight_clip_fn) - - def sparsify(self): - #self.core_encoder.module.sparsify() - self.core_decoder.module.sparsify() - - def get_decoder_chunks(self, z_frames, mode='split', chunks_per_offset = 4): - - enc_stride = self.enc_stride - dec_stride = self.dec_stride - - stride = dec_stride // enc_stride - - chunks = [] - - for offset in range(stride): - # start is the smalles number = offset mod stride that decodes to a valid range - start = offset - while enc_stride * (start + 1) - dec_stride < 0: - start += stride - - # check if start is a valid index - if start >= z_frames: - raise ValueError("get_decoder_chunks_generic: range too small") - - # stop is the smallest number outside [0, num_enc_frames] that's congruent to offset mod stride - stop = z_frames - (z_frames % stride) + offset - while stop < z_frames: - stop += stride - - # calculate split points - length = (stop - start) - if mode == 'split': - split_points = [start + stride * int(i * length / chunks_per_offset / stride) for i in range(chunks_per_offset)] + [stop] - elif mode == 'random_split': - split_points = [stride * x + start for x in random_split(0, (stop - start)//stride - 1, chunks_per_offset - 1, 1)] - else: - raise ValueError(f"get_decoder_chunks_generic: unknown mode {mode}") - - - for i in range(chunks_per_offset): - # (enc_frame_start, enc_frame_stop, enc_frame_stride, stride, feature_frame_start, feature_frame_stop) - # encoder range(i, j, stride) maps to feature range(enc_stride * (i + 1) - dec_stride, enc_stride * j) - # provided that i - j = 1 mod stride - chunks.append({ - 'z_start' : split_points[i], - 'z_stop' : split_points[i + 1] - stride + 1, - 'z_stride' : stride, - 'features_start' : enc_stride * (split_points[i] + 1) - dec_stride, - 'features_stop' : enc_stride * (split_points[i + 1] - stride + 1) - }) - - return chunks - - - def forward(self, features, q_id): - - # calculate statistical model from quantization ID - statistical_model = self.statistical_model(q_id) - - # run encoder - z, states = self.core_encoder(features) - - # scaling, dead-zone and quantization - z = z * statistical_model['quant_scale'][:,:,:self.latent_dim] - z = soft_dead_zone(z, statistical_model['dead_zone'][:,:,:self.latent_dim]) - - # quantization - z_q = hard_quantize(z) / statistical_model['quant_scale'][:,:,:self.latent_dim] - z_n = noise_quantize(z) / statistical_model['quant_scale'][:,:,:self.latent_dim] - #states_q = soft_pvq(states, self.pvq_num_pulses) - states = states * statistical_model['quant_scale'][:,:,self.latent_dim:] - states = soft_dead_zone(states, statistical_model['dead_zone'][:,:,self.latent_dim:]) - - states_q = hard_quantize(states) / statistical_model['quant_scale'][:,:,self.latent_dim:] - states_n = noise_quantize(states) / statistical_model['quant_scale'][:,:,self.latent_dim:] - - if self.state_dropout_rate > 0: - drop = torch.rand(states_q.size(0)) < self.state_dropout_rate - mask = torch.ones_like(states_q) - mask[drop] = 0 - states_q = states_q * mask - - # decoder - chunks = self.get_decoder_chunks(z.size(1), mode=self.split_mode) - - outputs_hq = [] - outputs_sq = [] - for chunk in chunks: - # decoder with hard quantized input - z_dec_reverse = torch.flip(z_q[..., chunk['z_start'] : chunk['z_stop'] : chunk['z_stride'], :], [1]) - dec_initial_state = states_q[..., chunk['z_stop'] - 1 : chunk['z_stop'], :] - features_reverse = self.core_decoder(z_dec_reverse, dec_initial_state) - outputs_hq.append((torch.flip(features_reverse, [1]), chunk['features_start'], chunk['features_stop'])) - - - # decoder with soft quantized input - z_dec_reverse = torch.flip(z_n[..., chunk['z_start'] : chunk['z_stop'] : chunk['z_stride'], :], [1]) - dec_initial_state = states_n[..., chunk['z_stop'] - 1 : chunk['z_stop'], :] - features_reverse = self.core_decoder(z_dec_reverse, dec_initial_state) - outputs_sq.append((torch.flip(features_reverse, [1]), chunk['features_start'], chunk['features_stop'])) - - return { - 'outputs_hard_quant' : outputs_hq, - 'outputs_soft_quant' : outputs_sq, - 'z' : z, - 'states' : states, - 'statistical_model' : statistical_model - } - - def encode(self, features): - """ encoder with quantization and rate estimation """ - - z, states = self.core_encoder(features) - - # quantization of initial states - states = soft_pvq(states, self.pvq_num_pulses) - state_size = m.log2(pvq_codebook_size(self.state_dim, self.pvq_num_pulses)) - - return z, states, state_size - - def decode(self, z, initial_state): - """ decoder (flips sequences by itself) """ - - z_reverse = torch.flip(z, [1]) - features_reverse = self.core_decoder(z_reverse, initial_state) - features = torch.flip(features_reverse, [1]) - - return features - - def quantize(self, z, q_ids): - """ quantization of latent vectors """ - - stats = self.statistical_model(q_ids) - - zq = z * stats['quant_scale'][:self.latent_dim] - zq = soft_dead_zone(zq, stats['dead_zone'][:self.latent_dim]) - zq = torch.round(zq) - - sizes = hard_rate_estimate(zq, stats['r_hard'][:,:,:self.latent_dim], stats['theta_hard'][:,:,:self.latent_dim], reduce=False) - - return zq, sizes - - def unquantize(self, zq, q_ids): - """ re-scaling of latent vector """ - - stats = self.statistical_model(q_ids) - - z = zq / stats['quant_scale'][:,:,:self.latent_dim] - - return z - - def freeze_model(self): - - # freeze all parameters - for p in self.parameters(): - p.requires_grad = False - - for p in self.statistical_model.parameters(): - p.requires_grad = True diff --git a/android/app/src/main/cpp/third_party/opus/dnn/torch/rdovae/requirements.txt b/android/app/src/main/cpp/third_party/opus/dnn/torch/rdovae/requirements.txt deleted file mode 100644 index 9225ea8..0000000 --- a/android/app/src/main/cpp/third_party/opus/dnn/torch/rdovae/requirements.txt +++ /dev/null @@ -1,4 +0,0 @@ -numpy -scipy -torch -tqdm \ No newline at end of file diff --git a/android/app/src/main/cpp/third_party/opus/dnn/torch/rdovae/train_rdovae.py b/android/app/src/main/cpp/third_party/opus/dnn/torch/rdovae/train_rdovae.py deleted file mode 100644 index d9a43b3..0000000 --- a/android/app/src/main/cpp/third_party/opus/dnn/torch/rdovae/train_rdovae.py +++ /dev/null @@ -1,278 +0,0 @@ -""" -/* Copyright (c) 2022 Amazon - Written by Jan Buethe */ -/* - Redistribution and use in source and binary forms, with or without - modification, are permitted provided that the following conditions - are met: - - - Redistributions of source code must retain the above copyright - notice, this list of conditions and the following disclaimer. - - - Redistributions in binary form must reproduce the above copyright - notice, this list of conditions and the following disclaimer in the - documentation and/or other materials provided with the distribution. - - THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS - ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT - LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR - A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER - OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, - EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, - PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR - PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF - LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING - NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS - SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. -*/ -""" - -import os -import argparse - -import torch -import tqdm - -from rdovae import RDOVAE, RDOVAEDataset, distortion_loss, hard_rate_estimate, soft_rate_estimate - - -parser = argparse.ArgumentParser() - -parser.add_argument('features', type=str, help='path to feature file in .f32 format') -parser.add_argument('output', type=str, help='path to output folder') - -parser.add_argument('--cuda-visible-devices', type=str, help="comma separates list of cuda visible device indices, default: ''", default="") - - -model_group = parser.add_argument_group(title="model parameters") -model_group.add_argument('--latent-dim', type=int, help="number of symbols produces by encoder, default: 80", default=80) -model_group.add_argument('--cond-size', type=int, help="first conditioning size, default: 256", default=256) -model_group.add_argument('--cond-size2', type=int, help="second conditioning size, default: 256", default=256) -model_group.add_argument('--state-dim', type=int, help="dimensionality of transfered state, default: 24", default=24) -model_group.add_argument('--quant-levels', type=int, help="number of quantization levels, default: 16", default=16) -model_group.add_argument('--lambda-min', type=float, help="minimal value for rate lambda, default: 0.0002", default=2e-4) -model_group.add_argument('--lambda-max', type=float, help="maximal value for rate lambda, default: 0.0104", default=0.0104) -model_group.add_argument('--pvq-num-pulses', type=int, help="number of pulses for PVQ, default: 82", default=82) -model_group.add_argument('--state-dropout-rate', type=float, help="state dropout rate, default: 0", default=0.0) - -training_group = parser.add_argument_group(title="training parameters") -training_group.add_argument('--batch-size', type=int, help="batch size, default: 32", default=32) -training_group.add_argument('--lr', type=float, help='learning rate, default: 3e-4', default=3e-4) -training_group.add_argument('--epochs', type=int, help='number of training epochs, default: 100', default=100) -training_group.add_argument('--sequence-length', type=int, help='sequence length, needs to be divisible by 4, default: 256', default=256) -training_group.add_argument('--lr-decay-factor', type=float, help='learning rate decay factor, default: 2.5e-5', default=2.5e-5) -training_group.add_argument('--split-mode', type=str, choices=['split', 'random_split'], help='splitting mode for decoder input, default: split', default='split') -training_group.add_argument('--enable-first-frame-loss', action='store_true', default=False, help='enables dedicated distortion loss on first 4 decoder frames') -training_group.add_argument('--initial-checkpoint', type=str, help='initial checkpoint to start training from, default: None', default=None) -training_group.add_argument('--train-decoder-only', action='store_true', help='freeze encoder and statistical model and train decoder only') - -args = parser.parse_args() - -# set visible devices -os.environ['CUDA_VISIBLE_DEVICES'] = args.cuda_visible_devices - -# checkpoints -checkpoint_dir = os.path.join(args.output, 'checkpoints') -checkpoint = dict() -os.makedirs(checkpoint_dir, exist_ok=True) - -# training parameters -batch_size = args.batch_size -lr = args.lr -epochs = args.epochs -sequence_length = args.sequence_length -lr_decay_factor = args.lr_decay_factor -split_mode = args.split_mode -# not exposed -adam_betas = [0.8, 0.95] -adam_eps = 1e-8 - -checkpoint['batch_size'] = batch_size -checkpoint['lr'] = lr -checkpoint['lr_decay_factor'] = lr_decay_factor -checkpoint['split_mode'] = split_mode -checkpoint['epochs'] = epochs -checkpoint['sequence_length'] = sequence_length -checkpoint['adam_betas'] = adam_betas - -# logging -log_interval = 10 - -# device -device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu") - -# model parameters -cond_size = args.cond_size -cond_size2 = args.cond_size2 -latent_dim = args.latent_dim -quant_levels = args.quant_levels -lambda_min = args.lambda_min -lambda_max = args.lambda_max -state_dim = args.state_dim -# not expsed -num_features = 20 - - -# training data -feature_file = args.features - -# model -checkpoint['model_args'] = (num_features, latent_dim, quant_levels, cond_size, cond_size2) -checkpoint['model_kwargs'] = {'state_dim': state_dim, 'split_mode' : split_mode, 'pvq_num_pulses': args.pvq_num_pulses, 'state_dropout_rate': args.state_dropout_rate} -model = RDOVAE(*checkpoint['model_args'], **checkpoint['model_kwargs']) - -if type(args.initial_checkpoint) != type(None): - checkpoint = torch.load(args.initial_checkpoint, map_location='cpu') - model.load_state_dict(checkpoint['state_dict'], strict=False) - -checkpoint['state_dict'] = model.state_dict() - -if args.train_decoder_only: - if args.initial_checkpoint is None: - print("warning: training decoder only without providing initial checkpoint") - - for p in model.core_encoder.module.parameters(): - p.requires_grad = False - - for p in model.statistical_model.parameters(): - p.requires_grad = False - -# dataloader -checkpoint['dataset_args'] = (feature_file, sequence_length, num_features, 36) -checkpoint['dataset_kwargs'] = {'lambda_min': lambda_min, 'lambda_max': lambda_max, 'enc_stride': model.enc_stride, 'quant_levels': quant_levels} -dataset = RDOVAEDataset(*checkpoint['dataset_args'], **checkpoint['dataset_kwargs']) -dataloader = torch.utils.data.DataLoader(dataset, batch_size=batch_size, shuffle=True, drop_last=True, num_workers=4) - - - -# optimizer -params = [p for p in model.parameters() if p.requires_grad] -optimizer = torch.optim.Adam(params, lr=lr, betas=adam_betas, eps=adam_eps) - - -# learning rate scheduler -scheduler = torch.optim.lr_scheduler.LambdaLR(optimizer=optimizer, lr_lambda=lambda x : 1 / (1 + lr_decay_factor * x)) - -if __name__ == '__main__': - - # push model to device - model.to(device) - - # training loop - - for epoch in range(1, epochs + 1): - - print(f"training epoch {epoch}...") - - # running stats - running_rate_loss = 0 - running_soft_dist_loss = 0 - running_hard_dist_loss = 0 - running_hard_rate_loss = 0 - running_soft_rate_loss = 0 - running_total_loss = 0 - running_rate_metric = 0 - running_states_rate_metric = 0 - previous_total_loss = 0 - running_first_frame_loss = 0 - - with tqdm.tqdm(dataloader, unit='batch') as tepoch: - for i, (features, rate_lambda, q_ids) in enumerate(tepoch): - - # zero out gradients - optimizer.zero_grad() - - # push inputs to device - features = features.to(device) - q_ids = q_ids.to(device) - rate_lambda = rate_lambda.to(device) - - - rate_lambda_upsamp = torch.repeat_interleave(rate_lambda, 2, 1) - - # run model - model_output = model(features, q_ids) - - # collect outputs - z = model_output['z'] - states = model_output['states'] - outputs_hard_quant = model_output['outputs_hard_quant'] - outputs_soft_quant = model_output['outputs_soft_quant'] - statistical_model = model_output['statistical_model'] - - # rate loss - hard_rate = hard_rate_estimate(z, statistical_model['r_hard'][:,:,:latent_dim], statistical_model['theta_hard'][:,:,:latent_dim], reduce=False) - soft_rate = soft_rate_estimate(z, statistical_model['r_soft'][:,:,:latent_dim], reduce=False) - states_hard_rate = hard_rate_estimate(states, statistical_model['r_hard'][:,:,latent_dim:], statistical_model['theta_hard'][:,:,latent_dim:], reduce=False) - states_soft_rate = soft_rate_estimate(states, statistical_model['r_soft'][:,:,latent_dim:], reduce=False) - soft_rate_loss = torch.mean(torch.sqrt(rate_lambda) * (soft_rate + .02*states_soft_rate)) - hard_rate_loss = torch.mean(torch.sqrt(rate_lambda) * (hard_rate + .02*states_hard_rate)) - rate_loss = (soft_rate_loss + 0.1 * hard_rate_loss) - hard_rate_metric = torch.mean(hard_rate) - states_rate_metric = torch.mean(states_hard_rate) - - ## distortion losses - - # hard quantized decoder input - distortion_loss_hard_quant = torch.zeros_like(rate_loss) - for dec_features, start, stop in outputs_hard_quant: - distortion_loss_hard_quant += distortion_loss(features[..., start : stop, :], dec_features, rate_lambda_upsamp[..., start : stop]) / len(outputs_hard_quant) - - first_frame_loss = torch.zeros_like(rate_loss) - for dec_features, start, stop in outputs_hard_quant: - first_frame_loss += distortion_loss(features[..., stop-4 : stop, :], dec_features[..., -4:, :], rate_lambda_upsamp[..., stop - 4 : stop]) / len(outputs_hard_quant) - - # soft quantized decoder input - distortion_loss_soft_quant = torch.zeros_like(rate_loss) - for dec_features, start, stop in outputs_soft_quant: - distortion_loss_soft_quant += distortion_loss(features[..., start : stop, :], dec_features, rate_lambda_upsamp[..., start : stop]) / len(outputs_soft_quant) - - # total loss - total_loss = rate_loss + (distortion_loss_hard_quant + distortion_loss_soft_quant) / 2 - - if args.enable_first_frame_loss: - total_loss = .97*total_loss + 0.03 * first_frame_loss - - - total_loss.backward() - - optimizer.step() - - model.clip_weights() - model.sparsify() - - scheduler.step() - - # collect running stats - running_hard_dist_loss += float(distortion_loss_hard_quant.detach().cpu()) - running_soft_dist_loss += float(distortion_loss_soft_quant.detach().cpu()) - running_rate_loss += float(rate_loss.detach().cpu()) - running_rate_metric += float(hard_rate_metric.detach().cpu()) - running_states_rate_metric += float(states_rate_metric.detach().cpu()) - running_total_loss += float(total_loss.detach().cpu()) - running_first_frame_loss += float(first_frame_loss.detach().cpu()) - running_soft_rate_loss += float(soft_rate_loss.detach().cpu()) - running_hard_rate_loss += float(hard_rate_loss.detach().cpu()) - - if (i + 1) % log_interval == 0: - current_loss = (running_total_loss - previous_total_loss) / log_interval - tepoch.set_postfix( - current_loss=current_loss, - total_loss=running_total_loss / (i + 1), - dist_hq=running_hard_dist_loss / (i + 1), - dist_sq=running_soft_dist_loss / (i + 1), - rate_loss=running_rate_loss / (i + 1), - rate=running_rate_metric / (i + 1), - states_rate=running_states_rate_metric / (i + 1), - ffloss=running_first_frame_loss / (i + 1), - rateloss_hard=running_hard_rate_loss / (i + 1), - rateloss_soft=running_soft_rate_loss / (i + 1) - ) - previous_total_loss = running_total_loss - - # save checkpoint - checkpoint_path = os.path.join(checkpoint_dir, f'checkpoint_epoch_{epoch}.pth') - checkpoint['state_dict'] = model.state_dict() - checkpoint['loss'] = running_total_loss / len(dataloader) - checkpoint['epoch'] = epoch - torch.save(checkpoint, checkpoint_path) diff --git a/android/app/src/main/cpp/third_party/opus/dnn/torch/testsuite/README.md b/android/app/src/main/cpp/third_party/opus/dnn/torch/testsuite/README.md deleted file mode 100644 index cc76965..0000000 --- a/android/app/src/main/cpp/third_party/opus/dnn/torch/testsuite/README.md +++ /dev/null @@ -1,46 +0,0 @@ -# lpcnet-testsuite - -## setup -The test script is written for Linux only. It requires sox to be installed and available. - -Setup is done as usual via - -``` -pip install -r requirements.txt -``` - -The test scrip run_warpq_test.py requires a setup file in yaml format, which specifies how -to generate a wave file OUTPUT from a wave file INPUT sampled resampled to the specified -sampling rate as a list of shell commands. This makes it easy to test other neural vocoders -with it as well. Two examples are given in examples. INPUT and OUTPUT will be replaced by using -the string.format(INPUT=input,OUTPUT=output) method. - -Here is one example: - -``` -test: "LPCNet reference test" -processing: - - "sox {INPUT} {INPUT}.raw" - - "/local/code/LPCNet/lpcnet_demo -features {INPUT}.raw {INPUT}.features.f32" - - "/local/code/LPCNet/lpcnet_demo -synthesis {INPUT}.features.f32 {INPUT}.decoded.raw" - - "sox -r 16000 -L -e signed-integer -b 16 -c 1 {INPUT}.decoded.raw {OUTPUT}" -``` - -The structure of the output folder is as follows: - -``` -output_folder -+-- html - +-- index.html - +-- items -+-- processing -+-- setup.yml -+-- stats.txt -+-- scores.txt -``` - -scores.txt contains the WARP-Q scores in descending order (best to worse) -stats.txt contains mean values over all, the 10 best and the 10 worst items -setup.yml contains all information to repeat the run -htms contains a self-contained website displaying the 10 best and 10 worst items -processing contains processing output \ No newline at end of file diff --git a/android/app/src/main/cpp/third_party/opus/dnn/torch/testsuite/examples/lpcnet_c_example.yml b/android/app/src/main/cpp/third_party/opus/dnn/torch/testsuite/examples/lpcnet_c_example.yml deleted file mode 100644 index 2858309..0000000 --- a/android/app/src/main/cpp/third_party/opus/dnn/torch/testsuite/examples/lpcnet_c_example.yml +++ /dev/null @@ -1,6 +0,0 @@ -test: "LPCNet reference test" -processing: - - "sox {INPUT} {INPUT}.raw" - - "/local/code/LPCNet/lpcnet_demo -features {INPUT}.raw {INPUT}.features.f32" - - "/local/code/LPCNet/lpcnet_demo -synthesis {INPUT}.features.f32 {INPUT}.decoded.raw" - - "sox -r 16000 -L -e signed-integer -b 16 -c 1 {INPUT}.decoded.raw {OUTPUT} trim 0.015" \ No newline at end of file diff --git a/android/app/src/main/cpp/third_party/opus/dnn/torch/testsuite/examples/lpcnet_c_plc_example.yml b/android/app/src/main/cpp/third_party/opus/dnn/torch/testsuite/examples/lpcnet_c_plc_example.yml deleted file mode 100644 index b97b26d..0000000 --- a/android/app/src/main/cpp/third_party/opus/dnn/torch/testsuite/examples/lpcnet_c_plc_example.yml +++ /dev/null @@ -1,5 +0,0 @@ -test: "LPCNet reference test" -processing: - - "sox {INPUT} {INPUT}.raw" - - "/local/code/LPCNet/lpcnet_demo -plc_file causal {PLCFILE} {INPUT}.raw {INPUT}.decoded.raw" - - "sox -r 16000 -L -e signed-integer -b 16 -c 1 {INPUT}.decoded.raw {OUTPUT}" \ No newline at end of file diff --git a/android/app/src/main/cpp/third_party/opus/dnn/torch/testsuite/examples/lpcnet_torch_example.yml b/android/app/src/main/cpp/third_party/opus/dnn/torch/testsuite/examples/lpcnet_torch_example.yml deleted file mode 100644 index 631cbfa..0000000 --- a/android/app/src/main/cpp/third_party/opus/dnn/torch/testsuite/examples/lpcnet_torch_example.yml +++ /dev/null @@ -1,5 +0,0 @@ -test: "no noise test" -processing: - - "sox {INPUT} {INPUT}.raw" - - "/home/ubuntu/bin/lpcnet_dump_data_v2 -test {INPUT}.raw {INPUT}.features.f32" - - "/home/ubuntu/opt/miniconda3/envs/torch/bin/python /local/code/lpcnext/test_lpcnet.py {INPUT}.features.f32 /local/experiments/noise_augmentation/output/lpcnet_384_2/checkpoints/checkpoint_epoch_20.pth {OUTPUT}" \ No newline at end of file diff --git a/android/app/src/main/cpp/third_party/opus/dnn/torch/testsuite/requirements.txt b/android/app/src/main/cpp/third_party/opus/dnn/torch/testsuite/requirements.txt deleted file mode 100644 index af2d991..0000000 --- a/android/app/src/main/cpp/third_party/opus/dnn/torch/testsuite/requirements.txt +++ /dev/null @@ -1,12 +0,0 @@ -scipy -librosa -numpy -scikit-image -pyvad -speechpy -soundfile -pyyaml -pesq -AMFM_decompy -matplotlib -multiprocess \ No newline at end of file diff --git a/android/app/src/main/cpp/third_party/opus/dnn/torch/testsuite/run_test.py b/android/app/src/main/cpp/third_party/opus/dnn/torch/testsuite/run_test.py deleted file mode 100644 index 69463dd..0000000 --- a/android/app/src/main/cpp/third_party/opus/dnn/torch/testsuite/run_test.py +++ /dev/null @@ -1,375 +0,0 @@ -""" -/* Copyright (c) 2023 Amazon - Written by Jan Buethe */ -/* - Redistribution and use in source and binary forms, with or without - modification, are permitted provided that the following conditions - are met: - - - Redistributions of source code must retain the above copyright - notice, this list of conditions and the following disclaimer. - - - Redistributions in binary form must reproduce the above copyright - notice, this list of conditions and the following disclaimer in the - documentation and/or other materials provided with the distribution. - - THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS - ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT - LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR - A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER - OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, - EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, - PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR - PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF - LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING - NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS - SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. -*/ -""" - -import os -import multiprocess as multiprocessing -import random -import subprocess -import argparse -import shutil - -import yaml - -from utils.files import get_wave_file_list -from utils.pesq import compute_PESQ -from utils.pitch import compute_pitch_error - - -parser = argparse.ArgumentParser() -parser.add_argument('setup', type=str, help='setup yaml specifying end to end processing with model under test') -parser.add_argument('input_folder', type=str, help='input folder path') -parser.add_argument('output_folder', type=str, help='output folder path') -parser.add_argument('--num-testitems', type=int, help="number of testitems to be processed (default 100)", default=100) -parser.add_argument('--seed', type=int, help='seed for random item selection', default=None) -parser.add_argument('--fs', type=int, help="sampling rate at which input is presented as wave file (defaults to 16000)", default=16000) -parser.add_argument('--num-workers', type=int, help="number of subprocesses to be used (default=4)", default=4) -parser.add_argument('--plc-suffix', type=str, default="_is_lost.txt", help="suffix of plc error pattern file: only relevant if command chain uses PLCFILE (default=_is_lost.txt)") -parser.add_argument('--metrics', type=str, default='pesq', help='comma separated string of metrics, supported: {{"pesq", "pitch_error", "voicing_error"}}, default="pesq"') -parser.add_argument('--verbose', action='store_true', help='enables printouts of all commands run in the pipeline') - -def check_for_sox_in_path(): - r = subprocess.run("sox -h", shell=True, stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL) - return r.returncode == 0 - - -def run_save_sh(command, verbose=False): - - if verbose: - print(f"[run_save_sh] running command {command}...") - - r = subprocess.run(command, shell=True) - if r.returncode != 0: - raise RuntimeError(f"command '{command}' failed with exit code {r.returncode}") - - -def run_processing_chain(input_path, output_path, model_commands, fs, metrics={'pesq'}, plc_suffix="_is_lost.txt", verbose=False): - - # prepare model input - model_input = output_path + ".resamp.wav" - run_save_sh(f"sox {input_path} -r {fs} {model_input}", verbose=verbose) - - plcfile = os.path.splitext(input_path)[0] + plc_suffix - if os.path.isfile(plcfile): - run_save_sh(f"cp {plcfile} {os.path.dirname(output_path)}") - - # generate model output - for command in model_commands: - run_save_sh(command.format(INPUT=model_input, OUTPUT=output_path, PLCFILE=plcfile), verbose=verbose) - - scores = dict() - cache = dict() - for metric in metrics: - if metric == 'pesq': - # run pesq - score = compute_PESQ(input_path, output_path, fs=fs) - elif metric == 'pitch_error': - if metric in cache: - score = cache[metric] - else: - rval = compute_pitch_error(input_path, output_path, fs=fs) - score = rval[metric] - cache['voicing_error'] = rval['voicing_error'] - elif metric == 'voicing_error': - if metric in cache: - score = cache[metric] - else: - rval = compute_pitch_error(input_path, output_path, fs=fs) - score = rval[metric] - cache['pitch_error'] = rval['pitch_error'] - else: - ValueError(f'error: unknown metric {metric}') - - scores[metric] = score - - return (output_path, scores) - - -def get_output_path(root_folder, input, output_folder): - - input_relpath = os.path.relpath(input, root_folder) - - os.makedirs(os.path.join(output_folder, 'processing', os.path.dirname(input_relpath)), exist_ok=True) - - output_path = os.path.join(output_folder, 'processing', input_relpath + '.output.wav') - - return output_path - - -def add_audio_table(f, html_folder, results, title, metric): - - item_folder = os.path.join(html_folder, 'items') - os.makedirs(item_folder, exist_ok=True) - - # table with results - f.write(f""" -
-

{title}

- - - - - - - - - """) - - for i, r in enumerate(results): - item, score = r - item_name = os.path.basename(item) - new_item_path = os.path.join(item_folder, item_name) - shutil.copyfile(item, new_item_path) - shutil.copyfile(item + '.resamp.wav', os.path.join(item_folder, item_name + '.orig.wav')) - - f.write(f""" - - - - - - - - """) - - # footer - f.write(""" -
Rank Name {metric.upper()} Audio (out) Audio (orig)
{i + 1} {item_name.split('.')[0]} {score:.3f} - - - -
-
- """) - - -def create_html(output_folder, results, title, metric): - - html_folder = output_folder - items_folder = os.path.join(html_folder, 'items') - os.makedirs(html_folder, exist_ok=True) - os.makedirs(items_folder, exist_ok=True) - - with open(os.path.join(html_folder, 'index.html'), 'w') as f: - # header and title - f.write(f""" - - - - - {title} - - - -

{title}

-
- """) - - # top 20 - add_audio_table(f, html_folder, results[:-21: -1], "Top 20", metric) - - # 20 around median - N = len(results) // 2 - add_audio_table(f, html_folder, results[N + 10 : N - 10: -1], "Median 20", metric) - - # flop 20 - add_audio_table(f, html_folder, results[:20], "Flop 20", metric) - - # footer - f.write(""" -
- - - """) - -metric_sorting_signs = { - 'pesq' : 1, - 'pitch_error' : -1, - 'voicing_error' : -1 -} - -def is_valid_result(data, metrics): - if not isinstance(data, dict): - return False - - for metric in metrics: - if not metric in data: - return False - - return True - - -def evaluate_results(output_folder, results, metric): - - results = sorted(results, key=lambda x : metric_sorting_signs[metric] * x[1]) - with open(os.path.join(args.output_folder, f'scores_{metric}.txt'), 'w') as f: - for result in results: - f.write(f"{os.path.relpath(result[0], args.output_folder)} {result[1]}\n") - - - # some statistics - mean = sum([r[1] for r in results]) / len(results) - top_mean = sum([r[1] for r in results[-20:]]) / 20 - bottom_mean = sum([r[1] for r in results[:20]]) / 20 - - with open(os.path.join(args.output_folder, f'stats_{metric}.txt'), 'w') as f: - f.write(f"mean score: {mean}\n") - f.write(f"bottom mean score: {bottom_mean}\n") - f.write(f"top mean score: {top_mean}\n") - - print(f"\nmean score: {mean}") - print(f"bottom mean score: {bottom_mean}") - print(f"top mean score: {top_mean}\n") - - # create output html - create_html(os.path.join(output_folder, 'html', metric), results, setup['test'], metric) - -if __name__ == "__main__": - args = parser.parse_args() - - # check for sox - if not check_for_sox_in_path(): - raise RuntimeError("script requires sox") - - - # prepare output folder - if os.path.exists(args.output_folder): - print("warning: output folder exists") - - reply = input('continue? (y/n): ') - while reply not in {'y', 'n'}: - reply = input('continue? (y/n): ') - - if reply == 'n': - os._exit() - else: - # start with a clean sleight - shutil.rmtree(args.output_folder) - - os.makedirs(args.output_folder, exist_ok=True) - - # extract metrics - metrics = args.metrics.split(",") - for metric in metrics: - if not metric in metric_sorting_signs: - print(f"unknown metric {metric}") - args.usage() - - # read setup - print(f"loading {args.setup}...") - with open(args.setup, "r") as f: - setup = yaml.load(f.read(), yaml.FullLoader) - - model_commands = setup['processing'] - - print("\nfound the following model commands:") - for command in model_commands: - print(command.format(INPUT='input.wav', OUTPUT='output.wav', PLCFILE='input_is_lost.txt')) - - # store setup to output folder - setup['input'] = os.path.abspath(args.input_folder) - setup['output'] = os.path.abspath(args.output_folder) - setup['seed'] = args.seed - with open(os.path.join(args.output_folder, 'setup.yml'), 'w') as f: - yaml.dump(setup, f) - - # get input - print(f"\nCollecting audio files from {args.input_folder}...") - file_list = get_wave_file_list(args.input_folder, check_for_features=False) - print(f"...{len(file_list)} files found\n") - - # sample from file list - file_list = sorted(file_list) - random.seed(args.seed) - random.shuffle(file_list) - num_testitems = min(args.num_testitems, len(file_list)) - file_list = file_list[:num_testitems] - - - print(f"\nlaunching test on {num_testitems} items...") - # helper function for parallel processing - def func(input_path): - output_path = get_output_path(args.input_folder, input_path, args.output_folder) - - try: - rval = run_processing_chain(input_path, output_path, model_commands, args.fs, metrics=metrics, plc_suffix=args.plc_suffix, verbose=args.verbose) - except: - rval = (input_path, -1) - - return rval - - with multiprocessing.Pool(args.num_workers) as p: - results = p.map(func, file_list) - - results_dict = dict() - for name, values in results: - if is_valid_result(values, metrics): - results_dict[name] = values - - print(results_dict) - - # evaluating results - num_failures = num_testitems - len(results_dict) - print(f"\nprocessing of {num_failures} items failed\n") - - for metric in metrics: - print(metric) - evaluate_results( - args.output_folder, - [(name, value[metric]) for name, value in results_dict.items()], - metric - ) \ No newline at end of file diff --git a/android/app/src/main/cpp/third_party/opus/dnn/torch/testsuite/utils/__init__.py b/android/app/src/main/cpp/third_party/opus/dnn/torch/testsuite/utils/__init__.py deleted file mode 100644 index e69de29..0000000 diff --git a/android/app/src/main/cpp/third_party/opus/dnn/torch/testsuite/utils/files.py b/android/app/src/main/cpp/third_party/opus/dnn/torch/testsuite/utils/files.py deleted file mode 100644 index c3a1553..0000000 --- a/android/app/src/main/cpp/third_party/opus/dnn/torch/testsuite/utils/files.py +++ /dev/null @@ -1,54 +0,0 @@ -""" -/* Copyright (c) 2023 Amazon - Written by Jan Buethe */ -/* - Redistribution and use in source and binary forms, with or without - modification, are permitted provided that the following conditions - are met: - - - Redistributions of source code must retain the above copyright - notice, this list of conditions and the following disclaimer. - - - Redistributions in binary form must reproduce the above copyright - notice, this list of conditions and the following disclaimer in the - documentation and/or other materials provided with the distribution. - - THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS - ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT - LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR - A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER - OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, - EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, - PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR - PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF - LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING - NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS - SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. -*/ -""" - -import os - - -def get_wave_file_list(parent_folder, extensions=[".wav", ".flac"], check_for_features=False): - """ traverses subfolders of parent_folder in search for files that match the given extension """ - - file_list = [] - - for root, dirs, files in os.walk(parent_folder, topdown=True): - - for file in files: - - stem, ext = os.path.splitext(file) - - #check for extension - if not ext in extensions: - continue - - # check if feature file exists - if check_for_features and not os.path.isfile(os.path.join(root, stem + "_features.f32")): - continue - - file_list.append(os.path.join(root, file)) - - return file_list \ No newline at end of file diff --git a/android/app/src/main/cpp/third_party/opus/dnn/torch/testsuite/utils/pesq.py b/android/app/src/main/cpp/third_party/opus/dnn/torch/testsuite/utils/pesq.py deleted file mode 100644 index 5a27f51..0000000 --- a/android/app/src/main/cpp/third_party/opus/dnn/torch/testsuite/utils/pesq.py +++ /dev/null @@ -1,43 +0,0 @@ -""" -/* Copyright (c) 2023 Amazon - Written by Jan Buethe */ -/* - Redistribution and use in source and binary forms, with or without - modification, are permitted provided that the following conditions - are met: - - - Redistributions of source code must retain the above copyright - notice, this list of conditions and the following disclaimer. - - - Redistributions in binary form must reproduce the above copyright - notice, this list of conditions and the following disclaimer in the - documentation and/or other materials provided with the distribution. - - THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS - ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT - LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR - A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER - OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, - EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, - PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR - PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF - LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING - NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS - SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. -*/ -""" - -import pesq -import librosa - -def compute_PESQ(ref, test, fs=16000): - - if not ref.endswith('.wav') or not test.endswith('.wav'): - raise ValueError('error: expecting .wav as file extension') - - ref_item, _ = librosa.load(ref, sr=fs) - test_item, _ = librosa.load(test, sr=fs) - - score = pesq.pesq(fs, ref_item, test_item) - - return score \ No newline at end of file diff --git a/android/app/src/main/cpp/third_party/opus/dnn/torch/testsuite/utils/pitch.py b/android/app/src/main/cpp/third_party/opus/dnn/torch/testsuite/utils/pitch.py deleted file mode 100644 index f942965..0000000 --- a/android/app/src/main/cpp/third_party/opus/dnn/torch/testsuite/utils/pitch.py +++ /dev/null @@ -1,61 +0,0 @@ -""" -/* Copyright (c) 2023 Amazon - Written by Jan Buethe */ -/* - Redistribution and use in source and binary forms, with or without - modification, are permitted provided that the following conditions - are met: - - - Redistributions of source code must retain the above copyright - notice, this list of conditions and the following disclaimer. - - - Redistributions in binary form must reproduce the above copyright - notice, this list of conditions and the following disclaimer in the - documentation and/or other materials provided with the distribution. - - THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS - ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT - LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR - A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER - OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, - EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, - PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR - PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF - LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING - NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS - SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. -*/ -""" - -import numpy as np -from scipy.io import wavfile -import amfm_decompy.pYAAPT as pYAAPT -import amfm_decompy.basic_tools as basic - -def get_voicing_info(x, sr=16000): - - signal = basic.SignalObj(x, sr) - pitch = pYAAPT.yaapt(signal, **{'frame_length' : 20.0, 'tda_frame_length' : 20.0}) - - pitch_values = pitch.samp_values - voiced_flags = pitch.vuv.astype('float') - - return pitch_values, voiced_flags - -def compute_pitch_error(ref_path, test_path, fs=16000): - fs_orig, x_orig = wavfile.read(ref_path) - fs_test, x_test = wavfile.read(test_path) - - min_length = min(len(x_orig), len(x_test)) - x_orig = x_orig[:min_length] - x_test = x_test[:min_length] - - assert fs_orig == fs_test == fs - - pitch_contour_orig, voicing_orig = get_voicing_info(x_orig.astype(np.float32)) - pitch_contour_test, voicing_test = get_voicing_info(x_test.astype(np.float32)) - - return { - 'pitch_error' : np.mean(np.abs(pitch_contour_orig - pitch_contour_test)).item(), - 'voicing_error' : np.sum(np.abs(voicing_orig - voicing_test)).item() / len(voicing_orig) - } \ No newline at end of file diff --git a/android/app/src/main/cpp/third_party/opus/dnn/torch/weight-exchange/README.md b/android/app/src/main/cpp/third_party/opus/dnn/torch/weight-exchange/README.md deleted file mode 100644 index f4818b5..0000000 --- a/android/app/src/main/cpp/third_party/opus/dnn/torch/weight-exchange/README.md +++ /dev/null @@ -1,21 +0,0 @@ -# weight-exchange - - - -## Weight Exchange -Repo wor exchanging weights betweeen torch an tensorflow.keras modules, using an intermediate numpy format. - -Routines for loading/dumping torch weights are located in exchange/torch and can be loaded with -``` -import exchange.torch -``` -and routines for loading/dumping tensorflow weights are located in exchange/tf and can be loaded with -``` -import exchange.tf -``` - -Note that `exchange.torch` requires torch to be installed and `exchange.tf` requires tensorflow. To avoid the necessity of installing both torch and tensorflow in the working environment, none of these submodules is imported when calling `import exchange`. Similarly, the requirements listed in `requirements.txt` do include neither Tensorflow or Pytorch. - - -## C export -The module `exchange.c_export` contains routines to export weights to C files. On the long run it will be possible to call all `dump_...` functions with either a path string or a `CWriter` instance based on which the export format is chosen. This is currently only implemented for `torch.nn.GRU`, `torch.nn.Linear` and `torch.nn.Conv1d`. \ No newline at end of file diff --git a/android/app/src/main/cpp/third_party/opus/dnn/torch/weight-exchange/requirements.txt b/android/app/src/main/cpp/third_party/opus/dnn/torch/weight-exchange/requirements.txt deleted file mode 100644 index 296d654..0000000 --- a/android/app/src/main/cpp/third_party/opus/dnn/torch/weight-exchange/requirements.txt +++ /dev/null @@ -1 +0,0 @@ -numpy \ No newline at end of file diff --git a/android/app/src/main/cpp/third_party/opus/dnn/torch/weight-exchange/setup.py b/android/app/src/main/cpp/third_party/opus/dnn/torch/weight-exchange/setup.py deleted file mode 100644 index 2b20440..0000000 --- a/android/app/src/main/cpp/third_party/opus/dnn/torch/weight-exchange/setup.py +++ /dev/null @@ -1,48 +0,0 @@ -""" -/* Copyright (c) 2023 Amazon - Written by Jan Buethe */ -/* - Redistribution and use in source and binary forms, with or without - modification, are permitted provided that the following conditions - are met: - - - Redistributions of source code must retain the above copyright - notice, this list of conditions and the following disclaimer. - - - Redistributions in binary form must reproduce the above copyright - notice, this list of conditions and the following disclaimer in the - documentation and/or other materials provided with the distribution. - - THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS - ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT - LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR - A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER - OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, - EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, - PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR - PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF - LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING - NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS - SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. -*/ -""" - -#!/usr/bin/env/python -import os -from setuptools import setup - -lib_folder = os.path.dirname(os.path.realpath(__file__)) - -with open(os.path.join(lib_folder, 'requirements.txt'), 'r') as f: - install_requires = list(f.read().splitlines()) - -print(install_requires) - -setup(name='wexchange', - version='1.6', - author='Jan Buethe', - author_email='jbuethe@amazon.de', - description='Weight-exchange library between Pytorch and Tensorflow', - packages=['wexchange', 'wexchange.tf', 'wexchange.torch', 'wexchange.c_export'], - install_requires=install_requires - ) diff --git a/android/app/src/main/cpp/third_party/opus/dnn/torch/weight-exchange/wexchange/__init__.py b/android/app/src/main/cpp/third_party/opus/dnn/torch/weight-exchange/wexchange/__init__.py deleted file mode 100644 index a028614..0000000 --- a/android/app/src/main/cpp/third_party/opus/dnn/torch/weight-exchange/wexchange/__init__.py +++ /dev/null @@ -1,30 +0,0 @@ -""" -/* Copyright (c) 2023 Amazon - Written by Jan Buethe */ -/* - Redistribution and use in source and binary forms, with or without - modification, are permitted provided that the following conditions - are met: - - - Redistributions of source code must retain the above copyright - notice, this list of conditions and the following disclaimer. - - - Redistributions in binary form must reproduce the above copyright - notice, this list of conditions and the following disclaimer in the - documentation and/or other materials provided with the distribution. - - THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS - ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT - LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR - A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER - OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, - EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, - PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR - PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF - LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING - NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS - SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. -*/ -""" - -from . import c_export \ No newline at end of file diff --git a/android/app/src/main/cpp/third_party/opus/dnn/torch/weight-exchange/wexchange/c_export/__init__.py b/android/app/src/main/cpp/third_party/opus/dnn/torch/weight-exchange/wexchange/c_export/__init__.py deleted file mode 100644 index 2a580c8..0000000 --- a/android/app/src/main/cpp/third_party/opus/dnn/torch/weight-exchange/wexchange/c_export/__init__.py +++ /dev/null @@ -1,31 +0,0 @@ -from .c_writer import CWriter -""" -/* Copyright (c) 2023 Amazon - Written by Jan Buethe */ -/* - Redistribution and use in source and binary forms, with or without - modification, are permitted provided that the following conditions - are met: - - - Redistributions of source code must retain the above copyright - notice, this list of conditions and the following disclaimer. - - - Redistributions in binary form must reproduce the above copyright - notice, this list of conditions and the following disclaimer in the - documentation and/or other materials provided with the distribution. - - THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS - ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT - LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR - A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER - OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, - EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, - PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR - PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF - LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING - NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS - SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. -*/ -""" - -from .common import print_gru_layer, print_dense_layer, print_conv1d_layer, print_tconv1d_layer, print_conv2d_layer, print_vector \ No newline at end of file diff --git a/android/app/src/main/cpp/third_party/opus/dnn/torch/weight-exchange/wexchange/c_export/c_writer.py b/android/app/src/main/cpp/third_party/opus/dnn/torch/weight-exchange/wexchange/c_export/c_writer.py deleted file mode 100644 index 2745f33..0000000 --- a/android/app/src/main/cpp/third_party/opus/dnn/torch/weight-exchange/wexchange/c_export/c_writer.py +++ /dev/null @@ -1,182 +0,0 @@ -""" -/* Copyright (c) 2023 Amazon - Written by Jan Buethe */ -/* - Redistribution and use in source and binary forms, with or without - modification, are permitted provided that the following conditions - are met: - - - Redistributions of source code must retain the above copyright - notice, this list of conditions and the following disclaimer. - - - Redistributions in binary form must reproduce the above copyright - notice, this list of conditions and the following disclaimer in the - documentation and/or other materials provided with the distribution. - - THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS - ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT - LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR - A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER - OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, - EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, - PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR - PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF - LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING - NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS - SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. -*/ -""" - -import os -from collections import OrderedDict - -class CWriter: - def __init__(self, - filename_without_extension, - message=None, - header_only=False, - create_state_struct=False, - enable_binary_blob=True, - model_struct_name="Model", - nnet_header="nnet.h", - add_typedef=False): - """ - Writer class for creating souce and header files for weight exports to C - - Parameters: - ----------- - - filename_without_extension: str - filename from which .c and .h files are created - - message: str, optional - if given and not None, this message will be printed as comment in the header file - - header_only: bool, optional - if True, only a header file is created; defaults to False - - enable_binary_blob: bool, optional - if True, export is done in binary blob format and a model type is created; defaults to False - - create_state_struct: bool, optional - if True, a state struct type is created in the header file; if False, state sizes are defined as macros; defaults to False - - model_struct_name: str, optional - name used for the model struct type; only relevant when enable_binary_blob is True; defaults to "Model" - - nnet_header: str, optional - name of header nnet header file; defaults to nnet.h - - """ - - - self.header_only = header_only - self.enable_binary_blob = enable_binary_blob - self.create_state_struct = create_state_struct - self.model_struct_name = model_struct_name - self.add_typedef = add_typedef - - # for binary blob format, format is key=, value=(, ) - self.layer_dict = OrderedDict() - - # for binary blob format, format is key=, value= - self.weight_arrays = [] - - # form model struct, format is key=, value= - self.state_dict = OrderedDict() - - self.header = open(filename_without_extension + ".h", "w") - header_name = os.path.basename(filename_without_extension) + '.h' - - if message is not None: - self.header.write(f"/* {message} */\n\n") - - self.header_guard = os.path.basename(filename_without_extension).upper() + "_H" - self.header.write( -f''' -#ifndef {self.header_guard} -#define {self.header_guard} - -#include "{nnet_header}" - -''' - ) - - if not self.header_only: - self.source = open(filename_without_extension + ".c", "w") - if message is not None: - self.source.write(f"/* {message} */\n\n") - - self.source.write( -f""" -#ifdef HAVE_CONFIG_H -#include "config.h" -#endif - -""") - self.source.write(f'#include "{header_name}"\n\n') - - - def _finalize_header(self): - - # create model type - if self.enable_binary_blob: - if self.add_typedef: - self.header.write(f"\ntypedef struct {{") - else: - self.header.write(f"\nstruct {self.model_struct_name} {{") - for name, data in self.layer_dict.items(): - layer_type = data[0] - self.header.write(f"\n {layer_type} {name};") - if self.add_typedef: - self.header.write(f"\n}} {self.model_struct_name};\n") - else: - self.header.write(f"\n}};\n") - - init_prototype = f"int init_{self.model_struct_name.lower()}({self.model_struct_name} *model, const WeightArray *arrays)" - self.header.write(f"\n{init_prototype};\n") - - self.header.write(f"\n#endif /* {self.header_guard} */\n") - - def _finalize_source(self): - - if self.enable_binary_blob: - # create weight array - if len(set(self.weight_arrays)) != len(self.weight_arrays): - raise ValueError("error: detected duplicates in weight arrays") - self.source.write("\n#ifndef USE_WEIGHTS_FILE\n") - self.source.write(f"const WeightArray {self.model_struct_name.lower()}_arrays[] = {{\n") - for name in self.weight_arrays: - self.source.write(f"#ifdef WEIGHTS_{name}_DEFINED\n") - self.source.write(f' {{"{name}", WEIGHTS_{name}_TYPE, sizeof({name}), {name}}},\n') - self.source.write(f"#endif\n") - self.source.write(" {NULL, 0, 0, NULL}\n") - self.source.write("};\n") - - self.source.write("#endif /* USE_WEIGHTS_FILE */\n") - - # create init function definition - init_prototype = f"int init_{self.model_struct_name.lower()}({self.model_struct_name} *model, const WeightArray *arrays)" - self.source.write("\n#ifndef DUMP_BINARY_WEIGHTS\n") - self.source.write(f"{init_prototype} {{\n") - for name, data in self.layer_dict.items(): - self.source.write(f" if ({data[1]}) return 1;\n") - self.source.write(" return 0;\n") - self.source.write("}\n") - self.source.write("#endif /* DUMP_BINARY_WEIGHTS */\n") - - - def close(self): - - if not self.header_only: - self._finalize_source() - self.source.close() - - self._finalize_header() - self.header.close() - - def __del__(self): - try: - self.close() - except: - pass \ No newline at end of file diff --git a/android/app/src/main/cpp/third_party/opus/dnn/torch/weight-exchange/wexchange/c_export/common.py b/android/app/src/main/cpp/third_party/opus/dnn/torch/weight-exchange/wexchange/c_export/common.py deleted file mode 100644 index 039edd9..0000000 --- a/android/app/src/main/cpp/third_party/opus/dnn/torch/weight-exchange/wexchange/c_export/common.py +++ /dev/null @@ -1,387 +0,0 @@ -'''Copyright (c) 2017-2018 Mozilla - Copyright (c) 2022 Amazon - - Redistribution and use in source and binary forms, with or without - modification, are permitted provided that the following conditions - are met: - - - Redistributions of source code must retain the above copyright - notice, this list of conditions and the following disclaimer. - - - Redistributions in binary form must reproduce the above copyright - notice, this list of conditions and the following disclaimer in the - documentation and/or other materials provided with the distribution. - - THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS - ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT - LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR - A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE FOUNDATION OR - CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, - EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, - PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR - PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF - LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING - NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS - SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. -''' - -import numpy as np - -from .c_writer import CWriter - -def print_vector(writer, vector, name, dtype='float', reshape_8x4=False, static=True, debug_float=False): - - if isinstance(writer, CWriter): - f = writer.source - binary_blob = writer.enable_binary_blob - else: - f = writer - binary_blob = False - - dtype_suffix = { - 'float' : 'float', - 'opus_int8' : 'int8', - 'opus_uint16' : 'uint16', - 'opus_int16' : 'int16', - 'int' : 'int', - 'qweight': 'qweight' - } - - - if binary_blob: - f.write( -f''' -#ifndef USE_WEIGHTS_FILE -''' - ) - writer.weight_arrays.append(name) - - if reshape_8x4: - vector = vector.reshape((vector.shape[0]//4, 4, vector.shape[1]//8, 8)) - vector = vector.transpose((2, 0, 3, 1)) - - v = np.reshape(vector, (-1)) - - if debug_float: - f.write('#ifndef DISABLE_DEBUG_FLOAT\n') - if binary_blob: - f.write( -f''' -#define WEIGHTS_{name}_DEFINED -#define WEIGHTS_{name}_TYPE WEIGHT_TYPE_{dtype_suffix[dtype]} -''' - ) - - if static: - f.write('static ') - - f.write(f'const {dtype} {name}[{len(v)}] = {{\n ') - - for i in range(0, len(v)): - - f.write(f'{v[i]}') - - if (i!=len(v)-1): - f.write(',') - else: - break - - if (i%8==7): - f.write("\n ") - else: - f.write(" ") - - f.write('\n};\n\n') - if debug_float: f.write('#endif /*DISABLE_DEBUG_FLOAT*/\n') - - if binary_blob: - f.write( -f''' -#endif /* USE_WEIGHTS_FILE */ -''' - ) - - return vector - - - -def extract_diagonal(A): - """ input shape is (N, k*N) """ - - N, M = A.shape - B = A.copy() - assert M % N == 0 - k = M // N - - diags = [] - for l in range(k): - diag = np.diag(B[:, l * N : (l+1) * N]).copy() - B[:, l * N : (l+1) * N] -= np.diag(diag) - diags.append(diag) - - diag = np.concatenate(diags) - - return diag, B - -def quantize_weight(weight, scale): - scale = scale + 1e-30 - Aq = np.round(weight / scale).astype('int') - if Aq.max() > 127 or Aq.min() <= -128: - raise ValueError("value out of bounds in quantize_weight") - Aq = np.clip(np.round(weight / scale).astype('int'), -128, 127) - return Aq - - -def print_sparse_weight(writer, A, name, scale=1/128, have_diag=True, quantize=False): - N = A.shape[0] - M = A.shape[1] - W = np.zeros((0,), dtype='int') - W0 = np.zeros((0,)) - - if have_diag: - diag, A = extract_diagonal(A) - print_vector(writer, diag, name + '_diag') - - if quantize: - Aq = quantize_weight(A, scale) - else: - Aq = A - - # extract blocks - idx = np.zeros((0,), dtype='int') - for i in range(M//8): - pos = idx.shape[0] - idx = np.append(idx, -1) - nb_nonzero = 0 - for j in range(N//4): - block = A[j*4:(j+1)*4, i*8:(i+1)*8] - qblock = Aq[j*4:(j+1)*4, i*8:(i+1)*8] - if np.sum(np.abs(block)) > 1e-10: - nb_nonzero = nb_nonzero + 1 - idx = np.append(idx, j*4) - vblock = qblock.transpose((1,0)).reshape((-1,)) - W0 = np.concatenate([W0, block.reshape((-1,))]) - W = np.concatenate([W, vblock]) - idx[pos] = nb_nonzero - - if quantize: print_vector(writer, W, name + '_int8', reshape_8x4=False, dtype='opus_int8') - print_vector(writer, W0, name + '_float', reshape_8x4=False, dtype='float', debug_float=quantize) - print_vector(writer, idx, name + '_idx', reshape_8x4=False, dtype='int') - - return Aq - - - -def compute_scaling(weight): - """ computes optimal scaling vector for weight of shape (features_in, features_out) """ - - n_in, n_out = weight.shape - assert n_in % 4 == 0 and n_out % 8 == 0 - - weight_max_abs = np.max(np.abs(weight), axis=0) - weight_max_sum = np.max(np.abs(weight[: n_in : 2] + weight[1 : n_in : 2]), axis=0) - scale_max = weight_max_abs / 127 - scale_sum = weight_max_sum / 129 - - scale = np.maximum(scale_max, scale_sum) - - return scale - -def qn(string): - if string == "NULL": return string - else: return '"' + string + '"' - -def print_linear_layer(writer : CWriter, - name : str, - weight : np.ndarray, - bias : np.ndarray, - scale : np.ndarray = None, - sparse : bool = False, - diagonal : bool = False, - quantize : bool = True): - - """ prints linear layer - - Parameters: - ----------- - name : str - layer name - weight: np.ndarray - ... - scale: np.ndarray or None - If None auto scaling will be applied. Otherwise, output channels will be multiplied by scale (the usual broadcasting rules apply). - - - """ - - if len(weight.shape) != 2: - raise ValueError('expecting 2-dim weight array in print_linear_layer') - - - bias_name = "NULL" if bias is None else name + "_bias" - subias_name = name + "_subias" if quantize else "NULL" - scale_name = name + "_scale" if quantize else "NULL" - idx_name = name + "_weights_idx" if sparse else "NULL" - float_weight_name = name + "_weights_float" - int_weight_name = name + "_weights_int8" if quantize else "NULL" - diag_name = name + "_weights_diag" if sparse and diagonal else "NULL" - - nb_inputs, nb_outputs = weight.shape - - if scale is None and quantize: - scale = compute_scaling(weight) - - - if sparse: - weight_q = print_sparse_weight(writer, weight, name + "_weights", scale=scale, have_diag=diagonal, quantize=quantize) - else: - if quantize: - weight_q = quantize_weight(weight, scale) - print_vector(writer, weight_q, name + "_weights_int8", dtype='opus_int8', reshape_8x4=True) - - print_vector(writer, weight, name + "_weights_float", dtype='float', reshape_8x4=False, debug_float=quantize) - - if quantize: - subias = (np.zeros(nb_outputs) if bias is None else bias) - np.sum(weight_q * scale, axis=0) - print_vector(writer, subias, name + "_subias") - - final_scale = scale / 127 * np.ones(nb_outputs) - print_vector(writer, final_scale, name + "_scale") - - if bias is not None: - print_vector(writer, bias, name + "_bias") - - - init_call = f'linear_init(&model->{name}, arrays, {qn(bias_name)}, {qn(subias_name)}, {qn(int_weight_name)},' \ - + f'{qn(float_weight_name)}, {qn(idx_name)}, {qn(diag_name)}, {qn(scale_name)}, {nb_inputs}, {nb_outputs})' - - writer.layer_dict[name] = ('LinearLayer', init_call) - - -def print_dense_layer(writer : CWriter, - name : str, - weight : np.ndarray, - bias : np.ndarray, - scale=1/128, - format : str = 'torch', - sparse=False, - diagonal=False, - quantize=False): - - if format == 'torch': - weight = weight.transpose() - - print_linear_layer(writer, name, weight, bias, scale=scale, sparse=sparse, diagonal=diagonal, quantize=quantize) - - writer.header.write(f"\n#define {name.upper()}_OUT_SIZE {weight.shape[1]}\n") - - -def print_conv1d_layer(writer : CWriter, - name : str, - weight : np.ndarray, - bias : np.ndarray, - scale=1/128, - format : str = 'torch', - quantize=False, - sparse=False): - - - if format == "torch": - # convert to channels last - weight = np.transpose(weight, (2, 1, 0)) - - lin_weight = np.reshape(weight, (-1, weight.shape[-1])) - print_linear_layer(writer, name, lin_weight, bias, scale=scale, sparse=sparse, diagonal=False, quantize=quantize) - - - writer.header.write(f"\n#define {name.upper()}_OUT_SIZE {weight.shape[2]}\n") - writer.header.write(f"\n#define {name.upper()}_IN_SIZE {weight.shape[1]}\n") - writer.header.write(f"\n#define {name.upper()}_STATE_SIZE ({weight.shape[1]} * ({weight.shape[0] - 1}))\n") - writer.header.write(f"\n#define {name.upper()}_DELAY {(weight.shape[0] - 1) // 2}\n") # CAVE: delay is not a property of the conv layer - - return weight.shape[0] * weight.shape[1] - -def print_conv2d_layer(writer : CWriter, - name : str, - weight : np.ndarray, - bias : np.ndarray, - scale : float=1/128, - quantize : bool=False): - - if quantize: - print("[print_conv2d_layer] warning: quantize argument ignored") - - bias_name = name + "_bias" - float_weight_name = name + "_weight_float" - - print_vector(writer, weight, float_weight_name) - print_vector(writer, bias, bias_name) - - # init function - out_channels, in_channels, ksize1, ksize2 = weight.shape - init_call = f'conv2d_init(&model->{name}, arrays, "{bias_name}", "{float_weight_name}", {in_channels}, {out_channels}, {ksize1}, {ksize2})' - - writer.layer_dict[name] = ('Conv2dLayer', init_call) - - - -def print_gru_layer(writer : CWriter, - name : str, - weight : np.ndarray, - recurrent_weight : np.ndarray, - bias : np.ndarray, - recurrent_bias : np.ndarray, - format : str = 'torch', - quantize : bool = False, - input_sparse : bool = False, - recurrent_sparse : bool = False, - scale=1/128, - recurrent_scale=1/128 - ): - - if format == "torch": - # change gate ordering from rzn to zrn - - N = weight.shape[0] // 3 - for x in [weight, recurrent_weight, bias, recurrent_bias]: - if x is None: continue - tmp = x[0:N].copy() - x[0:N] = x[N:2*N] - x[N:2*N] = tmp - - weight = weight.transpose() - recurrent_weight = recurrent_weight.transpose() - else: - N = weight.shape[1] // 3 - - print_linear_layer(writer, name + "_input", weight, bias, scale=scale, sparse=input_sparse, quantize=quantize) - print_linear_layer(writer, name + "_recurrent", recurrent_weight, recurrent_bias, scale=recurrent_scale, sparse=recurrent_sparse, diagonal=recurrent_sparse, quantize=quantize) - - # wrapping it up - writer.header.write(f"\n#define {name.upper()}_OUT_SIZE {N}\n") - writer.header.write(f"\n#define {name.upper()}_STATE_SIZE {N}\n") - - return N - - -def print_tconv1d_layer(writer : CWriter, - name : str, - weight : np.ndarray, - bias : np.ndarray, - stride: int, - scale=1/128, - quantize=False, - sparse=False): - - in_channels, out_channels, kernel_size = weight.shape - - - linear_weight = weight.transpose(2, 1, 0).reshape(kernel_size * out_channels, in_channels).transpose(1, 0) - linear_bias = np.repeat(bias[np.newaxis, :], kernel_size, 0).flatten() - - print_linear_layer(writer, name, linear_weight, linear_bias, scale=scale, quantize=quantize, sparse=sparse) - - writer.header.write(f"\n#define {name.upper()}_KERNEL_SIZE {kernel_size}\n") - writer.header.write(f"\n#define {name.upper()}_STRIDE {stride}\n") - writer.header.write(f"\n#define {name.upper()}_IN_CHANNELS {in_channels}\n") - writer.header.write(f"\n#define {name.upper()}_OUT_CHANNELS {out_channels}\n") \ No newline at end of file diff --git a/android/app/src/main/cpp/third_party/opus/dnn/torch/weight-exchange/wexchange/tf/__init__.py b/android/app/src/main/cpp/third_party/opus/dnn/torch/weight-exchange/wexchange/tf/__init__.py deleted file mode 100644 index 0267804..0000000 --- a/android/app/src/main/cpp/third_party/opus/dnn/torch/weight-exchange/wexchange/tf/__init__.py +++ /dev/null @@ -1,5 +0,0 @@ -from .tf import dump_tf_conv1d_weights, load_tf_conv1d_weights -from .tf import dump_tf_dense_weights, load_tf_dense_weights -from .tf import dump_tf_embedding_weights, load_tf_embedding_weights -from .tf import dump_tf_gru_weights, load_tf_gru_weights -from .tf import dump_tf_weights, load_tf_weights \ No newline at end of file diff --git a/android/app/src/main/cpp/third_party/opus/dnn/torch/weight-exchange/wexchange/tf/tf.py b/android/app/src/main/cpp/third_party/opus/dnn/torch/weight-exchange/wexchange/tf/tf.py deleted file mode 100644 index bebbb55..0000000 --- a/android/app/src/main/cpp/third_party/opus/dnn/torch/weight-exchange/wexchange/tf/tf.py +++ /dev/null @@ -1,188 +0,0 @@ -""" -/* Copyright (c) 2023 Amazon - Written by Jan Buethe */ -/* - Redistribution and use in source and binary forms, with or without - modification, are permitted provided that the following conditions - are met: - - - Redistributions of source code must retain the above copyright - notice, this list of conditions and the following disclaimer. - - - Redistributions in binary form must reproduce the above copyright - notice, this list of conditions and the following disclaimer in the - documentation and/or other materials provided with the distribution. - - THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS - ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT - LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR - A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER - OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, - EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, - PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR - PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF - LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING - NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS - SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. -*/ -""" - -import os - -import tensorflow as tf -import numpy as np - -from wexchange.c_export import CWriter, print_gru_layer, print_dense_layer, print_conv1d_layer - -def dump_tf_gru_weights(where, gru, name='gru', input_sparse=False, recurrent_sparse=False, quantize=False, scale=1/128, recurrent_scale=1/128): - - - assert gru.activation == tf.keras.activations.tanh - assert gru.recurrent_activation == tf.keras.activations.sigmoid - assert gru.reset_after == True - - w_ih = gru.weights[0].numpy().transpose().copy() - w_hh = gru.weights[1].numpy().transpose().copy() - b_ih = gru.weights[2].numpy()[0].copy() - b_hh = gru.weights[2].numpy()[1].copy() - - if isinstance(where, CWriter): - return print_gru_layer(where, name, w_ih, w_hh, b_ih, b_hh, format='tf', input_sparse=input_sparse, recurrent_sparse=recurrent_sparse, quantize=quantize, scale=scale, recurrent_scale=recurrent_scale) - else: - os.makedirs(where, exist_ok=True) - - # zrn => rzn - N = w_ih.shape[0] // 3 - for x in [w_ih, w_hh, b_ih, b_hh]: - tmp = x[0:N].copy() - x[0:N] = x[N:2*N] - x[N:2*N] = tmp - - np.save(os.path.join(where, 'weight_ih_rzn.npy'), w_ih) - np.save(os.path.join(where, 'weight_hh_rzn.npy'), w_hh) - np.save(os.path.join(where, 'bias_ih_rzn.npy'), b_ih) - np.save(os.path.join(where, 'bias_hh_rzn.npy'), b_hh) - - -def load_tf_gru_weights(path, gru): - - assert gru.activation == tf.keras.activations.tanh - assert gru.recurrent_activation == tf.keras.activations.sigmoid - assert gru.reset_after == True - - w_ih = np.load(os.path.join(path, 'weight_ih_rzn.npy')) - w_hh = np.load(os.path.join(path, 'weight_hh_rzn.npy')) - b_ih = np.load(os.path.join(path, 'bias_ih_rzn.npy')) - b_hh = np.load(os.path.join(path, 'bias_hh_rzn.npy')) - - # rzn => zrn - N = w_ih.shape[0] // 3 - for x in [w_ih, w_hh, b_ih, b_hh]: - tmp = x[0:N].copy() - x[0:N] = x[N:2*N] - x[N:2*N] = tmp - - gru.weights[0].assign(tf.convert_to_tensor(w_ih.transpose())) - gru.weights[1].assign(tf.convert_to_tensor(w_hh.transpose())) - gru.weights[2].assign(tf.convert_to_tensor(np.vstack((b_ih, b_hh)))) - - -def dump_tf_dense_weights(where, dense, name='dense', scale=1/128, sparse=False, diagonal=False, quantize=False): - - w = dense.weights[0].numpy() - if dense.bias is None: - b = np.zeros(dense.units, dtype=w.dtype) - else: - b = dense.bias.numpy() - - - - if isinstance(where, CWriter): - return print_dense_layer(where, name, w, b, scale=scale, format='tf', sparse=sparse, diagonal=diagonal, quantize=quantize) - - else: - os.makedirs(where, exist_ok=True) - - np.save(os.path.join(where, 'weight.npy'), w.transpose()) - np.save(os.path.join(where, 'bias.npy'), b) - - -def load_tf_dense_weights(path, dense): - - w = np.load(os.path.join(path, 'weight.npy')).transpose() - b = np.load(os.path.join(path, 'bias.npy')) - - dense.weights[0].assign(tf.convert_to_tensor(w)) - if dense.bias is not None: - dense.weights[1].assign(tf.convert_to_tensor(b)) - - -def dump_tf_conv1d_weights(where, conv, name='conv', scale=1/128, quantize=False): - - assert conv.data_format == 'channels_last' - - w = conv.weights[0].numpy().copy() - if conv.bias is None: - b = np.zeros(conv.filters, dtype=w.dtype) - else: - b = conv.bias.numpy() - - if isinstance(where, CWriter): - return print_conv1d_layer(where, name, w, b, scale=scale, format='tf', quantize=quantize) - else: - os.makedirs(where, exist_ok=True) - - w = np.transpose(w, (2, 1, 0)) - np.save(os.path.join(where, 'weight_oik.npy'), w) - np.save(os.path.join(where, 'bias.npy'), b) - - -def load_tf_conv1d_weights(path, conv): - - w = np.load(os.path.join(path, 'weight_oik.npy')) - b = np.load(os.path.join(path, 'bias.npy')) - - w = np.transpose(w, (2, 1, 0)) - - conv.weights[0].assign(tf.convert_to_tensor(w)) - if conv.bias is not None: - conv.weights[1].assign(tf.convert_to_tensor(b)) - - -def dump_tf_embedding_weights(path, emb): - os.makedirs(path, exist_ok=True) - - w = emb.weights[0].numpy() - np.save(os.path.join(path, 'weight.npy'), w) - - - -def load_tf_embedding_weights(path, emb): - - w = np.load(os.path.join(path, 'weight.npy')) - emb.weights[0].assign(tf.convert_to_tensor(w)) - - -def dump_tf_weights(path, module): - if isinstance(module, tf.keras.layers.Dense): - dump_tf_dense_weights(path, module) - elif isinstance(module, tf.keras.layers.GRU): - dump_tf_gru_weights(path, module) - elif isinstance(module, tf.keras.layers.Conv1D): - dump_tf_conv1d_weights(path, module) - elif isinstance(module, tf.keras.layers.Embedding): - dump_tf_embedding_weights(path, module) - else: - raise ValueError(f'dump_tf_weights: layer of type {type(module)} not supported') - -def load_tf_weights(path, module): - if isinstance(module, tf.keras.layers.Dense): - load_tf_dense_weights(path, module) - elif isinstance(module, tf.keras.layers.GRU): - load_tf_gru_weights(path, module) - elif isinstance(module, tf.keras.layers.Conv1D): - load_tf_conv1d_weights(path, module) - elif isinstance(module, tf.keras.layers.Embedding): - load_tf_embedding_weights(path, module) - else: - raise ValueError(f'dump_tf_weights: layer of type {type(module)} not supported') \ No newline at end of file diff --git a/android/app/src/main/cpp/third_party/opus/dnn/torch/weight-exchange/wexchange/torch/__init__.py b/android/app/src/main/cpp/third_party/opus/dnn/torch/weight-exchange/wexchange/torch/__init__.py deleted file mode 100644 index 8245566..0000000 --- a/android/app/src/main/cpp/third_party/opus/dnn/torch/weight-exchange/wexchange/torch/__init__.py +++ /dev/null @@ -1,37 +0,0 @@ -""" -/* Copyright (c) 2023 Amazon - Written by Jan Buethe */ -/* - Redistribution and use in source and binary forms, with or without - modification, are permitted provided that the following conditions - are met: - - - Redistributions of source code must retain the above copyright - notice, this list of conditions and the following disclaimer. - - - Redistributions in binary form must reproduce the above copyright - notice, this list of conditions and the following disclaimer in the - documentation and/or other materials provided with the distribution. - - THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS - ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT - LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR - A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER - OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, - EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, - PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR - PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF - LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING - NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS - SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. -*/ -""" - -from .torch import dump_torch_conv1d_weights, load_torch_conv1d_weights -from .torch import dump_torch_conv2d_weights, load_torch_conv2d_weights -from .torch import dump_torch_dense_weights, load_torch_dense_weights -from .torch import dump_torch_gru_weights, load_torch_gru_weights -from .torch import dump_torch_grucell_weights -from .torch import dump_torch_embedding_weights, load_torch_embedding_weights -from .torch import dump_torch_weights, load_torch_weights -from .torch import dump_torch_adaptive_conv1d_weights \ No newline at end of file diff --git a/android/app/src/main/cpp/third_party/opus/dnn/torch/weight-exchange/wexchange/torch/torch.py b/android/app/src/main/cpp/third_party/opus/dnn/torch/weight-exchange/wexchange/torch/torch.py deleted file mode 100644 index af5d3e5..0000000 --- a/android/app/src/main/cpp/third_party/opus/dnn/torch/weight-exchange/wexchange/torch/torch.py +++ /dev/null @@ -1,433 +0,0 @@ -""" -/* Copyright (c) 2023 Amazon - Written by Jan Buethe */ -/* - Redistribution and use in source and binary forms, with or without - modification, are permitted provided that the following conditions - are met: - - - Redistributions of source code must retain the above copyright - notice, this list of conditions and the following disclaimer. - - - Redistributions in binary form must reproduce the above copyright - notice, this list of conditions and the following disclaimer in the - documentation and/or other materials provided with the distribution. - - THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS - ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT - LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR - A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER - OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, - EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, - PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR - PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF - LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING - NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS - SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. -*/ -""" - -import os -import sys - -import torch -import numpy as np - -sys.path.append(sys.path.append(os.path.join(os.path.dirname(__file__), '../osce'))) -try: - import utils.layers as osce_layers - from utils.layers.limited_adaptive_conv1d import LimitedAdaptiveConv1d - from utils.layers.limited_adaptive_comb1d import LimitedAdaptiveComb1d - from utils.layers.td_shaper import TDShaper - has_osce=True -except: - has_osce=False - -from wexchange.c_export import CWriter, print_gru_layer, print_dense_layer, print_conv1d_layer, print_tconv1d_layer, print_conv2d_layer - -def dump_torch_adaptive_conv1d_weights(where, adaconv, name='adaconv', scale=1/128, quantize=False): - - - w_kernel = adaconv.conv_kernel.weight.detach().cpu().numpy().copy() - b_kernel = adaconv.conv_kernel.bias.detach().cpu().numpy().copy() - w_gain = adaconv.filter_gain.weight.detach().cpu().numpy().copy() - b_gain = adaconv.filter_gain.bias.detach().cpu().numpy().copy() - - if isinstance(where, CWriter): - # pad kernel for quantization - left_padding = adaconv.padding[0] - kernel_size = adaconv.kernel_size - in_channels = adaconv.in_channels - out_channels = adaconv.out_channels - feature_dim = adaconv.feature_dim - - if quantize and kernel_size % 8: - kernel_padding = 8 - (kernel_size % 8) - w_kernel = np.concatenate( - (np.zeros((out_channels, in_channels, kernel_padding, feature_dim)), w_kernel.reshape(out_channels, in_channels, kernel_size, feature_dim)), - dtype=w_kernel.dtype, - axis=2).reshape(-1, feature_dim) - b_kernel = np.concatenate( - (np.zeros((out_channels, in_channels, kernel_padding)), b_kernel.reshape(out_channels, in_channels, kernel_size)), - dtype=b_kernel.dtype, - axis=2).reshape(-1) - left_padding += kernel_padding - kernel_size += kernel_padding - - # write relevant scalar parameters to header file - where.header.write(f""" -#define {name.upper()}_FILTER_GAIN_A {adaconv.filter_gain_a:f}f -#define {name.upper()}_FILTER_GAIN_B {adaconv.filter_gain_b:f}f -#define {name.upper()}_SHAPE_GAIN {adaconv.shape_gain:f}f -#define {name.upper()}_KERNEL_SIZE {kernel_size} -#define {name.upper()}_FRAME_SIZE {adaconv.frame_size} -#define {name.upper()}_LEFT_PADDING {left_padding} -#define {name.upper()}_OVERLAP_SIZE {adaconv.overlap_size} -#define {name.upper()}_IN_CHANNELS {adaconv.in_channels} -#define {name.upper()}_OUT_CHANNELS {adaconv.out_channels} -#define {name.upper()}_NORM_P {adaconv.norm_p} -#define {name.upper()}_FEATURE_DIM {adaconv.feature_dim} -""" - ) - - print_dense_layer(where, name + "_kernel", w_kernel, b_kernel, scale=scale, format='torch', sparse=False, diagonal=False, quantize=quantize) - print_dense_layer(where, name + "_gain", w_gain, b_gain, format='torch', sparse=False, diagonal=False, quantize=False) - - - else: - np.save(where, 'weight_kernel.npy', w_kernel) - np.save(where, 'bias_kernel.npy', b_kernel) - np.save(where, 'weight_gain.npy', w_gain) - np.save(where, 'bias_gain.npy', b_gain) - - -def dump_torch_adaptive_comb1d_weights(where, adaconv, name='adaconv', scale=1/128, quantize=False): - - - w_kernel = adaconv.conv_kernel.weight.detach().cpu().numpy().copy() - b_kernel = adaconv.conv_kernel.bias.detach().cpu().numpy().copy() - w_gain = adaconv.filter_gain.weight.detach().cpu().numpy().copy() - b_gain = adaconv.filter_gain.bias.detach().cpu().numpy().copy() - w_global_gain = adaconv.global_filter_gain.weight.detach().cpu().numpy().copy() - b_global_gain = adaconv.global_filter_gain.bias.detach().cpu().numpy().copy() - - - if isinstance(where, CWriter): - # pad kernel for quantization - left_padding = adaconv.padding[0] - kernel_size = adaconv.kernel_size - - if quantize and w_kernel.shape[0] % 8: - kernel_padding = 8 - (w_kernel.shape[0] % 8) - w_kernel = np.concatenate((np.zeros((kernel_padding, w_kernel.shape[1])), w_kernel), dtype=w_kernel.dtype) - b_kernel = np.concatenate((np.zeros((kernel_padding)), b_kernel), dtype=b_kernel.dtype) - left_padding += kernel_padding - kernel_size += kernel_padding - # write relevant scalar parameters to header file - where.header.write(f""" -#define {name.upper()}_FILTER_GAIN_A {adaconv.filter_gain_a:f}f -#define {name.upper()}_FILTER_GAIN_B {adaconv.filter_gain_b:f}f -#define {name.upper()}_LOG_GAIN_LIMIT {adaconv.log_gain_limit:f}f -#define {name.upper()}_KERNEL_SIZE {kernel_size} -#define {name.upper()}_LEFT_PADDING {left_padding} -#define {name.upper()}_FRAME_SIZE {adaconv.frame_size} -#define {name.upper()}_OVERLAP_SIZE {adaconv.overlap_size} -#define {name.upper()}_IN_CHANNELS {adaconv.in_channels} -#define {name.upper()}_OUT_CHANNELS {adaconv.out_channels} -#define {name.upper()}_NORM_P {adaconv.norm_p} -#define {name.upper()}_FEATURE_DIM {adaconv.feature_dim} -#define {name.upper()}_MAX_LAG {adaconv.max_lag} -""" - ) - - print_dense_layer(where, name + "_kernel", w_kernel, b_kernel, scale=scale, format='torch', sparse=False, diagonal=False, quantize=quantize) - print_dense_layer(where, name + "_gain", w_gain, b_gain, format='torch', sparse=False, diagonal=False, quantize=False) - print_dense_layer(where, name + "_global_gain", w_global_gain, b_global_gain, format='torch', sparse=False, diagonal=False, quantize=False) - - - else: - np.save(where, 'weight_kernel.npy', w_kernel) - np.save(where, 'bias_kernel.npy', b_kernel) - np.save(where, 'weight_gain.npy', w_gain) - np.save(where, 'bias_gain.npy', b_gain) - np.save(where, 'weight_global_gain.npy', w_global_gain) - np.save(where, 'bias_global_gain.npy', b_global_gain) - -def dump_torch_tdshaper(where, shaper, name='tdshaper', quantize=False, scale=1/128): - - if isinstance(where, CWriter): - where.header.write(f""" -#define {name.upper()}_FEATURE_DIM {shaper.feature_dim} -#define {name.upper()}_FRAME_SIZE {shaper.frame_size} -#define {name.upper()}_AVG_POOL_K {shaper.avg_pool_k} -#define {name.upper()}_INNOVATE {1 if shaper.innovate else 0} -#define {name.upper()}_POOL_AFTER {1 if shaper.pool_after else 0} -""" - ) - - dump_torch_conv1d_weights(where, shaper.feature_alpha1_f, name + "_alpha1_f", quantize=quantize, scale=scale) - dump_torch_conv1d_weights(where, shaper.feature_alpha1_t, name + "_alpha1_t") - dump_torch_conv1d_weights(where, shaper.feature_alpha2, name + "_alpha2") - - if shaper.innovate: - dump_torch_conv1d_weights(where, shaper.feature_alpha1b, name + "_alpha1b") - dump_torch_conv1d_weights(where, shaper.feature_alpha1c, name + "_alpha1c") - dump_torch_conv1d_weights(where, shaper.feature_alpha2b, name + "_alpha2b") - dump_torch_conv1d_weights(where, shaper.feature_alpha2c, name + "_alpha2c") - - - -def dump_torch_gru_weights(where, gru, name='gru', input_sparse=False, recurrent_sparse=False, quantize=False, scale=1/128, recurrent_scale=1/128): - - assert gru.num_layers == 1 - assert gru.bidirectional == False - - w_ih = gru.weight_ih_l0.detach().cpu().numpy().copy() - w_hh = gru.weight_hh_l0.detach().cpu().numpy().copy() - if hasattr(gru, 'bias_ih_l0'): - b_ih = gru.bias_ih_l0.detach().cpu().numpy().copy() - else: - b_ih = None - if hasattr(gru, 'bias_hh_l0'): - b_hh = gru.bias_hh_l0.detach().cpu().numpy().copy() - else: - b_hh = None - - if isinstance(where, CWriter): - return print_gru_layer(where, name, w_ih, w_hh, b_ih, b_hh, format='torch', input_sparse=input_sparse, recurrent_sparse=recurrent_sparse, quantize=quantize, scale=scale, recurrent_scale=recurrent_scale) - else: - os.makedirs(where, exist_ok=True) - - np.save(os.path.join(where, 'weight_ih_rzn.npy'), w_ih) - np.save(os.path.join(where, 'weight_hh_rzn.npy'), w_hh) - np.save(os.path.join(where, 'bias_ih_rzn.npy'), b_ih) - np.save(os.path.join(where, 'bias_hh_rzn.npy'), b_hh) - - -def dump_torch_grucell_weights(where, gru, name='gru', input_sparse=False, recurrent_sparse=False, quantize=False, scale=1/128, recurrent_scale=1/128): - - w_ih = gru.weight_ih.detach().cpu().numpy().copy() - w_hh = gru.weight_hh.detach().cpu().numpy().copy() - if hasattr(gru, 'bias_ih') and gru.bias_ih is not None: - b_ih = gru.bias_ih.detach().cpu().numpy().copy() - else: - b_ih = None - if hasattr(gru, 'bias_hh') and gru.bias_hh is not None: - b_hh = gru.bias_hh.detach().cpu().numpy().copy() - else: - b_hh = None - - if isinstance(where, CWriter): - return print_gru_layer(where, name, w_ih, w_hh, b_ih, b_hh, format='torch', input_sparse=input_sparse, recurrent_sparse=recurrent_sparse, quantize=quantize, scale=scale, recurrent_scale=recurrent_scale) - else: - os.makedirs(where, exist_ok=True) - - np.save(os.path.join(where, 'weight_ih_rzn.npy'), w_ih) - np.save(os.path.join(where, 'weight_hh_rzn.npy'), w_hh) - np.save(os.path.join(where, 'bias_ih_rzn.npy'), b_ih) - np.save(os.path.join(where, 'bias_hh_rzn.npy'), b_hh) - - - -def load_torch_gru_weights(where, gru): - - assert gru.num_layers == 1 - assert gru.bidirectional == False - - w_ih = np.load(os.path.join(where, 'weight_ih_rzn.npy')) - w_hh = np.load(os.path.join(where, 'weight_hh_rzn.npy')) - b_ih = np.load(os.path.join(where, 'bias_ih_rzn.npy')) - b_hh = np.load(os.path.join(where, 'bias_hh_rzn.npy')) - - with torch.no_grad(): - gru.weight_ih_l0.set_(torch.from_numpy(w_ih)) - gru.weight_hh_l0.set_(torch.from_numpy(w_hh)) - gru.bias_ih_l0.set_(torch.from_numpy(b_ih)) - gru.bias_hh_l0.set_(torch.from_numpy(b_hh)) - - -def dump_torch_dense_weights(where, dense, name='dense', scale=1/128, sparse=False, diagonal=False, quantize=False): - - w = dense.weight.detach().cpu().numpy().copy() - if dense.bias is None: - b = np.zeros(dense.out_features, dtype=w.dtype) - else: - b = dense.bias.detach().cpu().numpy().copy() - - if isinstance(where, CWriter): - return print_dense_layer(where, name, w, b, scale=scale, format='torch', sparse=sparse, diagonal=diagonal, quantize=quantize) - - else: - os.makedirs(where, exist_ok=True) - - np.save(os.path.join(where, 'weight.npy'), w) - np.save(os.path.join(where, 'bias.npy'), b) - - -def load_torch_dense_weights(where, dense): - - w = np.load(os.path.join(where, 'weight.npy')) - b = np.load(os.path.join(where, 'bias.npy')) - - with torch.no_grad(): - dense.weight.set_(torch.from_numpy(w)) - if dense.bias is not None: - dense.bias.set_(torch.from_numpy(b)) - - -def dump_torch_conv1d_weights(where, conv, name='conv', scale=1/128, quantize=False, sparse=False): - - w = conv.weight.detach().cpu().numpy().copy() - if conv.bias is None: - b = np.zeros(conv.out_channels, dtype=w.dtype) - else: - b = conv.bias.detach().cpu().numpy().copy() - - if isinstance(where, CWriter): - - return print_conv1d_layer(where, name, w, b, scale=scale, format='torch', quantize=quantize, sparse=sparse) - else: - os.makedirs(where, exist_ok=True) - - np.save(os.path.join(where, 'weight_oik.npy'), w) - - np.save(os.path.join(where, 'bias.npy'), b) - - -def load_torch_conv1d_weights(where, conv): - - with torch.no_grad(): - w = np.load(os.path.join(where, 'weight_oik.npy')) - conv.weight.set_(torch.from_numpy(w)) - if type(conv.bias) != type(None): - b = np.load(os.path.join(where, 'bias.npy')) - if conv.bias is not None: - conv.bias.set_(torch.from_numpy(b)) - - -def dump_torch_tconv1d_weights(where, conv, name='conv', scale=1/128, quantize=False, sparse=False): - - w = conv.weight.detach().cpu().numpy().copy() - if conv.bias is None: - b = np.zeros(conv.out_channels, dtype=w.dtype) - else: - b = conv.bias.detach().cpu().numpy().copy() - - if isinstance(where, CWriter): - - return print_tconv1d_layer(where, name, w, b, conv.stride[0], scale=scale, quantize=quantize, sparse=sparse) - else: - os.makedirs(where, exist_ok=True) - - np.save(os.path.join(where, 'weight_oik.npy'), w) - - np.save(os.path.join(where, 'bias.npy'), b) - - -def load_torch_tconv1d_weights(where, conv): - - with torch.no_grad(): - w = np.load(os.path.join(where, 'weight_oik.npy')) - conv.weight.set_(torch.from_numpy(w)) - if type(conv.bias) != type(None): - b = np.load(os.path.join(where, 'bias.npy')) - if conv.bias is not None: - conv.bias.set_(torch.from_numpy(b)) - - -def dump_torch_conv2d_weights(where, conv, name='conv', scale=1/128, quantize=False): - w = conv.weight.detach().cpu().permute(0, 1, 3, 2).numpy().copy() - if conv.bias is None: - b = np.zeros(conv.out_channels, dtype=w.dtype) - else: - b = conv.bias.detach().cpu().numpy().copy() - - if isinstance(where, CWriter): - return print_conv2d_layer(where, name, w, b, scale=scale, quantize=quantize) - - else: - os.makedirs(where, exist_ok=True) - - np.save(os.path.join(where, 'weight_oiwh.npy'), w) - - np.save(os.path.join(where, 'bias.npy'), b) - -def load_torch_conv2d_weights(where, conv): - with torch.no_grad(): - w = np.load(os.path.join(where, 'weight_oiwh.npy')) - conv.weight.set_(torch.from_numpy(w).permute(0, 1, 3, 2)) - if type(conv.bias) != type(None): - b = np.load(os.path.join(where, 'bias.npy')) - if conv.bias is not None: - conv.bias.set_(torch.from_numpy(b)) - - -def dump_torch_embedding_weights(where, embed, name='embed', scale=1/128, sparse=False, diagonal=False, quantize=False): - - w = embed.weight.detach().cpu().numpy().copy().transpose() - b = np.zeros(w.shape[0], dtype=w.dtype) - - if isinstance(where, CWriter): - return print_dense_layer(where, name, w, b, scale=scale, format='torch', sparse=sparse, diagonal=diagonal, quantize=quantize) - - else: - os.makedirs(where, exist_ok=True) - - np.save(os.path.join(where, 'weight.npy'), w) - np.save(os.path.join(where, 'bias.npy'), b) - - -def load_torch_embedding_weights(where, emb): - - w = np.load(os.path.join(where, 'weight.npy')) - - with torch.no_grad(): - emb.weight.set_(torch.from_numpy(w)) - -def dump_torch_weights(where, module, name=None, verbose=False, **kwargs): - """ generic function for dumping weights of some torch.nn.Module """ - if verbose and name is not None: - print(f"printing layer {name} of type {type(module)}...") - if isinstance(module, torch.nn.Linear): - return dump_torch_dense_weights(where, module, name, **kwargs) - elif isinstance(module, torch.nn.GRU): - return dump_torch_gru_weights(where, module, name, **kwargs) - elif isinstance(module, torch.nn.GRUCell): - return dump_torch_grucell_weights(where, module, name, **kwargs) - elif isinstance(module, torch.nn.Conv1d): - return dump_torch_conv1d_weights(where, module, name, **kwargs) - elif isinstance(module, torch.nn.Conv2d): - return dump_torch_conv2d_weights(where, module, name, **kwargs) - elif isinstance(module, torch.nn.Embedding): - return dump_torch_embedding_weights(where, module, name, **kwargs) - elif isinstance(module, torch.nn.ConvTranspose1d): - return dump_torch_tconv1d_weights(where, module, name, **kwargs) - else: - if has_osce: - if isinstance(module, LimitedAdaptiveConv1d): - dump_torch_adaptive_conv1d_weights(where, module, name, **kwargs) - elif isinstance(module, LimitedAdaptiveComb1d): - dump_torch_adaptive_comb1d_weights(where, module, name, **kwargs) - elif isinstance(module, TDShaper): - dump_torch_tdshaper(where, module, name, **kwargs) - else: - raise ValueError(f'dump_torch_weights: layer of type {type(module)} not supported') - else: - raise ValueError(f'dump_torch_weights: layer of type {type(module)} not supported') - -def load_torch_weights(where, module): - """ generic function for loading weights of some torch.nn.Module """ - if isinstance(module, torch.nn.Linear): - load_torch_dense_weights(where, module) - elif isinstance(module, torch.nn.GRU): - load_torch_gru_weights(where, module) - elif isinstance(module, torch.nn.Conv1d): - load_torch_conv1d_weights(where, module) - elif isinstance(module, torch.nn.Conv2d): - load_torch_conv2d_weights(where, module) - elif isinstance(module, torch.nn.Embedding): - load_torch_embedding_weights(where, module) - elif isinstance(module, torch.nn.ConvTranspose1d): - return load_torch_tconv1d_weights(where, module) - else: - raise ValueError(f'load_torch_weights: layer of type {type(module)} not supported') diff --git a/android/app/src/main/java/com/tsmobile/app/data/BridgeContract.kt b/android/app/src/main/java/com/tsmobile/app/data/BridgeContract.kt new file mode 100644 index 0000000..8b55b25 --- /dev/null +++ b/android/app/src/main/java/com/tsmobile/app/data/BridgeContract.kt @@ -0,0 +1,102 @@ +package com.tsmobile.app.data + +import android.util.Log + +/** + * Go ↔ Kotlin 跨语言 JSON 契约的 Kotlin 侧守卫。 + * + * 背景：gomobile 不能导出 []string / []*T / Go error，复杂数据全走 JSON 字符串。 + * 编译器无法校验字段名——Go 侧改字段名，Kotlin 侧不会编译失败， + * 只会在运行时静默拿到默认值（"" / 0 / false）。 + * + * 两道防线： + * 1. **本例的版本校验**：AAR 与 Kotlin 代码的契约版本不一致时明确报错， + * 而不是带着错配的字段名继续跑。 + * 2. **契约测试**：`BridgeContractTest` 用与 Go 侧共享的 golden 样本 + * （`src/test/resources/contract/` 目录下的 json 文件）断言反序列化结果， + * 字段漂移会让两边测试同时变红。 + * + * 维护：Go 侧 `go/teamspeak/contract.go` 是单一事实来源， + * 任何不兼容变更都要同步这里的 [EXPECTED_VERSION] 与 `docs/bridge-contract.md`。 + */ +object BridgeContract { + + private const val TAG = "BridgeContract" + + /** + * Kotlin 侧期望的契约版本。 + * 必须与 `go/teamspeak/contract.go` 的 `BridgeContractVersion` 一致。 + */ + const val EXPECTED_VERSION = "1" + + /** 版本校验结果。 */ + sealed class CheckResult { + /** 版本一致。 */ + object Ok : CheckResult() + + /** + * AAR 未导出 getContractVersion()。 + * 说明 AAR 是加入契约版本常量之前编译的，属于「旧但可用」， + * 不阻断连接，只提示需要重编。 + */ + object NativeMethodMissing : CheckResult() + + /** 版本不一致——字段名极可能已经漂移，必须阻断。 */ + data class Mismatch(val nativeVersion: String, val expected: String) : CheckResult() + } + + /** + * 读取 AAR 中的契约版本。 + * + * 用反射而非直接调用：AAR 可能尚未重编（`GetContractVersion` 是后加的）， + * 直接调用会在类加载期抛 NoSuchMethodError，而反射可以优雅降级。 + * + * @return 版本字符串；AAR 未导出该方法时返回 null + */ + fun readNativeVersion(): String? = try { + val clazz = Class.forName("teamspeak.Teamspeak") + clazz.getMethod("getContractVersion").invoke(null) as? String + } catch (e: Throwable) { + // 包含 ClassNotFoundException / NoSuchMethodException / InvocationTargetException + Log.i(TAG, "native getContractVersion() unavailable: ${e.javaClass.simpleName}") + null + } + + /** + * 校验 AAR 契约版本。 + * + * @param nativeVersion [readNativeVersion] 的结果 + */ + fun verify(nativeVersion: String?): CheckResult = when { + nativeVersion == null -> CheckResult.NativeMethodMissing + nativeVersion == EXPECTED_VERSION -> CheckResult.Ok + else -> CheckResult.Mismatch(nativeVersion, EXPECTED_VERSION) + } + + /** + * 校验并返回面向用户的错误信息（空串 = 通过）。 + * + * 语义与桥接约定一致：空串成功，非空为错误。 + */ + fun verifyOrDescribeError(): String = when (val result = verify(readNativeVersion())) { + is CheckResult.Ok -> { + Log.i(TAG, "Bridge contract version OK ($EXPECTED_VERSION)") + "" + } + is CheckResult.NativeMethodMissing -> { + // 旧 AAR：不阻断，但要让开发者知道该重编了 + Log.w( + TAG, + "teamspeak.aar does not export getContractVersion(); " + + "it predates contract v$EXPECTED_VERSION. Rebuild the AAR (build.bat step 1)." + ) + "" + } + is CheckResult.Mismatch -> { + val msg = "协议层与 App 版本不匹配（AAR 契约 v${result.nativeVersion}，" + + "App 需要 v${result.expected}）。请重新构建 AAR 后安装。" + Log.e(TAG, msg) + msg + } + } +} diff --git a/android/app/src/main/java/com/tsmobile/app/data/Models.kt b/android/app/src/main/java/com/tsmobile/app/data/Models.kt index 1563e4f..73a2adf 100644 --- a/android/app/src/main/java/com/tsmobile/app/data/Models.kt +++ b/android/app/src/main/java/com/tsmobile/app/data/Models.kt @@ -150,14 +150,25 @@ sealed class MessageSendState { * 连接状态机。 * * 状态转换： + * Idle → Connected：连接并同步完成 * Connected → Disconnecting：主动断开 * Connected → Reconnecting：网络异常 * Connected → Disconnected(wasKicked=true)：被踢 * Reconnecting → Connected：重连成功 * Reconnecting → Disconnected：重连失败/用户放弃 * Disconnecting → Disconnected：断开完成 + * Disconnected → Idle：返回主页 */ sealed class ConnectionState { + /** + * 空闲 — 尚未发起连接，或已返回配置页等待用户输入。 + * + * 此前用 `null` 表达这个状态（`StateFlow`）， + * 导致每个消费点都要先判空。改为显式状态后类型不再可空， + * 且「未连接」与「已断开」可以区分对待。 + */ + object Idle : ConnectionState() + /** 已连接 — 正常会话中 */ object Connected : ConnectionState() diff --git a/android/app/src/main/java/com/tsmobile/app/data/Repository.kt b/android/app/src/main/java/com/tsmobile/app/data/Repository.kt index 1311861..c68c389 100644 --- a/android/app/src/main/java/com/tsmobile/app/data/Repository.kt +++ b/android/app/src/main/java/com/tsmobile/app/data/Repository.kt @@ -1,5 +1,6 @@ package com.tsmobile.app.data +import com.tsmobile.app.BuildConfig import com.tsmobile.app.TSBridge import kotlinx.coroutines.Dispatchers import kotlinx.coroutines.flow.MutableStateFlow @@ -21,6 +22,35 @@ object Repository { private val _channels = MutableStateFlow>(emptyList()) val channels: StateFlow> = _channels.asStateFlow() + /** + * 频道列表最后一次从服务器刷新的时刻（epoch ms）。 + * + * SDK 不提供 channel create/update/delete 事件，频道列表只在 + * 「陈旧超过 5 分钟」或「切换频道前」刷新（见 CLAUDE.md Known SDK Limitations）。 + * 因此 UI 上展示的频道树可能滞后，需要把这个事实暴露给用户， + * 而不是让界面假装数据总是最新的。 + * + * 0 表示尚未成功同步过。 + */ + private val _channelsFetchedAt = MutableStateFlow(0L) + val channelsFetchedAt: StateFlow = _channelsFetchedAt.asStateFlow() + + /** 频道数据视为陈旧的时间阈值（毫秒）——与 CLAUDE.md 记录的 5 分钟刷新策略一致。 */ + const val CHANNEL_STALENESS_MS = 5 * 60 * 1000L + + /** 标记频道列表刚刷新过。 */ + fun markChannelsFetched() { + _channelsFetchedAt.value = System.currentTimeMillis() + } + + /** + * 频道数据是否已陈旧（超过 [CHANNEL_STALENESS_MS] 未刷新）。 + * 尚未同步过（时间戳为 0）不算陈旧，UI 应展示加载态而非「数据过期」。 + */ + fun isChannelDataStale(now: Long = System.currentTimeMillis()): Boolean { + return isChannelDataStale(_channelsFetchedAt.value, now) + } + // --- 客户端列表 --- private val _clients = MutableStateFlow>(emptyList()) val clients: StateFlow> = _clients.asStateFlow() @@ -85,12 +115,15 @@ object Repository { return false } - // 打印频道 parentId 关系，用于检测循环引用 - for (ch in channels) { - android.util.Log.d("Repository", "Channel: id=${ch.id}, name=${ch.name}, parentId=${ch.parentId}, order=${ch.order}") - } - for (cl in clients) { - android.util.Log.d("Repository", "Client: id=${cl.id}, nickname=${cl.nickname}, channelId=${cl.channelId}") + // 逐个打印频道/成员明细会输出完整成员列表与 UID，属于隐私数据， + // 仅在 debug 包保留（release 包中 R8 会把整个分支消除）。 + if (BuildConfig.DEBUG) { + for (ch in channels) { + android.util.Log.d("Repository", "Channel: id=${ch.id}, name=${ch.name}, parentId=${ch.parentId}, order=${ch.order}") + } + for (cl in clients) { + android.util.Log.d("Repository", "Client: id=${cl.id}, nickname=${cl.nickname}, channelId=${cl.channelId}") + } } // 原子提交 @@ -161,29 +194,37 @@ object Repository { */ fun updateChannels(channels: List) { _channels.value = channels + markChannelsFetched() } /** * 应用客户端列表并过滤僵尸会话。 * - * 重连时旧连接可能尚未超时（僵尸会话），服务器会对同 UID 的新连接自动改名（如 "name" → "name_1"）。 - * 此处按 UID 过滤：保留当前 selfClientId，移除同 UID 但不同 clid 的僵尸条目。 + * 按频道做**差分更新**而非整表替换：`notifycliententerview` 的 ChannelID 不可靠， + * 导致 client enter/leave 一律触发 clientlist 全量刷新。高频进出场景下， + * 整表替换会让所有频道的成员列表都变成新实例，进而触发整个频道树重组。 + * 差分后只有真正变化的频道会换列表实例。 */ private fun applyClients(clients: List, selfId: Int) { - val selfUid = clients.find { it.id == selfId }?.uid - val filtered = if (selfUid != null) { - clients.filter { it.id == selfId || it.uid != selfUid } - } else { - clients - } + val filtered = filterZombieSessions(clients, selfId) if (filtered.size < clients.size) { android.util.Log.i("Repository", "Filtered ${clients.size - filtered.size} zombie session(s) from client list") } _clients.value = filtered clientMap = filtered.associateBy { it.id } clientUidMap = filtered.associateBy { it.uid } - _channelClients.value = filtered.groupBy { it.channelId } - android.util.Log.d("Repository", "applyClients: ${filtered.size} clients (${clients.size} before filtering)") + + val regrouped = filtered.groupBy { it.channelId } + val diffed = diffChannelClients(_channelClients.value, regrouped) + // 内容无变化时不重新赋值，避免无意义的下游重组 + if (diffed !== _channelClients.value) { + _channelClients.value = diffed + } + + if (android.util.Log.isLoggable("Repository", android.util.Log.DEBUG)) { + val changed = diffed.count { (ch, list) -> _channelClients.value[ch] !== list } + android.util.Log.d("Repository", "applyClients: ${filtered.size} clients (${clients.size} before filtering), $changed channel(s) changed") + } } /** @@ -198,6 +239,7 @@ object Repository { _channels.value = channels _selfClientId.value = selfClientId _currentChannelId.value = currentChannelId + markChannelsFetched() applyClients(clients, selfClientId) } @@ -338,6 +380,7 @@ object Repository { _currentChannelId.value = "0" _serverInfo.value = null _channelClients.value = emptyMap() + _channelsFetchedAt.value = 0L messageArchives.clear() activeChatKey = null } @@ -356,3 +399,79 @@ object Repository { return clientMap[_selfClientId.value]?.channelId ?: "0" } } + +/** + * 频道数据陈旧判定（纯函数，显式传入时间戳以便确定性测试）。 + * + * 边界为**开区间**：恰好等于阈值时**不算**陈旧。 + * 写成纯函数而非内联，是因为用 `System.currentTimeMillis()` 做断言的测试 + * 天然不确定——两次取时间之间有耗时，边界断言会随机失败。 + * + * @param fetchedAt 上次成功刷新的时刻（0 = 尚未同步过） + * @param now 当前时刻 + */ +internal fun isChannelDataStale(fetchedAt: Long, now: Long): Boolean { + if (fetchedAt <= 0) return false // 未同步 ≠ 陈旧 + return now - fetchedAt > Repository.CHANNEL_STALENESS_MS +} + +/** + * 按频道差分更新成员分组（纯函数，独立出来便于测试）。 + * + * 对每个频道：新列表与旧列表**内容相等**时复用旧列表实例，否则采用新列表。 + * 返回的 Map 若与 [previous] 完全一致，直接返回 [previous] 本身 + * （让调用方可以用 `===` 判定「无需更新」）。 + * + * 为什么保留实例而不只依赖 `List.equals`： + * `equals` 只能保证值相等，而 Compose 的跳过判定与协程的 StateFlow 去重 + * 都更依赖稳定引用；复用实例能让「未变化的频道」真正零成本。 + * + * 边界行为（由 RepositoryPureLogicTest 覆盖）： + * - 频道消失（最后一人离开）→ 该 key 从结果中移除 + * - 频道新出现 → 加入结果 + * - 同一频道成员顺序变化 → 视为变化（顺序影响 UI 展示） + */ +internal fun diffChannelClients( + previous: Map>, + next: Map>, +): Map> { + if (previous.isEmpty()) return next + if (next.isEmpty()) return if (previous.isEmpty()) previous else emptyMap() + + var changed = previous.size != next.size + val merged = LinkedHashMap>(next.size) + + for ((channelId, newList) in next) { + val oldList = previous[channelId] + // 内容相等 → 复用旧实例，下游可跳过重组 + if (oldList != null && oldList == newList) { + merged[channelId] = oldList + } else { + merged[channelId] = newList + changed = true + } + } + + return if (changed) merged else previous +} + +/** + * 过滤僵尸会话（纯函数，独立出来便于测试）。 + * + * 重连时旧连接可能尚未超时（僵尸会话），服务器会对同 UID 的新连接自动改名 + * （如 "name" → "name_1"）。此处按 UID 过滤：保留当前 selfId 对应的条目， + * 移除同 UID 但 clid 不同的僵尸条目。 + * + * 边界行为（均由 RepositoryPureLogicTest 覆盖）： + * - 列表中没有 selfId → 无法确定自身 UID → 原样返回，不做任何过滤 + * - 自身 UID 为空串 → 不参与过滤（空 UID 不是有效的身份标识， + * 否则会把所有 UID 缺失的条目误判为僵尸而全部删除） + * - 存在多个同 UID 条目且其中一个是 selfId → 只保留 selfId 那个 + * + * @param selfId 当前自身的客户端 ID + */ +internal fun filterZombieSessions(clients: List, selfId: Int): List { + val selfUid = clients.find { it.id == selfId }?.uid + if (selfUid.isNullOrEmpty()) return clients + return clients.filter { it.id == selfId || it.uid != selfUid } +} diff --git a/android/app/src/main/java/com/tsmobile/app/ui/components/ConnectionStatusIndicator.kt b/android/app/src/main/java/com/tsmobile/app/ui/components/ConnectionStatusIndicator.kt index c62397b..1ede1cf 100644 --- a/android/app/src/main/java/com/tsmobile/app/ui/components/ConnectionStatusIndicator.kt +++ b/android/app/src/main/java/com/tsmobile/app/ui/components/ConnectionStatusIndicator.kt @@ -21,7 +21,7 @@ import com.tsmobile.app.ui.theme.semanticColors */ @Composable fun ConnectionStatusIndicator( - connectionState: ConnectionState?, + connectionState: ConnectionState, modifier: Modifier = Modifier, ) { val colors = MaterialTheme.colorScheme @@ -32,7 +32,7 @@ fun ConnectionStatusIndicator( is ConnectionState.WaitingForNetwork -> semanticColors.warning to "等待网络" is ConnectionState.Reconnecting -> semanticColors.warning to "重连中" is ConnectionState.Disconnected -> colors.error to "已断开" - null -> colors.outline to "未连接" + is ConnectionState.Idle -> colors.outline to "未连接" } Row( diff --git a/android/app/src/main/java/com/tsmobile/app/ui/components/ServerDetailCard.kt b/android/app/src/main/java/com/tsmobile/app/ui/components/ServerDetailCard.kt index 390b39b..bc431b8 100644 --- a/android/app/src/main/java/com/tsmobile/app/ui/components/ServerDetailCard.kt +++ b/android/app/src/main/java/com/tsmobile/app/ui/components/ServerDetailCard.kt @@ -49,7 +49,7 @@ fun ServerDetailCard( serverInfo: ServerInfo?, serverName: String, serverAddress: String, - connectionState: ConnectionState?, + connectionState: ConnectionState, onDisconnect: () -> Unit, onDismiss: () -> Unit, ) { @@ -179,7 +179,7 @@ private fun SectionCard( @Composable private fun ServerInfoContent( serverInfo: ServerInfo?, - connectionState: ConnectionState?, + connectionState: ConnectionState, ) { Column(verticalArrangement = Arrangement.spacedBy(UiTokens.Spacing.Small)) { DetailInfoRow("状态", when (connectionState) { @@ -188,7 +188,7 @@ private fun ServerInfoContent( is ConnectionState.WaitingForNetwork -> "等待网络恢复..." is ConnectionState.Reconnecting -> "重连中 (${connectionState.attempt}/${connectionState.maxAttempts})" is ConnectionState.Disconnected -> "已断开" - null -> "未连接" + is ConnectionState.Idle -> "未连接" }) if (serverInfo == null) { Text( diff --git a/android/app/src/main/java/com/tsmobile/app/ui/screens/ChannelListScreen.kt b/android/app/src/main/java/com/tsmobile/app/ui/screens/ChannelListScreen.kt index e256f49..ddb6d63 100644 --- a/android/app/src/main/java/com/tsmobile/app/ui/screens/ChannelListScreen.kt +++ b/android/app/src/main/java/com/tsmobile/app/ui/screens/ChannelListScreen.kt @@ -33,26 +33,37 @@ import androidx.compose.foundation.shape.RoundedCornerShape import androidx.compose.ui.platform.LocalContext import androidx.compose.ui.text.font.FontWeight import androidx.compose.ui.text.style.TextOverflow +import androidx.compose.ui.unit.Dp import androidx.compose.ui.unit.dp import com.tsmobile.app.data.ChannelInfo import com.tsmobile.app.data.ClientInfo import com.tsmobile.app.data.ConnectionState +import com.tsmobile.app.data.Repository +import kotlinx.coroutines.launch import com.tsmobile.app.ui.components.* import com.tsmobile.app.ui.theme.ThemeMode import com.tsmobile.app.ui.theme.UiTokens import com.tsmobile.app.ui.theme.semanticColors import com.tsmobile.app.viewmodel.* -// 📐 各区域布局参数（分别控制左/右边距） +// 📐 各区域布局参数（统一取自 UiTokens.Spacing，不再使用 24.5/14.5 这类半像素局部修正值—— +// 半像素在 2.75x 等非整数密度屏幕上会产生子像素对齐问题，导致缩进线发虚） // 中部：频道行两端对齐 -private val TREE_LEFT_INDENT = 24.5.dp -private val TREE_RIGHT_INDENT = 24.5.dp +private val TREE_LEFT_INDENT = UiTokens.Spacing.ExtraLarge +private val TREE_RIGHT_INDENT = UiTokens.Spacing.ExtraLarge // 中部：成员行两端对齐（独立于频道行） -private val CLIENT_LEFT_INDENT = 0.dp -private val CLIENT_RIGHT_INDENT = 24.5.dp +private val CLIENT_LEFT_INDENT = UiTokens.Spacing.None +private val CLIENT_RIGHT_INDENT = UiTokens.Spacing.ExtraLarge // 底部：当前频道栏、语音控制栏两端对齐 -private val FOOTER_LEFT_INDENT = 14.5.dp -private val FOOTER_RIGHT_INDENT = 14.5.dp +private val FOOTER_LEFT_INDENT = UiTokens.Spacing.Large +private val FOOTER_RIGHT_INDENT = UiTokens.Spacing.Large + +/** 缩进封顶层级：TeamSpeak 频道可任意深层嵌套，超过此深度后不再累加，避免窄屏内容溢出。 */ +private const val MAX_INDENT_DEPTH = 4 + +/** 按层级计算左侧缩进（封顶 [MAX_INDENT_DEPTH] 层）。 */ +private fun indentFor(depth: Int): Dp = + UiTokens.Spacing.ExtraLarge * depth.coerceIn(0, MAX_INDENT_DEPTH) /** 去除 spacer 前缀和多余的破折号：`[spacer19]--- xxx ---` → `xxx` */ private fun formatChannelName(name: String): String { @@ -270,32 +281,38 @@ fun ChannelListScreen( } // 成员操作菜单 - if (showClientMenu && selectedClient != null) { - ClientActionMenu( - client = selectedClient!!, - isSelf = selectedClient!!.id == channelViewModel.selfClientId.collectAsState().value, - onPoke = { - pokeTarget = selectedClient!!.id to selectedClient!!.nickname - showClientMenu = false - selectedClient = null - }, - onCopyNickname = { - copyToClipboard(context, selectedClient!!.nickname) - }, - onCopyUID = { - copyToClipboard(context, selectedClient!!.uid) - }, - onKickFromServer = { - serverViewModel.kickClient(selectedClient!!.id, 5, "") - }, - onKickFromChannel = { - serverViewModel.kickClient(selectedClient!!.id, 4, "") - }, - onDismiss = { - showClientMenu = false - selectedClient = null - }, - ) + // 用 ?.let 把 client 收敛为不可空局部变量：既消除全部 !!， + // 也避免 onDismiss 与回调时序交错时对已置空状态的二次解引用。 + // selfClientId 在 composable 顶部订阅，而不是在参数位置 collectAsState()。 + val selfClientId by channelViewModel.selfClientId.collectAsState() + if (showClientMenu) { + selectedClient?.let { client -> + ClientActionMenu( + client = client, + isSelf = client.id == selfClientId, + onPoke = { + pokeTarget = client.id to client.nickname + showClientMenu = false + selectedClient = null + }, + onCopyNickname = { + copyToClipboard(context, client.nickname) + }, + onCopyUID = { + copyToClipboard(context, client.uid) + }, + onKickFromServer = { + serverViewModel.kickClient(client.id, 5, "") + }, + onKickFromChannel = { + serverViewModel.kickClient(client.id, 4, "") + }, + onDismiss = { + showClientMenu = false + selectedClient = null + }, + ) + } } // Poke 弹窗（成员列表长按） @@ -315,19 +332,21 @@ fun ChannelListScreen( // Poke 弹窗（语音卡成员长按）已经在 VoiceCard 内部处理 // 频道切换确认弹窗（有密码时带输入框） - if (showSwitchDialog && pendingChannel != null) { - ChannelConfirmDialog( - channelName = pendingChannel!!.name, - isPassword = pendingChannel!!.isPassword, - onConfirm = { password -> - channelViewModel.confirmSwitchDialog(password) - }, - onDismiss = { - channelViewModel.dismissSwitchDialog() - }, - isError = switchState is ChannelSwitchState.Failed, - errorMessage = (switchState as? ChannelSwitchState.Failed)?.error ?: "密码错误，请重试", - ) + if (showSwitchDialog) { + pendingChannel?.let { channel -> + ChannelConfirmDialog( + channelName = channel.name, + isPassword = channel.isPassword, + onConfirm = { password -> + channelViewModel.confirmSwitchDialog(password) + }, + onDismiss = { + channelViewModel.dismissSwitchDialog() + }, + isError = switchState is ChannelSwitchState.Failed, + errorMessage = (switchState as? ChannelSwitchState.Failed)?.error ?: "密码错误，请重试", + ) + } } } @@ -477,17 +496,73 @@ private fun ChannelTreeContent( channelViewModel.buildChannelTree(channels, clients, expandedChannelIds) } - LazyColumn(modifier = Modifier.fillMaxSize()) { - tree.forEach { node -> - channelTreeNodeItems( - node = node, - depth = 0, - selfClientId = selfClientId, - onChannelClick = onChannelClick, - onChannelLongClick = onChannelLongClick, - onClientClick = onClientClick, + // 频道数据时效：SDK 不提供频道创建/更新/删除事件，列表可能滞后。 + // 只在确实陈旧时提示，避免正常的短暂滞后造成噪音。 + val fetchedAt by Repository.channelsFetchedAt.collectAsState() + val isStale = remember(fetchedAt) { + fetchedAt > 0 && System.currentTimeMillis() - fetchedAt > Repository.CHANNEL_STALENESS_MS + } + + Column(modifier = Modifier.fillMaxSize()) { + if (isStale) { + // refreshChannelsIfNeeded 是 suspend，且内部会先判一次陈旧， + // 因此这里在 UI 作用域启动即可——不会重复请求。 + val scope = rememberCoroutineScope() + StaleChannelBanner( + onRefresh = { scope.launch { channelViewModel.refreshChannelsIfNeeded() } }, ) } + LazyColumn(modifier = Modifier.fillMaxSize()) { + tree.forEach { node -> + channelTreeNodeItems( + node = node, + depth = 0, + selfClientId = selfClientId, + onChannelClick = onChannelClick, + onChannelLongClick = onChannelLongClick, + onClientClick = onClientClick, + ) + } + } + } +} + +/** + * 频道数据陈旧提示条。 + * + * 对应 3.2：频道树可能滞后 5 分钟（SDK 无频道变更事件）， + * 之前 UI 没有任何「数据可能不是最新」的表达。 + */ +@Composable +private fun StaleChannelBanner(onRefresh: () -> Unit) { + Surface( + modifier = Modifier.fillMaxWidth(), + color = MaterialTheme.colorScheme.secondaryContainer, + ) { + Row( + modifier = Modifier.padding( + horizontal = UiTokens.Spacing.Medium, + vertical = UiTokens.Spacing.Small, + ), + verticalAlignment = Alignment.CenterVertically, + ) { + Icon( + Icons.Default.Refresh, + contentDescription = null, + modifier = Modifier.size(UiTokens.Size.IconSmall), + tint = MaterialTheme.colorScheme.onSecondaryContainer, + ) + Spacer(Modifier.width(UiTokens.Spacing.Small)) + Text( + text = "频道数据可能已过期", + style = MaterialTheme.typography.bodySmall, + color = MaterialTheme.colorScheme.onSecondaryContainer, + modifier = Modifier.weight(1f), + ) + TextButton(onClick = onRefresh) { + Text("刷新") + } + } } } @@ -562,11 +637,11 @@ private fun ChannelRow( modifier = Modifier .fillMaxWidth() .combinedClickable(onClick = onClick, onLongClick = onLongClick) - .padding(vertical = 12.dp), + .padding(vertical = UiTokens.Spacing.Medium), verticalAlignment = Alignment.CenterVertically, ) { // 📐 左侧内容缩进：修改 TREE_LEFT_INDENT - Spacer(modifier = Modifier.width(TREE_LEFT_INDENT + (depth * 24).dp)) + Spacer(modifier = Modifier.width(TREE_LEFT_INDENT + indentFor(depth))) Icon( imageVector = Icons.Default.Folder, contentDescription = null, @@ -642,11 +717,11 @@ private fun ClientRow( modifier = Modifier .fillMaxWidth() .clickable(onClick = onClick) - .padding(vertical = 12.dp), + .padding(vertical = UiTokens.Spacing.Medium), verticalAlignment = Alignment.CenterVertically, ) { // 📐 左侧内容缩进：修改 CLIENT_LEFT_INDENT - Spacer(modifier = Modifier.width(CLIENT_LEFT_INDENT + (depth * 24).dp)) + Spacer(modifier = Modifier.width(CLIENT_LEFT_INDENT + indentFor(depth))) Spacer(modifier = Modifier.width(24.dp)) // 对齐频道行的频道图标 // 用户图标 diff --git a/android/app/src/main/java/com/tsmobile/app/viewmodel/ChannelViewModel.kt b/android/app/src/main/java/com/tsmobile/app/viewmodel/ChannelViewModel.kt index 060c59d..0f32d47 100644 --- a/android/app/src/main/java/com/tsmobile/app/viewmodel/ChannelViewModel.kt +++ b/android/app/src/main/java/com/tsmobile/app/viewmodel/ChannelViewModel.kt @@ -62,7 +62,6 @@ class ChannelViewModel(application: Application) : AndroidViewModel(application) companion object { private const val TAG = "ChannelViewModel" - private const val CHANNEL_LIST_STALE_THRESHOLD = 5 * 60 * 1000L // 5 分钟 } private val json = Json { ignoreUnknownKeys = true } @@ -71,9 +70,6 @@ class ChannelViewModel(application: Application) : AndroidViewModel(application) // 主线程赋值，桥接回调解读，需 @Volatile 保证可见性。 @Volatile var chatViewModel: ChatViewModel? = null - // 频道列表最后刷新时间 - private var lastChannelRefreshTime: Long = 0 - // --- 同步状态 --- private val _syncState = MutableStateFlow(SyncState.Unsynced) val syncState: StateFlow = _syncState.asStateFlow() @@ -217,7 +213,8 @@ class ChannelViewModel(application: Application) : AndroidViewModel(application) autoExpandChannelsWithClients() _syncState.value = SyncState.Synchronized - lastChannelRefreshTime = System.currentTimeMillis() + // performInitialSync → updateBaseline 已调用 markChannelsFetched()， + // 时间戳由 Repository 统一维护，此处不再重复记录 Log.i(TAG, "Initial sync completed: ${Repository.channels.value.size} channels, ${Repository.clients.value.size} clients, syncState=Synchronized") } else { Log.e(TAG, "Initial sync failed, setting SyncFailed state") @@ -256,23 +253,17 @@ class ChannelViewModel(application: Application) : AndroidViewModel(application) } } - /** - * 检查频道列表是否过期（超过 5 分钟未刷新）。 - * - * SDK 不提供频道创建/更新/删除事件，因此频道列表可能随时间过期。 - * 在用户执行关键操作（如切换频道）时检查并刷新。 - */ - private fun isChannelListStale(): Boolean { - val elapsed = System.currentTimeMillis() - lastChannelRefreshTime - return elapsed > CHANNEL_LIST_STALE_THRESHOLD - } - /** * 如果频道列表过期，自动刷新。 * 刷新失败时不阻塞业务，使用旧数据。 + * + * 陈旧判定统一走 [Repository.isChannelDataStale]——此前 ViewModel 与 Repository + * 各维护一个时间戳，而 `Repository.updateChannels()` 只会更新自己那个， + * 导致刷新后这里的 `lastChannelRefreshTime` 仍指向更早的时刻， + * 会出现「刚刷新完又立刻判定过期再刷一次」的重复请求。 */ suspend fun refreshChannelsIfNeeded() { - if (!isChannelListStale()) return + if (!Repository.isChannelDataStale()) return try { Log.d(TAG, "Channel list stale, refreshing...") @@ -280,8 +271,8 @@ class ChannelViewModel(application: Application) : AndroidViewModel(application) TSBridge.getChannelsJSON() } val channels = json.decodeFromString>(channelsJson) + // updateChannels 内部会调用 markChannelsFetched()，统一时间戳来源 Repository.updateChannels(channels) - lastChannelRefreshTime = System.currentTimeMillis() Log.d(TAG, "Channel list refreshed: ${channels.size} channels") } catch (e: Exception) { Log.e(TAG, "Channel refresh failed, using stale data", e) @@ -425,8 +416,15 @@ class ChannelViewModel(application: Application) : AndroidViewModel(application) ) } + // 不打散服务器返回的顺序：TS3 的 channellist 响应本身就是树序 + // （子频道紧跟父频道），groupBy 保留插入顺序，直接递归即可。 + // + // 注意：不要在这里 sortedBy { it.order }——ChannelInfo.order 在 Go 侧 + // 根本不存在（channelJSON 无该字段），恒为 0，排序是 no-op； + // 且 TS3 的 channel_order 语义是「前驱频道的 ID」而非排序权重， + // 真按它排序反而会打乱频道树。 + // 见 docs/teamspeak-sdk-3.5.2/doc/client/channel-sort.html val children = (childrenMap[channel.id] ?: emptyList()) - .sortedBy { it.order } .map { buildNode(it) } return ChannelTreeNode( @@ -437,9 +435,8 @@ class ChannelViewModel(application: Application) : AndroidViewModel(application) ) } - // 其他顶级频道（parentId == "0" 且非自引用） + // 其他顶级频道（parentId == "0" 且非自引用）。同上，保持服务器返回顺序。 val topLevel = (childrenMap["0"] ?: emptyList()) - .sortedBy { it.order } // 自引用根频道放在最前面（无子频道），然后是其他顶级频道 val selfRefNodes = selfRefRoots.map { root -> diff --git a/android/app/src/main/java/com/tsmobile/app/viewmodel/ChatViewModel.kt b/android/app/src/main/java/com/tsmobile/app/viewmodel/ChatViewModel.kt index 333bb55..2ca6ff1 100644 --- a/android/app/src/main/java/com/tsmobile/app/viewmodel/ChatViewModel.kt +++ b/android/app/src/main/java/com/tsmobile/app/viewmodel/ChatViewModel.kt @@ -4,6 +4,7 @@ import android.app.Application import android.util.Log import androidx.lifecycle.AndroidViewModel import androidx.lifecycle.viewModelScope +import com.tsmobile.app.BuildConfig import com.tsmobile.app.TSBridge import com.tsmobile.app.data.ChatMessage import com.tsmobile.app.data.MessageDeliveryState @@ -248,18 +249,24 @@ class ChatViewModel(application: Application) : AndroidViewModel(application) { val selfId = Repository.selfClientId.value val isSelf = senderId == selfId - Log.d(TAG, "handleTextMessage: mode=$targetMode, target=$targetId, sender=$senderName($senderId), " + - "isSelf=$isSelf, current=[$currentTargetMode, $currentTargetId]") - // 打印完整的消息内容以便调试文件消息结构 - if (content.trimStart().startsWith("{")) { - Log.d(TAG, "handleTextMessage JSON content: $content") + // 消息正文（含发送者昵称）属于隐私数据，仅在 debug 包打印完整内容； + // release 包由 proguard-rules.pro 剥离 Log.d，这里再加 DebugConfig 双保险， + // 避免参数表达式（JSON 解析等）在 release 下空转。 + if (BuildConfig.DEBUG) { + Log.d(TAG, "handleTextMessage: mode=$targetMode, target=$targetId, sender=$senderName($senderId), " + + "isSelf=$isSelf, current=[$currentTargetMode, $currentTargetId]") + // 打印完整的消息内容以便调试文件消息结构 + if (content.trimStart().startsWith("{")) { + Log.d(TAG, "handleTextMessage JSON content: $content") + } } // 尝试匹配 PENDING 消息（送达确认）。 // AAR 已支持 InvokerID，senderId 可靠。仍保留降级逻辑：senderId=0 时仅匹配 content。 val confirmedId = Repository.confirmMessageDelivery(targetMode, targetId, senderId, content) if (confirmedId != null) { - Log.d(TAG, "Delivery confirmed: id=$confirmedId, content=$content") + // 只打印 id，不打印 content——消息正文属于用户隐私数据 + Log.d(TAG, "Delivery confirmed: id=$confirmedId") deliveryTimeoutJobs.remove(confirmedId)?.cancel() if (targetMode == currentTargetMode && targetId == currentTargetId) { refreshMessages() diff --git a/android/app/src/main/java/com/tsmobile/app/viewmodel/ServerViewModel.kt b/android/app/src/main/java/com/tsmobile/app/viewmodel/ServerViewModel.kt index ad662ac..1f65613 100644 --- a/android/app/src/main/java/com/tsmobile/app/viewmodel/ServerViewModel.kt +++ b/android/app/src/main/java/com/tsmobile/app/viewmodel/ServerViewModel.kt @@ -3,6 +3,7 @@ package com.tsmobile.app.viewmodel import android.app.Application import androidx.lifecycle.AndroidViewModel import androidx.lifecycle.viewModelScope +import com.tsmobile.app.BuildConfig import com.tsmobile.app.ConnectionService import com.tsmobile.app.NetworkMonitor import com.tsmobile.app.TSBridge @@ -98,9 +99,10 @@ class ServerViewModel(application: Application) : AndroidViewModel(application) .stateIn(viewModelScope, SharingStarted.WhileSubscribed(5000), emptyList()) // ── 连接生命周期状态（步骤 09） ── - private val _connectionState = MutableStateFlow(null) - /** null = 未进入频道列表页，非 null = 当前连接生命周期状态 */ - val connectionState: StateFlow = _connectionState.asStateFlow() + // 不再用 null 编码「未连接」——那是业务状态，用 ConnectionState.Idle 显式表达， + // 消费点因此无需判空。 + private val _connectionState = MutableStateFlow(ConnectionState.Idle) + val connectionState: StateFlow = _connectionState.asStateFlow() private val _kickReason = MutableStateFlow("") val kickReason: StateFlow = _kickReason.asStateFlow() @@ -460,6 +462,24 @@ class ServerViewModel(application: Application) : AndroidViewModel(application) * 调用前必须已设置 connectState = CONNECTING。 */ private fun doConnect() { + // 跨语言契约版本校验：AAR 与 Kotlin 的 JSON 字段约定必须一致。 + // 不一致时字段名极可能已漂移——那种情况下连接能成功， + // 但频道名/成员昵称会静默变成默认值，排查成本远高于直接拦住。 + // + // 校验放在 doConnect（而非 connect）是因为存在多个连接入口： + // connect() 走表单，quickConnect() 走最近记录直连。 + // 放在这里才能保证**所有**路径都被拦。 + val contractError = BridgeContract.verifyOrDescribeError() + if (contractError.isNotEmpty()) { + _state.update { + it.copy( + connectState = ConnectState.FAILED, + errorMessage = contractError, + ) + } + return + } + val current = _state.value val config = ServerConfig( address = current.address.trim(), @@ -824,7 +844,7 @@ class ServerViewModel(application: Application) : AndroidViewModel(application) chatViewModel?.clearMessages() // 6. 重置状态 - _connectionState.value = null + _connectionState.value = ConnectionState.Idle _state.update { it.copy(connectState = ConnectState.IDLE) } synchronized(reconnectLock) { lastConnectParams = null } } @@ -835,7 +855,7 @@ class ServerViewModel(application: Application) : AndroidViewModel(application) fun reconnectAfterKick() { val params = synchronized(reconnectLock) { lastConnectParams } ?: return _kickReason.value = "" - _connectionState.value = null + _connectionState.value = ConnectionState.Idle // 重新填充表单并连接 _state.update { @@ -861,7 +881,7 @@ class ServerViewModel(application: Application) : AndroidViewModel(application) } networkMonitor.reset() _kickReason.value = "" - _connectionState.value = null + _connectionState.value = ConnectionState.Idle Repository.clearSession() channelViewModel?.clearChannels() _state.update { it.copy(connectState = ConnectState.IDLE) } @@ -1153,8 +1173,12 @@ class ServerViewModel(application: Application) : AndroidViewModel(application) } override fun onTextMessage(msg: TextMsg) { - android.util.Log.d(TAG, "onTextMessage: targetMode=${msg.targetMode}, targetID=${msg.targetID}, " + - "invokerName=${msg.invokerName}, invokerUID=${msg.invokerUID}, message=${msg.message}") + // 这条日志会输出发送者 UID 与完整消息内容（隐私数据）， + // 必须限定在 debug 包——release 下 R8 会把整个分支消除。 + if (BuildConfig.DEBUG) { + android.util.Log.d(TAG, "onTextMessage: targetMode=${msg.targetMode}, targetID=${msg.targetID}, " + + "invokerName=${msg.invokerName}, invokerUID=${msg.invokerUID}, message=${msg.message}") + } var targetIdLong = msg.targetID.toLongOrNull() ?: run { android.util.Log.w(TAG, "onTextMessage: invalid targetID '${msg.targetID}', dropping") return @@ -1272,18 +1296,35 @@ class ServerViewModel(application: Application) : AndroidViewModel(application) // --- 错误分类 --- - private fun classifyError(raw: String): String { - val lower = raw.lowercase() - return when { - "password" in lower || "密码" in lower -> "服务器密码错误" - "nickname" in lower || "昵称" in lower -> "昵称已被使用，请更换" - "timeout" in lower || "unreachable" in lower || "network" in lower -> - "无法连接到服务器，请检查网络" - "resolve" in lower || "dns" in lower || "lookup" in lower -> - "服务器地址无法解析" - "full" in lower || "limit" in lower -> "服务器已满" - else -> raw - } + private fun classifyError(raw: String): String = classifyServerError(raw) +} + +/** + * 把 Go 侧/Bridge 的原始错误串映射为面向用户的中文提示（纯函数，便于测试）。 + * + * 匹配顺序有意义：先匹配更具体的特征，再落到宽泛的兜底。 + * 无法识别时**原样返回**——宁可显示英文原文，也不要谎报一个错误的分类。 + */ +internal fun classifyServerError(raw: String): String { + val lower = raw.lowercase() + return when { + "password" in lower || "密码" in lower -> "服务器密码错误" + "nickname" in lower || "昵称" in lower -> "昵称已被使用，请更换" + "timeout" in lower || "unreachable" in lower || "network" in lower -> + "无法连接到服务器，请检查网络" + "resolve" in lower || "dns" in lower || "lookup" in lower -> + "服务器地址无法解析" + // id=521：同一身份（UID）的并发连接数超限。 + // 常见诱因是上次异常断开留下的僵尸会话尚未被服务器回收（TS3 默认需数分钟）， + // 并非用户操作错误——文案要给出可执行的等待建议。 + // + // 必须排在 "limit" 分支之前：错误串可能形如 "clone limit exceeded"， + // 若先匹配 limit 会被误报为"服务器已满"（那是另一种问题，处置方式完全不同）。 + // 用 "clone" 而非 "clones" 以同时覆盖单复数。 + "clone" in lower || "521" in lower -> + "同一身份连接数过多，请等待几分钟后重试" + "full" in lower || "limit" in lower -> "服务器已满" + else -> raw } } diff --git a/android/app/src/test/java/com/tsmobile/app/data/BridgeContractTest.kt b/android/app/src/test/java/com/tsmobile/app/data/BridgeContractTest.kt new file mode 100644 index 0000000..bc8ef2a --- /dev/null +++ b/android/app/src/test/java/com/tsmobile/app/data/BridgeContractTest.kt @@ -0,0 +1,171 @@ +package com.tsmobile.app.data + +import kotlinx.serialization.json.Json +import org.junit.Assert.assertEquals +import org.junit.Assert.assertFalse +import org.junit.Assert.assertNotNull +import org.junit.Assert.assertTrue +import org.junit.Test + +/** + * Kotlin 侧契约测试。 + * + * 用与 Go 侧**共用**的 golden 样本（`src/test/resources/contract/` 目录下的 json 文件）断言 + * 数据类能正确解析。Go 侧的 `contract_golden_test.go` 断言同一批样本与 struct tag + * 一致——两边合起来构成闭环：改 Go 字段名 → Go 测试红 → 若不更新样本， + * Kotlin 测试也红。不存在「改了字段名但无人发现」的静默路径。 + * + * 注意这里的 Json 配置**故意与生产一致**（`ignoreUnknownKeys = true`）， + * 因为要验证的正是这个配置下缺失字段的默认值语义。 + */ +class BridgeContractTest { + + private val json = Json { ignoreUnknownKeys = true } + + private fun readSample(name: String): String = + javaClass.classLoader!!.getResourceAsStream("contract/$name") + ?.bufferedReader()?.use { it.readText() } + ?: error("契约样本缺失: contract/$name（Go 与 Kotlin 共用，不可删除）") + + // ─── 正常路径：字段名对得上，值必须正确 ───────────────────── + + @Test + fun `channels sample deserializes with correct field names`() { + val channels = json.decodeFromString>(readSample("channels.json")) + + assertEquals(2, channels.size) + + val hall = channels[0] + assertEquals("1", hall.id) + assertEquals("大厅", hall.name) + assertEquals("0", hall.parentId) + assertEquals("", hall.description) + assertFalse(hall.isPassword) + + val locked = channels[1] + assertEquals("2", locked.id) + assertEquals("1", locked.parentId) + assertTrue("isPassword 解析失败——加密频道会被当成公开频道", locked.isPassword) + } + + @Test + fun `clients sample deserializes with correct field names`() { + val clients = json.decodeFromString>(readSample("clients.json")) + + assertEquals(3, clients.size) + + val alice = clients[0] + assertEquals(1, alice.id) + assertEquals("Alice", alice.nickname) + assertEquals("abc123==", alice.uid) + assertEquals("1", alice.channelId) + assertTrue(alice.isSelf) + + val carol = clients[2] + assertEquals(listOf("5", "6"), carol.serverGroups) + assertFalse(carol.isSelf) + } + + @Test + fun `server info sample deserializes with correct field names`() { + val info = json.decodeFromString(readSample("server_info.json")) + + assertEquals("My Server", info.name) + assertEquals("Welcome to the server!", info.welcomeMessage) + assertEquals(32, info.maxClients) + assertEquals(3, info.clientsOnline) + assertEquals(2, info.channelsOnline) + assertEquals("3.13.7", info.version) + assertEquals("Linux", info.platform) + // uptime/created 在 Go 侧是格式化后的字符串，不是数值 + assertEquals("3600", info.uptime) + assertEquals("1700000000", info.created) + } + + // ─── 失效模式：字段名漂移时会静默拿到默认值 ───────────────── + + /** + * 这是本项目最危险的失效模式的可执行说明。 + * + * Go 侧若把 `channelId` 改名为 `channelID`，Kotlin 不会编译失败， + * 只会静默解析成默认值。本测试把这个行为**显式钉住**： + * 它证明「字段对不上」不会抛异常，所以只能靠契约测试发现。 + */ + @Test + fun `field name drift silently yields defaults - documents the failure mode`() { + val drifted = """[{"id":"1","nickname":"Alice","channelID":"1","serverGroups":[],"isSelf":true}]""" + val clients = json.decodeFromString>(drifted) + + assertEquals(1, clients.size) + assertEquals("Alice", clients[0].nickname) + // channelID 对不上 channelId → 静默落到默认值 ""，成员会从频道里"消失" + assertEquals("", clients[0].channelId) + } + + /** + * 缺失字段全部回落到默认值（Go 侧字段被删除时同样不发生异常）。 + */ + @Test + fun `missing fields fall back to defaults`() { + val minimal = """[{"id":"7"}]""" + val channels = json.decodeFromString>(minimal) + + assertEquals(1, channels.size) + assertEquals("7", channels[0].id) + assertEquals("", channels[0].name) + assertEquals("0", channels[0].parentId) // 默认值 "0" 而非 ""，是根频道的约定 + assertFalse(channels[0].isPassword) + assertEquals(0, channels[0].order) + } + + /** + * 未知字段被忽略（`ignoreUnknownKeys = true`）—— + * 这正是「Go 新增字段而 Kotlin 未跟进」时不会崩的原因， + * 也是它必须靠契约文档而非异常来发现的原因。 + */ + @Test + fun `unknown fields are ignored`() { + val withExtra = """[{"id":"1","name":"x","parentId":"0","brandNewField":123}]""" + val channels = json.decodeFromString>(withExtra) + + assertEquals("x", channels[0].name) + } + + @Test + fun `empty arrays parse to empty lists - bridge failure convention`() { + // 桥接约定：失败时返回 "[]"/"{}"，消费方必须能安全处理 + assertEquals(0, json.decodeFromString>("[]").size) + assertEquals(0, json.decodeFromString>("[]").size) + + val emptyServer = json.decodeFromString("{}") + assertEquals("", emptyServer.name) + assertEquals(0, emptyServer.clientsOnline) + } + + // ─── 契约版本守卫 ─────────────────────────────────────────── + + @Test + fun `contract version check accepts matching version`() { + assertTrue(BridgeContract.verify("1") is BridgeContract.CheckResult.Ok) + } + + @Test + fun `contract version check reports mismatch`() { + val result = BridgeContract.verify("2") + assertTrue("版本不一致必须被识别，不能静默放过", result is BridgeContract.CheckResult.Mismatch) + assertEquals("2", (result as BridgeContract.CheckResult.Mismatch).nativeVersion) + } + + @Test + fun `missing native method degrades without blocking`() { + // 旧 AAR 未导出 getContractVersion：不阻断连接（返回空串 = 成功） + assertTrue(BridgeContract.verify(null) is BridgeContract.CheckResult.NativeMethodMissing) + } + + @Test + fun `mismatch produces a user-visible error string`() { + val result = BridgeContract.verify("99") as BridgeContract.CheckResult.Mismatch + assertNotNull(result.expected) + assertEquals(BridgeContract.EXPECTED_VERSION, result.expected) + } +} diff --git a/android/app/src/test/java/com/tsmobile/app/data/ChannelOrderSemanticsTest.kt b/android/app/src/test/java/com/tsmobile/app/data/ChannelOrderSemanticsTest.kt new file mode 100644 index 0000000..83711b8 --- /dev/null +++ b/android/app/src/test/java/com/tsmobile/app/data/ChannelOrderSemanticsTest.kt @@ -0,0 +1,127 @@ +package com.tsmobile.app.data + +import org.junit.Assert.assertEquals +import org.junit.Assert.assertTrue +import org.junit.Test + +/** + * 钉住 TS3 `channel_order` 的语义，防止有人"顺手"把它当成排序权重。 + * + * 背景（真实踩坑）：`ChannelInfo.order` 在 Go 侧**根本不存在** + * （`channelJSON` 没有 `order` 字段），Kotlin 侧恒为默认值 `0`。 + * 曾经的 `buildChannelTree` 里写着 `.sortedBy { it.order }`—— + * 那是个 no-op（稳定排序 + 全等值 = 保持原序），但读起来像"已按 order 排序"， + * 会诱导后来者去"修复"它。 + * + * TS3 官方语义（docs/teamspeak-sdk-3.5.2/doc/client/channel-sort.html）： + * + * > The channel order is the ID of the predecessor channel after which the + * > given channel should be sorted. An order of 0 means the channel is + * > sorted on the top of its hirarchy. + * + * 即 `channel_order` 是**前驱频道的 ID**（链表指针），不是排序权重。 + * 官方示例：`Subsubchannel_2 ( ID = 7 , order = 6 )` 意为"排在 ID=6 之后"。 + * + * 若按该值数值排序，官方示例会得到 `1,4,6,2,3,5,7` —— 正好打乱频道树。 + */ +class ChannelOrderSemanticsTest { + + private fun ch(id: String, parentId: String, name: String, order: Int) = + ChannelInfo(id = id, parentId = parentId, name = name, order = order) + + /** + * 这是本测试的核心：按 order 数值排序会破坏 TS3 的链表语义。 + * 用官方文档的示例数据固化这个反例。 + */ + @Test + fun `sorting by order corrupts the tree - official SDK example`() { + // 官方文档示例：order 是「前驱频道的 ID」 + val official = listOf( + ch("1", "0", "Channel_1", order = 0), + ch("2", "0", "Channel_2", order = 1), // 排在 ID=1 之后 + ch("4", "0", "Subchannel_1", order = 0), // 本层最前 + ch("6", "0", "Subsubchannel_1", order = 0), + ch("7", "0", "Subsubchannel_2", order = 6), // 排在 ID=6 之后 + ch("5", "0", "Subchannel_2", order = 4), // 排在 ID=4 之后 + ch("3", "0", "Channel_3", order = 2), // 排在 ID=2 之后 + ) + + val byOrder = official.sortedBy { it.order }.map { it.id } + + // 排序结果 = 1,4,6,2,3,5,7（按 order 数值升序），与官方期望顺序 1,2,4,6,7,5,3 完全不同 + assertEquals( + "按 order 数值排序的结果（也就是不该做的做法的结果）", + listOf("1", "4", "6", "2", "3", "5", "7"), + byOrder + ) + + // 官方描述的正确顺序（子频道紧跟父频道） + val correct = listOf("1", "2", "4", "6", "7", "5", "3") + assertTrue( + "按 order 排序得到的顺序必须与正确顺序不同，否则这个反例就失效了", + byOrder != correct + ) + } + + /** + * order 全为 0 时（本项目的真实情况），sortedBy 是 no-op —— 保持服务器返回顺序。 + * 这正是当前代码依赖的行为，也是它"看起来能用"的原因。 + */ + @Test + fun `all-zero order keeps server response order`() { + // 模拟真机日志里 TS3 返回的顺序（子频道紧跟父频道） + val fromServer = listOf( + ch("95", "0", "Battlefield 6", order = 0), + ch("96", "95", "Battlefield 12", order = 0), + ch("36", "0", "Minecraft", order = 0), + ch("100", "36", "MC 2", order = 0), + ) + + val afterSort = fromServer.sortedBy { it.order } + + assertEquals( + "order 全等时 sortedBy 必须保持原序（稳定排序），" + + "这就是当前代码能不靠 order 也显示正确的原因", + fromServer.map { it.id }, + afterSort.map { it.id } + ) + } + + /** + * 子频道紧跟父频道 —— channellist 的响应约定，也是 buildChannelTree 依赖的前提。 + */ + @Test + fun `subchannels immediately follow their parent in response`() { + val fromServer = listOf( + "95" to "0", "96" to "95", // 父 → 子 + "36" to "0", "100" to "36", // 父 → 子 + "16" to "0", "93" to "16", // 父 → 子 + ) + + fromServer.forEachIndexed { index, (id, parentId) -> + if (parentId != "0") { + val prev = fromServer.getOrNull(index - 1) + assertEquals( + "子频道 $id 的前一行必须是它的父频道 $parentId", + parentId, + prev?.first + ) + } + } + } + + /** + * 记录 ChannelInfo.order 的默认值语义：Go 侧不发该字段，解析结果恒为 0。 + */ + @Test + fun `order defaults to zero because Go does not emit the field`() { + val parsed = kotlinx.serialization.json.Json { ignoreUnknownKeys = true } + .decodeFromString("""{"id":"1","name":"x","parentId":"0"}""") + + assertEquals( + "Go 的 channelJSON 不含 order，解析结果必须落到默认值 0", + 0, + parsed.order + ) + } +} diff --git a/android/app/src/test/java/com/tsmobile/app/data/InputValidatorTest.kt b/android/app/src/test/java/com/tsmobile/app/data/InputValidatorTest.kt new file mode 100644 index 0000000..2f7b34a --- /dev/null +++ b/android/app/src/test/java/com/tsmobile/app/data/InputValidatorTest.kt @@ -0,0 +1,173 @@ +package com.tsmobile.app.data + +import org.junit.Assert.assertFalse +import org.junit.Assert.assertTrue +import org.junit.Test + +/** + * InputValidator 单元测试。 + * + * 纯函数、无 Android 依赖，是投入产出比最高的一类测试。 + * 重点覆盖 address:port 解析与 1..65535 端口边界—— + * 这里是用户输入的第一道关口，且边界值容易写错。 + */ +class InputValidatorTest { + + // ─── 服务器地址 ───────────────────────────────────────────── + + @Test + fun `accepts plain hostname`() { + assertTrue(InputValidator.validateServerAddress("ts.example.com").isValid) + } + + @Test + fun `accepts ipv4 with valid port`() { + assertTrue(InputValidator.validateServerAddress("192.168.1.1:9987").isValid) + } + + @Test + fun `accepts ts3 protocol prefix`() { + assertTrue(InputValidator.validateServerAddress("ts3server://ts.example.com").isValid) + assertTrue(InputValidator.validateServerAddress("ts3://ts.example.com:9987").isValid) + } + + @Test + fun `rejects empty and blank input`() { + assertFalse(InputValidator.validateServerAddress("").isValid) + assertFalse(InputValidator.validateServerAddress(" ").isValid) + } + + @Test + fun `rejects port zero and negative`() { + assertFalse("端口 0 非法", InputValidator.validateServerAddress("host:0").isValid) + assertFalse("负端口非法", InputValidator.validateServerAddress("host:-1").isValid) + } + + @Test + fun `accepts port boundary values`() { + assertTrue("端口下界 1", InputValidator.validateServerAddress("host:1").isValid) + assertTrue("端口上界 65535", InputValidator.validateServerAddress("host:65535").isValid) + } + + @Test + fun `rejects port above upper bound`() { + assertFalse("端口 65536 越界", InputValidator.validateServerAddress("host:65536").isValid) + } + + @Test + fun `rejects non-numeric port`() { + assertFalse(InputValidator.validateServerAddress("host:abc").isValid) + } + + @Test + fun `rejects address containing space`() { + assertFalse(InputValidator.validateServerAddress("host name.com").isValid) + } + + @Test + fun `rejects overlong address`() { + val tooLong = "a".repeat(257) + assertFalse(InputValidator.validateServerAddress(tooLong).isValid) + } + + @Test + fun `accepts bracketed ipv6`() { + // 多冒号地址必须用 [] 包裹，否则无法与 port 区分 + assertTrue(InputValidator.validateServerAddress("[::1]:9987").isValid) + } + + @Test + fun `rejects unbracketed ipv6`() { + assertFalse(InputValidator.validateServerAddress("::1:9987").isValid) + } + + @Test + fun `trims surrounding whitespace before validating`() { + assertTrue(InputValidator.validateServerAddress(" ts.example.com ").isValid) + } + + // ─── 昵称 ─────────────────────────────────────────────────── + + @Test + fun `accepts normal nickname`() { + assertTrue(InputValidator.validateNickname("Alice").isValid) + } + + @Test + fun `rejects empty nickname`() { + assertFalse(InputValidator.validateNickname("").isValid) + } + + @Test + fun `accepts nickname at max length 30`() { + assertTrue(InputValidator.validateNickname("a".repeat(30)).isValid) + } + + @Test + fun `rejects nickname over 30 chars`() { + assertFalse(InputValidator.validateNickname("a".repeat(31)).isValid) + } + + @Test + fun `rejects TeamSpeak illegal characters`() { + for (ch in listOf("\\", "/", "|", "\n", "\r", "\t")) { + assertFalse( + "字符 ${ch.replace("\n", "\\n").replace("\r", "\\r").replace("\t", "\\t")} 应被拒绝", + InputValidator.validateNickname("bad${ch}name").isValid + ) + } + } + + @Test + fun `nickname length is measured after trimming`() { + // 前后空格被 trim，30 个字符加空格仍应通过 + assertTrue(InputValidator.validateNickname(" " + "a".repeat(30) + " ").isValid) + } + + // ─── 频道密码 ─────────────────────────────────────────────── + + @Test + fun `channel password must be non-empty`() { + assertFalse(InputValidator.validateChannelPassword("").isValid) + } + + @Test + fun `accepts channel password at max length 100`() { + assertTrue(InputValidator.validateChannelPassword("p".repeat(100)).isValid) + } + + @Test + fun `rejects channel password over 100 chars`() { + assertFalse(InputValidator.validateChannelPassword("p".repeat(101)).isValid) + } + + // ─── 聊天消息 ─────────────────────────────────────────────── + + @Test + fun `accepts normal message`() { + assertTrue(InputValidator.validateMessage("hello").isValid) + } + + @Test + fun `rejects blank message`() { + assertFalse(InputValidator.validateMessage("").isValid) + assertFalse(InputValidator.validateMessage(" ").isValid) + } + + @Test + fun `accepts message at max length 1024`() { + assertTrue(InputValidator.validateMessage("m".repeat(1024)).isValid) + } + + @Test + fun `rejects message over 1024 chars`() { + assertFalse(InputValidator.validateMessage("m".repeat(1025)).isValid) + } + + @Test + fun `error messages are user-facing Chinese`() { + val result = InputValidator.validateServerAddress("") + assertFalse(result.isValid) + assertTrue("错误信息必须是面向用户的中文", result.errorMessage.isNotEmpty()) + } +} diff --git a/android/app/src/test/java/com/tsmobile/app/data/MessageArchiveTest.kt b/android/app/src/test/java/com/tsmobile/app/data/MessageArchiveTest.kt new file mode 100644 index 0000000..f574306 --- /dev/null +++ b/android/app/src/test/java/com/tsmobile/app/data/MessageArchiveTest.kt @@ -0,0 +1,305 @@ +package com.tsmobile.app.data + +import org.junit.After +import org.junit.Assert.assertEquals +import org.junit.Assert.assertFalse +import org.junit.Assert.assertNotNull +import org.junit.Assert.assertNull +import org.junit.Assert.assertTrue +import org.junit.Before +import org.junit.Test + +/** + * 消息归档与送达确认测试。 + * + * 覆盖 `Repository.confirmMessageDelivery` / `markPendingMessagesFailed` / + * `markMessageDeliveryFailed`——消息送达确认的匹配逻辑。 + * + * 这块是「用户以为发出去了、实际没发出去」这类问题的关键： + * - PENDING → SENT 需要匹配到服务端回显 + * - 10 秒超时 → FAILED + * - senderId=0 的降级匹配路径（旧 AAR 不带 InvokerID） + * + * Repository 是 object 单例，测试间需清理状态。 + */ +class MessageArchiveTest { + + private val modeChannel = 2 + private val modePrivate = 1 + private val channelId = 42L + + @Before + @After + fun clearArchive() { + // 单例状态在测试间必须隔离，否则顺序会影响结果 + Repository.clearSession() + } + + private fun pendingMessage( + id: String, + content: String, + senderId: Int = 7, + targetMode: Int = modeChannel, + targetId: Long = channelId, + ) = ChatMessage( + id = id, + targetMode = targetMode, + targetId = targetId, + senderId = senderId, + senderName = "Me", + content = content, + timestamp = 1_000L, + isSelf = true, + deliveryState = MessageDeliveryState.PENDING, + ) + + // ─── 送达确认：正常匹配 ───────────────────────────────────── + + @Test + fun `confirms pending message on server echo`() { + Repository.archiveMessage(pendingMessage("m1", "hello")) + + val confirmedId = Repository.confirmMessageDelivery( + modeChannel, channelId, senderId = 7, content = "hello" + ) + + assertEquals("m1", confirmedId) + assertEquals(MessageDeliveryState.SENT, Repository.getMessages(modeChannel, channelId)[0].deliveryState) + } + + @Test + fun `does not confirm message with different content`() { + Repository.archiveMessage(pendingMessage("m1", "hello")) + + val confirmedId = Repository.confirmMessageDelivery( + modeChannel, channelId, senderId = 7, content = "different" + ) + + assertNull(confirmedId) + assertEquals(MessageDeliveryState.PENDING, Repository.getMessages(modeChannel, channelId)[0].deliveryState) + } + + @Test + fun `does not confirm when senderId mismatches`() { + Repository.archiveMessage(pendingMessage("m1", "hello", senderId = 7)) + + val confirmedId = Repository.confirmMessageDelivery( + modeChannel, channelId, senderId = 999, content = "hello" + ) + + assertNull("发送者不匹配时不得确认（否则会把别人的消息记成自己的）", confirmedId) + } + + @Test + fun `confirms with senderId zero - degraded matching path`() { + // 旧 AAR 不带 InvokerID，senderId 回落到 0 → 仅按 content 匹配 + Repository.archiveMessage(pendingMessage("m1", "hello", senderId = 7)) + + val confirmedId = Repository.confirmMessageDelivery( + modeChannel, channelId, senderId = 0, content = "hello" + ) + + assertEquals("senderId=0 是旧 AAR 的降级路径，必须仍能确认", "m1", confirmedId) + } + + @Test + fun `does not confirm already sent message`() { + Repository.archiveMessage(pendingMessage("m1", "hello")) + Repository.confirmMessageDelivery(modeChannel, channelId, 7, "hello") + + val second = Repository.confirmMessageDelivery(modeChannel, channelId, 7, "hello") + + assertNull("已确认的消息不应被重复确认", second) + } + + @Test + fun `does not touch other sessions`() { + Repository.archiveMessage(pendingMessage("m1", "hello", targetId = channelId)) + + val confirmedId = Repository.confirmMessageDelivery( + modeChannel, targetId = 999L, senderId = 7, content = "hello" + ) + + assertNull("不同会话的消息不得互相确认", confirmedId) + } + + @Test + fun `confirms most recent matching message first`() { + // 相同内容连发多条：应从最新一条开始匹配 + Repository.archiveMessage(pendingMessage("m1", "same")) + Repository.archiveMessage(pendingMessage("m2", "same")) + + val confirmedId = Repository.confirmMessageDelivery(modeChannel, channelId, 7, "same") + + assertEquals("应从最新一条开始匹配", "m2", confirmedId) + } + + // ─── 超时失败 ─────────────────────────────────────────────── + + @Test + fun `marks single message failed`() { + Repository.archiveMessage(pendingMessage("m1", "hello")) + + val ok = Repository.markMessageDeliveryFailed(modeChannel, channelId, "m1") + + assertTrue(ok) + assertEquals(MessageDeliveryState.FAILED, Repository.getMessages(modeChannel, channelId)[0].deliveryState) + } + + @Test + fun `marking unknown message failed returns false`() { + assertTrue(!Repository.markMessageDeliveryFailed(modeChannel, channelId, "nonexistent")) + } + + @Test + fun `marking already sent message failed is a no-op`() { + Repository.archiveMessage(pendingMessage("m1", "hello")) + Repository.confirmMessageDelivery(modeChannel, channelId, 7, "hello") + + val ok = Repository.markMessageDeliveryFailed(modeChannel, channelId, "m1") + + assertTrue("已确认的消息不应被改回 FAILED", !ok) + assertEquals(MessageDeliveryState.SENT, Repository.getMessages(modeChannel, channelId)[0].deliveryState) + } + + @Test + fun `marks all pending messages failed on disconnect`() { + Repository.archiveMessage(pendingMessage("m1", "a")) + Repository.archiveMessage(pendingMessage("m2", "b")) + Repository.archiveMessage(pendingMessage("m3", "c").copy(isSelf = false, deliveryState = MessageDeliveryState.SENT)) + + val failedIds = Repository.markPendingMessagesFailed(modeChannel, channelId) + + assertEquals(2, failedIds.size) + assertTrue(failedIds.containsAll(listOf("m1", "m2"))) + val messages = Repository.getMessages(modeChannel, channelId) + assertEquals(MessageDeliveryState.FAILED, messages.first { it.id == "m1" }.deliveryState) + assertEquals("非自己发送的消息不应被标记", MessageDeliveryState.SENT, messages.first { it.id == "m3" }.deliveryState) + } + + @Test + fun `failing pending messages in one session leaves others intact`() { + Repository.archiveMessage(pendingMessage("m1", "a", targetId = channelId)) + Repository.archiveMessage(pendingMessage("m2", "b", targetMode = modePrivate, targetId = 99L)) + + Repository.markPendingMessagesFailed(modeChannel, channelId) + + assertEquals(MessageDeliveryState.PENDING, Repository.getMessages(modePrivate, 99L)[0].deliveryState) + } + + // ─── 归档容量与隔离 ───────────────────────────────────────── + + @Test + fun `archive keeps insertion order`() { + Repository.archiveMessage(pendingMessage("m1", "a")) + Repository.archiveMessage(pendingMessage("m2", "b")) + Repository.archiveMessage(pendingMessage("m3", "c")) + + assertEquals(listOf("m1", "m2", "m3"), Repository.getMessages(modeChannel, channelId).map { it.id }) + } + + @Test + fun `archive caps at 500 messages per session`() { + repeat(520) { i -> + Repository.archiveMessage(pendingMessage("m$i", "content$i")) + } + + val messages = Repository.getMessages(modeChannel, channelId) + assertEquals(500, messages.size) + assertEquals("应淘汰最旧的，保留最新的", "m20", messages.first().id) + assertEquals("m519", messages.last().id) + } + + @Test + fun `unknown session returns empty list`() { + assertTrue(Repository.getMessages(modeChannel, 12345L).isEmpty()) + } + + @Test + fun `clearSession wipes all archives`() { + Repository.archiveMessage(pendingMessage("m1", "a")) + assertTrue(Repository.getMessages(modeChannel, channelId).isNotEmpty()) + + Repository.clearSession() + + assertTrue(Repository.getMessages(modeChannel, channelId).isEmpty()) + } + + // ─── 会话活跃标记（决定是否推送系统通知） ─────────────────── + + @Test + fun `active chat key reflects current session`() { + Repository.setActiveChat(modeChannel, channelId) + assertNotNull(Repository.activeChatKey) + assertEquals("${modeChannel}_$channelId", Repository.activeChatKey) + + Repository.setActiveChat(null, null) + assertNull(Repository.activeChatKey) + } + + // ─── 频道数据时效（3.2） ──────────────────────────────────── + // + // 全部用纯函数 isChannelDataStale(fetchedAt, now) 做确定性断言， + // 不依赖 System.currentTimeMillis()——那样两次取时间之间的耗时会 + // 让边界断言随机失败（曾经真的 flaky 过）。 + + @Test + fun `never-synced channel data is not reported stale`() { + assertFalse( + "未同步过（时间戳 0）不等于数据陈旧，UI 应显示加载态", + isChannelDataStale(fetchedAt = 0L, now = 10_000_000L) + ) + } + + @Test + fun `freshly fetched channel data is not stale`() { + val t = 1_000_000L + assertFalse(isChannelDataStale(fetchedAt = t, now = t)) + } + + @Test + fun `channel data past staleness threshold is stale`() { + val t = 1_000_000L + assertTrue( + "超过 5 分钟未刷新应判定为陈旧", + isChannelDataStale(fetchedAt = t, now = t + Repository.CHANNEL_STALENESS_MS + 1) + ) + } + + @Test + fun `channel data exactly at threshold is not yet stale`() { + val t = 1_000_000L + assertFalse( + "恰好等于阈值时不应判定为陈旧（边界为开区间）", + isChannelDataStale(fetchedAt = t, now = t + Repository.CHANNEL_STALENESS_MS) + ) + } + + @Test + fun `one millisecond before threshold is not stale`() { + val t = 1_000_000L + assertFalse(isChannelDataStale(fetchedAt = t, now = t + Repository.CHANNEL_STALENESS_MS - 1)) + } + + @Test + fun `Repository delegates to the pure predicate with its stored timestamp`() { + // 集成检查：markChannelsFetched 写入的时间戳会被 isChannelDataStale() 读到 + Repository.markChannelsFetched() + assertFalse("刚标记过就不该是陈旧", Repository.isChannelDataStale()) + + Repository.clearSession() + assertFalse( + "会话清理后应回到「未同步」而非「陈旧」", + Repository.isChannelDataStale(now = System.currentTimeMillis() + 10_000_000L) + ) + } + + @Test + fun `staleness threshold matches documented 5 minutes`() { + assertEquals( + "阈值必须与 CLAUDE.md 记录的 5 分钟刷新策略一致", + 5 * 60 * 1000L, + Repository.CHANNEL_STALENESS_MS + ) + } +} diff --git a/android/app/src/test/java/com/tsmobile/app/data/RepositoryPureLogicTest.kt b/android/app/src/test/java/com/tsmobile/app/data/RepositoryPureLogicTest.kt new file mode 100644 index 0000000..9de310e --- /dev/null +++ b/android/app/src/test/java/com/tsmobile/app/data/RepositoryPureLogicTest.kt @@ -0,0 +1,264 @@ +package com.tsmobile.app.data + +import org.junit.Assert.assertEquals +import org.junit.Assert.assertTrue +import org.junit.Test + +/** + * Repository 纯逻辑测试。 + * + * 覆盖 `filterZombieSessions`——重连正确性的关键路径。 + * 该逻辑原先内联在 `Repository.applyClients` 中无法测试（Repository 是 object 单例， + * 直接依赖 TSBridge），已提取为 internal 纯函数。 + * + * 为什么这块必须测：重连时若僵尸会话过滤出错， + * 用户会在成员列表里看到两个自己，或自己从列表里消失。 + */ +class RepositoryPureLogicTest { + + private fun client( + id: Int, + uid: String, + channelId: String = "1", + nickname: String = "user$id", + ) = ClientInfo(id = id, nickname = nickname, uid = uid, channelId = channelId) + + // ─── 正常场景 ─────────────────────────────────────────────── + + @Test + fun `no zombies - list passes through unchanged`() { + val clients = listOf( + client(1, "uid-self"), + client(2, "uid-bob"), + client(3, "uid-carol"), + ) + + val result = filterZombieSessions(clients, selfId = 1) + + assertEquals(clients, result) + } + + @Test + fun `drops zombie with same uid but different clid`() { + // 重连后服务器保留了旧会话：同 UID，clid 不同，昵称被自动改成 name_1 + val clients = listOf( + client(1, "uid-self", nickname = "Me"), + client(99, "uid-self", nickname = "Me_1"), + client(2, "uid-bob"), + ) + + val result = filterZombieSessions(clients, selfId = 1) + + assertEquals(2, result.size) + assertTrue("必须保留 selfId 对应的条目", result.any { it.id == 1 }) + assertTrue("同 UID 的僵尸条目必须被移除", result.none { it.id == 99 }) + assertTrue(result.any { it.id == 2 }) + } + + @Test + fun `keeps multiple zombies removed when self appears later in list`() { + val clients = listOf( + client(98, "uid-self", nickname = "Me_2"), + client(99, "uid-self", nickname = "Me_1"), + client(1, "uid-self", nickname = "Me"), + ) + + val result = filterZombieSessions(clients, selfId = 1) + + assertEquals(1, result.size) + assertEquals(1, result[0].id) + } + + // ─── 边界：无法确定自身身份时必须放弃过滤 ─────────────────── + + @Test + fun `self not in list - no filtering at all`() { + // 自身尚未出现在列表中 → 无法确定 selfUid → 不能误删任何条目 + val clients = listOf( + client(2, "uid-bob"), + client(3, "uid-carol"), + ) + + val result = filterZombieSessions(clients, selfId = 1) + + assertEquals(clients, result) + } + + @Test + fun `self with blank uid - no filtering`() { + // 空 UID 不是有效身份标识。若参与过滤，会把所有 UID 缺失的条目全部删掉。 + val clients = listOf( + client(1, ""), + client(2, ""), + client(3, ""), + ) + + val result = filterZombieSessions(clients, selfId = 1) + + assertEquals("空 UID 不得触发僵尸过滤，否则整个成员列表会被清空", 3, result.size) + } + + @Test + fun `empty list returns empty`() { + assertEquals(emptyList(), filterZombieSessions(emptyList(), selfId = 1)) + } + + @Test + fun `selfId zero - no false positives`() { + // selfId=0 表示尚未同步到自身 ID + val clients = listOf( + client(2, "uid-bob"), + client(3, "uid-carol"), + ) + + val result = filterZombieSessions(clients, selfId = 0) + + assertEquals(2, result.size) + } + + // ─── 保持顺序与内容完整 ───────────────────────────────────── + + @Test + fun `preserves original order after filtering`() { + val clients = listOf( + client(1, "uid-self"), + client(2, "uid-bob"), + client(99, "uid-self"), + client(3, "uid-carol"), + ) + + val result = filterZombieSessions(clients, selfId = 1) + + assertEquals(listOf(1, 2, 3), result.map { it.id }) + } + + @Test + fun `does not mutate the input list`() { + val clients = listOf(client(1, "uid-self"), client(99, "uid-self")) + val snapshot = clients.toList() + + filterZombieSessions(clients, selfId = 1) + + assertEquals("输入列表被修改——调用方可能持有同一引用", snapshot, clients) + } +} + +/** + * `diffChannelClients` 差分更新测试。 + * + * 这是 3.2 的核心：`notifycliententerview` 的 ChannelID 不可靠导致 + * client enter/leave 一律全量刷新，整表替换会让整个频道树重组。 + * 差分的目标是「只有真正变化的频道换实例」。 + */ +class DiffChannelClientsTest { + + private fun client(id: Int, channelId: String) = + ClientInfo(id = id, nickname = "user$id", uid = "uid$id", channelId = channelId) + + @Test + fun `identical content reuses previous instances`() { + val a = listOf(client(1, "1"), client(2, "1")) + val b = listOf(client(3, "2")) + val previous = mapOf("1" to a, "2" to b) + + // 内容等价但对象是新建的 + val next = mapOf( + "1" to listOf(client(1, "1"), client(2, "1")), + "2" to listOf(client(3, "2")), + ) + + val result = diffChannelClients(previous, next) + + assertTrue("无变化时必须返回原 Map 实例", result === previous) + assertTrue("未变化的频道必须复用旧列表实例", result["1"] === a) + assertTrue(result["2"] === b) + } + + @Test + fun `changed channel gets new list, unchanged keeps old instance`() { + val unchanged = listOf(client(1, "1")) + val previous = mapOf("1" to unchanged, "2" to listOf(client(2, "2"))) + + val next = mapOf( + "1" to unchanged, // 同一个实例，未变化 + "2" to listOf(client(2, "2"), client(3, "2")), // 新增成员 + ) + + val result = diffChannelClients(previous, next) + + assertTrue("未变化的频道应复用旧实例", result["1"] === unchanged) + assertEquals(2, result["2"]?.size) + assertTrue("有变化时必须返回新 Map", result !== previous) + } + + @Test + fun `member leaving removes empty channel`() { + val previous = mapOf("1" to listOf(client(1, "1")), "2" to listOf(client(2, "2"))) + val next = mapOf("1" to listOf(client(1, "1"))) + + val result = diffChannelClients(previous, next) + + assertEquals(1, result.size) + assertTrue("成员全部离开的频道应从结果中移除", !result.containsKey("2")) + } + + @Test + fun `new channel is added`() { + val previous = mapOf("1" to listOf(client(1, "1"))) + val next = mapOf( + "1" to listOf(client(1, "1")), + "9" to listOf(client(9, "9")), + ) + + val result = diffChannelClients(previous, next) + + assertEquals(2, result.size) + assertEquals(1, result["9"]?.size) + } + + @Test + fun `member order change counts as change`() { + val previous = mapOf("1" to listOf(client(1, "1"), client(2, "1"))) + val next = mapOf("1" to listOf(client(2, "1"), client(1, "1"))) + + val result = diffChannelClients(previous, next) + + assertTrue("顺序影响 UI 展示，必须视为变化", result !== previous) + assertEquals(listOf(2, 1), result["1"]?.map { it.id }) + } + + @Test + fun `member moving between channels updates both`() { + val previous = mapOf( + "1" to listOf(client(1, "1"), client(2, "1")), + "2" to emptyList(), + ) + val next = mapOf( + "1" to listOf(client(1, "1")), + "2" to listOf(client(2, "2")), + ) + + val result = diffChannelClients(previous, next) + + assertEquals(1, result["1"]?.size) + assertEquals(2, result["2"]?.first()?.id) + } + + @Test + fun `empty previous returns next directly`() { + val next = mapOf("1" to listOf(client(1, "1"))) + assertTrue(diffChannelClients(emptyMap(), next) === next) + } + + @Test + fun `empty next yields empty map`() { + val previous = mapOf("1" to listOf(client(1, "1"))) + assertTrue(diffChannelClients(previous, emptyMap()).isEmpty()) + } + + @Test + fun `both empty is a no-op`() { + val previous = emptyMap>() + assertTrue(diffChannelClients(previous, emptyMap()) === previous) + } +} diff --git a/android/app/src/test/java/com/tsmobile/app/viewmodel/ClassifyServerErrorTest.kt b/android/app/src/test/java/com/tsmobile/app/viewmodel/ClassifyServerErrorTest.kt new file mode 100644 index 0000000..510c8ad --- /dev/null +++ b/android/app/src/test/java/com/tsmobile/app/viewmodel/ClassifyServerErrorTest.kt @@ -0,0 +1,140 @@ +package com.tsmobile.app.viewmodel + +import org.junit.Assert.assertEquals +import org.junit.Assert.assertNotEquals +import org.junit.Assert.assertTrue +import org.junit.Test + +/** + * `classifyServerError` 单元测试。 + * + * 覆盖「Go 侧原始错误串 → 面向用户的中文提示」的映射。 + * 这段逻辑原先内联在 `ServerViewModel` 的 private 方法里、零测试覆盖， + * 已提取为 file-scope 纯函数。 + * + * 用例中的原始串**全部来自实测**（真机 logcat / ServerQuery 响应）， + * 不是编造的样例——保证测试描述的是真实会遇到的输入。 + */ +class ClassifyServerErrorTest { + + // ─── 实测来源的原始错误串 ─────────────────────────────────── + + @Test + fun `id 521 too many clones maps to wait guidance`() { + // 实测：连续探测导致同 UID 并发连接超限 + // Go 侧原文：TeamSpeak server error: too many clones already connected (id=521) + val raw = "TeamSpeak server error: too many clones already connected (id=521)" + val result = classifyServerError(raw) + + assertEquals("同一身份连接数过多，请等待几分钟后重试", result) + assertTrue("不得把英文原文透给用户", !result.contains("clone", ignoreCase = true)) + } + + @Test + fun `id 521 detected by code alone`() { + // 即使文案变化，错误码 521 也应能识别 + assertEquals( + "同一身份连接数过多，请等待几分钟后重试", + classifyServerError("server error id=521") + ) + } + + @Test + fun `invalid server password maps correctly`() { + // 实测：密码错误时服务器返回 + assertEquals( + "服务器密码错误", + classifyServerError("TeamSpeak server error: invalid server password (id=1028)") + ) + } + + @Test + fun `insufficient client permissions falls through unchanged`() { + // 实测：普通用户身份调用 clientlist 时返回 + // 这条没有专用文案，应原样返回（宁可显示原文，不要谎报分类） + val raw = "TeamSpeak server error: insufficient client permissions (id=2568)" + assertEquals("未识别的错误必须原样返回，不得误分类", raw, classifyServerError(raw)) + } + + // ─── 各类映射 ─────────────────────────────────────────────── + + @Test + fun `timeout and network variants map to connectivity message`() { + val expected = "无法连接到服务器，请检查网络" + assertEquals(expected, classifyServerError("connection timeout")) + assertEquals(expected, classifyServerError("host unreachable")) + assertEquals(expected, classifyServerError("network is down")) + } + + @Test + fun `dns failures map to address message`() { + val expected = "服务器地址无法解析" + assertEquals(expected, classifyServerError("dns lookup failed")) + assertEquals(expected, classifyServerError("could not resolve host")) + assertEquals(expected, classifyServerError("lookup ts.example.com: no such host")) + } + + @Test + fun `nickname errors map correctly`() { + assertEquals("昵称已被使用，请更换", classifyServerError("nickname already in use")) + assertEquals("昵称已被使用，请更换", classifyServerError("昵称无效")) + } + + @Test + fun `server full maps correctly`() { + assertEquals("服务器已满", classifyServerError("server is full")) + assertEquals("服务器已满", classifyServerError("max clients limit reached")) + } + + // ─── 匹配顺序与边界 ───────────────────────────────────────── + + @Test + fun `clones takes precedence over generic limit matching`() { + // "too many clones" 不以 limit 结尾，但若将来文案变化出现 + // "clone limit"，也必须仍归类为 521 而非"服务器已满" + assertEquals( + "同一身份连接数过多，请等待几分钟后重试", + classifyServerError("clone limit exceeded") + ) + } + + @Test + fun `password takes precedence when message contains multiple keywords`() { + // 复合错误串：密码相关应优先，因为它是用户可直接修正的问题 + val raw = "invalid server password (network timeout while resolving)" + assertEquals("服务器密码错误", classifyServerError(raw)) + } + + @Test + fun `unknown error returns raw text unchanged`() { + val raw = "some completely unknown bridge failure xyz" + assertEquals(raw, classifyServerError(raw)) + } + + @Test + fun `empty input returns empty`() { + assertEquals("", classifyServerError("")) + } + + @Test + fun `matching is case insensitive`() { + assertEquals("服务器密码错误", classifyServerError("INVALID SERVER PASSWORD")) + assertEquals("服务器已满", classifyServerError("SERVER IS FULL")) + } + + @Test + fun `chinese errors from bridge are recognised`() { + // 桥接约定：Kotlin 侧的错误串可能直接是中文 + assertEquals("服务器密码错误", classifyServerError("密码错误")) + assertEquals("昵称已被使用，请更换", classifyServerError("昵称已被占用")) + } + + @Test + fun `classified result is always non-empty for non-empty input`() { + // 保证 UI 不会显示空白错误 + val inputs = listOf("timeout", "dns", "password", "full", "unknown xyz") + for (input in inputs) { + assertNotEquals("输入 $input 不得映射为空串", "", classifyServerError(input)) + } + } +} diff --git a/android/app/src/test/resources/contract/channels.json b/android/app/src/test/resources/contract/channels.json new file mode 100644 index 0000000..3a4fda4 --- /dev/null +++ b/android/app/src/test/resources/contract/channels.json @@ -0,0 +1,16 @@ +[ + { + "id": "1", + "name": "大厅", + "parentId": "0", + "description": "", + "isPassword": false + }, + { + "id": "2", + "name": "加密频道", + "parentId": "1", + "description": "", + "isPassword": true + } +] diff --git a/android/app/src/test/resources/contract/clients.json b/android/app/src/test/resources/contract/clients.json new file mode 100644 index 0000000..57f1f97 --- /dev/null +++ b/android/app/src/test/resources/contract/clients.json @@ -0,0 +1,26 @@ +[ + { + "id": 1, + "nickname": "Alice", + "uid": "abc123==", + "channelId": "1", + "serverGroups": ["6"], + "isSelf": true + }, + { + "id": 2, + "nickname": "Bob", + "uid": "def456==", + "channelId": "2", + "serverGroups": [], + "isSelf": false + }, + { + "id": 3, + "nickname": "Carol", + "uid": "ghi789==", + "channelId": "0", + "serverGroups": ["5", "6"], + "isSelf": false + } +] diff --git a/android/app/src/test/resources/contract/server_info.json b/android/app/src/test/resources/contract/server_info.json new file mode 100644 index 0000000..a9f725e --- /dev/null +++ b/android/app/src/test/resources/contract/server_info.json @@ -0,0 +1,14 @@ +{ + "name": "My Server", + "welcomeMessage": "Welcome to the server!", + "maxClients": 32, + "clientsOnline": 3, + "channelsOnline": 2, + "uptime": "3600", + "version": "3.13.7", + "platform": "Linux", + "created": "1700000000", + "iconId": "0", + "defaultServerGroup": 6, + "defaultChannelGroup": 5 +} diff --git a/docs/TS Mobile UI UX 重构设计规范.md b/docs/TS Mobile UI UX 重构设计规范.md new file mode 100644 index 0000000..a76c647 --- /dev/null +++ b/docs/TS Mobile UI UX 重构设计规范.md @@ -0,0 +1,1605 @@ +# TS Mobile UI / UX 重构设计规范 + +## 1. 文档目标 + +本文档用于指导 TS Mobile Android 客户端下一阶段 UI / UX 重构。 + +重构目标不是简单地： + +- 修改颜色 +- 增加圆角 +- 增加动画 +- 更换 Material 组件 +- 美化现有页面 + +而是重新建立一套符合 TeamSpeak 使用模型的移动端信息架构。 + +核心目标： + +> 让用户始终清楚自己连接到了哪个服务器、当前处于哪个频道、频道里有哪些成员，以及当前语音和聊天状态。 + +------ + +# 2. 核心产品模型 + +TeamSpeak 与普通聊天软件最大的区别之一，是： + +> 用户连接服务器后，会直接处于某个频道上下文中。 + +因此 TS Mobile 不应该建立： + +```text +服务器 + ↓ +进入频道 +``` + +这样的用户流程。 + +正确模型： + +```text +未连接 + ↓ +连接服务器 + ↓ +已连接 + 已进入频道 + ↓ +服务器工作空间 +``` + +连接成功后： + +```text +Server +└── Current Channel + ├── Chat + ├── Members + └── Voice +``` + +因此： + +**Current Channel 是整个 App 最重要的上下文状态。** + +------ + +# 3. 顶层状态模型 + +建议 UI 层只暴露三个主要状态： + +```text +Disconnected + ↓ +Connecting + ↓ +Connected +``` + +其中： + +### Disconnected + +没有连接服务器。 + +显示： + +- Logo +- Server Address +- Nickname +- Password +- Recent Servers +- Connect + +### Connecting + +连接服务器过程中。 + +这是连接流程中的 Loading 状态，而不是独立产品页面。 + +可以显示： + +```text +Connecting... + +server.example.com:9987 +``` + +### Connected + +连接成功后直接进入当前频道。 + +```text +Server +Current Channel +Chat / Channels +Voice +Members +``` + +不应该存在： + +```text +Connected but no channel +``` + +这样的普通 UI 状态。 + +------ + +# 4. 产品信息架构 + +最终信息架构： + +```text +TS Mobile +│ +├── Connection +│ ├── Connect +│ ├── Connecting +│ └── Disconnected +│ +└── Server Space + │ + ├── Server Header + │ + ├── Current Channel + │ + ├── Chat + │ + ├── Channels + │ ├── Channel Tree + │ └── Channel Members + │ + ├── Voice + │ + └── Server Drawer + ├── Server Info + ├── Voice Settings + ├── Notifications + ├── Appearance + └── Disconnect +``` + +------ + +# 5. Server Space + +连接成功后，整个 App 进入一个统一的： + +> Server Space + +而不是分别进入： + +```text +ChannelScreen +ChatScreen +VoiceScreen +``` + +这些功能都属于当前 Server Space。 + +推荐结构： + +```text +┌────────────────────────────┐ +│ Server Header │ +├────────────────────────────┤ +│ │ +│ │ +│ Current Content │ +│ │ +│ │ +├────────────────────────────┤ +│ Current Channel │ +├────────────────────────────┤ +│ Chat Channels │ +└────────────────────────────┘ +``` + +------ + +# 6. Server Header + +Server Header 负责展示服务器上下文。 + +推荐： + +```text +┌────────────────────────────┐ +│ ☰ My Server ⋮ │ +│ ● Connected · 12 online│ +└────────────────────────────┘ +``` + +包含： + +- Server Name +- Connection Status +- Online Count +- Navigation / Drawer +- More + +不建议长期显示： + +```text +server.example.com:9987 +``` + +服务器地址属于 Server Detail。 + +------ + +# 7. Current Channel Context Bar + +这是本次重构最重要的 UI 组件之一。 + +它不是普通 Card。 + +它是： + +> 当前通信上下文。 + +推荐： + +```text +┌────────────────────────────┐ +│ # General 12 │ +│ ● ● ● ● +8 │ +└────────────────────────────┘ +``` + +显示： + +- Channel Name +- Channel Type +- Member Count +- 少量成员头像 +- 当前语音状态 + +例如： + +```text +# General +12 members +● ● ● ● +8 +``` + +用户切换频道后： + +```text +# General +``` + +直接变成： + +```text +# Gaming +``` + +同时： + +- Chat 改变 +- Members 改变 +- Voice Context 改变 + +------ + +# 8. Chat / Channels 导航 + +推荐保留底部双 Tab： + +```text +┌────────────────────────────┐ +│ │ +│ Main Content │ +│ │ +├────────────────────────────┤ +│ # General 12 │ +├────────────────────────────┤ +│ Chat Channels │ +└────────────────────────────┘ +``` + +但需要明确： + +> Chat / Channels 是视图，不是产品实体。 + +真正的实体关系： + +```text +Server +└── Current Channel + ├── Chat + └── Members / Channel Tree +``` + +------ + +# 9. Chat + +Chat 是当前频道的文字消息流。 + +推荐布局： + +```text +┌────────────────────────────┐ +│ # General 12 │ +├────────────────────────────┤ +│ │ +│ Alex │ +│ Hello │ +│ │ +│ Tom │ +│ Anyone playing? │ +│ │ +│ Alex │ +│ Let's go │ +│ │ +├────────────────────────────┤ +│ + Message... ↑ │ +└────────────────────────────┘ +``` + +------ + +# 10. Chat 消息设计 + +不建议过度模仿： + +- WhatsApp +- Telegram +- Discord + +TeamSpeak 更适合： + +> 紧凑型实时频道消息流。 + +连续消息建议合并： + +```text +Alex +Hello +Anyone playing? +Let's go +``` + +而不是： + +```text +Alex Hello +Alex Anyone playing? +Alex Let's go +``` + +这样可以明显减少垂直空间消耗。 + +------ + +# 11. Chat 输入框 + +推荐： + +```text +┌────────────────────────────┐ +│ ＋ Message... ↑ │ +└────────────────────────────┘ +``` + +输入区域保持较大的触控高度。 + +不要把： + +- Emoji +- 文件 +- 图片 +- 语音 +- 更多 +- 格式化 + +全部放到一级 UI。 + +TeamSpeak 的文字聊天不是核心功能。 + +------ + +# 12. Channels + +Channels 页面负责： + +> 浏览服务器结构 + 查看成员。 + +推荐： + +```text +CHANNELS + +▼ Lobby 5 + ● Alice + ● Bob + +▼ General 12 + ● Tom + ● Alex + ● Mike + +▶ Gaming 8 + +▶ Music 3 + +▶ AFK 2 +``` + +频道节点应该保持紧凑。 + +------ + +# 13. Channel Item + +频道 Item 推荐包含： + +```text +[展开] [频道类型] [名称] [人数] +``` + +例如： + +```text +▼ 🎙 General 12 +``` + +避免在频道 Item 内塞入过多状态。 + +详细信息通过： + +> Channel Detail + +查看。 + +------ + +# 14. Current Channel 与 Channel Tree 的关系 + +频道树中的当前频道应该具有明显但克制的选中状态。 + +例如： + +```text +▶ Lobby + +▼ General 12 + ● Alex + ● Tom + +▶ Gaming 8 +``` + +当前频道： + +```text +General +``` + +可以使用： + +- Surface 提升 +- Primary 色边 +- Icon 高亮 + +但不要使用非常强烈的纯色块。 + +------ + +# 15. Member Item + +成员列表推荐： + +```text +[Avatar] Alex + ● Online +``` + +如果正在说话： + +```text +[Avatar] Alex + ● Speaking +``` + +但正常状态不要一直显示文字。 + +推荐使用： + +```text +Avatar ++ +Status Indicator ++ +Nickname +``` + +------ + +# 16. Member 状态系统 + +至少定义： + +```text +Online +Idle +Away +Muted +Deafened +Speaking +Server Muted +Disconnected +``` + +其中： + +> Speaking + +是最重要的实时状态。 + +可以通过： + +- Avatar Ring +- 状态点 +- 小型音量指示器 + +表达。 + +避免大面积动态动画。 + +------ + +# 17. Member Action + +成员操作统一进入 Action Menu。 + +推荐： + +```text +Alex + +Interaction + Poke + +Information + Copy Nickname + Copy UID + +Management + Move + Kick from Channel + Kick from Server +``` + +危险操作单独处理。 + +例如： + +```text +Kick from Server +``` + +需要二次确认。 + +------ + +# 18. Voice + +Voice 不应该成为独立一级页面。 + +它属于： + +```text +Current Channel +└── Voice +``` + +因此： + +```text +Current Channel = General +``` + +意味着： + +```text +Voice Context = General +``` + +切换： + +```text +General → Gaming +``` + +Voice Context 同时切换。 + +------ + +# 19. Voice UI + +推荐将语音设计成一个状态组件。 + +### Connected + +```text +┌────────────────────────────┐ +│ 🎙 General 12 │ +│ ● Connected │ +│ │ +│ [ PTT ] │ +│ │ +│ Mic Speaker│ +└────────────────────────────┘ +``` + +### Speaking + +```text +┌────────────────────────────┐ +│ 🎙 General 12 │ +│ │ +│ [ SPEAKING ] │ +│ │ +└────────────────────────────┘ +``` + +PTT 是语音 UI 的核心操作。 + +------ + +# 20. PTT 状态 + +PTT 至少存在： + +```text +Idle + ↓ +Pressed + ↓ +Speaking +``` + +反馈必须： + +- 快 +- 明确 +- 稳定 +- 低干扰 + +不建议加入复杂粒子效果或大面积动画。 + +------ + +# 21. Server Drawer + +Server Drawer 的职责： + +> 管理当前服务器。 + +不是简单的服务器详情卡。 + +推荐： + +```text +┌────────────────────────────┐ +│ Server │ +│ │ +│ My Server │ +│ ● Connected │ +│ │ +├────────────────────────────┤ +│ Server Info │ +│ server.example.com:9987 │ +│ 12 / 64 online │ +├────────────────────────────┤ +│ Voice │ +│ Notifications │ +│ Appearance │ +│ Settings │ +├────────────────────────────┤ +│ │ +│ Disconnect │ +└────────────────────────────┘ +``` + +------ + +# 22. Server Detail + +Server Detail 可以展示： + +```text +Server Name +Address +Port +Version +Online Users +Max Users +Connection Quality +``` + +但这些信息不应该占用主界面。 + +------ + +# 23. Connection Screen + +连接页是唯一真正意义上的“未连接 UI”。 + +推荐： + +```text +TS Mobile + +Connect to your TeamSpeak server + +Server address +┌────────────────────────────┐ +│ server.example.com │ +└────────────────────────────┘ + +Port +┌────────────────────────────┐ +│ 9987 │ +└────────────────────────────┘ + +Nickname +┌────────────────────────────┐ +│ Player │ +└────────────────────────────┘ + + [ Connect ] + +Recent Servers +``` + +------ + +# 24. Recent Servers + +如果存在历史连接： + +```text +Recent Servers + +┌────────────────────────────┐ +│ My Server │ +│ server.example.com:9987 │ +│ › │ +└────────────────────────────┘ + +┌────────────────────────────┐ +│ Community Server │ +│ ts.example.net:9987 │ +│ › │ +└────────────────────────────┘ +``` + +最近连接应该成为高频入口。 + +首次使用才重点展示完整 Connect Form。 + +------ + +# 25. Connecting + +Connecting 不建议做成长期存在的独立页面。 + +推荐： + +```text +TS Mobile + + ◯ + +Connecting... + +server.example.com:9987 + + Cancel +``` + +连接完成： + +```text +直接进入 Server Space +``` + +而不是： + +```text +Connecting +→ Connected +→ Select Channel +``` + +------ + +# 26. Disconnect + +Disconnect 后： + +```text +Server Space + ↓ +Disconnected + ↓ +Connection Screen +``` + +重新连接： + +```text +Connect + ↓ +Connecting + ↓ +Current Channel +``` + +------ + +# 27. Channel Switch + +用户点击频道： + +```text +General +``` + +切换： + +```text +Gaming +``` + +本质上只是： + +```text +currentChannelId +``` + +发生变化。 + +UI 同步更新： + +```text +Current Channel +Chat +Members +Voice +``` + +不应该把它理解为： + +```text +Navigation → New Page +``` + +而应该是： + +```text +Context → New Channel +``` + +------ + +# 28. Bottom Sheet / Dialog / Drawer 职责 + +项目中已经存在多种 Sheet、Dialog、Card。 + +需要建立明确规则。 + +### Page + +用于主要任务。 + +```text +Connect +Chat +Channels +``` + +### Drawer + +用于全局导航和 Server 控制。 + +```text +Server +Settings +Disconnect +``` + +### Bottom Sheet + +用于当前上下文详情。 + +```text +Channel Detail +Member Detail +Voice Detail +``` + +### Dialog + +用于： + +```text +确认 +危险操作 +输入 +``` + +避免出现： + +```text +BottomSheet + ↓ +Dialog + ↓ +BottomSheet +``` + +这种复杂交互链。 + +------ + +# 29. Design Token + +建议继续强化现有： + +```text +Color +Shapes +Theme +Type +UiTokens +``` + +体系。 + +统一定义： + +```text +Spacing + +xs = 4dp +sm = 8dp +md = 12dp +lg = 16dp +xl = 24dp +``` + +------ + +# 30. Radius + +建议： + +```text +List Item 8~12dp +Card 12~16dp +Floating Card 16~20dp +Bottom Sheet 24dp +Avatar Circle +``` + +不要所有组件统一 24dp。 + +圆角本身应该承担层级表达作用。 + +------ + +# 31. 控件尺寸 + +建议建立固定 Control Token： + +```text +Small Button +40dp + +Normal Button +48dp + +Input +52dp + +List Item +52~56dp + +Avatar Small +32dp + +Avatar Medium +40dp + +Avatar Large +56dp +``` + +避免大量： + +```text +14.5dp +17dp +24.5dp +``` + +这种局部修正值。 + +如果确实需要特殊值，应有明确视觉原因。 + +------ + +# 32. Typography + +推荐建立： + +```text +Title +16~20sp / Bold + +Body +14~16sp / Regular + +Secondary +12~14sp / Regular + +Caption +11~12sp / Regular +``` + +移动端不要大量使用过小文字。 + +尤其： + +- Channel Name +- Nickname +- Message + +必须优先保证可读性。 + +------ + +# 33. Semantic Colors + +不要让每个组件自己定义颜色。 + +统一语义： + +```text +Primary +Accent +Success +Warning +Error +Muted +Disabled +Speaking +``` + +例如： + +```text +Connected +→ Success + +Speaking +→ Accent + +Reconnecting +→ Warning + +Disconnected +→ Error +``` + +------ + +# 34. 状态视觉规范 + +状态优先级： + +```text +Connected + ↓ +Current Channel + ↓ +Speaking + ↓ +Other User States +``` + +越重要的状态，视觉反馈越明显。 + +但不要同时让多个状态都高亮。 + +------ + +# 35. 动画规范 + +动画不应该成为 UI 主体。 + +优先： + +```text +状态变化动画 +``` + +而不是： + +```text +页面切换动画 +``` + +推荐： + +- Channel selected +- Speaking +- PTT pressed +- Connection state +- Reconnect banner + +使用短时、低幅度动画。 + +------ + +# 36. 视觉风格 + +最终建议采用： + +```text +Modern +Flat +Native +Compact +Readable +Low Decoration +``` + +不是： + +```text +Discord Clone +``` + +也不是： + +```text +Material 3 Demo +``` + +而是： + +> Modern Native Communication Client + +------ + +# 37. 推荐视觉层级 + +整体控制为： + +```text +Background + ↓ +Surface + ↓ +Elevated Surface + ↓ +Primary Action +``` + +例如： + +```text +Background + 页面背景 + +Surface + Chat / Channel List + +Elevated Surface + Current Channel / Voice + +Primary + Connect / PTT / Send +``` + +不要给每一个组件增加独立背景。 + +------ + +# 38. 主页面最终结构 + +最终推荐： + +```text +┌─────────────────────────────┐ +│ ☰ My Server ⋮ │ +│ ● Connected · 12 online │ +├─────────────────────────────┤ +│ │ +│ # General 12 │ +│ ● ● ● ● +8 │ +│ │ +├─────────────────────────────┤ +│ │ +│ │ +│ Chat Content │ +│ │ +│ │ +├─────────────────────────────┤ +│ + Message... ↑ │ +├─────────────────────────────┤ +│ Chat Channels │ +└─────────────────────────────┘ +``` + +Channels： + +```text +┌─────────────────────────────┐ +│ ☰ My Server ⋮ │ +│ ● Connected · 12 online │ +├─────────────────────────────┤ +│ # General 12 │ +├─────────────────────────────┤ +│ CHANNELS │ +│ │ +│ ▼ Lobby 5 │ +│ ● Alice │ +│ ● Bob │ +│ │ +│ ▼ General 12 │ +│ ● Alex │ +│ ● Tom │ +│ │ +│ ▶ Gaming 8 │ +│ ▶ Music 3 │ +│ ▶ AFK 2 │ +├─────────────────────────────┤ +│ Chat Channels │ +└─────────────────────────────┘ +``` + +------ + +# 39. UI 组件层级 + +建议最终组件结构： + +```text +App +│ +└── ServerSpace + │ + ├── ServerHeader + │ + ├── CurrentChannelBar + │ + ├── Content + │ ├── ChatView + │ │ ├── MessageList + │ │ └── MessageInput + │ │ + │ └── ChannelsView + │ ├── ChannelTree + │ └── MemberList + │ + ├── VoiceStatus + │ + ├── BottomNavigation + │ + └── ServerDrawer +``` + +------ + +# 40. 推荐代码重构方向 + +现有代码已经有： + +```text +AppTopBar +MessageInputBar +VoiceCard +VoiceControlBar +ServerDetailCard +ClientActionMenu +``` + +下一阶段建议进一步抽象为： + +```text +ui/components/ +│ +├── server/ +│ ├── ServerHeader +│ ├── ServerDrawer +│ └── ServerStatus +│ +├── channel/ +│ ├── CurrentChannelBar +│ ├── ChannelTree +│ ├── ChannelItem +│ └── ChannelDetail +│ +├── client/ +│ ├── ClientItem +│ ├── ClientAvatar +│ ├── ClientStatus +│ └── ClientActionMenu +│ +├── chat/ +│ ├── MessageList +│ ├── MessageItem +│ └── MessageInput +│ +└── voice/ + ├── VoiceStatus + ├── PTTButton + └── VoiceControl +``` + +这样比继续按照 Screen 切组件更加容易维护。 + +------ + +# 41. ViewModel 状态建议 + +UI 最终应该围绕一个核心 Server Space 状态： + +```text +ServerSpaceState + +connectionState + +server +currentChannel + +channels +clients + +chatMessages + +voiceState +``` + +其中： + +```text +currentChannel +``` + +是核心状态。 + +UI 不应该自己维护多个互相独立的： + +```text +selectedChannel +voiceChannel +chatChannel +memberChannel +``` + +否则很容易出现状态不同步。 + +应该： + +```text +currentChannelId +``` + +作为统一来源。 + +------ + +# 42. P0 重构优先级 + +## P0-1 + +重新定义 Server Space。 + +目标： + +```text +Connected + ↓ +Current Channel +``` + +作为整个 App 的核心模型。 + +------ + +## P0-2 + +重构： + +```text +CurrentChannelBar +``` + +让它成为全局 Context Bar。 + +------ + +## P0-3 + +重新整理： + +```text +Chat +Channels +``` + +两个 Tab 的关系。 + +------ + +## P0-4 + +重构： + +```text +Voice +PTT +Voice Status +``` + +让它们绑定 Current Channel。 + +------ + +## P0-5 + +统一： + +```text +Server Header +Server Drawer +``` + +------ + +# 43. P1 + +完成： + +```text +Channel Tree +Member Item +Member Detail +Channel Detail +Recent Servers +Server Settings +``` + +并统一 Sheet / Dialog / Drawer。 + +------ + +# 44. P2 + +最后处理： + +```text +Animation +Gesture +Motion +Micro Interaction +Advanced Voice Feedback +``` + +------ + +# 45. 最终设计原则 + +TS Mobile 的 UI 应该始终遵循以下五条原则： + +## 1. Server 是工作空间 + +连接服务器以后，用户进入 Server Space。 + +## 2. Current Channel 是核心上下文 + +所有通信行为围绕当前频道。 + +## 3. Chat / Channels 是视图 + +它们不是两个独立产品页面。 + +## 4. Voice / Members / Chat 都绑定 Current Channel + +切换频道意味着整个通信上下文切换。 + +## 5. UI 优先表达状态，而不是装饰 + +用户第一眼应该知道： + +```text +Server +Current Channel +Members +Voice State +Chat +``` + +而不是看到： + +```text +圆角 +阴影 +动画 +渐变 +装饰 +``` + +------ + +# 46. 最终目标 + +最终用户打开 TS Mobile 后： + +```text +第一次使用 + ↓ +连接服务器 + ↓ +直接进入服务器默认 / 当前频道 + ↓ +看到当前频道 + ↓ +可以聊天 + ↓ +可以查看频道树 + ↓ +可以看到成员 + ↓ +可以进行语音通信 +``` + +整个过程中不应该出现多余的： + +```text +服务器主页 +选择频道 +进入频道 +频道主页 +聊天主页 +语音主页 +``` + +而应该保持一个稳定的模型： + +```text +Server + + +Current Channel + + +Current View +``` + +其中： + +```text +Current View = +Chat +or +Channels +``` + +这是 TS Mobile 最适合移动端的 UI / UX 核心架构。 + +------ + +# 47. 一句话定义 + +如果最终需要给这个 UI 架构定一句设计原则： + +> **TS Mobile 不是“服务器列表 + 聊天 + 频道 + 语音”的功能集合，而是一个以 Server 为工作空间、以 Current Channel 为核心上下文的移动 TeamSpeak 客户端。** \ No newline at end of file diff --git a/docs/UI重构设计规范_审查报告.md b/docs/UI重构设计规范_审查报告.md new file mode 100644 index 0000000..9ed1995 --- /dev/null +++ b/docs/UI重构设计规范_审查报告.md @@ -0,0 +1,324 @@ +# 《TS Mobile UI / UX 重构设计规范》审查报告 + +审查对象：`E:\MyProject\ts-mobile-go\docs\TS Mobile UI UX 重构设计规范.md`（47 章） +交叉参照： +- `E:\MyProject\ts-mobile-go\docs\UI架构设计.md` +- `E:\MyProject\ts-mobile-go\docs\流程\00_总览.md` ~ `09_EventBus架构.md` +- `E:\MyProject\ts-mobile-go\docs\implementation\00_实施总览.md` ~ `12_主题与收尾.md` +- `E:\MyProject\ts-mobile-go\CLAUDE.md`（已知 SDK 限制） +- Android 实现：`android/app/src/main/java/com/tsmobile/app/`（Models、Repository、NavGraph、UiTokens、Shapes、SemanticColors、TSBridge、各 ViewModel 与组件） + +审查结论：**方向正确，但当前版本不具备落地条件。** + +"Server 是工作空间 + Current Channel 是核心上下文 + Chat/Channels 只是视图"这个模型判断是对的，比现有的三页跳转（server_config → channel_list → chat）更贴合 TeamSpeak 的使用心智。但文档停留在"理念陈述"层面：状态模型与代码不符、多个核心组件缺数据来源、异常路径几乎空白、Design Token 与现有实现两套命名并存。按此文档开工，实现者会在第 3 天卡住。 + +--- + +## 一、阻断级缺陷（不补齐无法开工） + +### A1. 顶层状态模型与代码不一致，且漏掉必然出现的中间态 + +文档第 3 章只定义 `Disconnected / Connecting / Connected` 三态，并断言"不应该存在 Connected but no channel"。 + +实际 `data/Models.kt` 的 `ConnectionState` 是 5 态： + +| 文档 | 代码实际 | +| --- | --- | +| Disconnected | `Disconnected(reason, wasKicked)` | +| Connecting | 无独立态（连接中由 ViewModel 的 `isConnecting` 类标志表达） | +| Connected | `Connected` | +| — 未定义 | `Disconnecting` | +| — 未定义 | `Reconnecting(attempt, maxAttempts, reason)` | +| — 未定义 | `WaitingForNetwork(since)` | + +更关键的是：**"Connected 但没有频道"在真实链路上必然存在**，文档把它当成不该出现的状态，但它不是 UI 设计问题，是协议时序问题： + +1. `ConnectionState.Connected` 之后还有独立的第二层状态 `SyncState`（`ChannelViewModel`：`Unsynced / Syncing / Synchronized / SyncFailed`）。首次同步期间频道树是空的，`UI架构设计.md` 2.2 节为此专门设计了"正在同步服务器数据..."加载态。新文档完全没提这一层。 +2. `Repository.currentChannelId` 初值就是 `"0"`；`NavGraph.kt` 里存在兜底逻辑——当 `currentChannelId <= 0` 时改用 `Repository.getSelfChannelId()` 从客户端列表反查。这说明 `GetChannelID` 失败是已知的真实场景。 + +**需要补的规格**：Connected 之后的完整状态矩阵，至少覆盖 +`Connected × {Unsynced, Syncing, Synchronized, SyncFailed}` × `currentChannelId ∈ {有效, "0", 反查失败}`， +并明确每种组合下 ServerHeader / CurrentChannelBar / Content 区各显示什么（骨架屏？占位名？"正在同步"？）。这是全文档最大的空洞——CurrentChannelBar 被称为"最重要的 UI 组件"，却没有定义它在数据未就绪时的样子。 + +### A2. 成员状态系统缺数据来源，且未列入任何优先级 + +文档第 16 章要求 8 种成员状态：`Online / Idle / Away / Muted / Deafened / Speaking / Server Muted / Disconnected`。 + +实际数据能力： +- `ClientInfo` 字段仅有 `id / nickname / uid / channelId / serverGroups / isSelf`——**没有 away、idle、muted、deafened、serverMuted、talkPower 任何一项**。 +- `TSBridge` 回调只有 `onClientSpeaking(clientID, speaking)`（Go 侧判定，Kotlin 不得自行从 PCM 推断）。 +- `setRemoteClientMuted` 是本地静音远端，不等于对方的 Muted 状态。 + +也就是说 8 种状态里只有 Speaking 有数据支撑，其余 6 种（Online 可由在场推断）需要改 `go/teamspeak/bridge.go`、扩展 `getClientsJSON` 字段、**重新编译 AAR**。文档第 40~44 章的重构方向和 P0/P1/P2 优先级里完全没有"需要改协议层"这一项，会让排期严重失真。 + +**需要补**：每个状态标注「数据已具备 / 需扩展 bridge / 本期不做」，并把 bridge 改造单列为 P0 前置项。 + +### A3. Avatar 体系凭空出现，无任何数据来源与降级规格 + +文档第 7、15、16、31、39 章大量依赖头像：CurrentChannelBar 的 `● ● ● ● +8`、Member Item 的 `[Avatar] Alex`、Speaking 的 Avatar Ring、第 31 章还定义了三档尺寸令牌（32/40/56dp）。 + +实际情况：TeamSpeak 协议**没有用户头像**。`ServerInfo.iconId` 和 `ChannelDetailInfo.iconId` 是服务器图标 / 频道图标，不是客户端头像；代码中不存在任何 avatar 相关实现。 + +文档从头到尾没有说明：头像内容从哪来？无头像时显示什么（昵称首字母？固定图形？服务器组图标？）？头像是否可上传（TS 无此能力）？ + +这不是细节问题——CurrentChannelBar 的成员预览、Member Item 的主视觉、Speaking 的主要表达方式全部建立在头像上。**必须先定义"无头像时的 Avatar 替代方案"，否则第 7、15、16 章的示意图无法实现。** + +建议：改为首字母色块（昵称首字符 + 由 UID 哈希出的稳定背景色），Avatar Ring 表达 Speaking，并把"服务器组徽章"作为唯一的真实图形来源。 + +### A4. PTT 在最终结构里没有位置 + +文档第 19 章说"PTT 是语音 UI 的核心操作"，第 20 章专门定义 PTT 状态，第 39 章组件树里有 `VoiceStatus`。 + +但第 38 章"主页面最终结构"的两张完整示意图（Chat / Channels）里**没有任何语音或 PTT 元素**，第 5 章的 Server Space 结构图里也只有 `Current Content`。 + +同时文档第 18 章否定了 Voice 独立页面，第 19 章又把 Voice UI 画成一个大卡片（含 🎙 / ● Connected / [PTT] / Mic / Speaker）——这个卡片挂在哪一层？常驻还是弹出？与底部 BottomNavigation 如何共存？现状 `UI架构设计.md` 是"底部语音控制栏常驻所有页面 + 展开为语音卡（BottomSheet）"，新文档既没说保留也没说取消。 + +**核心操作缺失布局定义，属于必须补齐的阻断项。** + +附带三个未定义问题： +- PTT 触发方式（屏内按钮 / 悬浮窗 / 音量键 / 声控）一个都没指定。 +- **App 在后台或锁屏时如何发言**——全文未涉及后台场景，但代码有 `ConnectionService` 前台服务与 keepalive 通知，说明后台常驻是真实需求，PTT 的后台可用性是移动端语音客户端的关键设计点。 +- 第 20 章的 `Idle → Pressed → Speaking` 与代码 `VoiceState`（`Idle / Transmitting / Blocked(reason)`）不匹配：文档漏了 `Blocked`（未连接、采集失败、发送异常）及其 UI 表现；且 `Speaking` 在代码语义里指**远端**用户在说话（`onClientSpeaking`），文档却把它当作自己 PTT 的第三态，概念混用需澄清。 + +### A5. Chat 范围收窄会砍掉私聊与服务器聊天，文档未声明取舍 + +文档第 9 章把 Chat 定义为"当前频道的文字消息流"，第 41 章 `ServerSpaceState` 只有单个 `chatMessages`，第 46 章 `Current View = Chat or Channels`。 + +实际现有能力覆盖三种会话：`ChatMessage.targetMode` 为 `1=私聊 / 2=频道 / 3=服务器`；`Repository` 按 `"${targetMode}_${targetId}"` 分档归档（每会话上限 500 条）；`ServerViewModel.sendMessageNotification` 会区分标题"频道消息"/"私聊消息"；`MainActivity.DeepLinkAction` 携带 `targetMode/targetId`，通知点击可直达指定会话。 + +新架构里私聊和服务器聊天**没有任何入口**。这会直接导致： +- 系统通知点击 → 跳转会话的 deep link 断链； +- Repository 的多会话归档能力被闲置； +- 从 Member Action Menu 发起私聊的路径消失（第 17 章的菜单里也只有 Poke / Copy / Move / Kick）。 + +**必须明确写**：本期是否支持私聊与服务器聊天？若不支持，通知 deep link 如何降级？若支持，入口在哪（Drawer？Member Menu？第三个 Tab？会话列表？），`ServerSpaceState` 需改为会话集合而非单个 `chatMessages`。 + +--- + +## 二、与现有文档 / 代码的直接冲突（需裁决，否则三套规范并存） + +| # | 冲突点 | 新规范 | 现状 | +| --- | --- | --- | --- | +| B1 | 断线重连 | 第 33 章 `Reconnecting → Warning`、第 35 章推荐 Reconnect banner 动画 | `UI架构设计.md` 5.4 为顶部横幅不阻塞页面、最多 5 次递增重连；代码实际是 `ReconnectOverlay` **全屏遮罩**；`CLAUDE.md` 又写明"自动重连默认禁用，频繁重连会触发服务端限流/封禁"。三方不一致，新文档未裁决 | +| B2 | 头部是否显示地址 | 第 6 章"不建议长期显示 server.example.com:9987" | `UI架构设计.md` 1.2 与 `AppTopBar(subtitle=...)` 明确显示 `192.168.1.1:9987`。文档未说明这是有意变更；且服务器重名时地址是唯一辨识信息，第 24 章 Recent Servers 又显示地址，自相矛盾 | +| B3 | 端口输入 | 第 23 章把 Port 拆为独立输入框 | `InputValidator.validateServerAddress` 支持 `address:port` 一体式并校验 1..65535；`ServerConfig` / `RecentConnection` 只有 `address` 字段；TSDNS 地址本身可能含端口。第 24 章 Recent Servers 又写成一体式 | +| B4 | 连接页字段 | 第 23 章表单无 Password、无默认频道 | 第 3 章 Disconnected 明确列了 Password；`ServerConfig` 有 `password / defaultChannel / defaultChannelPassword` | +| B5 | 被踢路径 | 第 26 章 Disconnect 流程只有 `Server Space → Disconnected → Connection Screen` | 代码有 `Disconnected(wasKicked=true)` → `Routes.KICKED` → `KickedScreen`（重连 / 返回主页）。流程图漏了这条已实现的路径 | +| B6 | 成员状态是否显示文字 | 第 15 章"正常状态不要一直显示文字" | `UI架构设计.md` 与现有实现用文字标注；纯颜色/圆点表达会降低可访问性（见 D4） | +| B7 | Drawer 条目 | 第 21 章：Server Info / Voice / Notifications / Appearance / Settings / Disconnect | 第 4 章信息架构：Server Info / Voice Settings / Notifications / Appearance / Disconnect。同一文档内两处条目不同（Settings 有无、Voice vs Voice Settings） | +| B8 | CurrentChannelBar 位置 | 第 38 章放在 Header 之下（顶部） | 第 5 章、第 8 章结构图放在 Content 之下、Tab 之上（底部）。同一文档内位置矛盾 | +| B9 | 主题切换入口 | 第 21 章移入 Server Drawer → Appearance | 现状在服务器配置页右上角，未连接时也可切换。移入 Drawer 后**未连接状态无法切换主题**，属交互回退，文档未察觉 | +| B10 | 频道切换语义 | 第 27 章"本质上只是 currentChannelId 发生变化" | 代码是异步 `ClientMove` + `ChannelSwitchState` 等服务端确认，可能因密码 / 满员 / 权限 / talkPower 失败（`mapMoveError`）。按文档描述实现会做成无回滚的乐观更新 | + +**文档治理问题（最需要先解决的一条）**：项目已有 `UI架构设计.md`（3 页 3 卡）、`docs/流程/00~09`、`docs/implementation/00~12` 三套体系，新文档**没有版本、日期、作者、适用范围、变更记录，也没有声明它与上述文档的关系**——是取代还是补充？冲突以谁为准？不明确这一点，上表 10 处冲突会全部变成实现期的反复拉扯。 + +建议在文档开头加一节「本文档效力」：明确取代 `UI架构设计.md` 的哪些章节、保留哪些、`docs/流程` 作为行为权威源不变。 + +--- + +## 三、覆盖缺口（现有功能在新规范里消失） + +新规范描述的是一个"理想晴天的正常路径"。以下已实现或已设计的能力在 47 章中**一次都没出现**： + +**异常与错误态**（现状代码已有对应实现，文档全部丢失） +- 连接失败 6 类错误分类与文案（`ServerViewModel.classifyError`：密码错误 / 昵称冲突 / 网络不可达 / 地址无效 / 超时 / 服务器满） +- 首次同步失败与重试（`SyncState.SyncFailed`） +- 频道切换失败（`ChannelSwitchState` / `mapMoveError`）、密码频道输入（`ChannelPasswordDialog`）、频道确认（`ChannelConfirmDialog`） +- 消息发送失败与重试（`MessageSendState.Failed` / `MessageDeliveryState.FAILED` / 10 秒超时） +- 空状态（`EmptyStateView`：空频道、无消息） +- 网络丢失等待（`WaitingForNetwork`） +- 应用更新提示（`UpdateChecker` / `UpdateBanner` / `UpdateCheckDialog`）在 Server Space 中的位置 + +**已存在的 Dialog / Menu 全部未纳入规范** +第 28 章只抽象地说 Dialog 用于"确认 / 危险操作 / 输入"，但没有清单。代码中已有：`ChannelPasswordDialog`、`ChannelConfirmDialog`、`DisconnectConfirmDialog`、`PokeDialog`、`ClientActionMenu`、`MessageContextMenu`、`UpdateCheckDialog`、`PokeNotification`。规范应逐个给出触发条件、内容、按钮、危险等级。 + +**Poke** +现状是核心交互（三个入口 + 顶部气泡 + 系统通知 + `App.POKE_CHANNEL_ID`）。新文档只在第 17 章 Member Action 里出现一次 "Poke"，未定义接收端表现。 + +**权限与降级** +第 17 章要求 Move / Kick from Channel / Kick from Server，但没定义**无权限时的表现**（隐藏 / 置灰 / 点击后报错）。TS 的权限由服务器组与频道组决定（`ServerInfo.defaultServerGroup / defaultChannelGroup`、`ChannelDetailInfo.neededTalkPower`），这是必须定义的分支。 + +**Recent Servers 管理** +第 24 章只给了两行卡片示意，缺：最多 10 条、按时间倒序、长按删除、清空、成功/失败标记（这些在 `UI架构设计.md` 2.1 有），以及**密码的存储安全规范**（`RecentConnectionsStore` 持久化了 password，文档未涉及是否加密、是否用 Keystore）。 + +**文件与富文本消息** +代码有 `Bbcode.kt`、`FileMessageMeta`（图片 / MyTS 文件）、`EmojiMapper`。文档第 10 章讲消息合并、第 11 章说"不要把 Emoji / 文件 / 图片放一级 UI"，但没说明**接收侧**这些内容如何渲染。"不放到一级输入 UI"和"能不能收发"是两件事，当前表述容易被误读为砍功能。 + +**后台与生命周期** +前台服务、keepalive 通知、`NetworkMonitor`、进程被杀后的状态恢复、`connectionGeneration` 防止陈旧回调——移动端语音客户端的核心体验，文档零覆盖。 + +--- + +## 四、Design Token 章节（29~34）的具体问题 + +### D1. 与现有 Token 两套命名并存 + +第 29 章给 `xs=4 sm=8 md=12 lg=16 xl=24`，代码 `UiTokens.Spacing` 已是 `None=0 ExtraSmall=4 Small=8 Medium=12 Large=16 ExtraLarge=24 Huge=32`。文档说"建议继续强化现有体系"，却给了一套新命名，且漏了 `None` 和 `Huge`。照文档写会出现 `Spacing.sm` 与 `Spacing.Small` 并存。 +→ 直接引用现有名称，补上 None / Huge。 + +### D2. 用区间代替确定值，等于没有约束 + +- 第 30 章 Radius：`List Item 8~12dp`、`Card 12~16dp`、`Floating Card 16~20dp` +- 第 32 章 Typography：`Title 16~20sp`、`Body 14~16sp`、`Secondary 12~14sp`、`Caption 11~12sp` +- 第 31 章：`List Item 52~56dp` + +给区间意味着两个开发者会各选一端，最终仍然不统一——这正是文档第 31 章想避免的"14.5dp / 17dp 局部修正值"问题的另一种形式。 +→ 规范应给确定映射，并复用代码已有档位：`Shapes`（extraSmall 4 / small 8 / medium 12 / large 16 / extraLarge 24）、`UiTokens.Size`（TouchTarget 48 / ControlHeight 40 / AppBarHeight 56 / Icon 16-20-24）、`MaterialTheme.typography`（`AppTopBar` 已用 `titleMedium`）。文档自建的 Title/Body/Secondary/Caption 与 M3 的 display/headline/title/body/label 是两套体系，未给映射关系。 + +### D3. Semantic Colors 与代码不匹配 + +第 33 章列 `Primary / Accent / Success / Warning / Error / Muted / Disabled / Speaking`；代码 `SemanticColors` 只有 `success / warning / info` 三组（各含 on / container 变体）。差异： +- 缺 `accent / muted / disabled / speaking` 四组，需明确新增字段 +- `Primary / Error` 在 M3 `colorScheme` 里已有（`primary / error / errorContainer`），文档另立一份会造成双源 +- `info` 在代码里有、文档没提 +- `Accent` 与 M3 的 `tertiary` 关系未定义；`Speaking → Accent` 与 `Primary Action` 同色会削弱第 37 章的视觉层级 +- 未给出 onXxx 前景色与 container 变体，而代码现有 token 都是成组定义的 + +### D4. 状态仅靠颜色区分，缺冗余编码 + +第 33~34 章把 Connected/Speaking/Reconnecting/Disconnected 全部映射到颜色，没有要求形状、图标或文字的第二重编码。这违反 WCAG 1.4.1（不能仅用颜色传达信息），对色觉障碍用户不可用。 +第 15 章"正常状态不要一直显示文字"进一步削弱了这一点。 +→ 建议规定：颜色 + 图标形状（如 Speaking 用波形而非仅高亮环）双重编码；纯装饰性状态点可只用颜色。 + +### D5. 暗色主题规范完全缺失 + +代码有 `ThemeMode.kt` / `ThemePreferences.kt`，`UI架构设计.md` 4.4 定义了主题切换与持久化。新文档第 21 章列了 Appearance、第 29 章提了 Theme，但**全文没有一条暗色规格**：Semantic Colors 在暗色下的取值、Speaking 高亮的对比度、第 37 章 Background/Surface/Elevated Surface 三级层级在暗色下如何区分（暗色主题通常靠 tonal elevation 而非阴影）。 +→ 第 33/34/37 章均需给出亮/暗双套取值，或明确引用 M3 tonal palette 的生成规则。 + +### D6. 动画规范无数值 + +第 35 章只说"短时、低幅度"，没有时长、easing、可打断性、并发规则。代码现状已有具体值（`tween(340/320/280/240, FastOutSlowInEasing)`）。 +→ 给出 duration token（如 `Fast=150ms / Normal=250ms / Slow=350ms`）与 easing token，并说明是否沿用现有值。同时缺"系统开启减弱动效时如何降级"。 + +--- + +## 五、组件级设计漏洞 + +### E1. CurrentChannelBar（第 7 章，自称最重要组件）缺规格 +- 成员头像预览的**排序规则**未定义（说话者优先？加入时间？权限高低？） +- 折叠阈值未定义（几个之后显示 `+N`） +- 点击行为未定义（跳 Channels Tab？弹 Member 列表 Sheet？） +- **缺变体定义**：第 38 章 Channels 视图里它被简化为 `# General 12`，第 7 章却是含头像的完整版。同一组件两种形态，正文未说明，也无变体命名 +- 与 Channels Tab 的 Channel Tree 成员列表**信息重复**：Channels 视图下当前频道成员会出现两次 +- 数据刷新抖动：`CLAUDE.md` 说明 client enter/leave 会触发**全量 clientlist 刷新**（因 `notifycliententerview` 的 ChannelID 不可靠），头像预览会整组重排。第 35 章讲动画却没考虑这个高频刷新场景 + +### E2. Channel Tree（第 12~14 章）缺关键规格 +- **嵌套缩进上限**未定义。TS 频道可深层嵌套，窄屏 5 层以上必然溢出。需要规定最大缩进层级、超限处理（截断缩进 / 横向滚动 / 面包屑） +- 排序规则未定义（`ChannelInfo.order` 字段存在但文档未提） +- 默认展开策略未定义（全部折叠？只展开当前频道路径？） +- 展开状态在切 Tab / 切频道后是否保持 +- 大量频道时的性能要求（LazyColumn、key 稳定性） +- 实时性假设不成立：`CLAUDE.md` 明确 SDK **无 channel create/update/delete 事件**，频道列表 5 分钟陈旧或切换前才刷新。第 12 章把它当实时树来设计，需注明刷新时机与"数据可能滞后"的表达 + +### E3. 消息合并（第 10 章）缺规则 +- 合并的**时间窗口**未定义（跨 5 分钟还合并吗？） +- 合并后时间戳如何显示 +- 系统消息（`MessageType.SYSTEM`）是否打断合并 +- 合并组内单条消息的 `deliveryState`（PENDING/SENT/FAILED）如何展示、失败重试入口在哪 + +### E4. 用 emoji 充当设计规格 +第 13 章 `▼ 🎙 General 12`、示意图中的 `☰ ⋮ ● ▼ ▶ 💬 🎤 🔒 🔇`。emoji 跨设备渲染不一致，且与第 36 章"Low Decoration / 不是 Material 3 Demo"的诉求冲突。 +→ 应指定 Material Icons 具体图标名，并补一张「频道类型 → 图标」映射表。文档提到"Channel Type"但从未枚举 TS 实际的频道属性（默认 / 密码 / 永久 / 半永久 / 临时 / codec / neededTalkPower / maxClients），而这些字段 `ChannelInfo` 和 `ChannelDetailInfo` 里都有。 + +### E5. 触控目标低于平台标准 +第 31 章 `Small Button 40dp`。Android 与 M3 推荐最小触控目标 48dp（代码 `UiTokens.Size.TouchTarget = 48`）。视觉高度 40dp 可以，但必须规定用 padding 补足到 48dp 命中区——文档未说明，实现者会直接做成 40dp 命中区。 + +### E6. 第 28 章 Sheet/Dialog/Drawer 分层规则不完整 +规则本身合理（避免 BottomSheet → Dialog → BottomSheet 链），但缺： +- Bottom Sheet 的档位（半屏 / 全屏 / 拖拽行为），现状语音卡是可拖拽半屏 +- 系统返回键在每种容器上的行为 +- 多层容器同时存在时的层级与遮罩规则 +- 键盘弹起时 Dialog / Sheet 内输入框的避让（现状 `UI架构设计.md` 2.3 有键盘联动规格，新文档丢失） + +--- + +## 六、可访问性与适配（全文零覆盖） + +一份移动端 UI/UX 重构规范里没有以下任何一项： + +- **无障碍语义**：contentDescription 规范、状态变化的 `liveRegion` 声明。Speaking 是纯视觉高频状态，TalkBack 用户完全无法感知谁在说话——这对语音客户端是严重问题 +- **字体缩放**：全用 sp 定义字号，但第 31 章的固定 dp 高度（Input 52 / List Item 52~56）在大字号下会截断，需定义最小/最大缩放下的行为 +- **横屏 / 平板 / 折叠屏**：全文只有一种窄屏竖版布局。Compose 项目大概率会跑在平板上，Server Space 是否用双栏（左频道树 + 右内容）？未提 +- **对比度**：第 33/37 章定义颜色与层级，无任何对比度要求（WCAG AA 4.5:1） +- **减弱动效**：第 35 章无降级方案 +- **单手可达性**：PTT、发送、Tab 都在底部是对的，但 CurrentChannelBar 按第 38 章在顶部，未讨论可达性 + +--- + +## 七、实时性能约束缺失 + +对一个语音客户端，这是不该缺的一章： + +- Speaking 状态由 Go 侧 400ms 静音超时判定，属高频变化。第 16 章要求 Avatar Ring + 状态点 + 音量指示器**三种同时表达**，12 人频道会引发成员列表大范围重组 +- 文档未规定重组边界（`derivedStateOf`、稳定 key、把高频状态下推到最小 composable） +- 未规定 Speaking 指示的更新节流（是否需要 100ms 级去抖） +- 未规定频道树全量刷新时避免闪烁的策略（结合 `CLAUDE.md` 的"client enter/leave 触发全量 clientlist 刷新"） + +--- + +## 八、文档形式与可执行性 + +### F1. 信噪比过低 +25KB / 47 章中大量是把一两个词包进 ```text 代码块（如单独一行的 `currentChannelId`、`Chat`、`Channels`）。真正的规格信息（确定数值、状态枚举、交互规则）占比很小。同样的内容可以压缩到 1/3 篇幅且更有用。 + +### F2. 措辞无约束力 +"推荐"约 20 次、"建议"约 25 次、"不要/避免"若干。规范里全是建议就无法作为验收依据。 +→ 改用 MUST / SHOULD / MAY（或 必须 / 应当 / 可以），并对每条给出可检验的判据。 + +### F3. 缺规范应有的载体 +没有：组件规格表（尺寸/间距/状态/交互的确定值）、状态枚举表、Do/Don't 对照、验收清单、视觉稿或线框图链接。ASCII 示意图只能表达布局意图，无法表达规格。 + +### F4. 术语不统一 +- `Client` / `Member` / `User` 混用：代码是 `ClientInfo` / `ClientActionMenu` / `ClientItem`，文档第 15~17 章叫 Member，第 40 章目录又叫 `client/` +- `Channel Tree` / `Channels` / `Channel List` 混用 +- `Server Space` / 服务器工作空间（第 2 章）混用 +→ 加一节术语表，锁定唯一名称（建议跟随代码用 Client）。 + +### F5. P0/P1/P2 不可排期 +第 42~44 章的优先级只有条目名，缺：工作量、依赖关系、验收标准、**是否需要改 Go 协议层**。按 A2/A3 的分析，成员状态与头像都需要先改 `bridge.go` 并重编 AAR，这必须是 P0 前置项，但文档把它归到了 P1（Member Item）和未提及（Avatar）。 + +### F6. 缺迁移映射 +第 40 章给了目标目录结构，但没有"现有文件 → 新结构"的对照表。当前实现是 4 路由（`server_config / channel_list / chat / kicked`）+ 独立 Screen；目标是 Server Space 单容器 + 内部 Tab。这需要重写 `NavGraph.kt`（含 deep link、`BackHandler`、`LaunchedEffect` 导航、`ReconnectOverlay` 与 `PokeNotification` 全局覆盖层），是本次重构成本最高的部分，文档一句未提。 + +建议补一张迁移表，例如： +| 现有 | 目标 | 处理 | +| --- | --- | --- | +| `AppTopBar` | `server/ServerHeader` | 改造（增加状态与在线数） | +| `ChannelListScreen` | `ServerSpace` + `channel/ChannelTree` | 拆分 | +| `ChatScreen` | `chat/ChatView` | 降级为视图 | +| `VoiceCard` + `VoiceControlBar` + `PTTButton` | `voice/*` | 合并（需先定 A4） | +| `ServerDetailCard` | `server/ServerDrawer` | 形态从 BottomSheet 改 Drawer | +| `NavGraph` 4 路由 | 2 路由（Connection / ServerSpace）+ Tab | 重写 | +| `KickedScreen` | ？ | 文档未定义（见 B5） | + +--- + +## 九、优化建议：文档结构重组 + +按当前 47 章平铺结构，实现者无法按需查阅。建议重组为 6 部分： + +1. **范围与效力**（新增，最高优先）：版本 / 日期 / 适用模块 / 与 `UI架构设计.md`、`docs/流程`、`docs/implementation` 的取代关系 / 冲突裁决规则 / 术语表 +2. **模型与状态**（现第 2~4、26~27、41 章）：连接状态机（补齐 5 态 + SyncState + currentChannelId 就绪态）、频道切换的异步语义与失败回滚 +3. **信息架构与导航**（现第 5、8、28、38、39 章）：Server Space 布局、Tab 规则、容器分层、迁移映射表 +4. **组件规格**（现第 6~7、9~25 章）：每个组件一节，统一模板 = 结构 / 尺寸 token / 状态枚举 / 交互 / 数据源 / 异常态 / 无障碍 / 变体 +5. **视觉与 Token**（现第 29~37 章）：确定值映射到现有代码 token，亮暗双套，对比度要求，动画数值 +6. **横切规范**（新增）：异常与错误态全集、空态、后台与生命周期、实时性能约束、可访问性、多窗口适配、凭据存储安全 + +另外两条具体建议： +- **把"数据可用性"作为每个组件规格的必填字段**。本次审查中 A2（成员状态）、A3（头像）、E2（频道实时性）三处阻断问题的共同根因，是文档从 UI 效果倒推、没有先核对协议层能提供什么。建议在每个组件规格里强制填「数据源 / 是否需改 bridge / 数据未就绪时的表现」。 +- **P0 之前先做一次"数据能力对齐"**：列出 `bridge.go` 当前导出的全部字段与事件，标出规范中依赖但缺失的项，形成 bridge 改造清单。这一步不做，P0 的 CurrentChannelBar 和 Voice 都只能做成半成品。 + +--- + +## 十、总体评价 + +| 维度 | 评价 | +| --- | --- | +| 产品模型判断 | 好。Server Space + Current Channel 上下文的方向正确，优于现有三页跳转 | +| 与代码现状的吻合度 | 差。状态机、成员数据、头像、会话范围四处脱节 | +| 完整性 | 不足。异常路径、后台、可访问性、暗色、性能基本空白 | +| 可执行性 | 不足。区间值代替确定值、"建议"代替约束、无迁移映射、无验收标准 | +| 文档治理 | 缺失。无版本信息，未声明与既有三套文档的关系 | +| 内部一致性 | 有 4 处自相矛盾（B3/B4/B7/B8） | + +**建议处理方式**：不要按当前版本直接进入实现。先补三样东西——① 效力声明与冲突裁决（第二节表格逐条定调）；② 数据能力对齐清单（Go bridge 现有字段 vs 规范依赖字段）；③ 状态矩阵（A1）。这三项完成后，第 40 章的组件重构才有确定的输入。其余缺口可在各组件规格细化时逐个补齐。 diff --git a/docs/bridge-contract.md b/docs/bridge-contract.md new file mode 100644 index 0000000..e43c556 --- /dev/null +++ b/docs/bridge-contract.md @@ -0,0 +1,218 @@ +# Go ↔ Kotlin Bridge 契约 + +> **状态：权威** ｜ 最后核对：2026-09-10 ｜ 契约版本：`1` +> +> 单一事实来源：`go/teamspeak/contract.go`（字段名常量 + `BridgeContractVersion`） +> Kotlin 侧守卫：`android/app/src/main/java/com/tsmobile/app/data/BridgeContract.kt` +> 契约测试：`go/teamspeak/contract_test.go`、`contract_golden_test.go`、 +> `android/app/src/test/java/com/tsmobile/app/data/BridgeContractTest.kt` + +--- + +## 为什么需要这份文档 + +gomobile 有三条硬限制（`CLAUDE.md` 已记录）：不能导出 `[]string`、`[]*T`、Go `error`。 +项目用 **JSON 字符串**绕过它们，代价是： + +> **Go 侧改一个字段名，Kotlin 侧不会编译失败，只会在运行时静默拿到默认值。** + +叠加 `Repository.kt` 的 `Json { ignoreUnknownKeys = true }`，字段对不上时 +`ChannelInfo` 直接填 `""` / `0` / `false`：频道列表看起来是空的， +或者密码频道显示成公开频道，而日志里什么都没有。 + +本契约用三道机制钉住这个边界： + +| 机制 | 防止的问题 | +| --- | --- | +| `contract.go` 字段名常量 | 集中声明，marshal 处不写字面量 | +| `contract_test.go` 反射断言 | 常量与 struct tag 漂移 → 测试红 | +| golden 样本（Go/Kotlin 共用） | 样本与 tag 漂移 → 两侧测试都红 | +| `BridgeContract.verifyOrDescribeError()` | AAR 与 App 版本不匹配 → 连接前明确报错 | + +--- + +## 边界数据类型分级 + +不是所有跨边界数据都同样脆弱。**改代码前先确认属于哪一类**： + +| 类别 | 保护强度 | 类型 | 说明 | +| --- | --- | --- | --- | +| **A 类** | 编译期检查 | `TextMsg`、`PokeEvent`、`Channel`、`Client` | gomobile 导出的 struct，字段是真实属性。Go 改名 → Kotlin 编译失败。**无需常量保护。** | +| **B 类** | 无（靠契约测试） | `channelJSON`、`clientJSON`、`serverInfoJSON`、文件下载 JSON | JSON 字符串。**本契约的主要保护对象。** | +| **C 类** | 无（无消费方） | `channelDetailedJSON`、`clientDetailedInfoJSON`、`initialSyncJSON` | 已导出但 Kotlin 未解析。字段名已声明以钉住形状；将来要消费时先跑契约测试。 | + +--- + +## B 类契约明细 + +图例：**✓** = 已有 Kotlin 消费方 ｜ **—** = 当前未消费 + +### 1. 频道列表 — `GetChannelsJSON()` → `Repository.fetchChannels()` + +Go：`channelJSON`（`bridge.go`）｜ Kotlin：`ChannelInfo`（`Models.kt`） + +| Go 字段 | JSON 字段 | Kotlin 字段 | 类型 | 可空 | 默认值语义 | +| --- | --- | --- | --- | --- | --- | +| `ID` | `id` | `id` | `String` | 否 | `""` — **字符串化数字**，见下方注意 | +| `Name` | `name` | `name` | `String` | 否 | `""` | +| `ParentID` | `parentId` | `parentId` | `String` | 否 | `"0"` — 顶级频道的约定值 | +| `Description` | `description` | `description` | `String` | 否 | `""`（Go 侧实际不填充） | +| `IsPassword` | `isPassword` | `isPassword` | `Boolean` | 否 | `false` | + +> ⚠️ **`id` / `parentId` 是字符串不是数字。** TeamSpeak 的频道 ID 是无符号 64 位， +> 超出 Kotlin `Int` 范围。`contract_test.go` 的 `TestChannelJSON_IDIsStringType` +> 钉住了这一点——改成数值类型会让 Kotlin 在运行时抛序列化异常。 + +> ⚠️ **`ChannelInfo.order`（`Int`）在 Go 侧不存在。** `channelJSON` 没有 `order` 字段， +> 所以该值恒为默认 `0`，`ChannelViewModel.buildChannelTree` 的 `.sortedBy { it.order }` +> 实际是不产生效果的 no-op（`sortedBy` 稳定，全等值等于保持原序）。 +> +> **频道顺序目前由服务器返回顺序决定，且这是正确的**——TS3 的 `channellist` +> 响应按频道树顺序返回，子频道紧跟父频道。显示顺序经真机验证与响应顺序一致。 +> +> 📌 **不要"按 order 排序"**：`channel_order` 不是排序权重，而是 +> **前驱频道的 ID**（链表指针，`0` = 排在本层最前）。见 +> `docs/teamspeak-sdk-3.5.2/doc/client/channel-sort.html`： +> +> > The channel order is the ID of the predecessor channel after which the +> > given channel should be sorted. An order of 0 means the channel is +> > sorted on the top of its hirarchy. +> +> 官方示例：`Subsubchannel_2 ( ID = 7 , order = 6 )` 表示"排在 ID=6 之后"， +> `Subchannel_2 ( ID = 5 , order = 4 )` 表示"排在 ID=4 之后"。 +> 若按该值**数值排序**会得到 `1,4,6,2,3,5,7`，正好把树结构打乱。 +> +> 若将来确实需要在客户端重建顺序（例如不再依赖响应顺序）， +> 正确做法是消费 `channelDetailedJSON.order` 后按前驱指针**串链**， +> 而不是排序。注意 API 侧 `ChannelDetailInfo` 里 `pid` 取自 +> `item["channel_order"]`（`api.go:265`），该处取值可疑，使用前需先核实。 + +### 2. 客户端列表 — `GetClientsJSON()` → `Repository.fetchClients()` + +Go：`clientJSON` ｜ Kotlin：`ClientInfo` + +| Go 字段 | JSON 字段 | Kotlin 字段 | 类型 | 可空 | 默认值语义 | +| --- | --- | --- | --- | --- | --- | +| `ID` | `id` | `id` | `Int` | 否 | `0` | +| `Nickname` | `nickname` | `nickname` | `String` | 否 | `""` | +| `UID` | `uid` | `uid` | `String` | 否 | `""` — **永久身份标识，等同账号 ID** | +| `ChannelID` | `channelId` | `channelId` | `String` | 否 | `"0"` — 字符串化 | +| `ServerGroups` | `serverGroups` | `serverGroups` | `List` | 否 | `[]` | +| `IsSelf` | `isSelf` | `isSelf` | `Boolean` | 否 | `false` | + +> `uid` 是敏感数据：`Repository.performInitialSync` 中的全量 dump 已改为 +> `BuildConfig.DEBUG` 保护 + `proguard-rules.pro` 剥离 `Log.d`。 + +### 3. 服务器信息 — `GetServerInfoJSON()` → `ServerViewModel` / `Repository` + +Go：`serverInfoJSON` ｜ Kotlin：`ServerInfo` + +| Go 字段 | JSON 字段 | Kotlin 字段 | 类型 | 可空 | 默认值语义 | +| --- | --- | --- | --- | --- | --- | +| `Name` | `name` | `name` | `String` | 否 | `""` | +| `WelcomeMessage` | `welcomeMessage` | `welcomeMessage` | `String` | 否 | `""` | +| `MaxClients` | `maxClients` | `maxClients` | `Int` | 否 | `0` | +| `ClientsOnline` | `clientsOnline` | `clientsOnline` | `Int` | 否 | `0` | +| `ChannelsOnline` | `channelsOnline` | `channelsOnline` | `Int` | 否 | `0` | +| `Uptime` | `uptime` | `uptime` | `String` | 否 | `""` — **字符串化秒数** | +| `Version` | `version` | `version` | `String` | 否 | `""` | +| `Platform` | `platform` | `platform` | `String` | 否 | `""` | +| `Created` | `created` | `created` | `String` | 否 | `""` — **字符串化秒级时间戳** | +| `IconID` | `iconId` | `iconId` | `String` | 否 | `""` | +| `DefaultServerGroup` | `defaultServerGroup` | `defaultServerGroup` | `Int` | 否 | `0` | +| `DefaultChannelGroup` | `defaultChannelGroup` | `defaultChannelGroup` | `Int` | 否 | `0` | + +> ⚠️ `uptime` / `created` 是**字符串**。`TestGolden_ServerInfoTypes` 钉住了类型—— +> 改成数值会让 Kotlin 的 `String` 字段静默变空。 + +### 4. 文件下载 — `DownloadFileBytesJSON()` → `FileDownloadManager` + +Go：`kotlin_api.go`（内联 map）｜ Kotlin：`TSBridge.downloadFileBytes()` + +| JSON 字段 | 类型 | 说明 | +| --- | --- | --- | +| `data` | `String` | base64 编码的文件内容 | +| `size` | `Int` | 解码后字节数 | + +> **失败约定与其它接口不同**：失败返回 `"{}"`（而非 `"[]"`），消费方以 `data` 为空判定失败。 +> 空字符串返回表示「未连接」，`"{}"` 表示「连接正常但下载失败」——两者不可混为一谈。 + +--- + +## 事件契约（A 类，编译期检查） + +这些类型由 gomobile 直接导出为 Java 类，字段访问经编译器校验，**不需要也不应该** +为它们添加 JSON 字段常量。 + +### `TextMsg`（`EventCallback.OnTextMessage`） + +| 字段 | 类型 | 说明 | +| --- | --- | --- | +| `TargetMode` | `Int` | 见下方 targetMode 表 | +| `TargetID` | `String` | 目标 ID（字符串化） | +| `InvokerID` | `Int` | 发送者 clid；旧 AAR 可能为 `0` | +| `InvokerName` | `String` | 发送者昵称 | +| `InvokerUID` | `String` | 发送者 UID | +| `Message` | `String` | 消息内容（纯文本或文件消息 JSON） | + +### `PokeEvent`（`EventCallback.OnPoked`） + +| 字段 | 类型 | +| --- | --- | +| `InvokerID` | `Int` | +| `InvokerName` | `String` | +| `InvokerUID` | `String` | +| `Message` | `String` | + +--- + +## targetMode 收发能力对照（重要） + +`TargetMode` 的**接收**与**发送**能力不对称，读代码时极易误判为「三种都支持」： + +| 值 | 含义 | 接收 | 发送 | +| --- | --- | --- | --- | +| `1` | 私聊 | ✅ 支持 | ❌ **未实现** | +| `2` | 频道 | ✅ 支持 | ✅ 支持 | +| `3` | 服务器 | ✅ 支持 | ❌ **未实现** | + +`TSBridge.sendTextMessage` 仅实现 `mode=2`；其余分支返回显式错误串 +`"暂不支持该消息类型"`（按桥接约定：非空 = 错误），由 `ChatViewModel` 映射为 +`MessageSendState.Failed` 并将消息标记为 `FAILED`。 + +**当前状态：接收侧支持三种会话（通知 deep link 可跳转到指定会话），发送侧只有频道消息。** +私聊能收到、能弹通知，但无法在 App 内回复。 + +--- + +## 版本升级流程 + +任何**不兼容**变更（字段改名、类型变更、字段删除）必须： + +1. 递增 `go/teamspeak/contract.go` 的 `BridgeContractVersion` +2. 同步 `BridgeContract.kt` 的 `EXPECTED_VERSION` +3. 更新本文档 +4. 重编 AAR（`build.bat` 第 1 步） + +只**新增可选字段**（Kotlin 侧有默认值）不需要递增版本。 + +> 版本校验不匹配时，`ServerViewModel.connect()` 会以 `ConnectState.FAILED` +> 明确报错并阻止连接——而不是带着错配的字段名继续跑。 +> 旧 AAR（未导出 `getContractVersion()`）不阻断，仅记录 warning。 + +--- + +## 跑契约测试 + +```bash +# Go 侧：字段名常量 + golden 样本一致性 +cd go && go test ./teamspeak/ -run "Contract|Golden" -v + +# Kotlin 侧：反序列化 + 失效模式 +cd android && ./gradlew testDebugUnitTest --tests '*BridgeContractTest*' +``` + +**golden 样本位于** `android/app/src/test/resources/contract/`， +由 Go 与 Kotlin **共用**：Go 测试断言样本字段集合 == struct tag， +Kotlin 测试断言样本能正确反序列化。 +字段漂移时两侧都会变红——这是「改了字段名却无人发现」的兜底。 diff --git a/docs/项目工程改进方案.md b/docs/项目工程改进方案.md new file mode 100644 index 0000000..ddabf6d --- /dev/null +++ b/docs/项目工程改进方案.md @@ -0,0 +1,480 @@ +# TS Mobile 工程改进方案 + +> 编写依据：对 `android/`、`go/`、`.git` 的实测，以及 `CLAUDE.md`、`docs/` 现有文档的交叉核对。 +> 立场：本文不复述通用 Android 最佳实践，只针对这个项目因**双层跨语言架构**而产生的真实风险。 +> 所有结论均附实测证据，未验证的地方标注为「推测」。 + +--- + +## 一、我的核心判断 + +这个项目最大的风险不在 UI，也不在 Kotlin 代码质量，而在**Go 与 Kotlin 之间那道没有类型保护的边界**。 + +架构是：Go 协议层 → gomobile 编译成 `teamspeak.aar` → Kotlin 通过 JNI 调用。gomobile 有三条硬限制（`CLAUDE.md` 已记录）：不能导出 `[]string`、不能导出 `[]*T`、不能导出 Go `error`。为了绕过它们，项目选择了**用 JSON 字符串传所有复杂数据**。 + +这个选择的代价是： + +> **Go 侧改一个字段名，Kotlin 侧不会编译失败，只会在运行时静默拿到默认值。** + +`Repository.kt:18` 的 `Json { ignoreUnknownKeys = true }` 让这个失效模式更隐蔽——字段名对不上时不报错，`ChannelInfo` 直接用默认值 `""` / `0` / `false` 填充。结果是频道列表看起来是空的，或者密码频道显示成公开频道，而日志里什么都没有。 + +而这条边界目前是**完全无保护**的： + +| 保护手段 | 现状 | +| --- | --- | +| 编译期类型检查 | 无（JSON 字符串） | +| 契约文档 | 无（`docs/sdk-bridge-api.md` 存在但未核对与 `bridge.go` 是否同步） | +| 契约测试 | 无 | +| Schema 校验 | 无 | +| 版本标识 | 无（AAR 无版本号，`bridge.go` 无契约版本常量） | + +叠加第二个事实：**重编 AAR 是手工两步流程，且产物入了 git**。`CLAUDE.md` 专门用一整节记录「忘记加 `-linkmode=external` 会 SIGSEGV」「忘记 `-Wl,--hash-style=both` 会 dlopen 失败」——这两个坑被写进文档，说明它们已经真实咬过人。而 `android/app/libs/teamspeak.aar`（23.13 MB）被 git 跟踪，意味着**你无法从提交历史判断某个 APK 用的是哪一版协议层**。 + +所以我的改进主线是一条： + +> **先把跨语言契约固化成可验证的东西，再谈架构解耦，最后才谈 UI。** + +顺序不能反。在一个契约不可靠的地基上拆 ViewModel、重构 Compose，只会把不确定性扩散到更多文件里。 + +--- + +## 二、实测到的问题分级 + +### 第一梯队 · 止血（半天内可完成，阻断其他改进） + +#### 1.1 Release 包泄漏完整成员列表与 UID + +`proguard-rules.pro` 全文只有 3 行： + +``` +-keep class teamspeak.** { *; } +-keep class com.tsmobile.app.TSBridge { *; } +``` + +没有任何日志剥离规则。而全项目有 **197 处 `Log.*` 调用**（实测统计），其中 `Repository.performInitialSync` 在 `Repository.kt:89-94` 逐条打印： + +```kotlin +for (ch in channels) { + android.util.Log.d("Repository", "Channel: id=${ch.id}, name=${ch.name}, parentId=${ch.parentId}...") +} +for (cl in clients) { + android.util.Log.d("Repository", "Client: id=${cl.id}, nickname=${cl.nickname}, channelId=${cl.channelId}") +} +``` + +`applyClients` 还打印 UID 过滤细节。这些在 release 包里全部保留——任何人 `adb logcat` 就能拿到服务器完整的频道结构和所有成员的昵称 + UID。UID 在 TeamSpeak 里是永久身份标识，等同于账号 ID。 + +**改法**（`proguard-rules.pro` 追加）： + +```proguard +-assumenosideeffects class android.util.Log { + public static int v(...); + public static int d(...); + public static int i(...); + public static int w(...); +} +``` + +保留 `Log.e`。注意 `-assumenosideeffects` 只删调用，不删参数表达式的求值——`Repository.kt` 里这些日志的参数是字符串模板，含 `channels` 遍历，删除调用后循环体变空，R8 会一并消除。 + +同时应把 `performInitialSync` 里的全量 dump 改成 debug-only 或只打印数量。 + +**验收**：`assembleRelease` 后反编译 `classes.dex`，grep 不到 `"Channel: id="` 字面量。 +**成本**：低（~1 小时）。**风险**：低。这是本方案性价比最高的一项。 + +#### 1.2 构建产物入库，`.git` 已 236 MB + +实测被 git 跟踪的二进制： + +| 文件 | 大小 | 问题 | +| --- | --- | --- | +| `android/app/libs/teamspeak.aar` | 23.13 MB | 每次重编 AAR 都产生新版本，历史永久膨胀 | +| `android/.gradle/8.11.1/executionHistory/executionHistory.bin` | 9.98 MB | Gradle 本地构建缓存，纯垃圾 | +| `go/teamspeak/.opus/{lib,install,build}/{abi}/libopus.a` | ~3.3 MB × 4 ABI × 3 份 | 同一份库存了三次 | +| `third_party/opus/dnn/torch/osce/resources/training_files.txt` | 5.32 MB | PyTorch DNN 训练资源，Android 构建完全用不到 | + +`.gitignore` 里已经写了 `android/.gradle/` 和 `go/teamspeak/.opus/build/`，但**对已跟踪文件无效**——需要显式 `git rm --cached`。另外 `.gitignore` 有 `*.so`、`*.dylib`，**唯独漏了 `*.a`**，这是 libopus.a 全量入库的直接原因。 + +这里有一个需要你决策的权衡，我不替你定： + +- `libopus.a` 入库是**有意的**（`CLAUDE.md` 写明「Pre-built static libraries live in ... for all four Android ABIs」，避免每次交叉编译 opus）。但 `lib/`、`install/`、`build/` 三份内容相同，**至少可以去掉两份**，省 ~26 MB。 +- `teamspeak.aar` 入库则是纯负担。它应该由 `build.bat` 现场生成。如果你担心 clone 后没装 gomobile 无法构建，正确做法是 Git LFS 或把 AAR 挂到 Release 附件，而不是塞进 git 对象库。 + +**改法**： +``` +# .gitignore 追加 +*.a +``` +``` +git rm --cached android/.gradle -r +git rm --cached go/teamspeak/.opus/build -r +git rm --cached go/teamspeak/.opus/install -r +git rm --cached "android/app/src/main/cpp/third_party/opus/dnn" -r +``` + +`git rm --cached` 只从索引移除，**不删工作区文件**，构建不受影响。 + +至于清理已有的 236 MB 历史（`git filter-repo`），那是**不可逆的破坏性操作**，会重写所有 commit hash、要求所有克隆重新拉取。除非仓库已经大到影响日常操作，我不建议现在做。先止住增量即可。 + +**验收**：`git ls-files | ForEach-Object { ... }` 统计跟踪文件总体积，从当前 ~50 MB 降到 ~25 MB；新 clone 后 `build.bat` 能正常产出 APK。 +**成本**：低（~1 小时）。**风险**：中——`third_party/opus/dnn` 是否真的不参与构建需要你确认一次（如果 `CMakeLists.txt` 引用了它，删掉会编译失败）。 + +#### 1.3 顺带确认：密钥管理是正确的 + +我特意查了这一项，结论是**没问题，不用改**：`keystore/`、`*.jks`、`*.keystore`、`android/local.properties` 都在 `.gitignore` 里，且 `git ls-files keystore/` 返回空——签名密钥从未入库。`build.gradle.kts:28-32` 通过 `providers.gradleProperty()` 读密码而非硬编码，也是对的。 + +写在这里是为了避免你在后续清理时误以为这里也有问题。 + +--- + +### 第二梯队 · 契约固化（本方案的核心，1~2 周） + +#### 2.1 给 bridge 建立单一事实来源 + +**现状证据**：`TSBridge.kt:255-258` 有一个已经存在的契约缺口： + +```kotlin +fun sendTextMessage(targetMode: Int, targetId: String, message: String): String { + // TODO: 重新编译 AAR 后使用 client?.sendTextMessage(targetMode, targetId, message) + // 当前 AAR 只有 sendChannelMessage，对应 targetMode=2 + return when (targetMode) { +``` + +也就是说：**接收侧支持三种会话**（`ChatMessage.targetMode` 为 1=私聊 / 2=频道 / 3=服务器，`ServerViewModel.sendMessageNotification` 还会把标题区分为"频道消息"/"私聊消息"，通知 deep link 还能跳转到指定会话），但**发送侧只实现了频道消息**。私聊能收到、能弹通知，但用户无法回复——收发能力不对称。 + +需要说明的是，这里**没有**静默失败：`else` 分支返回的是显式错误串 `"暂不支持该消息类型"`，按桥接约定（非空=错误）会被上层识别为失败。这个处理是正确的。 + +但它暴露了真正的问题：**这个能力缺口只存在于代码注释里**。`docs/` 的 27 份文档中没有任何一处记录「发送仅支持 mode=2」，`ChatMessage.targetMode` 的字段注释还写着 `1=私聊, 2=频道, 3=服务器`，读起来像三种都支持。下一个人（或下一个 AI 助手）在实现私聊 UI 时，会假设发送链路是通的。 + +> 待核实：该错误串在 UI 上的呈现路径我没有追到底——`ChatViewModel.sendMessage` 是否把它映射成 `MessageSendState.Failed` 并让用户看见，需要实测确认。如果只落日志不弹提示，用户会以为消息发出去了。 + +这类「文档与代码不同步」的缺口只能靠显式契约发现。建议： + +1. 在 `go/teamspeak/` 下新建 `contract.go`，用一个常量集中声明契约版本与所有 JSON 字段名： + ```go + const BridgeContractVersion = "1" + + // 字段名常量，禁止在 marshal 处写字面量 + const ( + FieldChannelID = "id" + FieldChannelParentID = "parentId" + FieldChannelName = "name" + // ... + ) + ``` + 导出 `GetContractVersion()`，Kotlin 启动时校验，不匹配就明确报错而不是静默降级。 + +2. 在 `docs/` 下建 `bridge-contract.md`，逐个结构体列出：Go 类型 → JSON 字段 → Kotlin 数据类字段 → 是否可空 → 默认值语义。**每个字段标注双向支持状态**（如 `sendTextMessage: mode=2 已实现 / mode=1,3 未实现`）。 + +3. 把 `TSBridge.kt` 的 TODO 转成显式失败：未实现的 `targetMode` 返回明确错误串，让 `ChatViewModel` 能把消息标成 `FAILED` 而不是 `PENDING` 挂着。 + +**验收**：`bridge-contract.md` 覆盖 `bridge.go` 导出的全部结构体；启动时有契约版本校验；私聊发送失败会在 UI 上可见。 +**成本**：中（2~4 天，主要是梳理）。**风险**：低（纯增量，不改行为）。 + +#### 2.2 给契约加测试 + +这是整个方案里我认为**最该投入**的一项。 + +现状：Kotlin 侧只有 2 个测试文件（`EmojiMapperTest.kt`、`MixedPcmFrameTest.kt`），Go 侧自有测试只有 `receive_audio_test.go`（343 行，其余 19 个 `_test.go` 属 vendored 的 teamspeak-go，不算你的资产）。 + +而核心逻辑规模是：`ServerViewModel.kt` ~1355 行、`bridge.go` 1542 行、`kotlin_api.go` 625 行、`ChannelViewModel.kt` 613 行、`Repository.kt` 325 行——**这些全部零测试**。 + +不需要追求覆盖率。只需要针对「跨边界 + 纯逻辑」这两类写测试，投入产出比最高： + +**A. 契约往返测试（Go 侧）**——最高优先 +```go +func TestChannelInfoJSON_ContractStable(t *testing.T) { + // 固定输入 → 断言 JSON 字段名与类型 + // 任何人改字段名，这个测试立刻红 +} +``` + +**B. 反序列化健壮性（Kotlin 侧）** +用 Go 侧产出的真实 JSON 样本作为 test resource，断言 `ChannelInfo` / `ClientInfo` / `ServerInfo` 能正确解析，且缺字段时的默认值是预期的。**特别是 `ignoreUnknownKeys = true` 掩盖的那些场景**。 + +**C. 纯逻辑单元测试**——这些现在就能写，无需 mock Android： +- `InputValidator`（4 个校验函数，含 `address:port` 解析与 1..65535 边界） +- `Repository.confirmMessageDelivery` / `markPendingMessagesFailed`（消息送达确认的匹配逻辑，含 `senderId=0` 降级路径） +- `ServerViewModel.classifyError`（6 类错误映射） +- `ChannelViewModel.mapMoveError` +- 僵尸会话过滤 `applyClients`（`Repository.kt:172-187`，同 UID 不同 clid 的过滤规则，这是重连正确性的关键） + +`Repository` 是 `object` 单例，直接依赖 `TSBridge`，不好测。改法是抽出纯函数：把 `applyClients` 的过滤逻辑提成 `internal fun filterZombieSessions(clients: List, selfId: Int): List`，单独测。这类重构成本极低但立刻可测。 + +**验收**：CI 上跑通；改动 `bridge.go` 任一字段名会导致测试失败。 +**成本**：中（3~5 天）。**风险**：低。 + +#### 2.3 拆掉 ViewModel 之间的手工交叉引用 + +`NavGraph.kt:66-71`： + +```kotlin +LaunchedEffect(Unit) { + serverViewModel.channelViewModel = channelViewModel + serverViewModel.chatViewModel = chatViewModel + serverViewModel.voiceViewModel = voiceViewModel + channelViewModel.chatViewModel = chatViewModel +} +``` + +配合 `ServerViewModel.kt:85-89` 的三个 `@Volatile var ...ViewModel?`。 + +这是**用可变引用做依赖注入**，问题有三： +1. 赋值发生在 `LaunchedEffect` 里，即首帧之后。在此之前的任何 bridge 回调都会走 `?:` 兜底分支——`ServerViewModel.kt:1206/1213/1220` 三处 `?: viewModelScope.launch { Repository.refreshClientList() }` 就是这个兜底。 +2. ViewModel 生命周期由 `viewModel()` 在 NavGraph 作用域创建，理论上同生共死，但这个不变量没有任何机制保证，靠注释维系。 +3. 状态所有权模糊：`Repository` 是真相源，但 `ChannelViewModel` 也持有 `currentChannelId`、`channels`、`clients` 的镜像 StateFlow，两边都要同步。 + +**根因**是 Go 回调只有一个入口（`createBridgeCallbacks()`，`ServerViewModel.kt:993` 起，一个函数吞掉所有事件再手工分发）。 + +**改法**（渐进，不需要引入 DI 框架）： +1. 新建 `BridgeEventBus`（单例 object，内部 `MutableSharedFlow`）。 +2. `TSBridge.Callbacks` 的实现从 `ServerViewModel` 移到一个独立的 `BridgeDispatcher`（由 `App` 或 `ConnectionService` 持有，生命周期与应用一致，而非与 ViewModel 一致）。 +3. `BridgeDispatcher` 只做一件事：把 JNI 回调转成 sealed class 事件投递到 bus。 +4. 各 ViewModel 自己 `collect` 关心的事件，不再互相持有引用。 + +这一步同时解决了一个潜在 bug：**当前 ViewModel 被清除后（如配置变更、进程重建），bridge 回调就没人接了**。把 dispatcher 挂到应用级生命周期更稳。 + +**验收**：`ServerViewModel` 中不再出现其他 ViewModel 的类型引用；grep `@Volatile var.*ViewModel` 无结果。 +**成本**：高（4~7 天）。**风险**：中——涉及所有回调路径，**必须在 2.2 的测试到位之后做**。这是梯队顺序不能反的具体原因。 + +#### 2.4 拆分 ServerViewModel + +~1355 行，实测承担 **9 类职责**：连接生命周期、重连状态机（含 `reconnectLock` / `reconnectAttempt` / `reconnectJob` / `lastConnectParams` / `lastChannelId` 五个并发字段）、网络监控、系统通知构建与发送（`sendPokeSystemNotification` / `sendMessageNotification` / `clearNotifications` / `triggerVibration`）、Poke 气泡、主题切换、应用更新检测（`checkForUpdate` / `manualCheckUpdate` / `closeUpdateDialog` / `openReleaseUrl` / `dismissUpdate`）、表单状态、bridge 回调分发、错误分类。 + +其中**更新检测和主题切换与"服务器"毫无关系**，纯粹是历史堆积。 + +**改法**（按可独立性排序，前三项可立刻做，互不阻塞）： +| 抽出 | 目标 | 依赖 | +| --- | --- | --- | +| 更新检测（5 个方法 + `UpdateInfo` + `updatePreferences`） | `UpdateViewModel` | 无，完全独立 | +| 主题（`toggleTheme` + `themeMode` + `themePreferences`） | `SettingsViewModel` 或 `CompositionLocal` | 无 | +| 通知构建 | `NotificationHelper`（非 ViewModel，纯工具类） | 需 `Application` context | +| 重连状态机 | `ReconnectController` | 依赖 2.3 完成 | +| bridge 回调分发 | `BridgeDispatcher` | 即 2.3 | + +拆完 `ServerViewModel` 应只剩连接生命周期 + 表单状态，预计 ~350 行。 + +**成本**：中（前三项 2~3 天，后两项依赖 2.3）。**风险**：低~中。 + +--- + +### 第三梯队 · 状态模型收敛（1 周） + +#### 3.1 七个状态机，两处定义，语义重叠 + +实测项目里的状态机： + +| 状态机 | 位置 | 取值 | +| --- | --- | --- | +| `ConnectionState` | `Models.kt:160` | Connected / Disconnecting / Reconnecting(attempt,max,reason) / WaitingForNetwork(since) / Disconnected(reason,wasKicked) | +| `ConnectState` | `ServerViewModel.kt:34` | IDLE / CONNECTING / SUCCESS / FAILED / TIMEOUT | +| `SyncState` | `ChannelViewModel.kt:20` | Unsynced / Syncing / Synchronized / SyncFailed | +| `ChannelSwitchState` | `Models.kt` | Requesting / Failed / ... | +| `VoiceState` | `Models.kt:196` | Idle / Transmitting / Blocked(reason) | +| `MessageSendState` | `Models.kt:138` | Idle / Sending / Failed | +| `MessageDeliveryState` | `Models.kt:72` | PENDING / SENT / FAILED | + +`ConnectState` 和 `ConnectionState` 是**同一件事的两套表达**：一个是 enum 一个是 sealed class，一个管按钮外观一个管导航。`NavGraph.kt:103` 用 `connectionState` 驱动导航，`ServerConfigScreen` 用 `state.connectState` 驱动按钮，两者的转换点分散在 `doConnect()` 里。这就是「Connected 但 UI 还停在 CONNECTING」这类 bug 的温床。 + +`connectionState` 还用了 `StateFlow`，`null` 表示"未进入频道列表页"（`ServerViewModel.kt:101-103` 注释）——**用 null 编码一个业务状态**，每个消费点都要先判空。 + +**改法**： +1. 删除 `ConnectState`，`ServerScreenState` 直接引用 `ConnectionState` 的派生值。按钮外观由 `when(connectionState)` 计算，不作为独立状态存储。 +2. `ConnectionState` 增加 `object Idle`（或 `data class Initial`）替代 `null`。 +3. 把 `SyncState` 合并进 `ConnectionState`——「已连接但数据未就绪」本来就是一个连接阶段，不该是另一个维度。建议： + ```kotlin + sealed class ConnectionState { + object Idle + data class Connecting(val target: String) + data class Syncing(val progress: SyncPhase) // ← 原 SyncState + object Connected // 数据已就绪 + data class Reconnecting(val attempt: Int, val maxAttempts: Int, val reason: String) + data class WaitingForNetwork(val since: Long) + object Disconnecting + data class Disconnected(val reason: String, val wasKicked: Boolean) + } + ``` + 这样「不存在 Connected but no channel」从一个**需要靠 UI 层自律维持的约定**，变成**类型系统保证的不变量**——只有 `Syncing` 完成后才可能是 `Connected`。 + +这一条直接消除了现有设计里最容易出错的一块，而且让 `NavGraph` 的导航逻辑从「监听多个状态 + 判空」简化为「单一 `when`」。 + +**验收**：grep 不到 `ConnectState`；`connectionState` 类型不再是可空。 +**成本**：中（3~5 天）。**风险**：中——影响导航与所有 UI 消费点，需 2.2 的测试兜底。 + +#### 3.2 频道切换的乐观更新语义 + +`CLAUDE.md` 记录了两个 SDK 硬限制： +- 无 channel create/update/delete 事件，频道列表靠「5 分钟陈旧或切换前刷新」 +- `notifycliententerview` 的 ChannelID 不可靠，client enter/leave 触发**全量 clientlist 刷新** + +这两条对 UI 有直接影响，但代码里没有体现为显式设计： +- 全量刷新意味着 `_channelClients` 整个 Map 被替换（`Repository.kt:185`），成员列表会整体重组。高频进出场景（公共服务器）下这是持续的重组压力。 +- 频道树数据可能滞后 5 分钟，UI 却没有任何「数据可能不是最新」的表达。 + +**改法**： +1. `applyClients` 改为**差分更新**：对比新旧列表，只对变化的 channelId 更新 `_channelClients` 的对应条目，而不是 `groupBy` 全量替换。 +2. 给频道数据加 `fetchedAt` 时间戳，超过阈值时在 UI 上标注（如频道页下拉刷新 + "数据更新于 X 分钟前"）。 +3. 频道切换必须是**悲观**的：`ChannelSwitchState.Requesting` 期间禁用其他频道点击，等服务端确认或超时失败后回滚。现有 `ChannelConfirmDialog` + `switchState is ChannelSwitchState.Failed` 已经是这个思路，需要确认超时路径完整（**推测**：未见明确超时，若服务端不响应可能永久停在 Requesting——需实测）。 + +**成本**：中。**风险**：中（差分更新容易引入不一致，必须有测试）。 + +--- + +### 第四梯队 · UI 层（1~2 周，可与第三梯队并行） + +#### 4.1 一个真实的 Compose 误用 + +`ChannelListScreen.kt:276`： + +```kotlin +ClientActionMenu( + client = selectedClient!!, + isSelf = selectedClient!!.id == channelViewModel.selfClientId.collectAsState().value, +``` + +`collectAsState()` 在**参数表达式位置**调用。虽然 `ClientActionMenu` 是 `@Composable`，参数在 composable 作用域内求值，所以订阅能生效——但 `.value` 立即取值再传入，导致 `isSelf` 是**一次性快照**，`selfClientId` 变化时 `ClientActionMenu` 不会重组（它收到的是 Boolean，不是 State）。 + +同文件内 `selectedClient!!` 出现 9 次非空断言，配合 `var selectedClient by remember { mutableStateOf(null) }`。这是典型的「用 !! 绕过可空性」，一旦 `onDismiss` 与回调时序交错就会崩。 + +**改法**： +```kotlin +selectedClient?.let { client -> + val selfId by channelViewModel.selfClientId.collectAsState() + ClientActionMenu(client = client, isSelf = client.id == selfId, ...) +} +``` +用 `?.let` 消掉全部 `!!`，`collectAsState()` 提到函数体顶部。 + +**成本**：低（~1 小时）。**风险**：低。建议优先做，这是可复现的正确性问题。 + +#### 4.2 拆分 838 行的 ChannelListScreen + +该文件含 12 个 `@Composable`/私有函数，单函数最长约 200 行（`ChannelListScreen` 本体，含 4 个弹窗的编排）。13 个 `collectAsState()` 集中在顶部——**任何一个状态变化都会重组整个屏幕**，包括频道树。 + +**改法**： +1. 把 4 个弹窗（`ChannelDetailCard` / `ClientActionMenu` / `PokeDialog` / `ChannelConfirmDialog`）提成一个 `ChannelListDialogs` composable，状态用参数传入。 +2. `ChannelTreeContent` 已经独立（`:464`），但它自己 `collectAsState` 了 `channels` / `clients` / `expandedChannelIds` / `selfClientId` 四个流——保持这样是对的，不要提到父级。 +3. `ChannelRow` / `ClientRow` 加 `key`（LazyColumn 已用 `channelTreeNodeItems` 扩展，需确认 key 稳定性）。 +4. 高频变化的 Speaking 状态应下推到 `ClientRow` 内部订阅，不经过父级。 + +**成本**：中（2~3 天）。**风险**：低。 + +#### 4.3 硬编码尺寸 + +`ChannelListScreen.kt:48-55`： + +```kotlin +private val TREE_LEFT_INDENT = 24.5.dp +private val TREE_RIGHT_INDENT = 24.5.dp +private val FOOTER_LEFT_INDENT = 14.5.dp +private val FOOTER_RIGHT_INDENT = 14.5.dp +``` + +以及 `:569` 的 `depth * 24).dp` 内联计算、`:565` / `:645` 的 `padding(vertical = 12.dp)`。 + +`UiTokens.Spacing` 已经有完整档位（None/ExtraSmall/Small/Medium/Large/ExtraLarge/Huge），这些 24.5 / 14.5 是绕开 token 的局部修正值。**24.5dp 这种半像素值在非整数密度屏幕（如 2.75x）上会产生子像素对齐问题**，导致缩进线模糊。 + +另外 `depth * 24` 无上限——TeamSpeak 频道可深层嵌套，5 层以上在窄屏必然溢出。需要定 `maxIndentDepth`（建议 4）并对超限部分做截断或横向滚动。 + +**改法**：全部替换为 `UiTokens.Spacing.*`，缩进改为 `UiTokens.Spacing.Large * min(depth, 4)`。 + +**成本**：低。**风险**：低。 + +#### 4.4 EmojiMapper.kt 1989 行 + +项目最大的 Kotlin 文件，是手写 emoji 映射表。这类**纯数据**不该占编译单元：它拖慢增量编译（改一行重编 1989 行），且无法被非 Kotlin 侧复用。 + +**改法**：移到 `assets/emoji-map.json`，启动时懒加载；或用 KSP 生成。有 `EmojiMapperTest.kt` 兜底，迁移风险可控。 + +**成本**：低~中。**风险**：低。 + +--- + +### 第五梯队 · 门禁与协作（持续） + +#### 5.1 没有 CI + +实测：仓库内无 `.github/workflows/`（`third_party/opus/.gitlab-ci.yml` 是上游 opus 自带的，与本项目无关），无 detekt/ktlint 配置，无 `lint.xml`。 + +构建全靠本地 `build.bat` / `build.sh`，而这两个脚本封装的正是 `CLAUDE.md` 里记录过会踩坑的手工两步流程。**没有 CI 意味着那两个 SIGSEGV / DT_HASH 坑只能靠人记住。** + +**改法**：加一个最小 GitHub Actions（或任何你用的 CI）： +```yaml +jobs: + go: # cd go && go vet ./teamspeak/ && go test ./teamspeak/ + kotlin: # cd android && ./gradlew lint testDebug assembleDebug + aar: # 用固定 flags 重编 AAR，校验产物可被 dlopen（这条最关键） +``` +第三项最有价值：把 `build.bat` 里的 `-ldflags` 写成 CI 里的唯一真源，让「忘记加 flag」变成不可能。 + +同时加 `detekt` + `ktlint`（Gradle 插件，配置成本低），并把 `hardcoded dp/sp` 之类规则打开——这能从机制上防止 4.3 的问题复发，比写进规范文档有效得多。 + +**成本**：中（1~2 天搭好）。**风险**：低。 + +#### 5.2 文档体系需要收口 + +`docs/` 下现有：`UI架构设计.md`（3 页 3 卡）、`状态控制设计.md`、`sdk-bridge-api.md`、`sdk文档-go.md`、`TS Mobile UI UX 重构设计规范.md`、`流程/00~09`（10 份）、`implementation/00~12`（13 份）——**27 份文档，总计约 400 KB**。 + +问题不是文档少，是**没有权威源声明**。`UI架构设计.md` 描述的三页三卡架构与 `TS Mobile UI UX 重构设计规范.md` 的 Server Space 架构直接冲突，而代码实现的是前者。一个新加入的人（或一个 AI 助手）无法判断该信哪份。 + +**改法**：在 `docs/README.md` 建一个索引表，每份文档标注：状态（权威 / 历史 / 草案）、覆盖范围、最后核对日期、与代码的一致性。冲突的明确标注「已被 X 取代」。 + +`CLAUDE.md` 是目前**唯一与代码高度一致**的文档（Known SDK Limitations、Thread Safety、Critical Build Details 三节我逐条核对过，全部属实）。建议把它提升为文档体系的入口，其余文档从它链接出去。 + +**成本**：低（半天）。**风险**：无。 + +--- + +## 三、明确不建议做的事 + +这部分和上面同样重要——避免你把力气花在错的地方。 + +1. **不要现在引入 Hilt / Koin**。项目是单人维护规模（`versionCode 12`、`versionName 1.0.12`），ViewModel 交叉引用的问题用 2.3 的事件总线就能解决，引入 DI 框架会带来 KSP 编译开销和学习成本，收益不成比例。 + +2. **不要重写 Compose UI**。`ui/components/` 下 20 个组件的粒度基本合理（多数 100~300 行），真正失控的只有 `ChannelListScreen`。局部拆分即可。 + +3. **不要动 Go 音频管线**。`receive_audio.go` 是全项目唯一有像样测试的模块（343 行测试对 281 行实现），且 `CLAUDE.md` 记录了 jitter buffer / Opus 解码 / 立体声混音的完整设计与「Speaking 检测由 Go 侧负责，Kotlin 不得从 PCM 推断」的所有权划分。这块是项目质量最高的部分，改它风险最大收益最小。 + +4. **不要清理 git 历史**（除非仓库已影响日常操作）。见 1.2，先止住增量。 + +5. **不要在契约固化之前做 2.3 / 3.1**。这两项都会大范围移动回调与状态流转路径。没有测试网兜着做，等于在无保护状态下重写核心链路。**这是我把它排在第二梯队而非第一梯队的唯一原因**——不是它不重要，是它有前置依赖。 + +6. **不要追求测试覆盖率指标**。这个项目该测的是「跨边界的契约」和「纯逻辑的状态转换」，UI 层用 Compose Preview + 手工验证足够。定一个 60% 覆盖率目标会让你去测 getter/setter。 + +--- + +## 四、落地顺序 + +按依赖关系排，不是按重要性排： + +``` +第 1 步（半天） 1.1 Release 剥离日志 ← 隐私问题，独立，立刻做 +第 2 步（半天） 1.2 构建产物出库 ← 独立，立刻做 +第 3 步（1 小时） 4.1 修 collectAsState 误用 ← 独立，正确性问题 +第 4 步（1 天） 5.1 CI 最小门禁 ← 独立，且为后续所有步骤提供安全网 +───────────────────────────────────────── +第 5 步（2~4 天） 2.1 契约文档 + 版本常量 ← 后续步骤的地基 +第 6 步（3~5 天） 2.2 契约测试 + 纯逻辑测试 ← 2.3 / 3.1 的前置条件 +───────────────────────────────────────── +第 7 步（2~3 天） 2.4 拆出 Update / Theme / Notification ← 独立，可提前做 +第 8 步（4~7 天） 2.3 事件总线，消除 ViewModel 交叉引用 +第 9 步（3~5 天） 3.1 状态模型收敛 +───────────────────────────────────────── +第 10 步（并行） 4.2 / 4.3 / 4.4 UI 层清理 +第 11 步（并行） 3.2 差分更新 + 频道数据时效 +第 12 步（半天） 5.2 文档收口 +``` + +第 1~4 步互不依赖，可以在一天内全部完成，且立刻消除一个隐私泄漏、一个正确性 bug，并建立安全网。**建议就从这四步开始。** + +第 5~6 步是投资，做完之后第 8~9 步的风险从「高」降到「中」。 + +--- + +## 五、如果只做一件事 + +如果时间只够做一项，我会选 **2.2 的契约往返测试**。 + +理由：这个项目所有的结构性风险——JSON 字段静默失效、收发能力不对称却无处记录、AAR 与源码不同步、重编 flag 遗漏——本质上都是**同一个问题的不同表现：跨语言边界上没有可执行的验证**。而契约测试是唯一能用最小成本（一个 Go 测试文件 + 几个 Kotlin 测试）把这个边界钉住的手段。 + +其他所有改进都是在已知问题上打补丁；只有这一项能防止**你还不知道的问题**继续产生。 diff --git a/go/teamspeak/.opus/build/arm64-v8a/.ninja_deps b/go/teamspeak/.opus/build/arm64-v8a/.ninja_deps deleted file mode 100644 index e07a413..0000000 Binary files a/go/teamspeak/.opus/build/arm64-v8a/.ninja_deps and /dev/null differ diff --git a/go/teamspeak/.opus/build/arm64-v8a/.ninja_log b/go/teamspeak/.opus/build/arm64-v8a/.ninja_log deleted file mode 100644 index 0d24f51..0000000 --- a/go/teamspeak/.opus/build/arm64-v8a/.ninja_log +++ /dev/null @@ -1,148 +0,0 @@ -# ninja log v5 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-#elif defined(__WATCOMC__) -# define COMPILER_ID "OpenWatcom" - /* __WATCOMC__ = VVRP + 1100 */ -# define COMPILER_VERSION_MAJOR DEC((__WATCOMC__ - 1100) / 100) -# define COMPILER_VERSION_MINOR DEC((__WATCOMC__ / 10) % 10) -# if (__WATCOMC__ % 10) > 0 -# define COMPILER_VERSION_PATCH DEC(__WATCOMC__ % 10) -# endif - -#elif defined(__SUNPRO_C) -# define COMPILER_ID "SunPro" -# if __SUNPRO_C >= 0x5100 - /* __SUNPRO_C = 0xVRRP */ -# define COMPILER_VERSION_MAJOR HEX(__SUNPRO_C>>12) -# define COMPILER_VERSION_MINOR HEX(__SUNPRO_C>>4 & 0xFF) -# define COMPILER_VERSION_PATCH HEX(__SUNPRO_C & 0xF) -# else - /* __SUNPRO_CC = 0xVRP */ -# define COMPILER_VERSION_MAJOR HEX(__SUNPRO_C>>8) -# define COMPILER_VERSION_MINOR HEX(__SUNPRO_C>>4 & 0xF) -# define COMPILER_VERSION_PATCH HEX(__SUNPRO_C & 0xF) -# endif - -#elif defined(__HP_cc) -# define COMPILER_ID "HP" - /* __HP_cc = VVRRPP */ -# define COMPILER_VERSION_MAJOR DEC(__HP_cc/10000) -# define COMPILER_VERSION_MINOR DEC(__HP_cc/100 % 100) -# define COMPILER_VERSION_PATCH DEC(__HP_cc % 100) - -#elif defined(__DECC) -# define COMPILER_ID "Compaq" - /* __DECC_VER = VVRRTPPPP */ -# define COMPILER_VERSION_MAJOR DEC(__DECC_VER/10000000) -# define COMPILER_VERSION_MINOR DEC(__DECC_VER/100000 % 100) -# define COMPILER_VERSION_PATCH DEC(__DECC_VER % 10000) - -#elif defined(__IBMC__) && defined(__COMPILER_VER__) -# define COMPILER_ID "zOS" - /* __IBMC__ = VRP */ -# define COMPILER_VERSION_MAJOR DEC(__IBMC__/100) -# define COMPILER_VERSION_MINOR DEC(__IBMC__/10 % 10) -# define COMPILER_VERSION_PATCH DEC(__IBMC__ % 10) - -#elif defined(__ibmxl__) && defined(__clang__) -# define COMPILER_ID "XLClang" -# define COMPILER_VERSION_MAJOR DEC(__ibmxl_version__) -# define COMPILER_VERSION_MINOR DEC(__ibmxl_release__) -# define COMPILER_VERSION_PATCH DEC(__ibmxl_modification__) -# define COMPILER_VERSION_TWEAK DEC(__ibmxl_ptf_fix_level__) - - -#elif defined(__IBMC__) && !defined(__COMPILER_VER__) && __IBMC__ >= 800 -# define COMPILER_ID "XL" - /* __IBMC__ = VRP */ -# define COMPILER_VERSION_MAJOR DEC(__IBMC__/100) -# define COMPILER_VERSION_MINOR DEC(__IBMC__/10 % 10) -# define COMPILER_VERSION_PATCH DEC(__IBMC__ % 10) - -#elif defined(__IBMC__) && !defined(__COMPILER_VER__) && __IBMC__ < 800 -# define COMPILER_ID "VisualAge" - /* __IBMC__ = VRP */ -# define COMPILER_VERSION_MAJOR DEC(__IBMC__/100) -# define COMPILER_VERSION_MINOR DEC(__IBMC__/10 % 10) -# define COMPILER_VERSION_PATCH DEC(__IBMC__ % 10) - -#elif defined(__NVCOMPILER) -# define COMPILER_ID "NVHPC" -# define COMPILER_VERSION_MAJOR DEC(__NVCOMPILER_MAJOR__) -# define COMPILER_VERSION_MINOR DEC(__NVCOMPILER_MINOR__) -# if defined(__NVCOMPILER_PATCHLEVEL__) -# define COMPILER_VERSION_PATCH DEC(__NVCOMPILER_PATCHLEVEL__) -# endif - -#elif defined(__PGI) -# define COMPILER_ID "PGI" -# define COMPILER_VERSION_MAJOR DEC(__PGIC__) -# define COMPILER_VERSION_MINOR DEC(__PGIC_MINOR__) -# if defined(__PGIC_PATCHLEVEL__) -# define COMPILER_VERSION_PATCH DEC(__PGIC_PATCHLEVEL__) -# endif - -#elif defined(_CRAYC) -# define COMPILER_ID "Cray" -# define COMPILER_VERSION_MAJOR DEC(_RELEASE_MAJOR) -# define COMPILER_VERSION_MINOR DEC(_RELEASE_MINOR) - -#elif defined(__TI_COMPILER_VERSION__) -# define COMPILER_ID "TI" - /* __TI_COMPILER_VERSION__ = VVVRRRPPP */ -# define COMPILER_VERSION_MAJOR DEC(__TI_COMPILER_VERSION__/1000000) -# define COMPILER_VERSION_MINOR DEC(__TI_COMPILER_VERSION__/1000 % 1000) -# define COMPILER_VERSION_PATCH DEC(__TI_COMPILER_VERSION__ % 1000) - -#elif defined(__CLANG_FUJITSU) -# define COMPILER_ID "FujitsuClang" -# define COMPILER_VERSION_MAJOR DEC(__FCC_major__) -# define COMPILER_VERSION_MINOR DEC(__FCC_minor__) -# define COMPILER_VERSION_PATCH DEC(__FCC_patchlevel__) -# define COMPILER_VERSION_INTERNAL_STR __clang_version__ - - -#elif defined(__FUJITSU) -# define COMPILER_ID "Fujitsu" -# if defined(__FCC_version__) -# define COMPILER_VERSION __FCC_version__ -# elif defined(__FCC_major__) -# define COMPILER_VERSION_MAJOR DEC(__FCC_major__) -# define COMPILER_VERSION_MINOR DEC(__FCC_minor__) -# define COMPILER_VERSION_PATCH DEC(__FCC_patchlevel__) -# endif -# if defined(__fcc_version) -# define COMPILER_VERSION_INTERNAL DEC(__fcc_version) -# elif defined(__FCC_VERSION) -# define COMPILER_VERSION_INTERNAL DEC(__FCC_VERSION) -# endif - - -#elif defined(__ghs__) -# define COMPILER_ID "GHS" -/* __GHS_VERSION_NUMBER = VVVVRP */ -# ifdef __GHS_VERSION_NUMBER -# define COMPILER_VERSION_MAJOR DEC(__GHS_VERSION_NUMBER / 100) -# define COMPILER_VERSION_MINOR DEC(__GHS_VERSION_NUMBER / 10 % 10) -# define COMPILER_VERSION_PATCH DEC(__GHS_VERSION_NUMBER % 10) -# endif - -#elif defined(__TINYC__) -# define COMPILER_ID "TinyCC" - -#elif defined(__BCC__) -# define COMPILER_ID "Bruce" - -#elif defined(__SCO_VERSION__) -# define COMPILER_ID "SCO" - -#elif defined(__ARMCC_VERSION) && !defined(__clang__) -# define COMPILER_ID "ARMCC" -#if __ARMCC_VERSION >= 1000000 - /* __ARMCC_VERSION = VRRPPPP */ - # define COMPILER_VERSION_MAJOR DEC(__ARMCC_VERSION/1000000) - # define COMPILER_VERSION_MINOR DEC(__ARMCC_VERSION/10000 % 100) - # define COMPILER_VERSION_PATCH DEC(__ARMCC_VERSION % 10000) -#else - /* __ARMCC_VERSION = VRPPPP */ - # define COMPILER_VERSION_MAJOR DEC(__ARMCC_VERSION/100000) - # define COMPILER_VERSION_MINOR DEC(__ARMCC_VERSION/10000 % 10) - # define COMPILER_VERSION_PATCH DEC(__ARMCC_VERSION % 10000) -#endif - - -#elif defined(__clang__) && defined(__apple_build_version__) -# define COMPILER_ID "AppleClang" -# if defined(_MSC_VER) -# define SIMULATE_ID "MSVC" -# endif -# define COMPILER_VERSION_MAJOR DEC(__clang_major__) -# define COMPILER_VERSION_MINOR DEC(__clang_minor__) -# define COMPILER_VERSION_PATCH DEC(__clang_patchlevel__) -# if defined(_MSC_VER) - /* _MSC_VER = VVRR */ -# define SIMULATE_VERSION_MAJOR DEC(_MSC_VER / 100) -# define SIMULATE_VERSION_MINOR DEC(_MSC_VER % 100) -# endif -# define COMPILER_VERSION_TWEAK DEC(__apple_build_version__) - -#elif defined(__clang__) && defined(__ARMCOMPILER_VERSION) -# define COMPILER_ID "ARMClang" - # define COMPILER_VERSION_MAJOR DEC(__ARMCOMPILER_VERSION/1000000) - # define COMPILER_VERSION_MINOR DEC(__ARMCOMPILER_VERSION/10000 % 100) - # define COMPILER_VERSION_PATCH DEC(__ARMCOMPILER_VERSION % 10000) -# define COMPILER_VERSION_INTERNAL DEC(__ARMCOMPILER_VERSION) - -#elif defined(__clang__) -# define COMPILER_ID "Clang" -# if defined(_MSC_VER) -# define SIMULATE_ID "MSVC" -# endif -# define COMPILER_VERSION_MAJOR DEC(__clang_major__) -# define COMPILER_VERSION_MINOR DEC(__clang_minor__) -# define COMPILER_VERSION_PATCH DEC(__clang_patchlevel__) -# if defined(_MSC_VER) - /* _MSC_VER = VVRR */ -# define SIMULATE_VERSION_MAJOR DEC(_MSC_VER / 100) -# define SIMULATE_VERSION_MINOR DEC(_MSC_VER % 100) -# endif - -#elif defined(__GNUC__) -# define COMPILER_ID "GNU" -# define COMPILER_VERSION_MAJOR DEC(__GNUC__) -# if defined(__GNUC_MINOR__) -# define COMPILER_VERSION_MINOR DEC(__GNUC_MINOR__) -# endif -# if defined(__GNUC_PATCHLEVEL__) -# define COMPILER_VERSION_PATCH DEC(__GNUC_PATCHLEVEL__) -# endif - -#elif defined(_MSC_VER) -# define COMPILER_ID "MSVC" - /* _MSC_VER = VVRR */ -# define COMPILER_VERSION_MAJOR DEC(_MSC_VER / 100) -# define COMPILER_VERSION_MINOR DEC(_MSC_VER % 100) -# if defined(_MSC_FULL_VER) -# if _MSC_VER >= 1400 - /* _MSC_FULL_VER = VVRRPPPPP */ -# define COMPILER_VERSION_PATCH DEC(_MSC_FULL_VER % 100000) -# else - /* _MSC_FULL_VER = VVRRPPPP */ -# define COMPILER_VERSION_PATCH DEC(_MSC_FULL_VER % 10000) -# endif -# endif -# if defined(_MSC_BUILD) -# define COMPILER_VERSION_TWEAK DEC(_MSC_BUILD) -# endif - -#elif defined(__VISUALDSPVERSION__) || defined(__ADSPBLACKFIN__) || defined(__ADSPTS__) || defined(__ADSP21000__) -# define COMPILER_ID "ADSP" -#if defined(__VISUALDSPVERSION__) - /* __VISUALDSPVERSION__ = 0xVVRRPP00 */ -# define COMPILER_VERSION_MAJOR HEX(__VISUALDSPVERSION__>>24) -# define COMPILER_VERSION_MINOR HEX(__VISUALDSPVERSION__>>16 & 0xFF) -# define COMPILER_VERSION_PATCH HEX(__VISUALDSPVERSION__>>8 & 0xFF) -#endif - -#elif defined(__IAR_SYSTEMS_ICC__) || defined(__IAR_SYSTEMS_ICC) -# define COMPILER_ID "IAR" -# if defined(__VER__) && defined(__ICCARM__) -# define COMPILER_VERSION_MAJOR DEC((__VER__) / 1000000) -# define COMPILER_VERSION_MINOR DEC(((__VER__) / 1000) % 1000) -# define COMPILER_VERSION_PATCH DEC((__VER__) % 1000) -# define COMPILER_VERSION_INTERNAL DEC(__IAR_SYSTEMS_ICC__) -# elif defined(__VER__) && (defined(__ICCAVR__) || defined(__ICCRX__) || defined(__ICCRH850__) || defined(__ICCRL78__) || defined(__ICC430__) || defined(__ICCRISCV__) || defined(__ICCV850__) || defined(__ICC8051__) || defined(__ICCSTM8__)) -# define COMPILER_VERSION_MAJOR DEC((__VER__) / 100) -# define COMPILER_VERSION_MINOR DEC((__VER__) - (((__VER__) / 100)*100)) -# define COMPILER_VERSION_PATCH DEC(__SUBVERSION__) -# define COMPILER_VERSION_INTERNAL DEC(__IAR_SYSTEMS_ICC__) -# endif - -#elif defined(__SDCC_VERSION_MAJOR) || defined(SDCC) -# define COMPILER_ID "SDCC" -# if defined(__SDCC_VERSION_MAJOR) -# define COMPILER_VERSION_MAJOR DEC(__SDCC_VERSION_MAJOR) -# define COMPILER_VERSION_MINOR DEC(__SDCC_VERSION_MINOR) -# define COMPILER_VERSION_PATCH DEC(__SDCC_VERSION_PATCH) -# else - /* SDCC = VRP */ -# define COMPILER_VERSION_MAJOR DEC(SDCC/100) -# define COMPILER_VERSION_MINOR DEC(SDCC/10 % 10) -# define COMPILER_VERSION_PATCH DEC(SDCC % 10) -# endif - - -/* These compilers are either not known or too old to define an - identification macro. Try to identify the platform and guess that - it is the native compiler. */ -#elif defined(__hpux) || defined(__hpua) -# define COMPILER_ID "HP" - -#else /* unknown compiler */ -# define COMPILER_ID "" -#endif - -/* Construct the string literal in pieces to prevent the source from - getting matched. Store it in a pointer rather than an array - because some compilers will just produce instructions to fill the - array rather than assigning a pointer to a static array. */ -char const* info_compiler = "INFO" ":" "compiler[" COMPILER_ID "]"; -#ifdef SIMULATE_ID -char const* info_simulate = "INFO" ":" "simulate[" SIMULATE_ID "]"; -#endif - -#ifdef __QNXNTO__ -char const* qnxnto = "INFO" ":" "qnxnto[]"; -#endif - -#if defined(__CRAYXT_COMPUTE_LINUX_TARGET) -char const *info_cray = "INFO" ":" "compiler_wrapper[CrayPrgEnv]"; -#endif - -#define STRINGIFY_HELPER(X) #X -#define STRINGIFY(X) STRINGIFY_HELPER(X) - -/* Identify known platforms by name. */ -#if defined(__linux) || defined(__linux__) || defined(linux) -# define PLATFORM_ID "Linux" - -#elif defined(__MSYS__) -# define PLATFORM_ID "MSYS" - -#elif defined(__CYGWIN__) -# define PLATFORM_ID "Cygwin" - -#elif defined(__MINGW32__) -# define PLATFORM_ID "MinGW" - -#elif defined(__APPLE__) -# define PLATFORM_ID "Darwin" - -#elif defined(_WIN32) || defined(__WIN32__) || defined(WIN32) -# define PLATFORM_ID "Windows" - -#elif defined(__FreeBSD__) || defined(__FreeBSD) -# define PLATFORM_ID "FreeBSD" - -#elif defined(__NetBSD__) || defined(__NetBSD) -# define PLATFORM_ID "NetBSD" - -#elif defined(__OpenBSD__) || defined(__OPENBSD) -# define PLATFORM_ID "OpenBSD" - -#elif defined(__sun) || defined(sun) -# define PLATFORM_ID "SunOS" - -#elif defined(_AIX) || defined(__AIX) || defined(__AIX__) || defined(__aix) || defined(__aix__) -# define PLATFORM_ID "AIX" - -#elif defined(__hpux) || defined(__hpux__) -# define PLATFORM_ID "HP-UX" - -#elif defined(__HAIKU__) -# define PLATFORM_ID "Haiku" - -#elif defined(__BeOS) || defined(__BEOS__) || defined(_BEOS) -# define PLATFORM_ID "BeOS" - -#elif defined(__QNX__) || defined(__QNXNTO__) -# define PLATFORM_ID "QNX" - -#elif defined(__tru64) || defined(_tru64) || defined(__TRU64__) -# define PLATFORM_ID "Tru64" - -#elif defined(__riscos) || defined(__riscos__) -# define PLATFORM_ID "RISCos" - -#elif defined(__sinix) || defined(__sinix__) || defined(__SINIX__) -# define PLATFORM_ID "SINIX" - -#elif defined(__UNIX_SV__) -# define PLATFORM_ID "UNIX_SV" - -#elif defined(__bsdos__) -# define PLATFORM_ID "BSDOS" - -#elif defined(_MPRAS) || defined(MPRAS) -# define PLATFORM_ID "MP-RAS" - -#elif defined(__osf) || defined(__osf__) -# define PLATFORM_ID "OSF1" - -#elif defined(_SCO_SV) || defined(SCO_SV) || defined(sco_sv) -# define PLATFORM_ID "SCO_SV" - -#elif defined(__ultrix) || defined(__ultrix__) || defined(_ULTRIX) -# define PLATFORM_ID "ULTRIX" - -#elif defined(__XENIX__) || defined(_XENIX) || defined(XENIX) -# define PLATFORM_ID "Xenix" - -#elif defined(__WATCOMC__) -# if defined(__LINUX__) -# define PLATFORM_ID "Linux" - -# elif defined(__DOS__) -# define PLATFORM_ID "DOS" - -# elif defined(__OS2__) -# define PLATFORM_ID "OS2" - -# elif defined(__WINDOWS__) -# define PLATFORM_ID "Windows3x" - -# elif defined(__VXWORKS__) -# define PLATFORM_ID "VxWorks" - -# else /* unknown platform */ -# define PLATFORM_ID -# endif - -#elif defined(__INTEGRITY) -# if defined(INT_178B) -# define PLATFORM_ID "Integrity178" - -# else /* regular Integrity */ -# define PLATFORM_ID "Integrity" -# endif - -#else /* unknown platform */ -# define PLATFORM_ID - -#endif - -/* For windows compilers MSVC and Intel we can determine - the architecture of the compiler being used. This is because - the compilers do not have flags that can change the architecture, - but rather depend on which compiler is being used -*/ -#if defined(_WIN32) && defined(_MSC_VER) -# if defined(_M_IA64) -# define ARCHITECTURE_ID "IA64" - -# elif defined(_M_ARM64EC) -# define ARCHITECTURE_ID "ARM64EC" - -# elif defined(_M_X64) || defined(_M_AMD64) -# define ARCHITECTURE_ID "x64" - -# elif defined(_M_IX86) -# define ARCHITECTURE_ID "X86" - -# elif defined(_M_ARM64) -# define ARCHITECTURE_ID "ARM64" - -# elif defined(_M_ARM) -# if _M_ARM == 4 -# define ARCHITECTURE_ID "ARMV4I" -# elif _M_ARM == 5 -# define ARCHITECTURE_ID "ARMV5I" -# else -# define ARCHITECTURE_ID "ARMV" STRINGIFY(_M_ARM) -# endif - -# elif defined(_M_MIPS) -# define ARCHITECTURE_ID "MIPS" - -# elif defined(_M_SH) -# define ARCHITECTURE_ID "SHx" - -# else /* unknown architecture */ -# define ARCHITECTURE_ID "" -# endif - -#elif defined(__WATCOMC__) -# if defined(_M_I86) -# define ARCHITECTURE_ID "I86" - -# elif defined(_M_IX86) -# define ARCHITECTURE_ID "X86" - -# else /* unknown architecture */ -# define ARCHITECTURE_ID "" -# endif - -#elif defined(__IAR_SYSTEMS_ICC__) || defined(__IAR_SYSTEMS_ICC) -# if defined(__ICCARM__) -# define ARCHITECTURE_ID "ARM" - -# elif defined(__ICCRX__) -# define ARCHITECTURE_ID "RX" - -# elif defined(__ICCRH850__) -# define ARCHITECTURE_ID "RH850" - -# elif defined(__ICCRL78__) -# define ARCHITECTURE_ID "RL78" - -# elif defined(__ICCRISCV__) -# define ARCHITECTURE_ID "RISCV" - -# elif defined(__ICCAVR__) -# define ARCHITECTURE_ID "AVR" - -# elif defined(__ICC430__) -# define ARCHITECTURE_ID "MSP430" - -# elif defined(__ICCV850__) -# define ARCHITECTURE_ID "V850" - -# elif defined(__ICC8051__) -# define ARCHITECTURE_ID "8051" - -# elif defined(__ICCSTM8__) -# define ARCHITECTURE_ID "STM8" - -# else /* unknown architecture */ -# define ARCHITECTURE_ID "" -# endif - -#elif defined(__ghs__) -# if defined(__PPC64__) -# define ARCHITECTURE_ID "PPC64" - -# elif defined(__ppc__) -# define ARCHITECTURE_ID "PPC" - -# elif defined(__ARM__) -# define ARCHITECTURE_ID "ARM" - -# elif defined(__x86_64__) -# define ARCHITECTURE_ID "x64" - -# elif defined(__i386__) -# define ARCHITECTURE_ID "X86" - -# else /* unknown architecture */ -# define ARCHITECTURE_ID "" -# endif - -#elif defined(__TI_COMPILER_VERSION__) -# if defined(__TI_ARM__) -# define ARCHITECTURE_ID "ARM" - -# elif defined(__MSP430__) -# define ARCHITECTURE_ID "MSP430" - -# elif defined(__TMS320C28XX__) -# define ARCHITECTURE_ID "TMS320C28x" - -# elif defined(__TMS320C6X__) || defined(_TMS320C6X) -# define ARCHITECTURE_ID "TMS320C6x" - -# else /* unknown architecture */ -# define ARCHITECTURE_ID "" -# endif - -#else -# define ARCHITECTURE_ID -#endif - -/* Convert integer to decimal digit literals. */ -#define DEC(n) \ - ('0' + (((n) / 10000000)%10)), \ - ('0' + (((n) / 1000000)%10)), \ - ('0' + (((n) / 100000)%10)), \ - ('0' + (((n) / 10000)%10)), \ - ('0' + (((n) / 1000)%10)), \ - ('0' + (((n) / 100)%10)), \ - ('0' + (((n) / 10)%10)), \ - ('0' + ((n) % 10)) - -/* Convert integer to hex digit literals. */ -#define HEX(n) \ - ('0' + ((n)>>28 & 0xF)), \ - ('0' + ((n)>>24 & 0xF)), \ - ('0' + ((n)>>20 & 0xF)), \ - ('0' + ((n)>>16 & 0xF)), \ - ('0' + ((n)>>12 & 0xF)), \ - ('0' + ((n)>>8 & 0xF)), \ - ('0' + ((n)>>4 & 0xF)), \ - ('0' + ((n) & 0xF)) - -/* Construct a string literal encoding the version number. */ -#ifdef COMPILER_VERSION -char const* info_version = "INFO" ":" "compiler_version[" COMPILER_VERSION "]"; - -/* Construct a string literal encoding the version number components. */ -#elif defined(COMPILER_VERSION_MAJOR) -char const info_version[] = { - 'I', 'N', 'F', 'O', ':', - 'c','o','m','p','i','l','e','r','_','v','e','r','s','i','o','n','[', - COMPILER_VERSION_MAJOR, -# ifdef COMPILER_VERSION_MINOR - '.', COMPILER_VERSION_MINOR, -# ifdef COMPILER_VERSION_PATCH - '.', COMPILER_VERSION_PATCH, -# ifdef COMPILER_VERSION_TWEAK - '.', COMPILER_VERSION_TWEAK, -# endif -# endif -# endif - ']','\0'}; -#endif - -/* Construct a string literal encoding the internal version number. */ -#ifdef COMPILER_VERSION_INTERNAL -char const info_version_internal[] = { - 'I', 'N', 'F', 'O', ':', - 'c','o','m','p','i','l','e','r','_','v','e','r','s','i','o','n','_', - 'i','n','t','e','r','n','a','l','[', - COMPILER_VERSION_INTERNAL,']','\0'}; -#elif defined(COMPILER_VERSION_INTERNAL_STR) -char const* info_version_internal = "INFO" ":" "compiler_version_internal[" COMPILER_VERSION_INTERNAL_STR "]"; -#endif - -/* Construct a string literal encoding the version number components. */ -#ifdef SIMULATE_VERSION_MAJOR -char const info_simulate_version[] = { - 'I', 'N', 'F', 'O', ':', - 's','i','m','u','l','a','t','e','_','v','e','r','s','i','o','n','[', - SIMULATE_VERSION_MAJOR, -# ifdef SIMULATE_VERSION_MINOR - '.', SIMULATE_VERSION_MINOR, -# ifdef SIMULATE_VERSION_PATCH - '.', SIMULATE_VERSION_PATCH, -# ifdef SIMULATE_VERSION_TWEAK - '.', SIMULATE_VERSION_TWEAK, -# endif -# endif -# endif - ']','\0'}; -#endif - -/* Construct the string literal in pieces to prevent the source from - getting matched. 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file mode 100644 index 78a3e9c..0000000 --- a/go/teamspeak/.opus/build/x86_64/config.h +++ /dev/null @@ -1 +0,0 @@ -/* #undef PACKAGE_VERSION */ diff --git a/go/teamspeak/.opus/build/x86_64/install_manifest.txt b/go/teamspeak/.opus/build/x86_64/install_manifest.txt deleted file mode 100644 index 0b55008..0000000 --- a/go/teamspeak/.opus/build/x86_64/install_manifest.txt +++ /dev/null @@ -1,6 +0,0 @@ -E:/MyProject/ts-mobile-go/go/teamspeak/.opus/install/x86_64/lib/libopus.a -E:/MyProject/ts-mobile-go/go/teamspeak/.opus/install/x86_64/include/opus/opus.h -E:/MyProject/ts-mobile-go/go/teamspeak/.opus/install/x86_64/include/opus/opus_defines.h -E:/MyProject/ts-mobile-go/go/teamspeak/.opus/install/x86_64/include/opus/opus_multistream.h -E:/MyProject/ts-mobile-go/go/teamspeak/.opus/install/x86_64/include/opus/opus_projection.h -E:/MyProject/ts-mobile-go/go/teamspeak/.opus/install/x86_64/include/opus/opus_types.h \ No newline at end of file diff --git a/go/teamspeak/.opus/build/x86_64/libopus.a b/go/teamspeak/.opus/build/x86_64/libopus.a deleted file mode 100644 index c68c3f3..0000000 Binary files a/go/teamspeak/.opus/build/x86_64/libopus.a and /dev/null differ diff --git a/go/teamspeak/.opus/install/arm64-v8a/include/opus/opus.h b/go/teamspeak/.opus/install/arm64-v8a/include/opus/opus.h deleted file mode 100644 index eadeda7..0000000 --- a/go/teamspeak/.opus/install/arm64-v8a/include/opus/opus.h +++ /dev/null @@ -1,1099 +0,0 @@ -/* Copyright (c) 2010-2011 Xiph.Org Foundation, Skype Limited - Written by Jean-Marc Valin and Koen Vos */ -/* - Redistribution and use in source and binary forms, with or without - modification, are permitted provided that the following conditions - are met: - - - Redistributions of source code must retain the above copyright - notice, this list of conditions and the following disclaimer. - - - Redistributions in binary form must reproduce the above copyright - notice, this list of conditions and the following disclaimer in the - documentation and/or other materials provided with the distribution. - - THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS - ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT - LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR - A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER - OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, - EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, - PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR - PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF - LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING - NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS - SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. -*/ - -/** - * @file opus.h - * @brief Opus reference implementation API - */ - -#ifndef OPUS_H -#define OPUS_H - -#include "opus_types.h" -#include "opus_defines.h" - -#ifdef __cplusplus -extern "C" { -#endif - -/** - * @mainpage Opus - * - * The Opus codec is designed for interactive speech and audio transmission over the Internet. - * It is designed by the IETF Codec Working Group and incorporates technology from - * Skype's SILK codec and Xiph.Org's CELT codec. - * - * The Opus codec is designed to handle a wide range of interactive audio applications, - * including Voice over IP, videoconferencing, in-game chat, and even remote live music - * performances. It can scale from low bit-rate narrowband speech to very high quality - * stereo music. Its main features are: - - * @li Sampling rates from 8 to 48 kHz - * @li Bit-rates from 6 kb/s to 510 kb/s - * @li Support for both constant bit-rate (CBR) and variable bit-rate (VBR) - * @li Audio bandwidth from narrowband to full-band - * @li Support for speech and music - * @li Support for mono and stereo - * @li Support for multichannel (up to 255 channels) - * @li Frame sizes from 2.5 ms to 60 ms - * @li Good loss robustness and packet loss concealment (PLC) - * @li Floating point and fixed-point implementation - * - * Documentation sections: - * @li @ref opus_encoder - * @li @ref opus_decoder - * @li @ref opus_repacketizer - * @li @ref opus_multistream - * @li @ref opus_libinfo - * @li @ref opus_custom - */ - -/** @defgroup opus_encoder Opus Encoder - * @{ - * - * @brief This page describes the process and functions used to encode Opus. - * - * Since Opus is a stateful codec, the encoding process starts with creating an encoder - * state. This can be done with: - * - * @code - * int error; - * OpusEncoder *enc; - * enc = opus_encoder_create(Fs, channels, application, &error); - * @endcode - * - * From this point, @c enc can be used for encoding an audio stream. An encoder state - * @b must @b not be used for more than one stream at the same time. Similarly, the encoder - * state @b must @b not be re-initialized for each frame. - * - * While opus_encoder_create() allocates memory for the state, it's also possible - * to initialize pre-allocated memory: - * - * @code - * int size; - * int error; - * OpusEncoder *enc; - * size = opus_encoder_get_size(channels); - * enc = malloc(size); - * error = opus_encoder_init(enc, Fs, channels, application); - * @endcode - * - * where opus_encoder_get_size() returns the required size for the encoder state. Note that - * future versions of this code may change the size, so no assumptions should be made about it. - * - * The encoder state is always continuous in memory and only a shallow copy is sufficient - * to copy it (e.g. memcpy()) - * - * It is possible to change some of the encoder's settings using the opus_encoder_ctl() - * interface. All these settings already default to the recommended value, so they should - * only be changed when necessary. The most common settings one may want to change are: - * - * @code - * opus_encoder_ctl(enc, OPUS_SET_BITRATE(bitrate)); - * opus_encoder_ctl(enc, OPUS_SET_COMPLEXITY(complexity)); - * opus_encoder_ctl(enc, OPUS_SET_SIGNAL(signal_type)); - * @endcode - * - * where - * - * @arg bitrate is in bits per second (b/s) - * @arg complexity is a value from 1 to 10, where 1 is the lowest complexity and 10 is the highest - * @arg signal_type is either OPUS_AUTO (default), OPUS_SIGNAL_VOICE, or OPUS_SIGNAL_MUSIC - * - * See @ref opus_encoderctls and @ref opus_genericctls for a complete list of parameters that can be set or queried. Most parameters can be set or changed at any time during a stream. - * - * To encode a frame, opus_encode() or opus_encode_float() must be called with exactly one frame (2.5, 5, 10, 20, 40 or 60 ms) of audio data: - * @code - * len = opus_encode(enc, audio_frame, frame_size, packet, max_packet); - * @endcode - * - * where - *
    - *
  • audio_frame is the audio data in opus_int16 (or float for opus_encode_float())
    • - *
  • frame_size is the duration of the frame in samples (per channel)
    • - *
  • packet is the byte array to which the compressed data is written
    • - *
  • max_packet is the maximum number of bytes that can be written in the packet (4000 bytes is recommended). - * Do not use max_packet to control VBR target bitrate, instead use the #OPUS_SET_BITRATE CTL.
    • - *
- * - * opus_encode() and opus_encode_float() return the number of bytes actually written to the packet. - * The return value can be negative, which indicates that an error has occurred. If the return value - * is 2 bytes or less, then the packet does not need to be transmitted (DTX). - * - * Once the encoder state if no longer needed, it can be destroyed with - * - * @code - * opus_encoder_destroy(enc); - * @endcode - * - * If the encoder was created with opus_encoder_init() rather than opus_encoder_create(), - * then no action is required aside from potentially freeing the memory that was manually - * allocated for it (calling free(enc) for the example above) - * - */ - -/** Opus encoder state. - * This contains the complete state of an Opus encoder. - * It is position independent and can be freely copied. - * @see opus_encoder_create,opus_encoder_init - */ -typedef struct OpusEncoder OpusEncoder; - -/** Gets the size of an OpusEncoder structure. - * @param[in] channels int: Number of channels. - * This must be 1 or 2. - * @returns The size in bytes. - */ -OPUS_EXPORT OPUS_WARN_UNUSED_RESULT int opus_encoder_get_size(int channels); - -/** - */ - -/** Allocates and initializes an encoder state. - * There are three coding modes: - * - * @ref OPUS_APPLICATION_VOIP gives best quality at a given bitrate for voice - * signals. It enhances the input signal by high-pass filtering and - * emphasizing formants and harmonics. Optionally it includes in-band - * forward error correction to protect against packet loss. Use this - * mode for typical VoIP applications. Because of the enhancement, - * even at high bitrates the output may sound different from the input. - * - * @ref OPUS_APPLICATION_AUDIO gives best quality at a given bitrate for most - * non-voice signals like music. Use this mode for music and mixed - * (music/voice) content, broadcast, and applications requiring less - * than 15 ms of coding delay. - * - * @ref OPUS_APPLICATION_RESTRICTED_LOWDELAY configures low-delay mode that - * disables the speech-optimized mode in exchange for slightly reduced delay. - * This mode can only be set on an newly initialized or freshly reset encoder - * because it changes the codec delay. - * - * This is useful when the caller knows that the speech-optimized modes will not be needed (use with caution). - * @param [in] Fs opus_int32: Sampling rate of input signal (Hz) - * This must be one of 8000, 12000, 16000, - * 24000, or 48000. - * @param [in] channels int: Number of channels (1 or 2) in input signal - * @param [in] application int: Coding mode (one of @ref OPUS_APPLICATION_VOIP, @ref OPUS_APPLICATION_AUDIO, or @ref OPUS_APPLICATION_RESTRICTED_LOWDELAY) - * @param [out] error int*: @ref opus_errorcodes - * @note Regardless of the sampling rate and number channels selected, the Opus encoder - * can switch to a lower audio bandwidth or number of channels if the bitrate - * selected is too low. This also means that it is safe to always use 48 kHz stereo input - * and let the encoder optimize the encoding. - */ -OPUS_EXPORT OPUS_WARN_UNUSED_RESULT OpusEncoder *opus_encoder_create( - opus_int32 Fs, - int channels, - int application, - int *error -); - -/** Initializes a previously allocated encoder state - * The memory pointed to by st must be at least the size returned by opus_encoder_get_size(). - * This is intended for applications which use their own allocator instead of malloc. - * @see opus_encoder_create(),opus_encoder_get_size() - * To reset a previously initialized state, use the #OPUS_RESET_STATE CTL. - * @param [in] st OpusEncoder*: Encoder state - * @param [in] Fs opus_int32: Sampling rate of input signal (Hz) - * This must be one of 8000, 12000, 16000, - * 24000, or 48000. - * @param [in] channels int: Number of channels (1 or 2) in input signal - * @param [in] application int: Coding mode (one of OPUS_APPLICATION_VOIP, OPUS_APPLICATION_AUDIO, or OPUS_APPLICATION_RESTRICTED_LOWDELAY) - * @retval #OPUS_OK Success or @ref opus_errorcodes - */ -OPUS_EXPORT int opus_encoder_init( - OpusEncoder *st, - opus_int32 Fs, - int channels, - int application -) OPUS_ARG_NONNULL(1); - -/** Encodes an Opus frame. - * @param [in] st OpusEncoder*: Encoder state - * @param [in] pcm opus_int16*: Input signal (interleaved if 2 channels). length is frame_size*channels*sizeof(opus_int16) - * @param [in] frame_size int: Number of samples per channel in the - * input signal. - * This must be an Opus frame size for - * the encoder's sampling rate. - * For example, at 48 kHz the permitted - * values are 120, 240, 480, 960, 1920, - * and 2880. - * Passing in a duration of less than - * 10 ms (480 samples at 48 kHz) will - * prevent the encoder from using the LPC - * or hybrid modes. - * @param [out] data unsigned char*: Output payload. - * This must contain storage for at - * least \a max_data_bytes. - * @param [in] max_data_bytes opus_int32: Size of the allocated - * memory for the output - * payload. This may be - * used to impose an upper limit on - * the instant bitrate, but should - * not be used as the only bitrate - * control. Use #OPUS_SET_BITRATE to - * control the bitrate. - * @returns The length of the encoded packet (in bytes) on success or a - * negative error code (see @ref opus_errorcodes) on failure. - */ -OPUS_EXPORT OPUS_WARN_UNUSED_RESULT opus_int32 opus_encode( - OpusEncoder *st, - const opus_int16 *pcm, - int frame_size, - unsigned char *data, - opus_int32 max_data_bytes -) OPUS_ARG_NONNULL(1) OPUS_ARG_NONNULL(2) OPUS_ARG_NONNULL(4); - -/** Encodes an Opus frame from floating point input. - * @param [in] st OpusEncoder*: Encoder state - * @param [in] pcm float*: Input in float format (interleaved if 2 channels), with a normal range of +/-1.0. - * Samples with a range beyond +/-1.0 are supported but will - * be clipped by decoders using the integer API and should - * only be used if it is known that the far end supports - * extended dynamic range. - * length is frame_size*channels*sizeof(float) - * @param [in] frame_size int: Number of samples per channel in the - * input signal. - * This must be an Opus frame size for - * the encoder's sampling rate. - * For example, at 48 kHz the permitted - * values are 120, 240, 480, 960, 1920, - * and 2880. - * Passing in a duration of less than - * 10 ms (480 samples at 48 kHz) will - * prevent the encoder from using the LPC - * or hybrid modes. - * @param [out] data unsigned char*: Output payload. - * This must contain storage for at - * least \a max_data_bytes. - * @param [in] max_data_bytes opus_int32: Size of the allocated - * memory for the output - * payload. This may be - * used to impose an upper limit on - * the instant bitrate, but should - * not be used as the only bitrate - * control. Use #OPUS_SET_BITRATE to - * control the bitrate. - * @returns The length of the encoded packet (in bytes) on success or a - * negative error code (see @ref opus_errorcodes) on failure. - */ -OPUS_EXPORT OPUS_WARN_UNUSED_RESULT opus_int32 opus_encode_float( - OpusEncoder *st, - const float *pcm, - int frame_size, - unsigned char *data, - opus_int32 max_data_bytes -) OPUS_ARG_NONNULL(1) OPUS_ARG_NONNULL(2) OPUS_ARG_NONNULL(4); - -/** Frees an OpusEncoder allocated by opus_encoder_create(). - * @param[in] st OpusEncoder*: State to be freed. - */ -OPUS_EXPORT void opus_encoder_destroy(OpusEncoder *st); - -/** Perform a CTL function on an Opus encoder. - * - * Generally the request and subsequent arguments are generated - * by a convenience macro. - * @param st OpusEncoder*: Encoder state. - * @param request This and all remaining parameters should be replaced by one - * of the convenience macros in @ref opus_genericctls or - * @ref opus_encoderctls. - * @see opus_genericctls - * @see opus_encoderctls - */ -OPUS_EXPORT int opus_encoder_ctl(OpusEncoder *st, int request, ...) OPUS_ARG_NONNULL(1); -/**@}*/ - -/** @defgroup opus_decoder Opus Decoder - * @{ - * - * @brief This page describes the process and functions used to decode Opus. - * - * The decoding process also starts with creating a decoder - * state. This can be done with: - * @code - * int error; - * OpusDecoder *dec; - * dec = opus_decoder_create(Fs, channels, &error); - * @endcode - * where - * @li Fs is the sampling rate and must be 8000, 12000, 16000, 24000, or 48000 - * @li channels is the number of channels (1 or 2) - * @li error will hold the error code in case of failure (or #OPUS_OK on success) - * @li the return value is a newly created decoder state to be used for decoding - * - * While opus_decoder_create() allocates memory for the state, it's also possible - * to initialize pre-allocated memory: - * @code - * int size; - * int error; - * OpusDecoder *dec; - * size = opus_decoder_get_size(channels); - * dec = malloc(size); - * error = opus_decoder_init(dec, Fs, channels); - * @endcode - * where opus_decoder_get_size() returns the required size for the decoder state. Note that - * future versions of this code may change the size, so no assumptions should be made about it. - * - * The decoder state is always continuous in memory and only a shallow copy is sufficient - * to copy it (e.g. memcpy()) - * - * To decode a frame, opus_decode() or opus_decode_float() must be called with a packet of compressed audio data: - * @code - * frame_size = opus_decode(dec, packet, len, decoded, max_size, 0); - * @endcode - * where - * - * @li packet is the byte array containing the compressed data - * @li len is the exact number of bytes contained in the packet - * @li decoded is the decoded audio data in opus_int16 (or float for opus_decode_float()) - * @li max_size is the max duration of the frame in samples (per channel) that can fit into the decoded_frame array - * - * opus_decode() and opus_decode_float() return the number of samples (per channel) decoded from the packet. - * If that value is negative, then an error has occurred. This can occur if the packet is corrupted or if the audio - * buffer is too small to hold the decoded audio. - * - * Opus is a stateful codec with overlapping blocks and as a result Opus - * packets are not coded independently of each other. Packets must be - * passed into the decoder serially and in the correct order for a correct - * decode. Lost packets can be replaced with loss concealment by calling - * the decoder with a null pointer and zero length for the missing packet. - * - * A single codec state may only be accessed from a single thread at - * a time and any required locking must be performed by the caller. Separate - * streams must be decoded with separate decoder states and can be decoded - * in parallel unless the library was compiled with NONTHREADSAFE_PSEUDOSTACK - * defined. - * - */ - -/** Opus decoder state. - * This contains the complete state of an Opus decoder. - * It is position independent and can be freely copied. - * @see opus_decoder_create,opus_decoder_init - */ -typedef struct OpusDecoder OpusDecoder; - -/** Opus DRED decoder. - * This contains the complete state of an Opus DRED decoder. - * It is position independent and can be freely copied. - * @see opus_dred_decoder_create,opus_dred_decoder_init - */ -typedef struct OpusDREDDecoder OpusDREDDecoder; - - -/** Opus DRED state. - * This contains the complete state of an Opus DRED packet. - * It is position independent and can be freely copied. - * @see opus_dred_create,opus_dred_init - */ -typedef struct OpusDRED OpusDRED; - -/** Gets the size of an OpusDecoder structure. - * @param [in] channels int: Number of channels. - * This must be 1 or 2. - * @returns The size in bytes. - */ -OPUS_EXPORT OPUS_WARN_UNUSED_RESULT int opus_decoder_get_size(int channels); - -/** Allocates and initializes a decoder state. - * @param [in] Fs opus_int32: Sample rate to decode at (Hz). - * This must be one of 8000, 12000, 16000, - * 24000, or 48000. - * @param [in] channels int: Number of channels (1 or 2) to decode - * @param [out] error int*: #OPUS_OK Success or @ref opus_errorcodes - * - * Internally Opus stores data at 48000 Hz, so that should be the default - * value for Fs. However, the decoder can efficiently decode to buffers - * at 8, 12, 16, and 24 kHz so if for some reason the caller cannot use - * data at the full sample rate, or knows the compressed data doesn't - * use the full frequency range, it can request decoding at a reduced - * rate. Likewise, the decoder is capable of filling in either mono or - * interleaved stereo pcm buffers, at the caller's request. - */ -OPUS_EXPORT OPUS_WARN_UNUSED_RESULT OpusDecoder *opus_decoder_create( - opus_int32 Fs, - int channels, - int *error -); - -/** Initializes a previously allocated decoder state. - * The state must be at least the size returned by opus_decoder_get_size(). - * This is intended for applications which use their own allocator instead of malloc. @see opus_decoder_create,opus_decoder_get_size - * To reset a previously initialized state, use the #OPUS_RESET_STATE CTL. - * @param [in] st OpusDecoder*: Decoder state. - * @param [in] Fs opus_int32: Sampling rate to decode to (Hz). - * This must be one of 8000, 12000, 16000, - * 24000, or 48000. - * @param [in] channels int: Number of channels (1 or 2) to decode - * @retval #OPUS_OK Success or @ref opus_errorcodes - */ -OPUS_EXPORT int opus_decoder_init( - OpusDecoder *st, - opus_int32 Fs, - int channels -) OPUS_ARG_NONNULL(1); - -/** Decode an Opus packet. - * @param [in] st OpusDecoder*: Decoder state - * @param [in] data char*: Input payload. Use a NULL pointer to indicate packet loss - * @param [in] len opus_int32: Number of bytes in payload* - * @param [out] pcm opus_int16*: Output signal (interleaved if 2 channels). length - * is frame_size*channels*sizeof(opus_int16) - * @param [in] frame_size Number of samples per channel of available space in \a pcm. - * If this is less than the maximum packet duration (120ms; 5760 for 48kHz), this function will - * not be capable of decoding some packets. In the case of PLC (data==NULL) or FEC (decode_fec=1), - * then frame_size needs to be exactly the duration of audio that is missing, otherwise the - * decoder will not be in the optimal state to decode the next incoming packet. For the PLC and - * FEC cases, frame_size must be a multiple of 2.5 ms. - * @param [in] decode_fec int: Flag (0 or 1) to request that any in-band forward error correction data be - * decoded. If no such data is available, the frame is decoded as if it were lost. - * @returns Number of decoded samples or @ref opus_errorcodes - */ -OPUS_EXPORT OPUS_WARN_UNUSED_RESULT int opus_decode( - OpusDecoder *st, - const unsigned char *data, - opus_int32 len, - opus_int16 *pcm, - int frame_size, - int decode_fec -) OPUS_ARG_NONNULL(1) OPUS_ARG_NONNULL(4); - -/** Decode an Opus packet with floating point output. - * @param [in] st OpusDecoder*: Decoder state - * @param [in] data char*: Input payload. Use a NULL pointer to indicate packet loss - * @param [in] len opus_int32: Number of bytes in payload - * @param [out] pcm float*: Output signal (interleaved if 2 channels). length - * is frame_size*channels*sizeof(float) - * @param [in] frame_size Number of samples per channel of available space in \a pcm. - * If this is less than the maximum packet duration (120ms; 5760 for 48kHz), this function will - * not be capable of decoding some packets. In the case of PLC (data==NULL) or FEC (decode_fec=1), - * then frame_size needs to be exactly the duration of audio that is missing, otherwise the - * decoder will not be in the optimal state to decode the next incoming packet. For the PLC and - * FEC cases, frame_size must be a multiple of 2.5 ms. - * @param [in] decode_fec int: Flag (0 or 1) to request that any in-band forward error correction data be - * decoded. If no such data is available the frame is decoded as if it were lost. - * @returns Number of decoded samples or @ref opus_errorcodes - */ -OPUS_EXPORT OPUS_WARN_UNUSED_RESULT int opus_decode_float( - OpusDecoder *st, - const unsigned char *data, - opus_int32 len, - float *pcm, - int frame_size, - int decode_fec -) OPUS_ARG_NONNULL(1) OPUS_ARG_NONNULL(4); - -/** Perform a CTL function on an Opus decoder. - * - * Generally the request and subsequent arguments are generated - * by a convenience macro. - * @param st OpusDecoder*: Decoder state. - * @param request This and all remaining parameters should be replaced by one - * of the convenience macros in @ref opus_genericctls or - * @ref opus_decoderctls. - * @see opus_genericctls - * @see opus_decoderctls - */ -OPUS_EXPORT int opus_decoder_ctl(OpusDecoder *st, int request, ...) OPUS_ARG_NONNULL(1); - -/** Frees an OpusDecoder allocated by opus_decoder_create(). - * @param[in] st OpusDecoder*: State to be freed. - */ -OPUS_EXPORT void opus_decoder_destroy(OpusDecoder *st); - -/** Gets the size of an OpusDREDDecoder structure. - * @returns The size in bytes. - */ -OPUS_EXPORT int opus_dred_decoder_get_size(void); - -/** Allocates and initializes an OpusDREDDecoder state. - * @param [out] error int*: #OPUS_OK Success or @ref opus_errorcodes - */ -OPUS_EXPORT OpusDREDDecoder *opus_dred_decoder_create(int *error); - -/** Initializes an OpusDREDDecoder state. - * @param[in] dec OpusDREDDecoder*: State to be initialized. - */ -OPUS_EXPORT int opus_dred_decoder_init(OpusDREDDecoder *dec); - -/** Frees an OpusDREDDecoder allocated by opus_dred_decoder_create(). - * @param[in] dec OpusDREDDecoder*: State to be freed. - */ -OPUS_EXPORT void opus_dred_decoder_destroy(OpusDREDDecoder *dec); - -/** Perform a CTL function on an Opus DRED decoder. - * - * Generally the request and subsequent arguments are generated - * by a convenience macro. - * @param dred_dec OpusDREDDecoder*: DRED Decoder state. - * @param request This and all remaining parameters should be replaced by one - * of the convenience macros in @ref opus_genericctls or - * @ref opus_decoderctls. - * @see opus_genericctls - * @see opus_decoderctls - */ -OPUS_EXPORT int opus_dred_decoder_ctl(OpusDREDDecoder *dred_dec, int request, ...); - -/** Gets the size of an OpusDRED structure. - * @returns The size in bytes. - */ -OPUS_EXPORT int opus_dred_get_size(void); - -/** Allocates and initializes a DRED state. - * @param [out] error int*: #OPUS_OK Success or @ref opus_errorcodes - */ -OPUS_EXPORT OpusDRED *opus_dred_alloc(int *error); - -/** Frees an OpusDRED allocated by opus_dred_create(). - * @param[in] dec OpusDRED*: State to be freed. - */ -OPUS_EXPORT void opus_dred_free(OpusDRED *dec); - -/** Decode an Opus DRED packet. - * @param [in] dred_dec OpusDRED*: DRED Decoder state - * @param [in] dred OpusDRED*: DRED state - * @param [in] data char*: Input payload - * @param [in] len opus_int32: Number of bytes in payload - * @param [in] max_dred_samples opus_int32: Maximum number of DRED samples that may be needed (if available in the packet). - * @param [in] sampling_rate opus_int32: Sampling rate used for max_dred_samples argument. Needs not match the actual sampling rate of the decoder. - * @param [out] dred_end opus_int32*: Number of non-encoded (silence) samples between the DRED timestamp and the last DRED sample. - * @param [in] defer_processing int: Flag (0 or 1). If set to one, the CPU-intensive part of the DRED decoding is deferred until opus_dred_process() is called. - * @returns Offset (positive) of the first decoded DRED samples, zero if no DRED is present, or @ref opus_errorcodes - */ -OPUS_EXPORT int opus_dred_parse(OpusDREDDecoder *dred_dec, OpusDRED *dred, const unsigned char *data, opus_int32 len, opus_int32 max_dred_samples, opus_int32 sampling_rate, int *dred_end, int defer_processing) OPUS_ARG_NONNULL(1); - -/** Finish decoding an Opus DRED packet. The function only needs to be called if opus_dred_parse() was called with defer_processing=1. - * The source and destination will often be the same DRED state. - * @param [in] dred_dec OpusDRED*: DRED Decoder state - * @param [in] src OpusDRED*: Source DRED state to start the processing from. - * @param [out] dst OpusDRED*: Destination DRED state to store the updated state after processing. - * @returns @ref opus_errorcodes - */ -OPUS_EXPORT int opus_dred_process(OpusDREDDecoder *dred_dec, const OpusDRED *src, OpusDRED *dst); - -/** Decode audio from an Opus DRED packet with floating point output. - * @param [in] st OpusDecoder*: Decoder state - * @param [in] dred OpusDRED*: DRED state - * @param [in] dred_offset opus_int32: position of the redundancy to decode (in samples before the beginning of the real audio data in the packet). - * @param [out] pcm opus_int16*: Output signal (interleaved if 2 channels). length - * is frame_size*channels*sizeof(opus_int16) - * @param [in] frame_size Number of samples per channel to decode in \a pcm. - * frame_size must be a multiple of 2.5 ms. - * @returns Number of decoded samples or @ref opus_errorcodes - */ -OPUS_EXPORT int opus_decoder_dred_decode(OpusDecoder *st, const OpusDRED *dred, opus_int32 dred_offset, opus_int16 *pcm, opus_int32 frame_size); - -/** Decode audio from an Opus DRED packet with floating point output. - * @param [in] st OpusDecoder*: Decoder state - * @param [in] dred OpusDRED*: DRED state - * @param [in] dred_offset opus_int32: position of the redundancy to decode (in samples before the beginning of the real audio data in the packet). - * @param [out] pcm float*: Output signal (interleaved if 2 channels). length - * is frame_size*channels*sizeof(float) - * @param [in] frame_size Number of samples per channel to decode in \a pcm. - * frame_size must be a multiple of 2.5 ms. - * @returns Number of decoded samples or @ref opus_errorcodes - */ -OPUS_EXPORT int opus_decoder_dred_decode_float(OpusDecoder *st, const OpusDRED *dred, opus_int32 dred_offset, float *pcm, opus_int32 frame_size); - - -/** Parse an opus packet into one or more frames. - * Opus_decode will perform this operation internally so most applications do - * not need to use this function. - * This function does not copy the frames, the returned pointers are pointers into - * the input packet. - * @param [in] data char*: Opus packet to be parsed - * @param [in] len opus_int32: size of data - * @param [out] out_toc char*: TOC pointer - * @param [out] frames char*[48] encapsulated frames - * @param [out] size opus_int16[48] sizes of the encapsulated frames - * @param [out] payload_offset int*: returns the position of the payload within the packet (in bytes) - * @returns number of frames - */ -OPUS_EXPORT int opus_packet_parse( - const unsigned char *data, - opus_int32 len, - unsigned char *out_toc, - const unsigned char *frames[48], - opus_int16 size[48], - int *payload_offset -) OPUS_ARG_NONNULL(1) OPUS_ARG_NONNULL(5); - -/** Gets the bandwidth of an Opus packet. - * @param [in] data char*: Opus packet - * @retval OPUS_BANDWIDTH_NARROWBAND Narrowband (4kHz bandpass) - * @retval OPUS_BANDWIDTH_MEDIUMBAND Mediumband (6kHz bandpass) - * @retval OPUS_BANDWIDTH_WIDEBAND Wideband (8kHz bandpass) - * @retval OPUS_BANDWIDTH_SUPERWIDEBAND Superwideband (12kHz bandpass) - * @retval OPUS_BANDWIDTH_FULLBAND Fullband (20kHz bandpass) - * @retval OPUS_INVALID_PACKET The compressed data passed is corrupted or of an unsupported type - */ -OPUS_EXPORT OPUS_WARN_UNUSED_RESULT int opus_packet_get_bandwidth(const unsigned char *data) OPUS_ARG_NONNULL(1); - -/** Gets the number of samples per frame from an Opus packet. - * @param [in] data char*: Opus packet. - * This must contain at least one byte of - * data. - * @param [in] Fs opus_int32: Sampling rate in Hz. - * This must be a multiple of 400, or - * inaccurate results will be returned. - * @returns Number of samples per frame. - */ -OPUS_EXPORT OPUS_WARN_UNUSED_RESULT int opus_packet_get_samples_per_frame(const unsigned char *data, opus_int32 Fs) OPUS_ARG_NONNULL(1); - -/** Gets the number of channels from an Opus packet. - * @param [in] data char*: Opus packet - * @returns Number of channels - * @retval OPUS_INVALID_PACKET The compressed data passed is corrupted or of an unsupported type - */ -OPUS_EXPORT OPUS_WARN_UNUSED_RESULT int opus_packet_get_nb_channels(const unsigned char *data) OPUS_ARG_NONNULL(1); - -/** Gets the number of frames in an Opus packet. - * @param [in] packet char*: Opus packet - * @param [in] len opus_int32: Length of packet - * @returns Number of frames - * @retval OPUS_BAD_ARG Insufficient data was passed to the function - * @retval OPUS_INVALID_PACKET The compressed data passed is corrupted or of an unsupported type - */ -OPUS_EXPORT OPUS_WARN_UNUSED_RESULT int opus_packet_get_nb_frames(const unsigned char packet[], opus_int32 len) OPUS_ARG_NONNULL(1); - -/** Gets the number of samples of an Opus packet. - * @param [in] packet char*: Opus packet - * @param [in] len opus_int32: Length of packet - * @param [in] Fs opus_int32: Sampling rate in Hz. - * This must be a multiple of 400, or - * inaccurate results will be returned. - * @returns Number of samples - * @retval OPUS_BAD_ARG Insufficient data was passed to the function - * @retval OPUS_INVALID_PACKET The compressed data passed is corrupted or of an unsupported type - */ -OPUS_EXPORT OPUS_WARN_UNUSED_RESULT int opus_packet_get_nb_samples(const unsigned char packet[], opus_int32 len, opus_int32 Fs) OPUS_ARG_NONNULL(1); - -/** Checks whether an Opus packet has LBRR. - * @param [in] packet char*: Opus packet - * @param [in] len opus_int32: Length of packet - * @returns 1 is LBRR is present, 0 otherwise - * @retval OPUS_INVALID_PACKET The compressed data passed is corrupted or of an unsupported type - */ -OPUS_EXPORT OPUS_WARN_UNUSED_RESULT int opus_packet_has_lbrr(const unsigned char packet[], opus_int32 len); - -/** Gets the number of samples of an Opus packet. - * @param [in] dec OpusDecoder*: Decoder state - * @param [in] packet char*: Opus packet - * @param [in] len opus_int32: Length of packet - * @returns Number of samples - * @retval OPUS_BAD_ARG Insufficient data was passed to the function - * @retval OPUS_INVALID_PACKET The compressed data passed is corrupted or of an unsupported type - */ -OPUS_EXPORT OPUS_WARN_UNUSED_RESULT int opus_decoder_get_nb_samples(const OpusDecoder *dec, const unsigned char packet[], opus_int32 len) OPUS_ARG_NONNULL(1) OPUS_ARG_NONNULL(2); - -/** Applies soft-clipping to bring a float signal within the [-1,1] range. If - * the signal is already in that range, nothing is done. If there are values - * outside of [-1,1], then the signal is clipped as smoothly as possible to - * both fit in the range and avoid creating excessive distortion in the - * process. - * @param [in,out] pcm float*: Input PCM and modified PCM - * @param [in] frame_size int Number of samples per channel to process - * @param [in] channels int: Number of channels - * @param [in,out] softclip_mem float*: State memory for the soft clipping process (one float per channel, initialized to zero) - */ -OPUS_EXPORT void opus_pcm_soft_clip(float *pcm, int frame_size, int channels, float *softclip_mem); - - -/**@}*/ - -/** @defgroup opus_repacketizer Repacketizer - * @{ - * - * The repacketizer can be used to merge multiple Opus packets into a single - * packet or alternatively to split Opus packets that have previously been - * merged. Splitting valid Opus packets is always guaranteed to succeed, - * whereas merging valid packets only succeeds if all frames have the same - * mode, bandwidth, and frame size, and when the total duration of the merged - * packet is no more than 120 ms. The 120 ms limit comes from the - * specification and limits decoder memory requirements at a point where - * framing overhead becomes negligible. - * - * The repacketizer currently only operates on elementary Opus - * streams. It will not manipualte multistream packets successfully, except in - * the degenerate case where they consist of data from a single stream. - * - * The repacketizing process starts with creating a repacketizer state, either - * by calling opus_repacketizer_create() or by allocating the memory yourself, - * e.g., - * @code - * OpusRepacketizer *rp; - * rp = (OpusRepacketizer*)malloc(opus_repacketizer_get_size()); - * if (rp != NULL) - * opus_repacketizer_init(rp); - * @endcode - * - * Then the application should submit packets with opus_repacketizer_cat(), - * extract new packets with opus_repacketizer_out() or - * opus_repacketizer_out_range(), and then reset the state for the next set of - * input packets via opus_repacketizer_init(). - * - * For example, to split a sequence of packets into individual frames: - * @code - * unsigned char *data; - * int len; - * while (get_next_packet(&data, &len)) - * { - * unsigned char out[1276]; - * opus_int32 out_len; - * int nb_frames; - * int err; - * int i; - * err = opus_repacketizer_cat(rp, data, len); - * if (err != OPUS_OK) - * { - * release_packet(data); - * return err; - * } - * nb_frames = opus_repacketizer_get_nb_frames(rp); - * for (i = 0; i < nb_frames; i++) - * { - * out_len = opus_repacketizer_out_range(rp, i, i+1, out, sizeof(out)); - * if (out_len < 0) - * { - * release_packet(data); - * return (int)out_len; - * } - * output_next_packet(out, out_len); - * } - * opus_repacketizer_init(rp); - * release_packet(data); - * } - * @endcode - * - * Alternatively, to combine a sequence of frames into packets that each - * contain up to TARGET_DURATION_MS milliseconds of data: - * @code - * // The maximum number of packets with duration TARGET_DURATION_MS occurs - * // when the frame size is 2.5 ms, for a total of (TARGET_DURATION_MS*2/5) - * // packets. - * unsigned char *data[(TARGET_DURATION_MS*2/5)+1]; - * opus_int32 len[(TARGET_DURATION_MS*2/5)+1]; - * int nb_packets; - * unsigned char out[1277*(TARGET_DURATION_MS*2/2)]; - * opus_int32 out_len; - * int prev_toc; - * nb_packets = 0; - * while (get_next_packet(data+nb_packets, len+nb_packets)) - * { - * int nb_frames; - * int err; - * nb_frames = opus_packet_get_nb_frames(data[nb_packets], len[nb_packets]); - * if (nb_frames < 1) - * { - * release_packets(data, nb_packets+1); - * return nb_frames; - * } - * nb_frames += opus_repacketizer_get_nb_frames(rp); - * // If adding the next packet would exceed our target, or it has an - * // incompatible TOC sequence, output the packets we already have before - * // submitting it. - * // N.B., The nb_packets > 0 check ensures we've submitted at least one - * // packet since the last call to opus_repacketizer_init(). Otherwise a - * // single packet longer than TARGET_DURATION_MS would cause us to try to - * // output an (invalid) empty packet. It also ensures that prev_toc has - * // been set to a valid value. Additionally, len[nb_packets] > 0 is - * // guaranteed by the call to opus_packet_get_nb_frames() above, so the - * // reference to data[nb_packets][0] should be valid. - * if (nb_packets > 0 && ( - * ((prev_toc & 0xFC) != (data[nb_packets][0] & 0xFC)) || - * opus_packet_get_samples_per_frame(data[nb_packets], 48000)*nb_frames > - * TARGET_DURATION_MS*48)) - * { - * out_len = opus_repacketizer_out(rp, out, sizeof(out)); - * if (out_len < 0) - * { - * release_packets(data, nb_packets+1); - * return (int)out_len; - * } - * output_next_packet(out, out_len); - * opus_repacketizer_init(rp); - * release_packets(data, nb_packets); - * data[0] = data[nb_packets]; - * len[0] = len[nb_packets]; - * nb_packets = 0; - * } - * err = opus_repacketizer_cat(rp, data[nb_packets], len[nb_packets]); - * if (err != OPUS_OK) - * { - * release_packets(data, nb_packets+1); - * return err; - * } - * prev_toc = data[nb_packets][0]; - * nb_packets++; - * } - * // Output the final, partial packet. - * if (nb_packets > 0) - * { - * out_len = opus_repacketizer_out(rp, out, sizeof(out)); - * release_packets(data, nb_packets); - * if (out_len < 0) - * return (int)out_len; - * output_next_packet(out, out_len); - * } - * @endcode - * - * An alternate way of merging packets is to simply call opus_repacketizer_cat() - * unconditionally until it fails. At that point, the merged packet can be - * obtained with opus_repacketizer_out() and the input packet for which - * opus_repacketizer_cat() needs to be re-added to a newly reinitialized - * repacketizer state. - */ - -typedef struct OpusRepacketizer OpusRepacketizer; - -/** Gets the size of an OpusRepacketizer structure. - * @returns The size in bytes. - */ -OPUS_EXPORT OPUS_WARN_UNUSED_RESULT int opus_repacketizer_get_size(void); - -/** (Re)initializes a previously allocated repacketizer state. - * The state must be at least the size returned by opus_repacketizer_get_size(). - * This can be used for applications which use their own allocator instead of - * malloc(). - * It must also be called to reset the queue of packets waiting to be - * repacketized, which is necessary if the maximum packet duration of 120 ms - * is reached or if you wish to submit packets with a different Opus - * configuration (coding mode, audio bandwidth, frame size, or channel count). - * Failure to do so will prevent a new packet from being added with - * opus_repacketizer_cat(). - * @see opus_repacketizer_create - * @see opus_repacketizer_get_size - * @see opus_repacketizer_cat - * @param rp OpusRepacketizer*: The repacketizer state to - * (re)initialize. - * @returns A pointer to the same repacketizer state that was passed in. - */ -OPUS_EXPORT OpusRepacketizer *opus_repacketizer_init(OpusRepacketizer *rp) OPUS_ARG_NONNULL(1); - -/** Allocates memory and initializes the new repacketizer with - * opus_repacketizer_init(). - */ -OPUS_EXPORT OPUS_WARN_UNUSED_RESULT OpusRepacketizer *opus_repacketizer_create(void); - -/** Frees an OpusRepacketizer allocated by - * opus_repacketizer_create(). - * @param[in] rp OpusRepacketizer*: State to be freed. - */ -OPUS_EXPORT void opus_repacketizer_destroy(OpusRepacketizer *rp); - -/** Add a packet to the current repacketizer state. - * This packet must match the configuration of any packets already submitted - * for repacketization since the last call to opus_repacketizer_init(). - * This means that it must have the same coding mode, audio bandwidth, frame - * size, and channel count. - * This can be checked in advance by examining the top 6 bits of the first - * byte of the packet, and ensuring they match the top 6 bits of the first - * byte of any previously submitted packet. - * The total duration of audio in the repacketizer state also must not exceed - * 120 ms, the maximum duration of a single packet, after adding this packet. - * - * The contents of the current repacketizer state can be extracted into new - * packets using opus_repacketizer_out() or opus_repacketizer_out_range(). - * - * In order to add a packet with a different configuration or to add more - * audio beyond 120 ms, you must clear the repacketizer state by calling - * opus_repacketizer_init(). - * If a packet is too large to add to the current repacketizer state, no part - * of it is added, even if it contains multiple frames, some of which might - * fit. - * If you wish to be able to add parts of such packets, you should first use - * another repacketizer to split the packet into pieces and add them - * individually. - * @see opus_repacketizer_out_range - * @see opus_repacketizer_out - * @see opus_repacketizer_init - * @param rp OpusRepacketizer*: The repacketizer state to which to - * add the packet. - * @param[in] data const unsigned char*: The packet data. - * The application must ensure - * this pointer remains valid - * until the next call to - * opus_repacketizer_init() or - * opus_repacketizer_destroy(). - * @param len opus_int32: The number of bytes in the packet data. - * @returns An error code indicating whether or not the operation succeeded. - * @retval #OPUS_OK The packet's contents have been added to the repacketizer - * state. - * @retval #OPUS_INVALID_PACKET The packet did not have a valid TOC sequence, - * the packet's TOC sequence was not compatible - * with previously submitted packets (because - * the coding mode, audio bandwidth, frame size, - * or channel count did not match), or adding - * this packet would increase the total amount of - * audio stored in the repacketizer state to more - * than 120 ms. - */ -OPUS_EXPORT int opus_repacketizer_cat(OpusRepacketizer *rp, const unsigned char *data, opus_int32 len) OPUS_ARG_NONNULL(1) OPUS_ARG_NONNULL(2); - - -/** Construct a new packet from data previously submitted to the repacketizer - * state via opus_repacketizer_cat(). - * @param rp OpusRepacketizer*: The repacketizer state from which to - * construct the new packet. - * @param begin int: The index of the first frame in the current - * repacketizer state to include in the output. - * @param end int: One past the index of the last frame in the - * current repacketizer state to include in the - * output. - * @param[out] data const unsigned char*: The buffer in which to - * store the output packet. - * @param maxlen opus_int32: The maximum number of bytes to store in - * the output buffer. In order to guarantee - * success, this should be at least - * 1276 for a single frame, - * or for multiple frames, - * 1277*(end-begin). - * However, 1*(end-begin) plus - * the size of all packet data submitted to - * the repacketizer since the last call to - * opus_repacketizer_init() or - * opus_repacketizer_create() is also - * sufficient, and possibly much smaller. - * @returns The total size of the output packet on success, or an error code - * on failure. - * @retval #OPUS_BAD_ARG [begin,end) was an invalid range of - * frames (begin < 0, begin >= end, or end > - * opus_repacketizer_get_nb_frames()). - * @retval #OPUS_BUFFER_TOO_SMALL \a maxlen was insufficient to contain the - * complete output packet. - */ -OPUS_EXPORT OPUS_WARN_UNUSED_RESULT opus_int32 opus_repacketizer_out_range(OpusRepacketizer *rp, int begin, int end, unsigned char *data, opus_int32 maxlen) OPUS_ARG_NONNULL(1) OPUS_ARG_NONNULL(4); - -/** Return the total number of frames contained in packet data submitted to - * the repacketizer state so far via opus_repacketizer_cat() since the last - * call to opus_repacketizer_init() or opus_repacketizer_create(). - * This defines the valid range of packets that can be extracted with - * opus_repacketizer_out_range() or opus_repacketizer_out(). - * @param rp OpusRepacketizer*: The repacketizer state containing the - * frames. - * @returns The total number of frames contained in the packet data submitted - * to the repacketizer state. - */ -OPUS_EXPORT OPUS_WARN_UNUSED_RESULT int opus_repacketizer_get_nb_frames(OpusRepacketizer *rp) OPUS_ARG_NONNULL(1); - -/** Construct a new packet from data previously submitted to the repacketizer - * state via opus_repacketizer_cat(). - * This is a convenience routine that returns all the data submitted so far - * in a single packet. - * It is equivalent to calling - * @code - * opus_repacketizer_out_range(rp, 0, opus_repacketizer_get_nb_frames(rp), - * data, maxlen) - * @endcode - * @param rp OpusRepacketizer*: The repacketizer state from which to - * construct the new packet. - * @param[out] data const unsigned char*: The buffer in which to - * store the output packet. - * @param maxlen opus_int32: The maximum number of bytes to store in - * the output buffer. In order to guarantee - * success, this should be at least - * 1277*opus_repacketizer_get_nb_frames(rp). - * However, - * 1*opus_repacketizer_get_nb_frames(rp) - * plus the size of all packet data - * submitted to the repacketizer since the - * last call to opus_repacketizer_init() or - * opus_repacketizer_create() is also - * sufficient, and possibly much smaller. - * @returns The total size of the output packet on success, or an error code - * on failure. - * @retval #OPUS_BUFFER_TOO_SMALL \a maxlen was insufficient to contain the - * complete output packet. - */ -OPUS_EXPORT OPUS_WARN_UNUSED_RESULT opus_int32 opus_repacketizer_out(OpusRepacketizer *rp, unsigned char *data, opus_int32 maxlen) OPUS_ARG_NONNULL(1); - -/** Pads a given Opus packet to a larger size (possibly changing the TOC sequence). - * @param[in,out] data const unsigned char*: The buffer containing the - * packet to pad. - * @param len opus_int32: The size of the packet. - * This must be at least 1. - * @param new_len opus_int32: The desired size of the packet after padding. - * This must be at least as large as len. - * @returns an error code - * @retval #OPUS_OK \a on success. - * @retval #OPUS_BAD_ARG \a len was less than 1 or new_len was less than len. - * @retval #OPUS_INVALID_PACKET \a data did not contain a valid Opus packet. - */ -OPUS_EXPORT int opus_packet_pad(unsigned char *data, opus_int32 len, opus_int32 new_len); - -/** Remove all padding from a given Opus packet and rewrite the TOC sequence to - * minimize space usage. - * @param[in,out] data const unsigned char*: The buffer containing the - * packet to strip. - * @param len opus_int32: The size of the packet. - * This must be at least 1. - * @returns The new size of the output packet on success, or an error code - * on failure. - * @retval #OPUS_BAD_ARG \a len was less than 1. - * @retval #OPUS_INVALID_PACKET \a data did not contain a valid Opus packet. - */ -OPUS_EXPORT OPUS_WARN_UNUSED_RESULT opus_int32 opus_packet_unpad(unsigned char *data, opus_int32 len); - -/** Pads a given Opus multi-stream packet to a larger size (possibly changing the TOC sequence). - * @param[in,out] data const unsigned char*: The buffer containing the - * packet to pad. - * @param len opus_int32: The size of the packet. - * This must be at least 1. - * @param new_len opus_int32: The desired size of the packet after padding. - * This must be at least 1. - * @param nb_streams opus_int32: The number of streams (not channels) in the packet. - * This must be at least as large as len. - * @returns an error code - * @retval #OPUS_OK \a on success. - * @retval #OPUS_BAD_ARG \a len was less than 1. - * @retval #OPUS_INVALID_PACKET \a data did not contain a valid Opus packet. - */ -OPUS_EXPORT int opus_multistream_packet_pad(unsigned char *data, opus_int32 len, opus_int32 new_len, int nb_streams); - -/** Remove all padding from a given Opus multi-stream packet and rewrite the TOC sequence to - * minimize space usage. - * @param[in,out] data const unsigned char*: The buffer containing the - * packet to strip. - * @param len opus_int32: The size of the packet. - * This must be at least 1. - * @param nb_streams opus_int32: The number of streams (not channels) in the packet. - * This must be at least 1. - * @returns The new size of the output packet on success, or an error code - * on failure. - * @retval #OPUS_BAD_ARG \a len was less than 1 or new_len was less than len. - * @retval #OPUS_INVALID_PACKET \a data did not contain a valid Opus packet. - */ -OPUS_EXPORT OPUS_WARN_UNUSED_RESULT opus_int32 opus_multistream_packet_unpad(unsigned char *data, opus_int32 len, int nb_streams); - -/**@}*/ - -#ifdef __cplusplus -} -#endif - -#endif /* OPUS_H */ diff --git a/go/teamspeak/.opus/install/arm64-v8a/include/opus/opus_defines.h b/go/teamspeak/.opus/install/arm64-v8a/include/opus/opus_defines.h deleted file mode 100644 index cd8f4dd..0000000 --- a/go/teamspeak/.opus/install/arm64-v8a/include/opus/opus_defines.h +++ /dev/null @@ -1,830 +0,0 @@ -/* Copyright (c) 2010-2011 Xiph.Org Foundation, Skype Limited - Written by Jean-Marc Valin and Koen Vos */ -/* - Redistribution and use in source and binary forms, with or without - modification, are permitted provided that the following conditions - are met: - - - Redistributions of source code must retain the above copyright - notice, this list of conditions and the following disclaimer. - - - Redistributions in binary form must reproduce the above copyright - notice, this list of conditions and the following disclaimer in the - documentation and/or other materials provided with the distribution. - - THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS - ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT - LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR - A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER - OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, - EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, - PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR - PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF - LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING - NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS - SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. -*/ - -/** - * @file opus_defines.h - * @brief Opus reference implementation constants - */ - -#ifndef OPUS_DEFINES_H -#define OPUS_DEFINES_H - -#include "opus_types.h" - -#ifdef __cplusplus -extern "C" { -#endif - -/** @defgroup opus_errorcodes Error codes - * @{ - */ -/** No error @hideinitializer*/ -#define OPUS_OK 0 -/** One or more invalid/out of range arguments @hideinitializer*/ -#define OPUS_BAD_ARG -1 -/** Not enough bytes allocated in the buffer @hideinitializer*/ -#define OPUS_BUFFER_TOO_SMALL -2 -/** An internal error was detected @hideinitializer*/ -#define OPUS_INTERNAL_ERROR -3 -/** The compressed data passed is corrupted @hideinitializer*/ -#define OPUS_INVALID_PACKET -4 -/** Invalid/unsupported request number @hideinitializer*/ -#define OPUS_UNIMPLEMENTED -5 -/** An encoder or decoder structure is invalid or already freed @hideinitializer*/ -#define OPUS_INVALID_STATE -6 -/** Memory allocation has failed @hideinitializer*/ -#define OPUS_ALLOC_FAIL -7 -/**@}*/ - -/** @cond OPUS_INTERNAL_DOC */ -/**Export control for opus functions */ - -#ifndef OPUS_EXPORT -# if defined(_WIN32) -# if defined(OPUS_BUILD) && defined(DLL_EXPORT) -# define OPUS_EXPORT __declspec(dllexport) -# else -# define OPUS_EXPORT -# endif -# elif defined(__GNUC__) && defined(OPUS_BUILD) -# define OPUS_EXPORT __attribute__ ((visibility ("default"))) -# else -# define OPUS_EXPORT -# endif -#endif - -# if !defined(OPUS_GNUC_PREREQ) -# if defined(__GNUC__)&&defined(__GNUC_MINOR__) -# define OPUS_GNUC_PREREQ(_maj,_min) \ - ((__GNUC__<<16)+__GNUC_MINOR__>=((_maj)<<16)+(_min)) -# else -# define OPUS_GNUC_PREREQ(_maj,_min) 0 -# endif -# endif - -#if (!defined(__STDC_VERSION__) || (__STDC_VERSION__ < 199901L) ) -# if OPUS_GNUC_PREREQ(3,0) -# define OPUS_RESTRICT __restrict__ -# elif (defined(_MSC_VER) && _MSC_VER >= 1400) -# define OPUS_RESTRICT __restrict -# else -# define OPUS_RESTRICT -# endif -#else -# define OPUS_RESTRICT restrict -#endif - -#if (!defined(__STDC_VERSION__) || (__STDC_VERSION__ < 199901L) ) -# if OPUS_GNUC_PREREQ(2,7) -# define OPUS_INLINE __inline__ -# elif (defined(_MSC_VER)) -# define OPUS_INLINE __inline -# else -# define OPUS_INLINE -# endif -#else -# define OPUS_INLINE inline -#endif - -/**Warning attributes for opus functions - * NONNULL is not used in OPUS_BUILD to avoid the compiler optimizing out - * some paranoid null checks. */ -#if defined(__GNUC__) && OPUS_GNUC_PREREQ(3, 4) -# define OPUS_WARN_UNUSED_RESULT __attribute__ ((__warn_unused_result__)) -#else -# define OPUS_WARN_UNUSED_RESULT -#endif -#if !defined(OPUS_BUILD) && defined(__GNUC__) && OPUS_GNUC_PREREQ(3, 4) -# define OPUS_ARG_NONNULL(_x) __attribute__ ((__nonnull__(_x))) -#else -# define OPUS_ARG_NONNULL(_x) -#endif - -/** These are the actual Encoder CTL ID numbers. - * They should not be used directly by applications. - * In general, SETs should be even and GETs should be odd.*/ -#define OPUS_SET_APPLICATION_REQUEST 4000 -#define OPUS_GET_APPLICATION_REQUEST 4001 -#define OPUS_SET_BITRATE_REQUEST 4002 -#define OPUS_GET_BITRATE_REQUEST 4003 -#define OPUS_SET_MAX_BANDWIDTH_REQUEST 4004 -#define OPUS_GET_MAX_BANDWIDTH_REQUEST 4005 -#define OPUS_SET_VBR_REQUEST 4006 -#define OPUS_GET_VBR_REQUEST 4007 -#define OPUS_SET_BANDWIDTH_REQUEST 4008 -#define OPUS_GET_BANDWIDTH_REQUEST 4009 -#define OPUS_SET_COMPLEXITY_REQUEST 4010 -#define OPUS_GET_COMPLEXITY_REQUEST 4011 -#define OPUS_SET_INBAND_FEC_REQUEST 4012 -#define OPUS_GET_INBAND_FEC_REQUEST 4013 -#define OPUS_SET_PACKET_LOSS_PERC_REQUEST 4014 -#define OPUS_GET_PACKET_LOSS_PERC_REQUEST 4015 -#define OPUS_SET_DTX_REQUEST 4016 -#define OPUS_GET_DTX_REQUEST 4017 -#define OPUS_SET_VBR_CONSTRAINT_REQUEST 4020 -#define OPUS_GET_VBR_CONSTRAINT_REQUEST 4021 -#define OPUS_SET_FORCE_CHANNELS_REQUEST 4022 -#define OPUS_GET_FORCE_CHANNELS_REQUEST 4023 -#define OPUS_SET_SIGNAL_REQUEST 4024 -#define OPUS_GET_SIGNAL_REQUEST 4025 -#define OPUS_GET_LOOKAHEAD_REQUEST 4027 -/* #define OPUS_RESET_STATE 4028 */ -#define OPUS_GET_SAMPLE_RATE_REQUEST 4029 -#define OPUS_GET_FINAL_RANGE_REQUEST 4031 -#define OPUS_GET_PITCH_REQUEST 4033 -#define OPUS_SET_GAIN_REQUEST 4034 -#define OPUS_GET_GAIN_REQUEST 4045 /* Should have been 4035 */ -#define OPUS_SET_LSB_DEPTH_REQUEST 4036 -#define OPUS_GET_LSB_DEPTH_REQUEST 4037 -#define OPUS_GET_LAST_PACKET_DURATION_REQUEST 4039 -#define OPUS_SET_EXPERT_FRAME_DURATION_REQUEST 4040 -#define OPUS_GET_EXPERT_FRAME_DURATION_REQUEST 4041 -#define OPUS_SET_PREDICTION_DISABLED_REQUEST 4042 -#define OPUS_GET_PREDICTION_DISABLED_REQUEST 4043 -/* Don't use 4045, it's already taken by OPUS_GET_GAIN_REQUEST */ -#define OPUS_SET_PHASE_INVERSION_DISABLED_REQUEST 4046 -#define OPUS_GET_PHASE_INVERSION_DISABLED_REQUEST 4047 -#define OPUS_GET_IN_DTX_REQUEST 4049 -#define OPUS_SET_DRED_DURATION_REQUEST 4050 -#define OPUS_GET_DRED_DURATION_REQUEST 4051 -#define OPUS_SET_DNN_BLOB_REQUEST 4052 -/*#define OPUS_GET_DNN_BLOB_REQUEST 4053 */ - -/** Defines for the presence of extended APIs. */ -#define OPUS_HAVE_OPUS_PROJECTION_H - -/* Macros to trigger compilation errors when the wrong types are provided to a CTL */ -#define __opus_check_int(x) (((void)((x) == (opus_int32)0)), (opus_int32)(x)) - -#ifdef DISABLE_PTR_CHECK -/* Disable checks to prevent ubsan from complaining about NULL checks - in test_opus_api. */ -#define __opus_check_int_ptr(ptr) (ptr) -#define __opus_check_uint_ptr(ptr) (ptr) -#define __opus_check_uint8_ptr(ptr) (ptr) -#define __opus_check_val16_ptr(ptr) (ptr) -#define __opus_check_void_ptr(ptr) (ptr) -#else -#define __opus_check_int_ptr(ptr) ((ptr) + ((ptr) - (opus_int32*)(ptr))) -#define __opus_check_uint_ptr(ptr) ((ptr) + ((ptr) - (opus_uint32*)(ptr))) -#define __opus_check_uint8_ptr(ptr) ((ptr) + ((ptr) - (opus_uint8*)(ptr))) -#define __opus_check_val16_ptr(ptr) ((ptr) + ((ptr) - (opus_val16*)(ptr))) -#define __opus_check_void_ptr(x) ((void)((void *)0 == (x)), (x)) -#endif -/** @endcond */ - -/** @defgroup opus_ctlvalues Pre-defined values for CTL interface - * @see opus_genericctls, opus_encoderctls - * @{ - */ -/* Values for the various encoder CTLs */ -#define OPUS_AUTO -1000 /**opus_int32: Allowed values: 0-10, inclusive. - * - * @hideinitializer */ -#define OPUS_SET_COMPLEXITY(x) OPUS_SET_COMPLEXITY_REQUEST, __opus_check_int(x) -/** Gets the encoder's complexity configuration. - * @see OPUS_SET_COMPLEXITY - * @param[out] x opus_int32 *: Returns a value in the range 0-10, - * inclusive. - * @hideinitializer */ -#define OPUS_GET_COMPLEXITY(x) OPUS_GET_COMPLEXITY_REQUEST, __opus_check_int_ptr(x) - -/** Configures the bitrate in the encoder. - * Rates from 500 to 512000 bits per second are meaningful, as well as the - * special values #OPUS_AUTO and #OPUS_BITRATE_MAX. - * The value #OPUS_BITRATE_MAX can be used to cause the codec to use as much - * rate as it can, which is useful for controlling the rate by adjusting the - * output buffer size. - * @see OPUS_GET_BITRATE - * @param[in] x opus_int32: Bitrate in bits per second. The default - * is determined based on the number of - * channels and the input sampling rate. - * @hideinitializer */ -#define OPUS_SET_BITRATE(x) OPUS_SET_BITRATE_REQUEST, __opus_check_int(x) -/** Gets the encoder's bitrate configuration. - * @see OPUS_SET_BITRATE - * @param[out] x opus_int32 *: Returns the bitrate in bits per second. - * The default is determined based on the - * number of channels and the input - * sampling rate. - * @hideinitializer */ -#define OPUS_GET_BITRATE(x) OPUS_GET_BITRATE_REQUEST, __opus_check_int_ptr(x) - -/** Enables or disables variable bitrate (VBR) in the encoder. - * The configured bitrate may not be met exactly because frames must - * be an integer number of bytes in length. - * @see OPUS_GET_VBR - * @see OPUS_SET_VBR_CONSTRAINT - * @param[in] x opus_int32: Allowed values: - *
- *
0
Hard CBR. For LPC/hybrid modes at very low bit-rate, this can - * cause noticeable quality degradation.
- *
1
VBR (default). The exact type of VBR is controlled by - * #OPUS_SET_VBR_CONSTRAINT.
- *
- * @hideinitializer */ -#define OPUS_SET_VBR(x) OPUS_SET_VBR_REQUEST, __opus_check_int(x) -/** Determine if variable bitrate (VBR) is enabled in the encoder. - * @see OPUS_SET_VBR - * @see OPUS_GET_VBR_CONSTRAINT - * @param[out] x opus_int32 *: Returns one of the following values: - *
- *
0
Hard CBR.
- *
1
VBR (default). The exact type of VBR may be retrieved via - * #OPUS_GET_VBR_CONSTRAINT.
- *
- * @hideinitializer */ -#define OPUS_GET_VBR(x) OPUS_GET_VBR_REQUEST, __opus_check_int_ptr(x) - -/** Enables or disables constrained VBR in the encoder. - * This setting is ignored when the encoder is in CBR mode. - * @warning Only the MDCT mode of Opus currently heeds the constraint. - * Speech mode ignores it completely, hybrid mode may fail to obey it - * if the LPC layer uses more bitrate than the constraint would have - * permitted. - * @see OPUS_GET_VBR_CONSTRAINT - * @see OPUS_SET_VBR - * @param[in] x opus_int32: Allowed values: - *
- *
0
Unconstrained VBR.
- *
1
Constrained VBR (default). This creates a maximum of one - * frame of buffering delay assuming a transport with a - * serialization speed of the nominal bitrate.
- *
- * @hideinitializer */ -#define OPUS_SET_VBR_CONSTRAINT(x) OPUS_SET_VBR_CONSTRAINT_REQUEST, __opus_check_int(x) -/** Determine if constrained VBR is enabled in the encoder. - * @see OPUS_SET_VBR_CONSTRAINT - * @see OPUS_GET_VBR - * @param[out] x opus_int32 *: Returns one of the following values: - *
- *
0
Unconstrained VBR.
- *
1
Constrained VBR (default).
- *
- * @hideinitializer */ -#define OPUS_GET_VBR_CONSTRAINT(x) OPUS_GET_VBR_CONSTRAINT_REQUEST, __opus_check_int_ptr(x) - -/** Configures mono/stereo forcing in the encoder. - * This can force the encoder to produce packets encoded as either mono or - * stereo, regardless of the format of the input audio. This is useful when - * the caller knows that the input signal is currently a mono source embedded - * in a stereo stream. - * @see OPUS_GET_FORCE_CHANNELS - * @param[in] x opus_int32: Allowed values: - *
- *
#OPUS_AUTO
Not forced (default)
- *
1
Forced mono
- *
2
Forced stereo
- *
- * @hideinitializer */ -#define OPUS_SET_FORCE_CHANNELS(x) OPUS_SET_FORCE_CHANNELS_REQUEST, __opus_check_int(x) -/** Gets the encoder's forced channel configuration. - * @see OPUS_SET_FORCE_CHANNELS - * @param[out] x opus_int32 *: - *
- *
#OPUS_AUTO
Not forced (default)
- *
1
Forced mono
- *
2
Forced stereo
- *
- * @hideinitializer */ -#define OPUS_GET_FORCE_CHANNELS(x) OPUS_GET_FORCE_CHANNELS_REQUEST, __opus_check_int_ptr(x) - -/** Configures the maximum bandpass that the encoder will select automatically. - * Applications should normally use this instead of #OPUS_SET_BANDWIDTH - * (leaving that set to the default, #OPUS_AUTO). This allows the - * application to set an upper bound based on the type of input it is - * providing, but still gives the encoder the freedom to reduce the bandpass - * when the bitrate becomes too low, for better overall quality. - * @see OPUS_GET_MAX_BANDWIDTH - * @param[in] x opus_int32: Allowed values: - *
- *
OPUS_BANDWIDTH_NARROWBAND
4 kHz passband
- *
OPUS_BANDWIDTH_MEDIUMBAND
6 kHz passband
- *
OPUS_BANDWIDTH_WIDEBAND
8 kHz passband
- *
OPUS_BANDWIDTH_SUPERWIDEBAND
12 kHz passband
- *
OPUS_BANDWIDTH_FULLBAND
20 kHz passband (default)
- *
- * @hideinitializer */ -#define OPUS_SET_MAX_BANDWIDTH(x) OPUS_SET_MAX_BANDWIDTH_REQUEST, __opus_check_int(x) - -/** Gets the encoder's configured maximum allowed bandpass. - * @see OPUS_SET_MAX_BANDWIDTH - * @param[out] x opus_int32 *: Allowed values: - *
- *
#OPUS_BANDWIDTH_NARROWBAND
4 kHz passband
- *
#OPUS_BANDWIDTH_MEDIUMBAND
6 kHz passband
- *
#OPUS_BANDWIDTH_WIDEBAND
8 kHz passband
- *
#OPUS_BANDWIDTH_SUPERWIDEBAND
12 kHz passband
- *
#OPUS_BANDWIDTH_FULLBAND
20 kHz passband (default)
- *
- * @hideinitializer */ -#define OPUS_GET_MAX_BANDWIDTH(x) OPUS_GET_MAX_BANDWIDTH_REQUEST, __opus_check_int_ptr(x) - -/** Sets the encoder's bandpass to a specific value. - * This prevents the encoder from automatically selecting the bandpass based - * on the available bitrate. If an application knows the bandpass of the input - * audio it is providing, it should normally use #OPUS_SET_MAX_BANDWIDTH - * instead, which still gives the encoder the freedom to reduce the bandpass - * when the bitrate becomes too low, for better overall quality. - * @see OPUS_GET_BANDWIDTH - * @param[in] x opus_int32: Allowed values: - *
- *
#OPUS_AUTO
(default)
- *
#OPUS_BANDWIDTH_NARROWBAND
4 kHz passband
- *
#OPUS_BANDWIDTH_MEDIUMBAND
6 kHz passband
- *
#OPUS_BANDWIDTH_WIDEBAND
8 kHz passband
- *
#OPUS_BANDWIDTH_SUPERWIDEBAND
12 kHz passband
- *
#OPUS_BANDWIDTH_FULLBAND
20 kHz passband
- *
- * @hideinitializer */ -#define OPUS_SET_BANDWIDTH(x) OPUS_SET_BANDWIDTH_REQUEST, __opus_check_int(x) - -/** Configures the type of signal being encoded. - * This is a hint which helps the encoder's mode selection. - * @see OPUS_GET_SIGNAL - * @param[in] x opus_int32: Allowed values: - *
- *
#OPUS_AUTO
(default)
- *
#OPUS_SIGNAL_VOICE
Bias thresholds towards choosing LPC or Hybrid modes.
- *
#OPUS_SIGNAL_MUSIC
Bias thresholds towards choosing MDCT modes.
- *
- * @hideinitializer */ -#define OPUS_SET_SIGNAL(x) OPUS_SET_SIGNAL_REQUEST, __opus_check_int(x) -/** Gets the encoder's configured signal type. - * @see OPUS_SET_SIGNAL - * @param[out] x opus_int32 *: Returns one of the following values: - *
- *
#OPUS_AUTO
(default)
- *
#OPUS_SIGNAL_VOICE
Bias thresholds towards choosing LPC or Hybrid modes.
- *
#OPUS_SIGNAL_MUSIC
Bias thresholds towards choosing MDCT modes.
- *
- * @hideinitializer */ -#define OPUS_GET_SIGNAL(x) OPUS_GET_SIGNAL_REQUEST, __opus_check_int_ptr(x) - - -/** Configures the encoder's intended application. - * The initial value is a mandatory argument to the encoder_create function. - * @see OPUS_GET_APPLICATION - * @param[in] x opus_int32: Returns one of the following values: - *
- *
#OPUS_APPLICATION_VOIP
- *
Process signal for improved speech intelligibility.
- *
#OPUS_APPLICATION_AUDIO
- *
Favor faithfulness to the original input.
- *
#OPUS_APPLICATION_RESTRICTED_LOWDELAY
- *
Configure the minimum possible coding delay by disabling certain modes - * of operation.
- *
- * @hideinitializer */ -#define OPUS_SET_APPLICATION(x) OPUS_SET_APPLICATION_REQUEST, __opus_check_int(x) -/** Gets the encoder's configured application. - * @see OPUS_SET_APPLICATION - * @param[out] x opus_int32 *: Returns one of the following values: - *
- *
#OPUS_APPLICATION_VOIP
- *
Process signal for improved speech intelligibility.
- *
#OPUS_APPLICATION_AUDIO
- *
Favor faithfulness to the original input.
- *
#OPUS_APPLICATION_RESTRICTED_LOWDELAY
- *
Configure the minimum possible coding delay by disabling certain modes - * of operation.
- *
- * @hideinitializer */ -#define OPUS_GET_APPLICATION(x) OPUS_GET_APPLICATION_REQUEST, __opus_check_int_ptr(x) - -/** Gets the total samples of delay added by the entire codec. - * This can be queried by the encoder and then the provided number of samples can be - * skipped on from the start of the decoder's output to provide time aligned input - * and output. From the perspective of a decoding application the real data begins this many - * samples late. - * - * The decoder contribution to this delay is identical for all decoders, but the - * encoder portion of the delay may vary from implementation to implementation, - * version to version, or even depend on the encoder's initial configuration. - * Applications needing delay compensation should call this CTL rather than - * hard-coding a value. - * @param[out] x opus_int32 *: Number of lookahead samples - * @hideinitializer */ -#define OPUS_GET_LOOKAHEAD(x) OPUS_GET_LOOKAHEAD_REQUEST, __opus_check_int_ptr(x) - -/** Configures the encoder's use of inband forward error correction (FEC). - * @note This is only applicable to the LPC layer - * @see OPUS_GET_INBAND_FEC - * @param[in] x opus_int32: Allowed values: - *
- *
0
Disable inband FEC (default).
- *
1
Inband FEC enabled. If the packet loss rate is sufficiently high, Opus will automatically switch to SILK even at high rates to enable use of that FEC.
- *
2
Inband FEC enabled, but does not necessarily switch to SILK if we have music.
- *
- * @hideinitializer */ -#define OPUS_SET_INBAND_FEC(x) OPUS_SET_INBAND_FEC_REQUEST, __opus_check_int(x) -/** Gets encoder's configured use of inband forward error correction. - * @see OPUS_SET_INBAND_FEC - * @param[out] x opus_int32 *: Returns one of the following values: - *
- *
0
Inband FEC disabled (default).
- *
1
Inband FEC enabled. If the packet loss rate is sufficiently high, Opus will automatically switch to SILK even at high rates to enable use of that FEC.
- *
2
Inband FEC enabled, but does not necessarily switch to SILK if we have music.
- *
- * @hideinitializer */ -#define OPUS_GET_INBAND_FEC(x) OPUS_GET_INBAND_FEC_REQUEST, __opus_check_int_ptr(x) - -/** Configures the encoder's expected packet loss percentage. - * Higher values trigger progressively more loss resistant behavior in the encoder - * at the expense of quality at a given bitrate in the absence of packet loss, but - * greater quality under loss. - * @see OPUS_GET_PACKET_LOSS_PERC - * @param[in] x opus_int32: Loss percentage in the range 0-100, inclusive (default: 0). - * @hideinitializer */ -#define OPUS_SET_PACKET_LOSS_PERC(x) OPUS_SET_PACKET_LOSS_PERC_REQUEST, __opus_check_int(x) -/** Gets the encoder's configured packet loss percentage. - * @see OPUS_SET_PACKET_LOSS_PERC - * @param[out] x opus_int32 *: Returns the configured loss percentage - * in the range 0-100, inclusive (default: 0). - * @hideinitializer */ -#define OPUS_GET_PACKET_LOSS_PERC(x) OPUS_GET_PACKET_LOSS_PERC_REQUEST, __opus_check_int_ptr(x) - -/** Configures the encoder's use of discontinuous transmission (DTX). - * @note This is only applicable to the LPC layer - * @see OPUS_GET_DTX - * @param[in] x opus_int32: Allowed values: - *
- *
0
Disable DTX (default).
- *
1
Enabled DTX.
- *
- * @hideinitializer */ -#define OPUS_SET_DTX(x) OPUS_SET_DTX_REQUEST, __opus_check_int(x) -/** Gets encoder's configured use of discontinuous transmission. - * @see OPUS_SET_DTX - * @param[out] x opus_int32 *: Returns one of the following values: - *
- *
0
DTX disabled (default).
- *
1
DTX enabled.
- *
- * @hideinitializer */ -#define OPUS_GET_DTX(x) OPUS_GET_DTX_REQUEST, __opus_check_int_ptr(x) -/** Configures the depth of signal being encoded. - * - * This is a hint which helps the encoder identify silence and near-silence. - * It represents the number of significant bits of linear intensity below - * which the signal contains ignorable quantization or other noise. - * - * For example, OPUS_SET_LSB_DEPTH(14) would be an appropriate setting - * for G.711 u-law input. OPUS_SET_LSB_DEPTH(16) would be appropriate - * for 16-bit linear pcm input with opus_encode_float(). - * - * When using opus_encode() instead of opus_encode_float(), or when libopus - * is compiled for fixed-point, the encoder uses the minimum of the value - * set here and the value 16. - * - * @see OPUS_GET_LSB_DEPTH - * @param[in] x opus_int32: Input precision in bits, between 8 and 24 - * (default: 24). - * @hideinitializer */ -#define OPUS_SET_LSB_DEPTH(x) OPUS_SET_LSB_DEPTH_REQUEST, __opus_check_int(x) -/** Gets the encoder's configured signal depth. - * @see OPUS_SET_LSB_DEPTH - * @param[out] x opus_int32 *: Input precision in bits, between 8 and - * 24 (default: 24). - * @hideinitializer */ -#define OPUS_GET_LSB_DEPTH(x) OPUS_GET_LSB_DEPTH_REQUEST, __opus_check_int_ptr(x) - -/** Configures the encoder's use of variable duration frames. - * When variable duration is enabled, the encoder is free to use a shorter frame - * size than the one requested in the opus_encode*() call. - * It is then the user's responsibility - * to verify how much audio was encoded by checking the ToC byte of the encoded - * packet. The part of the audio that was not encoded needs to be resent to the - * encoder for the next call. Do not use this option unless you really - * know what you are doing. - * @see OPUS_GET_EXPERT_FRAME_DURATION - * @param[in] x opus_int32: Allowed values: - *
- *
OPUS_FRAMESIZE_ARG
Select frame size from the argument (default).
- *
OPUS_FRAMESIZE_2_5_MS
Use 2.5 ms frames.
- *
OPUS_FRAMESIZE_5_MS
Use 5 ms frames.
- *
OPUS_FRAMESIZE_10_MS
Use 10 ms frames.
- *
OPUS_FRAMESIZE_20_MS
Use 20 ms frames.
- *
OPUS_FRAMESIZE_40_MS
Use 40 ms frames.
- *
OPUS_FRAMESIZE_60_MS
Use 60 ms frames.
- *
OPUS_FRAMESIZE_80_MS
Use 80 ms frames.
- *
OPUS_FRAMESIZE_100_MS
Use 100 ms frames.
- *
OPUS_FRAMESIZE_120_MS
Use 120 ms frames.
- *
- * @hideinitializer */ -#define OPUS_SET_EXPERT_FRAME_DURATION(x) OPUS_SET_EXPERT_FRAME_DURATION_REQUEST, __opus_check_int(x) -/** Gets the encoder's configured use of variable duration frames. - * @see OPUS_SET_EXPERT_FRAME_DURATION - * @param[out] x opus_int32 *: Returns one of the following values: - *
- *
OPUS_FRAMESIZE_ARG
Select frame size from the argument (default).
- *
OPUS_FRAMESIZE_2_5_MS
Use 2.5 ms frames.
- *
OPUS_FRAMESIZE_5_MS
Use 5 ms frames.
- *
OPUS_FRAMESIZE_10_MS
Use 10 ms frames.
- *
OPUS_FRAMESIZE_20_MS
Use 20 ms frames.
- *
OPUS_FRAMESIZE_40_MS
Use 40 ms frames.
- *
OPUS_FRAMESIZE_60_MS
Use 60 ms frames.
- *
OPUS_FRAMESIZE_80_MS
Use 80 ms frames.
- *
OPUS_FRAMESIZE_100_MS
Use 100 ms frames.
- *
OPUS_FRAMESIZE_120_MS
Use 120 ms frames.
- *
- * @hideinitializer */ -#define OPUS_GET_EXPERT_FRAME_DURATION(x) OPUS_GET_EXPERT_FRAME_DURATION_REQUEST, __opus_check_int_ptr(x) - -/** If set to 1, disables almost all use of prediction, making frames almost - * completely independent. This reduces quality. - * @see OPUS_GET_PREDICTION_DISABLED - * @param[in] x opus_int32: Allowed values: - *
- *
0
Enable prediction (default).
- *
1
Disable prediction.
- *
- * @hideinitializer */ -#define OPUS_SET_PREDICTION_DISABLED(x) OPUS_SET_PREDICTION_DISABLED_REQUEST, __opus_check_int(x) -/** Gets the encoder's configured prediction status. - * @see OPUS_SET_PREDICTION_DISABLED - * @param[out] x opus_int32 *: Returns one of the following values: - *
- *
0
Prediction enabled (default).
- *
1
Prediction disabled.
- *
- * @hideinitializer */ -#define OPUS_GET_PREDICTION_DISABLED(x) OPUS_GET_PREDICTION_DISABLED_REQUEST, __opus_check_int_ptr(x) - -/** If non-zero, enables Deep Redundancy (DRED) and use the specified maximum number of 10-ms redundant frames - * @hideinitializer */ -#define OPUS_SET_DRED_DURATION(x) OPUS_SET_DRED_DURATION_REQUEST, __opus_check_int(x) -/** Gets the encoder's configured Deep Redundancy (DRED) maximum number of frames. - * @hideinitializer */ -#define OPUS_GET_DRED_DURATION(x) OPUS_GET_DRED_DURATION_REQUEST, __opus_check_int_ptr(x) - -/** Provide external DNN weights from binary object (only when explicitly built without the weights) - * @hideinitializer */ -#define OPUS_SET_DNN_BLOB(data, len) OPUS_SET_DNN_BLOB_REQUEST, __opus_check_void_ptr(data), __opus_check_int(len) - - -/**@}*/ - -/** @defgroup opus_genericctls Generic CTLs - * - * These macros are used with the \c opus_decoder_ctl and - * \c opus_encoder_ctl calls to generate a particular - * request. - * - * When called on an \c OpusDecoder they apply to that - * particular decoder instance. When called on an - * \c OpusEncoder they apply to the corresponding setting - * on that encoder instance, if present. - * - * Some usage examples: - * - * @code - * int ret; - * opus_int32 pitch; - * ret = opus_decoder_ctl(dec_ctx, OPUS_GET_PITCH(&pitch)); - * if (ret == OPUS_OK) return ret; - * - * opus_encoder_ctl(enc_ctx, OPUS_RESET_STATE); - * opus_decoder_ctl(dec_ctx, OPUS_RESET_STATE); - * - * opus_int32 enc_bw, dec_bw; - * opus_encoder_ctl(enc_ctx, OPUS_GET_BANDWIDTH(&enc_bw)); - * opus_decoder_ctl(dec_ctx, OPUS_GET_BANDWIDTH(&dec_bw)); - * if (enc_bw != dec_bw) { - * printf("packet bandwidth mismatch!\n"); - * } - * @endcode - * - * @see opus_encoder, opus_decoder_ctl, opus_encoder_ctl, opus_decoderctls, opus_encoderctls - * @{ - */ - -/** Resets the codec state to be equivalent to a freshly initialized state. - * This should be called when switching streams in order to prevent - * the back to back decoding from giving different results from - * one at a time decoding. - * @hideinitializer */ -#define OPUS_RESET_STATE 4028 - -/** Gets the final state of the codec's entropy coder. - * This is used for testing purposes, - * The encoder and decoder state should be identical after coding a payload - * (assuming no data corruption or software bugs) - * - * @param[out] x opus_uint32 *: Entropy coder state - * - * @hideinitializer */ -#define OPUS_GET_FINAL_RANGE(x) OPUS_GET_FINAL_RANGE_REQUEST, __opus_check_uint_ptr(x) - -/** Gets the encoder's configured bandpass or the decoder's last bandpass. - * @see OPUS_SET_BANDWIDTH - * @param[out] x opus_int32 *: Returns one of the following values: - *
- *
#OPUS_AUTO
(default)
- *
#OPUS_BANDWIDTH_NARROWBAND
4 kHz passband
- *
#OPUS_BANDWIDTH_MEDIUMBAND
6 kHz passband
- *
#OPUS_BANDWIDTH_WIDEBAND
8 kHz passband
- *
#OPUS_BANDWIDTH_SUPERWIDEBAND
12 kHz passband
- *
#OPUS_BANDWIDTH_FULLBAND
20 kHz passband
- *
- * @hideinitializer */ -#define OPUS_GET_BANDWIDTH(x) OPUS_GET_BANDWIDTH_REQUEST, __opus_check_int_ptr(x) - -/** Gets the sampling rate the encoder or decoder was initialized with. - * This simply returns the Fs value passed to opus_encoder_init() - * or opus_decoder_init(). - * @param[out] x opus_int32 *: Sampling rate of encoder or decoder. - * @hideinitializer - */ -#define OPUS_GET_SAMPLE_RATE(x) OPUS_GET_SAMPLE_RATE_REQUEST, __opus_check_int_ptr(x) - -/** If set to 1, disables the use of phase inversion for intensity stereo, - * improving the quality of mono downmixes, but slightly reducing normal - * stereo quality. Disabling phase inversion in the decoder does not comply - * with RFC 6716, although it does not cause any interoperability issue and - * is expected to become part of the Opus standard once RFC 6716 is updated - * by draft-ietf-codec-opus-update. - * @see OPUS_GET_PHASE_INVERSION_DISABLED - * @param[in] x opus_int32: Allowed values: - *
- *
0
Enable phase inversion (default).
- *
1
Disable phase inversion.
- *
- * @hideinitializer */ -#define OPUS_SET_PHASE_INVERSION_DISABLED(x) OPUS_SET_PHASE_INVERSION_DISABLED_REQUEST, __opus_check_int(x) -/** Gets the encoder's configured phase inversion status. - * @see OPUS_SET_PHASE_INVERSION_DISABLED - * @param[out] x opus_int32 *: Returns one of the following values: - *
- *
0
Stereo phase inversion enabled (default).
- *
1
Stereo phase inversion disabled.
- *
- * @hideinitializer */ -#define OPUS_GET_PHASE_INVERSION_DISABLED(x) OPUS_GET_PHASE_INVERSION_DISABLED_REQUEST, __opus_check_int_ptr(x) -/** Gets the DTX state of the encoder. - * Returns whether the last encoded frame was either a comfort noise update - * during DTX or not encoded because of DTX. - * @param[out] x opus_int32 *: Returns one of the following values: - *
- *
0
The encoder is not in DTX.
- *
1
The encoder is in DTX.
- *
- * @hideinitializer */ -#define OPUS_GET_IN_DTX(x) OPUS_GET_IN_DTX_REQUEST, __opus_check_int_ptr(x) - -/**@}*/ - -/** @defgroup opus_decoderctls Decoder related CTLs - * @see opus_genericctls, opus_encoderctls, opus_decoder - * @{ - */ - -/** Configures decoder gain adjustment. - * Scales the decoded output by a factor specified in Q8 dB units. - * This has a maximum range of -32768 to 32767 inclusive, and returns - * OPUS_BAD_ARG otherwise. The default is zero indicating no adjustment. - * This setting survives decoder reset. - * - * gain = pow(10, x/(20.0*256)) - * - * @param[in] x opus_int32: Amount to scale PCM signal by in Q8 dB units. - * @hideinitializer */ -#define OPUS_SET_GAIN(x) OPUS_SET_GAIN_REQUEST, __opus_check_int(x) -/** Gets the decoder's configured gain adjustment. @see OPUS_SET_GAIN - * - * @param[out] x opus_int32 *: Amount to scale PCM signal by in Q8 dB units. - * @hideinitializer */ -#define OPUS_GET_GAIN(x) OPUS_GET_GAIN_REQUEST, __opus_check_int_ptr(x) - -/** Gets the duration (in samples) of the last packet successfully decoded or concealed. - * @param[out] x opus_int32 *: Number of samples (at current sampling rate). - * @hideinitializer */ -#define OPUS_GET_LAST_PACKET_DURATION(x) OPUS_GET_LAST_PACKET_DURATION_REQUEST, __opus_check_int_ptr(x) - -/** Gets the pitch of the last decoded frame, if available. - * This can be used for any post-processing algorithm requiring the use of pitch, - * e.g. time stretching/shortening. If the last frame was not voiced, or if the - * pitch was not coded in the frame, then zero is returned. - * - * This CTL is only implemented for decoder instances. - * - * @param[out] x opus_int32 *: pitch period at 48 kHz (or 0 if not available) - * - * @hideinitializer */ -#define OPUS_GET_PITCH(x) OPUS_GET_PITCH_REQUEST, __opus_check_int_ptr(x) - -/**@}*/ - -/** @defgroup opus_libinfo Opus library information functions - * @{ - */ - -/** Converts an opus error code into a human readable string. - * - * @param[in] error int: Error number - * @returns Error string - */ -OPUS_EXPORT const char *opus_strerror(int error); - -/** Gets the libopus version string. - * - * Applications may look for the substring "-fixed" in the version string to - * determine whether they have a fixed-point or floating-point build at - * runtime. - * - * @returns Version string - */ -OPUS_EXPORT const char *opus_get_version_string(void); -/**@}*/ - -#ifdef __cplusplus -} -#endif - -#endif /* OPUS_DEFINES_H */ diff --git a/go/teamspeak/.opus/install/arm64-v8a/include/opus/opus_multistream.h b/go/teamspeak/.opus/install/arm64-v8a/include/opus/opus_multistream.h deleted file mode 100644 index 824cc55..0000000 --- a/go/teamspeak/.opus/install/arm64-v8a/include/opus/opus_multistream.h +++ /dev/null @@ -1,660 +0,0 @@ -/* Copyright (c) 2011 Xiph.Org Foundation - Written by Jean-Marc Valin */ -/* - Redistribution and use in source and binary forms, with or without - modification, are permitted provided that the following conditions - are met: - - - Redistributions of source code must retain the above copyright - notice, this list of conditions and the following disclaimer. - - - Redistributions in binary form must reproduce the above copyright - notice, this list of conditions and the following disclaimer in the - documentation and/or other materials provided with the distribution. - - THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS - ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT - LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR - A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER - OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, - EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, - PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR - PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF - LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING - NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS - SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. -*/ - -/** - * @file opus_multistream.h - * @brief Opus reference implementation multistream API - */ - -#ifndef OPUS_MULTISTREAM_H -#define OPUS_MULTISTREAM_H - -#include "opus.h" - -#ifdef __cplusplus -extern "C" { -#endif - -/** @cond OPUS_INTERNAL_DOC */ - -/** Macros to trigger compilation errors when the wrong types are provided to a - * CTL. */ -/**@{*/ -#define __opus_check_encstate_ptr(ptr) ((ptr) + ((ptr) - (OpusEncoder**)(ptr))) -#define __opus_check_decstate_ptr(ptr) ((ptr) + ((ptr) - (OpusDecoder**)(ptr))) -/**@}*/ - -/** These are the actual encoder and decoder CTL ID numbers. - * They should not be used directly by applications. - * In general, SETs should be even and GETs should be odd.*/ -/**@{*/ -#define OPUS_MULTISTREAM_GET_ENCODER_STATE_REQUEST 5120 -#define OPUS_MULTISTREAM_GET_DECODER_STATE_REQUEST 5122 -/**@}*/ - -/** @endcond */ - -/** @defgroup opus_multistream_ctls Multistream specific encoder and decoder CTLs - * - * These are convenience macros that are specific to the - * opus_multistream_encoder_ctl() and opus_multistream_decoder_ctl() - * interface. - * The CTLs from @ref opus_genericctls, @ref opus_encoderctls, and - * @ref opus_decoderctls may be applied to a multistream encoder or decoder as - * well. - * In addition, you may retrieve the encoder or decoder state for an specific - * stream via #OPUS_MULTISTREAM_GET_ENCODER_STATE or - * #OPUS_MULTISTREAM_GET_DECODER_STATE and apply CTLs to it individually. - */ -/**@{*/ - -/** Gets the encoder state for an individual stream of a multistream encoder. - * @param[in] x opus_int32: The index of the stream whose encoder you - * wish to retrieve. - * This must be non-negative and less than - * the streams parameter used - * to initialize the encoder. - * @param[out] y OpusEncoder**: Returns a pointer to the given - * encoder state. - * @retval OPUS_BAD_ARG The index of the requested stream was out of range. - * @hideinitializer - */ -#define OPUS_MULTISTREAM_GET_ENCODER_STATE(x,y) OPUS_MULTISTREAM_GET_ENCODER_STATE_REQUEST, __opus_check_int(x), __opus_check_encstate_ptr(y) - -/** Gets the decoder state for an individual stream of a multistream decoder. - * @param[in] x opus_int32: The index of the stream whose decoder you - * wish to retrieve. - * This must be non-negative and less than - * the streams parameter used - * to initialize the decoder. - * @param[out] y OpusDecoder**: Returns a pointer to the given - * decoder state. - * @retval OPUS_BAD_ARG The index of the requested stream was out of range. - * @hideinitializer - */ -#define OPUS_MULTISTREAM_GET_DECODER_STATE(x,y) OPUS_MULTISTREAM_GET_DECODER_STATE_REQUEST, __opus_check_int(x), __opus_check_decstate_ptr(y) - -/**@}*/ - -/** @defgroup opus_multistream Opus Multistream API - * @{ - * - * The multistream API allows individual Opus streams to be combined into a - * single packet, enabling support for up to 255 channels. Unlike an - * elementary Opus stream, the encoder and decoder must negotiate the channel - * configuration before the decoder can successfully interpret the data in the - * packets produced by the encoder. Some basic information, such as packet - * duration, can be computed without any special negotiation. - * - * The format for multistream Opus packets is defined in - * RFC 7845 - * and is based on the self-delimited Opus framing described in Appendix B of - * RFC 6716. - * Normal Opus packets are just a degenerate case of multistream Opus packets, - * and can be encoded or decoded with the multistream API by setting - * streams to 1 when initializing the encoder or - * decoder. - * - * Multistream Opus streams can contain up to 255 elementary Opus streams. - * These may be either "uncoupled" or "coupled", indicating that the decoder - * is configured to decode them to either 1 or 2 channels, respectively. - * The streams are ordered so that all coupled streams appear at the - * beginning. - * - * A mapping table defines which decoded channel i - * should be used for each input/output (I/O) channel j. This table is - * typically provided as an unsigned char array. - * Let i = mapping[j] be the index for I/O channel j. - * If i < 2*coupled_streams, then I/O channel j is - * encoded as the left channel of stream (i/2) if i - * is even, or as the right channel of stream (i/2) if - * i is odd. Otherwise, I/O channel j is encoded as - * mono in stream (i - coupled_streams), unless it has the special - * value 255, in which case it is omitted from the encoding entirely (the - * decoder will reproduce it as silence). Each value i must either - * be the special value 255 or be less than streams + coupled_streams. - * - * The output channels specified by the encoder - * should use the - * Vorbis - * channel ordering. A decoder may wish to apply an additional permutation - * to the mapping the encoder used to achieve a different output channel - * order (e.g. for outputting in WAV order). - * - * Each multistream packet contains an Opus packet for each stream, and all of - * the Opus packets in a single multistream packet must have the same - * duration. Therefore the duration of a multistream packet can be extracted - * from the TOC sequence of the first stream, which is located at the - * beginning of the packet, just like an elementary Opus stream: - * - * @code - * int nb_samples; - * int nb_frames; - * nb_frames = opus_packet_get_nb_frames(data, len); - * if (nb_frames < 1) - * return nb_frames; - * nb_samples = opus_packet_get_samples_per_frame(data, 48000) * nb_frames; - * @endcode - * - * The general encoding and decoding process proceeds exactly the same as in - * the normal @ref opus_encoder and @ref opus_decoder APIs. - * See their documentation for an overview of how to use the corresponding - * multistream functions. - */ - -/** Opus multistream encoder state. - * This contains the complete state of a multistream Opus encoder. - * It is position independent and can be freely copied. - * @see opus_multistream_encoder_create - * @see opus_multistream_encoder_init - */ -typedef struct OpusMSEncoder OpusMSEncoder; - -/** Opus multistream decoder state. - * This contains the complete state of a multistream Opus decoder. - * It is position independent and can be freely copied. - * @see opus_multistream_decoder_create - * @see opus_multistream_decoder_init - */ -typedef struct OpusMSDecoder OpusMSDecoder; - -/**\name Multistream encoder functions */ -/**@{*/ - -/** Gets the size of an OpusMSEncoder structure. - * @param streams int: The total number of streams to encode from the - * input. - * This must be no more than 255. - * @param coupled_streams int: Number of coupled (2 channel) streams - * to encode. - * This must be no larger than the total - * number of streams. - * Additionally, The total number of - * encoded channels (streams + - * coupled_streams) must be no - * more than 255. - * @returns The size in bytes on success, or a negative error code - * (see @ref opus_errorcodes) on error. - */ -OPUS_EXPORT OPUS_WARN_UNUSED_RESULT opus_int32 opus_multistream_encoder_get_size( - int streams, - int coupled_streams -); - -OPUS_EXPORT OPUS_WARN_UNUSED_RESULT opus_int32 opus_multistream_surround_encoder_get_size( - int channels, - int mapping_family -); - - -/** Allocates and initializes a multistream encoder state. - * Call opus_multistream_encoder_destroy() to release - * this object when finished. - * @param Fs opus_int32: Sampling rate of the input signal (in Hz). - * This must be one of 8000, 12000, 16000, - * 24000, or 48000. - * @param channels int: Number of channels in the input signal. - * This must be at most 255. - * It may be greater than the number of - * coded channels (streams + - * coupled_streams). - * @param streams int: The total number of streams to encode from the - * input. - * This must be no more than the number of channels. - * @param coupled_streams int: Number of coupled (2 channel) streams - * to encode. - * This must be no larger than the total - * number of streams. - * Additionally, The total number of - * encoded channels (streams + - * coupled_streams) must be no - * more than the number of input channels. - * @param[in] mapping const unsigned char[channels]: Mapping from - * encoded channels to input channels, as described in - * @ref opus_multistream. As an extra constraint, the - * multistream encoder does not allow encoding coupled - * streams for which one channel is unused since this - * is never a good idea. - * @param application int: The target encoder application. - * This must be one of the following: - *
- *
#OPUS_APPLICATION_VOIP
- *
Process signal for improved speech intelligibility.
- *
#OPUS_APPLICATION_AUDIO
- *
Favor faithfulness to the original input.
- *
#OPUS_APPLICATION_RESTRICTED_LOWDELAY
- *
Configure the minimum possible coding delay by disabling certain modes - * of operation.
- *
- * @param[out] error int *: Returns #OPUS_OK on success, or an error - * code (see @ref opus_errorcodes) on - * failure. - */ -OPUS_EXPORT OPUS_WARN_UNUSED_RESULT OpusMSEncoder *opus_multistream_encoder_create( - opus_int32 Fs, - int channels, - int streams, - int coupled_streams, - const unsigned char *mapping, - int application, - int *error -) OPUS_ARG_NONNULL(5); - -OPUS_EXPORT OPUS_WARN_UNUSED_RESULT OpusMSEncoder *opus_multistream_surround_encoder_create( - opus_int32 Fs, - int channels, - int mapping_family, - int *streams, - int *coupled_streams, - unsigned char *mapping, - int application, - int *error -) OPUS_ARG_NONNULL(4) OPUS_ARG_NONNULL(5) OPUS_ARG_NONNULL(6); - -/** Initialize a previously allocated multistream encoder state. - * The memory pointed to by \a st must be at least the size returned by - * opus_multistream_encoder_get_size(). - * This is intended for applications which use their own allocator instead of - * malloc. - * To reset a previously initialized state, use the #OPUS_RESET_STATE CTL. - * @see opus_multistream_encoder_create - * @see opus_multistream_encoder_get_size - * @param st OpusMSEncoder*: Multistream encoder state to initialize. - * @param Fs opus_int32: Sampling rate of the input signal (in Hz). - * This must be one of 8000, 12000, 16000, - * 24000, or 48000. - * @param channels int: Number of channels in the input signal. - * This must be at most 255. - * It may be greater than the number of - * coded channels (streams + - * coupled_streams). - * @param streams int: The total number of streams to encode from the - * input. - * This must be no more than the number of channels. - * @param coupled_streams int: Number of coupled (2 channel) streams - * to encode. - * This must be no larger than the total - * number of streams. - * Additionally, The total number of - * encoded channels (streams + - * coupled_streams) must be no - * more than the number of input channels. - * @param[in] mapping const unsigned char[channels]: Mapping from - * encoded channels to input channels, as described in - * @ref opus_multistream. As an extra constraint, the - * multistream encoder does not allow encoding coupled - * streams for which one channel is unused since this - * is never a good idea. - * @param application int: The target encoder application. - * This must be one of the following: - *
- *
#OPUS_APPLICATION_VOIP
- *
Process signal for improved speech intelligibility.
- *
#OPUS_APPLICATION_AUDIO
- *
Favor faithfulness to the original input.
- *
#OPUS_APPLICATION_RESTRICTED_LOWDELAY
- *
Configure the minimum possible coding delay by disabling certain modes - * of operation.
- *
- * @returns #OPUS_OK on success, or an error code (see @ref opus_errorcodes) - * on failure. - */ -OPUS_EXPORT int opus_multistream_encoder_init( - OpusMSEncoder *st, - opus_int32 Fs, - int channels, - int streams, - int coupled_streams, - const unsigned char *mapping, - int application -) OPUS_ARG_NONNULL(1) OPUS_ARG_NONNULL(6); - -OPUS_EXPORT int opus_multistream_surround_encoder_init( - OpusMSEncoder *st, - opus_int32 Fs, - int channels, - int mapping_family, - int *streams, - int *coupled_streams, - unsigned char *mapping, - int application -) OPUS_ARG_NONNULL(1) OPUS_ARG_NONNULL(5) OPUS_ARG_NONNULL(6) OPUS_ARG_NONNULL(7); - -/** Encodes a multistream Opus frame. - * @param st OpusMSEncoder*: Multistream encoder state. - * @param[in] pcm const opus_int16*: The input signal as interleaved - * samples. - * This must contain - * frame_size*channels - * samples. - * @param frame_size int: Number of samples per channel in the input - * signal. - * This must be an Opus frame size for the - * encoder's sampling rate. - * For example, at 48 kHz the permitted values - * are 120, 240, 480, 960, 1920, and 2880. - * Passing in a duration of less than 10 ms - * (480 samples at 48 kHz) will prevent the - * encoder from using the LPC or hybrid modes. - * @param[out] data unsigned char*: Output payload. - * This must contain storage for at - * least \a max_data_bytes. - * @param [in] max_data_bytes opus_int32: Size of the allocated - * memory for the output - * payload. This may be - * used to impose an upper limit on - * the instant bitrate, but should - * not be used as the only bitrate - * control. Use #OPUS_SET_BITRATE to - * control the bitrate. - * @returns The length of the encoded packet (in bytes) on success or a - * negative error code (see @ref opus_errorcodes) on failure. - */ -OPUS_EXPORT OPUS_WARN_UNUSED_RESULT int opus_multistream_encode( - OpusMSEncoder *st, - const opus_int16 *pcm, - int frame_size, - unsigned char *data, - opus_int32 max_data_bytes -) OPUS_ARG_NONNULL(1) OPUS_ARG_NONNULL(2) OPUS_ARG_NONNULL(4); - -/** Encodes a multistream Opus frame from floating point input. - * @param st OpusMSEncoder*: Multistream encoder state. - * @param[in] pcm const float*: The input signal as interleaved - * samples with a normal range of - * +/-1.0. - * Samples with a range beyond +/-1.0 - * are supported but will be clipped by - * decoders using the integer API and - * should only be used if it is known - * that the far end supports extended - * dynamic range. - * This must contain - * frame_size*channels - * samples. - * @param frame_size int: Number of samples per channel in the input - * signal. - * This must be an Opus frame size for the - * encoder's sampling rate. - * For example, at 48 kHz the permitted values - * are 120, 240, 480, 960, 1920, and 2880. - * Passing in a duration of less than 10 ms - * (480 samples at 48 kHz) will prevent the - * encoder from using the LPC or hybrid modes. - * @param[out] data unsigned char*: Output payload. - * This must contain storage for at - * least \a max_data_bytes. - * @param [in] max_data_bytes opus_int32: Size of the allocated - * memory for the output - * payload. This may be - * used to impose an upper limit on - * the instant bitrate, but should - * not be used as the only bitrate - * control. Use #OPUS_SET_BITRATE to - * control the bitrate. - * @returns The length of the encoded packet (in bytes) on success or a - * negative error code (see @ref opus_errorcodes) on failure. - */ -OPUS_EXPORT OPUS_WARN_UNUSED_RESULT int opus_multistream_encode_float( - OpusMSEncoder *st, - const float *pcm, - int frame_size, - unsigned char *data, - opus_int32 max_data_bytes -) OPUS_ARG_NONNULL(1) OPUS_ARG_NONNULL(2) OPUS_ARG_NONNULL(4); - -/** Frees an OpusMSEncoder allocated by - * opus_multistream_encoder_create(). - * @param st OpusMSEncoder*: Multistream encoder state to be freed. - */ -OPUS_EXPORT void opus_multistream_encoder_destroy(OpusMSEncoder *st); - -/** Perform a CTL function on a multistream Opus encoder. - * - * Generally the request and subsequent arguments are generated by a - * convenience macro. - * @param st OpusMSEncoder*: Multistream encoder state. - * @param request This and all remaining parameters should be replaced by one - * of the convenience macros in @ref opus_genericctls, - * @ref opus_encoderctls, or @ref opus_multistream_ctls. - * @see opus_genericctls - * @see opus_encoderctls - * @see opus_multistream_ctls - */ -OPUS_EXPORT int opus_multistream_encoder_ctl(OpusMSEncoder *st, int request, ...) OPUS_ARG_NONNULL(1); - -/**@}*/ - -/**\name Multistream decoder functions */ -/**@{*/ - -/** Gets the size of an OpusMSDecoder structure. - * @param streams int: The total number of streams coded in the - * input. - * This must be no more than 255. - * @param coupled_streams int: Number streams to decode as coupled - * (2 channel) streams. - * This must be no larger than the total - * number of streams. - * Additionally, The total number of - * coded channels (streams + - * coupled_streams) must be no - * more than 255. - * @returns The size in bytes on success, or a negative error code - * (see @ref opus_errorcodes) on error. - */ -OPUS_EXPORT OPUS_WARN_UNUSED_RESULT opus_int32 opus_multistream_decoder_get_size( - int streams, - int coupled_streams -); - -/** Allocates and initializes a multistream decoder state. - * Call opus_multistream_decoder_destroy() to release - * this object when finished. - * @param Fs opus_int32: Sampling rate to decode at (in Hz). - * This must be one of 8000, 12000, 16000, - * 24000, or 48000. - * @param channels int: Number of channels to output. - * This must be at most 255. - * It may be different from the number of coded - * channels (streams + - * coupled_streams). - * @param streams int: The total number of streams coded in the - * input. - * This must be no more than 255. - * @param coupled_streams int: Number of streams to decode as coupled - * (2 channel) streams. - * This must be no larger than the total - * number of streams. - * Additionally, The total number of - * coded channels (streams + - * coupled_streams) must be no - * more than 255. - * @param[in] mapping const unsigned char[channels]: Mapping from - * coded channels to output channels, as described in - * @ref opus_multistream. - * @param[out] error int *: Returns #OPUS_OK on success, or an error - * code (see @ref opus_errorcodes) on - * failure. - */ -OPUS_EXPORT OPUS_WARN_UNUSED_RESULT OpusMSDecoder *opus_multistream_decoder_create( - opus_int32 Fs, - int channels, - int streams, - int coupled_streams, - const unsigned char *mapping, - int *error -) OPUS_ARG_NONNULL(5); - -/** Intialize a previously allocated decoder state object. - * The memory pointed to by \a st must be at least the size returned by - * opus_multistream_encoder_get_size(). - * This is intended for applications which use their own allocator instead of - * malloc. - * To reset a previously initialized state, use the #OPUS_RESET_STATE CTL. - * @see opus_multistream_decoder_create - * @see opus_multistream_deocder_get_size - * @param st OpusMSEncoder*: Multistream encoder state to initialize. - * @param Fs opus_int32: Sampling rate to decode at (in Hz). - * This must be one of 8000, 12000, 16000, - * 24000, or 48000. - * @param channels int: Number of channels to output. - * This must be at most 255. - * It may be different from the number of coded - * channels (streams + - * coupled_streams). - * @param streams int: The total number of streams coded in the - * input. - * This must be no more than 255. - * @param coupled_streams int: Number of streams to decode as coupled - * (2 channel) streams. - * This must be no larger than the total - * number of streams. - * Additionally, The total number of - * coded channels (streams + - * coupled_streams) must be no - * more than 255. - * @param[in] mapping const unsigned char[channels]: Mapping from - * coded channels to output channels, as described in - * @ref opus_multistream. - * @returns #OPUS_OK on success, or an error code (see @ref opus_errorcodes) - * on failure. - */ -OPUS_EXPORT int opus_multistream_decoder_init( - OpusMSDecoder *st, - opus_int32 Fs, - int channels, - int streams, - int coupled_streams, - const unsigned char *mapping -) OPUS_ARG_NONNULL(1) OPUS_ARG_NONNULL(6); - -/** Decode a multistream Opus packet. - * @param st OpusMSDecoder*: Multistream decoder state. - * @param[in] data const unsigned char*: Input payload. - * Use a NULL - * pointer to indicate packet - * loss. - * @param len opus_int32: Number of bytes in payload. - * @param[out] pcm opus_int16*: Output signal, with interleaved - * samples. - * This must contain room for - * frame_size*channels - * samples. - * @param frame_size int: The number of samples per channel of - * available space in \a pcm. - * If this is less than the maximum packet duration - * (120 ms; 5760 for 48kHz), this function will not be capable - * of decoding some packets. In the case of PLC (data==NULL) - * or FEC (decode_fec=1), then frame_size needs to be exactly - * the duration of audio that is missing, otherwise the - * decoder will not be in the optimal state to decode the - * next incoming packet. For the PLC and FEC cases, frame_size - * must be a multiple of 2.5 ms. - * @param decode_fec int: Flag (0 or 1) to request that any in-band - * forward error correction data be decoded. - * If no such data is available, the frame is - * decoded as if it were lost. - * @returns Number of samples decoded on success or a negative error code - * (see @ref opus_errorcodes) on failure. - */ -OPUS_EXPORT OPUS_WARN_UNUSED_RESULT int opus_multistream_decode( - OpusMSDecoder *st, - const unsigned char *data, - opus_int32 len, - opus_int16 *pcm, - int frame_size, - int decode_fec -) OPUS_ARG_NONNULL(1) OPUS_ARG_NONNULL(4); - -/** Decode a multistream Opus packet with floating point output. - * @param st OpusMSDecoder*: Multistream decoder state. - * @param[in] data const unsigned char*: Input payload. - * Use a NULL - * pointer to indicate packet - * loss. - * @param len opus_int32: Number of bytes in payload. - * @param[out] pcm opus_int16*: Output signal, with interleaved - * samples. - * This must contain room for - * frame_size*channels - * samples. - * @param frame_size int: The number of samples per channel of - * available space in \a pcm. - * If this is less than the maximum packet duration - * (120 ms; 5760 for 48kHz), this function will not be capable - * of decoding some packets. In the case of PLC (data==NULL) - * or FEC (decode_fec=1), then frame_size needs to be exactly - * the duration of audio that is missing, otherwise the - * decoder will not be in the optimal state to decode the - * next incoming packet. For the PLC and FEC cases, frame_size - * must be a multiple of 2.5 ms. - * @param decode_fec int: Flag (0 or 1) to request that any in-band - * forward error correction data be decoded. - * If no such data is available, the frame is - * decoded as if it were lost. - * @returns Number of samples decoded on success or a negative error code - * (see @ref opus_errorcodes) on failure. - */ -OPUS_EXPORT OPUS_WARN_UNUSED_RESULT int opus_multistream_decode_float( - OpusMSDecoder *st, - const unsigned char *data, - opus_int32 len, - float *pcm, - int frame_size, - int decode_fec -) OPUS_ARG_NONNULL(1) OPUS_ARG_NONNULL(4); - -/** Perform a CTL function on a multistream Opus decoder. - * - * Generally the request and subsequent arguments are generated by a - * convenience macro. - * @param st OpusMSDecoder*: Multistream decoder state. - * @param request This and all remaining parameters should be replaced by one - * of the convenience macros in @ref opus_genericctls, - * @ref opus_decoderctls, or @ref opus_multistream_ctls. - * @see opus_genericctls - * @see opus_decoderctls - * @see opus_multistream_ctls - */ -OPUS_EXPORT int opus_multistream_decoder_ctl(OpusMSDecoder *st, int request, ...) OPUS_ARG_NONNULL(1); - -/** Frees an OpusMSDecoder allocated by - * opus_multistream_decoder_create(). - * @param st OpusMSDecoder: Multistream decoder state to be freed. - */ -OPUS_EXPORT void opus_multistream_decoder_destroy(OpusMSDecoder *st); - -/**@}*/ - -/**@}*/ - -#ifdef __cplusplus -} -#endif - -#endif /* OPUS_MULTISTREAM_H */ diff --git a/go/teamspeak/.opus/install/arm64-v8a/include/opus/opus_projection.h b/go/teamspeak/.opus/install/arm64-v8a/include/opus/opus_projection.h deleted file mode 100644 index 9dabf4e..0000000 --- a/go/teamspeak/.opus/install/arm64-v8a/include/opus/opus_projection.h +++ /dev/null @@ -1,568 +0,0 @@ -/* Copyright (c) 2017 Google Inc. - Written by Andrew Allen */ -/* - Redistribution and use in source and binary forms, with or without - modification, are permitted provided that the following conditions - are met: - - - Redistributions of source code must retain the above copyright - notice, this list of conditions and the following disclaimer. - - - Redistributions in binary form must reproduce the above copyright - notice, this list of conditions and the following disclaimer in the - documentation and/or other materials provided with the distribution. - - THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS - ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT - LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR - A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER - OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, - EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, - PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR - PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF - LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING - NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS - SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. -*/ - -/** - * @file opus_projection.h - * @brief Opus projection reference API - */ - -#ifndef OPUS_PROJECTION_H -#define OPUS_PROJECTION_H - -#include "opus_multistream.h" - -#ifdef __cplusplus -extern "C" { -#endif - -/** @cond OPUS_INTERNAL_DOC */ - -/** These are the actual encoder and decoder CTL ID numbers. - * They should not be used directly by applications.c - * In general, SETs should be even and GETs should be odd.*/ -/**@{*/ -#define OPUS_PROJECTION_GET_DEMIXING_MATRIX_GAIN_REQUEST 6001 -#define OPUS_PROJECTION_GET_DEMIXING_MATRIX_SIZE_REQUEST 6003 -#define OPUS_PROJECTION_GET_DEMIXING_MATRIX_REQUEST 6005 -/**@}*/ - - -/** @endcond */ - -/** @defgroup opus_projection_ctls Projection specific encoder and decoder CTLs - * - * These are convenience macros that are specific to the - * opus_projection_encoder_ctl() and opus_projection_decoder_ctl() - * interface. - * The CTLs from @ref opus_genericctls, @ref opus_encoderctls, - * @ref opus_decoderctls, and @ref opus_multistream_ctls may be applied to a - * projection encoder or decoder as well. - */ -/**@{*/ - -/** Gets the gain (in dB. S7.8-format) of the demixing matrix from the encoder. - * @param[out] x opus_int32 *: Returns the gain (in dB. S7.8-format) - * of the demixing matrix. - * @hideinitializer - */ -#define OPUS_PROJECTION_GET_DEMIXING_MATRIX_GAIN(x) OPUS_PROJECTION_GET_DEMIXING_MATRIX_GAIN_REQUEST, __opus_check_int_ptr(x) - - -/** Gets the size in bytes of the demixing matrix from the encoder. - * @param[out] x opus_int32 *: Returns the size in bytes of the - * demixing matrix. - * @hideinitializer - */ -#define OPUS_PROJECTION_GET_DEMIXING_MATRIX_SIZE(x) OPUS_PROJECTION_GET_DEMIXING_MATRIX_SIZE_REQUEST, __opus_check_int_ptr(x) - - -/** Copies the demixing matrix to the supplied pointer location. - * @param[out] x unsigned char *: Returns the demixing matrix to the - * supplied pointer location. - * @param y opus_int32: The size in bytes of the reserved memory at the - * pointer location. - * @hideinitializer - */ -#define OPUS_PROJECTION_GET_DEMIXING_MATRIX(x,y) OPUS_PROJECTION_GET_DEMIXING_MATRIX_REQUEST, x, __opus_check_int(y) - - -/**@}*/ - -/** Opus projection encoder state. - * This contains the complete state of a projection Opus encoder. - * It is position independent and can be freely copied. - * @see opus_projection_ambisonics_encoder_create - */ -typedef struct OpusProjectionEncoder OpusProjectionEncoder; - - -/** Opus projection decoder state. - * This contains the complete state of a projection Opus decoder. - * It is position independent and can be freely copied. - * @see opus_projection_decoder_create - * @see opus_projection_decoder_init - */ -typedef struct OpusProjectionDecoder OpusProjectionDecoder; - - -/**\name Projection encoder functions */ -/**@{*/ - -/** Gets the size of an OpusProjectionEncoder structure. - * @param channels int: The total number of input channels to encode. - * This must be no more than 255. - * @param mapping_family int: The mapping family to use for selecting - * the appropriate projection. - * @returns The size in bytes on success, or a negative error code - * (see @ref opus_errorcodes) on error. - */ -OPUS_EXPORT OPUS_WARN_UNUSED_RESULT opus_int32 opus_projection_ambisonics_encoder_get_size( - int channels, - int mapping_family -); - - -/** Allocates and initializes a projection encoder state. - * Call opus_projection_encoder_destroy() to release - * this object when finished. - * @param Fs opus_int32: Sampling rate of the input signal (in Hz). - * This must be one of 8000, 12000, 16000, - * 24000, or 48000. - * @param channels int: Number of channels in the input signal. - * This must be at most 255. - * It may be greater than the number of - * coded channels (streams + - * coupled_streams). - * @param mapping_family int: The mapping family to use for selecting - * the appropriate projection. - * @param[out] streams int *: The total number of streams that will - * be encoded from the input. - * @param[out] coupled_streams int *: Number of coupled (2 channel) - * streams that will be encoded from the input. - * @param application int: The target encoder application. - * This must be one of the following: - *
- *
#OPUS_APPLICATION_VOIP
- *
Process signal for improved speech intelligibility.
- *
#OPUS_APPLICATION_AUDIO
- *
Favor faithfulness to the original input.
- *
#OPUS_APPLICATION_RESTRICTED_LOWDELAY
- *
Configure the minimum possible coding delay by disabling certain modes - * of operation.
- *
- * @param[out] error int *: Returns #OPUS_OK on success, or an error - * code (see @ref opus_errorcodes) on - * failure. - */ -OPUS_EXPORT OPUS_WARN_UNUSED_RESULT OpusProjectionEncoder *opus_projection_ambisonics_encoder_create( - opus_int32 Fs, - int channels, - int mapping_family, - int *streams, - int *coupled_streams, - int application, - int *error -) OPUS_ARG_NONNULL(4) OPUS_ARG_NONNULL(5); - - -/** Initialize a previously allocated projection encoder state. - * The memory pointed to by \a st must be at least the size returned by - * opus_projection_ambisonics_encoder_get_size(). - * This is intended for applications which use their own allocator instead of - * malloc. - * To reset a previously initialized state, use the #OPUS_RESET_STATE CTL. - * @see opus_projection_ambisonics_encoder_create - * @see opus_projection_ambisonics_encoder_get_size - * @param st OpusProjectionEncoder*: Projection encoder state to initialize. - * @param Fs opus_int32: Sampling rate of the input signal (in Hz). - * This must be one of 8000, 12000, 16000, - * 24000, or 48000. - * @param channels int: Number of channels in the input signal. - * This must be at most 255. - * It may be greater than the number of - * coded channels (streams + - * coupled_streams). - * @param streams int: The total number of streams to encode from the - * input. - * This must be no more than the number of channels. - * @param coupled_streams int: Number of coupled (2 channel) streams - * to encode. - * This must be no larger than the total - * number of streams. - * Additionally, The total number of - * encoded channels (streams + - * coupled_streams) must be no - * more than the number of input channels. - * @param application int: The target encoder application. - * This must be one of the following: - *
- *
#OPUS_APPLICATION_VOIP
- *
Process signal for improved speech intelligibility.
- *
#OPUS_APPLICATION_AUDIO
- *
Favor faithfulness to the original input.
- *
#OPUS_APPLICATION_RESTRICTED_LOWDELAY
- *
Configure the minimum possible coding delay by disabling certain modes - * of operation.
- *
- * @returns #OPUS_OK on success, or an error code (see @ref opus_errorcodes) - * on failure. - */ -OPUS_EXPORT int opus_projection_ambisonics_encoder_init( - OpusProjectionEncoder *st, - opus_int32 Fs, - int channels, - int mapping_family, - int *streams, - int *coupled_streams, - int application -) OPUS_ARG_NONNULL(1) OPUS_ARG_NONNULL(5) OPUS_ARG_NONNULL(6); - - -/** Encodes a projection Opus frame. - * @param st OpusProjectionEncoder*: Projection encoder state. - * @param[in] pcm const opus_int16*: The input signal as interleaved - * samples. - * This must contain - * frame_size*channels - * samples. - * @param frame_size int: Number of samples per channel in the input - * signal. - * This must be an Opus frame size for the - * encoder's sampling rate. - * For example, at 48 kHz the permitted values - * are 120, 240, 480, 960, 1920, and 2880. - * Passing in a duration of less than 10 ms - * (480 samples at 48 kHz) will prevent the - * encoder from using the LPC or hybrid modes. - * @param[out] data unsigned char*: Output payload. - * This must contain storage for at - * least \a max_data_bytes. - * @param [in] max_data_bytes opus_int32: Size of the allocated - * memory for the output - * payload. This may be - * used to impose an upper limit on - * the instant bitrate, but should - * not be used as the only bitrate - * control. Use #OPUS_SET_BITRATE to - * control the bitrate. - * @returns The length of the encoded packet (in bytes) on success or a - * negative error code (see @ref opus_errorcodes) on failure. - */ -OPUS_EXPORT OPUS_WARN_UNUSED_RESULT int opus_projection_encode( - OpusProjectionEncoder *st, - const opus_int16 *pcm, - int frame_size, - unsigned char *data, - opus_int32 max_data_bytes -) OPUS_ARG_NONNULL(1) OPUS_ARG_NONNULL(2) OPUS_ARG_NONNULL(4); - - -/** Encodes a projection Opus frame from floating point input. - * @param st OpusProjectionEncoder*: Projection encoder state. - * @param[in] pcm const float*: The input signal as interleaved - * samples with a normal range of - * +/-1.0. - * Samples with a range beyond +/-1.0 - * are supported but will be clipped by - * decoders using the integer API and - * should only be used if it is known - * that the far end supports extended - * dynamic range. - * This must contain - * frame_size*channels - * samples. - * @param frame_size int: Number of samples per channel in the input - * signal. - * This must be an Opus frame size for the - * encoder's sampling rate. - * For example, at 48 kHz the permitted values - * are 120, 240, 480, 960, 1920, and 2880. - * Passing in a duration of less than 10 ms - * (480 samples at 48 kHz) will prevent the - * encoder from using the LPC or hybrid modes. - * @param[out] data unsigned char*: Output payload. - * This must contain storage for at - * least \a max_data_bytes. - * @param [in] max_data_bytes opus_int32: Size of the allocated - * memory for the output - * payload. This may be - * used to impose an upper limit on - * the instant bitrate, but should - * not be used as the only bitrate - * control. Use #OPUS_SET_BITRATE to - * control the bitrate. - * @returns The length of the encoded packet (in bytes) on success or a - * negative error code (see @ref opus_errorcodes) on failure. - */ -OPUS_EXPORT OPUS_WARN_UNUSED_RESULT int opus_projection_encode_float( - OpusProjectionEncoder *st, - const float *pcm, - int frame_size, - unsigned char *data, - opus_int32 max_data_bytes -) OPUS_ARG_NONNULL(1) OPUS_ARG_NONNULL(2) OPUS_ARG_NONNULL(4); - - -/** Frees an OpusProjectionEncoder allocated by - * opus_projection_ambisonics_encoder_create(). - * @param st OpusProjectionEncoder*: Projection encoder state to be freed. - */ -OPUS_EXPORT void opus_projection_encoder_destroy(OpusProjectionEncoder *st); - - -/** Perform a CTL function on a projection Opus encoder. - * - * Generally the request and subsequent arguments are generated by a - * convenience macro. - * @param st OpusProjectionEncoder*: Projection encoder state. - * @param request This and all remaining parameters should be replaced by one - * of the convenience macros in @ref opus_genericctls, - * @ref opus_encoderctls, @ref opus_multistream_ctls, or - * @ref opus_projection_ctls - * @see opus_genericctls - * @see opus_encoderctls - * @see opus_multistream_ctls - * @see opus_projection_ctls - */ -OPUS_EXPORT int opus_projection_encoder_ctl(OpusProjectionEncoder *st, int request, ...) OPUS_ARG_NONNULL(1); - - -/**@}*/ - -/**\name Projection decoder functions */ -/**@{*/ - -/** Gets the size of an OpusProjectionDecoder structure. - * @param channels int: The total number of output channels. - * This must be no more than 255. - * @param streams int: The total number of streams coded in the - * input. - * This must be no more than 255. - * @param coupled_streams int: Number streams to decode as coupled - * (2 channel) streams. - * This must be no larger than the total - * number of streams. - * Additionally, The total number of - * coded channels (streams + - * coupled_streams) must be no - * more than 255. - * @returns The size in bytes on success, or a negative error code - * (see @ref opus_errorcodes) on error. - */ -OPUS_EXPORT OPUS_WARN_UNUSED_RESULT opus_int32 opus_projection_decoder_get_size( - int channels, - int streams, - int coupled_streams -); - - -/** Allocates and initializes a projection decoder state. - * Call opus_projection_decoder_destroy() to release - * this object when finished. - * @param Fs opus_int32: Sampling rate to decode at (in Hz). - * This must be one of 8000, 12000, 16000, - * 24000, or 48000. - * @param channels int: Number of channels to output. - * This must be at most 255. - * It may be different from the number of coded - * channels (streams + - * coupled_streams). - * @param streams int: The total number of streams coded in the - * input. - * This must be no more than 255. - * @param coupled_streams int: Number of streams to decode as coupled - * (2 channel) streams. - * This must be no larger than the total - * number of streams. - * Additionally, The total number of - * coded channels (streams + - * coupled_streams) must be no - * more than 255. - * @param[in] demixing_matrix const unsigned char[demixing_matrix_size]: Demixing matrix - * that mapping from coded channels to output channels, - * as described in @ref opus_projection and - * @ref opus_projection_ctls. - * @param demixing_matrix_size opus_int32: The size in bytes of the - * demixing matrix, as - * described in @ref - * opus_projection_ctls. - * @param[out] error int *: Returns #OPUS_OK on success, or an error - * code (see @ref opus_errorcodes) on - * failure. - */ -OPUS_EXPORT OPUS_WARN_UNUSED_RESULT OpusProjectionDecoder *opus_projection_decoder_create( - opus_int32 Fs, - int channels, - int streams, - int coupled_streams, - unsigned char *demixing_matrix, - opus_int32 demixing_matrix_size, - int *error -) OPUS_ARG_NONNULL(5); - - -/** Intialize a previously allocated projection decoder state object. - * The memory pointed to by \a st must be at least the size returned by - * opus_projection_decoder_get_size(). - * This is intended for applications which use their own allocator instead of - * malloc. - * To reset a previously initialized state, use the #OPUS_RESET_STATE CTL. - * @see opus_projection_decoder_create - * @see opus_projection_deocder_get_size - * @param st OpusProjectionDecoder*: Projection encoder state to initialize. - * @param Fs opus_int32: Sampling rate to decode at (in Hz). - * This must be one of 8000, 12000, 16000, - * 24000, or 48000. - * @param channels int: Number of channels to output. - * This must be at most 255. - * It may be different from the number of coded - * channels (streams + - * coupled_streams). - * @param streams int: The total number of streams coded in the - * input. - * This must be no more than 255. - * @param coupled_streams int: Number of streams to decode as coupled - * (2 channel) streams. - * This must be no larger than the total - * number of streams. - * Additionally, The total number of - * coded channels (streams + - * coupled_streams) must be no - * more than 255. - * @param[in] demixing_matrix const unsigned char[demixing_matrix_size]: Demixing matrix - * that mapping from coded channels to output channels, - * as described in @ref opus_projection and - * @ref opus_projection_ctls. - * @param demixing_matrix_size opus_int32: The size in bytes of the - * demixing matrix, as - * described in @ref - * opus_projection_ctls. - * @returns #OPUS_OK on success, or an error code (see @ref opus_errorcodes) - * on failure. - */ -OPUS_EXPORT int opus_projection_decoder_init( - OpusProjectionDecoder *st, - opus_int32 Fs, - int channels, - int streams, - int coupled_streams, - unsigned char *demixing_matrix, - opus_int32 demixing_matrix_size -) OPUS_ARG_NONNULL(1) OPUS_ARG_NONNULL(6); - - -/** Decode a projection Opus packet. - * @param st OpusProjectionDecoder*: Projection decoder state. - * @param[in] data const unsigned char*: Input payload. - * Use a NULL - * pointer to indicate packet - * loss. - * @param len opus_int32: Number of bytes in payload. - * @param[out] pcm opus_int16*: Output signal, with interleaved - * samples. - * This must contain room for - * frame_size*channels - * samples. - * @param frame_size int: The number of samples per channel of - * available space in \a pcm. - * If this is less than the maximum packet duration - * (120 ms; 5760 for 48kHz), this function will not be capable - * of decoding some packets. In the case of PLC (data==NULL) - * or FEC (decode_fec=1), then frame_size needs to be exactly - * the duration of audio that is missing, otherwise the - * decoder will not be in the optimal state to decode the - * next incoming packet. For the PLC and FEC cases, frame_size - * must be a multiple of 2.5 ms. - * @param decode_fec int: Flag (0 or 1) to request that any in-band - * forward error correction data be decoded. - * If no such data is available, the frame is - * decoded as if it were lost. - * @returns Number of samples decoded on success or a negative error code - * (see @ref opus_errorcodes) on failure. - */ -OPUS_EXPORT OPUS_WARN_UNUSED_RESULT int opus_projection_decode( - OpusProjectionDecoder *st, - const unsigned char *data, - opus_int32 len, - opus_int16 *pcm, - int frame_size, - int decode_fec -) OPUS_ARG_NONNULL(1) OPUS_ARG_NONNULL(4); - - -/** Decode a projection Opus packet with floating point output. - * @param st OpusProjectionDecoder*: Projection decoder state. - * @param[in] data const unsigned char*: Input payload. - * Use a NULL - * pointer to indicate packet - * loss. - * @param len opus_int32: Number of bytes in payload. - * @param[out] pcm opus_int16*: Output signal, with interleaved - * samples. - * This must contain room for - * frame_size*channels - * samples. - * @param frame_size int: The number of samples per channel of - * available space in \a pcm. - * If this is less than the maximum packet duration - * (120 ms; 5760 for 48kHz), this function will not be capable - * of decoding some packets. In the case of PLC (data==NULL) - * or FEC (decode_fec=1), then frame_size needs to be exactly - * the duration of audio that is missing, otherwise the - * decoder will not be in the optimal state to decode the - * next incoming packet. For the PLC and FEC cases, frame_size - * must be a multiple of 2.5 ms. - * @param decode_fec int: Flag (0 or 1) to request that any in-band - * forward error correction data be decoded. - * If no such data is available, the frame is - * decoded as if it were lost. - * @returns Number of samples decoded on success or a negative error code - * (see @ref opus_errorcodes) on failure. - */ -OPUS_EXPORT OPUS_WARN_UNUSED_RESULT int opus_projection_decode_float( - OpusProjectionDecoder *st, - const unsigned char *data, - opus_int32 len, - float *pcm, - int frame_size, - int decode_fec -) OPUS_ARG_NONNULL(1) OPUS_ARG_NONNULL(4); - - -/** Perform a CTL function on a projection Opus decoder. - * - * Generally the request and subsequent arguments are generated by a - * convenience macro. - * @param st OpusProjectionDecoder*: Projection decoder state. - * @param request This and all remaining parameters should be replaced by one - * of the convenience macros in @ref opus_genericctls, - * @ref opus_decoderctls, @ref opus_multistream_ctls, or - * @ref opus_projection_ctls. - * @see opus_genericctls - * @see opus_decoderctls - * @see opus_multistream_ctls - * @see opus_projection_ctls - */ -OPUS_EXPORT int opus_projection_decoder_ctl(OpusProjectionDecoder *st, int request, ...) OPUS_ARG_NONNULL(1); - - -/** Frees an OpusProjectionDecoder allocated by - * opus_projection_decoder_create(). - * @param st OpusProjectionDecoder: Projection decoder state to be freed. - */ -OPUS_EXPORT void opus_projection_decoder_destroy(OpusProjectionDecoder *st); - - -/**@}*/ - -/**@}*/ - -#ifdef __cplusplus -} -#endif - -#endif /* OPUS_PROJECTION_H */ diff --git a/go/teamspeak/.opus/install/arm64-v8a/include/opus/opus_types.h b/go/teamspeak/.opus/install/arm64-v8a/include/opus/opus_types.h deleted file mode 100644 index 7cf6755..0000000 --- a/go/teamspeak/.opus/install/arm64-v8a/include/opus/opus_types.h +++ /dev/null @@ -1,166 +0,0 @@ -/* (C) COPYRIGHT 1994-2002 Xiph.Org Foundation */ -/* Modified by Jean-Marc Valin */ -/* - Redistribution and use in source and binary forms, with or without - modification, are permitted provided that the following conditions - are met: - - - Redistributions of source code must retain the above copyright - notice, this list of conditions and the following disclaimer. - - - Redistributions in binary form must reproduce the above copyright - notice, this list of conditions and the following disclaimer in the - documentation and/or other materials provided with the distribution. - - THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS - ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT - LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR - A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER - OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, - EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, - PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR - PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF - LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING - NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS - SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. -*/ -/* opus_types.h based on ogg_types.h from libogg */ - -/** - @file opus_types.h - @brief Opus reference implementation types -*/ -#ifndef OPUS_TYPES_H -#define OPUS_TYPES_H - -#define opus_int int /* used for counters etc; at least 16 bits */ -#define opus_int64 long long -#define opus_int8 signed char - -#define opus_uint unsigned int /* used for counters etc; at least 16 bits */ -#define opus_uint64 unsigned long long -#define opus_uint8 unsigned char - -/* Use the real stdint.h if it's there (taken from Paul Hsieh's pstdint.h) */ -#if (defined(__STDC__) && __STDC__ && defined(__STDC_VERSION__) && __STDC_VERSION__ >= 199901L) || (defined(__GNUC__) && (defined(_STDINT_H) || defined(_STDINT_H_)) || defined (HAVE_STDINT_H)) -#include -# undef opus_int64 -# undef opus_int8 -# undef opus_uint64 -# undef opus_uint8 - typedef int8_t opus_int8; - typedef uint8_t opus_uint8; - typedef int16_t opus_int16; - typedef uint16_t opus_uint16; - typedef int32_t opus_int32; - typedef uint32_t opus_uint32; - typedef int64_t opus_int64; - typedef uint64_t opus_uint64; -#elif defined(_WIN32) - -# if defined(__CYGWIN__) -# include <_G_config.h> - typedef _G_int32_t opus_int32; - typedef _G_uint32_t opus_uint32; - typedef _G_int16 opus_int16; - typedef _G_uint16 opus_uint16; -# elif defined(__MINGW32__) - typedef short opus_int16; - typedef unsigned short opus_uint16; - typedef int opus_int32; - typedef unsigned int opus_uint32; -# elif defined(__MWERKS__) - typedef int opus_int32; - typedef unsigned int opus_uint32; - typedef short opus_int16; - typedef unsigned short opus_uint16; -# else - /* MSVC/Borland */ - typedef __int32 opus_int32; - typedef unsigned __int32 opus_uint32; - typedef __int16 opus_int16; - typedef unsigned __int16 opus_uint16; -# endif - -#elif defined(__MACOS__) - -# include - typedef SInt16 opus_int16; - typedef UInt16 opus_uint16; - typedef SInt32 opus_int32; - typedef UInt32 opus_uint32; - -#elif (defined(__APPLE__) && defined(__MACH__)) /* MacOS X Framework build */ - -# include - typedef int16_t opus_int16; - typedef u_int16_t opus_uint16; - typedef int32_t opus_int32; - typedef u_int32_t opus_uint32; - -#elif defined(__BEOS__) - - /* Be */ -# include - typedef int16 opus_int16; - typedef u_int16 opus_uint16; - typedef int32_t opus_int32; - typedef u_int32_t opus_uint32; - -#elif defined (__EMX__) - - /* OS/2 GCC */ - typedef short opus_int16; - typedef unsigned short opus_uint16; - typedef int opus_int32; - typedef unsigned int opus_uint32; - -#elif defined (DJGPP) - - /* DJGPP */ - typedef short opus_int16; - typedef unsigned short opus_uint16; - typedef int opus_int32; - typedef unsigned int opus_uint32; - -#elif defined(R5900) - - /* PS2 EE */ - typedef int opus_int32; - typedef unsigned opus_uint32; - typedef short opus_int16; - typedef unsigned short opus_uint16; - -#elif defined(__SYMBIAN32__) - - /* Symbian GCC */ - typedef signed short opus_int16; - typedef unsigned short opus_uint16; - typedef signed int opus_int32; - typedef unsigned int opus_uint32; - -#elif defined(CONFIG_TI_C54X) || defined (CONFIG_TI_C55X) - - typedef short opus_int16; - typedef unsigned short opus_uint16; - typedef long opus_int32; - typedef unsigned long opus_uint32; - -#elif defined(CONFIG_TI_C6X) - - typedef short opus_int16; - typedef unsigned short opus_uint16; - typedef int opus_int32; - typedef unsigned int opus_uint32; - -#else - - /* Give up, take a reasonable guess */ - typedef short opus_int16; - typedef unsigned short opus_uint16; - typedef int opus_int32; - typedef unsigned int opus_uint32; - -#endif - -#endif /* OPUS_TYPES_H */ diff --git a/go/teamspeak/.opus/install/arm64-v8a/lib/libopus.a b/go/teamspeak/.opus/install/arm64-v8a/lib/libopus.a deleted file mode 100644 index c0632bc..0000000 Binary files a/go/teamspeak/.opus/install/arm64-v8a/lib/libopus.a and /dev/null differ diff --git a/go/teamspeak/.opus/install/armeabi-v7a/include/opus/opus.h b/go/teamspeak/.opus/install/armeabi-v7a/include/opus/opus.h deleted file mode 100644 index eadeda7..0000000 --- a/go/teamspeak/.opus/install/armeabi-v7a/include/opus/opus.h +++ /dev/null @@ -1,1099 +0,0 @@ -/* Copyright (c) 2010-2011 Xiph.Org Foundation, Skype Limited - Written by Jean-Marc Valin and Koen Vos */ -/* - Redistribution and use in source and binary forms, with or without - modification, are permitted provided that the following conditions - are met: - - - Redistributions of source code must retain the above copyright - notice, this list of conditions and the following disclaimer. - - - Redistributions in binary form must reproduce the above copyright - notice, this list of conditions and the following disclaimer in the - documentation and/or other materials provided with the distribution. - - THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS - ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT - LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR - A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER - OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, - EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, - PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR - PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF - LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING - NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS - SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. -*/ - -/** - * @file opus.h - * @brief Opus reference implementation API - */ - -#ifndef OPUS_H -#define OPUS_H - -#include "opus_types.h" -#include "opus_defines.h" - -#ifdef __cplusplus -extern "C" { -#endif - -/** - * @mainpage Opus - * - * The Opus codec is designed for interactive speech and audio transmission over the Internet. - * It is designed by the IETF Codec Working Group and incorporates technology from - * Skype's SILK codec and Xiph.Org's CELT codec. - * - * The Opus codec is designed to handle a wide range of interactive audio applications, - * including Voice over IP, videoconferencing, in-game chat, and even remote live music - * performances. It can scale from low bit-rate narrowband speech to very high quality - * stereo music. Its main features are: - - * @li Sampling rates from 8 to 48 kHz - * @li Bit-rates from 6 kb/s to 510 kb/s - * @li Support for both constant bit-rate (CBR) and variable bit-rate (VBR) - * @li Audio bandwidth from narrowband to full-band - * @li Support for speech and music - * @li Support for mono and stereo - * @li Support for multichannel (up to 255 channels) - * @li Frame sizes from 2.5 ms to 60 ms - * @li Good loss robustness and packet loss concealment (PLC) - * @li Floating point and fixed-point implementation - * - * Documentation sections: - * @li @ref opus_encoder - * @li @ref opus_decoder - * @li @ref opus_repacketizer - * @li @ref opus_multistream - * @li @ref opus_libinfo - * @li @ref opus_custom - */ - -/** @defgroup opus_encoder Opus Encoder - * @{ - * - * @brief This page describes the process and functions used to encode Opus. - * - * Since Opus is a stateful codec, the encoding process starts with creating an encoder - * state. This can be done with: - * - * @code - * int error; - * OpusEncoder *enc; - * enc = opus_encoder_create(Fs, channels, application, &error); - * @endcode - * - * From this point, @c enc can be used for encoding an audio stream. An encoder state - * @b must @b not be used for more than one stream at the same time. Similarly, the encoder - * state @b must @b not be re-initialized for each frame. - * - * While opus_encoder_create() allocates memory for the state, it's also possible - * to initialize pre-allocated memory: - * - * @code - * int size; - * int error; - * OpusEncoder *enc; - * size = opus_encoder_get_size(channels); - * enc = malloc(size); - * error = opus_encoder_init(enc, Fs, channels, application); - * @endcode - * - * where opus_encoder_get_size() returns the required size for the encoder state. Note that - * future versions of this code may change the size, so no assumptions should be made about it. - * - * The encoder state is always continuous in memory and only a shallow copy is sufficient - * to copy it (e.g. memcpy()) - * - * It is possible to change some of the encoder's settings using the opus_encoder_ctl() - * interface. All these settings already default to the recommended value, so they should - * only be changed when necessary. The most common settings one may want to change are: - * - * @code - * opus_encoder_ctl(enc, OPUS_SET_BITRATE(bitrate)); - * opus_encoder_ctl(enc, OPUS_SET_COMPLEXITY(complexity)); - * opus_encoder_ctl(enc, OPUS_SET_SIGNAL(signal_type)); - * @endcode - * - * where - * - * @arg bitrate is in bits per second (b/s) - * @arg complexity is a value from 1 to 10, where 1 is the lowest complexity and 10 is the highest - * @arg signal_type is either OPUS_AUTO (default), OPUS_SIGNAL_VOICE, or OPUS_SIGNAL_MUSIC - * - * See @ref opus_encoderctls and @ref opus_genericctls for a complete list of parameters that can be set or queried. Most parameters can be set or changed at any time during a stream. - * - * To encode a frame, opus_encode() or opus_encode_float() must be called with exactly one frame (2.5, 5, 10, 20, 40 or 60 ms) of audio data: - * @code - * len = opus_encode(enc, audio_frame, frame_size, packet, max_packet); - * @endcode - * - * where - *
    - *
  • audio_frame is the audio data in opus_int16 (or float for opus_encode_float())
    • - *
  • frame_size is the duration of the frame in samples (per channel)
    • - *
  • packet is the byte array to which the compressed data is written
    • - *
  • max_packet is the maximum number of bytes that can be written in the packet (4000 bytes is recommended). - * Do not use max_packet to control VBR target bitrate, instead use the #OPUS_SET_BITRATE CTL.
    • - *
- * - * opus_encode() and opus_encode_float() return the number of bytes actually written to the packet. - * The return value can be negative, which indicates that an error has occurred. If the return value - * is 2 bytes or less, then the packet does not need to be transmitted (DTX). - * - * Once the encoder state if no longer needed, it can be destroyed with - * - * @code - * opus_encoder_destroy(enc); - * @endcode - * - * If the encoder was created with opus_encoder_init() rather than opus_encoder_create(), - * then no action is required aside from potentially freeing the memory that was manually - * allocated for it (calling free(enc) for the example above) - * - */ - -/** Opus encoder state. - * This contains the complete state of an Opus encoder. - * It is position independent and can be freely copied. - * @see opus_encoder_create,opus_encoder_init - */ -typedef struct OpusEncoder OpusEncoder; - -/** Gets the size of an OpusEncoder structure. - * @param[in] channels int: Number of channels. - * This must be 1 or 2. - * @returns The size in bytes. - */ -OPUS_EXPORT OPUS_WARN_UNUSED_RESULT int opus_encoder_get_size(int channels); - -/** - */ - -/** Allocates and initializes an encoder state. - * There are three coding modes: - * - * @ref OPUS_APPLICATION_VOIP gives best quality at a given bitrate for voice - * signals. It enhances the input signal by high-pass filtering and - * emphasizing formants and harmonics. Optionally it includes in-band - * forward error correction to protect against packet loss. Use this - * mode for typical VoIP applications. Because of the enhancement, - * even at high bitrates the output may sound different from the input. - * - * @ref OPUS_APPLICATION_AUDIO gives best quality at a given bitrate for most - * non-voice signals like music. Use this mode for music and mixed - * (music/voice) content, broadcast, and applications requiring less - * than 15 ms of coding delay. - * - * @ref OPUS_APPLICATION_RESTRICTED_LOWDELAY configures low-delay mode that - * disables the speech-optimized mode in exchange for slightly reduced delay. - * This mode can only be set on an newly initialized or freshly reset encoder - * because it changes the codec delay. - * - * This is useful when the caller knows that the speech-optimized modes will not be needed (use with caution). - * @param [in] Fs opus_int32: Sampling rate of input signal (Hz) - * This must be one of 8000, 12000, 16000, - * 24000, or 48000. - * @param [in] channels int: Number of channels (1 or 2) in input signal - * @param [in] application int: Coding mode (one of @ref OPUS_APPLICATION_VOIP, @ref OPUS_APPLICATION_AUDIO, or @ref OPUS_APPLICATION_RESTRICTED_LOWDELAY) - * @param [out] error int*: @ref opus_errorcodes - * @note Regardless of the sampling rate and number channels selected, the Opus encoder - * can switch to a lower audio bandwidth or number of channels if the bitrate - * selected is too low. This also means that it is safe to always use 48 kHz stereo input - * and let the encoder optimize the encoding. - */ -OPUS_EXPORT OPUS_WARN_UNUSED_RESULT OpusEncoder *opus_encoder_create( - opus_int32 Fs, - int channels, - int application, - int *error -); - -/** Initializes a previously allocated encoder state - * The memory pointed to by st must be at least the size returned by opus_encoder_get_size(). - * This is intended for applications which use their own allocator instead of malloc. - * @see opus_encoder_create(),opus_encoder_get_size() - * To reset a previously initialized state, use the #OPUS_RESET_STATE CTL. - * @param [in] st OpusEncoder*: Encoder state - * @param [in] Fs opus_int32: Sampling rate of input signal (Hz) - * This must be one of 8000, 12000, 16000, - * 24000, or 48000. - * @param [in] channels int: Number of channels (1 or 2) in input signal - * @param [in] application int: Coding mode (one of OPUS_APPLICATION_VOIP, OPUS_APPLICATION_AUDIO, or OPUS_APPLICATION_RESTRICTED_LOWDELAY) - * @retval #OPUS_OK Success or @ref opus_errorcodes - */ -OPUS_EXPORT int opus_encoder_init( - OpusEncoder *st, - opus_int32 Fs, - int channels, - int application -) OPUS_ARG_NONNULL(1); - -/** Encodes an Opus frame. - * @param [in] st OpusEncoder*: Encoder state - * @param [in] pcm opus_int16*: Input signal (interleaved if 2 channels). length is frame_size*channels*sizeof(opus_int16) - * @param [in] frame_size int: Number of samples per channel in the - * input signal. - * This must be an Opus frame size for - * the encoder's sampling rate. - * For example, at 48 kHz the permitted - * values are 120, 240, 480, 960, 1920, - * and 2880. - * Passing in a duration of less than - * 10 ms (480 samples at 48 kHz) will - * prevent the encoder from using the LPC - * or hybrid modes. - * @param [out] data unsigned char*: Output payload. - * This must contain storage for at - * least \a max_data_bytes. - * @param [in] max_data_bytes opus_int32: Size of the allocated - * memory for the output - * payload. This may be - * used to impose an upper limit on - * the instant bitrate, but should - * not be used as the only bitrate - * control. Use #OPUS_SET_BITRATE to - * control the bitrate. - * @returns The length of the encoded packet (in bytes) on success or a - * negative error code (see @ref opus_errorcodes) on failure. - */ -OPUS_EXPORT OPUS_WARN_UNUSED_RESULT opus_int32 opus_encode( - OpusEncoder *st, - const opus_int16 *pcm, - int frame_size, - unsigned char *data, - opus_int32 max_data_bytes -) OPUS_ARG_NONNULL(1) OPUS_ARG_NONNULL(2) OPUS_ARG_NONNULL(4); - -/** Encodes an Opus frame from floating point input. - * @param [in] st OpusEncoder*: Encoder state - * @param [in] pcm float*: Input in float format (interleaved if 2 channels), with a normal range of +/-1.0. - * Samples with a range beyond +/-1.0 are supported but will - * be clipped by decoders using the integer API and should - * only be used if it is known that the far end supports - * extended dynamic range. - * length is frame_size*channels*sizeof(float) - * @param [in] frame_size int: Number of samples per channel in the - * input signal. - * This must be an Opus frame size for - * the encoder's sampling rate. - * For example, at 48 kHz the permitted - * values are 120, 240, 480, 960, 1920, - * and 2880. - * Passing in a duration of less than - * 10 ms (480 samples at 48 kHz) will - * prevent the encoder from using the LPC - * or hybrid modes. - * @param [out] data unsigned char*: Output payload. - * This must contain storage for at - * least \a max_data_bytes. - * @param [in] max_data_bytes opus_int32: Size of the allocated - * memory for the output - * payload. This may be - * used to impose an upper limit on - * the instant bitrate, but should - * not be used as the only bitrate - * control. Use #OPUS_SET_BITRATE to - * control the bitrate. - * @returns The length of the encoded packet (in bytes) on success or a - * negative error code (see @ref opus_errorcodes) on failure. - */ -OPUS_EXPORT OPUS_WARN_UNUSED_RESULT opus_int32 opus_encode_float( - OpusEncoder *st, - const float *pcm, - int frame_size, - unsigned char *data, - opus_int32 max_data_bytes -) OPUS_ARG_NONNULL(1) OPUS_ARG_NONNULL(2) OPUS_ARG_NONNULL(4); - -/** Frees an OpusEncoder allocated by opus_encoder_create(). - * @param[in] st OpusEncoder*: State to be freed. - */ -OPUS_EXPORT void opus_encoder_destroy(OpusEncoder *st); - -/** Perform a CTL function on an Opus encoder. - * - * Generally the request and subsequent arguments are generated - * by a convenience macro. - * @param st OpusEncoder*: Encoder state. - * @param request This and all remaining parameters should be replaced by one - * of the convenience macros in @ref opus_genericctls or - * @ref opus_encoderctls. - * @see opus_genericctls - * @see opus_encoderctls - */ -OPUS_EXPORT int opus_encoder_ctl(OpusEncoder *st, int request, ...) OPUS_ARG_NONNULL(1); -/**@}*/ - -/** @defgroup opus_decoder Opus Decoder - * @{ - * - * @brief This page describes the process and functions used to decode Opus. - * - * The decoding process also starts with creating a decoder - * state. This can be done with: - * @code - * int error; - * OpusDecoder *dec; - * dec = opus_decoder_create(Fs, channels, &error); - * @endcode - * where - * @li Fs is the sampling rate and must be 8000, 12000, 16000, 24000, or 48000 - * @li channels is the number of channels (1 or 2) - * @li error will hold the error code in case of failure (or #OPUS_OK on success) - * @li the return value is a newly created decoder state to be used for decoding - * - * While opus_decoder_create() allocates memory for the state, it's also possible - * to initialize pre-allocated memory: - * @code - * int size; - * int error; - * OpusDecoder *dec; - * size = opus_decoder_get_size(channels); - * dec = malloc(size); - * error = opus_decoder_init(dec, Fs, channels); - * @endcode - * where opus_decoder_get_size() returns the required size for the decoder state. Note that - * future versions of this code may change the size, so no assumptions should be made about it. - * - * The decoder state is always continuous in memory and only a shallow copy is sufficient - * to copy it (e.g. memcpy()) - * - * To decode a frame, opus_decode() or opus_decode_float() must be called with a packet of compressed audio data: - * @code - * frame_size = opus_decode(dec, packet, len, decoded, max_size, 0); - * @endcode - * where - * - * @li packet is the byte array containing the compressed data - * @li len is the exact number of bytes contained in the packet - * @li decoded is the decoded audio data in opus_int16 (or float for opus_decode_float()) - * @li max_size is the max duration of the frame in samples (per channel) that can fit into the decoded_frame array - * - * opus_decode() and opus_decode_float() return the number of samples (per channel) decoded from the packet. - * If that value is negative, then an error has occurred. This can occur if the packet is corrupted or if the audio - * buffer is too small to hold the decoded audio. - * - * Opus is a stateful codec with overlapping blocks and as a result Opus - * packets are not coded independently of each other. Packets must be - * passed into the decoder serially and in the correct order for a correct - * decode. Lost packets can be replaced with loss concealment by calling - * the decoder with a null pointer and zero length for the missing packet. - * - * A single codec state may only be accessed from a single thread at - * a time and any required locking must be performed by the caller. Separate - * streams must be decoded with separate decoder states and can be decoded - * in parallel unless the library was compiled with NONTHREADSAFE_PSEUDOSTACK - * defined. - * - */ - -/** Opus decoder state. - * This contains the complete state of an Opus decoder. - * It is position independent and can be freely copied. - * @see opus_decoder_create,opus_decoder_init - */ -typedef struct OpusDecoder OpusDecoder; - -/** Opus DRED decoder. - * This contains the complete state of an Opus DRED decoder. - * It is position independent and can be freely copied. - * @see opus_dred_decoder_create,opus_dred_decoder_init - */ -typedef struct OpusDREDDecoder OpusDREDDecoder; - - -/** Opus DRED state. - * This contains the complete state of an Opus DRED packet. - * It is position independent and can be freely copied. - * @see opus_dred_create,opus_dred_init - */ -typedef struct OpusDRED OpusDRED; - -/** Gets the size of an OpusDecoder structure. - * @param [in] channels int: Number of channels. - * This must be 1 or 2. - * @returns The size in bytes. - */ -OPUS_EXPORT OPUS_WARN_UNUSED_RESULT int opus_decoder_get_size(int channels); - -/** Allocates and initializes a decoder state. - * @param [in] Fs opus_int32: Sample rate to decode at (Hz). - * This must be one of 8000, 12000, 16000, - * 24000, or 48000. - * @param [in] channels int: Number of channels (1 or 2) to decode - * @param [out] error int*: #OPUS_OK Success or @ref opus_errorcodes - * - * Internally Opus stores data at 48000 Hz, so that should be the default - * value for Fs. However, the decoder can efficiently decode to buffers - * at 8, 12, 16, and 24 kHz so if for some reason the caller cannot use - * data at the full sample rate, or knows the compressed data doesn't - * use the full frequency range, it can request decoding at a reduced - * rate. Likewise, the decoder is capable of filling in either mono or - * interleaved stereo pcm buffers, at the caller's request. - */ -OPUS_EXPORT OPUS_WARN_UNUSED_RESULT OpusDecoder *opus_decoder_create( - opus_int32 Fs, - int channels, - int *error -); - -/** Initializes a previously allocated decoder state. - * The state must be at least the size returned by opus_decoder_get_size(). - * This is intended for applications which use their own allocator instead of malloc. @see opus_decoder_create,opus_decoder_get_size - * To reset a previously initialized state, use the #OPUS_RESET_STATE CTL. - * @param [in] st OpusDecoder*: Decoder state. - * @param [in] Fs opus_int32: Sampling rate to decode to (Hz). - * This must be one of 8000, 12000, 16000, - * 24000, or 48000. - * @param [in] channels int: Number of channels (1 or 2) to decode - * @retval #OPUS_OK Success or @ref opus_errorcodes - */ -OPUS_EXPORT int opus_decoder_init( - OpusDecoder *st, - opus_int32 Fs, - int channels -) OPUS_ARG_NONNULL(1); - -/** Decode an Opus packet. - * @param [in] st OpusDecoder*: Decoder state - * @param [in] data char*: Input payload. Use a NULL pointer to indicate packet loss - * @param [in] len opus_int32: Number of bytes in payload* - * @param [out] pcm opus_int16*: Output signal (interleaved if 2 channels). length - * is frame_size*channels*sizeof(opus_int16) - * @param [in] frame_size Number of samples per channel of available space in \a pcm. - * If this is less than the maximum packet duration (120ms; 5760 for 48kHz), this function will - * not be capable of decoding some packets. In the case of PLC (data==NULL) or FEC (decode_fec=1), - * then frame_size needs to be exactly the duration of audio that is missing, otherwise the - * decoder will not be in the optimal state to decode the next incoming packet. For the PLC and - * FEC cases, frame_size must be a multiple of 2.5 ms. - * @param [in] decode_fec int: Flag (0 or 1) to request that any in-band forward error correction data be - * decoded. If no such data is available, the frame is decoded as if it were lost. - * @returns Number of decoded samples or @ref opus_errorcodes - */ -OPUS_EXPORT OPUS_WARN_UNUSED_RESULT int opus_decode( - OpusDecoder *st, - const unsigned char *data, - opus_int32 len, - opus_int16 *pcm, - int frame_size, - int decode_fec -) OPUS_ARG_NONNULL(1) OPUS_ARG_NONNULL(4); - -/** Decode an Opus packet with floating point output. - * @param [in] st OpusDecoder*: Decoder state - * @param [in] data char*: Input payload. Use a NULL pointer to indicate packet loss - * @param [in] len opus_int32: Number of bytes in payload - * @param [out] pcm float*: Output signal (interleaved if 2 channels). length - * is frame_size*channels*sizeof(float) - * @param [in] frame_size Number of samples per channel of available space in \a pcm. - * If this is less than the maximum packet duration (120ms; 5760 for 48kHz), this function will - * not be capable of decoding some packets. In the case of PLC (data==NULL) or FEC (decode_fec=1), - * then frame_size needs to be exactly the duration of audio that is missing, otherwise the - * decoder will not be in the optimal state to decode the next incoming packet. For the PLC and - * FEC cases, frame_size must be a multiple of 2.5 ms. - * @param [in] decode_fec int: Flag (0 or 1) to request that any in-band forward error correction data be - * decoded. If no such data is available the frame is decoded as if it were lost. - * @returns Number of decoded samples or @ref opus_errorcodes - */ -OPUS_EXPORT OPUS_WARN_UNUSED_RESULT int opus_decode_float( - OpusDecoder *st, - const unsigned char *data, - opus_int32 len, - float *pcm, - int frame_size, - int decode_fec -) OPUS_ARG_NONNULL(1) OPUS_ARG_NONNULL(4); - -/** Perform a CTL function on an Opus decoder. - * - * Generally the request and subsequent arguments are generated - * by a convenience macro. - * @param st OpusDecoder*: Decoder state. - * @param request This and all remaining parameters should be replaced by one - * of the convenience macros in @ref opus_genericctls or - * @ref opus_decoderctls. - * @see opus_genericctls - * @see opus_decoderctls - */ -OPUS_EXPORT int opus_decoder_ctl(OpusDecoder *st, int request, ...) OPUS_ARG_NONNULL(1); - -/** Frees an OpusDecoder allocated by opus_decoder_create(). - * @param[in] st OpusDecoder*: State to be freed. - */ -OPUS_EXPORT void opus_decoder_destroy(OpusDecoder *st); - -/** Gets the size of an OpusDREDDecoder structure. - * @returns The size in bytes. - */ -OPUS_EXPORT int opus_dred_decoder_get_size(void); - -/** Allocates and initializes an OpusDREDDecoder state. - * @param [out] error int*: #OPUS_OK Success or @ref opus_errorcodes - */ -OPUS_EXPORT OpusDREDDecoder *opus_dred_decoder_create(int *error); - -/** Initializes an OpusDREDDecoder state. - * @param[in] dec OpusDREDDecoder*: State to be initialized. - */ -OPUS_EXPORT int opus_dred_decoder_init(OpusDREDDecoder *dec); - -/** Frees an OpusDREDDecoder allocated by opus_dred_decoder_create(). - * @param[in] dec OpusDREDDecoder*: State to be freed. - */ -OPUS_EXPORT void opus_dred_decoder_destroy(OpusDREDDecoder *dec); - -/** Perform a CTL function on an Opus DRED decoder. - * - * Generally the request and subsequent arguments are generated - * by a convenience macro. - * @param dred_dec OpusDREDDecoder*: DRED Decoder state. - * @param request This and all remaining parameters should be replaced by one - * of the convenience macros in @ref opus_genericctls or - * @ref opus_decoderctls. - * @see opus_genericctls - * @see opus_decoderctls - */ -OPUS_EXPORT int opus_dred_decoder_ctl(OpusDREDDecoder *dred_dec, int request, ...); - -/** Gets the size of an OpusDRED structure. - * @returns The size in bytes. - */ -OPUS_EXPORT int opus_dred_get_size(void); - -/** Allocates and initializes a DRED state. - * @param [out] error int*: #OPUS_OK Success or @ref opus_errorcodes - */ -OPUS_EXPORT OpusDRED *opus_dred_alloc(int *error); - -/** Frees an OpusDRED allocated by opus_dred_create(). - * @param[in] dec OpusDRED*: State to be freed. - */ -OPUS_EXPORT void opus_dred_free(OpusDRED *dec); - -/** Decode an Opus DRED packet. - * @param [in] dred_dec OpusDRED*: DRED Decoder state - * @param [in] dred OpusDRED*: DRED state - * @param [in] data char*: Input payload - * @param [in] len opus_int32: Number of bytes in payload - * @param [in] max_dred_samples opus_int32: Maximum number of DRED samples that may be needed (if available in the packet). - * @param [in] sampling_rate opus_int32: Sampling rate used for max_dred_samples argument. Needs not match the actual sampling rate of the decoder. - * @param [out] dred_end opus_int32*: Number of non-encoded (silence) samples between the DRED timestamp and the last DRED sample. - * @param [in] defer_processing int: Flag (0 or 1). If set to one, the CPU-intensive part of the DRED decoding is deferred until opus_dred_process() is called. - * @returns Offset (positive) of the first decoded DRED samples, zero if no DRED is present, or @ref opus_errorcodes - */ -OPUS_EXPORT int opus_dred_parse(OpusDREDDecoder *dred_dec, OpusDRED *dred, const unsigned char *data, opus_int32 len, opus_int32 max_dred_samples, opus_int32 sampling_rate, int *dred_end, int defer_processing) OPUS_ARG_NONNULL(1); - -/** Finish decoding an Opus DRED packet. The function only needs to be called if opus_dred_parse() was called with defer_processing=1. - * The source and destination will often be the same DRED state. - * @param [in] dred_dec OpusDRED*: DRED Decoder state - * @param [in] src OpusDRED*: Source DRED state to start the processing from. - * @param [out] dst OpusDRED*: Destination DRED state to store the updated state after processing. - * @returns @ref opus_errorcodes - */ -OPUS_EXPORT int opus_dred_process(OpusDREDDecoder *dred_dec, const OpusDRED *src, OpusDRED *dst); - -/** Decode audio from an Opus DRED packet with floating point output. - * @param [in] st OpusDecoder*: Decoder state - * @param [in] dred OpusDRED*: DRED state - * @param [in] dred_offset opus_int32: position of the redundancy to decode (in samples before the beginning of the real audio data in the packet). - * @param [out] pcm opus_int16*: Output signal (interleaved if 2 channels). length - * is frame_size*channels*sizeof(opus_int16) - * @param [in] frame_size Number of samples per channel to decode in \a pcm. - * frame_size must be a multiple of 2.5 ms. - * @returns Number of decoded samples or @ref opus_errorcodes - */ -OPUS_EXPORT int opus_decoder_dred_decode(OpusDecoder *st, const OpusDRED *dred, opus_int32 dred_offset, opus_int16 *pcm, opus_int32 frame_size); - -/** Decode audio from an Opus DRED packet with floating point output. - * @param [in] st OpusDecoder*: Decoder state - * @param [in] dred OpusDRED*: DRED state - * @param [in] dred_offset opus_int32: position of the redundancy to decode (in samples before the beginning of the real audio data in the packet). - * @param [out] pcm float*: Output signal (interleaved if 2 channels). length - * is frame_size*channels*sizeof(float) - * @param [in] frame_size Number of samples per channel to decode in \a pcm. - * frame_size must be a multiple of 2.5 ms. - * @returns Number of decoded samples or @ref opus_errorcodes - */ -OPUS_EXPORT int opus_decoder_dred_decode_float(OpusDecoder *st, const OpusDRED *dred, opus_int32 dred_offset, float *pcm, opus_int32 frame_size); - - -/** Parse an opus packet into one or more frames. - * Opus_decode will perform this operation internally so most applications do - * not need to use this function. - * This function does not copy the frames, the returned pointers are pointers into - * the input packet. - * @param [in] data char*: Opus packet to be parsed - * @param [in] len opus_int32: size of data - * @param [out] out_toc char*: TOC pointer - * @param [out] frames char*[48] encapsulated frames - * @param [out] size opus_int16[48] sizes of the encapsulated frames - * @param [out] payload_offset int*: returns the position of the payload within the packet (in bytes) - * @returns number of frames - */ -OPUS_EXPORT int opus_packet_parse( - const unsigned char *data, - opus_int32 len, - unsigned char *out_toc, - const unsigned char *frames[48], - opus_int16 size[48], - int *payload_offset -) OPUS_ARG_NONNULL(1) OPUS_ARG_NONNULL(5); - -/** Gets the bandwidth of an Opus packet. - * @param [in] data char*: Opus packet - * @retval OPUS_BANDWIDTH_NARROWBAND Narrowband (4kHz bandpass) - * @retval OPUS_BANDWIDTH_MEDIUMBAND Mediumband (6kHz bandpass) - * @retval OPUS_BANDWIDTH_WIDEBAND Wideband (8kHz bandpass) - * @retval OPUS_BANDWIDTH_SUPERWIDEBAND Superwideband (12kHz bandpass) - * @retval OPUS_BANDWIDTH_FULLBAND Fullband (20kHz bandpass) - * @retval OPUS_INVALID_PACKET The compressed data passed is corrupted or of an unsupported type - */ -OPUS_EXPORT OPUS_WARN_UNUSED_RESULT int opus_packet_get_bandwidth(const unsigned char *data) OPUS_ARG_NONNULL(1); - -/** Gets the number of samples per frame from an Opus packet. - * @param [in] data char*: Opus packet. - * This must contain at least one byte of - * data. - * @param [in] Fs opus_int32: Sampling rate in Hz. - * This must be a multiple of 400, or - * inaccurate results will be returned. - * @returns Number of samples per frame. - */ -OPUS_EXPORT OPUS_WARN_UNUSED_RESULT int opus_packet_get_samples_per_frame(const unsigned char *data, opus_int32 Fs) OPUS_ARG_NONNULL(1); - -/** Gets the number of channels from an Opus packet. - * @param [in] data char*: Opus packet - * @returns Number of channels - * @retval OPUS_INVALID_PACKET The compressed data passed is corrupted or of an unsupported type - */ -OPUS_EXPORT OPUS_WARN_UNUSED_RESULT int opus_packet_get_nb_channels(const unsigned char *data) OPUS_ARG_NONNULL(1); - -/** Gets the number of frames in an Opus packet. - * @param [in] packet char*: Opus packet - * @param [in] len opus_int32: Length of packet - * @returns Number of frames - * @retval OPUS_BAD_ARG Insufficient data was passed to the function - * @retval OPUS_INVALID_PACKET The compressed data passed is corrupted or of an unsupported type - */ -OPUS_EXPORT OPUS_WARN_UNUSED_RESULT int opus_packet_get_nb_frames(const unsigned char packet[], opus_int32 len) OPUS_ARG_NONNULL(1); - -/** Gets the number of samples of an Opus packet. - * @param [in] packet char*: Opus packet - * @param [in] len opus_int32: Length of packet - * @param [in] Fs opus_int32: Sampling rate in Hz. - * This must be a multiple of 400, or - * inaccurate results will be returned. - * @returns Number of samples - * @retval OPUS_BAD_ARG Insufficient data was passed to the function - * @retval OPUS_INVALID_PACKET The compressed data passed is corrupted or of an unsupported type - */ -OPUS_EXPORT OPUS_WARN_UNUSED_RESULT int opus_packet_get_nb_samples(const unsigned char packet[], opus_int32 len, opus_int32 Fs) OPUS_ARG_NONNULL(1); - -/** Checks whether an Opus packet has LBRR. - * @param [in] packet char*: Opus packet - * @param [in] len opus_int32: Length of packet - * @returns 1 is LBRR is present, 0 otherwise - * @retval OPUS_INVALID_PACKET The compressed data passed is corrupted or of an unsupported type - */ -OPUS_EXPORT OPUS_WARN_UNUSED_RESULT int opus_packet_has_lbrr(const unsigned char packet[], opus_int32 len); - -/** Gets the number of samples of an Opus packet. - * @param [in] dec OpusDecoder*: Decoder state - * @param [in] packet char*: Opus packet - * @param [in] len opus_int32: Length of packet - * @returns Number of samples - * @retval OPUS_BAD_ARG Insufficient data was passed to the function - * @retval OPUS_INVALID_PACKET The compressed data passed is corrupted or of an unsupported type - */ -OPUS_EXPORT OPUS_WARN_UNUSED_RESULT int opus_decoder_get_nb_samples(const OpusDecoder *dec, const unsigned char packet[], opus_int32 len) OPUS_ARG_NONNULL(1) OPUS_ARG_NONNULL(2); - -/** Applies soft-clipping to bring a float signal within the [-1,1] range. If - * the signal is already in that range, nothing is done. If there are values - * outside of [-1,1], then the signal is clipped as smoothly as possible to - * both fit in the range and avoid creating excessive distortion in the - * process. - * @param [in,out] pcm float*: Input PCM and modified PCM - * @param [in] frame_size int Number of samples per channel to process - * @param [in] channels int: Number of channels - * @param [in,out] softclip_mem float*: State memory for the soft clipping process (one float per channel, initialized to zero) - */ -OPUS_EXPORT void opus_pcm_soft_clip(float *pcm, int frame_size, int channels, float *softclip_mem); - - -/**@}*/ - -/** @defgroup opus_repacketizer Repacketizer - * @{ - * - * The repacketizer can be used to merge multiple Opus packets into a single - * packet or alternatively to split Opus packets that have previously been - * merged. Splitting valid Opus packets is always guaranteed to succeed, - * whereas merging valid packets only succeeds if all frames have the same - * mode, bandwidth, and frame size, and when the total duration of the merged - * packet is no more than 120 ms. The 120 ms limit comes from the - * specification and limits decoder memory requirements at a point where - * framing overhead becomes negligible. - * - * The repacketizer currently only operates on elementary Opus - * streams. It will not manipualte multistream packets successfully, except in - * the degenerate case where they consist of data from a single stream. - * - * The repacketizing process starts with creating a repacketizer state, either - * by calling opus_repacketizer_create() or by allocating the memory yourself, - * e.g., - * @code - * OpusRepacketizer *rp; - * rp = (OpusRepacketizer*)malloc(opus_repacketizer_get_size()); - * if (rp != NULL) - * opus_repacketizer_init(rp); - * @endcode - * - * Then the application should submit packets with opus_repacketizer_cat(), - * extract new packets with opus_repacketizer_out() or - * opus_repacketizer_out_range(), and then reset the state for the next set of - * input packets via opus_repacketizer_init(). - * - * For example, to split a sequence of packets into individual frames: - * @code - * unsigned char *data; - * int len; - * while (get_next_packet(&data, &len)) - * { - * unsigned char out[1276]; - * opus_int32 out_len; - * int nb_frames; - * int err; - * int i; - * err = opus_repacketizer_cat(rp, data, len); - * if (err != OPUS_OK) - * { - * release_packet(data); - * return err; - * } - * nb_frames = opus_repacketizer_get_nb_frames(rp); - * for (i = 0; i < nb_frames; i++) - * { - * out_len = opus_repacketizer_out_range(rp, i, i+1, out, sizeof(out)); - * if (out_len < 0) - * { - * release_packet(data); - * return (int)out_len; - * } - * output_next_packet(out, out_len); - * } - * opus_repacketizer_init(rp); - * release_packet(data); - * } - * @endcode - * - * Alternatively, to combine a sequence of frames into packets that each - * contain up to TARGET_DURATION_MS milliseconds of data: - * @code - * // The maximum number of packets with duration TARGET_DURATION_MS occurs - * // when the frame size is 2.5 ms, for a total of (TARGET_DURATION_MS*2/5) - * // packets. - * unsigned char *data[(TARGET_DURATION_MS*2/5)+1]; - * opus_int32 len[(TARGET_DURATION_MS*2/5)+1]; - * int nb_packets; - * unsigned char out[1277*(TARGET_DURATION_MS*2/2)]; - * opus_int32 out_len; - * int prev_toc; - * nb_packets = 0; - * while (get_next_packet(data+nb_packets, len+nb_packets)) - * { - * int nb_frames; - * int err; - * nb_frames = opus_packet_get_nb_frames(data[nb_packets], len[nb_packets]); - * if (nb_frames < 1) - * { - * release_packets(data, nb_packets+1); - * return nb_frames; - * } - * nb_frames += opus_repacketizer_get_nb_frames(rp); - * // If adding the next packet would exceed our target, or it has an - * // incompatible TOC sequence, output the packets we already have before - * // submitting it. - * // N.B., The nb_packets > 0 check ensures we've submitted at least one - * // packet since the last call to opus_repacketizer_init(). Otherwise a - * // single packet longer than TARGET_DURATION_MS would cause us to try to - * // output an (invalid) empty packet. It also ensures that prev_toc has - * // been set to a valid value. Additionally, len[nb_packets] > 0 is - * // guaranteed by the call to opus_packet_get_nb_frames() above, so the - * // reference to data[nb_packets][0] should be valid. - * if (nb_packets > 0 && ( - * ((prev_toc & 0xFC) != (data[nb_packets][0] & 0xFC)) || - * opus_packet_get_samples_per_frame(data[nb_packets], 48000)*nb_frames > - * TARGET_DURATION_MS*48)) - * { - * out_len = opus_repacketizer_out(rp, out, sizeof(out)); - * if (out_len < 0) - * { - * release_packets(data, nb_packets+1); - * return (int)out_len; - * } - * output_next_packet(out, out_len); - * opus_repacketizer_init(rp); - * release_packets(data, nb_packets); - * data[0] = data[nb_packets]; - * len[0] = len[nb_packets]; - * nb_packets = 0; - * } - * err = opus_repacketizer_cat(rp, data[nb_packets], len[nb_packets]); - * if (err != OPUS_OK) - * { - * release_packets(data, nb_packets+1); - * return err; - * } - * prev_toc = data[nb_packets][0]; - * nb_packets++; - * } - * // Output the final, partial packet. - * if (nb_packets > 0) - * { - * out_len = opus_repacketizer_out(rp, out, sizeof(out)); - * release_packets(data, nb_packets); - * if (out_len < 0) - * return (int)out_len; - * output_next_packet(out, out_len); - * } - * @endcode - * - * An alternate way of merging packets is to simply call opus_repacketizer_cat() - * unconditionally until it fails. At that point, the merged packet can be - * obtained with opus_repacketizer_out() and the input packet for which - * opus_repacketizer_cat() needs to be re-added to a newly reinitialized - * repacketizer state. - */ - -typedef struct OpusRepacketizer OpusRepacketizer; - -/** Gets the size of an OpusRepacketizer structure. - * @returns The size in bytes. - */ -OPUS_EXPORT OPUS_WARN_UNUSED_RESULT int opus_repacketizer_get_size(void); - -/** (Re)initializes a previously allocated repacketizer state. - * The state must be at least the size returned by opus_repacketizer_get_size(). - * This can be used for applications which use their own allocator instead of - * malloc(). - * It must also be called to reset the queue of packets waiting to be - * repacketized, which is necessary if the maximum packet duration of 120 ms - * is reached or if you wish to submit packets with a different Opus - * configuration (coding mode, audio bandwidth, frame size, or channel count). - * Failure to do so will prevent a new packet from being added with - * opus_repacketizer_cat(). - * @see opus_repacketizer_create - * @see opus_repacketizer_get_size - * @see opus_repacketizer_cat - * @param rp OpusRepacketizer*: The repacketizer state to - * (re)initialize. - * @returns A pointer to the same repacketizer state that was passed in. - */ -OPUS_EXPORT OpusRepacketizer *opus_repacketizer_init(OpusRepacketizer *rp) OPUS_ARG_NONNULL(1); - -/** Allocates memory and initializes the new repacketizer with - * opus_repacketizer_init(). - */ -OPUS_EXPORT OPUS_WARN_UNUSED_RESULT OpusRepacketizer *opus_repacketizer_create(void); - -/** Frees an OpusRepacketizer allocated by - * opus_repacketizer_create(). - * @param[in] rp OpusRepacketizer*: State to be freed. - */ -OPUS_EXPORT void opus_repacketizer_destroy(OpusRepacketizer *rp); - -/** Add a packet to the current repacketizer state. - * This packet must match the configuration of any packets already submitted - * for repacketization since the last call to opus_repacketizer_init(). - * This means that it must have the same coding mode, audio bandwidth, frame - * size, and channel count. - * This can be checked in advance by examining the top 6 bits of the first - * byte of the packet, and ensuring they match the top 6 bits of the first - * byte of any previously submitted packet. - * The total duration of audio in the repacketizer state also must not exceed - * 120 ms, the maximum duration of a single packet, after adding this packet. - * - * The contents of the current repacketizer state can be extracted into new - * packets using opus_repacketizer_out() or opus_repacketizer_out_range(). - * - * In order to add a packet with a different configuration or to add more - * audio beyond 120 ms, you must clear the repacketizer state by calling - * opus_repacketizer_init(). - * If a packet is too large to add to the current repacketizer state, no part - * of it is added, even if it contains multiple frames, some of which might - * fit. - * If you wish to be able to add parts of such packets, you should first use - * another repacketizer to split the packet into pieces and add them - * individually. - * @see opus_repacketizer_out_range - * @see opus_repacketizer_out - * @see opus_repacketizer_init - * @param rp OpusRepacketizer*: The repacketizer state to which to - * add the packet. - * @param[in] data const unsigned char*: The packet data. - * The application must ensure - * this pointer remains valid - * until the next call to - * opus_repacketizer_init() or - * opus_repacketizer_destroy(). - * @param len opus_int32: The number of bytes in the packet data. - * @returns An error code indicating whether or not the operation succeeded. - * @retval #OPUS_OK The packet's contents have been added to the repacketizer - * state. - * @retval #OPUS_INVALID_PACKET The packet did not have a valid TOC sequence, - * the packet's TOC sequence was not compatible - * with previously submitted packets (because - * the coding mode, audio bandwidth, frame size, - * or channel count did not match), or adding - * this packet would increase the total amount of - * audio stored in the repacketizer state to more - * than 120 ms. - */ -OPUS_EXPORT int opus_repacketizer_cat(OpusRepacketizer *rp, const unsigned char *data, opus_int32 len) OPUS_ARG_NONNULL(1) OPUS_ARG_NONNULL(2); - - -/** Construct a new packet from data previously submitted to the repacketizer - * state via opus_repacketizer_cat(). - * @param rp OpusRepacketizer*: The repacketizer state from which to - * construct the new packet. - * @param begin int: The index of the first frame in the current - * repacketizer state to include in the output. - * @param end int: One past the index of the last frame in the - * current repacketizer state to include in the - * output. - * @param[out] data const unsigned char*: The buffer in which to - * store the output packet. - * @param maxlen opus_int32: The maximum number of bytes to store in - * the output buffer. In order to guarantee - * success, this should be at least - * 1276 for a single frame, - * or for multiple frames, - * 1277*(end-begin). - * However, 1*(end-begin) plus - * the size of all packet data submitted to - * the repacketizer since the last call to - * opus_repacketizer_init() or - * opus_repacketizer_create() is also - * sufficient, and possibly much smaller. - * @returns The total size of the output packet on success, or an error code - * on failure. - * @retval #OPUS_BAD_ARG [begin,end) was an invalid range of - * frames (begin < 0, begin >= end, or end > - * opus_repacketizer_get_nb_frames()). - * @retval #OPUS_BUFFER_TOO_SMALL \a maxlen was insufficient to contain the - * complete output packet. - */ -OPUS_EXPORT OPUS_WARN_UNUSED_RESULT opus_int32 opus_repacketizer_out_range(OpusRepacketizer *rp, int begin, int end, unsigned char *data, opus_int32 maxlen) OPUS_ARG_NONNULL(1) OPUS_ARG_NONNULL(4); - -/** Return the total number of frames contained in packet data submitted to - * the repacketizer state so far via opus_repacketizer_cat() since the last - * call to opus_repacketizer_init() or opus_repacketizer_create(). - * This defines the valid range of packets that can be extracted with - * opus_repacketizer_out_range() or opus_repacketizer_out(). - * @param rp OpusRepacketizer*: The repacketizer state containing the - * frames. - * @returns The total number of frames contained in the packet data submitted - * to the repacketizer state. - */ -OPUS_EXPORT OPUS_WARN_UNUSED_RESULT int opus_repacketizer_get_nb_frames(OpusRepacketizer *rp) OPUS_ARG_NONNULL(1); - -/** Construct a new packet from data previously submitted to the repacketizer - * state via opus_repacketizer_cat(). - * This is a convenience routine that returns all the data submitted so far - * in a single packet. - * It is equivalent to calling - * @code - * opus_repacketizer_out_range(rp, 0, opus_repacketizer_get_nb_frames(rp), - * data, maxlen) - * @endcode - * @param rp OpusRepacketizer*: The repacketizer state from which to - * construct the new packet. - * @param[out] data const unsigned char*: The buffer in which to - * store the output packet. - * @param maxlen opus_int32: The maximum number of bytes to store in - * the output buffer. In order to guarantee - * success, this should be at least - * 1277*opus_repacketizer_get_nb_frames(rp). - * However, - * 1*opus_repacketizer_get_nb_frames(rp) - * plus the size of all packet data - * submitted to the repacketizer since the - * last call to opus_repacketizer_init() or - * opus_repacketizer_create() is also - * sufficient, and possibly much smaller. - * @returns The total size of the output packet on success, or an error code - * on failure. - * @retval #OPUS_BUFFER_TOO_SMALL \a maxlen was insufficient to contain the - * complete output packet. - */ -OPUS_EXPORT OPUS_WARN_UNUSED_RESULT opus_int32 opus_repacketizer_out(OpusRepacketizer *rp, unsigned char *data, opus_int32 maxlen) OPUS_ARG_NONNULL(1); - -/** Pads a given Opus packet to a larger size (possibly changing the TOC sequence). - * @param[in,out] data const unsigned char*: The buffer containing the - * packet to pad. - * @param len opus_int32: The size of the packet. - * This must be at least 1. - * @param new_len opus_int32: The desired size of the packet after padding. - * This must be at least as large as len. - * @returns an error code - * @retval #OPUS_OK \a on success. - * @retval #OPUS_BAD_ARG \a len was less than 1 or new_len was less than len. - * @retval #OPUS_INVALID_PACKET \a data did not contain a valid Opus packet. - */ -OPUS_EXPORT int opus_packet_pad(unsigned char *data, opus_int32 len, opus_int32 new_len); - -/** Remove all padding from a given Opus packet and rewrite the TOC sequence to - * minimize space usage. - * @param[in,out] data const unsigned char*: The buffer containing the - * packet to strip. - * @param len opus_int32: The size of the packet. - * This must be at least 1. - * @returns The new size of the output packet on success, or an error code - * on failure. - * @retval #OPUS_BAD_ARG \a len was less than 1. - * @retval #OPUS_INVALID_PACKET \a data did not contain a valid Opus packet. - */ -OPUS_EXPORT OPUS_WARN_UNUSED_RESULT opus_int32 opus_packet_unpad(unsigned char *data, opus_int32 len); - -/** Pads a given Opus multi-stream packet to a larger size (possibly changing the TOC sequence). - * @param[in,out] data const unsigned char*: The buffer containing the - * packet to pad. - * @param len opus_int32: The size of the packet. - * This must be at least 1. - * @param new_len opus_int32: The desired size of the packet after padding. - * This must be at least 1. - * @param nb_streams opus_int32: The number of streams (not channels) in the packet. - * This must be at least as large as len. - * @returns an error code - * @retval #OPUS_OK \a on success. - * @retval #OPUS_BAD_ARG \a len was less than 1. - * @retval #OPUS_INVALID_PACKET \a data did not contain a valid Opus packet. - */ -OPUS_EXPORT int opus_multistream_packet_pad(unsigned char *data, opus_int32 len, opus_int32 new_len, int nb_streams); - -/** Remove all padding from a given Opus multi-stream packet and rewrite the TOC sequence to - * minimize space usage. - * @param[in,out] data const unsigned char*: The buffer containing the - * packet to strip. - * @param len opus_int32: The size of the packet. - * This must be at least 1. - * @param nb_streams opus_int32: The number of streams (not channels) in the packet. - * This must be at least 1. - * @returns The new size of the output packet on success, or an error code - * on failure. - * @retval #OPUS_BAD_ARG \a len was less than 1 or new_len was less than len. - * @retval #OPUS_INVALID_PACKET \a data did not contain a valid Opus packet. - */ -OPUS_EXPORT OPUS_WARN_UNUSED_RESULT opus_int32 opus_multistream_packet_unpad(unsigned char *data, opus_int32 len, int nb_streams); - -/**@}*/ - -#ifdef __cplusplus -} -#endif - -#endif /* OPUS_H */ diff --git a/go/teamspeak/.opus/install/armeabi-v7a/include/opus/opus_defines.h b/go/teamspeak/.opus/install/armeabi-v7a/include/opus/opus_defines.h deleted file mode 100644 index cd8f4dd..0000000 --- a/go/teamspeak/.opus/install/armeabi-v7a/include/opus/opus_defines.h +++ /dev/null @@ -1,830 +0,0 @@ -/* Copyright (c) 2010-2011 Xiph.Org Foundation, Skype Limited - Written by Jean-Marc Valin and Koen Vos */ -/* - Redistribution and use in source and binary forms, with or without - modification, are permitted provided that the following conditions - are met: - - - Redistributions of source code must retain the above copyright - notice, this list of conditions and the following disclaimer. - - - Redistributions in binary form must reproduce the above copyright - notice, this list of conditions and the following disclaimer in the - documentation and/or other materials provided with the distribution. - - THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS - ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT - LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR - A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER - OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, - EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, - PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR - PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF - LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING - NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS - SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. -*/ - -/** - * @file opus_defines.h - * @brief Opus reference implementation constants - */ - -#ifndef OPUS_DEFINES_H -#define OPUS_DEFINES_H - -#include "opus_types.h" - -#ifdef __cplusplus -extern "C" { -#endif - -/** @defgroup opus_errorcodes Error codes - * @{ - */ -/** No error @hideinitializer*/ -#define OPUS_OK 0 -/** One or more invalid/out of range arguments @hideinitializer*/ -#define OPUS_BAD_ARG -1 -/** Not enough bytes allocated in the buffer @hideinitializer*/ -#define OPUS_BUFFER_TOO_SMALL -2 -/** An internal error was detected @hideinitializer*/ -#define OPUS_INTERNAL_ERROR -3 -/** The compressed data passed is corrupted @hideinitializer*/ -#define OPUS_INVALID_PACKET -4 -/** Invalid/unsupported request number @hideinitializer*/ -#define OPUS_UNIMPLEMENTED -5 -/** An encoder or decoder structure is invalid or already freed @hideinitializer*/ -#define OPUS_INVALID_STATE -6 -/** Memory allocation has failed @hideinitializer*/ -#define OPUS_ALLOC_FAIL -7 -/**@}*/ - -/** @cond OPUS_INTERNAL_DOC */ -/**Export control for opus functions */ - -#ifndef OPUS_EXPORT -# if defined(_WIN32) -# if defined(OPUS_BUILD) && defined(DLL_EXPORT) -# define OPUS_EXPORT __declspec(dllexport) -# else -# define OPUS_EXPORT -# endif -# elif defined(__GNUC__) && defined(OPUS_BUILD) -# define OPUS_EXPORT __attribute__ ((visibility ("default"))) -# else -# define OPUS_EXPORT -# endif -#endif - -# if !defined(OPUS_GNUC_PREREQ) -# if defined(__GNUC__)&&defined(__GNUC_MINOR__) -# define OPUS_GNUC_PREREQ(_maj,_min) \ - ((__GNUC__<<16)+__GNUC_MINOR__>=((_maj)<<16)+(_min)) -# else -# define OPUS_GNUC_PREREQ(_maj,_min) 0 -# endif -# endif - -#if (!defined(__STDC_VERSION__) || (__STDC_VERSION__ < 199901L) ) -# if OPUS_GNUC_PREREQ(3,0) -# define OPUS_RESTRICT __restrict__ -# elif (defined(_MSC_VER) && _MSC_VER >= 1400) -# define OPUS_RESTRICT __restrict -# else -# define OPUS_RESTRICT -# endif -#else -# define OPUS_RESTRICT restrict -#endif - -#if (!defined(__STDC_VERSION__) || (__STDC_VERSION__ < 199901L) ) -# if OPUS_GNUC_PREREQ(2,7) -# define OPUS_INLINE __inline__ -# elif (defined(_MSC_VER)) -# define OPUS_INLINE __inline -# else -# define OPUS_INLINE -# endif -#else -# define OPUS_INLINE inline -#endif - -/**Warning attributes for opus functions - * NONNULL is not used in OPUS_BUILD to avoid the compiler optimizing out - * some paranoid null checks. */ -#if defined(__GNUC__) && OPUS_GNUC_PREREQ(3, 4) -# define OPUS_WARN_UNUSED_RESULT __attribute__ ((__warn_unused_result__)) -#else -# define OPUS_WARN_UNUSED_RESULT -#endif -#if !defined(OPUS_BUILD) && defined(__GNUC__) && OPUS_GNUC_PREREQ(3, 4) -# define OPUS_ARG_NONNULL(_x) __attribute__ ((__nonnull__(_x))) -#else -# define OPUS_ARG_NONNULL(_x) -#endif - -/** These are the actual Encoder CTL ID numbers. - * They should not be used directly by applications. - * In general, SETs should be even and GETs should be odd.*/ -#define OPUS_SET_APPLICATION_REQUEST 4000 -#define OPUS_GET_APPLICATION_REQUEST 4001 -#define OPUS_SET_BITRATE_REQUEST 4002 -#define OPUS_GET_BITRATE_REQUEST 4003 -#define OPUS_SET_MAX_BANDWIDTH_REQUEST 4004 -#define OPUS_GET_MAX_BANDWIDTH_REQUEST 4005 -#define OPUS_SET_VBR_REQUEST 4006 -#define OPUS_GET_VBR_REQUEST 4007 -#define OPUS_SET_BANDWIDTH_REQUEST 4008 -#define OPUS_GET_BANDWIDTH_REQUEST 4009 -#define OPUS_SET_COMPLEXITY_REQUEST 4010 -#define OPUS_GET_COMPLEXITY_REQUEST 4011 -#define OPUS_SET_INBAND_FEC_REQUEST 4012 -#define OPUS_GET_INBAND_FEC_REQUEST 4013 -#define OPUS_SET_PACKET_LOSS_PERC_REQUEST 4014 -#define OPUS_GET_PACKET_LOSS_PERC_REQUEST 4015 -#define OPUS_SET_DTX_REQUEST 4016 -#define OPUS_GET_DTX_REQUEST 4017 -#define OPUS_SET_VBR_CONSTRAINT_REQUEST 4020 -#define OPUS_GET_VBR_CONSTRAINT_REQUEST 4021 -#define OPUS_SET_FORCE_CHANNELS_REQUEST 4022 -#define OPUS_GET_FORCE_CHANNELS_REQUEST 4023 -#define OPUS_SET_SIGNAL_REQUEST 4024 -#define OPUS_GET_SIGNAL_REQUEST 4025 -#define OPUS_GET_LOOKAHEAD_REQUEST 4027 -/* #define OPUS_RESET_STATE 4028 */ -#define OPUS_GET_SAMPLE_RATE_REQUEST 4029 -#define OPUS_GET_FINAL_RANGE_REQUEST 4031 -#define OPUS_GET_PITCH_REQUEST 4033 -#define OPUS_SET_GAIN_REQUEST 4034 -#define OPUS_GET_GAIN_REQUEST 4045 /* Should have been 4035 */ -#define OPUS_SET_LSB_DEPTH_REQUEST 4036 -#define OPUS_GET_LSB_DEPTH_REQUEST 4037 -#define OPUS_GET_LAST_PACKET_DURATION_REQUEST 4039 -#define OPUS_SET_EXPERT_FRAME_DURATION_REQUEST 4040 -#define OPUS_GET_EXPERT_FRAME_DURATION_REQUEST 4041 -#define OPUS_SET_PREDICTION_DISABLED_REQUEST 4042 -#define OPUS_GET_PREDICTION_DISABLED_REQUEST 4043 -/* Don't use 4045, it's already taken by OPUS_GET_GAIN_REQUEST */ -#define OPUS_SET_PHASE_INVERSION_DISABLED_REQUEST 4046 -#define OPUS_GET_PHASE_INVERSION_DISABLED_REQUEST 4047 -#define OPUS_GET_IN_DTX_REQUEST 4049 -#define OPUS_SET_DRED_DURATION_REQUEST 4050 -#define OPUS_GET_DRED_DURATION_REQUEST 4051 -#define OPUS_SET_DNN_BLOB_REQUEST 4052 -/*#define OPUS_GET_DNN_BLOB_REQUEST 4053 */ - -/** Defines for the presence of extended APIs. */ -#define OPUS_HAVE_OPUS_PROJECTION_H - -/* Macros to trigger compilation errors when the wrong types are provided to a CTL */ -#define __opus_check_int(x) (((void)((x) == (opus_int32)0)), (opus_int32)(x)) - -#ifdef DISABLE_PTR_CHECK -/* Disable checks to prevent ubsan from complaining about NULL checks - in test_opus_api. */ -#define __opus_check_int_ptr(ptr) (ptr) -#define __opus_check_uint_ptr(ptr) (ptr) -#define __opus_check_uint8_ptr(ptr) (ptr) -#define __opus_check_val16_ptr(ptr) (ptr) -#define __opus_check_void_ptr(ptr) (ptr) -#else -#define __opus_check_int_ptr(ptr) ((ptr) + ((ptr) - (opus_int32*)(ptr))) -#define __opus_check_uint_ptr(ptr) ((ptr) + ((ptr) - (opus_uint32*)(ptr))) -#define __opus_check_uint8_ptr(ptr) ((ptr) + ((ptr) - (opus_uint8*)(ptr))) -#define __opus_check_val16_ptr(ptr) ((ptr) + ((ptr) - (opus_val16*)(ptr))) -#define __opus_check_void_ptr(x) ((void)((void *)0 == (x)), (x)) -#endif -/** @endcond */ - -/** @defgroup opus_ctlvalues Pre-defined values for CTL interface - * @see opus_genericctls, opus_encoderctls - * @{ - */ -/* Values for the various encoder CTLs */ -#define OPUS_AUTO -1000 /**opus_int32: Allowed values: 0-10, inclusive. - * - * @hideinitializer */ -#define OPUS_SET_COMPLEXITY(x) OPUS_SET_COMPLEXITY_REQUEST, __opus_check_int(x) -/** Gets the encoder's complexity configuration. - * @see OPUS_SET_COMPLEXITY - * @param[out] x opus_int32 *: Returns a value in the range 0-10, - * inclusive. - * @hideinitializer */ -#define OPUS_GET_COMPLEXITY(x) OPUS_GET_COMPLEXITY_REQUEST, __opus_check_int_ptr(x) - -/** Configures the bitrate in the encoder. - * Rates from 500 to 512000 bits per second are meaningful, as well as the - * special values #OPUS_AUTO and #OPUS_BITRATE_MAX. - * The value #OPUS_BITRATE_MAX can be used to cause the codec to use as much - * rate as it can, which is useful for controlling the rate by adjusting the - * output buffer size. - * @see OPUS_GET_BITRATE - * @param[in] x opus_int32: Bitrate in bits per second. The default - * is determined based on the number of - * channels and the input sampling rate. - * @hideinitializer */ -#define OPUS_SET_BITRATE(x) OPUS_SET_BITRATE_REQUEST, __opus_check_int(x) -/** Gets the encoder's bitrate configuration. - * @see OPUS_SET_BITRATE - * @param[out] x opus_int32 *: Returns the bitrate in bits per second. - * The default is determined based on the - * number of channels and the input - * sampling rate. - * @hideinitializer */ -#define OPUS_GET_BITRATE(x) OPUS_GET_BITRATE_REQUEST, __opus_check_int_ptr(x) - -/** Enables or disables variable bitrate (VBR) in the encoder. - * The configured bitrate may not be met exactly because frames must - * be an integer number of bytes in length. - * @see OPUS_GET_VBR - * @see OPUS_SET_VBR_CONSTRAINT - * @param[in] x opus_int32: Allowed values: - *
- *
0
Hard CBR. For LPC/hybrid modes at very low bit-rate, this can - * cause noticeable quality degradation.
- *
1
VBR (default). The exact type of VBR is controlled by - * #OPUS_SET_VBR_CONSTRAINT.
- *
- * @hideinitializer */ -#define OPUS_SET_VBR(x) OPUS_SET_VBR_REQUEST, __opus_check_int(x) -/** Determine if variable bitrate (VBR) is enabled in the encoder. - * @see OPUS_SET_VBR - * @see OPUS_GET_VBR_CONSTRAINT - * @param[out] x opus_int32 *: Returns one of the following values: - *
- *
0
Hard CBR.
- *
1
VBR (default). The exact type of VBR may be retrieved via - * #OPUS_GET_VBR_CONSTRAINT.
- *
- * @hideinitializer */ -#define OPUS_GET_VBR(x) OPUS_GET_VBR_REQUEST, __opus_check_int_ptr(x) - -/** Enables or disables constrained VBR in the encoder. - * This setting is ignored when the encoder is in CBR mode. - * @warning Only the MDCT mode of Opus currently heeds the constraint. - * Speech mode ignores it completely, hybrid mode may fail to obey it - * if the LPC layer uses more bitrate than the constraint would have - * permitted. - * @see OPUS_GET_VBR_CONSTRAINT - * @see OPUS_SET_VBR - * @param[in] x opus_int32: Allowed values: - *
- *
0
Unconstrained VBR.
- *
1
Constrained VBR (default). This creates a maximum of one - * frame of buffering delay assuming a transport with a - * serialization speed of the nominal bitrate.
- *
- * @hideinitializer */ -#define OPUS_SET_VBR_CONSTRAINT(x) OPUS_SET_VBR_CONSTRAINT_REQUEST, __opus_check_int(x) -/** Determine if constrained VBR is enabled in the encoder. - * @see OPUS_SET_VBR_CONSTRAINT - * @see OPUS_GET_VBR - * @param[out] x opus_int32 *: Returns one of the following values: - *
- *
0
Unconstrained VBR.
- *
1
Constrained VBR (default).
- *
- * @hideinitializer */ -#define OPUS_GET_VBR_CONSTRAINT(x) OPUS_GET_VBR_CONSTRAINT_REQUEST, __opus_check_int_ptr(x) - -/** Configures mono/stereo forcing in the encoder. - * This can force the encoder to produce packets encoded as either mono or - * stereo, regardless of the format of the input audio. This is useful when - * the caller knows that the input signal is currently a mono source embedded - * in a stereo stream. - * @see OPUS_GET_FORCE_CHANNELS - * @param[in] x opus_int32: Allowed values: - *
- *
#OPUS_AUTO
Not forced (default)
- *
1
Forced mono
- *
2
Forced stereo
- *
- * @hideinitializer */ -#define OPUS_SET_FORCE_CHANNELS(x) OPUS_SET_FORCE_CHANNELS_REQUEST, __opus_check_int(x) -/** Gets the encoder's forced channel configuration. - * @see OPUS_SET_FORCE_CHANNELS - * @param[out] x opus_int32 *: - *
- *
#OPUS_AUTO
Not forced (default)
- *
1
Forced mono
- *
2
Forced stereo
- *
- * @hideinitializer */ -#define OPUS_GET_FORCE_CHANNELS(x) OPUS_GET_FORCE_CHANNELS_REQUEST, __opus_check_int_ptr(x) - -/** Configures the maximum bandpass that the encoder will select automatically. - * Applications should normally use this instead of #OPUS_SET_BANDWIDTH - * (leaving that set to the default, #OPUS_AUTO). This allows the - * application to set an upper bound based on the type of input it is - * providing, but still gives the encoder the freedom to reduce the bandpass - * when the bitrate becomes too low, for better overall quality. - * @see OPUS_GET_MAX_BANDWIDTH - * @param[in] x opus_int32: Allowed values: - *
- *
OPUS_BANDWIDTH_NARROWBAND
4 kHz passband
- *
OPUS_BANDWIDTH_MEDIUMBAND
6 kHz passband
- *
OPUS_BANDWIDTH_WIDEBAND
8 kHz passband
- *
OPUS_BANDWIDTH_SUPERWIDEBAND
12 kHz passband
- *
OPUS_BANDWIDTH_FULLBAND
20 kHz passband (default)
- *
- * @hideinitializer */ -#define OPUS_SET_MAX_BANDWIDTH(x) OPUS_SET_MAX_BANDWIDTH_REQUEST, __opus_check_int(x) - -/** Gets the encoder's configured maximum allowed bandpass. - * @see OPUS_SET_MAX_BANDWIDTH - * @param[out] x opus_int32 *: Allowed values: - *
- *
#OPUS_BANDWIDTH_NARROWBAND
4 kHz passband
- *
#OPUS_BANDWIDTH_MEDIUMBAND
6 kHz passband
- *
#OPUS_BANDWIDTH_WIDEBAND
8 kHz passband
- *
#OPUS_BANDWIDTH_SUPERWIDEBAND
12 kHz passband
- *
#OPUS_BANDWIDTH_FULLBAND
20 kHz passband (default)
- *
- * @hideinitializer */ -#define OPUS_GET_MAX_BANDWIDTH(x) OPUS_GET_MAX_BANDWIDTH_REQUEST, __opus_check_int_ptr(x) - -/** Sets the encoder's bandpass to a specific value. - * This prevents the encoder from automatically selecting the bandpass based - * on the available bitrate. If an application knows the bandpass of the input - * audio it is providing, it should normally use #OPUS_SET_MAX_BANDWIDTH - * instead, which still gives the encoder the freedom to reduce the bandpass - * when the bitrate becomes too low, for better overall quality. - * @see OPUS_GET_BANDWIDTH - * @param[in] x opus_int32: Allowed values: - *
- *
#OPUS_AUTO
(default)
- *
#OPUS_BANDWIDTH_NARROWBAND
4 kHz passband
- *
#OPUS_BANDWIDTH_MEDIUMBAND
6 kHz passband
- *
#OPUS_BANDWIDTH_WIDEBAND
8 kHz passband
- *
#OPUS_BANDWIDTH_SUPERWIDEBAND
12 kHz passband
- *
#OPUS_BANDWIDTH_FULLBAND
20 kHz passband
- *
- * @hideinitializer */ -#define OPUS_SET_BANDWIDTH(x) OPUS_SET_BANDWIDTH_REQUEST, __opus_check_int(x) - -/** Configures the type of signal being encoded. - * This is a hint which helps the encoder's mode selection. - * @see OPUS_GET_SIGNAL - * @param[in] x opus_int32: Allowed values: - *
- *
#OPUS_AUTO
(default)
- *
#OPUS_SIGNAL_VOICE
Bias thresholds towards choosing LPC or Hybrid modes.
- *
#OPUS_SIGNAL_MUSIC
Bias thresholds towards choosing MDCT modes.
- *
- * @hideinitializer */ -#define OPUS_SET_SIGNAL(x) OPUS_SET_SIGNAL_REQUEST, __opus_check_int(x) -/** Gets the encoder's configured signal type. - * @see OPUS_SET_SIGNAL - * @param[out] x opus_int32 *: Returns one of the following values: - *
- *
#OPUS_AUTO
(default)
- *
#OPUS_SIGNAL_VOICE
Bias thresholds towards choosing LPC or Hybrid modes.
- *
#OPUS_SIGNAL_MUSIC
Bias thresholds towards choosing MDCT modes.
- *
- * @hideinitializer */ -#define OPUS_GET_SIGNAL(x) OPUS_GET_SIGNAL_REQUEST, __opus_check_int_ptr(x) - - -/** Configures the encoder's intended application. - * The initial value is a mandatory argument to the encoder_create function. - * @see OPUS_GET_APPLICATION - * @param[in] x opus_int32: Returns one of the following values: - *
- *
#OPUS_APPLICATION_VOIP
- *
Process signal for improved speech intelligibility.
- *
#OPUS_APPLICATION_AUDIO
- *
Favor faithfulness to the original input.
- *
#OPUS_APPLICATION_RESTRICTED_LOWDELAY
- *
Configure the minimum possible coding delay by disabling certain modes - * of operation.
- *
- * @hideinitializer */ -#define OPUS_SET_APPLICATION(x) OPUS_SET_APPLICATION_REQUEST, __opus_check_int(x) -/** Gets the encoder's configured application. - * @see OPUS_SET_APPLICATION - * @param[out] x opus_int32 *: Returns one of the following values: - *
- *
#OPUS_APPLICATION_VOIP
- *
Process signal for improved speech intelligibility.
- *
#OPUS_APPLICATION_AUDIO
- *
Favor faithfulness to the original input.
- *
#OPUS_APPLICATION_RESTRICTED_LOWDELAY
- *
Configure the minimum possible coding delay by disabling certain modes - * of operation.
- *
- * @hideinitializer */ -#define OPUS_GET_APPLICATION(x) OPUS_GET_APPLICATION_REQUEST, __opus_check_int_ptr(x) - -/** Gets the total samples of delay added by the entire codec. - * This can be queried by the encoder and then the provided number of samples can be - * skipped on from the start of the decoder's output to provide time aligned input - * and output. From the perspective of a decoding application the real data begins this many - * samples late. - * - * The decoder contribution to this delay is identical for all decoders, but the - * encoder portion of the delay may vary from implementation to implementation, - * version to version, or even depend on the encoder's initial configuration. - * Applications needing delay compensation should call this CTL rather than - * hard-coding a value. - * @param[out] x opus_int32 *: Number of lookahead samples - * @hideinitializer */ -#define OPUS_GET_LOOKAHEAD(x) OPUS_GET_LOOKAHEAD_REQUEST, __opus_check_int_ptr(x) - -/** Configures the encoder's use of inband forward error correction (FEC). - * @note This is only applicable to the LPC layer - * @see OPUS_GET_INBAND_FEC - * @param[in] x opus_int32: Allowed values: - *
- *
0
Disable inband FEC (default).
- *
1
Inband FEC enabled. If the packet loss rate is sufficiently high, Opus will automatically switch to SILK even at high rates to enable use of that FEC.
- *
2
Inband FEC enabled, but does not necessarily switch to SILK if we have music.
- *
- * @hideinitializer */ -#define OPUS_SET_INBAND_FEC(x) OPUS_SET_INBAND_FEC_REQUEST, __opus_check_int(x) -/** Gets encoder's configured use of inband forward error correction. - * @see OPUS_SET_INBAND_FEC - * @param[out] x opus_int32 *: Returns one of the following values: - *
- *
0
Inband FEC disabled (default).
- *
1
Inband FEC enabled. If the packet loss rate is sufficiently high, Opus will automatically switch to SILK even at high rates to enable use of that FEC.
- *
2
Inband FEC enabled, but does not necessarily switch to SILK if we have music.
- *
- * @hideinitializer */ -#define OPUS_GET_INBAND_FEC(x) OPUS_GET_INBAND_FEC_REQUEST, __opus_check_int_ptr(x) - -/** Configures the encoder's expected packet loss percentage. - * Higher values trigger progressively more loss resistant behavior in the encoder - * at the expense of quality at a given bitrate in the absence of packet loss, but - * greater quality under loss. - * @see OPUS_GET_PACKET_LOSS_PERC - * @param[in] x opus_int32: Loss percentage in the range 0-100, inclusive (default: 0). - * @hideinitializer */ -#define OPUS_SET_PACKET_LOSS_PERC(x) OPUS_SET_PACKET_LOSS_PERC_REQUEST, __opus_check_int(x) -/** Gets the encoder's configured packet loss percentage. - * @see OPUS_SET_PACKET_LOSS_PERC - * @param[out] x opus_int32 *: Returns the configured loss percentage - * in the range 0-100, inclusive (default: 0). - * @hideinitializer */ -#define OPUS_GET_PACKET_LOSS_PERC(x) OPUS_GET_PACKET_LOSS_PERC_REQUEST, __opus_check_int_ptr(x) - -/** Configures the encoder's use of discontinuous transmission (DTX). - * @note This is only applicable to the LPC layer - * @see OPUS_GET_DTX - * @param[in] x opus_int32: Allowed values: - *
- *
0
Disable DTX (default).
- *
1
Enabled DTX.
- *
- * @hideinitializer */ -#define OPUS_SET_DTX(x) OPUS_SET_DTX_REQUEST, __opus_check_int(x) -/** Gets encoder's configured use of discontinuous transmission. - * @see OPUS_SET_DTX - * @param[out] x opus_int32 *: Returns one of the following values: - *
- *
0
DTX disabled (default).
- *
1
DTX enabled.
- *
- * @hideinitializer */ -#define OPUS_GET_DTX(x) OPUS_GET_DTX_REQUEST, __opus_check_int_ptr(x) -/** Configures the depth of signal being encoded. - * - * This is a hint which helps the encoder identify silence and near-silence. - * It represents the number of significant bits of linear intensity below - * which the signal contains ignorable quantization or other noise. - * - * For example, OPUS_SET_LSB_DEPTH(14) would be an appropriate setting - * for G.711 u-law input. OPUS_SET_LSB_DEPTH(16) would be appropriate - * for 16-bit linear pcm input with opus_encode_float(). - * - * When using opus_encode() instead of opus_encode_float(), or when libopus - * is compiled for fixed-point, the encoder uses the minimum of the value - * set here and the value 16. - * - * @see OPUS_GET_LSB_DEPTH - * @param[in] x opus_int32: Input precision in bits, between 8 and 24 - * (default: 24). - * @hideinitializer */ -#define OPUS_SET_LSB_DEPTH(x) OPUS_SET_LSB_DEPTH_REQUEST, __opus_check_int(x) -/** Gets the encoder's configured signal depth. - * @see OPUS_SET_LSB_DEPTH - * @param[out] x opus_int32 *: Input precision in bits, between 8 and - * 24 (default: 24). - * @hideinitializer */ -#define OPUS_GET_LSB_DEPTH(x) OPUS_GET_LSB_DEPTH_REQUEST, __opus_check_int_ptr(x) - -/** Configures the encoder's use of variable duration frames. - * When variable duration is enabled, the encoder is free to use a shorter frame - * size than the one requested in the opus_encode*() call. - * It is then the user's responsibility - * to verify how much audio was encoded by checking the ToC byte of the encoded - * packet. The part of the audio that was not encoded needs to be resent to the - * encoder for the next call. Do not use this option unless you really - * know what you are doing. - * @see OPUS_GET_EXPERT_FRAME_DURATION - * @param[in] x opus_int32: Allowed values: - *
- *
OPUS_FRAMESIZE_ARG
Select frame size from the argument (default).
- *
OPUS_FRAMESIZE_2_5_MS
Use 2.5 ms frames.
- *
OPUS_FRAMESIZE_5_MS
Use 5 ms frames.
- *
OPUS_FRAMESIZE_10_MS
Use 10 ms frames.
- *
OPUS_FRAMESIZE_20_MS
Use 20 ms frames.
- *
OPUS_FRAMESIZE_40_MS
Use 40 ms frames.
- *
OPUS_FRAMESIZE_60_MS
Use 60 ms frames.
- *
OPUS_FRAMESIZE_80_MS
Use 80 ms frames.
- *
OPUS_FRAMESIZE_100_MS
Use 100 ms frames.
- *
OPUS_FRAMESIZE_120_MS
Use 120 ms frames.
- *
- * @hideinitializer */ -#define OPUS_SET_EXPERT_FRAME_DURATION(x) OPUS_SET_EXPERT_FRAME_DURATION_REQUEST, __opus_check_int(x) -/** Gets the encoder's configured use of variable duration frames. - * @see OPUS_SET_EXPERT_FRAME_DURATION - * @param[out] x opus_int32 *: Returns one of the following values: - *
- *
OPUS_FRAMESIZE_ARG
Select frame size from the argument (default).
- *
OPUS_FRAMESIZE_2_5_MS
Use 2.5 ms frames.
- *
OPUS_FRAMESIZE_5_MS
Use 5 ms frames.
- *
OPUS_FRAMESIZE_10_MS
Use 10 ms frames.
- *
OPUS_FRAMESIZE_20_MS
Use 20 ms frames.
- *
OPUS_FRAMESIZE_40_MS
Use 40 ms frames.
- *
OPUS_FRAMESIZE_60_MS
Use 60 ms frames.
- *
OPUS_FRAMESIZE_80_MS
Use 80 ms frames.
- *
OPUS_FRAMESIZE_100_MS
Use 100 ms frames.
- *
OPUS_FRAMESIZE_120_MS
Use 120 ms frames.
- *
- * @hideinitializer */ -#define OPUS_GET_EXPERT_FRAME_DURATION(x) OPUS_GET_EXPERT_FRAME_DURATION_REQUEST, __opus_check_int_ptr(x) - -/** If set to 1, disables almost all use of prediction, making frames almost - * completely independent. This reduces quality. - * @see OPUS_GET_PREDICTION_DISABLED - * @param[in] x opus_int32: Allowed values: - *
- *
0
Enable prediction (default).
- *
1
Disable prediction.
- *
- * @hideinitializer */ -#define OPUS_SET_PREDICTION_DISABLED(x) OPUS_SET_PREDICTION_DISABLED_REQUEST, __opus_check_int(x) -/** Gets the encoder's configured prediction status. - * @see OPUS_SET_PREDICTION_DISABLED - * @param[out] x opus_int32 *: Returns one of the following values: - *
- *
0
Prediction enabled (default).
- *
1
Prediction disabled.
- *
- * @hideinitializer */ -#define OPUS_GET_PREDICTION_DISABLED(x) OPUS_GET_PREDICTION_DISABLED_REQUEST, __opus_check_int_ptr(x) - -/** If non-zero, enables Deep Redundancy (DRED) and use the specified maximum number of 10-ms redundant frames - * @hideinitializer */ -#define OPUS_SET_DRED_DURATION(x) OPUS_SET_DRED_DURATION_REQUEST, __opus_check_int(x) -/** Gets the encoder's configured Deep Redundancy (DRED) maximum number of frames. - * @hideinitializer */ -#define OPUS_GET_DRED_DURATION(x) OPUS_GET_DRED_DURATION_REQUEST, __opus_check_int_ptr(x) - -/** Provide external DNN weights from binary object (only when explicitly built without the weights) - * @hideinitializer */ -#define OPUS_SET_DNN_BLOB(data, len) OPUS_SET_DNN_BLOB_REQUEST, __opus_check_void_ptr(data), __opus_check_int(len) - - -/**@}*/ - -/** @defgroup opus_genericctls Generic CTLs - * - * These macros are used with the \c opus_decoder_ctl and - * \c opus_encoder_ctl calls to generate a particular - * request. - * - * When called on an \c OpusDecoder they apply to that - * particular decoder instance. When called on an - * \c OpusEncoder they apply to the corresponding setting - * on that encoder instance, if present. - * - * Some usage examples: - * - * @code - * int ret; - * opus_int32 pitch; - * ret = opus_decoder_ctl(dec_ctx, OPUS_GET_PITCH(&pitch)); - * if (ret == OPUS_OK) return ret; - * - * opus_encoder_ctl(enc_ctx, OPUS_RESET_STATE); - * opus_decoder_ctl(dec_ctx, OPUS_RESET_STATE); - * - * opus_int32 enc_bw, dec_bw; - * opus_encoder_ctl(enc_ctx, OPUS_GET_BANDWIDTH(&enc_bw)); - * opus_decoder_ctl(dec_ctx, OPUS_GET_BANDWIDTH(&dec_bw)); - * if (enc_bw != dec_bw) { - * printf("packet bandwidth mismatch!\n"); - * } - * @endcode - * - * @see opus_encoder, opus_decoder_ctl, opus_encoder_ctl, opus_decoderctls, opus_encoderctls - * @{ - */ - -/** Resets the codec state to be equivalent to a freshly initialized state. - * This should be called when switching streams in order to prevent - * the back to back decoding from giving different results from - * one at a time decoding. - * @hideinitializer */ -#define OPUS_RESET_STATE 4028 - -/** Gets the final state of the codec's entropy coder. - * This is used for testing purposes, - * The encoder and decoder state should be identical after coding a payload - * (assuming no data corruption or software bugs) - * - * @param[out] x opus_uint32 *: Entropy coder state - * - * @hideinitializer */ -#define OPUS_GET_FINAL_RANGE(x) OPUS_GET_FINAL_RANGE_REQUEST, __opus_check_uint_ptr(x) - -/** Gets the encoder's configured bandpass or the decoder's last bandpass. - * @see OPUS_SET_BANDWIDTH - * @param[out] x opus_int32 *: Returns one of the following values: - *
- *
#OPUS_AUTO
(default)
- *
#OPUS_BANDWIDTH_NARROWBAND
4 kHz passband
- *
#OPUS_BANDWIDTH_MEDIUMBAND
6 kHz passband
- *
#OPUS_BANDWIDTH_WIDEBAND
8 kHz passband
- *
#OPUS_BANDWIDTH_SUPERWIDEBAND
12 kHz passband
- *
#OPUS_BANDWIDTH_FULLBAND
20 kHz passband
- *
- * @hideinitializer */ -#define OPUS_GET_BANDWIDTH(x) OPUS_GET_BANDWIDTH_REQUEST, __opus_check_int_ptr(x) - -/** Gets the sampling rate the encoder or decoder was initialized with. - * This simply returns the Fs value passed to opus_encoder_init() - * or opus_decoder_init(). - * @param[out] x opus_int32 *: Sampling rate of encoder or decoder. - * @hideinitializer - */ -#define OPUS_GET_SAMPLE_RATE(x) OPUS_GET_SAMPLE_RATE_REQUEST, __opus_check_int_ptr(x) - -/** If set to 1, disables the use of phase inversion for intensity stereo, - * improving the quality of mono downmixes, but slightly reducing normal - * stereo quality. Disabling phase inversion in the decoder does not comply - * with RFC 6716, although it does not cause any interoperability issue and - * is expected to become part of the Opus standard once RFC 6716 is updated - * by draft-ietf-codec-opus-update. - * @see OPUS_GET_PHASE_INVERSION_DISABLED - * @param[in] x opus_int32: Allowed values: - *
- *
0
Enable phase inversion (default).
- *
1
Disable phase inversion.
- *
- * @hideinitializer */ -#define OPUS_SET_PHASE_INVERSION_DISABLED(x) OPUS_SET_PHASE_INVERSION_DISABLED_REQUEST, __opus_check_int(x) -/** Gets the encoder's configured phase inversion status. - * @see OPUS_SET_PHASE_INVERSION_DISABLED - * @param[out] x opus_int32 *: Returns one of the following values: - *
- *
0
Stereo phase inversion enabled (default).
- *
1
Stereo phase inversion disabled.
- *
- * @hideinitializer */ -#define OPUS_GET_PHASE_INVERSION_DISABLED(x) OPUS_GET_PHASE_INVERSION_DISABLED_REQUEST, __opus_check_int_ptr(x) -/** Gets the DTX state of the encoder. - * Returns whether the last encoded frame was either a comfort noise update - * during DTX or not encoded because of DTX. - * @param[out] x opus_int32 *: Returns one of the following values: - *
- *
0
The encoder is not in DTX.
- *
1
The encoder is in DTX.
- *
- * @hideinitializer */ -#define OPUS_GET_IN_DTX(x) OPUS_GET_IN_DTX_REQUEST, __opus_check_int_ptr(x) - -/**@}*/ - -/** @defgroup opus_decoderctls Decoder related CTLs - * @see opus_genericctls, opus_encoderctls, opus_decoder - * @{ - */ - -/** Configures decoder gain adjustment. - * Scales the decoded output by a factor specified in Q8 dB units. - * This has a maximum range of -32768 to 32767 inclusive, and returns - * OPUS_BAD_ARG otherwise. The default is zero indicating no adjustment. - * This setting survives decoder reset. - * - * gain = pow(10, x/(20.0*256)) - * - * @param[in] x opus_int32: Amount to scale PCM signal by in Q8 dB units. - * @hideinitializer */ -#define OPUS_SET_GAIN(x) OPUS_SET_GAIN_REQUEST, __opus_check_int(x) -/** Gets the decoder's configured gain adjustment. @see OPUS_SET_GAIN - * - * @param[out] x opus_int32 *: Amount to scale PCM signal by in Q8 dB units. - * @hideinitializer */ -#define OPUS_GET_GAIN(x) OPUS_GET_GAIN_REQUEST, __opus_check_int_ptr(x) - -/** Gets the duration (in samples) of the last packet successfully decoded or concealed. - * @param[out] x opus_int32 *: Number of samples (at current sampling rate). - * @hideinitializer */ -#define OPUS_GET_LAST_PACKET_DURATION(x) OPUS_GET_LAST_PACKET_DURATION_REQUEST, __opus_check_int_ptr(x) - -/** Gets the pitch of the last decoded frame, if available. - * This can be used for any post-processing algorithm requiring the use of pitch, - * e.g. time stretching/shortening. If the last frame was not voiced, or if the - * pitch was not coded in the frame, then zero is returned. - * - * This CTL is only implemented for decoder instances. - * - * @param[out] x opus_int32 *: pitch period at 48 kHz (or 0 if not available) - * - * @hideinitializer */ -#define OPUS_GET_PITCH(x) OPUS_GET_PITCH_REQUEST, __opus_check_int_ptr(x) - -/**@}*/ - -/** @defgroup opus_libinfo Opus library information functions - * @{ - */ - -/** Converts an opus error code into a human readable string. - * - * @param[in] error int: Error number - * @returns Error string - */ -OPUS_EXPORT const char *opus_strerror(int error); - -/** Gets the libopus version string. - * - * Applications may look for the substring "-fixed" in the version string to - * determine whether they have a fixed-point or floating-point build at - * runtime. - * - * @returns Version string - */ -OPUS_EXPORT const char *opus_get_version_string(void); -/**@}*/ - -#ifdef __cplusplus -} -#endif - -#endif /* OPUS_DEFINES_H */ diff --git a/go/teamspeak/.opus/install/armeabi-v7a/include/opus/opus_multistream.h b/go/teamspeak/.opus/install/armeabi-v7a/include/opus/opus_multistream.h deleted file mode 100644 index 824cc55..0000000 --- a/go/teamspeak/.opus/install/armeabi-v7a/include/opus/opus_multistream.h +++ /dev/null @@ -1,660 +0,0 @@ -/* Copyright (c) 2011 Xiph.Org Foundation - Written by Jean-Marc Valin */ -/* - Redistribution and use in source and binary forms, with or without - modification, are permitted provided that the following conditions - are met: - - - Redistributions of source code must retain the above copyright - notice, this list of conditions and the following disclaimer. - - - Redistributions in binary form must reproduce the above copyright - notice, this list of conditions and the following disclaimer in the - documentation and/or other materials provided with the distribution. - - THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS - ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT - LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR - A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER - OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, - EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, - PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR - PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF - LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING - NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS - SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. -*/ - -/** - * @file opus_multistream.h - * @brief Opus reference implementation multistream API - */ - -#ifndef OPUS_MULTISTREAM_H -#define OPUS_MULTISTREAM_H - -#include "opus.h" - -#ifdef __cplusplus -extern "C" { -#endif - -/** @cond OPUS_INTERNAL_DOC */ - -/** Macros to trigger compilation errors when the wrong types are provided to a - * CTL. */ -/**@{*/ -#define __opus_check_encstate_ptr(ptr) ((ptr) + ((ptr) - (OpusEncoder**)(ptr))) -#define __opus_check_decstate_ptr(ptr) ((ptr) + ((ptr) - (OpusDecoder**)(ptr))) -/**@}*/ - -/** These are the actual encoder and decoder CTL ID numbers. - * They should not be used directly by applications. - * In general, SETs should be even and GETs should be odd.*/ -/**@{*/ -#define OPUS_MULTISTREAM_GET_ENCODER_STATE_REQUEST 5120 -#define OPUS_MULTISTREAM_GET_DECODER_STATE_REQUEST 5122 -/**@}*/ - -/** @endcond */ - -/** @defgroup opus_multistream_ctls Multistream specific encoder and decoder CTLs - * - * These are convenience macros that are specific to the - * opus_multistream_encoder_ctl() and opus_multistream_decoder_ctl() - * interface. - * The CTLs from @ref opus_genericctls, @ref opus_encoderctls, and - * @ref opus_decoderctls may be applied to a multistream encoder or decoder as - * well. - * In addition, you may retrieve the encoder or decoder state for an specific - * stream via #OPUS_MULTISTREAM_GET_ENCODER_STATE or - * #OPUS_MULTISTREAM_GET_DECODER_STATE and apply CTLs to it individually. - */ -/**@{*/ - -/** Gets the encoder state for an individual stream of a multistream encoder. - * @param[in] x opus_int32: The index of the stream whose encoder you - * wish to retrieve. - * This must be non-negative and less than - * the streams parameter used - * to initialize the encoder. - * @param[out] y OpusEncoder**: Returns a pointer to the given - * encoder state. - * @retval OPUS_BAD_ARG The index of the requested stream was out of range. - * @hideinitializer - */ -#define OPUS_MULTISTREAM_GET_ENCODER_STATE(x,y) OPUS_MULTISTREAM_GET_ENCODER_STATE_REQUEST, __opus_check_int(x), __opus_check_encstate_ptr(y) - -/** Gets the decoder state for an individual stream of a multistream decoder. - * @param[in] x opus_int32: The index of the stream whose decoder you - * wish to retrieve. - * This must be non-negative and less than - * the streams parameter used - * to initialize the decoder. - * @param[out] y OpusDecoder**: Returns a pointer to the given - * decoder state. - * @retval OPUS_BAD_ARG The index of the requested stream was out of range. - * @hideinitializer - */ -#define OPUS_MULTISTREAM_GET_DECODER_STATE(x,y) OPUS_MULTISTREAM_GET_DECODER_STATE_REQUEST, __opus_check_int(x), __opus_check_decstate_ptr(y) - -/**@}*/ - -/** @defgroup opus_multistream Opus Multistream API - * @{ - * - * The multistream API allows individual Opus streams to be combined into a - * single packet, enabling support for up to 255 channels. Unlike an - * elementary Opus stream, the encoder and decoder must negotiate the channel - * configuration before the decoder can successfully interpret the data in the - * packets produced by the encoder. Some basic information, such as packet - * duration, can be computed without any special negotiation. - * - * The format for multistream Opus packets is defined in - * RFC 7845 - * and is based on the self-delimited Opus framing described in Appendix B of - * RFC 6716. - * Normal Opus packets are just a degenerate case of multistream Opus packets, - * and can be encoded or decoded with the multistream API by setting - * streams to 1 when initializing the encoder or - * decoder. - * - * Multistream Opus streams can contain up to 255 elementary Opus streams. - * These may be either "uncoupled" or "coupled", indicating that the decoder - * is configured to decode them to either 1 or 2 channels, respectively. - * The streams are ordered so that all coupled streams appear at the - * beginning. - * - * A mapping table defines which decoded channel i - * should be used for each input/output (I/O) channel j. This table is - * typically provided as an unsigned char array. - * Let i = mapping[j] be the index for I/O channel j. - * If i < 2*coupled_streams, then I/O channel j is - * encoded as the left channel of stream (i/2) if i - * is even, or as the right channel of stream (i/2) if - * i is odd. Otherwise, I/O channel j is encoded as - * mono in stream (i - coupled_streams), unless it has the special - * value 255, in which case it is omitted from the encoding entirely (the - * decoder will reproduce it as silence). Each value i must either - * be the special value 255 or be less than streams + coupled_streams. - * - * The output channels specified by the encoder - * should use the - * Vorbis - * channel ordering. A decoder may wish to apply an additional permutation - * to the mapping the encoder used to achieve a different output channel - * order (e.g. for outputting in WAV order). - * - * Each multistream packet contains an Opus packet for each stream, and all of - * the Opus packets in a single multistream packet must have the same - * duration. Therefore the duration of a multistream packet can be extracted - * from the TOC sequence of the first stream, which is located at the - * beginning of the packet, just like an elementary Opus stream: - * - * @code - * int nb_samples; - * int nb_frames; - * nb_frames = opus_packet_get_nb_frames(data, len); - * if (nb_frames < 1) - * return nb_frames; - * nb_samples = opus_packet_get_samples_per_frame(data, 48000) * nb_frames; - * @endcode - * - * The general encoding and decoding process proceeds exactly the same as in - * the normal @ref opus_encoder and @ref opus_decoder APIs. - * See their documentation for an overview of how to use the corresponding - * multistream functions. - */ - -/** Opus multistream encoder state. - * This contains the complete state of a multistream Opus encoder. - * It is position independent and can be freely copied. - * @see opus_multistream_encoder_create - * @see opus_multistream_encoder_init - */ -typedef struct OpusMSEncoder OpusMSEncoder; - -/** Opus multistream decoder state. - * This contains the complete state of a multistream Opus decoder. - * It is position independent and can be freely copied. - * @see opus_multistream_decoder_create - * @see opus_multistream_decoder_init - */ -typedef struct OpusMSDecoder OpusMSDecoder; - -/**\name Multistream encoder functions */ -/**@{*/ - -/** Gets the size of an OpusMSEncoder structure. - * @param streams int: The total number of streams to encode from the - * input. - * This must be no more than 255. - * @param coupled_streams int: Number of coupled (2 channel) streams - * to encode. - * This must be no larger than the total - * number of streams. - * Additionally, The total number of - * encoded channels (streams + - * coupled_streams) must be no - * more than 255. - * @returns The size in bytes on success, or a negative error code - * (see @ref opus_errorcodes) on error. - */ -OPUS_EXPORT OPUS_WARN_UNUSED_RESULT opus_int32 opus_multistream_encoder_get_size( - int streams, - int coupled_streams -); - -OPUS_EXPORT OPUS_WARN_UNUSED_RESULT opus_int32 opus_multistream_surround_encoder_get_size( - int channels, - int mapping_family -); - - -/** Allocates and initializes a multistream encoder state. - * Call opus_multistream_encoder_destroy() to release - * this object when finished. - * @param Fs opus_int32: Sampling rate of the input signal (in Hz). - * This must be one of 8000, 12000, 16000, - * 24000, or 48000. - * @param channels int: Number of channels in the input signal. - * This must be at most 255. - * It may be greater than the number of - * coded channels (streams + - * coupled_streams). - * @param streams int: The total number of streams to encode from the - * input. - * This must be no more than the number of channels. - * @param coupled_streams int: Number of coupled (2 channel) streams - * to encode. - * This must be no larger than the total - * number of streams. - * Additionally, The total number of - * encoded channels (streams + - * coupled_streams) must be no - * more than the number of input channels. - * @param[in] mapping const unsigned char[channels]: Mapping from - * encoded channels to input channels, as described in - * @ref opus_multistream. As an extra constraint, the - * multistream encoder does not allow encoding coupled - * streams for which one channel is unused since this - * is never a good idea. - * @param application int: The target encoder application. - * This must be one of the following: - *
- *
#OPUS_APPLICATION_VOIP
- *
Process signal for improved speech intelligibility.
- *
#OPUS_APPLICATION_AUDIO
- *
Favor faithfulness to the original input.
- *
#OPUS_APPLICATION_RESTRICTED_LOWDELAY
- *
Configure the minimum possible coding delay by disabling certain modes - * of operation.
- *
- * @param[out] error int *: Returns #OPUS_OK on success, or an error - * code (see @ref opus_errorcodes) on - * failure. - */ -OPUS_EXPORT OPUS_WARN_UNUSED_RESULT OpusMSEncoder *opus_multistream_encoder_create( - opus_int32 Fs, - int channels, - int streams, - int coupled_streams, - const unsigned char *mapping, - int application, - int *error -) OPUS_ARG_NONNULL(5); - -OPUS_EXPORT OPUS_WARN_UNUSED_RESULT OpusMSEncoder *opus_multistream_surround_encoder_create( - opus_int32 Fs, - int channels, - int mapping_family, - int *streams, - int *coupled_streams, - unsigned char *mapping, - int application, - int *error -) OPUS_ARG_NONNULL(4) OPUS_ARG_NONNULL(5) OPUS_ARG_NONNULL(6); - -/** Initialize a previously allocated multistream encoder state. - * The memory pointed to by \a st must be at least the size returned by - * opus_multistream_encoder_get_size(). - * This is intended for applications which use their own allocator instead of - * malloc. - * To reset a previously initialized state, use the #OPUS_RESET_STATE CTL. - * @see opus_multistream_encoder_create - * @see opus_multistream_encoder_get_size - * @param st OpusMSEncoder*: Multistream encoder state to initialize. - * @param Fs opus_int32: Sampling rate of the input signal (in Hz). - * This must be one of 8000, 12000, 16000, - * 24000, or 48000. - * @param channels int: Number of channels in the input signal. - * This must be at most 255. - * It may be greater than the number of - * coded channels (streams + - * coupled_streams). - * @param streams int: The total number of streams to encode from the - * input. - * This must be no more than the number of channels. - * @param coupled_streams int: Number of coupled (2 channel) streams - * to encode. - * This must be no larger than the total - * number of streams. - * Additionally, The total number of - * encoded channels (streams + - * coupled_streams) must be no - * more than the number of input channels. - * @param[in] mapping const unsigned char[channels]: Mapping from - * encoded channels to input channels, as described in - * @ref opus_multistream. As an extra constraint, the - * multistream encoder does not allow encoding coupled - * streams for which one channel is unused since this - * is never a good idea. - * @param application int: The target encoder application. - * This must be one of the following: - *
- *
#OPUS_APPLICATION_VOIP
- *
Process signal for improved speech intelligibility.
- *
#OPUS_APPLICATION_AUDIO
- *
Favor faithfulness to the original input.
- *
#OPUS_APPLICATION_RESTRICTED_LOWDELAY
- *
Configure the minimum possible coding delay by disabling certain modes - * of operation.
- *
- * @returns #OPUS_OK on success, or an error code (see @ref opus_errorcodes) - * on failure. - */ -OPUS_EXPORT int opus_multistream_encoder_init( - OpusMSEncoder *st, - opus_int32 Fs, - int channels, - int streams, - int coupled_streams, - const unsigned char *mapping, - int application -) OPUS_ARG_NONNULL(1) OPUS_ARG_NONNULL(6); - -OPUS_EXPORT int opus_multistream_surround_encoder_init( - OpusMSEncoder *st, - opus_int32 Fs, - int channels, - int mapping_family, - int *streams, - int *coupled_streams, - unsigned char *mapping, - int application -) OPUS_ARG_NONNULL(1) OPUS_ARG_NONNULL(5) OPUS_ARG_NONNULL(6) OPUS_ARG_NONNULL(7); - -/** Encodes a multistream Opus frame. - * @param st OpusMSEncoder*: Multistream encoder state. - * @param[in] pcm const opus_int16*: The input signal as interleaved - * samples. - * This must contain - * frame_size*channels - * samples. - * @param frame_size int: Number of samples per channel in the input - * signal. - * This must be an Opus frame size for the - * encoder's sampling rate. - * For example, at 48 kHz the permitted values - * are 120, 240, 480, 960, 1920, and 2880. - * Passing in a duration of less than 10 ms - * (480 samples at 48 kHz) will prevent the - * encoder from using the LPC or hybrid modes. - * @param[out] data unsigned char*: Output payload. - * This must contain storage for at - * least \a max_data_bytes. - * @param [in] max_data_bytes opus_int32: Size of the allocated - * memory for the output - * payload. This may be - * used to impose an upper limit on - * the instant bitrate, but should - * not be used as the only bitrate - * control. Use #OPUS_SET_BITRATE to - * control the bitrate. - * @returns The length of the encoded packet (in bytes) on success or a - * negative error code (see @ref opus_errorcodes) on failure. - */ -OPUS_EXPORT OPUS_WARN_UNUSED_RESULT int opus_multistream_encode( - OpusMSEncoder *st, - const opus_int16 *pcm, - int frame_size, - unsigned char *data, - opus_int32 max_data_bytes -) OPUS_ARG_NONNULL(1) OPUS_ARG_NONNULL(2) OPUS_ARG_NONNULL(4); - -/** Encodes a multistream Opus frame from floating point input. - * @param st OpusMSEncoder*: Multistream encoder state. - * @param[in] pcm const float*: The input signal as interleaved - * samples with a normal range of - * +/-1.0. - * Samples with a range beyond +/-1.0 - * are supported but will be clipped by - * decoders using the integer API and - * should only be used if it is known - * that the far end supports extended - * dynamic range. - * This must contain - * frame_size*channels - * samples. - * @param frame_size int: Number of samples per channel in the input - * signal. - * This must be an Opus frame size for the - * encoder's sampling rate. - * For example, at 48 kHz the permitted values - * are 120, 240, 480, 960, 1920, and 2880. - * Passing in a duration of less than 10 ms - * (480 samples at 48 kHz) will prevent the - * encoder from using the LPC or hybrid modes. - * @param[out] data unsigned char*: Output payload. - * This must contain storage for at - * least \a max_data_bytes. - * @param [in] max_data_bytes opus_int32: Size of the allocated - * memory for the output - * payload. This may be - * used to impose an upper limit on - * the instant bitrate, but should - * not be used as the only bitrate - * control. Use #OPUS_SET_BITRATE to - * control the bitrate. - * @returns The length of the encoded packet (in bytes) on success or a - * negative error code (see @ref opus_errorcodes) on failure. - */ -OPUS_EXPORT OPUS_WARN_UNUSED_RESULT int opus_multistream_encode_float( - OpusMSEncoder *st, - const float *pcm, - int frame_size, - unsigned char *data, - opus_int32 max_data_bytes -) OPUS_ARG_NONNULL(1) OPUS_ARG_NONNULL(2) OPUS_ARG_NONNULL(4); - -/** Frees an OpusMSEncoder allocated by - * opus_multistream_encoder_create(). - * @param st OpusMSEncoder*: Multistream encoder state to be freed. - */ -OPUS_EXPORT void opus_multistream_encoder_destroy(OpusMSEncoder *st); - -/** Perform a CTL function on a multistream Opus encoder. - * - * Generally the request and subsequent arguments are generated by a - * convenience macro. - * @param st OpusMSEncoder*: Multistream encoder state. - * @param request This and all remaining parameters should be replaced by one - * of the convenience macros in @ref opus_genericctls, - * @ref opus_encoderctls, or @ref opus_multistream_ctls. - * @see opus_genericctls - * @see opus_encoderctls - * @see opus_multistream_ctls - */ -OPUS_EXPORT int opus_multistream_encoder_ctl(OpusMSEncoder *st, int request, ...) OPUS_ARG_NONNULL(1); - -/**@}*/ - -/**\name Multistream decoder functions */ -/**@{*/ - -/** Gets the size of an OpusMSDecoder structure. - * @param streams int: The total number of streams coded in the - * input. - * This must be no more than 255. - * @param coupled_streams int: Number streams to decode as coupled - * (2 channel) streams. - * This must be no larger than the total - * number of streams. - * Additionally, The total number of - * coded channels (streams + - * coupled_streams) must be no - * more than 255. - * @returns The size in bytes on success, or a negative error code - * (see @ref opus_errorcodes) on error. - */ -OPUS_EXPORT OPUS_WARN_UNUSED_RESULT opus_int32 opus_multistream_decoder_get_size( - int streams, - int coupled_streams -); - -/** Allocates and initializes a multistream decoder state. - * Call opus_multistream_decoder_destroy() to release - * this object when finished. - * @param Fs opus_int32: Sampling rate to decode at (in Hz). - * This must be one of 8000, 12000, 16000, - * 24000, or 48000. - * @param channels int: Number of channels to output. - * This must be at most 255. - * It may be different from the number of coded - * channels (streams + - * coupled_streams). - * @param streams int: The total number of streams coded in the - * input. - * This must be no more than 255. - * @param coupled_streams int: Number of streams to decode as coupled - * (2 channel) streams. - * This must be no larger than the total - * number of streams. - * Additionally, The total number of - * coded channels (streams + - * coupled_streams) must be no - * more than 255. - * @param[in] mapping const unsigned char[channels]: Mapping from - * coded channels to output channels, as described in - * @ref opus_multistream. - * @param[out] error int *: Returns #OPUS_OK on success, or an error - * code (see @ref opus_errorcodes) on - * failure. - */ -OPUS_EXPORT OPUS_WARN_UNUSED_RESULT OpusMSDecoder *opus_multistream_decoder_create( - opus_int32 Fs, - int channels, - int streams, - int coupled_streams, - const unsigned char *mapping, - int *error -) OPUS_ARG_NONNULL(5); - -/** Intialize a previously allocated decoder state object. - * The memory pointed to by \a st must be at least the size returned by - * opus_multistream_encoder_get_size(). - * This is intended for applications which use their own allocator instead of - * malloc. - * To reset a previously initialized state, use the #OPUS_RESET_STATE CTL. - * @see opus_multistream_decoder_create - * @see opus_multistream_deocder_get_size - * @param st OpusMSEncoder*: Multistream encoder state to initialize. - * @param Fs opus_int32: Sampling rate to decode at (in Hz). - * This must be one of 8000, 12000, 16000, - * 24000, or 48000. - * @param channels int: Number of channels to output. - * This must be at most 255. - * It may be different from the number of coded - * channels (streams + - * coupled_streams). - * @param streams int: The total number of streams coded in the - * input. - * This must be no more than 255. - * @param coupled_streams int: Number of streams to decode as coupled - * (2 channel) streams. - * This must be no larger than the total - * number of streams. - * Additionally, The total number of - * coded channels (streams + - * coupled_streams) must be no - * more than 255. - * @param[in] mapping const unsigned char[channels]: Mapping from - * coded channels to output channels, as described in - * @ref opus_multistream. - * @returns #OPUS_OK on success, or an error code (see @ref opus_errorcodes) - * on failure. - */ -OPUS_EXPORT int opus_multistream_decoder_init( - OpusMSDecoder *st, - opus_int32 Fs, - int channels, - int streams, - int coupled_streams, - const unsigned char *mapping -) OPUS_ARG_NONNULL(1) OPUS_ARG_NONNULL(6); - -/** Decode a multistream Opus packet. - * @param st OpusMSDecoder*: Multistream decoder state. - * @param[in] data const unsigned char*: Input payload. - * Use a NULL - * pointer to indicate packet - * loss. - * @param len opus_int32: Number of bytes in payload. - * @param[out] pcm opus_int16*: Output signal, with interleaved - * samples. - * This must contain room for - * frame_size*channels - * samples. - * @param frame_size int: The number of samples per channel of - * available space in \a pcm. - * If this is less than the maximum packet duration - * (120 ms; 5760 for 48kHz), this function will not be capable - * of decoding some packets. In the case of PLC (data==NULL) - * or FEC (decode_fec=1), then frame_size needs to be exactly - * the duration of audio that is missing, otherwise the - * decoder will not be in the optimal state to decode the - * next incoming packet. For the PLC and FEC cases, frame_size - * must be a multiple of 2.5 ms. - * @param decode_fec int: Flag (0 or 1) to request that any in-band - * forward error correction data be decoded. - * If no such data is available, the frame is - * decoded as if it were lost. - * @returns Number of samples decoded on success or a negative error code - * (see @ref opus_errorcodes) on failure. - */ -OPUS_EXPORT OPUS_WARN_UNUSED_RESULT int opus_multistream_decode( - OpusMSDecoder *st, - const unsigned char *data, - opus_int32 len, - opus_int16 *pcm, - int frame_size, - int decode_fec -) OPUS_ARG_NONNULL(1) OPUS_ARG_NONNULL(4); - -/** Decode a multistream Opus packet with floating point output. - * @param st OpusMSDecoder*: Multistream decoder state. - * @param[in] data const unsigned char*: Input payload. - * Use a NULL - * pointer to indicate packet - * loss. - * @param len opus_int32: Number of bytes in payload. - * @param[out] pcm opus_int16*: Output signal, with interleaved - * samples. - * This must contain room for - * frame_size*channels - * samples. - * @param frame_size int: The number of samples per channel of - * available space in \a pcm. - * If this is less than the maximum packet duration - * (120 ms; 5760 for 48kHz), this function will not be capable - * of decoding some packets. In the case of PLC (data==NULL) - * or FEC (decode_fec=1), then frame_size needs to be exactly - * the duration of audio that is missing, otherwise the - * decoder will not be in the optimal state to decode the - * next incoming packet. For the PLC and FEC cases, frame_size - * must be a multiple of 2.5 ms. - * @param decode_fec int: Flag (0 or 1) to request that any in-band - * forward error correction data be decoded. - * If no such data is available, the frame is - * decoded as if it were lost. - * @returns Number of samples decoded on success or a negative error code - * (see @ref opus_errorcodes) on failure. - */ -OPUS_EXPORT OPUS_WARN_UNUSED_RESULT int opus_multistream_decode_float( - OpusMSDecoder *st, - const unsigned char *data, - opus_int32 len, - float *pcm, - int frame_size, - int decode_fec -) OPUS_ARG_NONNULL(1) OPUS_ARG_NONNULL(4); - -/** Perform a CTL function on a multistream Opus decoder. - * - * Generally the request and subsequent arguments are generated by a - * convenience macro. - * @param st OpusMSDecoder*: Multistream decoder state. - * @param request This and all remaining parameters should be replaced by one - * of the convenience macros in @ref opus_genericctls, - * @ref opus_decoderctls, or @ref opus_multistream_ctls. - * @see opus_genericctls - * @see opus_decoderctls - * @see opus_multistream_ctls - */ -OPUS_EXPORT int opus_multistream_decoder_ctl(OpusMSDecoder *st, int request, ...) OPUS_ARG_NONNULL(1); - -/** Frees an OpusMSDecoder allocated by - * opus_multistream_decoder_create(). - * @param st OpusMSDecoder: Multistream decoder state to be freed. - */ -OPUS_EXPORT void opus_multistream_decoder_destroy(OpusMSDecoder *st); - -/**@}*/ - -/**@}*/ - -#ifdef __cplusplus -} -#endif - -#endif /* OPUS_MULTISTREAM_H */ diff --git a/go/teamspeak/.opus/install/armeabi-v7a/include/opus/opus_projection.h b/go/teamspeak/.opus/install/armeabi-v7a/include/opus/opus_projection.h deleted file mode 100644 index 9dabf4e..0000000 --- a/go/teamspeak/.opus/install/armeabi-v7a/include/opus/opus_projection.h +++ /dev/null @@ -1,568 +0,0 @@ -/* Copyright (c) 2017 Google Inc. - Written by Andrew Allen */ -/* - Redistribution and use in source and binary forms, with or without - modification, are permitted provided that the following conditions - are met: - - - Redistributions of source code must retain the above copyright - notice, this list of conditions and the following disclaimer. - - - Redistributions in binary form must reproduce the above copyright - notice, this list of conditions and the following disclaimer in the - documentation and/or other materials provided with the distribution. - - THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS - ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT - LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR - A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER - OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, - EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, - PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR - PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF - LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING - NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS - SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. -*/ - -/** - * @file opus_projection.h - * @brief Opus projection reference API - */ - -#ifndef OPUS_PROJECTION_H -#define OPUS_PROJECTION_H - -#include "opus_multistream.h" - -#ifdef __cplusplus -extern "C" { -#endif - -/** @cond OPUS_INTERNAL_DOC */ - -/** These are the actual encoder and decoder CTL ID numbers. - * They should not be used directly by applications.c - * In general, SETs should be even and GETs should be odd.*/ -/**@{*/ -#define OPUS_PROJECTION_GET_DEMIXING_MATRIX_GAIN_REQUEST 6001 -#define OPUS_PROJECTION_GET_DEMIXING_MATRIX_SIZE_REQUEST 6003 -#define OPUS_PROJECTION_GET_DEMIXING_MATRIX_REQUEST 6005 -/**@}*/ - - -/** @endcond */ - -/** @defgroup opus_projection_ctls Projection specific encoder and decoder CTLs - * - * These are convenience macros that are specific to the - * opus_projection_encoder_ctl() and opus_projection_decoder_ctl() - * interface. - * The CTLs from @ref opus_genericctls, @ref opus_encoderctls, - * @ref opus_decoderctls, and @ref opus_multistream_ctls may be applied to a - * projection encoder or decoder as well. - */ -/**@{*/ - -/** Gets the gain (in dB. S7.8-format) of the demixing matrix from the encoder. - * @param[out] x opus_int32 *: Returns the gain (in dB. S7.8-format) - * of the demixing matrix. - * @hideinitializer - */ -#define OPUS_PROJECTION_GET_DEMIXING_MATRIX_GAIN(x) OPUS_PROJECTION_GET_DEMIXING_MATRIX_GAIN_REQUEST, __opus_check_int_ptr(x) - - -/** Gets the size in bytes of the demixing matrix from the encoder. - * @param[out] x opus_int32 *: Returns the size in bytes of the - * demixing matrix. - * @hideinitializer - */ -#define OPUS_PROJECTION_GET_DEMIXING_MATRIX_SIZE(x) OPUS_PROJECTION_GET_DEMIXING_MATRIX_SIZE_REQUEST, __opus_check_int_ptr(x) - - -/** Copies the demixing matrix to the supplied pointer location. - * @param[out] x unsigned char *: Returns the demixing matrix to the - * supplied pointer location. - * @param y opus_int32: The size in bytes of the reserved memory at the - * pointer location. - * @hideinitializer - */ -#define OPUS_PROJECTION_GET_DEMIXING_MATRIX(x,y) OPUS_PROJECTION_GET_DEMIXING_MATRIX_REQUEST, x, __opus_check_int(y) - - -/**@}*/ - -/** Opus projection encoder state. - * This contains the complete state of a projection Opus encoder. - * It is position independent and can be freely copied. - * @see opus_projection_ambisonics_encoder_create - */ -typedef struct OpusProjectionEncoder OpusProjectionEncoder; - - -/** Opus projection decoder state. - * This contains the complete state of a projection Opus decoder. - * It is position independent and can be freely copied. - * @see opus_projection_decoder_create - * @see opus_projection_decoder_init - */ -typedef struct OpusProjectionDecoder OpusProjectionDecoder; - - -/**\name Projection encoder functions */ -/**@{*/ - -/** Gets the size of an OpusProjectionEncoder structure. - * @param channels int: The total number of input channels to encode. - * This must be no more than 255. - * @param mapping_family int: The mapping family to use for selecting - * the appropriate projection. - * @returns The size in bytes on success, or a negative error code - * (see @ref opus_errorcodes) on error. - */ -OPUS_EXPORT OPUS_WARN_UNUSED_RESULT opus_int32 opus_projection_ambisonics_encoder_get_size( - int channels, - int mapping_family -); - - -/** Allocates and initializes a projection encoder state. - * Call opus_projection_encoder_destroy() to release - * this object when finished. - * @param Fs opus_int32: Sampling rate of the input signal (in Hz). - * This must be one of 8000, 12000, 16000, - * 24000, or 48000. - * @param channels int: Number of channels in the input signal. - * This must be at most 255. - * It may be greater than the number of - * coded channels (streams + - * coupled_streams). - * @param mapping_family int: The mapping family to use for selecting - * the appropriate projection. - * @param[out] streams int *: The total number of streams that will - * be encoded from the input. - * @param[out] coupled_streams int *: Number of coupled (2 channel) - * streams that will be encoded from the input. - * @param application int: The target encoder application. - * This must be one of the following: - *
- *
#OPUS_APPLICATION_VOIP
- *
Process signal for improved speech intelligibility.
- *
#OPUS_APPLICATION_AUDIO
- *
Favor faithfulness to the original input.
- *
#OPUS_APPLICATION_RESTRICTED_LOWDELAY
- *
Configure the minimum possible coding delay by disabling certain modes - * of operation.
- *
- * @param[out] error int *: Returns #OPUS_OK on success, or an error - * code (see @ref opus_errorcodes) on - * failure. - */ -OPUS_EXPORT OPUS_WARN_UNUSED_RESULT OpusProjectionEncoder *opus_projection_ambisonics_encoder_create( - opus_int32 Fs, - int channels, - int mapping_family, - int *streams, - int *coupled_streams, - int application, - int *error -) OPUS_ARG_NONNULL(4) OPUS_ARG_NONNULL(5); - - -/** Initialize a previously allocated projection encoder state. - * The memory pointed to by \a st must be at least the size returned by - * opus_projection_ambisonics_encoder_get_size(). - * This is intended for applications which use their own allocator instead of - * malloc. - * To reset a previously initialized state, use the #OPUS_RESET_STATE CTL. - * @see opus_projection_ambisonics_encoder_create - * @see opus_projection_ambisonics_encoder_get_size - * @param st OpusProjectionEncoder*: Projection encoder state to initialize. - * @param Fs opus_int32: Sampling rate of the input signal (in Hz). - * This must be one of 8000, 12000, 16000, - * 24000, or 48000. - * @param channels int: Number of channels in the input signal. - * This must be at most 255. - * It may be greater than the number of - * coded channels (streams + - * coupled_streams). - * @param streams int: The total number of streams to encode from the - * input. - * This must be no more than the number of channels. - * @param coupled_streams int: Number of coupled (2 channel) streams - * to encode. - * This must be no larger than the total - * number of streams. - * Additionally, The total number of - * encoded channels (streams + - * coupled_streams) must be no - * more than the number of input channels. - * @param application int: The target encoder application. - * This must be one of the following: - *
- *
#OPUS_APPLICATION_VOIP
- *
Process signal for improved speech intelligibility.
- *
#OPUS_APPLICATION_AUDIO
- *
Favor faithfulness to the original input.
- *
#OPUS_APPLICATION_RESTRICTED_LOWDELAY
- *
Configure the minimum possible coding delay by disabling certain modes - * of operation.
- *
- * @returns #OPUS_OK on success, or an error code (see @ref opus_errorcodes) - * on failure. - */ -OPUS_EXPORT int opus_projection_ambisonics_encoder_init( - OpusProjectionEncoder *st, - opus_int32 Fs, - int channels, - int mapping_family, - int *streams, - int *coupled_streams, - int application -) OPUS_ARG_NONNULL(1) OPUS_ARG_NONNULL(5) OPUS_ARG_NONNULL(6); - - -/** Encodes a projection Opus frame. - * @param st OpusProjectionEncoder*: Projection encoder state. - * @param[in] pcm const opus_int16*: The input signal as interleaved - * samples. - * This must contain - * frame_size*channels - * samples. - * @param frame_size int: Number of samples per channel in the input - * signal. - * This must be an Opus frame size for the - * encoder's sampling rate. - * For example, at 48 kHz the permitted values - * are 120, 240, 480, 960, 1920, and 2880. - * Passing in a duration of less than 10 ms - * (480 samples at 48 kHz) will prevent the - * encoder from using the LPC or hybrid modes. - * @param[out] data unsigned char*: Output payload. - * This must contain storage for at - * least \a max_data_bytes. - * @param [in] max_data_bytes opus_int32: Size of the allocated - * memory for the output - * payload. This may be - * used to impose an upper limit on - * the instant bitrate, but should - * not be used as the only bitrate - * control. Use #OPUS_SET_BITRATE to - * control the bitrate. - * @returns The length of the encoded packet (in bytes) on success or a - * negative error code (see @ref opus_errorcodes) on failure. - */ -OPUS_EXPORT OPUS_WARN_UNUSED_RESULT int opus_projection_encode( - OpusProjectionEncoder *st, - const opus_int16 *pcm, - int frame_size, - unsigned char *data, - opus_int32 max_data_bytes -) OPUS_ARG_NONNULL(1) OPUS_ARG_NONNULL(2) OPUS_ARG_NONNULL(4); - - -/** Encodes a projection Opus frame from floating point input. - * @param st OpusProjectionEncoder*: Projection encoder state. - * @param[in] pcm const float*: The input signal as interleaved - * samples with a normal range of - * +/-1.0. - * Samples with a range beyond +/-1.0 - * are supported but will be clipped by - * decoders using the integer API and - * should only be used if it is known - * that the far end supports extended - * dynamic range. - * This must contain - * frame_size*channels - * samples. - * @param frame_size int: Number of samples per channel in the input - * signal. - * This must be an Opus frame size for the - * encoder's sampling rate. - * For example, at 48 kHz the permitted values - * are 120, 240, 480, 960, 1920, and 2880. - * Passing in a duration of less than 10 ms - * (480 samples at 48 kHz) will prevent the - * encoder from using the LPC or hybrid modes. - * @param[out] data unsigned char*: Output payload. - * This must contain storage for at - * least \a max_data_bytes. - * @param [in] max_data_bytes opus_int32: Size of the allocated - * memory for the output - * payload. This may be - * used to impose an upper limit on - * the instant bitrate, but should - * not be used as the only bitrate - * control. Use #OPUS_SET_BITRATE to - * control the bitrate. - * @returns The length of the encoded packet (in bytes) on success or a - * negative error code (see @ref opus_errorcodes) on failure. - */ -OPUS_EXPORT OPUS_WARN_UNUSED_RESULT int opus_projection_encode_float( - OpusProjectionEncoder *st, - const float *pcm, - int frame_size, - unsigned char *data, - opus_int32 max_data_bytes -) OPUS_ARG_NONNULL(1) OPUS_ARG_NONNULL(2) OPUS_ARG_NONNULL(4); - - -/** Frees an OpusProjectionEncoder allocated by - * opus_projection_ambisonics_encoder_create(). - * @param st OpusProjectionEncoder*: Projection encoder state to be freed. - */ -OPUS_EXPORT void opus_projection_encoder_destroy(OpusProjectionEncoder *st); - - -/** Perform a CTL function on a projection Opus encoder. - * - * Generally the request and subsequent arguments are generated by a - * convenience macro. - * @param st OpusProjectionEncoder*: Projection encoder state. - * @param request This and all remaining parameters should be replaced by one - * of the convenience macros in @ref opus_genericctls, - * @ref opus_encoderctls, @ref opus_multistream_ctls, or - * @ref opus_projection_ctls - * @see opus_genericctls - * @see opus_encoderctls - * @see opus_multistream_ctls - * @see opus_projection_ctls - */ -OPUS_EXPORT int opus_projection_encoder_ctl(OpusProjectionEncoder *st, int request, ...) OPUS_ARG_NONNULL(1); - - -/**@}*/ - -/**\name Projection decoder functions */ -/**@{*/ - -/** Gets the size of an OpusProjectionDecoder structure. - * @param channels int: The total number of output channels. - * This must be no more than 255. - * @param streams int: The total number of streams coded in the - * input. - * This must be no more than 255. - * @param coupled_streams int: Number streams to decode as coupled - * (2 channel) streams. - * This must be no larger than the total - * number of streams. - * Additionally, The total number of - * coded channels (streams + - * coupled_streams) must be no - * more than 255. - * @returns The size in bytes on success, or a negative error code - * (see @ref opus_errorcodes) on error. - */ -OPUS_EXPORT OPUS_WARN_UNUSED_RESULT opus_int32 opus_projection_decoder_get_size( - int channels, - int streams, - int coupled_streams -); - - -/** Allocates and initializes a projection decoder state. - * Call opus_projection_decoder_destroy() to release - * this object when finished. - * @param Fs opus_int32: Sampling rate to decode at (in Hz). - * This must be one of 8000, 12000, 16000, - * 24000, or 48000. - * @param channels int: Number of channels to output. - * This must be at most 255. - * It may be different from the number of coded - * channels (streams + - * coupled_streams). - * @param streams int: The total number of streams coded in the - * input. - * This must be no more than 255. - * @param coupled_streams int: Number of streams to decode as coupled - * (2 channel) streams. - * This must be no larger than the total - * number of streams. - * Additionally, The total number of - * coded channels (streams + - * coupled_streams) must be no - * more than 255. - * @param[in] demixing_matrix const unsigned char[demixing_matrix_size]: Demixing matrix - * that mapping from coded channels to output channels, - * as described in @ref opus_projection and - * @ref opus_projection_ctls. - * @param demixing_matrix_size opus_int32: The size in bytes of the - * demixing matrix, as - * described in @ref - * opus_projection_ctls. - * @param[out] error int *: Returns #OPUS_OK on success, or an error - * code (see @ref opus_errorcodes) on - * failure. - */ -OPUS_EXPORT OPUS_WARN_UNUSED_RESULT OpusProjectionDecoder *opus_projection_decoder_create( - opus_int32 Fs, - int channels, - int streams, - int coupled_streams, - unsigned char *demixing_matrix, - opus_int32 demixing_matrix_size, - int *error -) OPUS_ARG_NONNULL(5); - - -/** Intialize a previously allocated projection decoder state object. - * The memory pointed to by \a st must be at least the size returned by - * opus_projection_decoder_get_size(). - * This is intended for applications which use their own allocator instead of - * malloc. - * To reset a previously initialized state, use the #OPUS_RESET_STATE CTL. - * @see opus_projection_decoder_create - * @see opus_projection_deocder_get_size - * @param st OpusProjectionDecoder*: Projection encoder state to initialize. - * @param Fs opus_int32: Sampling rate to decode at (in Hz). - * This must be one of 8000, 12000, 16000, - * 24000, or 48000. - * @param channels int: Number of channels to output. - * This must be at most 255. - * It may be different from the number of coded - * channels (streams + - * coupled_streams). - * @param streams int: The total number of streams coded in the - * input. - * This must be no more than 255. - * @param coupled_streams int: Number of streams to decode as coupled - * (2 channel) streams. - * This must be no larger than the total - * number of streams. - * Additionally, The total number of - * coded channels (streams + - * coupled_streams) must be no - * more than 255. - * @param[in] demixing_matrix const unsigned char[demixing_matrix_size]: Demixing matrix - * that mapping from coded channels to output channels, - * as described in @ref opus_projection and - * @ref opus_projection_ctls. - * @param demixing_matrix_size opus_int32: The size in bytes of the - * demixing matrix, as - * described in @ref - * opus_projection_ctls. - * @returns #OPUS_OK on success, or an error code (see @ref opus_errorcodes) - * on failure. - */ -OPUS_EXPORT int opus_projection_decoder_init( - OpusProjectionDecoder *st, - opus_int32 Fs, - int channels, - int streams, - int coupled_streams, - unsigned char *demixing_matrix, - opus_int32 demixing_matrix_size -) OPUS_ARG_NONNULL(1) OPUS_ARG_NONNULL(6); - - -/** Decode a projection Opus packet. - * @param st OpusProjectionDecoder*: Projection decoder state. - * @param[in] data const unsigned char*: Input payload. - * Use a NULL - * pointer to indicate packet - * loss. - * @param len opus_int32: Number of bytes in payload. - * @param[out] pcm opus_int16*: Output signal, with interleaved - * samples. - * This must contain room for - * frame_size*channels - * samples. - * @param frame_size int: The number of samples per channel of - * available space in \a pcm. - * If this is less than the maximum packet duration - * (120 ms; 5760 for 48kHz), this function will not be capable - * of decoding some packets. In the case of PLC (data==NULL) - * or FEC (decode_fec=1), then frame_size needs to be exactly - * the duration of audio that is missing, otherwise the - * decoder will not be in the optimal state to decode the - * next incoming packet. For the PLC and FEC cases, frame_size - * must be a multiple of 2.5 ms. - * @param decode_fec int: Flag (0 or 1) to request that any in-band - * forward error correction data be decoded. - * If no such data is available, the frame is - * decoded as if it were lost. - * @returns Number of samples decoded on success or a negative error code - * (see @ref opus_errorcodes) on failure. - */ -OPUS_EXPORT OPUS_WARN_UNUSED_RESULT int opus_projection_decode( - OpusProjectionDecoder *st, - const unsigned char *data, - opus_int32 len, - opus_int16 *pcm, - int frame_size, - int decode_fec -) OPUS_ARG_NONNULL(1) OPUS_ARG_NONNULL(4); - - -/** Decode a projection Opus packet with floating point output. - * @param st OpusProjectionDecoder*: Projection decoder state. - * @param[in] data const unsigned char*: Input payload. - * Use a NULL - * pointer to indicate packet - * loss. - * @param len opus_int32: Number of bytes in payload. - * @param[out] pcm opus_int16*: Output signal, with interleaved - * samples. - * This must contain room for - * frame_size*channels - * samples. - * @param frame_size int: The number of samples per channel of - * available space in \a pcm. - * If this is less than the maximum packet duration - * (120 ms; 5760 for 48kHz), this function will not be capable - * of decoding some packets. In the case of PLC (data==NULL) - * or FEC (decode_fec=1), then frame_size needs to be exactly - * the duration of audio that is missing, otherwise the - * decoder will not be in the optimal state to decode the - * next incoming packet. For the PLC and FEC cases, frame_size - * must be a multiple of 2.5 ms. - * @param decode_fec int: Flag (0 or 1) to request that any in-band - * forward error correction data be decoded. - * If no such data is available, the frame is - * decoded as if it were lost. - * @returns Number of samples decoded on success or a negative error code - * (see @ref opus_errorcodes) on failure. - */ -OPUS_EXPORT OPUS_WARN_UNUSED_RESULT int opus_projection_decode_float( - OpusProjectionDecoder *st, - const unsigned char *data, - opus_int32 len, - float *pcm, - int frame_size, - int decode_fec -) OPUS_ARG_NONNULL(1) OPUS_ARG_NONNULL(4); - - -/** Perform a CTL function on a projection Opus decoder. - * - * Generally the request and subsequent arguments are generated by a - * convenience macro. - * @param st OpusProjectionDecoder*: Projection decoder state. - * @param request This and all remaining parameters should be replaced by one - * of the convenience macros in @ref opus_genericctls, - * @ref opus_decoderctls, @ref opus_multistream_ctls, or - * @ref opus_projection_ctls. - * @see opus_genericctls - * @see opus_decoderctls - * @see opus_multistream_ctls - * @see opus_projection_ctls - */ -OPUS_EXPORT int opus_projection_decoder_ctl(OpusProjectionDecoder *st, int request, ...) OPUS_ARG_NONNULL(1); - - -/** Frees an OpusProjectionDecoder allocated by - * opus_projection_decoder_create(). - * @param st OpusProjectionDecoder: Projection decoder state to be freed. - */ -OPUS_EXPORT void opus_projection_decoder_destroy(OpusProjectionDecoder *st); - - -/**@}*/ - -/**@}*/ - -#ifdef __cplusplus -} -#endif - -#endif /* OPUS_PROJECTION_H */ diff --git a/go/teamspeak/.opus/install/armeabi-v7a/include/opus/opus_types.h b/go/teamspeak/.opus/install/armeabi-v7a/include/opus/opus_types.h deleted file mode 100644 index 7cf6755..0000000 --- a/go/teamspeak/.opus/install/armeabi-v7a/include/opus/opus_types.h +++ /dev/null @@ -1,166 +0,0 @@ -/* (C) COPYRIGHT 1994-2002 Xiph.Org Foundation */ -/* Modified by Jean-Marc Valin */ -/* - Redistribution and use in source and binary forms, with or without - modification, are permitted provided that the following conditions - are met: - - - Redistributions of source code must retain the above copyright - notice, this list of conditions and the following disclaimer. - - - Redistributions in binary form must reproduce the above copyright - notice, this list of conditions and the following disclaimer in the - documentation and/or other materials provided with the distribution. - - THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS - ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT - LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR - A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER - OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, - EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, - PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR - PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF - LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING - NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS - SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. -*/ -/* opus_types.h based on ogg_types.h from libogg */ - -/** - @file opus_types.h - @brief Opus reference implementation types -*/ -#ifndef OPUS_TYPES_H -#define OPUS_TYPES_H - -#define opus_int int /* used for counters etc; at least 16 bits */ -#define opus_int64 long long -#define opus_int8 signed char - -#define opus_uint unsigned int /* used for counters etc; at least 16 bits */ -#define opus_uint64 unsigned long long -#define opus_uint8 unsigned char - -/* Use the real stdint.h if it's there (taken from Paul Hsieh's pstdint.h) */ -#if (defined(__STDC__) && __STDC__ && defined(__STDC_VERSION__) && __STDC_VERSION__ >= 199901L) || (defined(__GNUC__) && (defined(_STDINT_H) || defined(_STDINT_H_)) || defined (HAVE_STDINT_H)) -#include -# undef opus_int64 -# undef opus_int8 -# undef opus_uint64 -# undef opus_uint8 - typedef int8_t opus_int8; - typedef uint8_t opus_uint8; - typedef int16_t opus_int16; - typedef uint16_t opus_uint16; - typedef int32_t opus_int32; - typedef uint32_t opus_uint32; - typedef int64_t opus_int64; - typedef uint64_t opus_uint64; -#elif defined(_WIN32) - -# if defined(__CYGWIN__) -# include <_G_config.h> - typedef _G_int32_t opus_int32; - typedef _G_uint32_t opus_uint32; - typedef _G_int16 opus_int16; - typedef _G_uint16 opus_uint16; -# elif defined(__MINGW32__) - typedef short opus_int16; - typedef unsigned short opus_uint16; - typedef int opus_int32; - typedef unsigned int opus_uint32; -# elif defined(__MWERKS__) - typedef int opus_int32; - typedef unsigned int opus_uint32; - typedef short opus_int16; - typedef unsigned short opus_uint16; -# else - /* MSVC/Borland */ - typedef __int32 opus_int32; - typedef unsigned __int32 opus_uint32; - typedef __int16 opus_int16; - typedef unsigned __int16 opus_uint16; -# endif - -#elif defined(__MACOS__) - -# include - typedef SInt16 opus_int16; - typedef UInt16 opus_uint16; - typedef SInt32 opus_int32; - typedef UInt32 opus_uint32; - -#elif (defined(__APPLE__) && defined(__MACH__)) /* MacOS X Framework build */ - -# include - typedef int16_t opus_int16; - typedef u_int16_t opus_uint16; - typedef int32_t opus_int32; - typedef u_int32_t opus_uint32; - -#elif defined(__BEOS__) - - /* Be */ -# include - typedef int16 opus_int16; - typedef u_int16 opus_uint16; - typedef int32_t opus_int32; - typedef u_int32_t opus_uint32; - -#elif defined (__EMX__) - - /* OS/2 GCC */ - typedef short opus_int16; - typedef unsigned short opus_uint16; - typedef int opus_int32; - typedef unsigned int opus_uint32; - -#elif defined (DJGPP) - - /* DJGPP */ - typedef short opus_int16; - typedef unsigned short opus_uint16; - typedef int opus_int32; - typedef unsigned int opus_uint32; - -#elif defined(R5900) - - /* PS2 EE */ - typedef int opus_int32; - typedef unsigned opus_uint32; - typedef short opus_int16; - typedef unsigned short opus_uint16; - -#elif defined(__SYMBIAN32__) - - /* Symbian GCC */ - typedef signed short opus_int16; - typedef unsigned short opus_uint16; - typedef signed int opus_int32; - typedef unsigned int opus_uint32; - -#elif defined(CONFIG_TI_C54X) || defined (CONFIG_TI_C55X) - - typedef short opus_int16; - typedef unsigned short opus_uint16; - typedef long opus_int32; - typedef unsigned long opus_uint32; - -#elif defined(CONFIG_TI_C6X) - - typedef short opus_int16; - typedef unsigned short opus_uint16; - typedef int opus_int32; - typedef unsigned int opus_uint32; - -#else - - /* Give up, take a reasonable guess */ - typedef short opus_int16; - typedef unsigned short opus_uint16; - typedef int opus_int32; - typedef unsigned int opus_uint32; - -#endif - -#endif /* OPUS_TYPES_H */ diff --git a/go/teamspeak/.opus/install/armeabi-v7a/lib/libopus.a b/go/teamspeak/.opus/install/armeabi-v7a/lib/libopus.a deleted file mode 100644 index 9b492dc..0000000 Binary files a/go/teamspeak/.opus/install/armeabi-v7a/lib/libopus.a and /dev/null differ diff --git a/go/teamspeak/.opus/install/x86/include/opus/opus.h b/go/teamspeak/.opus/install/x86/include/opus/opus.h deleted file mode 100644 index eadeda7..0000000 --- a/go/teamspeak/.opus/install/x86/include/opus/opus.h +++ /dev/null @@ -1,1099 +0,0 @@ -/* Copyright (c) 2010-2011 Xiph.Org Foundation, Skype Limited - Written by Jean-Marc Valin and Koen Vos */ -/* - Redistribution and use in source and binary forms, with or without - modification, are permitted provided that the following conditions - are met: - - - Redistributions of source code must retain the above copyright - notice, this list of conditions and the following disclaimer. - - - Redistributions in binary form must reproduce the above copyright - notice, this list of conditions and the following disclaimer in the - documentation and/or other materials provided with the distribution. - - THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS - ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT - LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR - A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER - OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, - EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, - PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR - PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF - LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING - NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS - SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. -*/ - -/** - * @file opus.h - * @brief Opus reference implementation API - */ - -#ifndef OPUS_H -#define OPUS_H - -#include "opus_types.h" -#include "opus_defines.h" - -#ifdef __cplusplus -extern "C" { -#endif - -/** - * @mainpage Opus - * - * The Opus codec is designed for interactive speech and audio transmission over the Internet. - * It is designed by the IETF Codec Working Group and incorporates technology from - * Skype's SILK codec and Xiph.Org's CELT codec. - * - * The Opus codec is designed to handle a wide range of interactive audio applications, - * including Voice over IP, videoconferencing, in-game chat, and even remote live music - * performances. It can scale from low bit-rate narrowband speech to very high quality - * stereo music. Its main features are: - - * @li Sampling rates from 8 to 48 kHz - * @li Bit-rates from 6 kb/s to 510 kb/s - * @li Support for both constant bit-rate (CBR) and variable bit-rate (VBR) - * @li Audio bandwidth from narrowband to full-band - * @li Support for speech and music - * @li Support for mono and stereo - * @li Support for multichannel (up to 255 channels) - * @li Frame sizes from 2.5 ms to 60 ms - * @li Good loss robustness and packet loss concealment (PLC) - * @li Floating point and fixed-point implementation - * - * Documentation sections: - * @li @ref opus_encoder - * @li @ref opus_decoder - * @li @ref opus_repacketizer - * @li @ref opus_multistream - * @li @ref opus_libinfo - * @li @ref opus_custom - */ - -/** @defgroup opus_encoder Opus Encoder - * @{ - * - * @brief This page describes the process and functions used to encode Opus. - * - * Since Opus is a stateful codec, the encoding process starts with creating an encoder - * state. This can be done with: - * - * @code - * int error; - * OpusEncoder *enc; - * enc = opus_encoder_create(Fs, channels, application, &error); - * @endcode - * - * From this point, @c enc can be used for encoding an audio stream. An encoder state - * @b must @b not be used for more than one stream at the same time. Similarly, the encoder - * state @b must @b not be re-initialized for each frame. - * - * While opus_encoder_create() allocates memory for the state, it's also possible - * to initialize pre-allocated memory: - * - * @code - * int size; - * int error; - * OpusEncoder *enc; - * size = opus_encoder_get_size(channels); - * enc = malloc(size); - * error = opus_encoder_init(enc, Fs, channels, application); - * @endcode - * - * where opus_encoder_get_size() returns the required size for the encoder state. Note that - * future versions of this code may change the size, so no assumptions should be made about it. - * - * The encoder state is always continuous in memory and only a shallow copy is sufficient - * to copy it (e.g. memcpy()) - * - * It is possible to change some of the encoder's settings using the opus_encoder_ctl() - * interface. All these settings already default to the recommended value, so they should - * only be changed when necessary. The most common settings one may want to change are: - * - * @code - * opus_encoder_ctl(enc, OPUS_SET_BITRATE(bitrate)); - * opus_encoder_ctl(enc, OPUS_SET_COMPLEXITY(complexity)); - * opus_encoder_ctl(enc, OPUS_SET_SIGNAL(signal_type)); - * @endcode - * - * where - * - * @arg bitrate is in bits per second (b/s) - * @arg complexity is a value from 1 to 10, where 1 is the lowest complexity and 10 is the highest - * @arg signal_type is either OPUS_AUTO (default), OPUS_SIGNAL_VOICE, or OPUS_SIGNAL_MUSIC - * - * See @ref opus_encoderctls and @ref opus_genericctls for a complete list of parameters that can be set or queried. Most parameters can be set or changed at any time during a stream. - * - * To encode a frame, opus_encode() or opus_encode_float() must be called with exactly one frame (2.5, 5, 10, 20, 40 or 60 ms) of audio data: - * @code - * len = opus_encode(enc, audio_frame, frame_size, packet, max_packet); - * @endcode - * - * where - *
    - *
  • audio_frame is the audio data in opus_int16 (or float for opus_encode_float())
    • - *
  • frame_size is the duration of the frame in samples (per channel)
    • - *
  • packet is the byte array to which the compressed data is written
    • - *
  • max_packet is the maximum number of bytes that can be written in the packet (4000 bytes is recommended). - * Do not use max_packet to control VBR target bitrate, instead use the #OPUS_SET_BITRATE CTL.
    • - *
- * - * opus_encode() and opus_encode_float() return the number of bytes actually written to the packet. - * The return value can be negative, which indicates that an error has occurred. If the return value - * is 2 bytes or less, then the packet does not need to be transmitted (DTX). - * - * Once the encoder state if no longer needed, it can be destroyed with - * - * @code - * opus_encoder_destroy(enc); - * @endcode - * - * If the encoder was created with opus_encoder_init() rather than opus_encoder_create(), - * then no action is required aside from potentially freeing the memory that was manually - * allocated for it (calling free(enc) for the example above) - * - */ - -/** Opus encoder state. - * This contains the complete state of an Opus encoder. - * It is position independent and can be freely copied. - * @see opus_encoder_create,opus_encoder_init - */ -typedef struct OpusEncoder OpusEncoder; - -/** Gets the size of an OpusEncoder structure. - * @param[in] channels int: Number of channels. - * This must be 1 or 2. - * @returns The size in bytes. - */ -OPUS_EXPORT OPUS_WARN_UNUSED_RESULT int opus_encoder_get_size(int channels); - -/** - */ - -/** Allocates and initializes an encoder state. - * There are three coding modes: - * - * @ref OPUS_APPLICATION_VOIP gives best quality at a given bitrate for voice - * signals. It enhances the input signal by high-pass filtering and - * emphasizing formants and harmonics. Optionally it includes in-band - * forward error correction to protect against packet loss. Use this - * mode for typical VoIP applications. Because of the enhancement, - * even at high bitrates the output may sound different from the input. - * - * @ref OPUS_APPLICATION_AUDIO gives best quality at a given bitrate for most - * non-voice signals like music. Use this mode for music and mixed - * (music/voice) content, broadcast, and applications requiring less - * than 15 ms of coding delay. - * - * @ref OPUS_APPLICATION_RESTRICTED_LOWDELAY configures low-delay mode that - * disables the speech-optimized mode in exchange for slightly reduced delay. - * This mode can only be set on an newly initialized or freshly reset encoder - * because it changes the codec delay. - * - * This is useful when the caller knows that the speech-optimized modes will not be needed (use with caution). - * @param [in] Fs opus_int32: Sampling rate of input signal (Hz) - * This must be one of 8000, 12000, 16000, - * 24000, or 48000. - * @param [in] channels int: Number of channels (1 or 2) in input signal - * @param [in] application int: Coding mode (one of @ref OPUS_APPLICATION_VOIP, @ref OPUS_APPLICATION_AUDIO, or @ref OPUS_APPLICATION_RESTRICTED_LOWDELAY) - * @param [out] error int*: @ref opus_errorcodes - * @note Regardless of the sampling rate and number channels selected, the Opus encoder - * can switch to a lower audio bandwidth or number of channels if the bitrate - * selected is too low. This also means that it is safe to always use 48 kHz stereo input - * and let the encoder optimize the encoding. - */ -OPUS_EXPORT OPUS_WARN_UNUSED_RESULT OpusEncoder *opus_encoder_create( - opus_int32 Fs, - int channels, - int application, - int *error -); - -/** Initializes a previously allocated encoder state - * The memory pointed to by st must be at least the size returned by opus_encoder_get_size(). - * This is intended for applications which use their own allocator instead of malloc. - * @see opus_encoder_create(),opus_encoder_get_size() - * To reset a previously initialized state, use the #OPUS_RESET_STATE CTL. - * @param [in] st OpusEncoder*: Encoder state - * @param [in] Fs opus_int32: Sampling rate of input signal (Hz) - * This must be one of 8000, 12000, 16000, - * 24000, or 48000. - * @param [in] channels int: Number of channels (1 or 2) in input signal - * @param [in] application int: Coding mode (one of OPUS_APPLICATION_VOIP, OPUS_APPLICATION_AUDIO, or OPUS_APPLICATION_RESTRICTED_LOWDELAY) - * @retval #OPUS_OK Success or @ref opus_errorcodes - */ -OPUS_EXPORT int opus_encoder_init( - OpusEncoder *st, - opus_int32 Fs, - int channels, - int application -) OPUS_ARG_NONNULL(1); - -/** Encodes an Opus frame. - * @param [in] st OpusEncoder*: Encoder state - * @param [in] pcm opus_int16*: Input signal (interleaved if 2 channels). length is frame_size*channels*sizeof(opus_int16) - * @param [in] frame_size int: Number of samples per channel in the - * input signal. - * This must be an Opus frame size for - * the encoder's sampling rate. - * For example, at 48 kHz the permitted - * values are 120, 240, 480, 960, 1920, - * and 2880. - * Passing in a duration of less than - * 10 ms (480 samples at 48 kHz) will - * prevent the encoder from using the LPC - * or hybrid modes. - * @param [out] data unsigned char*: Output payload. - * This must contain storage for at - * least \a max_data_bytes. - * @param [in] max_data_bytes opus_int32: Size of the allocated - * memory for the output - * payload. This may be - * used to impose an upper limit on - * the instant bitrate, but should - * not be used as the only bitrate - * control. Use #OPUS_SET_BITRATE to - * control the bitrate. - * @returns The length of the encoded packet (in bytes) on success or a - * negative error code (see @ref opus_errorcodes) on failure. - */ -OPUS_EXPORT OPUS_WARN_UNUSED_RESULT opus_int32 opus_encode( - OpusEncoder *st, - const opus_int16 *pcm, - int frame_size, - unsigned char *data, - opus_int32 max_data_bytes -) OPUS_ARG_NONNULL(1) OPUS_ARG_NONNULL(2) OPUS_ARG_NONNULL(4); - -/** Encodes an Opus frame from floating point input. - * @param [in] st OpusEncoder*: Encoder state - * @param [in] pcm float*: Input in float format (interleaved if 2 channels), with a normal range of +/-1.0. - * Samples with a range beyond +/-1.0 are supported but will - * be clipped by decoders using the integer API and should - * only be used if it is known that the far end supports - * extended dynamic range. - * length is frame_size*channels*sizeof(float) - * @param [in] frame_size int: Number of samples per channel in the - * input signal. - * This must be an Opus frame size for - * the encoder's sampling rate. - * For example, at 48 kHz the permitted - * values are 120, 240, 480, 960, 1920, - * and 2880. - * Passing in a duration of less than - * 10 ms (480 samples at 48 kHz) will - * prevent the encoder from using the LPC - * or hybrid modes. - * @param [out] data unsigned char*: Output payload. - * This must contain storage for at - * least \a max_data_bytes. - * @param [in] max_data_bytes opus_int32: Size of the allocated - * memory for the output - * payload. This may be - * used to impose an upper limit on - * the instant bitrate, but should - * not be used as the only bitrate - * control. Use #OPUS_SET_BITRATE to - * control the bitrate. - * @returns The length of the encoded packet (in bytes) on success or a - * negative error code (see @ref opus_errorcodes) on failure. - */ -OPUS_EXPORT OPUS_WARN_UNUSED_RESULT opus_int32 opus_encode_float( - OpusEncoder *st, - const float *pcm, - int frame_size, - unsigned char *data, - opus_int32 max_data_bytes -) OPUS_ARG_NONNULL(1) OPUS_ARG_NONNULL(2) OPUS_ARG_NONNULL(4); - -/** Frees an OpusEncoder allocated by opus_encoder_create(). - * @param[in] st OpusEncoder*: State to be freed. - */ -OPUS_EXPORT void opus_encoder_destroy(OpusEncoder *st); - -/** Perform a CTL function on an Opus encoder. - * - * Generally the request and subsequent arguments are generated - * by a convenience macro. - * @param st OpusEncoder*: Encoder state. - * @param request This and all remaining parameters should be replaced by one - * of the convenience macros in @ref opus_genericctls or - * @ref opus_encoderctls. - * @see opus_genericctls - * @see opus_encoderctls - */ -OPUS_EXPORT int opus_encoder_ctl(OpusEncoder *st, int request, ...) OPUS_ARG_NONNULL(1); -/**@}*/ - -/** @defgroup opus_decoder Opus Decoder - * @{ - * - * @brief This page describes the process and functions used to decode Opus. - * - * The decoding process also starts with creating a decoder - * state. This can be done with: - * @code - * int error; - * OpusDecoder *dec; - * dec = opus_decoder_create(Fs, channels, &error); - * @endcode - * where - * @li Fs is the sampling rate and must be 8000, 12000, 16000, 24000, or 48000 - * @li channels is the number of channels (1 or 2) - * @li error will hold the error code in case of failure (or #OPUS_OK on success) - * @li the return value is a newly created decoder state to be used for decoding - * - * While opus_decoder_create() allocates memory for the state, it's also possible - * to initialize pre-allocated memory: - * @code - * int size; - * int error; - * OpusDecoder *dec; - * size = opus_decoder_get_size(channels); - * dec = malloc(size); - * error = opus_decoder_init(dec, Fs, channels); - * @endcode - * where opus_decoder_get_size() returns the required size for the decoder state. Note that - * future versions of this code may change the size, so no assumptions should be made about it. - * - * The decoder state is always continuous in memory and only a shallow copy is sufficient - * to copy it (e.g. memcpy()) - * - * To decode a frame, opus_decode() or opus_decode_float() must be called with a packet of compressed audio data: - * @code - * frame_size = opus_decode(dec, packet, len, decoded, max_size, 0); - * @endcode - * where - * - * @li packet is the byte array containing the compressed data - * @li len is the exact number of bytes contained in the packet - * @li decoded is the decoded audio data in opus_int16 (or float for opus_decode_float()) - * @li max_size is the max duration of the frame in samples (per channel) that can fit into the decoded_frame array - * - * opus_decode() and opus_decode_float() return the number of samples (per channel) decoded from the packet. - * If that value is negative, then an error has occurred. This can occur if the packet is corrupted or if the audio - * buffer is too small to hold the decoded audio. - * - * Opus is a stateful codec with overlapping blocks and as a result Opus - * packets are not coded independently of each other. Packets must be - * passed into the decoder serially and in the correct order for a correct - * decode. Lost packets can be replaced with loss concealment by calling - * the decoder with a null pointer and zero length for the missing packet. - * - * A single codec state may only be accessed from a single thread at - * a time and any required locking must be performed by the caller. Separate - * streams must be decoded with separate decoder states and can be decoded - * in parallel unless the library was compiled with NONTHREADSAFE_PSEUDOSTACK - * defined. - * - */ - -/** Opus decoder state. - * This contains the complete state of an Opus decoder. - * It is position independent and can be freely copied. - * @see opus_decoder_create,opus_decoder_init - */ -typedef struct OpusDecoder OpusDecoder; - -/** Opus DRED decoder. - * This contains the complete state of an Opus DRED decoder. - * It is position independent and can be freely copied. - * @see opus_dred_decoder_create,opus_dred_decoder_init - */ -typedef struct OpusDREDDecoder OpusDREDDecoder; - - -/** Opus DRED state. - * This contains the complete state of an Opus DRED packet. - * It is position independent and can be freely copied. - * @see opus_dred_create,opus_dred_init - */ -typedef struct OpusDRED OpusDRED; - -/** Gets the size of an OpusDecoder structure. - * @param [in] channels int: Number of channels. - * This must be 1 or 2. - * @returns The size in bytes. - */ -OPUS_EXPORT OPUS_WARN_UNUSED_RESULT int opus_decoder_get_size(int channels); - -/** Allocates and initializes a decoder state. - * @param [in] Fs opus_int32: Sample rate to decode at (Hz). - * This must be one of 8000, 12000, 16000, - * 24000, or 48000. - * @param [in] channels int: Number of channels (1 or 2) to decode - * @param [out] error int*: #OPUS_OK Success or @ref opus_errorcodes - * - * Internally Opus stores data at 48000 Hz, so that should be the default - * value for Fs. However, the decoder can efficiently decode to buffers - * at 8, 12, 16, and 24 kHz so if for some reason the caller cannot use - * data at the full sample rate, or knows the compressed data doesn't - * use the full frequency range, it can request decoding at a reduced - * rate. Likewise, the decoder is capable of filling in either mono or - * interleaved stereo pcm buffers, at the caller's request. - */ -OPUS_EXPORT OPUS_WARN_UNUSED_RESULT OpusDecoder *opus_decoder_create( - opus_int32 Fs, - int channels, - int *error -); - -/** Initializes a previously allocated decoder state. - * The state must be at least the size returned by opus_decoder_get_size(). - * This is intended for applications which use their own allocator instead of malloc. @see opus_decoder_create,opus_decoder_get_size - * To reset a previously initialized state, use the #OPUS_RESET_STATE CTL. - * @param [in] st OpusDecoder*: Decoder state. - * @param [in] Fs opus_int32: Sampling rate to decode to (Hz). - * This must be one of 8000, 12000, 16000, - * 24000, or 48000. - * @param [in] channels int: Number of channels (1 or 2) to decode - * @retval #OPUS_OK Success or @ref opus_errorcodes - */ -OPUS_EXPORT int opus_decoder_init( - OpusDecoder *st, - opus_int32 Fs, - int channels -) OPUS_ARG_NONNULL(1); - -/** Decode an Opus packet. - * @param [in] st OpusDecoder*: Decoder state - * @param [in] data char*: Input payload. Use a NULL pointer to indicate packet loss - * @param [in] len opus_int32: Number of bytes in payload* - * @param [out] pcm opus_int16*: Output signal (interleaved if 2 channels). length - * is frame_size*channels*sizeof(opus_int16) - * @param [in] frame_size Number of samples per channel of available space in \a pcm. - * If this is less than the maximum packet duration (120ms; 5760 for 48kHz), this function will - * not be capable of decoding some packets. In the case of PLC (data==NULL) or FEC (decode_fec=1), - * then frame_size needs to be exactly the duration of audio that is missing, otherwise the - * decoder will not be in the optimal state to decode the next incoming packet. For the PLC and - * FEC cases, frame_size must be a multiple of 2.5 ms. - * @param [in] decode_fec int: Flag (0 or 1) to request that any in-band forward error correction data be - * decoded. If no such data is available, the frame is decoded as if it were lost. - * @returns Number of decoded samples or @ref opus_errorcodes - */ -OPUS_EXPORT OPUS_WARN_UNUSED_RESULT int opus_decode( - OpusDecoder *st, - const unsigned char *data, - opus_int32 len, - opus_int16 *pcm, - int frame_size, - int decode_fec -) OPUS_ARG_NONNULL(1) OPUS_ARG_NONNULL(4); - -/** Decode an Opus packet with floating point output. - * @param [in] st OpusDecoder*: Decoder state - * @param [in] data char*: Input payload. Use a NULL pointer to indicate packet loss - * @param [in] len opus_int32: Number of bytes in payload - * @param [out] pcm float*: Output signal (interleaved if 2 channels). length - * is frame_size*channels*sizeof(float) - * @param [in] frame_size Number of samples per channel of available space in \a pcm. - * If this is less than the maximum packet duration (120ms; 5760 for 48kHz), this function will - * not be capable of decoding some packets. In the case of PLC (data==NULL) or FEC (decode_fec=1), - * then frame_size needs to be exactly the duration of audio that is missing, otherwise the - * decoder will not be in the optimal state to decode the next incoming packet. For the PLC and - * FEC cases, frame_size must be a multiple of 2.5 ms. - * @param [in] decode_fec int: Flag (0 or 1) to request that any in-band forward error correction data be - * decoded. If no such data is available the frame is decoded as if it were lost. - * @returns Number of decoded samples or @ref opus_errorcodes - */ -OPUS_EXPORT OPUS_WARN_UNUSED_RESULT int opus_decode_float( - OpusDecoder *st, - const unsigned char *data, - opus_int32 len, - float *pcm, - int frame_size, - int decode_fec -) OPUS_ARG_NONNULL(1) OPUS_ARG_NONNULL(4); - -/** Perform a CTL function on an Opus decoder. - * - * Generally the request and subsequent arguments are generated - * by a convenience macro. - * @param st OpusDecoder*: Decoder state. - * @param request This and all remaining parameters should be replaced by one - * of the convenience macros in @ref opus_genericctls or - * @ref opus_decoderctls. - * @see opus_genericctls - * @see opus_decoderctls - */ -OPUS_EXPORT int opus_decoder_ctl(OpusDecoder *st, int request, ...) OPUS_ARG_NONNULL(1); - -/** Frees an OpusDecoder allocated by opus_decoder_create(). - * @param[in] st OpusDecoder*: State to be freed. - */ -OPUS_EXPORT void opus_decoder_destroy(OpusDecoder *st); - -/** Gets the size of an OpusDREDDecoder structure. - * @returns The size in bytes. - */ -OPUS_EXPORT int opus_dred_decoder_get_size(void); - -/** Allocates and initializes an OpusDREDDecoder state. - * @param [out] error int*: #OPUS_OK Success or @ref opus_errorcodes - */ -OPUS_EXPORT OpusDREDDecoder *opus_dred_decoder_create(int *error); - -/** Initializes an OpusDREDDecoder state. - * @param[in] dec OpusDREDDecoder*: State to be initialized. - */ -OPUS_EXPORT int opus_dred_decoder_init(OpusDREDDecoder *dec); - -/** Frees an OpusDREDDecoder allocated by opus_dred_decoder_create(). - * @param[in] dec OpusDREDDecoder*: State to be freed. - */ -OPUS_EXPORT void opus_dred_decoder_destroy(OpusDREDDecoder *dec); - -/** Perform a CTL function on an Opus DRED decoder. - * - * Generally the request and subsequent arguments are generated - * by a convenience macro. - * @param dred_dec OpusDREDDecoder*: DRED Decoder state. - * @param request This and all remaining parameters should be replaced by one - * of the convenience macros in @ref opus_genericctls or - * @ref opus_decoderctls. - * @see opus_genericctls - * @see opus_decoderctls - */ -OPUS_EXPORT int opus_dred_decoder_ctl(OpusDREDDecoder *dred_dec, int request, ...); - -/** Gets the size of an OpusDRED structure. - * @returns The size in bytes. - */ -OPUS_EXPORT int opus_dred_get_size(void); - -/** Allocates and initializes a DRED state. - * @param [out] error int*: #OPUS_OK Success or @ref opus_errorcodes - */ -OPUS_EXPORT OpusDRED *opus_dred_alloc(int *error); - -/** Frees an OpusDRED allocated by opus_dred_create(). - * @param[in] dec OpusDRED*: State to be freed. - */ -OPUS_EXPORT void opus_dred_free(OpusDRED *dec); - -/** Decode an Opus DRED packet. - * @param [in] dred_dec OpusDRED*: DRED Decoder state - * @param [in] dred OpusDRED*: DRED state - * @param [in] data char*: Input payload - * @param [in] len opus_int32: Number of bytes in payload - * @param [in] max_dred_samples opus_int32: Maximum number of DRED samples that may be needed (if available in the packet). - * @param [in] sampling_rate opus_int32: Sampling rate used for max_dred_samples argument. Needs not match the actual sampling rate of the decoder. - * @param [out] dred_end opus_int32*: Number of non-encoded (silence) samples between the DRED timestamp and the last DRED sample. - * @param [in] defer_processing int: Flag (0 or 1). If set to one, the CPU-intensive part of the DRED decoding is deferred until opus_dred_process() is called. - * @returns Offset (positive) of the first decoded DRED samples, zero if no DRED is present, or @ref opus_errorcodes - */ -OPUS_EXPORT int opus_dred_parse(OpusDREDDecoder *dred_dec, OpusDRED *dred, const unsigned char *data, opus_int32 len, opus_int32 max_dred_samples, opus_int32 sampling_rate, int *dred_end, int defer_processing) OPUS_ARG_NONNULL(1); - -/** Finish decoding an Opus DRED packet. The function only needs to be called if opus_dred_parse() was called with defer_processing=1. - * The source and destination will often be the same DRED state. - * @param [in] dred_dec OpusDRED*: DRED Decoder state - * @param [in] src OpusDRED*: Source DRED state to start the processing from. - * @param [out] dst OpusDRED*: Destination DRED state to store the updated state after processing. - * @returns @ref opus_errorcodes - */ -OPUS_EXPORT int opus_dred_process(OpusDREDDecoder *dred_dec, const OpusDRED *src, OpusDRED *dst); - -/** Decode audio from an Opus DRED packet with floating point output. - * @param [in] st OpusDecoder*: Decoder state - * @param [in] dred OpusDRED*: DRED state - * @param [in] dred_offset opus_int32: position of the redundancy to decode (in samples before the beginning of the real audio data in the packet). - * @param [out] pcm opus_int16*: Output signal (interleaved if 2 channels). length - * is frame_size*channels*sizeof(opus_int16) - * @param [in] frame_size Number of samples per channel to decode in \a pcm. - * frame_size must be a multiple of 2.5 ms. - * @returns Number of decoded samples or @ref opus_errorcodes - */ -OPUS_EXPORT int opus_decoder_dred_decode(OpusDecoder *st, const OpusDRED *dred, opus_int32 dred_offset, opus_int16 *pcm, opus_int32 frame_size); - -/** Decode audio from an Opus DRED packet with floating point output. - * @param [in] st OpusDecoder*: Decoder state - * @param [in] dred OpusDRED*: DRED state - * @param [in] dred_offset opus_int32: position of the redundancy to decode (in samples before the beginning of the real audio data in the packet). - * @param [out] pcm float*: Output signal (interleaved if 2 channels). length - * is frame_size*channels*sizeof(float) - * @param [in] frame_size Number of samples per channel to decode in \a pcm. - * frame_size must be a multiple of 2.5 ms. - * @returns Number of decoded samples or @ref opus_errorcodes - */ -OPUS_EXPORT int opus_decoder_dred_decode_float(OpusDecoder *st, const OpusDRED *dred, opus_int32 dred_offset, float *pcm, opus_int32 frame_size); - - -/** Parse an opus packet into one or more frames. - * Opus_decode will perform this operation internally so most applications do - * not need to use this function. - * This function does not copy the frames, the returned pointers are pointers into - * the input packet. - * @param [in] data char*: Opus packet to be parsed - * @param [in] len opus_int32: size of data - * @param [out] out_toc char*: TOC pointer - * @param [out] frames char*[48] encapsulated frames - * @param [out] size opus_int16[48] sizes of the encapsulated frames - * @param [out] payload_offset int*: returns the position of the payload within the packet (in bytes) - * @returns number of frames - */ -OPUS_EXPORT int opus_packet_parse( - const unsigned char *data, - opus_int32 len, - unsigned char *out_toc, - const unsigned char *frames[48], - opus_int16 size[48], - int *payload_offset -) OPUS_ARG_NONNULL(1) OPUS_ARG_NONNULL(5); - -/** Gets the bandwidth of an Opus packet. - * @param [in] data char*: Opus packet - * @retval OPUS_BANDWIDTH_NARROWBAND Narrowband (4kHz bandpass) - * @retval OPUS_BANDWIDTH_MEDIUMBAND Mediumband (6kHz bandpass) - * @retval OPUS_BANDWIDTH_WIDEBAND Wideband (8kHz bandpass) - * @retval OPUS_BANDWIDTH_SUPERWIDEBAND Superwideband (12kHz bandpass) - * @retval OPUS_BANDWIDTH_FULLBAND Fullband (20kHz bandpass) - * @retval OPUS_INVALID_PACKET The compressed data passed is corrupted or of an unsupported type - */ -OPUS_EXPORT OPUS_WARN_UNUSED_RESULT int opus_packet_get_bandwidth(const unsigned char *data) OPUS_ARG_NONNULL(1); - -/** Gets the number of samples per frame from an Opus packet. - * @param [in] data char*: Opus packet. - * This must contain at least one byte of - * data. - * @param [in] Fs opus_int32: Sampling rate in Hz. - * This must be a multiple of 400, or - * inaccurate results will be returned. - * @returns Number of samples per frame. - */ -OPUS_EXPORT OPUS_WARN_UNUSED_RESULT int opus_packet_get_samples_per_frame(const unsigned char *data, opus_int32 Fs) OPUS_ARG_NONNULL(1); - -/** Gets the number of channels from an Opus packet. - * @param [in] data char*: Opus packet - * @returns Number of channels - * @retval OPUS_INVALID_PACKET The compressed data passed is corrupted or of an unsupported type - */ -OPUS_EXPORT OPUS_WARN_UNUSED_RESULT int opus_packet_get_nb_channels(const unsigned char *data) OPUS_ARG_NONNULL(1); - -/** Gets the number of frames in an Opus packet. - * @param [in] packet char*: Opus packet - * @param [in] len opus_int32: Length of packet - * @returns Number of frames - * @retval OPUS_BAD_ARG Insufficient data was passed to the function - * @retval OPUS_INVALID_PACKET The compressed data passed is corrupted or of an unsupported type - */ -OPUS_EXPORT OPUS_WARN_UNUSED_RESULT int opus_packet_get_nb_frames(const unsigned char packet[], opus_int32 len) OPUS_ARG_NONNULL(1); - -/** Gets the number of samples of an Opus packet. - * @param [in] packet char*: Opus packet - * @param [in] len opus_int32: Length of packet - * @param [in] Fs opus_int32: Sampling rate in Hz. - * This must be a multiple of 400, or - * inaccurate results will be returned. - * @returns Number of samples - * @retval OPUS_BAD_ARG Insufficient data was passed to the function - * @retval OPUS_INVALID_PACKET The compressed data passed is corrupted or of an unsupported type - */ -OPUS_EXPORT OPUS_WARN_UNUSED_RESULT int opus_packet_get_nb_samples(const unsigned char packet[], opus_int32 len, opus_int32 Fs) OPUS_ARG_NONNULL(1); - -/** Checks whether an Opus packet has LBRR. - * @param [in] packet char*: Opus packet - * @param [in] len opus_int32: Length of packet - * @returns 1 is LBRR is present, 0 otherwise - * @retval OPUS_INVALID_PACKET The compressed data passed is corrupted or of an unsupported type - */ -OPUS_EXPORT OPUS_WARN_UNUSED_RESULT int opus_packet_has_lbrr(const unsigned char packet[], opus_int32 len); - -/** Gets the number of samples of an Opus packet. - * @param [in] dec OpusDecoder*: Decoder state - * @param [in] packet char*: Opus packet - * @param [in] len opus_int32: Length of packet - * @returns Number of samples - * @retval OPUS_BAD_ARG Insufficient data was passed to the function - * @retval OPUS_INVALID_PACKET The compressed data passed is corrupted or of an unsupported type - */ -OPUS_EXPORT OPUS_WARN_UNUSED_RESULT int opus_decoder_get_nb_samples(const OpusDecoder *dec, const unsigned char packet[], opus_int32 len) OPUS_ARG_NONNULL(1) OPUS_ARG_NONNULL(2); - -/** Applies soft-clipping to bring a float signal within the [-1,1] range. If - * the signal is already in that range, nothing is done. If there are values - * outside of [-1,1], then the signal is clipped as smoothly as possible to - * both fit in the range and avoid creating excessive distortion in the - * process. - * @param [in,out] pcm float*: Input PCM and modified PCM - * @param [in] frame_size int Number of samples per channel to process - * @param [in] channels int: Number of channels - * @param [in,out] softclip_mem float*: State memory for the soft clipping process (one float per channel, initialized to zero) - */ -OPUS_EXPORT void opus_pcm_soft_clip(float *pcm, int frame_size, int channels, float *softclip_mem); - - -/**@}*/ - -/** @defgroup opus_repacketizer Repacketizer - * @{ - * - * The repacketizer can be used to merge multiple Opus packets into a single - * packet or alternatively to split Opus packets that have previously been - * merged. Splitting valid Opus packets is always guaranteed to succeed, - * whereas merging valid packets only succeeds if all frames have the same - * mode, bandwidth, and frame size, and when the total duration of the merged - * packet is no more than 120 ms. The 120 ms limit comes from the - * specification and limits decoder memory requirements at a point where - * framing overhead becomes negligible. - * - * The repacketizer currently only operates on elementary Opus - * streams. It will not manipualte multistream packets successfully, except in - * the degenerate case where they consist of data from a single stream. - * - * The repacketizing process starts with creating a repacketizer state, either - * by calling opus_repacketizer_create() or by allocating the memory yourself, - * e.g., - * @code - * OpusRepacketizer *rp; - * rp = (OpusRepacketizer*)malloc(opus_repacketizer_get_size()); - * if (rp != NULL) - * opus_repacketizer_init(rp); - * @endcode - * - * Then the application should submit packets with opus_repacketizer_cat(), - * extract new packets with opus_repacketizer_out() or - * opus_repacketizer_out_range(), and then reset the state for the next set of - * input packets via opus_repacketizer_init(). - * - * For example, to split a sequence of packets into individual frames: - * @code - * unsigned char *data; - * int len; - * while (get_next_packet(&data, &len)) - * { - * unsigned char out[1276]; - * opus_int32 out_len; - * int nb_frames; - * int err; - * int i; - * err = opus_repacketizer_cat(rp, data, len); - * if (err != OPUS_OK) - * { - * release_packet(data); - * return err; - * } - * nb_frames = opus_repacketizer_get_nb_frames(rp); - * for (i = 0; i < nb_frames; i++) - * { - * out_len = opus_repacketizer_out_range(rp, i, i+1, out, sizeof(out)); - * if (out_len < 0) - * { - * release_packet(data); - * return (int)out_len; - * } - * output_next_packet(out, out_len); - * } - * opus_repacketizer_init(rp); - * release_packet(data); - * } - * @endcode - * - * Alternatively, to combine a sequence of frames into packets that each - * contain up to TARGET_DURATION_MS milliseconds of data: - * @code - * // The maximum number of packets with duration TARGET_DURATION_MS occurs - * // when the frame size is 2.5 ms, for a total of (TARGET_DURATION_MS*2/5) - * // packets. - * unsigned char *data[(TARGET_DURATION_MS*2/5)+1]; - * opus_int32 len[(TARGET_DURATION_MS*2/5)+1]; - * int nb_packets; - * unsigned char out[1277*(TARGET_DURATION_MS*2/2)]; - * opus_int32 out_len; - * int prev_toc; - * nb_packets = 0; - * while (get_next_packet(data+nb_packets, len+nb_packets)) - * { - * int nb_frames; - * int err; - * nb_frames = opus_packet_get_nb_frames(data[nb_packets], len[nb_packets]); - * if (nb_frames < 1) - * { - * release_packets(data, nb_packets+1); - * return nb_frames; - * } - * nb_frames += opus_repacketizer_get_nb_frames(rp); - * // If adding the next packet would exceed our target, or it has an - * // incompatible TOC sequence, output the packets we already have before - * // submitting it. - * // N.B., The nb_packets > 0 check ensures we've submitted at least one - * // packet since the last call to opus_repacketizer_init(). Otherwise a - * // single packet longer than TARGET_DURATION_MS would cause us to try to - * // output an (invalid) empty packet. It also ensures that prev_toc has - * // been set to a valid value. Additionally, len[nb_packets] > 0 is - * // guaranteed by the call to opus_packet_get_nb_frames() above, so the - * // reference to data[nb_packets][0] should be valid. - * if (nb_packets > 0 && ( - * ((prev_toc & 0xFC) != (data[nb_packets][0] & 0xFC)) || - * opus_packet_get_samples_per_frame(data[nb_packets], 48000)*nb_frames > - * TARGET_DURATION_MS*48)) - * { - * out_len = opus_repacketizer_out(rp, out, sizeof(out)); - * if (out_len < 0) - * { - * release_packets(data, nb_packets+1); - * return (int)out_len; - * } - * output_next_packet(out, out_len); - * opus_repacketizer_init(rp); - * release_packets(data, nb_packets); - * data[0] = data[nb_packets]; - * len[0] = len[nb_packets]; - * nb_packets = 0; - * } - * err = opus_repacketizer_cat(rp, data[nb_packets], len[nb_packets]); - * if (err != OPUS_OK) - * { - * release_packets(data, nb_packets+1); - * return err; - * } - * prev_toc = data[nb_packets][0]; - * nb_packets++; - * } - * // Output the final, partial packet. - * if (nb_packets > 0) - * { - * out_len = opus_repacketizer_out(rp, out, sizeof(out)); - * release_packets(data, nb_packets); - * if (out_len < 0) - * return (int)out_len; - * output_next_packet(out, out_len); - * } - * @endcode - * - * An alternate way of merging packets is to simply call opus_repacketizer_cat() - * unconditionally until it fails. At that point, the merged packet can be - * obtained with opus_repacketizer_out() and the input packet for which - * opus_repacketizer_cat() needs to be re-added to a newly reinitialized - * repacketizer state. - */ - -typedef struct OpusRepacketizer OpusRepacketizer; - -/** Gets the size of an OpusRepacketizer structure. - * @returns The size in bytes. - */ -OPUS_EXPORT OPUS_WARN_UNUSED_RESULT int opus_repacketizer_get_size(void); - -/** (Re)initializes a previously allocated repacketizer state. - * The state must be at least the size returned by opus_repacketizer_get_size(). - * This can be used for applications which use their own allocator instead of - * malloc(). - * It must also be called to reset the queue of packets waiting to be - * repacketized, which is necessary if the maximum packet duration of 120 ms - * is reached or if you wish to submit packets with a different Opus - * configuration (coding mode, audio bandwidth, frame size, or channel count). - * Failure to do so will prevent a new packet from being added with - * opus_repacketizer_cat(). - * @see opus_repacketizer_create - * @see opus_repacketizer_get_size - * @see opus_repacketizer_cat - * @param rp OpusRepacketizer*: The repacketizer state to - * (re)initialize. - * @returns A pointer to the same repacketizer state that was passed in. - */ -OPUS_EXPORT OpusRepacketizer *opus_repacketizer_init(OpusRepacketizer *rp) OPUS_ARG_NONNULL(1); - -/** Allocates memory and initializes the new repacketizer with - * opus_repacketizer_init(). - */ -OPUS_EXPORT OPUS_WARN_UNUSED_RESULT OpusRepacketizer *opus_repacketizer_create(void); - -/** Frees an OpusRepacketizer allocated by - * opus_repacketizer_create(). - * @param[in] rp OpusRepacketizer*: State to be freed. - */ -OPUS_EXPORT void opus_repacketizer_destroy(OpusRepacketizer *rp); - -/** Add a packet to the current repacketizer state. - * This packet must match the configuration of any packets already submitted - * for repacketization since the last call to opus_repacketizer_init(). - * This means that it must have the same coding mode, audio bandwidth, frame - * size, and channel count. - * This can be checked in advance by examining the top 6 bits of the first - * byte of the packet, and ensuring they match the top 6 bits of the first - * byte of any previously submitted packet. - * The total duration of audio in the repacketizer state also must not exceed - * 120 ms, the maximum duration of a single packet, after adding this packet. - * - * The contents of the current repacketizer state can be extracted into new - * packets using opus_repacketizer_out() or opus_repacketizer_out_range(). - * - * In order to add a packet with a different configuration or to add more - * audio beyond 120 ms, you must clear the repacketizer state by calling - * opus_repacketizer_init(). - * If a packet is too large to add to the current repacketizer state, no part - * of it is added, even if it contains multiple frames, some of which might - * fit. - * If you wish to be able to add parts of such packets, you should first use - * another repacketizer to split the packet into pieces and add them - * individually. - * @see opus_repacketizer_out_range - * @see opus_repacketizer_out - * @see opus_repacketizer_init - * @param rp OpusRepacketizer*: The repacketizer state to which to - * add the packet. - * @param[in] data const unsigned char*: The packet data. - * The application must ensure - * this pointer remains valid - * until the next call to - * opus_repacketizer_init() or - * opus_repacketizer_destroy(). - * @param len opus_int32: The number of bytes in the packet data. - * @returns An error code indicating whether or not the operation succeeded. - * @retval #OPUS_OK The packet's contents have been added to the repacketizer - * state. - * @retval #OPUS_INVALID_PACKET The packet did not have a valid TOC sequence, - * the packet's TOC sequence was not compatible - * with previously submitted packets (because - * the coding mode, audio bandwidth, frame size, - * or channel count did not match), or adding - * this packet would increase the total amount of - * audio stored in the repacketizer state to more - * than 120 ms. - */ -OPUS_EXPORT int opus_repacketizer_cat(OpusRepacketizer *rp, const unsigned char *data, opus_int32 len) OPUS_ARG_NONNULL(1) OPUS_ARG_NONNULL(2); - - -/** Construct a new packet from data previously submitted to the repacketizer - * state via opus_repacketizer_cat(). - * @param rp OpusRepacketizer*: The repacketizer state from which to - * construct the new packet. - * @param begin int: The index of the first frame in the current - * repacketizer state to include in the output. - * @param end int: One past the index of the last frame in the - * current repacketizer state to include in the - * output. - * @param[out] data const unsigned char*: The buffer in which to - * store the output packet. - * @param maxlen opus_int32: The maximum number of bytes to store in - * the output buffer. In order to guarantee - * success, this should be at least - * 1276 for a single frame, - * or for multiple frames, - * 1277*(end-begin). - * However, 1*(end-begin) plus - * the size of all packet data submitted to - * the repacketizer since the last call to - * opus_repacketizer_init() or - * opus_repacketizer_create() is also - * sufficient, and possibly much smaller. - * @returns The total size of the output packet on success, or an error code - * on failure. - * @retval #OPUS_BAD_ARG [begin,end) was an invalid range of - * frames (begin < 0, begin >= end, or end > - * opus_repacketizer_get_nb_frames()). - * @retval #OPUS_BUFFER_TOO_SMALL \a maxlen was insufficient to contain the - * complete output packet. - */ -OPUS_EXPORT OPUS_WARN_UNUSED_RESULT opus_int32 opus_repacketizer_out_range(OpusRepacketizer *rp, int begin, int end, unsigned char *data, opus_int32 maxlen) OPUS_ARG_NONNULL(1) OPUS_ARG_NONNULL(4); - -/** Return the total number of frames contained in packet data submitted to - * the repacketizer state so far via opus_repacketizer_cat() since the last - * call to opus_repacketizer_init() or opus_repacketizer_create(). - * This defines the valid range of packets that can be extracted with - * opus_repacketizer_out_range() or opus_repacketizer_out(). - * @param rp OpusRepacketizer*: The repacketizer state containing the - * frames. - * @returns The total number of frames contained in the packet data submitted - * to the repacketizer state. - */ -OPUS_EXPORT OPUS_WARN_UNUSED_RESULT int opus_repacketizer_get_nb_frames(OpusRepacketizer *rp) OPUS_ARG_NONNULL(1); - -/** Construct a new packet from data previously submitted to the repacketizer - * state via opus_repacketizer_cat(). - * This is a convenience routine that returns all the data submitted so far - * in a single packet. - * It is equivalent to calling - * @code - * opus_repacketizer_out_range(rp, 0, opus_repacketizer_get_nb_frames(rp), - * data, maxlen) - * @endcode - * @param rp OpusRepacketizer*: The repacketizer state from which to - * construct the new packet. - * @param[out] data const unsigned char*: The buffer in which to - * store the output packet. - * @param maxlen opus_int32: The maximum number of bytes to store in - * the output buffer. In order to guarantee - * success, this should be at least - * 1277*opus_repacketizer_get_nb_frames(rp). - * However, - * 1*opus_repacketizer_get_nb_frames(rp) - * plus the size of all packet data - * submitted to the repacketizer since the - * last call to opus_repacketizer_init() or - * opus_repacketizer_create() is also - * sufficient, and possibly much smaller. - * @returns The total size of the output packet on success, or an error code - * on failure. - * @retval #OPUS_BUFFER_TOO_SMALL \a maxlen was insufficient to contain the - * complete output packet. - */ -OPUS_EXPORT OPUS_WARN_UNUSED_RESULT opus_int32 opus_repacketizer_out(OpusRepacketizer *rp, unsigned char *data, opus_int32 maxlen) OPUS_ARG_NONNULL(1); - -/** Pads a given Opus packet to a larger size (possibly changing the TOC sequence). - * @param[in,out] data const unsigned char*: The buffer containing the - * packet to pad. - * @param len opus_int32: The size of the packet. - * This must be at least 1. - * @param new_len opus_int32: The desired size of the packet after padding. - * This must be at least as large as len. - * @returns an error code - * @retval #OPUS_OK \a on success. - * @retval #OPUS_BAD_ARG \a len was less than 1 or new_len was less than len. - * @retval #OPUS_INVALID_PACKET \a data did not contain a valid Opus packet. - */ -OPUS_EXPORT int opus_packet_pad(unsigned char *data, opus_int32 len, opus_int32 new_len); - -/** Remove all padding from a given Opus packet and rewrite the TOC sequence to - * minimize space usage. - * @param[in,out] data const unsigned char*: The buffer containing the - * packet to strip. - * @param len opus_int32: The size of the packet. - * This must be at least 1. - * @returns The new size of the output packet on success, or an error code - * on failure. - * @retval #OPUS_BAD_ARG \a len was less than 1. - * @retval #OPUS_INVALID_PACKET \a data did not contain a valid Opus packet. - */ -OPUS_EXPORT OPUS_WARN_UNUSED_RESULT opus_int32 opus_packet_unpad(unsigned char *data, opus_int32 len); - -/** Pads a given Opus multi-stream packet to a larger size (possibly changing the TOC sequence). - * @param[in,out] data const unsigned char*: The buffer containing the - * packet to pad. - * @param len opus_int32: The size of the packet. - * This must be at least 1. - * @param new_len opus_int32: The desired size of the packet after padding. - * This must be at least 1. - * @param nb_streams opus_int32: The number of streams (not channels) in the packet. - * This must be at least as large as len. - * @returns an error code - * @retval #OPUS_OK \a on success. - * @retval #OPUS_BAD_ARG \a len was less than 1. - * @retval #OPUS_INVALID_PACKET \a data did not contain a valid Opus packet. - */ -OPUS_EXPORT int opus_multistream_packet_pad(unsigned char *data, opus_int32 len, opus_int32 new_len, int nb_streams); - -/** Remove all padding from a given Opus multi-stream packet and rewrite the TOC sequence to - * minimize space usage. - * @param[in,out] data const unsigned char*: The buffer containing the - * packet to strip. - * @param len opus_int32: The size of the packet. - * This must be at least 1. - * @param nb_streams opus_int32: The number of streams (not channels) in the packet. - * This must be at least 1. - * @returns The new size of the output packet on success, or an error code - * on failure. - * @retval #OPUS_BAD_ARG \a len was less than 1 or new_len was less than len. - * @retval #OPUS_INVALID_PACKET \a data did not contain a valid Opus packet. - */ -OPUS_EXPORT OPUS_WARN_UNUSED_RESULT opus_int32 opus_multistream_packet_unpad(unsigned char *data, opus_int32 len, int nb_streams); - -/**@}*/ - -#ifdef __cplusplus -} -#endif - -#endif /* OPUS_H */ diff --git a/go/teamspeak/.opus/install/x86/include/opus/opus_defines.h b/go/teamspeak/.opus/install/x86/include/opus/opus_defines.h deleted file mode 100644 index cd8f4dd..0000000 --- a/go/teamspeak/.opus/install/x86/include/opus/opus_defines.h +++ /dev/null @@ -1,830 +0,0 @@ -/* Copyright (c) 2010-2011 Xiph.Org Foundation, Skype Limited - Written by Jean-Marc Valin and Koen Vos */ -/* - Redistribution and use in source and binary forms, with or without - modification, are permitted provided that the following conditions - are met: - - - Redistributions of source code must retain the above copyright - notice, this list of conditions and the following disclaimer. - - - Redistributions in binary form must reproduce the above copyright - notice, this list of conditions and the following disclaimer in the - documentation and/or other materials provided with the distribution. - - THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS - ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT - LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR - A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER - OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, - EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, - PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR - PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF - LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING - NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS - SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. -*/ - -/** - * @file opus_defines.h - * @brief Opus reference implementation constants - */ - -#ifndef OPUS_DEFINES_H -#define OPUS_DEFINES_H - -#include "opus_types.h" - -#ifdef __cplusplus -extern "C" { -#endif - -/** @defgroup opus_errorcodes Error codes - * @{ - */ -/** No error @hideinitializer*/ -#define OPUS_OK 0 -/** One or more invalid/out of range arguments @hideinitializer*/ -#define OPUS_BAD_ARG -1 -/** Not enough bytes allocated in the buffer @hideinitializer*/ -#define OPUS_BUFFER_TOO_SMALL -2 -/** An internal error was detected @hideinitializer*/ -#define OPUS_INTERNAL_ERROR -3 -/** The compressed data passed is corrupted @hideinitializer*/ -#define OPUS_INVALID_PACKET -4 -/** Invalid/unsupported request number @hideinitializer*/ -#define OPUS_UNIMPLEMENTED -5 -/** An encoder or decoder structure is invalid or already freed @hideinitializer*/ -#define OPUS_INVALID_STATE -6 -/** Memory allocation has failed @hideinitializer*/ -#define OPUS_ALLOC_FAIL -7 -/**@}*/ - -/** @cond OPUS_INTERNAL_DOC */ -/**Export control for opus functions */ - -#ifndef OPUS_EXPORT -# if defined(_WIN32) -# if defined(OPUS_BUILD) && defined(DLL_EXPORT) -# define OPUS_EXPORT __declspec(dllexport) -# else -# define OPUS_EXPORT -# endif -# elif defined(__GNUC__) && defined(OPUS_BUILD) -# define OPUS_EXPORT __attribute__ ((visibility ("default"))) -# else -# define OPUS_EXPORT -# endif -#endif - -# if !defined(OPUS_GNUC_PREREQ) -# if defined(__GNUC__)&&defined(__GNUC_MINOR__) -# define OPUS_GNUC_PREREQ(_maj,_min) \ - ((__GNUC__<<16)+__GNUC_MINOR__>=((_maj)<<16)+(_min)) -# else -# define OPUS_GNUC_PREREQ(_maj,_min) 0 -# endif -# endif - -#if (!defined(__STDC_VERSION__) || (__STDC_VERSION__ < 199901L) ) -# if OPUS_GNUC_PREREQ(3,0) -# define OPUS_RESTRICT __restrict__ -# elif (defined(_MSC_VER) && _MSC_VER >= 1400) -# define OPUS_RESTRICT __restrict -# else -# define OPUS_RESTRICT -# endif -#else -# define OPUS_RESTRICT restrict -#endif - -#if (!defined(__STDC_VERSION__) || (__STDC_VERSION__ < 199901L) ) -# if OPUS_GNUC_PREREQ(2,7) -# define OPUS_INLINE __inline__ -# elif (defined(_MSC_VER)) -# define OPUS_INLINE __inline -# else -# define OPUS_INLINE -# endif -#else -# define OPUS_INLINE inline -#endif - -/**Warning attributes for opus functions - * NONNULL is not used in OPUS_BUILD to avoid the compiler optimizing out - * some paranoid null checks. */ -#if defined(__GNUC__) && OPUS_GNUC_PREREQ(3, 4) -# define OPUS_WARN_UNUSED_RESULT __attribute__ ((__warn_unused_result__)) -#else -# define OPUS_WARN_UNUSED_RESULT -#endif -#if !defined(OPUS_BUILD) && defined(__GNUC__) && OPUS_GNUC_PREREQ(3, 4) -# define OPUS_ARG_NONNULL(_x) __attribute__ ((__nonnull__(_x))) -#else -# define OPUS_ARG_NONNULL(_x) -#endif - -/** These are the actual Encoder CTL ID numbers. - * They should not be used directly by applications. - * In general, SETs should be even and GETs should be odd.*/ -#define OPUS_SET_APPLICATION_REQUEST 4000 -#define OPUS_GET_APPLICATION_REQUEST 4001 -#define OPUS_SET_BITRATE_REQUEST 4002 -#define OPUS_GET_BITRATE_REQUEST 4003 -#define OPUS_SET_MAX_BANDWIDTH_REQUEST 4004 -#define OPUS_GET_MAX_BANDWIDTH_REQUEST 4005 -#define OPUS_SET_VBR_REQUEST 4006 -#define OPUS_GET_VBR_REQUEST 4007 -#define OPUS_SET_BANDWIDTH_REQUEST 4008 -#define OPUS_GET_BANDWIDTH_REQUEST 4009 -#define OPUS_SET_COMPLEXITY_REQUEST 4010 -#define OPUS_GET_COMPLEXITY_REQUEST 4011 -#define OPUS_SET_INBAND_FEC_REQUEST 4012 -#define OPUS_GET_INBAND_FEC_REQUEST 4013 -#define OPUS_SET_PACKET_LOSS_PERC_REQUEST 4014 -#define OPUS_GET_PACKET_LOSS_PERC_REQUEST 4015 -#define OPUS_SET_DTX_REQUEST 4016 -#define OPUS_GET_DTX_REQUEST 4017 -#define OPUS_SET_VBR_CONSTRAINT_REQUEST 4020 -#define OPUS_GET_VBR_CONSTRAINT_REQUEST 4021 -#define OPUS_SET_FORCE_CHANNELS_REQUEST 4022 -#define OPUS_GET_FORCE_CHANNELS_REQUEST 4023 -#define OPUS_SET_SIGNAL_REQUEST 4024 -#define OPUS_GET_SIGNAL_REQUEST 4025 -#define OPUS_GET_LOOKAHEAD_REQUEST 4027 -/* #define OPUS_RESET_STATE 4028 */ -#define OPUS_GET_SAMPLE_RATE_REQUEST 4029 -#define OPUS_GET_FINAL_RANGE_REQUEST 4031 -#define OPUS_GET_PITCH_REQUEST 4033 -#define OPUS_SET_GAIN_REQUEST 4034 -#define OPUS_GET_GAIN_REQUEST 4045 /* Should have been 4035 */ -#define OPUS_SET_LSB_DEPTH_REQUEST 4036 -#define OPUS_GET_LSB_DEPTH_REQUEST 4037 -#define OPUS_GET_LAST_PACKET_DURATION_REQUEST 4039 -#define OPUS_SET_EXPERT_FRAME_DURATION_REQUEST 4040 -#define OPUS_GET_EXPERT_FRAME_DURATION_REQUEST 4041 -#define OPUS_SET_PREDICTION_DISABLED_REQUEST 4042 -#define OPUS_GET_PREDICTION_DISABLED_REQUEST 4043 -/* Don't use 4045, it's already taken by OPUS_GET_GAIN_REQUEST */ -#define OPUS_SET_PHASE_INVERSION_DISABLED_REQUEST 4046 -#define OPUS_GET_PHASE_INVERSION_DISABLED_REQUEST 4047 -#define OPUS_GET_IN_DTX_REQUEST 4049 -#define OPUS_SET_DRED_DURATION_REQUEST 4050 -#define OPUS_GET_DRED_DURATION_REQUEST 4051 -#define OPUS_SET_DNN_BLOB_REQUEST 4052 -/*#define OPUS_GET_DNN_BLOB_REQUEST 4053 */ - -/** Defines for the presence of extended APIs. */ -#define OPUS_HAVE_OPUS_PROJECTION_H - -/* Macros to trigger compilation errors when the wrong types are provided to a CTL */ -#define __opus_check_int(x) (((void)((x) == (opus_int32)0)), (opus_int32)(x)) - -#ifdef DISABLE_PTR_CHECK -/* Disable checks to prevent ubsan from complaining about NULL checks - in test_opus_api. */ -#define __opus_check_int_ptr(ptr) (ptr) -#define __opus_check_uint_ptr(ptr) (ptr) -#define __opus_check_uint8_ptr(ptr) (ptr) -#define __opus_check_val16_ptr(ptr) (ptr) -#define __opus_check_void_ptr(ptr) (ptr) -#else -#define __opus_check_int_ptr(ptr) ((ptr) + ((ptr) - (opus_int32*)(ptr))) -#define __opus_check_uint_ptr(ptr) ((ptr) + ((ptr) - (opus_uint32*)(ptr))) -#define __opus_check_uint8_ptr(ptr) ((ptr) + ((ptr) - (opus_uint8*)(ptr))) -#define __opus_check_val16_ptr(ptr) ((ptr) + ((ptr) - (opus_val16*)(ptr))) -#define __opus_check_void_ptr(x) ((void)((void *)0 == (x)), (x)) -#endif -/** @endcond */ - -/** @defgroup opus_ctlvalues Pre-defined values for CTL interface - * @see opus_genericctls, opus_encoderctls - * @{ - */ -/* Values for the various encoder CTLs */ -#define OPUS_AUTO -1000 /**opus_int32: Allowed values: 0-10, inclusive. - * - * @hideinitializer */ -#define OPUS_SET_COMPLEXITY(x) OPUS_SET_COMPLEXITY_REQUEST, __opus_check_int(x) -/** Gets the encoder's complexity configuration. - * @see OPUS_SET_COMPLEXITY - * @param[out] x opus_int32 *: Returns a value in the range 0-10, - * inclusive. - * @hideinitializer */ -#define OPUS_GET_COMPLEXITY(x) OPUS_GET_COMPLEXITY_REQUEST, __opus_check_int_ptr(x) - -/** Configures the bitrate in the encoder. - * Rates from 500 to 512000 bits per second are meaningful, as well as the - * special values #OPUS_AUTO and #OPUS_BITRATE_MAX. - * The value #OPUS_BITRATE_MAX can be used to cause the codec to use as much - * rate as it can, which is useful for controlling the rate by adjusting the - * output buffer size. - * @see OPUS_GET_BITRATE - * @param[in] x opus_int32: Bitrate in bits per second. The default - * is determined based on the number of - * channels and the input sampling rate. - * @hideinitializer */ -#define OPUS_SET_BITRATE(x) OPUS_SET_BITRATE_REQUEST, __opus_check_int(x) -/** Gets the encoder's bitrate configuration. - * @see OPUS_SET_BITRATE - * @param[out] x opus_int32 *: Returns the bitrate in bits per second. - * The default is determined based on the - * number of channels and the input - * sampling rate. - * @hideinitializer */ -#define OPUS_GET_BITRATE(x) OPUS_GET_BITRATE_REQUEST, __opus_check_int_ptr(x) - -/** Enables or disables variable bitrate (VBR) in the encoder. - * The configured bitrate may not be met exactly because frames must - * be an integer number of bytes in length. - * @see OPUS_GET_VBR - * @see OPUS_SET_VBR_CONSTRAINT - * @param[in] x opus_int32: Allowed values: - *
- *
0
Hard CBR. For LPC/hybrid modes at very low bit-rate, this can - * cause noticeable quality degradation.
- *
1
VBR (default). The exact type of VBR is controlled by - * #OPUS_SET_VBR_CONSTRAINT.
- *
- * @hideinitializer */ -#define OPUS_SET_VBR(x) OPUS_SET_VBR_REQUEST, __opus_check_int(x) -/** Determine if variable bitrate (VBR) is enabled in the encoder. - * @see OPUS_SET_VBR - * @see OPUS_GET_VBR_CONSTRAINT - * @param[out] x opus_int32 *: Returns one of the following values: - *
- *
0
Hard CBR.
- *
1
VBR (default). The exact type of VBR may be retrieved via - * #OPUS_GET_VBR_CONSTRAINT.
- *
- * @hideinitializer */ -#define OPUS_GET_VBR(x) OPUS_GET_VBR_REQUEST, __opus_check_int_ptr(x) - -/** Enables or disables constrained VBR in the encoder. - * This setting is ignored when the encoder is in CBR mode. - * @warning Only the MDCT mode of Opus currently heeds the constraint. - * Speech mode ignores it completely, hybrid mode may fail to obey it - * if the LPC layer uses more bitrate than the constraint would have - * permitted. - * @see OPUS_GET_VBR_CONSTRAINT - * @see OPUS_SET_VBR - * @param[in] x opus_int32: Allowed values: - *
- *
0
Unconstrained VBR.
- *
1
Constrained VBR (default). This creates a maximum of one - * frame of buffering delay assuming a transport with a - * serialization speed of the nominal bitrate.
- *
- * @hideinitializer */ -#define OPUS_SET_VBR_CONSTRAINT(x) OPUS_SET_VBR_CONSTRAINT_REQUEST, __opus_check_int(x) -/** Determine if constrained VBR is enabled in the encoder. - * @see OPUS_SET_VBR_CONSTRAINT - * @see OPUS_GET_VBR - * @param[out] x opus_int32 *: Returns one of the following values: - *
- *
0
Unconstrained VBR.
- *
1
Constrained VBR (default).
- *
- * @hideinitializer */ -#define OPUS_GET_VBR_CONSTRAINT(x) OPUS_GET_VBR_CONSTRAINT_REQUEST, __opus_check_int_ptr(x) - -/** Configures mono/stereo forcing in the encoder. - * This can force the encoder to produce packets encoded as either mono or - * stereo, regardless of the format of the input audio. This is useful when - * the caller knows that the input signal is currently a mono source embedded - * in a stereo stream. - * @see OPUS_GET_FORCE_CHANNELS - * @param[in] x opus_int32: Allowed values: - *
- *
#OPUS_AUTO
Not forced (default)
- *
1
Forced mono
- *
2
Forced stereo
- *
- * @hideinitializer */ -#define OPUS_SET_FORCE_CHANNELS(x) OPUS_SET_FORCE_CHANNELS_REQUEST, __opus_check_int(x) -/** Gets the encoder's forced channel configuration. - * @see OPUS_SET_FORCE_CHANNELS - * @param[out] x opus_int32 *: - *
- *
#OPUS_AUTO
Not forced (default)
- *
1
Forced mono
- *
2
Forced stereo
- *
- * @hideinitializer */ -#define OPUS_GET_FORCE_CHANNELS(x) OPUS_GET_FORCE_CHANNELS_REQUEST, __opus_check_int_ptr(x) - -/** Configures the maximum bandpass that the encoder will select automatically. - * Applications should normally use this instead of #OPUS_SET_BANDWIDTH - * (leaving that set to the default, #OPUS_AUTO). This allows the - * application to set an upper bound based on the type of input it is - * providing, but still gives the encoder the freedom to reduce the bandpass - * when the bitrate becomes too low, for better overall quality. - * @see OPUS_GET_MAX_BANDWIDTH - * @param[in] x opus_int32: Allowed values: - *
- *
OPUS_BANDWIDTH_NARROWBAND
4 kHz passband
- *
OPUS_BANDWIDTH_MEDIUMBAND
6 kHz passband
- *
OPUS_BANDWIDTH_WIDEBAND
8 kHz passband
- *
OPUS_BANDWIDTH_SUPERWIDEBAND
12 kHz passband
- *
OPUS_BANDWIDTH_FULLBAND
20 kHz passband (default)
- *
- * @hideinitializer */ -#define OPUS_SET_MAX_BANDWIDTH(x) OPUS_SET_MAX_BANDWIDTH_REQUEST, __opus_check_int(x) - -/** Gets the encoder's configured maximum allowed bandpass. - * @see OPUS_SET_MAX_BANDWIDTH - * @param[out] x opus_int32 *: Allowed values: - *
- *
#OPUS_BANDWIDTH_NARROWBAND
4 kHz passband
- *
#OPUS_BANDWIDTH_MEDIUMBAND
6 kHz passband
- *
#OPUS_BANDWIDTH_WIDEBAND
8 kHz passband
- *
#OPUS_BANDWIDTH_SUPERWIDEBAND
12 kHz passband
- *
#OPUS_BANDWIDTH_FULLBAND
20 kHz passband (default)
- *
- * @hideinitializer */ -#define OPUS_GET_MAX_BANDWIDTH(x) OPUS_GET_MAX_BANDWIDTH_REQUEST, __opus_check_int_ptr(x) - -/** Sets the encoder's bandpass to a specific value. - * This prevents the encoder from automatically selecting the bandpass based - * on the available bitrate. If an application knows the bandpass of the input - * audio it is providing, it should normally use #OPUS_SET_MAX_BANDWIDTH - * instead, which still gives the encoder the freedom to reduce the bandpass - * when the bitrate becomes too low, for better overall quality. - * @see OPUS_GET_BANDWIDTH - * @param[in] x opus_int32: Allowed values: - *
- *
#OPUS_AUTO
(default)
- *
#OPUS_BANDWIDTH_NARROWBAND
4 kHz passband
- *
#OPUS_BANDWIDTH_MEDIUMBAND
6 kHz passband
- *
#OPUS_BANDWIDTH_WIDEBAND
8 kHz passband
- *
#OPUS_BANDWIDTH_SUPERWIDEBAND
12 kHz passband
- *
#OPUS_BANDWIDTH_FULLBAND
20 kHz passband
- *
- * @hideinitializer */ -#define OPUS_SET_BANDWIDTH(x) OPUS_SET_BANDWIDTH_REQUEST, __opus_check_int(x) - -/** Configures the type of signal being encoded. - * This is a hint which helps the encoder's mode selection. - * @see OPUS_GET_SIGNAL - * @param[in] x opus_int32: Allowed values: - *
- *
#OPUS_AUTO
(default)
- *
#OPUS_SIGNAL_VOICE
Bias thresholds towards choosing LPC or Hybrid modes.
- *
#OPUS_SIGNAL_MUSIC
Bias thresholds towards choosing MDCT modes.
- *
- * @hideinitializer */ -#define OPUS_SET_SIGNAL(x) OPUS_SET_SIGNAL_REQUEST, __opus_check_int(x) -/** Gets the encoder's configured signal type. - * @see OPUS_SET_SIGNAL - * @param[out] x opus_int32 *: Returns one of the following values: - *
- *
#OPUS_AUTO
(default)
- *
#OPUS_SIGNAL_VOICE
Bias thresholds towards choosing LPC or Hybrid modes.
- *
#OPUS_SIGNAL_MUSIC
Bias thresholds towards choosing MDCT modes.
- *
- * @hideinitializer */ -#define OPUS_GET_SIGNAL(x) OPUS_GET_SIGNAL_REQUEST, __opus_check_int_ptr(x) - - -/** Configures the encoder's intended application. - * The initial value is a mandatory argument to the encoder_create function. - * @see OPUS_GET_APPLICATION - * @param[in] x opus_int32: Returns one of the following values: - *
- *
#OPUS_APPLICATION_VOIP
- *
Process signal for improved speech intelligibility.
- *
#OPUS_APPLICATION_AUDIO
- *
Favor faithfulness to the original input.
- *
#OPUS_APPLICATION_RESTRICTED_LOWDELAY
- *
Configure the minimum possible coding delay by disabling certain modes - * of operation.
- *
- * @hideinitializer */ -#define OPUS_SET_APPLICATION(x) OPUS_SET_APPLICATION_REQUEST, __opus_check_int(x) -/** Gets the encoder's configured application. - * @see OPUS_SET_APPLICATION - * @param[out] x opus_int32 *: Returns one of the following values: - *
- *
#OPUS_APPLICATION_VOIP
- *
Process signal for improved speech intelligibility.
- *
#OPUS_APPLICATION_AUDIO
- *
Favor faithfulness to the original input.
- *
#OPUS_APPLICATION_RESTRICTED_LOWDELAY
- *
Configure the minimum possible coding delay by disabling certain modes - * of operation.
- *
- * @hideinitializer */ -#define OPUS_GET_APPLICATION(x) OPUS_GET_APPLICATION_REQUEST, __opus_check_int_ptr(x) - -/** Gets the total samples of delay added by the entire codec. - * This can be queried by the encoder and then the provided number of samples can be - * skipped on from the start of the decoder's output to provide time aligned input - * and output. From the perspective of a decoding application the real data begins this many - * samples late. - * - * The decoder contribution to this delay is identical for all decoders, but the - * encoder portion of the delay may vary from implementation to implementation, - * version to version, or even depend on the encoder's initial configuration. - * Applications needing delay compensation should call this CTL rather than - * hard-coding a value. - * @param[out] x opus_int32 *: Number of lookahead samples - * @hideinitializer */ -#define OPUS_GET_LOOKAHEAD(x) OPUS_GET_LOOKAHEAD_REQUEST, __opus_check_int_ptr(x) - -/** Configures the encoder's use of inband forward error correction (FEC). - * @note This is only applicable to the LPC layer - * @see OPUS_GET_INBAND_FEC - * @param[in] x opus_int32: Allowed values: - *
- *
0
Disable inband FEC (default).
- *
1
Inband FEC enabled. If the packet loss rate is sufficiently high, Opus will automatically switch to SILK even at high rates to enable use of that FEC.
- *
2
Inband FEC enabled, but does not necessarily switch to SILK if we have music.
- *
- * @hideinitializer */ -#define OPUS_SET_INBAND_FEC(x) OPUS_SET_INBAND_FEC_REQUEST, __opus_check_int(x) -/** Gets encoder's configured use of inband forward error correction. - * @see OPUS_SET_INBAND_FEC - * @param[out] x opus_int32 *: Returns one of the following values: - *
- *
0
Inband FEC disabled (default).
- *
1
Inband FEC enabled. If the packet loss rate is sufficiently high, Opus will automatically switch to SILK even at high rates to enable use of that FEC.
- *
2
Inband FEC enabled, but does not necessarily switch to SILK if we have music.
- *
- * @hideinitializer */ -#define OPUS_GET_INBAND_FEC(x) OPUS_GET_INBAND_FEC_REQUEST, __opus_check_int_ptr(x) - -/** Configures the encoder's expected packet loss percentage. - * Higher values trigger progressively more loss resistant behavior in the encoder - * at the expense of quality at a given bitrate in the absence of packet loss, but - * greater quality under loss. - * @see OPUS_GET_PACKET_LOSS_PERC - * @param[in] x opus_int32: Loss percentage in the range 0-100, inclusive (default: 0). - * @hideinitializer */ -#define OPUS_SET_PACKET_LOSS_PERC(x) OPUS_SET_PACKET_LOSS_PERC_REQUEST, __opus_check_int(x) -/** Gets the encoder's configured packet loss percentage. - * @see OPUS_SET_PACKET_LOSS_PERC - * @param[out] x opus_int32 *: Returns the configured loss percentage - * in the range 0-100, inclusive (default: 0). - * @hideinitializer */ -#define OPUS_GET_PACKET_LOSS_PERC(x) OPUS_GET_PACKET_LOSS_PERC_REQUEST, __opus_check_int_ptr(x) - -/** Configures the encoder's use of discontinuous transmission (DTX). - * @note This is only applicable to the LPC layer - * @see OPUS_GET_DTX - * @param[in] x opus_int32: Allowed values: - *
- *
0
Disable DTX (default).
- *
1
Enabled DTX.
- *
- * @hideinitializer */ -#define OPUS_SET_DTX(x) OPUS_SET_DTX_REQUEST, __opus_check_int(x) -/** Gets encoder's configured use of discontinuous transmission. - * @see OPUS_SET_DTX - * @param[out] x opus_int32 *: Returns one of the following values: - *
- *
0
DTX disabled (default).
- *
1
DTX enabled.
- *
- * @hideinitializer */ -#define OPUS_GET_DTX(x) OPUS_GET_DTX_REQUEST, __opus_check_int_ptr(x) -/** Configures the depth of signal being encoded. - * - * This is a hint which helps the encoder identify silence and near-silence. - * It represents the number of significant bits of linear intensity below - * which the signal contains ignorable quantization or other noise. - * - * For example, OPUS_SET_LSB_DEPTH(14) would be an appropriate setting - * for G.711 u-law input. OPUS_SET_LSB_DEPTH(16) would be appropriate - * for 16-bit linear pcm input with opus_encode_float(). - * - * When using opus_encode() instead of opus_encode_float(), or when libopus - * is compiled for fixed-point, the encoder uses the minimum of the value - * set here and the value 16. - * - * @see OPUS_GET_LSB_DEPTH - * @param[in] x opus_int32: Input precision in bits, between 8 and 24 - * (default: 24). - * @hideinitializer */ -#define OPUS_SET_LSB_DEPTH(x) OPUS_SET_LSB_DEPTH_REQUEST, __opus_check_int(x) -/** Gets the encoder's configured signal depth. - * @see OPUS_SET_LSB_DEPTH - * @param[out] x opus_int32 *: Input precision in bits, between 8 and - * 24 (default: 24). - * @hideinitializer */ -#define OPUS_GET_LSB_DEPTH(x) OPUS_GET_LSB_DEPTH_REQUEST, __opus_check_int_ptr(x) - -/** Configures the encoder's use of variable duration frames. - * When variable duration is enabled, the encoder is free to use a shorter frame - * size than the one requested in the opus_encode*() call. - * It is then the user's responsibility - * to verify how much audio was encoded by checking the ToC byte of the encoded - * packet. The part of the audio that was not encoded needs to be resent to the - * encoder for the next call. Do not use this option unless you really - * know what you are doing. - * @see OPUS_GET_EXPERT_FRAME_DURATION - * @param[in] x opus_int32: Allowed values: - *
- *
OPUS_FRAMESIZE_ARG
Select frame size from the argument (default).
- *
OPUS_FRAMESIZE_2_5_MS
Use 2.5 ms frames.
- *
OPUS_FRAMESIZE_5_MS
Use 5 ms frames.
- *
OPUS_FRAMESIZE_10_MS
Use 10 ms frames.
- *
OPUS_FRAMESIZE_20_MS
Use 20 ms frames.
- *
OPUS_FRAMESIZE_40_MS
Use 40 ms frames.
- *
OPUS_FRAMESIZE_60_MS
Use 60 ms frames.
- *
OPUS_FRAMESIZE_80_MS
Use 80 ms frames.
- *
OPUS_FRAMESIZE_100_MS
Use 100 ms frames.
- *
OPUS_FRAMESIZE_120_MS
Use 120 ms frames.
- *
- * @hideinitializer */ -#define OPUS_SET_EXPERT_FRAME_DURATION(x) OPUS_SET_EXPERT_FRAME_DURATION_REQUEST, __opus_check_int(x) -/** Gets the encoder's configured use of variable duration frames. - * @see OPUS_SET_EXPERT_FRAME_DURATION - * @param[out] x opus_int32 *: Returns one of the following values: - *
- *
OPUS_FRAMESIZE_ARG
Select frame size from the argument (default).
- *
OPUS_FRAMESIZE_2_5_MS
Use 2.5 ms frames.
- *
OPUS_FRAMESIZE_5_MS
Use 5 ms frames.
- *
OPUS_FRAMESIZE_10_MS
Use 10 ms frames.
- *
OPUS_FRAMESIZE_20_MS
Use 20 ms frames.
- *
OPUS_FRAMESIZE_40_MS
Use 40 ms frames.
- *
OPUS_FRAMESIZE_60_MS
Use 60 ms frames.
- *
OPUS_FRAMESIZE_80_MS
Use 80 ms frames.
- *
OPUS_FRAMESIZE_100_MS
Use 100 ms frames.
- *
OPUS_FRAMESIZE_120_MS
Use 120 ms frames.
- *
- * @hideinitializer */ -#define OPUS_GET_EXPERT_FRAME_DURATION(x) OPUS_GET_EXPERT_FRAME_DURATION_REQUEST, __opus_check_int_ptr(x) - -/** If set to 1, disables almost all use of prediction, making frames almost - * completely independent. This reduces quality. - * @see OPUS_GET_PREDICTION_DISABLED - * @param[in] x opus_int32: Allowed values: - *
- *
0
Enable prediction (default).
- *
1
Disable prediction.
- *
- * @hideinitializer */ -#define OPUS_SET_PREDICTION_DISABLED(x) OPUS_SET_PREDICTION_DISABLED_REQUEST, __opus_check_int(x) -/** Gets the encoder's configured prediction status. - * @see OPUS_SET_PREDICTION_DISABLED - * @param[out] x opus_int32 *: Returns one of the following values: - *
- *
0
Prediction enabled (default).
- *
1
Prediction disabled.
- *
- * @hideinitializer */ -#define OPUS_GET_PREDICTION_DISABLED(x) OPUS_GET_PREDICTION_DISABLED_REQUEST, __opus_check_int_ptr(x) - -/** If non-zero, enables Deep Redundancy (DRED) and use the specified maximum number of 10-ms redundant frames - * @hideinitializer */ -#define OPUS_SET_DRED_DURATION(x) OPUS_SET_DRED_DURATION_REQUEST, __opus_check_int(x) -/** Gets the encoder's configured Deep Redundancy (DRED) maximum number of frames. - * @hideinitializer */ -#define OPUS_GET_DRED_DURATION(x) OPUS_GET_DRED_DURATION_REQUEST, __opus_check_int_ptr(x) - -/** Provide external DNN weights from binary object (only when explicitly built without the weights) - * @hideinitializer */ -#define OPUS_SET_DNN_BLOB(data, len) OPUS_SET_DNN_BLOB_REQUEST, __opus_check_void_ptr(data), __opus_check_int(len) - - -/**@}*/ - -/** @defgroup opus_genericctls Generic CTLs - * - * These macros are used with the \c opus_decoder_ctl and - * \c opus_encoder_ctl calls to generate a particular - * request. - * - * When called on an \c OpusDecoder they apply to that - * particular decoder instance. When called on an - * \c OpusEncoder they apply to the corresponding setting - * on that encoder instance, if present. - * - * Some usage examples: - * - * @code - * int ret; - * opus_int32 pitch; - * ret = opus_decoder_ctl(dec_ctx, OPUS_GET_PITCH(&pitch)); - * if (ret == OPUS_OK) return ret; - * - * opus_encoder_ctl(enc_ctx, OPUS_RESET_STATE); - * opus_decoder_ctl(dec_ctx, OPUS_RESET_STATE); - * - * opus_int32 enc_bw, dec_bw; - * opus_encoder_ctl(enc_ctx, OPUS_GET_BANDWIDTH(&enc_bw)); - * opus_decoder_ctl(dec_ctx, OPUS_GET_BANDWIDTH(&dec_bw)); - * if (enc_bw != dec_bw) { - * printf("packet bandwidth mismatch!\n"); - * } - * @endcode - * - * @see opus_encoder, opus_decoder_ctl, opus_encoder_ctl, opus_decoderctls, opus_encoderctls - * @{ - */ - -/** Resets the codec state to be equivalent to a freshly initialized state. - * This should be called when switching streams in order to prevent - * the back to back decoding from giving different results from - * one at a time decoding. - * @hideinitializer */ -#define OPUS_RESET_STATE 4028 - -/** Gets the final state of the codec's entropy coder. - * This is used for testing purposes, - * The encoder and decoder state should be identical after coding a payload - * (assuming no data corruption or software bugs) - * - * @param[out] x opus_uint32 *: Entropy coder state - * - * @hideinitializer */ -#define OPUS_GET_FINAL_RANGE(x) OPUS_GET_FINAL_RANGE_REQUEST, __opus_check_uint_ptr(x) - -/** Gets the encoder's configured bandpass or the decoder's last bandpass. - * @see OPUS_SET_BANDWIDTH - * @param[out] x opus_int32 *: Returns one of the following values: - *
- *
#OPUS_AUTO
(default)
- *
#OPUS_BANDWIDTH_NARROWBAND
4 kHz passband
- *
#OPUS_BANDWIDTH_MEDIUMBAND
6 kHz passband
- *
#OPUS_BANDWIDTH_WIDEBAND
8 kHz passband
- *
#OPUS_BANDWIDTH_SUPERWIDEBAND
12 kHz passband
- *
#OPUS_BANDWIDTH_FULLBAND
20 kHz passband
- *
- * @hideinitializer */ -#define OPUS_GET_BANDWIDTH(x) OPUS_GET_BANDWIDTH_REQUEST, __opus_check_int_ptr(x) - -/** Gets the sampling rate the encoder or decoder was initialized with. - * This simply returns the Fs value passed to opus_encoder_init() - * or opus_decoder_init(). - * @param[out] x opus_int32 *: Sampling rate of encoder or decoder. - * @hideinitializer - */ -#define OPUS_GET_SAMPLE_RATE(x) OPUS_GET_SAMPLE_RATE_REQUEST, __opus_check_int_ptr(x) - -/** If set to 1, disables the use of phase inversion for intensity stereo, - * improving the quality of mono downmixes, but slightly reducing normal - * stereo quality. Disabling phase inversion in the decoder does not comply - * with RFC 6716, although it does not cause any interoperability issue and - * is expected to become part of the Opus standard once RFC 6716 is updated - * by draft-ietf-codec-opus-update. - * @see OPUS_GET_PHASE_INVERSION_DISABLED - * @param[in] x opus_int32: Allowed values: - *
- *
0
Enable phase inversion (default).
- *
1
Disable phase inversion.
- *
- * @hideinitializer */ -#define OPUS_SET_PHASE_INVERSION_DISABLED(x) OPUS_SET_PHASE_INVERSION_DISABLED_REQUEST, __opus_check_int(x) -/** Gets the encoder's configured phase inversion status. - * @see OPUS_SET_PHASE_INVERSION_DISABLED - * @param[out] x opus_int32 *: Returns one of the following values: - *
- *
0
Stereo phase inversion enabled (default).
- *
1
Stereo phase inversion disabled.
- *
- * @hideinitializer */ -#define OPUS_GET_PHASE_INVERSION_DISABLED(x) OPUS_GET_PHASE_INVERSION_DISABLED_REQUEST, __opus_check_int_ptr(x) -/** Gets the DTX state of the encoder. - * Returns whether the last encoded frame was either a comfort noise update - * during DTX or not encoded because of DTX. - * @param[out] x opus_int32 *: Returns one of the following values: - *
- *
0
The encoder is not in DTX.
- *
1
The encoder is in DTX.
- *
- * @hideinitializer */ -#define OPUS_GET_IN_DTX(x) OPUS_GET_IN_DTX_REQUEST, __opus_check_int_ptr(x) - -/**@}*/ - -/** @defgroup opus_decoderctls Decoder related CTLs - * @see opus_genericctls, opus_encoderctls, opus_decoder - * @{ - */ - -/** Configures decoder gain adjustment. - * Scales the decoded output by a factor specified in Q8 dB units. - * This has a maximum range of -32768 to 32767 inclusive, and returns - * OPUS_BAD_ARG otherwise. The default is zero indicating no adjustment. - * This setting survives decoder reset. - * - * gain = pow(10, x/(20.0*256)) - * - * @param[in] x opus_int32: Amount to scale PCM signal by in Q8 dB units. - * @hideinitializer */ -#define OPUS_SET_GAIN(x) OPUS_SET_GAIN_REQUEST, __opus_check_int(x) -/** Gets the decoder's configured gain adjustment. @see OPUS_SET_GAIN - * - * @param[out] x opus_int32 *: Amount to scale PCM signal by in Q8 dB units. - * @hideinitializer */ -#define OPUS_GET_GAIN(x) OPUS_GET_GAIN_REQUEST, __opus_check_int_ptr(x) - -/** Gets the duration (in samples) of the last packet successfully decoded or concealed. - * @param[out] x opus_int32 *: Number of samples (at current sampling rate). - * @hideinitializer */ -#define OPUS_GET_LAST_PACKET_DURATION(x) OPUS_GET_LAST_PACKET_DURATION_REQUEST, __opus_check_int_ptr(x) - -/** Gets the pitch of the last decoded frame, if available. - * This can be used for any post-processing algorithm requiring the use of pitch, - * e.g. time stretching/shortening. If the last frame was not voiced, or if the - * pitch was not coded in the frame, then zero is returned. - * - * This CTL is only implemented for decoder instances. - * - * @param[out] x opus_int32 *: pitch period at 48 kHz (or 0 if not available) - * - * @hideinitializer */ -#define OPUS_GET_PITCH(x) OPUS_GET_PITCH_REQUEST, __opus_check_int_ptr(x) - -/**@}*/ - -/** @defgroup opus_libinfo Opus library information functions - * @{ - */ - -/** Converts an opus error code into a human readable string. - * - * @param[in] error int: Error number - * @returns Error string - */ -OPUS_EXPORT const char *opus_strerror(int error); - -/** Gets the libopus version string. - * - * Applications may look for the substring "-fixed" in the version string to - * determine whether they have a fixed-point or floating-point build at - * runtime. - * - * @returns Version string - */ -OPUS_EXPORT const char *opus_get_version_string(void); -/**@}*/ - -#ifdef __cplusplus -} -#endif - -#endif /* OPUS_DEFINES_H */ diff --git a/go/teamspeak/.opus/install/x86/include/opus/opus_multistream.h b/go/teamspeak/.opus/install/x86/include/opus/opus_multistream.h deleted file mode 100644 index 824cc55..0000000 --- a/go/teamspeak/.opus/install/x86/include/opus/opus_multistream.h +++ /dev/null @@ -1,660 +0,0 @@ -/* Copyright (c) 2011 Xiph.Org Foundation - Written by Jean-Marc Valin */ -/* - Redistribution and use in source and binary forms, with or without - modification, are permitted provided that the following conditions - are met: - - - Redistributions of source code must retain the above copyright - notice, this list of conditions and the following disclaimer. - - - Redistributions in binary form must reproduce the above copyright - notice, this list of conditions and the following disclaimer in the - documentation and/or other materials provided with the distribution. - - THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS - ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT - LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR - A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER - OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, - EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, - PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR - PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF - LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING - NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS - SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. -*/ - -/** - * @file opus_multistream.h - * @brief Opus reference implementation multistream API - */ - -#ifndef OPUS_MULTISTREAM_H -#define OPUS_MULTISTREAM_H - -#include "opus.h" - -#ifdef __cplusplus -extern "C" { -#endif - -/** @cond OPUS_INTERNAL_DOC */ - -/** Macros to trigger compilation errors when the wrong types are provided to a - * CTL. */ -/**@{*/ -#define __opus_check_encstate_ptr(ptr) ((ptr) + ((ptr) - (OpusEncoder**)(ptr))) -#define __opus_check_decstate_ptr(ptr) ((ptr) + ((ptr) - (OpusDecoder**)(ptr))) -/**@}*/ - -/** These are the actual encoder and decoder CTL ID numbers. - * They should not be used directly by applications. - * In general, SETs should be even and GETs should be odd.*/ -/**@{*/ -#define OPUS_MULTISTREAM_GET_ENCODER_STATE_REQUEST 5120 -#define OPUS_MULTISTREAM_GET_DECODER_STATE_REQUEST 5122 -/**@}*/ - -/** @endcond */ - -/** @defgroup opus_multistream_ctls Multistream specific encoder and decoder CTLs - * - * These are convenience macros that are specific to the - * opus_multistream_encoder_ctl() and opus_multistream_decoder_ctl() - * interface. - * The CTLs from @ref opus_genericctls, @ref opus_encoderctls, and - * @ref opus_decoderctls may be applied to a multistream encoder or decoder as - * well. - * In addition, you may retrieve the encoder or decoder state for an specific - * stream via #OPUS_MULTISTREAM_GET_ENCODER_STATE or - * #OPUS_MULTISTREAM_GET_DECODER_STATE and apply CTLs to it individually. - */ -/**@{*/ - -/** Gets the encoder state for an individual stream of a multistream encoder. - * @param[in] x opus_int32: The index of the stream whose encoder you - * wish to retrieve. - * This must be non-negative and less than - * the streams parameter used - * to initialize the encoder. - * @param[out] y OpusEncoder**: Returns a pointer to the given - * encoder state. - * @retval OPUS_BAD_ARG The index of the requested stream was out of range. - * @hideinitializer - */ -#define OPUS_MULTISTREAM_GET_ENCODER_STATE(x,y) OPUS_MULTISTREAM_GET_ENCODER_STATE_REQUEST, __opus_check_int(x), __opus_check_encstate_ptr(y) - -/** Gets the decoder state for an individual stream of a multistream decoder. - * @param[in] x opus_int32: The index of the stream whose decoder you - * wish to retrieve. - * This must be non-negative and less than - * the streams parameter used - * to initialize the decoder. - * @param[out] y OpusDecoder**: Returns a pointer to the given - * decoder state. - * @retval OPUS_BAD_ARG The index of the requested stream was out of range. - * @hideinitializer - */ -#define OPUS_MULTISTREAM_GET_DECODER_STATE(x,y) OPUS_MULTISTREAM_GET_DECODER_STATE_REQUEST, __opus_check_int(x), __opus_check_decstate_ptr(y) - -/**@}*/ - -/** @defgroup opus_multistream Opus Multistream API - * @{ - * - * The multistream API allows individual Opus streams to be combined into a - * single packet, enabling support for up to 255 channels. Unlike an - * elementary Opus stream, the encoder and decoder must negotiate the channel - * configuration before the decoder can successfully interpret the data in the - * packets produced by the encoder. Some basic information, such as packet - * duration, can be computed without any special negotiation. - * - * The format for multistream Opus packets is defined in - * RFC 7845 - * and is based on the self-delimited Opus framing described in Appendix B of - * RFC 6716. - * Normal Opus packets are just a degenerate case of multistream Opus packets, - * and can be encoded or decoded with the multistream API by setting - * streams to 1 when initializing the encoder or - * decoder. - * - * Multistream Opus streams can contain up to 255 elementary Opus streams. - * These may be either "uncoupled" or "coupled", indicating that the decoder - * is configured to decode them to either 1 or 2 channels, respectively. - * The streams are ordered so that all coupled streams appear at the - * beginning. - * - * A mapping table defines which decoded channel i - * should be used for each input/output (I/O) channel j. This table is - * typically provided as an unsigned char array. - * Let i = mapping[j] be the index for I/O channel j. - * If i < 2*coupled_streams, then I/O channel j is - * encoded as the left channel of stream (i/2) if i - * is even, or as the right channel of stream (i/2) if - * i is odd. Otherwise, I/O channel j is encoded as - * mono in stream (i - coupled_streams), unless it has the special - * value 255, in which case it is omitted from the encoding entirely (the - * decoder will reproduce it as silence). Each value i must either - * be the special value 255 or be less than streams + coupled_streams. - * - * The output channels specified by the encoder - * should use the - * Vorbis - * channel ordering. A decoder may wish to apply an additional permutation - * to the mapping the encoder used to achieve a different output channel - * order (e.g. for outputting in WAV order). - * - * Each multistream packet contains an Opus packet for each stream, and all of - * the Opus packets in a single multistream packet must have the same - * duration. Therefore the duration of a multistream packet can be extracted - * from the TOC sequence of the first stream, which is located at the - * beginning of the packet, just like an elementary Opus stream: - * - * @code - * int nb_samples; - * int nb_frames; - * nb_frames = opus_packet_get_nb_frames(data, len); - * if (nb_frames < 1) - * return nb_frames; - * nb_samples = opus_packet_get_samples_per_frame(data, 48000) * nb_frames; - * @endcode - * - * The general encoding and decoding process proceeds exactly the same as in - * the normal @ref opus_encoder and @ref opus_decoder APIs. - * See their documentation for an overview of how to use the corresponding - * multistream functions. - */ - -/** Opus multistream encoder state. - * This contains the complete state of a multistream Opus encoder. - * It is position independent and can be freely copied. - * @see opus_multistream_encoder_create - * @see opus_multistream_encoder_init - */ -typedef struct OpusMSEncoder OpusMSEncoder; - -/** Opus multistream decoder state. - * This contains the complete state of a multistream Opus decoder. - * It is position independent and can be freely copied. - * @see opus_multistream_decoder_create - * @see opus_multistream_decoder_init - */ -typedef struct OpusMSDecoder OpusMSDecoder; - -/**\name Multistream encoder functions */ -/**@{*/ - -/** Gets the size of an OpusMSEncoder structure. - * @param streams int: The total number of streams to encode from the - * input. - * This must be no more than 255. - * @param coupled_streams int: Number of coupled (2 channel) streams - * to encode. - * This must be no larger than the total - * number of streams. - * Additionally, The total number of - * encoded channels (streams + - * coupled_streams) must be no - * more than 255. - * @returns The size in bytes on success, or a negative error code - * (see @ref opus_errorcodes) on error. - */ -OPUS_EXPORT OPUS_WARN_UNUSED_RESULT opus_int32 opus_multistream_encoder_get_size( - int streams, - int coupled_streams -); - -OPUS_EXPORT OPUS_WARN_UNUSED_RESULT opus_int32 opus_multistream_surround_encoder_get_size( - int channels, - int mapping_family -); - - -/** Allocates and initializes a multistream encoder state. - * Call opus_multistream_encoder_destroy() to release - * this object when finished. - * @param Fs opus_int32: Sampling rate of the input signal (in Hz). - * This must be one of 8000, 12000, 16000, - * 24000, or 48000. - * @param channels int: Number of channels in the input signal. - * This must be at most 255. - * It may be greater than the number of - * coded channels (streams + - * coupled_streams). - * @param streams int: The total number of streams to encode from the - * input. - * This must be no more than the number of channels. - * @param coupled_streams int: Number of coupled (2 channel) streams - * to encode. - * This must be no larger than the total - * number of streams. - * Additionally, The total number of - * encoded channels (streams + - * coupled_streams) must be no - * more than the number of input channels. - * @param[in] mapping const unsigned char[channels]: Mapping from - * encoded channels to input channels, as described in - * @ref opus_multistream. As an extra constraint, the - * multistream encoder does not allow encoding coupled - * streams for which one channel is unused since this - * is never a good idea. - * @param application int: The target encoder application. - * This must be one of the following: - *
- *
#OPUS_APPLICATION_VOIP
- *
Process signal for improved speech intelligibility.
- *
#OPUS_APPLICATION_AUDIO
- *
Favor faithfulness to the original input.
- *
#OPUS_APPLICATION_RESTRICTED_LOWDELAY
- *
Configure the minimum possible coding delay by disabling certain modes - * of operation.
- *
- * @param[out] error int *: Returns #OPUS_OK on success, or an error - * code (see @ref opus_errorcodes) on - * failure. - */ -OPUS_EXPORT OPUS_WARN_UNUSED_RESULT OpusMSEncoder *opus_multistream_encoder_create( - opus_int32 Fs, - int channels, - int streams, - int coupled_streams, - const unsigned char *mapping, - int application, - int *error -) OPUS_ARG_NONNULL(5); - -OPUS_EXPORT OPUS_WARN_UNUSED_RESULT OpusMSEncoder *opus_multistream_surround_encoder_create( - opus_int32 Fs, - int channels, - int mapping_family, - int *streams, - int *coupled_streams, - unsigned char *mapping, - int application, - int *error -) OPUS_ARG_NONNULL(4) OPUS_ARG_NONNULL(5) OPUS_ARG_NONNULL(6); - -/** Initialize a previously allocated multistream encoder state. - * The memory pointed to by \a st must be at least the size returned by - * opus_multistream_encoder_get_size(). - * This is intended for applications which use their own allocator instead of - * malloc. - * To reset a previously initialized state, use the #OPUS_RESET_STATE CTL. - * @see opus_multistream_encoder_create - * @see opus_multistream_encoder_get_size - * @param st OpusMSEncoder*: Multistream encoder state to initialize. - * @param Fs opus_int32: Sampling rate of the input signal (in Hz). - * This must be one of 8000, 12000, 16000, - * 24000, or 48000. - * @param channels int: Number of channels in the input signal. - * This must be at most 255. - * It may be greater than the number of - * coded channels (streams + - * coupled_streams). - * @param streams int: The total number of streams to encode from the - * input. - * This must be no more than the number of channels. - * @param coupled_streams int: Number of coupled (2 channel) streams - * to encode. - * This must be no larger than the total - * number of streams. - * Additionally, The total number of - * encoded channels (streams + - * coupled_streams) must be no - * more than the number of input channels. - * @param[in] mapping const unsigned char[channels]: Mapping from - * encoded channels to input channels, as described in - * @ref opus_multistream. As an extra constraint, the - * multistream encoder does not allow encoding coupled - * streams for which one channel is unused since this - * is never a good idea. - * @param application int: The target encoder application. - * This must be one of the following: - *
- *
#OPUS_APPLICATION_VOIP
- *
Process signal for improved speech intelligibility.
- *
#OPUS_APPLICATION_AUDIO
- *
Favor faithfulness to the original input.
- *
#OPUS_APPLICATION_RESTRICTED_LOWDELAY
- *
Configure the minimum possible coding delay by disabling certain modes - * of operation.
- *
- * @returns #OPUS_OK on success, or an error code (see @ref opus_errorcodes) - * on failure. - */ -OPUS_EXPORT int opus_multistream_encoder_init( - OpusMSEncoder *st, - opus_int32 Fs, - int channels, - int streams, - int coupled_streams, - const unsigned char *mapping, - int application -) OPUS_ARG_NONNULL(1) OPUS_ARG_NONNULL(6); - -OPUS_EXPORT int opus_multistream_surround_encoder_init( - OpusMSEncoder *st, - opus_int32 Fs, - int channels, - int mapping_family, - int *streams, - int *coupled_streams, - unsigned char *mapping, - int application -) OPUS_ARG_NONNULL(1) OPUS_ARG_NONNULL(5) OPUS_ARG_NONNULL(6) OPUS_ARG_NONNULL(7); - -/** Encodes a multistream Opus frame. - * @param st OpusMSEncoder*: Multistream encoder state. - * @param[in] pcm const opus_int16*: The input signal as interleaved - * samples. - * This must contain - * frame_size*channels - * samples. - * @param frame_size int: Number of samples per channel in the input - * signal. - * This must be an Opus frame size for the - * encoder's sampling rate. - * For example, at 48 kHz the permitted values - * are 120, 240, 480, 960, 1920, and 2880. - * Passing in a duration of less than 10 ms - * (480 samples at 48 kHz) will prevent the - * encoder from using the LPC or hybrid modes. - * @param[out] data unsigned char*: Output payload. - * This must contain storage for at - * least \a max_data_bytes. - * @param [in] max_data_bytes opus_int32: Size of the allocated - * memory for the output - * payload. This may be - * used to impose an upper limit on - * the instant bitrate, but should - * not be used as the only bitrate - * control. Use #OPUS_SET_BITRATE to - * control the bitrate. - * @returns The length of the encoded packet (in bytes) on success or a - * negative error code (see @ref opus_errorcodes) on failure. - */ -OPUS_EXPORT OPUS_WARN_UNUSED_RESULT int opus_multistream_encode( - OpusMSEncoder *st, - const opus_int16 *pcm, - int frame_size, - unsigned char *data, - opus_int32 max_data_bytes -) OPUS_ARG_NONNULL(1) OPUS_ARG_NONNULL(2) OPUS_ARG_NONNULL(4); - -/** Encodes a multistream Opus frame from floating point input. - * @param st OpusMSEncoder*: Multistream encoder state. - * @param[in] pcm const float*: The input signal as interleaved - * samples with a normal range of - * +/-1.0. - * Samples with a range beyond +/-1.0 - * are supported but will be clipped by - * decoders using the integer API and - * should only be used if it is known - * that the far end supports extended - * dynamic range. - * This must contain - * frame_size*channels - * samples. - * @param frame_size int: Number of samples per channel in the input - * signal. - * This must be an Opus frame size for the - * encoder's sampling rate. - * For example, at 48 kHz the permitted values - * are 120, 240, 480, 960, 1920, and 2880. - * Passing in a duration of less than 10 ms - * (480 samples at 48 kHz) will prevent the - * encoder from using the LPC or hybrid modes. - * @param[out] data unsigned char*: Output payload. - * This must contain storage for at - * least \a max_data_bytes. - * @param [in] max_data_bytes opus_int32: Size of the allocated - * memory for the output - * payload. This may be - * used to impose an upper limit on - * the instant bitrate, but should - * not be used as the only bitrate - * control. Use #OPUS_SET_BITRATE to - * control the bitrate. - * @returns The length of the encoded packet (in bytes) on success or a - * negative error code (see @ref opus_errorcodes) on failure. - */ -OPUS_EXPORT OPUS_WARN_UNUSED_RESULT int opus_multistream_encode_float( - OpusMSEncoder *st, - const float *pcm, - int frame_size, - unsigned char *data, - opus_int32 max_data_bytes -) OPUS_ARG_NONNULL(1) OPUS_ARG_NONNULL(2) OPUS_ARG_NONNULL(4); - -/** Frees an OpusMSEncoder allocated by - * opus_multistream_encoder_create(). - * @param st OpusMSEncoder*: Multistream encoder state to be freed. - */ -OPUS_EXPORT void opus_multistream_encoder_destroy(OpusMSEncoder *st); - -/** Perform a CTL function on a multistream Opus encoder. - * - * Generally the request and subsequent arguments are generated by a - * convenience macro. - * @param st OpusMSEncoder*: Multistream encoder state. - * @param request This and all remaining parameters should be replaced by one - * of the convenience macros in @ref opus_genericctls, - * @ref opus_encoderctls, or @ref opus_multistream_ctls. - * @see opus_genericctls - * @see opus_encoderctls - * @see opus_multistream_ctls - */ -OPUS_EXPORT int opus_multistream_encoder_ctl(OpusMSEncoder *st, int request, ...) OPUS_ARG_NONNULL(1); - -/**@}*/ - -/**\name Multistream decoder functions */ -/**@{*/ - -/** Gets the size of an OpusMSDecoder structure. - * @param streams int: The total number of streams coded in the - * input. - * This must be no more than 255. - * @param coupled_streams int: Number streams to decode as coupled - * (2 channel) streams. - * This must be no larger than the total - * number of streams. - * Additionally, The total number of - * coded channels (streams + - * coupled_streams) must be no - * more than 255. - * @returns The size in bytes on success, or a negative error code - * (see @ref opus_errorcodes) on error. - */ -OPUS_EXPORT OPUS_WARN_UNUSED_RESULT opus_int32 opus_multistream_decoder_get_size( - int streams, - int coupled_streams -); - -/** Allocates and initializes a multistream decoder state. - * Call opus_multistream_decoder_destroy() to release - * this object when finished. - * @param Fs opus_int32: Sampling rate to decode at (in Hz). - * This must be one of 8000, 12000, 16000, - * 24000, or 48000. - * @param channels int: Number of channels to output. - * This must be at most 255. - * It may be different from the number of coded - * channels (streams + - * coupled_streams). - * @param streams int: The total number of streams coded in the - * input. - * This must be no more than 255. - * @param coupled_streams int: Number of streams to decode as coupled - * (2 channel) streams. - * This must be no larger than the total - * number of streams. - * Additionally, The total number of - * coded channels (streams + - * coupled_streams) must be no - * more than 255. - * @param[in] mapping const unsigned char[channels]: Mapping from - * coded channels to output channels, as described in - * @ref opus_multistream. - * @param[out] error int *: Returns #OPUS_OK on success, or an error - * code (see @ref opus_errorcodes) on - * failure. - */ -OPUS_EXPORT OPUS_WARN_UNUSED_RESULT OpusMSDecoder *opus_multistream_decoder_create( - opus_int32 Fs, - int channels, - int streams, - int coupled_streams, - const unsigned char *mapping, - int *error -) OPUS_ARG_NONNULL(5); - -/** Intialize a previously allocated decoder state object. - * The memory pointed to by \a st must be at least the size returned by - * opus_multistream_encoder_get_size(). - * This is intended for applications which use their own allocator instead of - * malloc. - * To reset a previously initialized state, use the #OPUS_RESET_STATE CTL. - * @see opus_multistream_decoder_create - * @see opus_multistream_deocder_get_size - * @param st OpusMSEncoder*: Multistream encoder state to initialize. - * @param Fs opus_int32: Sampling rate to decode at (in Hz). - * This must be one of 8000, 12000, 16000, - * 24000, or 48000. - * @param channels int: Number of channels to output. - * This must be at most 255. - * It may be different from the number of coded - * channels (streams + - * coupled_streams). - * @param streams int: The total number of streams coded in the - * input. - * This must be no more than 255. - * @param coupled_streams int: Number of streams to decode as coupled - * (2 channel) streams. - * This must be no larger than the total - * number of streams. - * Additionally, The total number of - * coded channels (streams + - * coupled_streams) must be no - * more than 255. - * @param[in] mapping const unsigned char[channels]: Mapping from - * coded channels to output channels, as described in - * @ref opus_multistream. - * @returns #OPUS_OK on success, or an error code (see @ref opus_errorcodes) - * on failure. - */ -OPUS_EXPORT int opus_multistream_decoder_init( - OpusMSDecoder *st, - opus_int32 Fs, - int channels, - int streams, - int coupled_streams, - const unsigned char *mapping -) OPUS_ARG_NONNULL(1) OPUS_ARG_NONNULL(6); - -/** Decode a multistream Opus packet. - * @param st OpusMSDecoder*: Multistream decoder state. - * @param[in] data const unsigned char*: Input payload. - * Use a NULL - * pointer to indicate packet - * loss. - * @param len opus_int32: Number of bytes in payload. - * @param[out] pcm opus_int16*: Output signal, with interleaved - * samples. - * This must contain room for - * frame_size*channels - * samples. - * @param frame_size int: The number of samples per channel of - * available space in \a pcm. - * If this is less than the maximum packet duration - * (120 ms; 5760 for 48kHz), this function will not be capable - * of decoding some packets. In the case of PLC (data==NULL) - * or FEC (decode_fec=1), then frame_size needs to be exactly - * the duration of audio that is missing, otherwise the - * decoder will not be in the optimal state to decode the - * next incoming packet. For the PLC and FEC cases, frame_size - * must be a multiple of 2.5 ms. - * @param decode_fec int: Flag (0 or 1) to request that any in-band - * forward error correction data be decoded. - * If no such data is available, the frame is - * decoded as if it were lost. - * @returns Number of samples decoded on success or a negative error code - * (see @ref opus_errorcodes) on failure. - */ -OPUS_EXPORT OPUS_WARN_UNUSED_RESULT int opus_multistream_decode( - OpusMSDecoder *st, - const unsigned char *data, - opus_int32 len, - opus_int16 *pcm, - int frame_size, - int decode_fec -) OPUS_ARG_NONNULL(1) OPUS_ARG_NONNULL(4); - -/** Decode a multistream Opus packet with floating point output. - * @param st OpusMSDecoder*: Multistream decoder state. - * @param[in] data const unsigned char*: Input payload. - * Use a NULL - * pointer to indicate packet - * loss. - * @param len opus_int32: Number of bytes in payload. - * @param[out] pcm opus_int16*: Output signal, with interleaved - * samples. - * This must contain room for - * frame_size*channels - * samples. - * @param frame_size int: The number of samples per channel of - * available space in \a pcm. - * If this is less than the maximum packet duration - * (120 ms; 5760 for 48kHz), this function will not be capable - * of decoding some packets. In the case of PLC (data==NULL) - * or FEC (decode_fec=1), then frame_size needs to be exactly - * the duration of audio that is missing, otherwise the - * decoder will not be in the optimal state to decode the - * next incoming packet. For the PLC and FEC cases, frame_size - * must be a multiple of 2.5 ms. - * @param decode_fec int: Flag (0 or 1) to request that any in-band - * forward error correction data be decoded. - * If no such data is available, the frame is - * decoded as if it were lost. - * @returns Number of samples decoded on success or a negative error code - * (see @ref opus_errorcodes) on failure. - */ -OPUS_EXPORT OPUS_WARN_UNUSED_RESULT int opus_multistream_decode_float( - OpusMSDecoder *st, - const unsigned char *data, - opus_int32 len, - float *pcm, - int frame_size, - int decode_fec -) OPUS_ARG_NONNULL(1) OPUS_ARG_NONNULL(4); - -/** Perform a CTL function on a multistream Opus decoder. - * - * Generally the request and subsequent arguments are generated by a - * convenience macro. - * @param st OpusMSDecoder*: Multistream decoder state. - * @param request This and all remaining parameters should be replaced by one - * of the convenience macros in @ref opus_genericctls, - * @ref opus_decoderctls, or @ref opus_multistream_ctls. - * @see opus_genericctls - * @see opus_decoderctls - * @see opus_multistream_ctls - */ -OPUS_EXPORT int opus_multistream_decoder_ctl(OpusMSDecoder *st, int request, ...) OPUS_ARG_NONNULL(1); - -/** Frees an OpusMSDecoder allocated by - * opus_multistream_decoder_create(). - * @param st OpusMSDecoder: Multistream decoder state to be freed. - */ -OPUS_EXPORT void opus_multistream_decoder_destroy(OpusMSDecoder *st); - -/**@}*/ - -/**@}*/ - -#ifdef __cplusplus -} -#endif - -#endif /* OPUS_MULTISTREAM_H */ diff --git a/go/teamspeak/.opus/install/x86/include/opus/opus_projection.h b/go/teamspeak/.opus/install/x86/include/opus/opus_projection.h deleted file mode 100644 index 9dabf4e..0000000 --- a/go/teamspeak/.opus/install/x86/include/opus/opus_projection.h +++ /dev/null @@ -1,568 +0,0 @@ -/* Copyright (c) 2017 Google Inc. - Written by Andrew Allen */ -/* - Redistribution and use in source and binary forms, with or without - modification, are permitted provided that the following conditions - are met: - - - Redistributions of source code must retain the above copyright - notice, this list of conditions and the following disclaimer. - - - Redistributions in binary form must reproduce the above copyright - notice, this list of conditions and the following disclaimer in the - documentation and/or other materials provided with the distribution. - - THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS - ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT - LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR - A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER - OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, - EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, - PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR - PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF - LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING - NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS - SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. -*/ - -/** - * @file opus_projection.h - * @brief Opus projection reference API - */ - -#ifndef OPUS_PROJECTION_H -#define OPUS_PROJECTION_H - -#include "opus_multistream.h" - -#ifdef __cplusplus -extern "C" { -#endif - -/** @cond OPUS_INTERNAL_DOC */ - -/** These are the actual encoder and decoder CTL ID numbers. - * They should not be used directly by applications.c - * In general, SETs should be even and GETs should be odd.*/ -/**@{*/ -#define OPUS_PROJECTION_GET_DEMIXING_MATRIX_GAIN_REQUEST 6001 -#define OPUS_PROJECTION_GET_DEMIXING_MATRIX_SIZE_REQUEST 6003 -#define OPUS_PROJECTION_GET_DEMIXING_MATRIX_REQUEST 6005 -/**@}*/ - - -/** @endcond */ - -/** @defgroup opus_projection_ctls Projection specific encoder and decoder CTLs - * - * These are convenience macros that are specific to the - * opus_projection_encoder_ctl() and opus_projection_decoder_ctl() - * interface. - * The CTLs from @ref opus_genericctls, @ref opus_encoderctls, - * @ref opus_decoderctls, and @ref opus_multistream_ctls may be applied to a - * projection encoder or decoder as well. - */ -/**@{*/ - -/** Gets the gain (in dB. S7.8-format) of the demixing matrix from the encoder. - * @param[out] x opus_int32 *: Returns the gain (in dB. S7.8-format) - * of the demixing matrix. - * @hideinitializer - */ -#define OPUS_PROJECTION_GET_DEMIXING_MATRIX_GAIN(x) OPUS_PROJECTION_GET_DEMIXING_MATRIX_GAIN_REQUEST, __opus_check_int_ptr(x) - - -/** Gets the size in bytes of the demixing matrix from the encoder. - * @param[out] x opus_int32 *: Returns the size in bytes of the - * demixing matrix. - * @hideinitializer - */ -#define OPUS_PROJECTION_GET_DEMIXING_MATRIX_SIZE(x) OPUS_PROJECTION_GET_DEMIXING_MATRIX_SIZE_REQUEST, __opus_check_int_ptr(x) - - -/** Copies the demixing matrix to the supplied pointer location. - * @param[out] x unsigned char *: Returns the demixing matrix to the - * supplied pointer location. - * @param y opus_int32: The size in bytes of the reserved memory at the - * pointer location. - * @hideinitializer - */ -#define OPUS_PROJECTION_GET_DEMIXING_MATRIX(x,y) OPUS_PROJECTION_GET_DEMIXING_MATRIX_REQUEST, x, __opus_check_int(y) - - -/**@}*/ - -/** Opus projection encoder state. - * This contains the complete state of a projection Opus encoder. - * It is position independent and can be freely copied. - * @see opus_projection_ambisonics_encoder_create - */ -typedef struct OpusProjectionEncoder OpusProjectionEncoder; - - -/** Opus projection decoder state. - * This contains the complete state of a projection Opus decoder. - * It is position independent and can be freely copied. - * @see opus_projection_decoder_create - * @see opus_projection_decoder_init - */ -typedef struct OpusProjectionDecoder OpusProjectionDecoder; - - -/**\name Projection encoder functions */ -/**@{*/ - -/** Gets the size of an OpusProjectionEncoder structure. - * @param channels int: The total number of input channels to encode. - * This must be no more than 255. - * @param mapping_family int: The mapping family to use for selecting - * the appropriate projection. - * @returns The size in bytes on success, or a negative error code - * (see @ref opus_errorcodes) on error. - */ -OPUS_EXPORT OPUS_WARN_UNUSED_RESULT opus_int32 opus_projection_ambisonics_encoder_get_size( - int channels, - int mapping_family -); - - -/** Allocates and initializes a projection encoder state. - * Call opus_projection_encoder_destroy() to release - * this object when finished. - * @param Fs opus_int32: Sampling rate of the input signal (in Hz). - * This must be one of 8000, 12000, 16000, - * 24000, or 48000. - * @param channels int: Number of channels in the input signal. - * This must be at most 255. - * It may be greater than the number of - * coded channels (streams + - * coupled_streams). - * @param mapping_family int: The mapping family to use for selecting - * the appropriate projection. - * @param[out] streams int *: The total number of streams that will - * be encoded from the input. - * @param[out] coupled_streams int *: Number of coupled (2 channel) - * streams that will be encoded from the input. - * @param application int: The target encoder application. - * This must be one of the following: - *
- *
#OPUS_APPLICATION_VOIP
- *
Process signal for improved speech intelligibility.
- *
#OPUS_APPLICATION_AUDIO
- *
Favor faithfulness to the original input.
- *
#OPUS_APPLICATION_RESTRICTED_LOWDELAY
- *
Configure the minimum possible coding delay by disabling certain modes - * of operation.
- *
- * @param[out] error int *: Returns #OPUS_OK on success, or an error - * code (see @ref opus_errorcodes) on - * failure. - */ -OPUS_EXPORT OPUS_WARN_UNUSED_RESULT OpusProjectionEncoder *opus_projection_ambisonics_encoder_create( - opus_int32 Fs, - int channels, - int mapping_family, - int *streams, - int *coupled_streams, - int application, - int *error -) OPUS_ARG_NONNULL(4) OPUS_ARG_NONNULL(5); - - -/** Initialize a previously allocated projection encoder state. - * The memory pointed to by \a st must be at least the size returned by - * opus_projection_ambisonics_encoder_get_size(). - * This is intended for applications which use their own allocator instead of - * malloc. - * To reset a previously initialized state, use the #OPUS_RESET_STATE CTL. - * @see opus_projection_ambisonics_encoder_create - * @see opus_projection_ambisonics_encoder_get_size - * @param st OpusProjectionEncoder*: Projection encoder state to initialize. - * @param Fs opus_int32: Sampling rate of the input signal (in Hz). - * This must be one of 8000, 12000, 16000, - * 24000, or 48000. - * @param channels int: Number of channels in the input signal. - * This must be at most 255. - * It may be greater than the number of - * coded channels (streams + - * coupled_streams). - * @param streams int: The total number of streams to encode from the - * input. - * This must be no more than the number of channels. - * @param coupled_streams int: Number of coupled (2 channel) streams - * to encode. - * This must be no larger than the total - * number of streams. - * Additionally, The total number of - * encoded channels (streams + - * coupled_streams) must be no - * more than the number of input channels. - * @param application int: The target encoder application. - * This must be one of the following: - *
- *
#OPUS_APPLICATION_VOIP
- *
Process signal for improved speech intelligibility.
- *
#OPUS_APPLICATION_AUDIO
- *
Favor faithfulness to the original input.
- *
#OPUS_APPLICATION_RESTRICTED_LOWDELAY
- *
Configure the minimum possible coding delay by disabling certain modes - * of operation.
- *
- * @returns #OPUS_OK on success, or an error code (see @ref opus_errorcodes) - * on failure. - */ -OPUS_EXPORT int opus_projection_ambisonics_encoder_init( - OpusProjectionEncoder *st, - opus_int32 Fs, - int channels, - int mapping_family, - int *streams, - int *coupled_streams, - int application -) OPUS_ARG_NONNULL(1) OPUS_ARG_NONNULL(5) OPUS_ARG_NONNULL(6); - - -/** Encodes a projection Opus frame. - * @param st OpusProjectionEncoder*: Projection encoder state. - * @param[in] pcm const opus_int16*: The input signal as interleaved - * samples. - * This must contain - * frame_size*channels - * samples. - * @param frame_size int: Number of samples per channel in the input - * signal. - * This must be an Opus frame size for the - * encoder's sampling rate. - * For example, at 48 kHz the permitted values - * are 120, 240, 480, 960, 1920, and 2880. - * Passing in a duration of less than 10 ms - * (480 samples at 48 kHz) will prevent the - * encoder from using the LPC or hybrid modes. - * @param[out] data unsigned char*: Output payload. - * This must contain storage for at - * least \a max_data_bytes. - * @param [in] max_data_bytes opus_int32: Size of the allocated - * memory for the output - * payload. This may be - * used to impose an upper limit on - * the instant bitrate, but should - * not be used as the only bitrate - * control. Use #OPUS_SET_BITRATE to - * control the bitrate. - * @returns The length of the encoded packet (in bytes) on success or a - * negative error code (see @ref opus_errorcodes) on failure. - */ -OPUS_EXPORT OPUS_WARN_UNUSED_RESULT int opus_projection_encode( - OpusProjectionEncoder *st, - const opus_int16 *pcm, - int frame_size, - unsigned char *data, - opus_int32 max_data_bytes -) OPUS_ARG_NONNULL(1) OPUS_ARG_NONNULL(2) OPUS_ARG_NONNULL(4); - - -/** Encodes a projection Opus frame from floating point input. - * @param st OpusProjectionEncoder*: Projection encoder state. - * @param[in] pcm const float*: The input signal as interleaved - * samples with a normal range of - * +/-1.0. - * Samples with a range beyond +/-1.0 - * are supported but will be clipped by - * decoders using the integer API and - * should only be used if it is known - * that the far end supports extended - * dynamic range. - * This must contain - * frame_size*channels - * samples. - * @param frame_size int: Number of samples per channel in the input - * signal. - * This must be an Opus frame size for the - * encoder's sampling rate. - * For example, at 48 kHz the permitted values - * are 120, 240, 480, 960, 1920, and 2880. - * Passing in a duration of less than 10 ms - * (480 samples at 48 kHz) will prevent the - * encoder from using the LPC or hybrid modes. - * @param[out] data unsigned char*: Output payload. - * This must contain storage for at - * least \a max_data_bytes. - * @param [in] max_data_bytes opus_int32: Size of the allocated - * memory for the output - * payload. This may be - * used to impose an upper limit on - * the instant bitrate, but should - * not be used as the only bitrate - * control. Use #OPUS_SET_BITRATE to - * control the bitrate. - * @returns The length of the encoded packet (in bytes) on success or a - * negative error code (see @ref opus_errorcodes) on failure. - */ -OPUS_EXPORT OPUS_WARN_UNUSED_RESULT int opus_projection_encode_float( - OpusProjectionEncoder *st, - const float *pcm, - int frame_size, - unsigned char *data, - opus_int32 max_data_bytes -) OPUS_ARG_NONNULL(1) OPUS_ARG_NONNULL(2) OPUS_ARG_NONNULL(4); - - -/** Frees an OpusProjectionEncoder allocated by - * opus_projection_ambisonics_encoder_create(). - * @param st OpusProjectionEncoder*: Projection encoder state to be freed. - */ -OPUS_EXPORT void opus_projection_encoder_destroy(OpusProjectionEncoder *st); - - -/** Perform a CTL function on a projection Opus encoder. - * - * Generally the request and subsequent arguments are generated by a - * convenience macro. - * @param st OpusProjectionEncoder*: Projection encoder state. - * @param request This and all remaining parameters should be replaced by one - * of the convenience macros in @ref opus_genericctls, - * @ref opus_encoderctls, @ref opus_multistream_ctls, or - * @ref opus_projection_ctls - * @see opus_genericctls - * @see opus_encoderctls - * @see opus_multistream_ctls - * @see opus_projection_ctls - */ -OPUS_EXPORT int opus_projection_encoder_ctl(OpusProjectionEncoder *st, int request, ...) OPUS_ARG_NONNULL(1); - - -/**@}*/ - -/**\name Projection decoder functions */ -/**@{*/ - -/** Gets the size of an OpusProjectionDecoder structure. - * @param channels int: The total number of output channels. - * This must be no more than 255. - * @param streams int: The total number of streams coded in the - * input. - * This must be no more than 255. - * @param coupled_streams int: Number streams to decode as coupled - * (2 channel) streams. - * This must be no larger than the total - * number of streams. - * Additionally, The total number of - * coded channels (streams + - * coupled_streams) must be no - * more than 255. - * @returns The size in bytes on success, or a negative error code - * (see @ref opus_errorcodes) on error. - */ -OPUS_EXPORT OPUS_WARN_UNUSED_RESULT opus_int32 opus_projection_decoder_get_size( - int channels, - int streams, - int coupled_streams -); - - -/** Allocates and initializes a projection decoder state. - * Call opus_projection_decoder_destroy() to release - * this object when finished. - * @param Fs opus_int32: Sampling rate to decode at (in Hz). - * This must be one of 8000, 12000, 16000, - * 24000, or 48000. - * @param channels int: Number of channels to output. - * This must be at most 255. - * It may be different from the number of coded - * channels (streams + - * coupled_streams). - * @param streams int: The total number of streams coded in the - * input. - * This must be no more than 255. - * @param coupled_streams int: Number of streams to decode as coupled - * (2 channel) streams. - * This must be no larger than the total - * number of streams. - * Additionally, The total number of - * coded channels (streams + - * coupled_streams) must be no - * more than 255. - * @param[in] demixing_matrix const unsigned char[demixing_matrix_size]: Demixing matrix - * that mapping from coded channels to output channels, - * as described in @ref opus_projection and - * @ref opus_projection_ctls. - * @param demixing_matrix_size opus_int32: The size in bytes of the - * demixing matrix, as - * described in @ref - * opus_projection_ctls. - * @param[out] error int *: Returns #OPUS_OK on success, or an error - * code (see @ref opus_errorcodes) on - * failure. - */ -OPUS_EXPORT OPUS_WARN_UNUSED_RESULT OpusProjectionDecoder *opus_projection_decoder_create( - opus_int32 Fs, - int channels, - int streams, - int coupled_streams, - unsigned char *demixing_matrix, - opus_int32 demixing_matrix_size, - int *error -) OPUS_ARG_NONNULL(5); - - -/** Intialize a previously allocated projection decoder state object. - * The memory pointed to by \a st must be at least the size returned by - * opus_projection_decoder_get_size(). - * This is intended for applications which use their own allocator instead of - * malloc. - * To reset a previously initialized state, use the #OPUS_RESET_STATE CTL. - * @see opus_projection_decoder_create - * @see opus_projection_deocder_get_size - * @param st OpusProjectionDecoder*: Projection encoder state to initialize. - * @param Fs opus_int32: Sampling rate to decode at (in Hz). - * This must be one of 8000, 12000, 16000, - * 24000, or 48000. - * @param channels int: Number of channels to output. - * This must be at most 255. - * It may be different from the number of coded - * channels (streams + - * coupled_streams). - * @param streams int: The total number of streams coded in the - * input. - * This must be no more than 255. - * @param coupled_streams int: Number of streams to decode as coupled - * (2 channel) streams. - * This must be no larger than the total - * number of streams. - * Additionally, The total number of - * coded channels (streams + - * coupled_streams) must be no - * more than 255. - * @param[in] demixing_matrix const unsigned char[demixing_matrix_size]: Demixing matrix - * that mapping from coded channels to output channels, - * as described in @ref opus_projection and - * @ref opus_projection_ctls. - * @param demixing_matrix_size opus_int32: The size in bytes of the - * demixing matrix, as - * described in @ref - * opus_projection_ctls. - * @returns #OPUS_OK on success, or an error code (see @ref opus_errorcodes) - * on failure. - */ -OPUS_EXPORT int opus_projection_decoder_init( - OpusProjectionDecoder *st, - opus_int32 Fs, - int channels, - int streams, - int coupled_streams, - unsigned char *demixing_matrix, - opus_int32 demixing_matrix_size -) OPUS_ARG_NONNULL(1) OPUS_ARG_NONNULL(6); - - -/** Decode a projection Opus packet. - * @param st OpusProjectionDecoder*: Projection decoder state. - * @param[in] data const unsigned char*: Input payload. - * Use a NULL - * pointer to indicate packet - * loss. - * @param len opus_int32: Number of bytes in payload. - * @param[out] pcm opus_int16*: Output signal, with interleaved - * samples. - * This must contain room for - * frame_size*channels - * samples. - * @param frame_size int: The number of samples per channel of - * available space in \a pcm. - * If this is less than the maximum packet duration - * (120 ms; 5760 for 48kHz), this function will not be capable - * of decoding some packets. In the case of PLC (data==NULL) - * or FEC (decode_fec=1), then frame_size needs to be exactly - * the duration of audio that is missing, otherwise the - * decoder will not be in the optimal state to decode the - * next incoming packet. For the PLC and FEC cases, frame_size - * must be a multiple of 2.5 ms. - * @param decode_fec int: Flag (0 or 1) to request that any in-band - * forward error correction data be decoded. - * If no such data is available, the frame is - * decoded as if it were lost. - * @returns Number of samples decoded on success or a negative error code - * (see @ref opus_errorcodes) on failure. - */ -OPUS_EXPORT OPUS_WARN_UNUSED_RESULT int opus_projection_decode( - OpusProjectionDecoder *st, - const unsigned char *data, - opus_int32 len, - opus_int16 *pcm, - int frame_size, - int decode_fec -) OPUS_ARG_NONNULL(1) OPUS_ARG_NONNULL(4); - - -/** Decode a projection Opus packet with floating point output. - * @param st OpusProjectionDecoder*: Projection decoder state. - * @param[in] data const unsigned char*: Input payload. - * Use a NULL - * pointer to indicate packet - * loss. - * @param len opus_int32: Number of bytes in payload. - * @param[out] pcm opus_int16*: Output signal, with interleaved - * samples. - * This must contain room for - * frame_size*channels - * samples. - * @param frame_size int: The number of samples per channel of - * available space in \a pcm. - * If this is less than the maximum packet duration - * (120 ms; 5760 for 48kHz), this function will not be capable - * of decoding some packets. In the case of PLC (data==NULL) - * or FEC (decode_fec=1), then frame_size needs to be exactly - * the duration of audio that is missing, otherwise the - * decoder will not be in the optimal state to decode the - * next incoming packet. For the PLC and FEC cases, frame_size - * must be a multiple of 2.5 ms. - * @param decode_fec int: Flag (0 or 1) to request that any in-band - * forward error correction data be decoded. - * If no such data is available, the frame is - * decoded as if it were lost. - * @returns Number of samples decoded on success or a negative error code - * (see @ref opus_errorcodes) on failure. - */ -OPUS_EXPORT OPUS_WARN_UNUSED_RESULT int opus_projection_decode_float( - OpusProjectionDecoder *st, - const unsigned char *data, - opus_int32 len, - float *pcm, - int frame_size, - int decode_fec -) OPUS_ARG_NONNULL(1) OPUS_ARG_NONNULL(4); - - -/** Perform a CTL function on a projection Opus decoder. - * - * Generally the request and subsequent arguments are generated by a - * convenience macro. - * @param st OpusProjectionDecoder*: Projection decoder state. - * @param request This and all remaining parameters should be replaced by one - * of the convenience macros in @ref opus_genericctls, - * @ref opus_decoderctls, @ref opus_multistream_ctls, or - * @ref opus_projection_ctls. - * @see opus_genericctls - * @see opus_decoderctls - * @see opus_multistream_ctls - * @see opus_projection_ctls - */ -OPUS_EXPORT int opus_projection_decoder_ctl(OpusProjectionDecoder *st, int request, ...) OPUS_ARG_NONNULL(1); - - -/** Frees an OpusProjectionDecoder allocated by - * opus_projection_decoder_create(). - * @param st OpusProjectionDecoder: Projection decoder state to be freed. - */ -OPUS_EXPORT void opus_projection_decoder_destroy(OpusProjectionDecoder *st); - - -/**@}*/ - -/**@}*/ - -#ifdef __cplusplus -} -#endif - -#endif /* OPUS_PROJECTION_H */ diff --git a/go/teamspeak/.opus/install/x86/include/opus/opus_types.h b/go/teamspeak/.opus/install/x86/include/opus/opus_types.h deleted file mode 100644 index 7cf6755..0000000 --- a/go/teamspeak/.opus/install/x86/include/opus/opus_types.h +++ /dev/null @@ -1,166 +0,0 @@ -/* (C) COPYRIGHT 1994-2002 Xiph.Org Foundation */ -/* Modified by Jean-Marc Valin */ -/* - Redistribution and use in source and binary forms, with or without - modification, are permitted provided that the following conditions - are met: - - - Redistributions of source code must retain the above copyright - notice, this list of conditions and the following disclaimer. - - - Redistributions in binary form must reproduce the above copyright - notice, this list of conditions and the following disclaimer in the - documentation and/or other materials provided with the distribution. - - THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS - ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT - LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR - A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER - OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, - EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, - PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR - PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF - LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING - NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS - SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. -*/ -/* opus_types.h based on ogg_types.h from libogg */ - -/** - @file opus_types.h - @brief Opus reference implementation types -*/ -#ifndef OPUS_TYPES_H -#define OPUS_TYPES_H - -#define opus_int int /* used for counters etc; at least 16 bits */ -#define opus_int64 long long -#define opus_int8 signed char - -#define opus_uint unsigned int /* used for counters etc; at least 16 bits */ -#define opus_uint64 unsigned long long -#define opus_uint8 unsigned char - -/* Use the real stdint.h if it's there (taken from Paul Hsieh's pstdint.h) */ -#if (defined(__STDC__) && __STDC__ && defined(__STDC_VERSION__) && __STDC_VERSION__ >= 199901L) || (defined(__GNUC__) && (defined(_STDINT_H) || defined(_STDINT_H_)) || defined (HAVE_STDINT_H)) -#include -# undef opus_int64 -# undef opus_int8 -# undef opus_uint64 -# undef opus_uint8 - typedef int8_t opus_int8; - typedef uint8_t opus_uint8; - typedef int16_t opus_int16; - typedef uint16_t opus_uint16; - typedef int32_t opus_int32; - typedef uint32_t opus_uint32; - typedef int64_t opus_int64; - typedef uint64_t opus_uint64; -#elif defined(_WIN32) - -# if defined(__CYGWIN__) -# include <_G_config.h> - typedef _G_int32_t opus_int32; - typedef _G_uint32_t opus_uint32; - typedef _G_int16 opus_int16; - typedef _G_uint16 opus_uint16; -# elif defined(__MINGW32__) - typedef short opus_int16; - typedef unsigned short opus_uint16; - typedef int opus_int32; - typedef unsigned int opus_uint32; -# elif defined(__MWERKS__) - typedef int opus_int32; - typedef unsigned int opus_uint32; - typedef short opus_int16; - typedef unsigned short opus_uint16; -# else - /* MSVC/Borland */ - typedef __int32 opus_int32; - typedef unsigned __int32 opus_uint32; - typedef __int16 opus_int16; - typedef unsigned __int16 opus_uint16; -# endif - -#elif defined(__MACOS__) - -# include - typedef SInt16 opus_int16; - typedef UInt16 opus_uint16; - typedef SInt32 opus_int32; - typedef UInt32 opus_uint32; - -#elif (defined(__APPLE__) && defined(__MACH__)) /* MacOS X Framework build */ - -# include - typedef int16_t opus_int16; - typedef u_int16_t opus_uint16; - typedef int32_t opus_int32; - typedef u_int32_t opus_uint32; - -#elif defined(__BEOS__) - - /* Be */ -# include - typedef int16 opus_int16; - typedef u_int16 opus_uint16; - typedef int32_t opus_int32; - typedef u_int32_t opus_uint32; - -#elif defined (__EMX__) - - /* OS/2 GCC */ - typedef short opus_int16; - typedef unsigned short opus_uint16; - typedef int opus_int32; - typedef unsigned int opus_uint32; - -#elif defined (DJGPP) - - /* DJGPP */ - typedef short opus_int16; - typedef unsigned short opus_uint16; - typedef int opus_int32; - typedef unsigned int opus_uint32; - -#elif defined(R5900) - - /* PS2 EE */ - typedef int opus_int32; - typedef unsigned opus_uint32; - typedef short opus_int16; - typedef unsigned short opus_uint16; - -#elif defined(__SYMBIAN32__) - - /* Symbian GCC */ - typedef signed short opus_int16; - typedef unsigned short opus_uint16; - typedef signed int opus_int32; - typedef unsigned int opus_uint32; - -#elif defined(CONFIG_TI_C54X) || defined (CONFIG_TI_C55X) - - typedef short opus_int16; - typedef unsigned short opus_uint16; - typedef long opus_int32; - typedef unsigned long opus_uint32; - -#elif defined(CONFIG_TI_C6X) - - typedef short opus_int16; - typedef unsigned short opus_uint16; - typedef int opus_int32; - typedef unsigned int opus_uint32; - -#else - - /* Give up, take a reasonable guess */ - typedef short opus_int16; - typedef unsigned short opus_uint16; - typedef int opus_int32; - typedef unsigned int opus_uint32; - -#endif - -#endif /* OPUS_TYPES_H */ diff --git a/go/teamspeak/.opus/install/x86/lib/libopus.a b/go/teamspeak/.opus/install/x86/lib/libopus.a deleted file mode 100644 index 1bb8e30..0000000 Binary files a/go/teamspeak/.opus/install/x86/lib/libopus.a and /dev/null differ diff --git a/go/teamspeak/.opus/install/x86_64/include/opus/opus.h b/go/teamspeak/.opus/install/x86_64/include/opus/opus.h deleted file mode 100644 index eadeda7..0000000 --- a/go/teamspeak/.opus/install/x86_64/include/opus/opus.h +++ /dev/null @@ -1,1099 +0,0 @@ -/* Copyright (c) 2010-2011 Xiph.Org Foundation, Skype Limited - Written by Jean-Marc Valin and Koen Vos */ -/* - Redistribution and use in source and binary forms, with or without - modification, are permitted provided that the following conditions - are met: - - - Redistributions of source code must retain the above copyright - notice, this list of conditions and the following disclaimer. - - - Redistributions in binary form must reproduce the above copyright - notice, this list of conditions and the following disclaimer in the - documentation and/or other materials provided with the distribution. - - THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS - ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT - LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR - A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER - OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, - EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, - PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR - PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF - LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING - NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS - SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. -*/ - -/** - * @file opus.h - * @brief Opus reference implementation API - */ - -#ifndef OPUS_H -#define OPUS_H - -#include "opus_types.h" -#include "opus_defines.h" - -#ifdef __cplusplus -extern "C" { -#endif - -/** - * @mainpage Opus - * - * The Opus codec is designed for interactive speech and audio transmission over the Internet. - * It is designed by the IETF Codec Working Group and incorporates technology from - * Skype's SILK codec and Xiph.Org's CELT codec. - * - * The Opus codec is designed to handle a wide range of interactive audio applications, - * including Voice over IP, videoconferencing, in-game chat, and even remote live music - * performances. It can scale from low bit-rate narrowband speech to very high quality - * stereo music. Its main features are: - - * @li Sampling rates from 8 to 48 kHz - * @li Bit-rates from 6 kb/s to 510 kb/s - * @li Support for both constant bit-rate (CBR) and variable bit-rate (VBR) - * @li Audio bandwidth from narrowband to full-band - * @li Support for speech and music - * @li Support for mono and stereo - * @li Support for multichannel (up to 255 channels) - * @li Frame sizes from 2.5 ms to 60 ms - * @li Good loss robustness and packet loss concealment (PLC) - * @li Floating point and fixed-point implementation - * - * Documentation sections: - * @li @ref opus_encoder - * @li @ref opus_decoder - * @li @ref opus_repacketizer - * @li @ref opus_multistream - * @li @ref opus_libinfo - * @li @ref opus_custom - */ - -/** @defgroup opus_encoder Opus Encoder - * @{ - * - * @brief This page describes the process and functions used to encode Opus. - * - * Since Opus is a stateful codec, the encoding process starts with creating an encoder - * state. This can be done with: - * - * @code - * int error; - * OpusEncoder *enc; - * enc = opus_encoder_create(Fs, channels, application, &error); - * @endcode - * - * From this point, @c enc can be used for encoding an audio stream. An encoder state - * @b must @b not be used for more than one stream at the same time. Similarly, the encoder - * state @b must @b not be re-initialized for each frame. - * - * While opus_encoder_create() allocates memory for the state, it's also possible - * to initialize pre-allocated memory: - * - * @code - * int size; - * int error; - * OpusEncoder *enc; - * size = opus_encoder_get_size(channels); - * enc = malloc(size); - * error = opus_encoder_init(enc, Fs, channels, application); - * @endcode - * - * where opus_encoder_get_size() returns the required size for the encoder state. Note that - * future versions of this code may change the size, so no assumptions should be made about it. - * - * The encoder state is always continuous in memory and only a shallow copy is sufficient - * to copy it (e.g. memcpy()) - * - * It is possible to change some of the encoder's settings using the opus_encoder_ctl() - * interface. All these settings already default to the recommended value, so they should - * only be changed when necessary. The most common settings one may want to change are: - * - * @code - * opus_encoder_ctl(enc, OPUS_SET_BITRATE(bitrate)); - * opus_encoder_ctl(enc, OPUS_SET_COMPLEXITY(complexity)); - * opus_encoder_ctl(enc, OPUS_SET_SIGNAL(signal_type)); - * @endcode - * - * where - * - * @arg bitrate is in bits per second (b/s) - * @arg complexity is a value from 1 to 10, where 1 is the lowest complexity and 10 is the highest - * @arg signal_type is either OPUS_AUTO (default), OPUS_SIGNAL_VOICE, or OPUS_SIGNAL_MUSIC - * - * See @ref opus_encoderctls and @ref opus_genericctls for a complete list of parameters that can be set or queried. Most parameters can be set or changed at any time during a stream. - * - * To encode a frame, opus_encode() or opus_encode_float() must be called with exactly one frame (2.5, 5, 10, 20, 40 or 60 ms) of audio data: - * @code - * len = opus_encode(enc, audio_frame, frame_size, packet, max_packet); - * @endcode - * - * where - *
    - *
  • audio_frame is the audio data in opus_int16 (or float for opus_encode_float())
    • - *
  • frame_size is the duration of the frame in samples (per channel)
    • - *
  • packet is the byte array to which the compressed data is written
    • - *
  • max_packet is the maximum number of bytes that can be written in the packet (4000 bytes is recommended). - * Do not use max_packet to control VBR target bitrate, instead use the #OPUS_SET_BITRATE CTL.
    • - *
- * - * opus_encode() and opus_encode_float() return the number of bytes actually written to the packet. - * The return value can be negative, which indicates that an error has occurred. If the return value - * is 2 bytes or less, then the packet does not need to be transmitted (DTX). - * - * Once the encoder state if no longer needed, it can be destroyed with - * - * @code - * opus_encoder_destroy(enc); - * @endcode - * - * If the encoder was created with opus_encoder_init() rather than opus_encoder_create(), - * then no action is required aside from potentially freeing the memory that was manually - * allocated for it (calling free(enc) for the example above) - * - */ - -/** Opus encoder state. - * This contains the complete state of an Opus encoder. - * It is position independent and can be freely copied. - * @see opus_encoder_create,opus_encoder_init - */ -typedef struct OpusEncoder OpusEncoder; - -/** Gets the size of an OpusEncoder structure. - * @param[in] channels int: Number of channels. - * This must be 1 or 2. - * @returns The size in bytes. - */ -OPUS_EXPORT OPUS_WARN_UNUSED_RESULT int opus_encoder_get_size(int channels); - -/** - */ - -/** Allocates and initializes an encoder state. - * There are three coding modes: - * - * @ref OPUS_APPLICATION_VOIP gives best quality at a given bitrate for voice - * signals. It enhances the input signal by high-pass filtering and - * emphasizing formants and harmonics. Optionally it includes in-band - * forward error correction to protect against packet loss. Use this - * mode for typical VoIP applications. Because of the enhancement, - * even at high bitrates the output may sound different from the input. - * - * @ref OPUS_APPLICATION_AUDIO gives best quality at a given bitrate for most - * non-voice signals like music. Use this mode for music and mixed - * (music/voice) content, broadcast, and applications requiring less - * than 15 ms of coding delay. - * - * @ref OPUS_APPLICATION_RESTRICTED_LOWDELAY configures low-delay mode that - * disables the speech-optimized mode in exchange for slightly reduced delay. - * This mode can only be set on an newly initialized or freshly reset encoder - * because it changes the codec delay. - * - * This is useful when the caller knows that the speech-optimized modes will not be needed (use with caution). - * @param [in] Fs opus_int32: Sampling rate of input signal (Hz) - * This must be one of 8000, 12000, 16000, - * 24000, or 48000. - * @param [in] channels int: Number of channels (1 or 2) in input signal - * @param [in] application int: Coding mode (one of @ref OPUS_APPLICATION_VOIP, @ref OPUS_APPLICATION_AUDIO, or @ref OPUS_APPLICATION_RESTRICTED_LOWDELAY) - * @param [out] error int*: @ref opus_errorcodes - * @note Regardless of the sampling rate and number channels selected, the Opus encoder - * can switch to a lower audio bandwidth or number of channels if the bitrate - * selected is too low. This also means that it is safe to always use 48 kHz stereo input - * and let the encoder optimize the encoding. - */ -OPUS_EXPORT OPUS_WARN_UNUSED_RESULT OpusEncoder *opus_encoder_create( - opus_int32 Fs, - int channels, - int application, - int *error -); - -/** Initializes a previously allocated encoder state - * The memory pointed to by st must be at least the size returned by opus_encoder_get_size(). - * This is intended for applications which use their own allocator instead of malloc. - * @see opus_encoder_create(),opus_encoder_get_size() - * To reset a previously initialized state, use the #OPUS_RESET_STATE CTL. - * @param [in] st OpusEncoder*: Encoder state - * @param [in] Fs opus_int32: Sampling rate of input signal (Hz) - * This must be one of 8000, 12000, 16000, - * 24000, or 48000. - * @param [in] channels int: Number of channels (1 or 2) in input signal - * @param [in] application int: Coding mode (one of OPUS_APPLICATION_VOIP, OPUS_APPLICATION_AUDIO, or OPUS_APPLICATION_RESTRICTED_LOWDELAY) - * @retval #OPUS_OK Success or @ref opus_errorcodes - */ -OPUS_EXPORT int opus_encoder_init( - OpusEncoder *st, - opus_int32 Fs, - int channels, - int application -) OPUS_ARG_NONNULL(1); - -/** Encodes an Opus frame. - * @param [in] st OpusEncoder*: Encoder state - * @param [in] pcm opus_int16*: Input signal (interleaved if 2 channels). length is frame_size*channels*sizeof(opus_int16) - * @param [in] frame_size int: Number of samples per channel in the - * input signal. - * This must be an Opus frame size for - * the encoder's sampling rate. - * For example, at 48 kHz the permitted - * values are 120, 240, 480, 960, 1920, - * and 2880. - * Passing in a duration of less than - * 10 ms (480 samples at 48 kHz) will - * prevent the encoder from using the LPC - * or hybrid modes. - * @param [out] data unsigned char*: Output payload. - * This must contain storage for at - * least \a max_data_bytes. - * @param [in] max_data_bytes opus_int32: Size of the allocated - * memory for the output - * payload. This may be - * used to impose an upper limit on - * the instant bitrate, but should - * not be used as the only bitrate - * control. Use #OPUS_SET_BITRATE to - * control the bitrate. - * @returns The length of the encoded packet (in bytes) on success or a - * negative error code (see @ref opus_errorcodes) on failure. - */ -OPUS_EXPORT OPUS_WARN_UNUSED_RESULT opus_int32 opus_encode( - OpusEncoder *st, - const opus_int16 *pcm, - int frame_size, - unsigned char *data, - opus_int32 max_data_bytes -) OPUS_ARG_NONNULL(1) OPUS_ARG_NONNULL(2) OPUS_ARG_NONNULL(4); - -/** Encodes an Opus frame from floating point input. - * @param [in] st OpusEncoder*: Encoder state - * @param [in] pcm float*: Input in float format (interleaved if 2 channels), with a normal range of +/-1.0. - * Samples with a range beyond +/-1.0 are supported but will - * be clipped by decoders using the integer API and should - * only be used if it is known that the far end supports - * extended dynamic range. - * length is frame_size*channels*sizeof(float) - * @param [in] frame_size int: Number of samples per channel in the - * input signal. - * This must be an Opus frame size for - * the encoder's sampling rate. - * For example, at 48 kHz the permitted - * values are 120, 240, 480, 960, 1920, - * and 2880. - * Passing in a duration of less than - * 10 ms (480 samples at 48 kHz) will - * prevent the encoder from using the LPC - * or hybrid modes. - * @param [out] data unsigned char*: Output payload. - * This must contain storage for at - * least \a max_data_bytes. - * @param [in] max_data_bytes opus_int32: Size of the allocated - * memory for the output - * payload. This may be - * used to impose an upper limit on - * the instant bitrate, but should - * not be used as the only bitrate - * control. Use #OPUS_SET_BITRATE to - * control the bitrate. - * @returns The length of the encoded packet (in bytes) on success or a - * negative error code (see @ref opus_errorcodes) on failure. - */ -OPUS_EXPORT OPUS_WARN_UNUSED_RESULT opus_int32 opus_encode_float( - OpusEncoder *st, - const float *pcm, - int frame_size, - unsigned char *data, - opus_int32 max_data_bytes -) OPUS_ARG_NONNULL(1) OPUS_ARG_NONNULL(2) OPUS_ARG_NONNULL(4); - -/** Frees an OpusEncoder allocated by opus_encoder_create(). - * @param[in] st OpusEncoder*: State to be freed. - */ -OPUS_EXPORT void opus_encoder_destroy(OpusEncoder *st); - -/** Perform a CTL function on an Opus encoder. - * - * Generally the request and subsequent arguments are generated - * by a convenience macro. - * @param st OpusEncoder*: Encoder state. - * @param request This and all remaining parameters should be replaced by one - * of the convenience macros in @ref opus_genericctls or - * @ref opus_encoderctls. - * @see opus_genericctls - * @see opus_encoderctls - */ -OPUS_EXPORT int opus_encoder_ctl(OpusEncoder *st, int request, ...) OPUS_ARG_NONNULL(1); -/**@}*/ - -/** @defgroup opus_decoder Opus Decoder - * @{ - * - * @brief This page describes the process and functions used to decode Opus. - * - * The decoding process also starts with creating a decoder - * state. This can be done with: - * @code - * int error; - * OpusDecoder *dec; - * dec = opus_decoder_create(Fs, channels, &error); - * @endcode - * where - * @li Fs is the sampling rate and must be 8000, 12000, 16000, 24000, or 48000 - * @li channels is the number of channels (1 or 2) - * @li error will hold the error code in case of failure (or #OPUS_OK on success) - * @li the return value is a newly created decoder state to be used for decoding - * - * While opus_decoder_create() allocates memory for the state, it's also possible - * to initialize pre-allocated memory: - * @code - * int size; - * int error; - * OpusDecoder *dec; - * size = opus_decoder_get_size(channels); - * dec = malloc(size); - * error = opus_decoder_init(dec, Fs, channels); - * @endcode - * where opus_decoder_get_size() returns the required size for the decoder state. Note that - * future versions of this code may change the size, so no assumptions should be made about it. - * - * The decoder state is always continuous in memory and only a shallow copy is sufficient - * to copy it (e.g. memcpy()) - * - * To decode a frame, opus_decode() or opus_decode_float() must be called with a packet of compressed audio data: - * @code - * frame_size = opus_decode(dec, packet, len, decoded, max_size, 0); - * @endcode - * where - * - * @li packet is the byte array containing the compressed data - * @li len is the exact number of bytes contained in the packet - * @li decoded is the decoded audio data in opus_int16 (or float for opus_decode_float()) - * @li max_size is the max duration of the frame in samples (per channel) that can fit into the decoded_frame array - * - * opus_decode() and opus_decode_float() return the number of samples (per channel) decoded from the packet. - * If that value is negative, then an error has occurred. This can occur if the packet is corrupted or if the audio - * buffer is too small to hold the decoded audio. - * - * Opus is a stateful codec with overlapping blocks and as a result Opus - * packets are not coded independently of each other. Packets must be - * passed into the decoder serially and in the correct order for a correct - * decode. Lost packets can be replaced with loss concealment by calling - * the decoder with a null pointer and zero length for the missing packet. - * - * A single codec state may only be accessed from a single thread at - * a time and any required locking must be performed by the caller. Separate - * streams must be decoded with separate decoder states and can be decoded - * in parallel unless the library was compiled with NONTHREADSAFE_PSEUDOSTACK - * defined. - * - */ - -/** Opus decoder state. - * This contains the complete state of an Opus decoder. - * It is position independent and can be freely copied. - * @see opus_decoder_create,opus_decoder_init - */ -typedef struct OpusDecoder OpusDecoder; - -/** Opus DRED decoder. - * This contains the complete state of an Opus DRED decoder. - * It is position independent and can be freely copied. - * @see opus_dred_decoder_create,opus_dred_decoder_init - */ -typedef struct OpusDREDDecoder OpusDREDDecoder; - - -/** Opus DRED state. - * This contains the complete state of an Opus DRED packet. - * It is position independent and can be freely copied. - * @see opus_dred_create,opus_dred_init - */ -typedef struct OpusDRED OpusDRED; - -/** Gets the size of an OpusDecoder structure. - * @param [in] channels int: Number of channels. - * This must be 1 or 2. - * @returns The size in bytes. - */ -OPUS_EXPORT OPUS_WARN_UNUSED_RESULT int opus_decoder_get_size(int channels); - -/** Allocates and initializes a decoder state. - * @param [in] Fs opus_int32: Sample rate to decode at (Hz). - * This must be one of 8000, 12000, 16000, - * 24000, or 48000. - * @param [in] channels int: Number of channels (1 or 2) to decode - * @param [out] error int*: #OPUS_OK Success or @ref opus_errorcodes - * - * Internally Opus stores data at 48000 Hz, so that should be the default - * value for Fs. However, the decoder can efficiently decode to buffers - * at 8, 12, 16, and 24 kHz so if for some reason the caller cannot use - * data at the full sample rate, or knows the compressed data doesn't - * use the full frequency range, it can request decoding at a reduced - * rate. Likewise, the decoder is capable of filling in either mono or - * interleaved stereo pcm buffers, at the caller's request. - */ -OPUS_EXPORT OPUS_WARN_UNUSED_RESULT OpusDecoder *opus_decoder_create( - opus_int32 Fs, - int channels, - int *error -); - -/** Initializes a previously allocated decoder state. - * The state must be at least the size returned by opus_decoder_get_size(). - * This is intended for applications which use their own allocator instead of malloc. @see opus_decoder_create,opus_decoder_get_size - * To reset a previously initialized state, use the #OPUS_RESET_STATE CTL. - * @param [in] st OpusDecoder*: Decoder state. - * @param [in] Fs opus_int32: Sampling rate to decode to (Hz). - * This must be one of 8000, 12000, 16000, - * 24000, or 48000. - * @param [in] channels int: Number of channels (1 or 2) to decode - * @retval #OPUS_OK Success or @ref opus_errorcodes - */ -OPUS_EXPORT int opus_decoder_init( - OpusDecoder *st, - opus_int32 Fs, - int channels -) OPUS_ARG_NONNULL(1); - -/** Decode an Opus packet. - * @param [in] st OpusDecoder*: Decoder state - * @param [in] data char*: Input payload. Use a NULL pointer to indicate packet loss - * @param [in] len opus_int32: Number of bytes in payload* - * @param [out] pcm opus_int16*: Output signal (interleaved if 2 channels). length - * is frame_size*channels*sizeof(opus_int16) - * @param [in] frame_size Number of samples per channel of available space in \a pcm. - * If this is less than the maximum packet duration (120ms; 5760 for 48kHz), this function will - * not be capable of decoding some packets. In the case of PLC (data==NULL) or FEC (decode_fec=1), - * then frame_size needs to be exactly the duration of audio that is missing, otherwise the - * decoder will not be in the optimal state to decode the next incoming packet. For the PLC and - * FEC cases, frame_size must be a multiple of 2.5 ms. - * @param [in] decode_fec int: Flag (0 or 1) to request that any in-band forward error correction data be - * decoded. If no such data is available, the frame is decoded as if it were lost. - * @returns Number of decoded samples or @ref opus_errorcodes - */ -OPUS_EXPORT OPUS_WARN_UNUSED_RESULT int opus_decode( - OpusDecoder *st, - const unsigned char *data, - opus_int32 len, - opus_int16 *pcm, - int frame_size, - int decode_fec -) OPUS_ARG_NONNULL(1) OPUS_ARG_NONNULL(4); - -/** Decode an Opus packet with floating point output. - * @param [in] st OpusDecoder*: Decoder state - * @param [in] data char*: Input payload. Use a NULL pointer to indicate packet loss - * @param [in] len opus_int32: Number of bytes in payload - * @param [out] pcm float*: Output signal (interleaved if 2 channels). length - * is frame_size*channels*sizeof(float) - * @param [in] frame_size Number of samples per channel of available space in \a pcm. - * If this is less than the maximum packet duration (120ms; 5760 for 48kHz), this function will - * not be capable of decoding some packets. In the case of PLC (data==NULL) or FEC (decode_fec=1), - * then frame_size needs to be exactly the duration of audio that is missing, otherwise the - * decoder will not be in the optimal state to decode the next incoming packet. For the PLC and - * FEC cases, frame_size must be a multiple of 2.5 ms. - * @param [in] decode_fec int: Flag (0 or 1) to request that any in-band forward error correction data be - * decoded. If no such data is available the frame is decoded as if it were lost. - * @returns Number of decoded samples or @ref opus_errorcodes - */ -OPUS_EXPORT OPUS_WARN_UNUSED_RESULT int opus_decode_float( - OpusDecoder *st, - const unsigned char *data, - opus_int32 len, - float *pcm, - int frame_size, - int decode_fec -) OPUS_ARG_NONNULL(1) OPUS_ARG_NONNULL(4); - -/** Perform a CTL function on an Opus decoder. - * - * Generally the request and subsequent arguments are generated - * by a convenience macro. - * @param st OpusDecoder*: Decoder state. - * @param request This and all remaining parameters should be replaced by one - * of the convenience macros in @ref opus_genericctls or - * @ref opus_decoderctls. - * @see opus_genericctls - * @see opus_decoderctls - */ -OPUS_EXPORT int opus_decoder_ctl(OpusDecoder *st, int request, ...) OPUS_ARG_NONNULL(1); - -/** Frees an OpusDecoder allocated by opus_decoder_create(). - * @param[in] st OpusDecoder*: State to be freed. - */ -OPUS_EXPORT void opus_decoder_destroy(OpusDecoder *st); - -/** Gets the size of an OpusDREDDecoder structure. - * @returns The size in bytes. - */ -OPUS_EXPORT int opus_dred_decoder_get_size(void); - -/** Allocates and initializes an OpusDREDDecoder state. - * @param [out] error int*: #OPUS_OK Success or @ref opus_errorcodes - */ -OPUS_EXPORT OpusDREDDecoder *opus_dred_decoder_create(int *error); - -/** Initializes an OpusDREDDecoder state. - * @param[in] dec OpusDREDDecoder*: State to be initialized. - */ -OPUS_EXPORT int opus_dred_decoder_init(OpusDREDDecoder *dec); - -/** Frees an OpusDREDDecoder allocated by opus_dred_decoder_create(). - * @param[in] dec OpusDREDDecoder*: State to be freed. - */ -OPUS_EXPORT void opus_dred_decoder_destroy(OpusDREDDecoder *dec); - -/** Perform a CTL function on an Opus DRED decoder. - * - * Generally the request and subsequent arguments are generated - * by a convenience macro. - * @param dred_dec OpusDREDDecoder*: DRED Decoder state. - * @param request This and all remaining parameters should be replaced by one - * of the convenience macros in @ref opus_genericctls or - * @ref opus_decoderctls. - * @see opus_genericctls - * @see opus_decoderctls - */ -OPUS_EXPORT int opus_dred_decoder_ctl(OpusDREDDecoder *dred_dec, int request, ...); - -/** Gets the size of an OpusDRED structure. - * @returns The size in bytes. - */ -OPUS_EXPORT int opus_dred_get_size(void); - -/** Allocates and initializes a DRED state. - * @param [out] error int*: #OPUS_OK Success or @ref opus_errorcodes - */ -OPUS_EXPORT OpusDRED *opus_dred_alloc(int *error); - -/** Frees an OpusDRED allocated by opus_dred_create(). - * @param[in] dec OpusDRED*: State to be freed. - */ -OPUS_EXPORT void opus_dred_free(OpusDRED *dec); - -/** Decode an Opus DRED packet. - * @param [in] dred_dec OpusDRED*: DRED Decoder state - * @param [in] dred OpusDRED*: DRED state - * @param [in] data char*: Input payload - * @param [in] len opus_int32: Number of bytes in payload - * @param [in] max_dred_samples opus_int32: Maximum number of DRED samples that may be needed (if available in the packet). - * @param [in] sampling_rate opus_int32: Sampling rate used for max_dred_samples argument. Needs not match the actual sampling rate of the decoder. - * @param [out] dred_end opus_int32*: Number of non-encoded (silence) samples between the DRED timestamp and the last DRED sample. - * @param [in] defer_processing int: Flag (0 or 1). If set to one, the CPU-intensive part of the DRED decoding is deferred until opus_dred_process() is called. - * @returns Offset (positive) of the first decoded DRED samples, zero if no DRED is present, or @ref opus_errorcodes - */ -OPUS_EXPORT int opus_dred_parse(OpusDREDDecoder *dred_dec, OpusDRED *dred, const unsigned char *data, opus_int32 len, opus_int32 max_dred_samples, opus_int32 sampling_rate, int *dred_end, int defer_processing) OPUS_ARG_NONNULL(1); - -/** Finish decoding an Opus DRED packet. The function only needs to be called if opus_dred_parse() was called with defer_processing=1. - * The source and destination will often be the same DRED state. - * @param [in] dred_dec OpusDRED*: DRED Decoder state - * @param [in] src OpusDRED*: Source DRED state to start the processing from. - * @param [out] dst OpusDRED*: Destination DRED state to store the updated state after processing. - * @returns @ref opus_errorcodes - */ -OPUS_EXPORT int opus_dred_process(OpusDREDDecoder *dred_dec, const OpusDRED *src, OpusDRED *dst); - -/** Decode audio from an Opus DRED packet with floating point output. - * @param [in] st OpusDecoder*: Decoder state - * @param [in] dred OpusDRED*: DRED state - * @param [in] dred_offset opus_int32: position of the redundancy to decode (in samples before the beginning of the real audio data in the packet). - * @param [out] pcm opus_int16*: Output signal (interleaved if 2 channels). length - * is frame_size*channels*sizeof(opus_int16) - * @param [in] frame_size Number of samples per channel to decode in \a pcm. - * frame_size must be a multiple of 2.5 ms. - * @returns Number of decoded samples or @ref opus_errorcodes - */ -OPUS_EXPORT int opus_decoder_dred_decode(OpusDecoder *st, const OpusDRED *dred, opus_int32 dred_offset, opus_int16 *pcm, opus_int32 frame_size); - -/** Decode audio from an Opus DRED packet with floating point output. - * @param [in] st OpusDecoder*: Decoder state - * @param [in] dred OpusDRED*: DRED state - * @param [in] dred_offset opus_int32: position of the redundancy to decode (in samples before the beginning of the real audio data in the packet). - * @param [out] pcm float*: Output signal (interleaved if 2 channels). length - * is frame_size*channels*sizeof(float) - * @param [in] frame_size Number of samples per channel to decode in \a pcm. - * frame_size must be a multiple of 2.5 ms. - * @returns Number of decoded samples or @ref opus_errorcodes - */ -OPUS_EXPORT int opus_decoder_dred_decode_float(OpusDecoder *st, const OpusDRED *dred, opus_int32 dred_offset, float *pcm, opus_int32 frame_size); - - -/** Parse an opus packet into one or more frames. - * Opus_decode will perform this operation internally so most applications do - * not need to use this function. - * This function does not copy the frames, the returned pointers are pointers into - * the input packet. - * @param [in] data char*: Opus packet to be parsed - * @param [in] len opus_int32: size of data - * @param [out] out_toc char*: TOC pointer - * @param [out] frames char*[48] encapsulated frames - * @param [out] size opus_int16[48] sizes of the encapsulated frames - * @param [out] payload_offset int*: returns the position of the payload within the packet (in bytes) - * @returns number of frames - */ -OPUS_EXPORT int opus_packet_parse( - const unsigned char *data, - opus_int32 len, - unsigned char *out_toc, - const unsigned char *frames[48], - opus_int16 size[48], - int *payload_offset -) OPUS_ARG_NONNULL(1) OPUS_ARG_NONNULL(5); - -/** Gets the bandwidth of an Opus packet. - * @param [in] data char*: Opus packet - * @retval OPUS_BANDWIDTH_NARROWBAND Narrowband (4kHz bandpass) - * @retval OPUS_BANDWIDTH_MEDIUMBAND Mediumband (6kHz bandpass) - * @retval OPUS_BANDWIDTH_WIDEBAND Wideband (8kHz bandpass) - * @retval OPUS_BANDWIDTH_SUPERWIDEBAND Superwideband (12kHz bandpass) - * @retval OPUS_BANDWIDTH_FULLBAND Fullband (20kHz bandpass) - * @retval OPUS_INVALID_PACKET The compressed data passed is corrupted or of an unsupported type - */ -OPUS_EXPORT OPUS_WARN_UNUSED_RESULT int opus_packet_get_bandwidth(const unsigned char *data) OPUS_ARG_NONNULL(1); - -/** Gets the number of samples per frame from an Opus packet. - * @param [in] data char*: Opus packet. - * This must contain at least one byte of - * data. - * @param [in] Fs opus_int32: Sampling rate in Hz. - * This must be a multiple of 400, or - * inaccurate results will be returned. - * @returns Number of samples per frame. - */ -OPUS_EXPORT OPUS_WARN_UNUSED_RESULT int opus_packet_get_samples_per_frame(const unsigned char *data, opus_int32 Fs) OPUS_ARG_NONNULL(1); - -/** Gets the number of channels from an Opus packet. - * @param [in] data char*: Opus packet - * @returns Number of channels - * @retval OPUS_INVALID_PACKET The compressed data passed is corrupted or of an unsupported type - */ -OPUS_EXPORT OPUS_WARN_UNUSED_RESULT int opus_packet_get_nb_channels(const unsigned char *data) OPUS_ARG_NONNULL(1); - -/** Gets the number of frames in an Opus packet. - * @param [in] packet char*: Opus packet - * @param [in] len opus_int32: Length of packet - * @returns Number of frames - * @retval OPUS_BAD_ARG Insufficient data was passed to the function - * @retval OPUS_INVALID_PACKET The compressed data passed is corrupted or of an unsupported type - */ -OPUS_EXPORT OPUS_WARN_UNUSED_RESULT int opus_packet_get_nb_frames(const unsigned char packet[], opus_int32 len) OPUS_ARG_NONNULL(1); - -/** Gets the number of samples of an Opus packet. - * @param [in] packet char*: Opus packet - * @param [in] len opus_int32: Length of packet - * @param [in] Fs opus_int32: Sampling rate in Hz. - * This must be a multiple of 400, or - * inaccurate results will be returned. - * @returns Number of samples - * @retval OPUS_BAD_ARG Insufficient data was passed to the function - * @retval OPUS_INVALID_PACKET The compressed data passed is corrupted or of an unsupported type - */ -OPUS_EXPORT OPUS_WARN_UNUSED_RESULT int opus_packet_get_nb_samples(const unsigned char packet[], opus_int32 len, opus_int32 Fs) OPUS_ARG_NONNULL(1); - -/** Checks whether an Opus packet has LBRR. - * @param [in] packet char*: Opus packet - * @param [in] len opus_int32: Length of packet - * @returns 1 is LBRR is present, 0 otherwise - * @retval OPUS_INVALID_PACKET The compressed data passed is corrupted or of an unsupported type - */ -OPUS_EXPORT OPUS_WARN_UNUSED_RESULT int opus_packet_has_lbrr(const unsigned char packet[], opus_int32 len); - -/** Gets the number of samples of an Opus packet. - * @param [in] dec OpusDecoder*: Decoder state - * @param [in] packet char*: Opus packet - * @param [in] len opus_int32: Length of packet - * @returns Number of samples - * @retval OPUS_BAD_ARG Insufficient data was passed to the function - * @retval OPUS_INVALID_PACKET The compressed data passed is corrupted or of an unsupported type - */ -OPUS_EXPORT OPUS_WARN_UNUSED_RESULT int opus_decoder_get_nb_samples(const OpusDecoder *dec, const unsigned char packet[], opus_int32 len) OPUS_ARG_NONNULL(1) OPUS_ARG_NONNULL(2); - -/** Applies soft-clipping to bring a float signal within the [-1,1] range. If - * the signal is already in that range, nothing is done. If there are values - * outside of [-1,1], then the signal is clipped as smoothly as possible to - * both fit in the range and avoid creating excessive distortion in the - * process. - * @param [in,out] pcm float*: Input PCM and modified PCM - * @param [in] frame_size int Number of samples per channel to process - * @param [in] channels int: Number of channels - * @param [in,out] softclip_mem float*: State memory for the soft clipping process (one float per channel, initialized to zero) - */ -OPUS_EXPORT void opus_pcm_soft_clip(float *pcm, int frame_size, int channels, float *softclip_mem); - - -/**@}*/ - -/** @defgroup opus_repacketizer Repacketizer - * @{ - * - * The repacketizer can be used to merge multiple Opus packets into a single - * packet or alternatively to split Opus packets that have previously been - * merged. Splitting valid Opus packets is always guaranteed to succeed, - * whereas merging valid packets only succeeds if all frames have the same - * mode, bandwidth, and frame size, and when the total duration of the merged - * packet is no more than 120 ms. The 120 ms limit comes from the - * specification and limits decoder memory requirements at a point where - * framing overhead becomes negligible. - * - * The repacketizer currently only operates on elementary Opus - * streams. It will not manipualte multistream packets successfully, except in - * the degenerate case where they consist of data from a single stream. - * - * The repacketizing process starts with creating a repacketizer state, either - * by calling opus_repacketizer_create() or by allocating the memory yourself, - * e.g., - * @code - * OpusRepacketizer *rp; - * rp = (OpusRepacketizer*)malloc(opus_repacketizer_get_size()); - * if (rp != NULL) - * opus_repacketizer_init(rp); - * @endcode - * - * Then the application should submit packets with opus_repacketizer_cat(), - * extract new packets with opus_repacketizer_out() or - * opus_repacketizer_out_range(), and then reset the state for the next set of - * input packets via opus_repacketizer_init(). - * - * For example, to split a sequence of packets into individual frames: - * @code - * unsigned char *data; - * int len; - * while (get_next_packet(&data, &len)) - * { - * unsigned char out[1276]; - * opus_int32 out_len; - * int nb_frames; - * int err; - * int i; - * err = opus_repacketizer_cat(rp, data, len); - * if (err != OPUS_OK) - * { - * release_packet(data); - * return err; - * } - * nb_frames = opus_repacketizer_get_nb_frames(rp); - * for (i = 0; i < nb_frames; i++) - * { - * out_len = opus_repacketizer_out_range(rp, i, i+1, out, sizeof(out)); - * if (out_len < 0) - * { - * release_packet(data); - * return (int)out_len; - * } - * output_next_packet(out, out_len); - * } - * opus_repacketizer_init(rp); - * release_packet(data); - * } - * @endcode - * - * Alternatively, to combine a sequence of frames into packets that each - * contain up to TARGET_DURATION_MS milliseconds of data: - * @code - * // The maximum number of packets with duration TARGET_DURATION_MS occurs - * // when the frame size is 2.5 ms, for a total of (TARGET_DURATION_MS*2/5) - * // packets. - * unsigned char *data[(TARGET_DURATION_MS*2/5)+1]; - * opus_int32 len[(TARGET_DURATION_MS*2/5)+1]; - * int nb_packets; - * unsigned char out[1277*(TARGET_DURATION_MS*2/2)]; - * opus_int32 out_len; - * int prev_toc; - * nb_packets = 0; - * while (get_next_packet(data+nb_packets, len+nb_packets)) - * { - * int nb_frames; - * int err; - * nb_frames = opus_packet_get_nb_frames(data[nb_packets], len[nb_packets]); - * if (nb_frames < 1) - * { - * release_packets(data, nb_packets+1); - * return nb_frames; - * } - * nb_frames += opus_repacketizer_get_nb_frames(rp); - * // If adding the next packet would exceed our target, or it has an - * // incompatible TOC sequence, output the packets we already have before - * // submitting it. - * // N.B., The nb_packets > 0 check ensures we've submitted at least one - * // packet since the last call to opus_repacketizer_init(). Otherwise a - * // single packet longer than TARGET_DURATION_MS would cause us to try to - * // output an (invalid) empty packet. It also ensures that prev_toc has - * // been set to a valid value. Additionally, len[nb_packets] > 0 is - * // guaranteed by the call to opus_packet_get_nb_frames() above, so the - * // reference to data[nb_packets][0] should be valid. - * if (nb_packets > 0 && ( - * ((prev_toc & 0xFC) != (data[nb_packets][0] & 0xFC)) || - * opus_packet_get_samples_per_frame(data[nb_packets], 48000)*nb_frames > - * TARGET_DURATION_MS*48)) - * { - * out_len = opus_repacketizer_out(rp, out, sizeof(out)); - * if (out_len < 0) - * { - * release_packets(data, nb_packets+1); - * return (int)out_len; - * } - * output_next_packet(out, out_len); - * opus_repacketizer_init(rp); - * release_packets(data, nb_packets); - * data[0] = data[nb_packets]; - * len[0] = len[nb_packets]; - * nb_packets = 0; - * } - * err = opus_repacketizer_cat(rp, data[nb_packets], len[nb_packets]); - * if (err != OPUS_OK) - * { - * release_packets(data, nb_packets+1); - * return err; - * } - * prev_toc = data[nb_packets][0]; - * nb_packets++; - * } - * // Output the final, partial packet. - * if (nb_packets > 0) - * { - * out_len = opus_repacketizer_out(rp, out, sizeof(out)); - * release_packets(data, nb_packets); - * if (out_len < 0) - * return (int)out_len; - * output_next_packet(out, out_len); - * } - * @endcode - * - * An alternate way of merging packets is to simply call opus_repacketizer_cat() - * unconditionally until it fails. At that point, the merged packet can be - * obtained with opus_repacketizer_out() and the input packet for which - * opus_repacketizer_cat() needs to be re-added to a newly reinitialized - * repacketizer state. - */ - -typedef struct OpusRepacketizer OpusRepacketizer; - -/** Gets the size of an OpusRepacketizer structure. - * @returns The size in bytes. - */ -OPUS_EXPORT OPUS_WARN_UNUSED_RESULT int opus_repacketizer_get_size(void); - -/** (Re)initializes a previously allocated repacketizer state. - * The state must be at least the size returned by opus_repacketizer_get_size(). - * This can be used for applications which use their own allocator instead of - * malloc(). - * It must also be called to reset the queue of packets waiting to be - * repacketized, which is necessary if the maximum packet duration of 120 ms - * is reached or if you wish to submit packets with a different Opus - * configuration (coding mode, audio bandwidth, frame size, or channel count). - * Failure to do so will prevent a new packet from being added with - * opus_repacketizer_cat(). - * @see opus_repacketizer_create - * @see opus_repacketizer_get_size - * @see opus_repacketizer_cat - * @param rp OpusRepacketizer*: The repacketizer state to - * (re)initialize. - * @returns A pointer to the same repacketizer state that was passed in. - */ -OPUS_EXPORT OpusRepacketizer *opus_repacketizer_init(OpusRepacketizer *rp) OPUS_ARG_NONNULL(1); - -/** Allocates memory and initializes the new repacketizer with - * opus_repacketizer_init(). - */ -OPUS_EXPORT OPUS_WARN_UNUSED_RESULT OpusRepacketizer *opus_repacketizer_create(void); - -/** Frees an OpusRepacketizer allocated by - * opus_repacketizer_create(). - * @param[in] rp OpusRepacketizer*: State to be freed. - */ -OPUS_EXPORT void opus_repacketizer_destroy(OpusRepacketizer *rp); - -/** Add a packet to the current repacketizer state. - * This packet must match the configuration of any packets already submitted - * for repacketization since the last call to opus_repacketizer_init(). - * This means that it must have the same coding mode, audio bandwidth, frame - * size, and channel count. - * This can be checked in advance by examining the top 6 bits of the first - * byte of the packet, and ensuring they match the top 6 bits of the first - * byte of any previously submitted packet. - * The total duration of audio in the repacketizer state also must not exceed - * 120 ms, the maximum duration of a single packet, after adding this packet. - * - * The contents of the current repacketizer state can be extracted into new - * packets using opus_repacketizer_out() or opus_repacketizer_out_range(). - * - * In order to add a packet with a different configuration or to add more - * audio beyond 120 ms, you must clear the repacketizer state by calling - * opus_repacketizer_init(). - * If a packet is too large to add to the current repacketizer state, no part - * of it is added, even if it contains multiple frames, some of which might - * fit. - * If you wish to be able to add parts of such packets, you should first use - * another repacketizer to split the packet into pieces and add them - * individually. - * @see opus_repacketizer_out_range - * @see opus_repacketizer_out - * @see opus_repacketizer_init - * @param rp OpusRepacketizer*: The repacketizer state to which to - * add the packet. - * @param[in] data const unsigned char*: The packet data. - * The application must ensure - * this pointer remains valid - * until the next call to - * opus_repacketizer_init() or - * opus_repacketizer_destroy(). - * @param len opus_int32: The number of bytes in the packet data. - * @returns An error code indicating whether or not the operation succeeded. - * @retval #OPUS_OK The packet's contents have been added to the repacketizer - * state. - * @retval #OPUS_INVALID_PACKET The packet did not have a valid TOC sequence, - * the packet's TOC sequence was not compatible - * with previously submitted packets (because - * the coding mode, audio bandwidth, frame size, - * or channel count did not match), or adding - * this packet would increase the total amount of - * audio stored in the repacketizer state to more - * than 120 ms. - */ -OPUS_EXPORT int opus_repacketizer_cat(OpusRepacketizer *rp, const unsigned char *data, opus_int32 len) OPUS_ARG_NONNULL(1) OPUS_ARG_NONNULL(2); - - -/** Construct a new packet from data previously submitted to the repacketizer - * state via opus_repacketizer_cat(). - * @param rp OpusRepacketizer*: The repacketizer state from which to - * construct the new packet. - * @param begin int: The index of the first frame in the current - * repacketizer state to include in the output. - * @param end int: One past the index of the last frame in the - * current repacketizer state to include in the - * output. - * @param[out] data const unsigned char*: The buffer in which to - * store the output packet. - * @param maxlen opus_int32: The maximum number of bytes to store in - * the output buffer. In order to guarantee - * success, this should be at least - * 1276 for a single frame, - * or for multiple frames, - * 1277*(end-begin). - * However, 1*(end-begin) plus - * the size of all packet data submitted to - * the repacketizer since the last call to - * opus_repacketizer_init() or - * opus_repacketizer_create() is also - * sufficient, and possibly much smaller. - * @returns The total size of the output packet on success, or an error code - * on failure. - * @retval #OPUS_BAD_ARG [begin,end) was an invalid range of - * frames (begin < 0, begin >= end, or end > - * opus_repacketizer_get_nb_frames()). - * @retval #OPUS_BUFFER_TOO_SMALL \a maxlen was insufficient to contain the - * complete output packet. - */ -OPUS_EXPORT OPUS_WARN_UNUSED_RESULT opus_int32 opus_repacketizer_out_range(OpusRepacketizer *rp, int begin, int end, unsigned char *data, opus_int32 maxlen) OPUS_ARG_NONNULL(1) OPUS_ARG_NONNULL(4); - -/** Return the total number of frames contained in packet data submitted to - * the repacketizer state so far via opus_repacketizer_cat() since the last - * call to opus_repacketizer_init() or opus_repacketizer_create(). - * This defines the valid range of packets that can be extracted with - * opus_repacketizer_out_range() or opus_repacketizer_out(). - * @param rp OpusRepacketizer*: The repacketizer state containing the - * frames. - * @returns The total number of frames contained in the packet data submitted - * to the repacketizer state. - */ -OPUS_EXPORT OPUS_WARN_UNUSED_RESULT int opus_repacketizer_get_nb_frames(OpusRepacketizer *rp) OPUS_ARG_NONNULL(1); - -/** Construct a new packet from data previously submitted to the repacketizer - * state via opus_repacketizer_cat(). - * This is a convenience routine that returns all the data submitted so far - * in a single packet. - * It is equivalent to calling - * @code - * opus_repacketizer_out_range(rp, 0, opus_repacketizer_get_nb_frames(rp), - * data, maxlen) - * @endcode - * @param rp OpusRepacketizer*: The repacketizer state from which to - * construct the new packet. - * @param[out] data const unsigned char*: The buffer in which to - * store the output packet. - * @param maxlen opus_int32: The maximum number of bytes to store in - * the output buffer. In order to guarantee - * success, this should be at least - * 1277*opus_repacketizer_get_nb_frames(rp). - * However, - * 1*opus_repacketizer_get_nb_frames(rp) - * plus the size of all packet data - * submitted to the repacketizer since the - * last call to opus_repacketizer_init() or - * opus_repacketizer_create() is also - * sufficient, and possibly much smaller. - * @returns The total size of the output packet on success, or an error code - * on failure. - * @retval #OPUS_BUFFER_TOO_SMALL \a maxlen was insufficient to contain the - * complete output packet. - */ -OPUS_EXPORT OPUS_WARN_UNUSED_RESULT opus_int32 opus_repacketizer_out(OpusRepacketizer *rp, unsigned char *data, opus_int32 maxlen) OPUS_ARG_NONNULL(1); - -/** Pads a given Opus packet to a larger size (possibly changing the TOC sequence). - * @param[in,out] data const unsigned char*: The buffer containing the - * packet to pad. - * @param len opus_int32: The size of the packet. - * This must be at least 1. - * @param new_len opus_int32: The desired size of the packet after padding. - * This must be at least as large as len. - * @returns an error code - * @retval #OPUS_OK \a on success. - * @retval #OPUS_BAD_ARG \a len was less than 1 or new_len was less than len. - * @retval #OPUS_INVALID_PACKET \a data did not contain a valid Opus packet. - */ -OPUS_EXPORT int opus_packet_pad(unsigned char *data, opus_int32 len, opus_int32 new_len); - -/** Remove all padding from a given Opus packet and rewrite the TOC sequence to - * minimize space usage. - * @param[in,out] data const unsigned char*: The buffer containing the - * packet to strip. - * @param len opus_int32: The size of the packet. - * This must be at least 1. - * @returns The new size of the output packet on success, or an error code - * on failure. - * @retval #OPUS_BAD_ARG \a len was less than 1. - * @retval #OPUS_INVALID_PACKET \a data did not contain a valid Opus packet. - */ -OPUS_EXPORT OPUS_WARN_UNUSED_RESULT opus_int32 opus_packet_unpad(unsigned char *data, opus_int32 len); - -/** Pads a given Opus multi-stream packet to a larger size (possibly changing the TOC sequence). - * @param[in,out] data const unsigned char*: The buffer containing the - * packet to pad. - * @param len opus_int32: The size of the packet. - * This must be at least 1. - * @param new_len opus_int32: The desired size of the packet after padding. - * This must be at least 1. - * @param nb_streams opus_int32: The number of streams (not channels) in the packet. - * This must be at least as large as len. - * @returns an error code - * @retval #OPUS_OK \a on success. - * @retval #OPUS_BAD_ARG \a len was less than 1. - * @retval #OPUS_INVALID_PACKET \a data did not contain a valid Opus packet. - */ -OPUS_EXPORT int opus_multistream_packet_pad(unsigned char *data, opus_int32 len, opus_int32 new_len, int nb_streams); - -/** Remove all padding from a given Opus multi-stream packet and rewrite the TOC sequence to - * minimize space usage. - * @param[in,out] data const unsigned char*: The buffer containing the - * packet to strip. - * @param len opus_int32: The size of the packet. - * This must be at least 1. - * @param nb_streams opus_int32: The number of streams (not channels) in the packet. - * This must be at least 1. - * @returns The new size of the output packet on success, or an error code - * on failure. - * @retval #OPUS_BAD_ARG \a len was less than 1 or new_len was less than len. - * @retval #OPUS_INVALID_PACKET \a data did not contain a valid Opus packet. - */ -OPUS_EXPORT OPUS_WARN_UNUSED_RESULT opus_int32 opus_multistream_packet_unpad(unsigned char *data, opus_int32 len, int nb_streams); - -/**@}*/ - -#ifdef __cplusplus -} -#endif - -#endif /* OPUS_H */ diff --git a/go/teamspeak/.opus/install/x86_64/include/opus/opus_defines.h b/go/teamspeak/.opus/install/x86_64/include/opus/opus_defines.h deleted file mode 100644 index cd8f4dd..0000000 --- a/go/teamspeak/.opus/install/x86_64/include/opus/opus_defines.h +++ /dev/null @@ -1,830 +0,0 @@ -/* Copyright (c) 2010-2011 Xiph.Org Foundation, Skype Limited - Written by Jean-Marc Valin and Koen Vos */ -/* - Redistribution and use in source and binary forms, with or without - modification, are permitted provided that the following conditions - are met: - - - Redistributions of source code must retain the above copyright - notice, this list of conditions and the following disclaimer. - - - Redistributions in binary form must reproduce the above copyright - notice, this list of conditions and the following disclaimer in the - documentation and/or other materials provided with the distribution. - - THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS - ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT - LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR - A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER - OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, - EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, - PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR - PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF - LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING - NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS - SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. -*/ - -/** - * @file opus_defines.h - * @brief Opus reference implementation constants - */ - -#ifndef OPUS_DEFINES_H -#define OPUS_DEFINES_H - -#include "opus_types.h" - -#ifdef __cplusplus -extern "C" { -#endif - -/** @defgroup opus_errorcodes Error codes - * @{ - */ -/** No error @hideinitializer*/ -#define OPUS_OK 0 -/** One or more invalid/out of range arguments @hideinitializer*/ -#define OPUS_BAD_ARG -1 -/** Not enough bytes allocated in the buffer @hideinitializer*/ -#define OPUS_BUFFER_TOO_SMALL -2 -/** An internal error was detected @hideinitializer*/ -#define OPUS_INTERNAL_ERROR -3 -/** The compressed data passed is corrupted @hideinitializer*/ -#define OPUS_INVALID_PACKET -4 -/** Invalid/unsupported request number @hideinitializer*/ -#define OPUS_UNIMPLEMENTED -5 -/** An encoder or decoder structure is invalid or already freed @hideinitializer*/ -#define OPUS_INVALID_STATE -6 -/** Memory allocation has failed @hideinitializer*/ -#define OPUS_ALLOC_FAIL -7 -/**@}*/ - -/** @cond OPUS_INTERNAL_DOC */ -/**Export control for opus functions */ - -#ifndef OPUS_EXPORT -# if defined(_WIN32) -# if defined(OPUS_BUILD) && defined(DLL_EXPORT) -# define OPUS_EXPORT __declspec(dllexport) -# else -# define OPUS_EXPORT -# endif -# elif defined(__GNUC__) && defined(OPUS_BUILD) -# define OPUS_EXPORT __attribute__ ((visibility ("default"))) -# else -# define OPUS_EXPORT -# endif -#endif - -# if !defined(OPUS_GNUC_PREREQ) -# if defined(__GNUC__)&&defined(__GNUC_MINOR__) -# define OPUS_GNUC_PREREQ(_maj,_min) \ - ((__GNUC__<<16)+__GNUC_MINOR__>=((_maj)<<16)+(_min)) -# else -# define OPUS_GNUC_PREREQ(_maj,_min) 0 -# endif -# endif - -#if (!defined(__STDC_VERSION__) || (__STDC_VERSION__ < 199901L) ) -# if OPUS_GNUC_PREREQ(3,0) -# define OPUS_RESTRICT __restrict__ -# elif (defined(_MSC_VER) && _MSC_VER >= 1400) -# define OPUS_RESTRICT __restrict -# else -# define OPUS_RESTRICT -# endif -#else -# define OPUS_RESTRICT restrict -#endif - -#if (!defined(__STDC_VERSION__) || (__STDC_VERSION__ < 199901L) ) -# if OPUS_GNUC_PREREQ(2,7) -# define OPUS_INLINE __inline__ -# elif (defined(_MSC_VER)) -# define OPUS_INLINE __inline -# else -# define OPUS_INLINE -# endif -#else -# define OPUS_INLINE inline -#endif - -/**Warning attributes for opus functions - * NONNULL is not used in OPUS_BUILD to avoid the compiler optimizing out - * some paranoid null checks. */ -#if defined(__GNUC__) && OPUS_GNUC_PREREQ(3, 4) -# define OPUS_WARN_UNUSED_RESULT __attribute__ ((__warn_unused_result__)) -#else -# define OPUS_WARN_UNUSED_RESULT -#endif -#if !defined(OPUS_BUILD) && defined(__GNUC__) && OPUS_GNUC_PREREQ(3, 4) -# define OPUS_ARG_NONNULL(_x) __attribute__ ((__nonnull__(_x))) -#else -# define OPUS_ARG_NONNULL(_x) -#endif - -/** These are the actual Encoder CTL ID numbers. - * They should not be used directly by applications. - * In general, SETs should be even and GETs should be odd.*/ -#define OPUS_SET_APPLICATION_REQUEST 4000 -#define OPUS_GET_APPLICATION_REQUEST 4001 -#define OPUS_SET_BITRATE_REQUEST 4002 -#define OPUS_GET_BITRATE_REQUEST 4003 -#define OPUS_SET_MAX_BANDWIDTH_REQUEST 4004 -#define OPUS_GET_MAX_BANDWIDTH_REQUEST 4005 -#define OPUS_SET_VBR_REQUEST 4006 -#define OPUS_GET_VBR_REQUEST 4007 -#define OPUS_SET_BANDWIDTH_REQUEST 4008 -#define OPUS_GET_BANDWIDTH_REQUEST 4009 -#define OPUS_SET_COMPLEXITY_REQUEST 4010 -#define OPUS_GET_COMPLEXITY_REQUEST 4011 -#define OPUS_SET_INBAND_FEC_REQUEST 4012 -#define OPUS_GET_INBAND_FEC_REQUEST 4013 -#define OPUS_SET_PACKET_LOSS_PERC_REQUEST 4014 -#define OPUS_GET_PACKET_LOSS_PERC_REQUEST 4015 -#define OPUS_SET_DTX_REQUEST 4016 -#define OPUS_GET_DTX_REQUEST 4017 -#define OPUS_SET_VBR_CONSTRAINT_REQUEST 4020 -#define OPUS_GET_VBR_CONSTRAINT_REQUEST 4021 -#define OPUS_SET_FORCE_CHANNELS_REQUEST 4022 -#define OPUS_GET_FORCE_CHANNELS_REQUEST 4023 -#define OPUS_SET_SIGNAL_REQUEST 4024 -#define OPUS_GET_SIGNAL_REQUEST 4025 -#define OPUS_GET_LOOKAHEAD_REQUEST 4027 -/* #define OPUS_RESET_STATE 4028 */ -#define OPUS_GET_SAMPLE_RATE_REQUEST 4029 -#define OPUS_GET_FINAL_RANGE_REQUEST 4031 -#define OPUS_GET_PITCH_REQUEST 4033 -#define OPUS_SET_GAIN_REQUEST 4034 -#define OPUS_GET_GAIN_REQUEST 4045 /* Should have been 4035 */ -#define OPUS_SET_LSB_DEPTH_REQUEST 4036 -#define OPUS_GET_LSB_DEPTH_REQUEST 4037 -#define OPUS_GET_LAST_PACKET_DURATION_REQUEST 4039 -#define OPUS_SET_EXPERT_FRAME_DURATION_REQUEST 4040 -#define OPUS_GET_EXPERT_FRAME_DURATION_REQUEST 4041 -#define OPUS_SET_PREDICTION_DISABLED_REQUEST 4042 -#define OPUS_GET_PREDICTION_DISABLED_REQUEST 4043 -/* Don't use 4045, it's already taken by OPUS_GET_GAIN_REQUEST */ -#define OPUS_SET_PHASE_INVERSION_DISABLED_REQUEST 4046 -#define OPUS_GET_PHASE_INVERSION_DISABLED_REQUEST 4047 -#define OPUS_GET_IN_DTX_REQUEST 4049 -#define OPUS_SET_DRED_DURATION_REQUEST 4050 -#define OPUS_GET_DRED_DURATION_REQUEST 4051 -#define OPUS_SET_DNN_BLOB_REQUEST 4052 -/*#define OPUS_GET_DNN_BLOB_REQUEST 4053 */ - -/** Defines for the presence of extended APIs. */ -#define OPUS_HAVE_OPUS_PROJECTION_H - -/* Macros to trigger compilation errors when the wrong types are provided to a CTL */ -#define __opus_check_int(x) (((void)((x) == (opus_int32)0)), (opus_int32)(x)) - -#ifdef DISABLE_PTR_CHECK -/* Disable checks to prevent ubsan from complaining about NULL checks - in test_opus_api. */ -#define __opus_check_int_ptr(ptr) (ptr) -#define __opus_check_uint_ptr(ptr) (ptr) -#define __opus_check_uint8_ptr(ptr) (ptr) -#define __opus_check_val16_ptr(ptr) (ptr) -#define __opus_check_void_ptr(ptr) (ptr) -#else -#define __opus_check_int_ptr(ptr) ((ptr) + ((ptr) - (opus_int32*)(ptr))) -#define __opus_check_uint_ptr(ptr) ((ptr) + ((ptr) - (opus_uint32*)(ptr))) -#define __opus_check_uint8_ptr(ptr) ((ptr) + ((ptr) - (opus_uint8*)(ptr))) -#define __opus_check_val16_ptr(ptr) ((ptr) + ((ptr) - (opus_val16*)(ptr))) -#define __opus_check_void_ptr(x) ((void)((void *)0 == (x)), (x)) -#endif -/** @endcond */ - -/** @defgroup opus_ctlvalues Pre-defined values for CTL interface - * @see opus_genericctls, opus_encoderctls - * @{ - */ -/* Values for the various encoder CTLs */ -#define OPUS_AUTO -1000 /**opus_int32: Allowed values: 0-10, inclusive. - * - * @hideinitializer */ -#define OPUS_SET_COMPLEXITY(x) OPUS_SET_COMPLEXITY_REQUEST, __opus_check_int(x) -/** Gets the encoder's complexity configuration. - * @see OPUS_SET_COMPLEXITY - * @param[out] x opus_int32 *: Returns a value in the range 0-10, - * inclusive. - * @hideinitializer */ -#define OPUS_GET_COMPLEXITY(x) OPUS_GET_COMPLEXITY_REQUEST, __opus_check_int_ptr(x) - -/** Configures the bitrate in the encoder. - * Rates from 500 to 512000 bits per second are meaningful, as well as the - * special values #OPUS_AUTO and #OPUS_BITRATE_MAX. - * The value #OPUS_BITRATE_MAX can be used to cause the codec to use as much - * rate as it can, which is useful for controlling the rate by adjusting the - * output buffer size. - * @see OPUS_GET_BITRATE - * @param[in] x opus_int32: Bitrate in bits per second. The default - * is determined based on the number of - * channels and the input sampling rate. - * @hideinitializer */ -#define OPUS_SET_BITRATE(x) OPUS_SET_BITRATE_REQUEST, __opus_check_int(x) -/** Gets the encoder's bitrate configuration. - * @see OPUS_SET_BITRATE - * @param[out] x opus_int32 *: Returns the bitrate in bits per second. - * The default is determined based on the - * number of channels and the input - * sampling rate. - * @hideinitializer */ -#define OPUS_GET_BITRATE(x) OPUS_GET_BITRATE_REQUEST, __opus_check_int_ptr(x) - -/** Enables or disables variable bitrate (VBR) in the encoder. - * The configured bitrate may not be met exactly because frames must - * be an integer number of bytes in length. - * @see OPUS_GET_VBR - * @see OPUS_SET_VBR_CONSTRAINT - * @param[in] x opus_int32: Allowed values: - *
- *
0
Hard CBR. For LPC/hybrid modes at very low bit-rate, this can - * cause noticeable quality degradation.
- *
1
VBR (default). The exact type of VBR is controlled by - * #OPUS_SET_VBR_CONSTRAINT.
- *
- * @hideinitializer */ -#define OPUS_SET_VBR(x) OPUS_SET_VBR_REQUEST, __opus_check_int(x) -/** Determine if variable bitrate (VBR) is enabled in the encoder. - * @see OPUS_SET_VBR - * @see OPUS_GET_VBR_CONSTRAINT - * @param[out] x opus_int32 *: Returns one of the following values: - *
- *
0
Hard CBR.
- *
1
VBR (default). The exact type of VBR may be retrieved via - * #OPUS_GET_VBR_CONSTRAINT.
- *
- * @hideinitializer */ -#define OPUS_GET_VBR(x) OPUS_GET_VBR_REQUEST, __opus_check_int_ptr(x) - -/** Enables or disables constrained VBR in the encoder. - * This setting is ignored when the encoder is in CBR mode. - * @warning Only the MDCT mode of Opus currently heeds the constraint. - * Speech mode ignores it completely, hybrid mode may fail to obey it - * if the LPC layer uses more bitrate than the constraint would have - * permitted. - * @see OPUS_GET_VBR_CONSTRAINT - * @see OPUS_SET_VBR - * @param[in] x opus_int32: Allowed values: - *
- *
0
Unconstrained VBR.
- *
1
Constrained VBR (default). This creates a maximum of one - * frame of buffering delay assuming a transport with a - * serialization speed of the nominal bitrate.
- *
- * @hideinitializer */ -#define OPUS_SET_VBR_CONSTRAINT(x) OPUS_SET_VBR_CONSTRAINT_REQUEST, __opus_check_int(x) -/** Determine if constrained VBR is enabled in the encoder. - * @see OPUS_SET_VBR_CONSTRAINT - * @see OPUS_GET_VBR - * @param[out] x opus_int32 *: Returns one of the following values: - *
- *
0
Unconstrained VBR.
- *
1
Constrained VBR (default).
- *
- * @hideinitializer */ -#define OPUS_GET_VBR_CONSTRAINT(x) OPUS_GET_VBR_CONSTRAINT_REQUEST, __opus_check_int_ptr(x) - -/** Configures mono/stereo forcing in the encoder. - * This can force the encoder to produce packets encoded as either mono or - * stereo, regardless of the format of the input audio. This is useful when - * the caller knows that the input signal is currently a mono source embedded - * in a stereo stream. - * @see OPUS_GET_FORCE_CHANNELS - * @param[in] x opus_int32: Allowed values: - *
- *
#OPUS_AUTO
Not forced (default)
- *
1
Forced mono
- *
2
Forced stereo
- *
- * @hideinitializer */ -#define OPUS_SET_FORCE_CHANNELS(x) OPUS_SET_FORCE_CHANNELS_REQUEST, __opus_check_int(x) -/** Gets the encoder's forced channel configuration. - * @see OPUS_SET_FORCE_CHANNELS - * @param[out] x opus_int32 *: - *
- *
#OPUS_AUTO
Not forced (default)
- *
1
Forced mono
- *
2
Forced stereo
- *
- * @hideinitializer */ -#define OPUS_GET_FORCE_CHANNELS(x) OPUS_GET_FORCE_CHANNELS_REQUEST, __opus_check_int_ptr(x) - -/** Configures the maximum bandpass that the encoder will select automatically. - * Applications should normally use this instead of #OPUS_SET_BANDWIDTH - * (leaving that set to the default, #OPUS_AUTO). This allows the - * application to set an upper bound based on the type of input it is - * providing, but still gives the encoder the freedom to reduce the bandpass - * when the bitrate becomes too low, for better overall quality. - * @see OPUS_GET_MAX_BANDWIDTH - * @param[in] x opus_int32: Allowed values: - *
- *
OPUS_BANDWIDTH_NARROWBAND
4 kHz passband
- *
OPUS_BANDWIDTH_MEDIUMBAND
6 kHz passband
- *
OPUS_BANDWIDTH_WIDEBAND
8 kHz passband
- *
OPUS_BANDWIDTH_SUPERWIDEBAND
12 kHz passband
- *
OPUS_BANDWIDTH_FULLBAND
20 kHz passband (default)
- *
- * @hideinitializer */ -#define OPUS_SET_MAX_BANDWIDTH(x) OPUS_SET_MAX_BANDWIDTH_REQUEST, __opus_check_int(x) - -/** Gets the encoder's configured maximum allowed bandpass. - * @see OPUS_SET_MAX_BANDWIDTH - * @param[out] x opus_int32 *: Allowed values: - *
- *
#OPUS_BANDWIDTH_NARROWBAND
4 kHz passband
- *
#OPUS_BANDWIDTH_MEDIUMBAND
6 kHz passband
- *
#OPUS_BANDWIDTH_WIDEBAND
8 kHz passband
- *
#OPUS_BANDWIDTH_SUPERWIDEBAND
12 kHz passband
- *
#OPUS_BANDWIDTH_FULLBAND
20 kHz passband (default)
- *
- * @hideinitializer */ -#define OPUS_GET_MAX_BANDWIDTH(x) OPUS_GET_MAX_BANDWIDTH_REQUEST, __opus_check_int_ptr(x) - -/** Sets the encoder's bandpass to a specific value. - * This prevents the encoder from automatically selecting the bandpass based - * on the available bitrate. If an application knows the bandpass of the input - * audio it is providing, it should normally use #OPUS_SET_MAX_BANDWIDTH - * instead, which still gives the encoder the freedom to reduce the bandpass - * when the bitrate becomes too low, for better overall quality. - * @see OPUS_GET_BANDWIDTH - * @param[in] x opus_int32: Allowed values: - *
- *
#OPUS_AUTO
(default)
- *
#OPUS_BANDWIDTH_NARROWBAND
4 kHz passband
- *
#OPUS_BANDWIDTH_MEDIUMBAND
6 kHz passband
- *
#OPUS_BANDWIDTH_WIDEBAND
8 kHz passband
- *
#OPUS_BANDWIDTH_SUPERWIDEBAND
12 kHz passband
- *
#OPUS_BANDWIDTH_FULLBAND
20 kHz passband
- *
- * @hideinitializer */ -#define OPUS_SET_BANDWIDTH(x) OPUS_SET_BANDWIDTH_REQUEST, __opus_check_int(x) - -/** Configures the type of signal being encoded. - * This is a hint which helps the encoder's mode selection. - * @see OPUS_GET_SIGNAL - * @param[in] x opus_int32: Allowed values: - *
- *
#OPUS_AUTO
(default)
- *
#OPUS_SIGNAL_VOICE
Bias thresholds towards choosing LPC or Hybrid modes.
- *
#OPUS_SIGNAL_MUSIC
Bias thresholds towards choosing MDCT modes.
- *
- * @hideinitializer */ -#define OPUS_SET_SIGNAL(x) OPUS_SET_SIGNAL_REQUEST, __opus_check_int(x) -/** Gets the encoder's configured signal type. - * @see OPUS_SET_SIGNAL - * @param[out] x opus_int32 *: Returns one of the following values: - *
- *
#OPUS_AUTO
(default)
- *
#OPUS_SIGNAL_VOICE
Bias thresholds towards choosing LPC or Hybrid modes.
- *
#OPUS_SIGNAL_MUSIC
Bias thresholds towards choosing MDCT modes.
- *
- * @hideinitializer */ -#define OPUS_GET_SIGNAL(x) OPUS_GET_SIGNAL_REQUEST, __opus_check_int_ptr(x) - - -/** Configures the encoder's intended application. - * The initial value is a mandatory argument to the encoder_create function. - * @see OPUS_GET_APPLICATION - * @param[in] x opus_int32: Returns one of the following values: - *
- *
#OPUS_APPLICATION_VOIP
- *
Process signal for improved speech intelligibility.
- *
#OPUS_APPLICATION_AUDIO
- *
Favor faithfulness to the original input.
- *
#OPUS_APPLICATION_RESTRICTED_LOWDELAY
- *
Configure the minimum possible coding delay by disabling certain modes - * of operation.
- *
- * @hideinitializer */ -#define OPUS_SET_APPLICATION(x) OPUS_SET_APPLICATION_REQUEST, __opus_check_int(x) -/** Gets the encoder's configured application. - * @see OPUS_SET_APPLICATION - * @param[out] x opus_int32 *: Returns one of the following values: - *
- *
#OPUS_APPLICATION_VOIP
- *
Process signal for improved speech intelligibility.
- *
#OPUS_APPLICATION_AUDIO
- *
Favor faithfulness to the original input.
- *
#OPUS_APPLICATION_RESTRICTED_LOWDELAY
- *
Configure the minimum possible coding delay by disabling certain modes - * of operation.
- *
- * @hideinitializer */ -#define OPUS_GET_APPLICATION(x) OPUS_GET_APPLICATION_REQUEST, __opus_check_int_ptr(x) - -/** Gets the total samples of delay added by the entire codec. - * This can be queried by the encoder and then the provided number of samples can be - * skipped on from the start of the decoder's output to provide time aligned input - * and output. From the perspective of a decoding application the real data begins this many - * samples late. - * - * The decoder contribution to this delay is identical for all decoders, but the - * encoder portion of the delay may vary from implementation to implementation, - * version to version, or even depend on the encoder's initial configuration. - * Applications needing delay compensation should call this CTL rather than - * hard-coding a value. - * @param[out] x opus_int32 *: Number of lookahead samples - * @hideinitializer */ -#define OPUS_GET_LOOKAHEAD(x) OPUS_GET_LOOKAHEAD_REQUEST, __opus_check_int_ptr(x) - -/** Configures the encoder's use of inband forward error correction (FEC). - * @note This is only applicable to the LPC layer - * @see OPUS_GET_INBAND_FEC - * @param[in] x opus_int32: Allowed values: - *
- *
0
Disable inband FEC (default).
- *
1
Inband FEC enabled. If the packet loss rate is sufficiently high, Opus will automatically switch to SILK even at high rates to enable use of that FEC.
- *
2
Inband FEC enabled, but does not necessarily switch to SILK if we have music.
- *
- * @hideinitializer */ -#define OPUS_SET_INBAND_FEC(x) OPUS_SET_INBAND_FEC_REQUEST, __opus_check_int(x) -/** Gets encoder's configured use of inband forward error correction. - * @see OPUS_SET_INBAND_FEC - * @param[out] x opus_int32 *: Returns one of the following values: - *
- *
0
Inband FEC disabled (default).
- *
1
Inband FEC enabled. If the packet loss rate is sufficiently high, Opus will automatically switch to SILK even at high rates to enable use of that FEC.
- *
2
Inband FEC enabled, but does not necessarily switch to SILK if we have music.
- *
- * @hideinitializer */ -#define OPUS_GET_INBAND_FEC(x) OPUS_GET_INBAND_FEC_REQUEST, __opus_check_int_ptr(x) - -/** Configures the encoder's expected packet loss percentage. - * Higher values trigger progressively more loss resistant behavior in the encoder - * at the expense of quality at a given bitrate in the absence of packet loss, but - * greater quality under loss. - * @see OPUS_GET_PACKET_LOSS_PERC - * @param[in] x opus_int32: Loss percentage in the range 0-100, inclusive (default: 0). - * @hideinitializer */ -#define OPUS_SET_PACKET_LOSS_PERC(x) OPUS_SET_PACKET_LOSS_PERC_REQUEST, __opus_check_int(x) -/** Gets the encoder's configured packet loss percentage. - * @see OPUS_SET_PACKET_LOSS_PERC - * @param[out] x opus_int32 *: Returns the configured loss percentage - * in the range 0-100, inclusive (default: 0). - * @hideinitializer */ -#define OPUS_GET_PACKET_LOSS_PERC(x) OPUS_GET_PACKET_LOSS_PERC_REQUEST, __opus_check_int_ptr(x) - -/** Configures the encoder's use of discontinuous transmission (DTX). - * @note This is only applicable to the LPC layer - * @see OPUS_GET_DTX - * @param[in] x opus_int32: Allowed values: - *
- *
0
Disable DTX (default).
- *
1
Enabled DTX.
- *
- * @hideinitializer */ -#define OPUS_SET_DTX(x) OPUS_SET_DTX_REQUEST, __opus_check_int(x) -/** Gets encoder's configured use of discontinuous transmission. - * @see OPUS_SET_DTX - * @param[out] x opus_int32 *: Returns one of the following values: - *
- *
0
DTX disabled (default).
- *
1
DTX enabled.
- *
- * @hideinitializer */ -#define OPUS_GET_DTX(x) OPUS_GET_DTX_REQUEST, __opus_check_int_ptr(x) -/** Configures the depth of signal being encoded. - * - * This is a hint which helps the encoder identify silence and near-silence. - * It represents the number of significant bits of linear intensity below - * which the signal contains ignorable quantization or other noise. - * - * For example, OPUS_SET_LSB_DEPTH(14) would be an appropriate setting - * for G.711 u-law input. OPUS_SET_LSB_DEPTH(16) would be appropriate - * for 16-bit linear pcm input with opus_encode_float(). - * - * When using opus_encode() instead of opus_encode_float(), or when libopus - * is compiled for fixed-point, the encoder uses the minimum of the value - * set here and the value 16. - * - * @see OPUS_GET_LSB_DEPTH - * @param[in] x opus_int32: Input precision in bits, between 8 and 24 - * (default: 24). - * @hideinitializer */ -#define OPUS_SET_LSB_DEPTH(x) OPUS_SET_LSB_DEPTH_REQUEST, __opus_check_int(x) -/** Gets the encoder's configured signal depth. - * @see OPUS_SET_LSB_DEPTH - * @param[out] x opus_int32 *: Input precision in bits, between 8 and - * 24 (default: 24). - * @hideinitializer */ -#define OPUS_GET_LSB_DEPTH(x) OPUS_GET_LSB_DEPTH_REQUEST, __opus_check_int_ptr(x) - -/** Configures the encoder's use of variable duration frames. - * When variable duration is enabled, the encoder is free to use a shorter frame - * size than the one requested in the opus_encode*() call. - * It is then the user's responsibility - * to verify how much audio was encoded by checking the ToC byte of the encoded - * packet. The part of the audio that was not encoded needs to be resent to the - * encoder for the next call. Do not use this option unless you really - * know what you are doing. - * @see OPUS_GET_EXPERT_FRAME_DURATION - * @param[in] x opus_int32: Allowed values: - *
- *
OPUS_FRAMESIZE_ARG
Select frame size from the argument (default).
- *
OPUS_FRAMESIZE_2_5_MS
Use 2.5 ms frames.
- *
OPUS_FRAMESIZE_5_MS
Use 5 ms frames.
- *
OPUS_FRAMESIZE_10_MS
Use 10 ms frames.
- *
OPUS_FRAMESIZE_20_MS
Use 20 ms frames.
- *
OPUS_FRAMESIZE_40_MS
Use 40 ms frames.
- *
OPUS_FRAMESIZE_60_MS
Use 60 ms frames.
- *
OPUS_FRAMESIZE_80_MS
Use 80 ms frames.
- *
OPUS_FRAMESIZE_100_MS
Use 100 ms frames.
- *
OPUS_FRAMESIZE_120_MS
Use 120 ms frames.
- *
- * @hideinitializer */ -#define OPUS_SET_EXPERT_FRAME_DURATION(x) OPUS_SET_EXPERT_FRAME_DURATION_REQUEST, __opus_check_int(x) -/** Gets the encoder's configured use of variable duration frames. - * @see OPUS_SET_EXPERT_FRAME_DURATION - * @param[out] x opus_int32 *: Returns one of the following values: - *
- *
OPUS_FRAMESIZE_ARG
Select frame size from the argument (default).
- *
OPUS_FRAMESIZE_2_5_MS
Use 2.5 ms frames.
- *
OPUS_FRAMESIZE_5_MS
Use 5 ms frames.
- *
OPUS_FRAMESIZE_10_MS
Use 10 ms frames.
- *
OPUS_FRAMESIZE_20_MS
Use 20 ms frames.
- *
OPUS_FRAMESIZE_40_MS
Use 40 ms frames.
- *
OPUS_FRAMESIZE_60_MS
Use 60 ms frames.
- *
OPUS_FRAMESIZE_80_MS
Use 80 ms frames.
- *
OPUS_FRAMESIZE_100_MS
Use 100 ms frames.
- *
OPUS_FRAMESIZE_120_MS
Use 120 ms frames.
- *
- * @hideinitializer */ -#define OPUS_GET_EXPERT_FRAME_DURATION(x) OPUS_GET_EXPERT_FRAME_DURATION_REQUEST, __opus_check_int_ptr(x) - -/** If set to 1, disables almost all use of prediction, making frames almost - * completely independent. This reduces quality. - * @see OPUS_GET_PREDICTION_DISABLED - * @param[in] x opus_int32: Allowed values: - *
- *
0
Enable prediction (default).
- *
1
Disable prediction.
- *
- * @hideinitializer */ -#define OPUS_SET_PREDICTION_DISABLED(x) OPUS_SET_PREDICTION_DISABLED_REQUEST, __opus_check_int(x) -/** Gets the encoder's configured prediction status. - * @see OPUS_SET_PREDICTION_DISABLED - * @param[out] x opus_int32 *: Returns one of the following values: - *
- *
0
Prediction enabled (default).
- *
1
Prediction disabled.
- *
- * @hideinitializer */ -#define OPUS_GET_PREDICTION_DISABLED(x) OPUS_GET_PREDICTION_DISABLED_REQUEST, __opus_check_int_ptr(x) - -/** If non-zero, enables Deep Redundancy (DRED) and use the specified maximum number of 10-ms redundant frames - * @hideinitializer */ -#define OPUS_SET_DRED_DURATION(x) OPUS_SET_DRED_DURATION_REQUEST, __opus_check_int(x) -/** Gets the encoder's configured Deep Redundancy (DRED) maximum number of frames. - * @hideinitializer */ -#define OPUS_GET_DRED_DURATION(x) OPUS_GET_DRED_DURATION_REQUEST, __opus_check_int_ptr(x) - -/** Provide external DNN weights from binary object (only when explicitly built without the weights) - * @hideinitializer */ -#define OPUS_SET_DNN_BLOB(data, len) OPUS_SET_DNN_BLOB_REQUEST, __opus_check_void_ptr(data), __opus_check_int(len) - - -/**@}*/ - -/** @defgroup opus_genericctls Generic CTLs - * - * These macros are used with the \c opus_decoder_ctl and - * \c opus_encoder_ctl calls to generate a particular - * request. - * - * When called on an \c OpusDecoder they apply to that - * particular decoder instance. When called on an - * \c OpusEncoder they apply to the corresponding setting - * on that encoder instance, if present. - * - * Some usage examples: - * - * @code - * int ret; - * opus_int32 pitch; - * ret = opus_decoder_ctl(dec_ctx, OPUS_GET_PITCH(&pitch)); - * if (ret == OPUS_OK) return ret; - * - * opus_encoder_ctl(enc_ctx, OPUS_RESET_STATE); - * opus_decoder_ctl(dec_ctx, OPUS_RESET_STATE); - * - * opus_int32 enc_bw, dec_bw; - * opus_encoder_ctl(enc_ctx, OPUS_GET_BANDWIDTH(&enc_bw)); - * opus_decoder_ctl(dec_ctx, OPUS_GET_BANDWIDTH(&dec_bw)); - * if (enc_bw != dec_bw) { - * printf("packet bandwidth mismatch!\n"); - * } - * @endcode - * - * @see opus_encoder, opus_decoder_ctl, opus_encoder_ctl, opus_decoderctls, opus_encoderctls - * @{ - */ - -/** Resets the codec state to be equivalent to a freshly initialized state. - * This should be called when switching streams in order to prevent - * the back to back decoding from giving different results from - * one at a time decoding. - * @hideinitializer */ -#define OPUS_RESET_STATE 4028 - -/** Gets the final state of the codec's entropy coder. - * This is used for testing purposes, - * The encoder and decoder state should be identical after coding a payload - * (assuming no data corruption or software bugs) - * - * @param[out] x opus_uint32 *: Entropy coder state - * - * @hideinitializer */ -#define OPUS_GET_FINAL_RANGE(x) OPUS_GET_FINAL_RANGE_REQUEST, __opus_check_uint_ptr(x) - -/** Gets the encoder's configured bandpass or the decoder's last bandpass. - * @see OPUS_SET_BANDWIDTH - * @param[out] x opus_int32 *: Returns one of the following values: - *
- *
#OPUS_AUTO
(default)
- *
#OPUS_BANDWIDTH_NARROWBAND
4 kHz passband
- *
#OPUS_BANDWIDTH_MEDIUMBAND
6 kHz passband
- *
#OPUS_BANDWIDTH_WIDEBAND
8 kHz passband
- *
#OPUS_BANDWIDTH_SUPERWIDEBAND
12 kHz passband
- *
#OPUS_BANDWIDTH_FULLBAND
20 kHz passband
- *
- * @hideinitializer */ -#define OPUS_GET_BANDWIDTH(x) OPUS_GET_BANDWIDTH_REQUEST, __opus_check_int_ptr(x) - -/** Gets the sampling rate the encoder or decoder was initialized with. - * This simply returns the Fs value passed to opus_encoder_init() - * or opus_decoder_init(). - * @param[out] x opus_int32 *: Sampling rate of encoder or decoder. - * @hideinitializer - */ -#define OPUS_GET_SAMPLE_RATE(x) OPUS_GET_SAMPLE_RATE_REQUEST, __opus_check_int_ptr(x) - -/** If set to 1, disables the use of phase inversion for intensity stereo, - * improving the quality of mono downmixes, but slightly reducing normal - * stereo quality. Disabling phase inversion in the decoder does not comply - * with RFC 6716, although it does not cause any interoperability issue and - * is expected to become part of the Opus standard once RFC 6716 is updated - * by draft-ietf-codec-opus-update. - * @see OPUS_GET_PHASE_INVERSION_DISABLED - * @param[in] x opus_int32: Allowed values: - *
- *
0
Enable phase inversion (default).
- *
1
Disable phase inversion.
- *
- * @hideinitializer */ -#define OPUS_SET_PHASE_INVERSION_DISABLED(x) OPUS_SET_PHASE_INVERSION_DISABLED_REQUEST, __opus_check_int(x) -/** Gets the encoder's configured phase inversion status. - * @see OPUS_SET_PHASE_INVERSION_DISABLED - * @param[out] x opus_int32 *: Returns one of the following values: - *
- *
0
Stereo phase inversion enabled (default).
- *
1
Stereo phase inversion disabled.
- *
- * @hideinitializer */ -#define OPUS_GET_PHASE_INVERSION_DISABLED(x) OPUS_GET_PHASE_INVERSION_DISABLED_REQUEST, __opus_check_int_ptr(x) -/** Gets the DTX state of the encoder. - * Returns whether the last encoded frame was either a comfort noise update - * during DTX or not encoded because of DTX. - * @param[out] x opus_int32 *: Returns one of the following values: - *
- *
0
The encoder is not in DTX.
- *
1
The encoder is in DTX.
- *
- * @hideinitializer */ -#define OPUS_GET_IN_DTX(x) OPUS_GET_IN_DTX_REQUEST, __opus_check_int_ptr(x) - -/**@}*/ - -/** @defgroup opus_decoderctls Decoder related CTLs - * @see opus_genericctls, opus_encoderctls, opus_decoder - * @{ - */ - -/** Configures decoder gain adjustment. - * Scales the decoded output by a factor specified in Q8 dB units. - * This has a maximum range of -32768 to 32767 inclusive, and returns - * OPUS_BAD_ARG otherwise. The default is zero indicating no adjustment. - * This setting survives decoder reset. - * - * gain = pow(10, x/(20.0*256)) - * - * @param[in] x opus_int32: Amount to scale PCM signal by in Q8 dB units. - * @hideinitializer */ -#define OPUS_SET_GAIN(x) OPUS_SET_GAIN_REQUEST, __opus_check_int(x) -/** Gets the decoder's configured gain adjustment. @see OPUS_SET_GAIN - * - * @param[out] x opus_int32 *: Amount to scale PCM signal by in Q8 dB units. - * @hideinitializer */ -#define OPUS_GET_GAIN(x) OPUS_GET_GAIN_REQUEST, __opus_check_int_ptr(x) - -/** Gets the duration (in samples) of the last packet successfully decoded or concealed. - * @param[out] x opus_int32 *: Number of samples (at current sampling rate). - * @hideinitializer */ -#define OPUS_GET_LAST_PACKET_DURATION(x) OPUS_GET_LAST_PACKET_DURATION_REQUEST, __opus_check_int_ptr(x) - -/** Gets the pitch of the last decoded frame, if available. - * This can be used for any post-processing algorithm requiring the use of pitch, - * e.g. time stretching/shortening. If the last frame was not voiced, or if the - * pitch was not coded in the frame, then zero is returned. - * - * This CTL is only implemented for decoder instances. - * - * @param[out] x opus_int32 *: pitch period at 48 kHz (or 0 if not available) - * - * @hideinitializer */ -#define OPUS_GET_PITCH(x) OPUS_GET_PITCH_REQUEST, __opus_check_int_ptr(x) - -/**@}*/ - -/** @defgroup opus_libinfo Opus library information functions - * @{ - */ - -/** Converts an opus error code into a human readable string. - * - * @param[in] error int: Error number - * @returns Error string - */ -OPUS_EXPORT const char *opus_strerror(int error); - -/** Gets the libopus version string. - * - * Applications may look for the substring "-fixed" in the version string to - * determine whether they have a fixed-point or floating-point build at - * runtime. - * - * @returns Version string - */ -OPUS_EXPORT const char *opus_get_version_string(void); -/**@}*/ - -#ifdef __cplusplus -} -#endif - -#endif /* OPUS_DEFINES_H */ diff --git a/go/teamspeak/.opus/install/x86_64/include/opus/opus_multistream.h b/go/teamspeak/.opus/install/x86_64/include/opus/opus_multistream.h deleted file mode 100644 index 824cc55..0000000 --- a/go/teamspeak/.opus/install/x86_64/include/opus/opus_multistream.h +++ /dev/null @@ -1,660 +0,0 @@ -/* Copyright (c) 2011 Xiph.Org Foundation - Written by Jean-Marc Valin */ -/* - Redistribution and use in source and binary forms, with or without - modification, are permitted provided that the following conditions - are met: - - - Redistributions of source code must retain the above copyright - notice, this list of conditions and the following disclaimer. - - - Redistributions in binary form must reproduce the above copyright - notice, this list of conditions and the following disclaimer in the - documentation and/or other materials provided with the distribution. - - THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS - ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT - LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR - A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER - OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, - EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, - PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR - PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF - LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING - NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS - SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. -*/ - -/** - * @file opus_multistream.h - * @brief Opus reference implementation multistream API - */ - -#ifndef OPUS_MULTISTREAM_H -#define OPUS_MULTISTREAM_H - -#include "opus.h" - -#ifdef __cplusplus -extern "C" { -#endif - -/** @cond OPUS_INTERNAL_DOC */ - -/** Macros to trigger compilation errors when the wrong types are provided to a - * CTL. */ -/**@{*/ -#define __opus_check_encstate_ptr(ptr) ((ptr) + ((ptr) - (OpusEncoder**)(ptr))) -#define __opus_check_decstate_ptr(ptr) ((ptr) + ((ptr) - (OpusDecoder**)(ptr))) -/**@}*/ - -/** These are the actual encoder and decoder CTL ID numbers. - * They should not be used directly by applications. - * In general, SETs should be even and GETs should be odd.*/ -/**@{*/ -#define OPUS_MULTISTREAM_GET_ENCODER_STATE_REQUEST 5120 -#define OPUS_MULTISTREAM_GET_DECODER_STATE_REQUEST 5122 -/**@}*/ - -/** @endcond */ - -/** @defgroup opus_multistream_ctls Multistream specific encoder and decoder CTLs - * - * These are convenience macros that are specific to the - * opus_multistream_encoder_ctl() and opus_multistream_decoder_ctl() - * interface. - * The CTLs from @ref opus_genericctls, @ref opus_encoderctls, and - * @ref opus_decoderctls may be applied to a multistream encoder or decoder as - * well. - * In addition, you may retrieve the encoder or decoder state for an specific - * stream via #OPUS_MULTISTREAM_GET_ENCODER_STATE or - * #OPUS_MULTISTREAM_GET_DECODER_STATE and apply CTLs to it individually. - */ -/**@{*/ - -/** Gets the encoder state for an individual stream of a multistream encoder. - * @param[in] x opus_int32: The index of the stream whose encoder you - * wish to retrieve. - * This must be non-negative and less than - * the streams parameter used - * to initialize the encoder. - * @param[out] y OpusEncoder**: Returns a pointer to the given - * encoder state. - * @retval OPUS_BAD_ARG The index of the requested stream was out of range. - * @hideinitializer - */ -#define OPUS_MULTISTREAM_GET_ENCODER_STATE(x,y) OPUS_MULTISTREAM_GET_ENCODER_STATE_REQUEST, __opus_check_int(x), __opus_check_encstate_ptr(y) - -/** Gets the decoder state for an individual stream of a multistream decoder. - * @param[in] x opus_int32: The index of the stream whose decoder you - * wish to retrieve. - * This must be non-negative and less than - * the streams parameter used - * to initialize the decoder. - * @param[out] y OpusDecoder**: Returns a pointer to the given - * decoder state. - * @retval OPUS_BAD_ARG The index of the requested stream was out of range. - * @hideinitializer - */ -#define OPUS_MULTISTREAM_GET_DECODER_STATE(x,y) OPUS_MULTISTREAM_GET_DECODER_STATE_REQUEST, __opus_check_int(x), __opus_check_decstate_ptr(y) - -/**@}*/ - -/** @defgroup opus_multistream Opus Multistream API - * @{ - * - * The multistream API allows individual Opus streams to be combined into a - * single packet, enabling support for up to 255 channels. Unlike an - * elementary Opus stream, the encoder and decoder must negotiate the channel - * configuration before the decoder can successfully interpret the data in the - * packets produced by the encoder. Some basic information, such as packet - * duration, can be computed without any special negotiation. - * - * The format for multistream Opus packets is defined in - * RFC 7845 - * and is based on the self-delimited Opus framing described in Appendix B of - * RFC 6716. - * Normal Opus packets are just a degenerate case of multistream Opus packets, - * and can be encoded or decoded with the multistream API by setting - * streams to 1 when initializing the encoder or - * decoder. - * - * Multistream Opus streams can contain up to 255 elementary Opus streams. - * These may be either "uncoupled" or "coupled", indicating that the decoder - * is configured to decode them to either 1 or 2 channels, respectively. - * The streams are ordered so that all coupled streams appear at the - * beginning. - * - * A mapping table defines which decoded channel i - * should be used for each input/output (I/O) channel j. This table is - * typically provided as an unsigned char array. - * Let i = mapping[j] be the index for I/O channel j. - * If i < 2*coupled_streams, then I/O channel j is - * encoded as the left channel of stream (i/2) if i - * is even, or as the right channel of stream (i/2) if - * i is odd. Otherwise, I/O channel j is encoded as - * mono in stream (i - coupled_streams), unless it has the special - * value 255, in which case it is omitted from the encoding entirely (the - * decoder will reproduce it as silence). Each value i must either - * be the special value 255 or be less than streams + coupled_streams. - * - * The output channels specified by the encoder - * should use the - * Vorbis - * channel ordering. A decoder may wish to apply an additional permutation - * to the mapping the encoder used to achieve a different output channel - * order (e.g. for outputting in WAV order). - * - * Each multistream packet contains an Opus packet for each stream, and all of - * the Opus packets in a single multistream packet must have the same - * duration. Therefore the duration of a multistream packet can be extracted - * from the TOC sequence of the first stream, which is located at the - * beginning of the packet, just like an elementary Opus stream: - * - * @code - * int nb_samples; - * int nb_frames; - * nb_frames = opus_packet_get_nb_frames(data, len); - * if (nb_frames < 1) - * return nb_frames; - * nb_samples = opus_packet_get_samples_per_frame(data, 48000) * nb_frames; - * @endcode - * - * The general encoding and decoding process proceeds exactly the same as in - * the normal @ref opus_encoder and @ref opus_decoder APIs. - * See their documentation for an overview of how to use the corresponding - * multistream functions. - */ - -/** Opus multistream encoder state. - * This contains the complete state of a multistream Opus encoder. - * It is position independent and can be freely copied. - * @see opus_multistream_encoder_create - * @see opus_multistream_encoder_init - */ -typedef struct OpusMSEncoder OpusMSEncoder; - -/** Opus multistream decoder state. - * This contains the complete state of a multistream Opus decoder. - * It is position independent and can be freely copied. - * @see opus_multistream_decoder_create - * @see opus_multistream_decoder_init - */ -typedef struct OpusMSDecoder OpusMSDecoder; - -/**\name Multistream encoder functions */ -/**@{*/ - -/** Gets the size of an OpusMSEncoder structure. - * @param streams int: The total number of streams to encode from the - * input. - * This must be no more than 255. - * @param coupled_streams int: Number of coupled (2 channel) streams - * to encode. - * This must be no larger than the total - * number of streams. - * Additionally, The total number of - * encoded channels (streams + - * coupled_streams) must be no - * more than 255. - * @returns The size in bytes on success, or a negative error code - * (see @ref opus_errorcodes) on error. - */ -OPUS_EXPORT OPUS_WARN_UNUSED_RESULT opus_int32 opus_multistream_encoder_get_size( - int streams, - int coupled_streams -); - -OPUS_EXPORT OPUS_WARN_UNUSED_RESULT opus_int32 opus_multistream_surround_encoder_get_size( - int channels, - int mapping_family -); - - -/** Allocates and initializes a multistream encoder state. - * Call opus_multistream_encoder_destroy() to release - * this object when finished. - * @param Fs opus_int32: Sampling rate of the input signal (in Hz). - * This must be one of 8000, 12000, 16000, - * 24000, or 48000. - * @param channels int: Number of channels in the input signal. - * This must be at most 255. - * It may be greater than the number of - * coded channels (streams + - * coupled_streams). - * @param streams int: The total number of streams to encode from the - * input. - * This must be no more than the number of channels. - * @param coupled_streams int: Number of coupled (2 channel) streams - * to encode. - * This must be no larger than the total - * number of streams. - * Additionally, The total number of - * encoded channels (streams + - * coupled_streams) must be no - * more than the number of input channels. - * @param[in] mapping const unsigned char[channels]: Mapping from - * encoded channels to input channels, as described in - * @ref opus_multistream. As an extra constraint, the - * multistream encoder does not allow encoding coupled - * streams for which one channel is unused since this - * is never a good idea. - * @param application int: The target encoder application. - * This must be one of the following: - *
- *
#OPUS_APPLICATION_VOIP
- *
Process signal for improved speech intelligibility.
- *
#OPUS_APPLICATION_AUDIO
- *
Favor faithfulness to the original input.
- *
#OPUS_APPLICATION_RESTRICTED_LOWDELAY
- *
Configure the minimum possible coding delay by disabling certain modes - * of operation.
- *
- * @param[out] error int *: Returns #OPUS_OK on success, or an error - * code (see @ref opus_errorcodes) on - * failure. - */ -OPUS_EXPORT OPUS_WARN_UNUSED_RESULT OpusMSEncoder *opus_multistream_encoder_create( - opus_int32 Fs, - int channels, - int streams, - int coupled_streams, - const unsigned char *mapping, - int application, - int *error -) OPUS_ARG_NONNULL(5); - -OPUS_EXPORT OPUS_WARN_UNUSED_RESULT OpusMSEncoder *opus_multistream_surround_encoder_create( - opus_int32 Fs, - int channels, - int mapping_family, - int *streams, - int *coupled_streams, - unsigned char *mapping, - int application, - int *error -) OPUS_ARG_NONNULL(4) OPUS_ARG_NONNULL(5) OPUS_ARG_NONNULL(6); - -/** Initialize a previously allocated multistream encoder state. - * The memory pointed to by \a st must be at least the size returned by - * opus_multistream_encoder_get_size(). - * This is intended for applications which use their own allocator instead of - * malloc. - * To reset a previously initialized state, use the #OPUS_RESET_STATE CTL. - * @see opus_multistream_encoder_create - * @see opus_multistream_encoder_get_size - * @param st OpusMSEncoder*: Multistream encoder state to initialize. - * @param Fs opus_int32: Sampling rate of the input signal (in Hz). - * This must be one of 8000, 12000, 16000, - * 24000, or 48000. - * @param channels int: Number of channels in the input signal. - * This must be at most 255. - * It may be greater than the number of - * coded channels (streams + - * coupled_streams). - * @param streams int: The total number of streams to encode from the - * input. - * This must be no more than the number of channels. - * @param coupled_streams int: Number of coupled (2 channel) streams - * to encode. - * This must be no larger than the total - * number of streams. - * Additionally, The total number of - * encoded channels (streams + - * coupled_streams) must be no - * more than the number of input channels. - * @param[in] mapping const unsigned char[channels]: Mapping from - * encoded channels to input channels, as described in - * @ref opus_multistream. As an extra constraint, the - * multistream encoder does not allow encoding coupled - * streams for which one channel is unused since this - * is never a good idea. - * @param application int: The target encoder application. - * This must be one of the following: - *
- *
#OPUS_APPLICATION_VOIP
- *
Process signal for improved speech intelligibility.
- *
#OPUS_APPLICATION_AUDIO
- *
Favor faithfulness to the original input.
- *
#OPUS_APPLICATION_RESTRICTED_LOWDELAY
- *
Configure the minimum possible coding delay by disabling certain modes - * of operation.
- *
- * @returns #OPUS_OK on success, or an error code (see @ref opus_errorcodes) - * on failure. - */ -OPUS_EXPORT int opus_multistream_encoder_init( - OpusMSEncoder *st, - opus_int32 Fs, - int channels, - int streams, - int coupled_streams, - const unsigned char *mapping, - int application -) OPUS_ARG_NONNULL(1) OPUS_ARG_NONNULL(6); - -OPUS_EXPORT int opus_multistream_surround_encoder_init( - OpusMSEncoder *st, - opus_int32 Fs, - int channels, - int mapping_family, - int *streams, - int *coupled_streams, - unsigned char *mapping, - int application -) OPUS_ARG_NONNULL(1) OPUS_ARG_NONNULL(5) OPUS_ARG_NONNULL(6) OPUS_ARG_NONNULL(7); - -/** Encodes a multistream Opus frame. - * @param st OpusMSEncoder*: Multistream encoder state. - * @param[in] pcm const opus_int16*: The input signal as interleaved - * samples. - * This must contain - * frame_size*channels - * samples. - * @param frame_size int: Number of samples per channel in the input - * signal. - * This must be an Opus frame size for the - * encoder's sampling rate. - * For example, at 48 kHz the permitted values - * are 120, 240, 480, 960, 1920, and 2880. - * Passing in a duration of less than 10 ms - * (480 samples at 48 kHz) will prevent the - * encoder from using the LPC or hybrid modes. - * @param[out] data unsigned char*: Output payload. - * This must contain storage for at - * least \a max_data_bytes. - * @param [in] max_data_bytes opus_int32: Size of the allocated - * memory for the output - * payload. This may be - * used to impose an upper limit on - * the instant bitrate, but should - * not be used as the only bitrate - * control. Use #OPUS_SET_BITRATE to - * control the bitrate. - * @returns The length of the encoded packet (in bytes) on success or a - * negative error code (see @ref opus_errorcodes) on failure. - */ -OPUS_EXPORT OPUS_WARN_UNUSED_RESULT int opus_multistream_encode( - OpusMSEncoder *st, - const opus_int16 *pcm, - int frame_size, - unsigned char *data, - opus_int32 max_data_bytes -) OPUS_ARG_NONNULL(1) OPUS_ARG_NONNULL(2) OPUS_ARG_NONNULL(4); - -/** Encodes a multistream Opus frame from floating point input. - * @param st OpusMSEncoder*: Multistream encoder state. - * @param[in] pcm const float*: The input signal as interleaved - * samples with a normal range of - * +/-1.0. - * Samples with a range beyond +/-1.0 - * are supported but will be clipped by - * decoders using the integer API and - * should only be used if it is known - * that the far end supports extended - * dynamic range. - * This must contain - * frame_size*channels - * samples. - * @param frame_size int: Number of samples per channel in the input - * signal. - * This must be an Opus frame size for the - * encoder's sampling rate. - * For example, at 48 kHz the permitted values - * are 120, 240, 480, 960, 1920, and 2880. - * Passing in a duration of less than 10 ms - * (480 samples at 48 kHz) will prevent the - * encoder from using the LPC or hybrid modes. - * @param[out] data unsigned char*: Output payload. - * This must contain storage for at - * least \a max_data_bytes. - * @param [in] max_data_bytes opus_int32: Size of the allocated - * memory for the output - * payload. This may be - * used to impose an upper limit on - * the instant bitrate, but should - * not be used as the only bitrate - * control. Use #OPUS_SET_BITRATE to - * control the bitrate. - * @returns The length of the encoded packet (in bytes) on success or a - * negative error code (see @ref opus_errorcodes) on failure. - */ -OPUS_EXPORT OPUS_WARN_UNUSED_RESULT int opus_multistream_encode_float( - OpusMSEncoder *st, - const float *pcm, - int frame_size, - unsigned char *data, - opus_int32 max_data_bytes -) OPUS_ARG_NONNULL(1) OPUS_ARG_NONNULL(2) OPUS_ARG_NONNULL(4); - -/** Frees an OpusMSEncoder allocated by - * opus_multistream_encoder_create(). - * @param st OpusMSEncoder*: Multistream encoder state to be freed. - */ -OPUS_EXPORT void opus_multistream_encoder_destroy(OpusMSEncoder *st); - -/** Perform a CTL function on a multistream Opus encoder. - * - * Generally the request and subsequent arguments are generated by a - * convenience macro. - * @param st OpusMSEncoder*: Multistream encoder state. - * @param request This and all remaining parameters should be replaced by one - * of the convenience macros in @ref opus_genericctls, - * @ref opus_encoderctls, or @ref opus_multistream_ctls. - * @see opus_genericctls - * @see opus_encoderctls - * @see opus_multistream_ctls - */ -OPUS_EXPORT int opus_multistream_encoder_ctl(OpusMSEncoder *st, int request, ...) OPUS_ARG_NONNULL(1); - -/**@}*/ - -/**\name Multistream decoder functions */ -/**@{*/ - -/** Gets the size of an OpusMSDecoder structure. - * @param streams int: The total number of streams coded in the - * input. - * This must be no more than 255. - * @param coupled_streams int: Number streams to decode as coupled - * (2 channel) streams. - * This must be no larger than the total - * number of streams. - * Additionally, The total number of - * coded channels (streams + - * coupled_streams) must be no - * more than 255. - * @returns The size in bytes on success, or a negative error code - * (see @ref opus_errorcodes) on error. - */ -OPUS_EXPORT OPUS_WARN_UNUSED_RESULT opus_int32 opus_multistream_decoder_get_size( - int streams, - int coupled_streams -); - -/** Allocates and initializes a multistream decoder state. - * Call opus_multistream_decoder_destroy() to release - * this object when finished. - * @param Fs opus_int32: Sampling rate to decode at (in Hz). - * This must be one of 8000, 12000, 16000, - * 24000, or 48000. - * @param channels int: Number of channels to output. - * This must be at most 255. - * It may be different from the number of coded - * channels (streams + - * coupled_streams). - * @param streams int: The total number of streams coded in the - * input. - * This must be no more than 255. - * @param coupled_streams int: Number of streams to decode as coupled - * (2 channel) streams. - * This must be no larger than the total - * number of streams. - * Additionally, The total number of - * coded channels (streams + - * coupled_streams) must be no - * more than 255. - * @param[in] mapping const unsigned char[channels]: Mapping from - * coded channels to output channels, as described in - * @ref opus_multistream. - * @param[out] error int *: Returns #OPUS_OK on success, or an error - * code (see @ref opus_errorcodes) on - * failure. - */ -OPUS_EXPORT OPUS_WARN_UNUSED_RESULT OpusMSDecoder *opus_multistream_decoder_create( - opus_int32 Fs, - int channels, - int streams, - int coupled_streams, - const unsigned char *mapping, - int *error -) OPUS_ARG_NONNULL(5); - -/** Intialize a previously allocated decoder state object. - * The memory pointed to by \a st must be at least the size returned by - * opus_multistream_encoder_get_size(). - * This is intended for applications which use their own allocator instead of - * malloc. - * To reset a previously initialized state, use the #OPUS_RESET_STATE CTL. - * @see opus_multistream_decoder_create - * @see opus_multistream_deocder_get_size - * @param st OpusMSEncoder*: Multistream encoder state to initialize. - * @param Fs opus_int32: Sampling rate to decode at (in Hz). - * This must be one of 8000, 12000, 16000, - * 24000, or 48000. - * @param channels int: Number of channels to output. - * This must be at most 255. - * It may be different from the number of coded - * channels (streams + - * coupled_streams). - * @param streams int: The total number of streams coded in the - * input. - * This must be no more than 255. - * @param coupled_streams int: Number of streams to decode as coupled - * (2 channel) streams. - * This must be no larger than the total - * number of streams. - * Additionally, The total number of - * coded channels (streams + - * coupled_streams) must be no - * more than 255. - * @param[in] mapping const unsigned char[channels]: Mapping from - * coded channels to output channels, as described in - * @ref opus_multistream. - * @returns #OPUS_OK on success, or an error code (see @ref opus_errorcodes) - * on failure. - */ -OPUS_EXPORT int opus_multistream_decoder_init( - OpusMSDecoder *st, - opus_int32 Fs, - int channels, - int streams, - int coupled_streams, - const unsigned char *mapping -) OPUS_ARG_NONNULL(1) OPUS_ARG_NONNULL(6); - -/** Decode a multistream Opus packet. - * @param st OpusMSDecoder*: Multistream decoder state. - * @param[in] data const unsigned char*: Input payload. - * Use a NULL - * pointer to indicate packet - * loss. - * @param len opus_int32: Number of bytes in payload. - * @param[out] pcm opus_int16*: Output signal, with interleaved - * samples. - * This must contain room for - * frame_size*channels - * samples. - * @param frame_size int: The number of samples per channel of - * available space in \a pcm. - * If this is less than the maximum packet duration - * (120 ms; 5760 for 48kHz), this function will not be capable - * of decoding some packets. In the case of PLC (data==NULL) - * or FEC (decode_fec=1), then frame_size needs to be exactly - * the duration of audio that is missing, otherwise the - * decoder will not be in the optimal state to decode the - * next incoming packet. For the PLC and FEC cases, frame_size - * must be a multiple of 2.5 ms. - * @param decode_fec int: Flag (0 or 1) to request that any in-band - * forward error correction data be decoded. - * If no such data is available, the frame is - * decoded as if it were lost. - * @returns Number of samples decoded on success or a negative error code - * (see @ref opus_errorcodes) on failure. - */ -OPUS_EXPORT OPUS_WARN_UNUSED_RESULT int opus_multistream_decode( - OpusMSDecoder *st, - const unsigned char *data, - opus_int32 len, - opus_int16 *pcm, - int frame_size, - int decode_fec -) OPUS_ARG_NONNULL(1) OPUS_ARG_NONNULL(4); - -/** Decode a multistream Opus packet with floating point output. - * @param st OpusMSDecoder*: Multistream decoder state. - * @param[in] data const unsigned char*: Input payload. - * Use a NULL - * pointer to indicate packet - * loss. - * @param len opus_int32: Number of bytes in payload. - * @param[out] pcm opus_int16*: Output signal, with interleaved - * samples. - * This must contain room for - * frame_size*channels - * samples. - * @param frame_size int: The number of samples per channel of - * available space in \a pcm. - * If this is less than the maximum packet duration - * (120 ms; 5760 for 48kHz), this function will not be capable - * of decoding some packets. In the case of PLC (data==NULL) - * or FEC (decode_fec=1), then frame_size needs to be exactly - * the duration of audio that is missing, otherwise the - * decoder will not be in the optimal state to decode the - * next incoming packet. For the PLC and FEC cases, frame_size - * must be a multiple of 2.5 ms. - * @param decode_fec int: Flag (0 or 1) to request that any in-band - * forward error correction data be decoded. - * If no such data is available, the frame is - * decoded as if it were lost. - * @returns Number of samples decoded on success or a negative error code - * (see @ref opus_errorcodes) on failure. - */ -OPUS_EXPORT OPUS_WARN_UNUSED_RESULT int opus_multistream_decode_float( - OpusMSDecoder *st, - const unsigned char *data, - opus_int32 len, - float *pcm, - int frame_size, - int decode_fec -) OPUS_ARG_NONNULL(1) OPUS_ARG_NONNULL(4); - -/** Perform a CTL function on a multistream Opus decoder. - * - * Generally the request and subsequent arguments are generated by a - * convenience macro. - * @param st OpusMSDecoder*: Multistream decoder state. - * @param request This and all remaining parameters should be replaced by one - * of the convenience macros in @ref opus_genericctls, - * @ref opus_decoderctls, or @ref opus_multistream_ctls. - * @see opus_genericctls - * @see opus_decoderctls - * @see opus_multistream_ctls - */ -OPUS_EXPORT int opus_multistream_decoder_ctl(OpusMSDecoder *st, int request, ...) OPUS_ARG_NONNULL(1); - -/** Frees an OpusMSDecoder allocated by - * opus_multistream_decoder_create(). - * @param st OpusMSDecoder: Multistream decoder state to be freed. - */ -OPUS_EXPORT void opus_multistream_decoder_destroy(OpusMSDecoder *st); - -/**@}*/ - -/**@}*/ - -#ifdef __cplusplus -} -#endif - -#endif /* OPUS_MULTISTREAM_H */ diff --git a/go/teamspeak/.opus/install/x86_64/include/opus/opus_projection.h b/go/teamspeak/.opus/install/x86_64/include/opus/opus_projection.h deleted file mode 100644 index 9dabf4e..0000000 --- a/go/teamspeak/.opus/install/x86_64/include/opus/opus_projection.h +++ /dev/null @@ -1,568 +0,0 @@ -/* Copyright (c) 2017 Google Inc. - Written by Andrew Allen */ -/* - Redistribution and use in source and binary forms, with or without - modification, are permitted provided that the following conditions - are met: - - - Redistributions of source code must retain the above copyright - notice, this list of conditions and the following disclaimer. - - - Redistributions in binary form must reproduce the above copyright - notice, this list of conditions and the following disclaimer in the - documentation and/or other materials provided with the distribution. - - THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS - ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT - LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR - A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER - OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, - EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, - PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR - PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF - LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING - NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS - SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. -*/ - -/** - * @file opus_projection.h - * @brief Opus projection reference API - */ - -#ifndef OPUS_PROJECTION_H -#define OPUS_PROJECTION_H - -#include "opus_multistream.h" - -#ifdef __cplusplus -extern "C" { -#endif - -/** @cond OPUS_INTERNAL_DOC */ - -/** These are the actual encoder and decoder CTL ID numbers. - * They should not be used directly by applications.c - * In general, SETs should be even and GETs should be odd.*/ -/**@{*/ -#define OPUS_PROJECTION_GET_DEMIXING_MATRIX_GAIN_REQUEST 6001 -#define OPUS_PROJECTION_GET_DEMIXING_MATRIX_SIZE_REQUEST 6003 -#define OPUS_PROJECTION_GET_DEMIXING_MATRIX_REQUEST 6005 -/**@}*/ - - -/** @endcond */ - -/** @defgroup opus_projection_ctls Projection specific encoder and decoder CTLs - * - * These are convenience macros that are specific to the - * opus_projection_encoder_ctl() and opus_projection_decoder_ctl() - * interface. - * The CTLs from @ref opus_genericctls, @ref opus_encoderctls, - * @ref opus_decoderctls, and @ref opus_multistream_ctls may be applied to a - * projection encoder or decoder as well. - */ -/**@{*/ - -/** Gets the gain (in dB. S7.8-format) of the demixing matrix from the encoder. - * @param[out] x opus_int32 *: Returns the gain (in dB. S7.8-format) - * of the demixing matrix. - * @hideinitializer - */ -#define OPUS_PROJECTION_GET_DEMIXING_MATRIX_GAIN(x) OPUS_PROJECTION_GET_DEMIXING_MATRIX_GAIN_REQUEST, __opus_check_int_ptr(x) - - -/** Gets the size in bytes of the demixing matrix from the encoder. - * @param[out] x opus_int32 *: Returns the size in bytes of the - * demixing matrix. - * @hideinitializer - */ -#define OPUS_PROJECTION_GET_DEMIXING_MATRIX_SIZE(x) OPUS_PROJECTION_GET_DEMIXING_MATRIX_SIZE_REQUEST, __opus_check_int_ptr(x) - - -/** Copies the demixing matrix to the supplied pointer location. - * @param[out] x unsigned char *: Returns the demixing matrix to the - * supplied pointer location. - * @param y opus_int32: The size in bytes of the reserved memory at the - * pointer location. - * @hideinitializer - */ -#define OPUS_PROJECTION_GET_DEMIXING_MATRIX(x,y) OPUS_PROJECTION_GET_DEMIXING_MATRIX_REQUEST, x, __opus_check_int(y) - - -/**@}*/ - -/** Opus projection encoder state. - * This contains the complete state of a projection Opus encoder. - * It is position independent and can be freely copied. - * @see opus_projection_ambisonics_encoder_create - */ -typedef struct OpusProjectionEncoder OpusProjectionEncoder; - - -/** Opus projection decoder state. - * This contains the complete state of a projection Opus decoder. - * It is position independent and can be freely copied. - * @see opus_projection_decoder_create - * @see opus_projection_decoder_init - */ -typedef struct OpusProjectionDecoder OpusProjectionDecoder; - - -/**\name Projection encoder functions */ -/**@{*/ - -/** Gets the size of an OpusProjectionEncoder structure. - * @param channels int: The total number of input channels to encode. - * This must be no more than 255. - * @param mapping_family int: The mapping family to use for selecting - * the appropriate projection. - * @returns The size in bytes on success, or a negative error code - * (see @ref opus_errorcodes) on error. - */ -OPUS_EXPORT OPUS_WARN_UNUSED_RESULT opus_int32 opus_projection_ambisonics_encoder_get_size( - int channels, - int mapping_family -); - - -/** Allocates and initializes a projection encoder state. - * Call opus_projection_encoder_destroy() to release - * this object when finished. - * @param Fs opus_int32: Sampling rate of the input signal (in Hz). - * This must be one of 8000, 12000, 16000, - * 24000, or 48000. - * @param channels int: Number of channels in the input signal. - * This must be at most 255. - * It may be greater than the number of - * coded channels (streams + - * coupled_streams). - * @param mapping_family int: The mapping family to use for selecting - * the appropriate projection. - * @param[out] streams int *: The total number of streams that will - * be encoded from the input. - * @param[out] coupled_streams int *: Number of coupled (2 channel) - * streams that will be encoded from the input. - * @param application int: The target encoder application. - * This must be one of the following: - *
- *
#OPUS_APPLICATION_VOIP
- *
Process signal for improved speech intelligibility.
- *
#OPUS_APPLICATION_AUDIO
- *
Favor faithfulness to the original input.
- *
#OPUS_APPLICATION_RESTRICTED_LOWDELAY
- *
Configure the minimum possible coding delay by disabling certain modes - * of operation.
- *
- * @param[out] error int *: Returns #OPUS_OK on success, or an error - * code (see @ref opus_errorcodes) on - * failure. - */ -OPUS_EXPORT OPUS_WARN_UNUSED_RESULT OpusProjectionEncoder *opus_projection_ambisonics_encoder_create( - opus_int32 Fs, - int channels, - int mapping_family, - int *streams, - int *coupled_streams, - int application, - int *error -) OPUS_ARG_NONNULL(4) OPUS_ARG_NONNULL(5); - - -/** Initialize a previously allocated projection encoder state. - * The memory pointed to by \a st must be at least the size returned by - * opus_projection_ambisonics_encoder_get_size(). - * This is intended for applications which use their own allocator instead of - * malloc. - * To reset a previously initialized state, use the #OPUS_RESET_STATE CTL. - * @see opus_projection_ambisonics_encoder_create - * @see opus_projection_ambisonics_encoder_get_size - * @param st OpusProjectionEncoder*: Projection encoder state to initialize. - * @param Fs opus_int32: Sampling rate of the input signal (in Hz). - * This must be one of 8000, 12000, 16000, - * 24000, or 48000. - * @param channels int: Number of channels in the input signal. - * This must be at most 255. - * It may be greater than the number of - * coded channels (streams + - * coupled_streams). - * @param streams int: The total number of streams to encode from the - * input. - * This must be no more than the number of channels. - * @param coupled_streams int: Number of coupled (2 channel) streams - * to encode. - * This must be no larger than the total - * number of streams. - * Additionally, The total number of - * encoded channels (streams + - * coupled_streams) must be no - * more than the number of input channels. - * @param application int: The target encoder application. - * This must be one of the following: - *
- *
#OPUS_APPLICATION_VOIP
- *
Process signal for improved speech intelligibility.
- *
#OPUS_APPLICATION_AUDIO
- *
Favor faithfulness to the original input.
- *
#OPUS_APPLICATION_RESTRICTED_LOWDELAY
- *
Configure the minimum possible coding delay by disabling certain modes - * of operation.
- *
- * @returns #OPUS_OK on success, or an error code (see @ref opus_errorcodes) - * on failure. - */ -OPUS_EXPORT int opus_projection_ambisonics_encoder_init( - OpusProjectionEncoder *st, - opus_int32 Fs, - int channels, - int mapping_family, - int *streams, - int *coupled_streams, - int application -) OPUS_ARG_NONNULL(1) OPUS_ARG_NONNULL(5) OPUS_ARG_NONNULL(6); - - -/** Encodes a projection Opus frame. - * @param st OpusProjectionEncoder*: Projection encoder state. - * @param[in] pcm const opus_int16*: The input signal as interleaved - * samples. - * This must contain - * frame_size*channels - * samples. - * @param frame_size int: Number of samples per channel in the input - * signal. - * This must be an Opus frame size for the - * encoder's sampling rate. - * For example, at 48 kHz the permitted values - * are 120, 240, 480, 960, 1920, and 2880. - * Passing in a duration of less than 10 ms - * (480 samples at 48 kHz) will prevent the - * encoder from using the LPC or hybrid modes. - * @param[out] data unsigned char*: Output payload. - * This must contain storage for at - * least \a max_data_bytes. - * @param [in] max_data_bytes opus_int32: Size of the allocated - * memory for the output - * payload. This may be - * used to impose an upper limit on - * the instant bitrate, but should - * not be used as the only bitrate - * control. Use #OPUS_SET_BITRATE to - * control the bitrate. - * @returns The length of the encoded packet (in bytes) on success or a - * negative error code (see @ref opus_errorcodes) on failure. - */ -OPUS_EXPORT OPUS_WARN_UNUSED_RESULT int opus_projection_encode( - OpusProjectionEncoder *st, - const opus_int16 *pcm, - int frame_size, - unsigned char *data, - opus_int32 max_data_bytes -) OPUS_ARG_NONNULL(1) OPUS_ARG_NONNULL(2) OPUS_ARG_NONNULL(4); - - -/** Encodes a projection Opus frame from floating point input. - * @param st OpusProjectionEncoder*: Projection encoder state. - * @param[in] pcm const float*: The input signal as interleaved - * samples with a normal range of - * +/-1.0. - * Samples with a range beyond +/-1.0 - * are supported but will be clipped by - * decoders using the integer API and - * should only be used if it is known - * that the far end supports extended - * dynamic range. - * This must contain - * frame_size*channels - * samples. - * @param frame_size int: Number of samples per channel in the input - * signal. - * This must be an Opus frame size for the - * encoder's sampling rate. - * For example, at 48 kHz the permitted values - * are 120, 240, 480, 960, 1920, and 2880. - * Passing in a duration of less than 10 ms - * (480 samples at 48 kHz) will prevent the - * encoder from using the LPC or hybrid modes. - * @param[out] data unsigned char*: Output payload. - * This must contain storage for at - * least \a max_data_bytes. - * @param [in] max_data_bytes opus_int32: Size of the allocated - * memory for the output - * payload. This may be - * used to impose an upper limit on - * the instant bitrate, but should - * not be used as the only bitrate - * control. Use #OPUS_SET_BITRATE to - * control the bitrate. - * @returns The length of the encoded packet (in bytes) on success or a - * negative error code (see @ref opus_errorcodes) on failure. - */ -OPUS_EXPORT OPUS_WARN_UNUSED_RESULT int opus_projection_encode_float( - OpusProjectionEncoder *st, - const float *pcm, - int frame_size, - unsigned char *data, - opus_int32 max_data_bytes -) OPUS_ARG_NONNULL(1) OPUS_ARG_NONNULL(2) OPUS_ARG_NONNULL(4); - - -/** Frees an OpusProjectionEncoder allocated by - * opus_projection_ambisonics_encoder_create(). - * @param st OpusProjectionEncoder*: Projection encoder state to be freed. - */ -OPUS_EXPORT void opus_projection_encoder_destroy(OpusProjectionEncoder *st); - - -/** Perform a CTL function on a projection Opus encoder. - * - * Generally the request and subsequent arguments are generated by a - * convenience macro. - * @param st OpusProjectionEncoder*: Projection encoder state. - * @param request This and all remaining parameters should be replaced by one - * of the convenience macros in @ref opus_genericctls, - * @ref opus_encoderctls, @ref opus_multistream_ctls, or - * @ref opus_projection_ctls - * @see opus_genericctls - * @see opus_encoderctls - * @see opus_multistream_ctls - * @see opus_projection_ctls - */ -OPUS_EXPORT int opus_projection_encoder_ctl(OpusProjectionEncoder *st, int request, ...) OPUS_ARG_NONNULL(1); - - -/**@}*/ - -/**\name Projection decoder functions */ -/**@{*/ - -/** Gets the size of an OpusProjectionDecoder structure. - * @param channels int: The total number of output channels. - * This must be no more than 255. - * @param streams int: The total number of streams coded in the - * input. - * This must be no more than 255. - * @param coupled_streams int: Number streams to decode as coupled - * (2 channel) streams. - * This must be no larger than the total - * number of streams. - * Additionally, The total number of - * coded channels (streams + - * coupled_streams) must be no - * more than 255. - * @returns The size in bytes on success, or a negative error code - * (see @ref opus_errorcodes) on error. - */ -OPUS_EXPORT OPUS_WARN_UNUSED_RESULT opus_int32 opus_projection_decoder_get_size( - int channels, - int streams, - int coupled_streams -); - - -/** Allocates and initializes a projection decoder state. - * Call opus_projection_decoder_destroy() to release - * this object when finished. - * @param Fs opus_int32: Sampling rate to decode at (in Hz). - * This must be one of 8000, 12000, 16000, - * 24000, or 48000. - * @param channels int: Number of channels to output. - * This must be at most 255. - * It may be different from the number of coded - * channels (streams + - * coupled_streams). - * @param streams int: The total number of streams coded in the - * input. - * This must be no more than 255. - * @param coupled_streams int: Number of streams to decode as coupled - * (2 channel) streams. - * This must be no larger than the total - * number of streams. - * Additionally, The total number of - * coded channels (streams + - * coupled_streams) must be no - * more than 255. - * @param[in] demixing_matrix const unsigned char[demixing_matrix_size]: Demixing matrix - * that mapping from coded channels to output channels, - * as described in @ref opus_projection and - * @ref opus_projection_ctls. - * @param demixing_matrix_size opus_int32: The size in bytes of the - * demixing matrix, as - * described in @ref - * opus_projection_ctls. - * @param[out] error int *: Returns #OPUS_OK on success, or an error - * code (see @ref opus_errorcodes) on - * failure. - */ -OPUS_EXPORT OPUS_WARN_UNUSED_RESULT OpusProjectionDecoder *opus_projection_decoder_create( - opus_int32 Fs, - int channels, - int streams, - int coupled_streams, - unsigned char *demixing_matrix, - opus_int32 demixing_matrix_size, - int *error -) OPUS_ARG_NONNULL(5); - - -/** Intialize a previously allocated projection decoder state object. - * The memory pointed to by \a st must be at least the size returned by - * opus_projection_decoder_get_size(). - * This is intended for applications which use their own allocator instead of - * malloc. - * To reset a previously initialized state, use the #OPUS_RESET_STATE CTL. - * @see opus_projection_decoder_create - * @see opus_projection_deocder_get_size - * @param st OpusProjectionDecoder*: Projection encoder state to initialize. - * @param Fs opus_int32: Sampling rate to decode at (in Hz). - * This must be one of 8000, 12000, 16000, - * 24000, or 48000. - * @param channels int: Number of channels to output. - * This must be at most 255. - * It may be different from the number of coded - * channels (streams + - * coupled_streams). - * @param streams int: The total number of streams coded in the - * input. - * This must be no more than 255. - * @param coupled_streams int: Number of streams to decode as coupled - * (2 channel) streams. - * This must be no larger than the total - * number of streams. - * Additionally, The total number of - * coded channels (streams + - * coupled_streams) must be no - * more than 255. - * @param[in] demixing_matrix const unsigned char[demixing_matrix_size]: Demixing matrix - * that mapping from coded channels to output channels, - * as described in @ref opus_projection and - * @ref opus_projection_ctls. - * @param demixing_matrix_size opus_int32: The size in bytes of the - * demixing matrix, as - * described in @ref - * opus_projection_ctls. - * @returns #OPUS_OK on success, or an error code (see @ref opus_errorcodes) - * on failure. - */ -OPUS_EXPORT int opus_projection_decoder_init( - OpusProjectionDecoder *st, - opus_int32 Fs, - int channels, - int streams, - int coupled_streams, - unsigned char *demixing_matrix, - opus_int32 demixing_matrix_size -) OPUS_ARG_NONNULL(1) OPUS_ARG_NONNULL(6); - - -/** Decode a projection Opus packet. - * @param st OpusProjectionDecoder*: Projection decoder state. - * @param[in] data const unsigned char*: Input payload. - * Use a NULL - * pointer to indicate packet - * loss. - * @param len opus_int32: Number of bytes in payload. - * @param[out] pcm opus_int16*: Output signal, with interleaved - * samples. - * This must contain room for - * frame_size*channels - * samples. - * @param frame_size int: The number of samples per channel of - * available space in \a pcm. - * If this is less than the maximum packet duration - * (120 ms; 5760 for 48kHz), this function will not be capable - * of decoding some packets. In the case of PLC (data==NULL) - * or FEC (decode_fec=1), then frame_size needs to be exactly - * the duration of audio that is missing, otherwise the - * decoder will not be in the optimal state to decode the - * next incoming packet. For the PLC and FEC cases, frame_size - * must be a multiple of 2.5 ms. - * @param decode_fec int: Flag (0 or 1) to request that any in-band - * forward error correction data be decoded. - * If no such data is available, the frame is - * decoded as if it were lost. - * @returns Number of samples decoded on success or a negative error code - * (see @ref opus_errorcodes) on failure. - */ -OPUS_EXPORT OPUS_WARN_UNUSED_RESULT int opus_projection_decode( - OpusProjectionDecoder *st, - const unsigned char *data, - opus_int32 len, - opus_int16 *pcm, - int frame_size, - int decode_fec -) OPUS_ARG_NONNULL(1) OPUS_ARG_NONNULL(4); - - -/** Decode a projection Opus packet with floating point output. - * @param st OpusProjectionDecoder*: Projection decoder state. - * @param[in] data const unsigned char*: Input payload. - * Use a NULL - * pointer to indicate packet - * loss. - * @param len opus_int32: Number of bytes in payload. - * @param[out] pcm opus_int16*: Output signal, with interleaved - * samples. - * This must contain room for - * frame_size*channels - * samples. - * @param frame_size int: The number of samples per channel of - * available space in \a pcm. - * If this is less than the maximum packet duration - * (120 ms; 5760 for 48kHz), this function will not be capable - * of decoding some packets. In the case of PLC (data==NULL) - * or FEC (decode_fec=1), then frame_size needs to be exactly - * the duration of audio that is missing, otherwise the - * decoder will not be in the optimal state to decode the - * next incoming packet. For the PLC and FEC cases, frame_size - * must be a multiple of 2.5 ms. - * @param decode_fec int: Flag (0 or 1) to request that any in-band - * forward error correction data be decoded. - * If no such data is available, the frame is - * decoded as if it were lost. - * @returns Number of samples decoded on success or a negative error code - * (see @ref opus_errorcodes) on failure. - */ -OPUS_EXPORT OPUS_WARN_UNUSED_RESULT int opus_projection_decode_float( - OpusProjectionDecoder *st, - const unsigned char *data, - opus_int32 len, - float *pcm, - int frame_size, - int decode_fec -) OPUS_ARG_NONNULL(1) OPUS_ARG_NONNULL(4); - - -/** Perform a CTL function on a projection Opus decoder. - * - * Generally the request and subsequent arguments are generated by a - * convenience macro. - * @param st OpusProjectionDecoder*: Projection decoder state. - * @param request This and all remaining parameters should be replaced by one - * of the convenience macros in @ref opus_genericctls, - * @ref opus_decoderctls, @ref opus_multistream_ctls, or - * @ref opus_projection_ctls. - * @see opus_genericctls - * @see opus_decoderctls - * @see opus_multistream_ctls - * @see opus_projection_ctls - */ -OPUS_EXPORT int opus_projection_decoder_ctl(OpusProjectionDecoder *st, int request, ...) OPUS_ARG_NONNULL(1); - - -/** Frees an OpusProjectionDecoder allocated by - * opus_projection_decoder_create(). - * @param st OpusProjectionDecoder: Projection decoder state to be freed. - */ -OPUS_EXPORT void opus_projection_decoder_destroy(OpusProjectionDecoder *st); - - -/**@}*/ - -/**@}*/ - -#ifdef __cplusplus -} -#endif - -#endif /* OPUS_PROJECTION_H */ diff --git a/go/teamspeak/.opus/install/x86_64/include/opus/opus_types.h b/go/teamspeak/.opus/install/x86_64/include/opus/opus_types.h deleted file mode 100644 index 7cf6755..0000000 --- a/go/teamspeak/.opus/install/x86_64/include/opus/opus_types.h +++ /dev/null @@ -1,166 +0,0 @@ -/* (C) COPYRIGHT 1994-2002 Xiph.Org Foundation */ -/* Modified by Jean-Marc Valin */ -/* - Redistribution and use in source and binary forms, with or without - modification, are permitted provided that the following conditions - are met: - - - Redistributions of source code must retain the above copyright - notice, this list of conditions and the following disclaimer. - - - Redistributions in binary form must reproduce the above copyright - notice, this list of conditions and the following disclaimer in the - documentation and/or other materials provided with the distribution. - - THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS - ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT - LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR - A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER - OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, - EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, - PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR - PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF - LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING - NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS - SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. -*/ -/* opus_types.h based on ogg_types.h from libogg */ - -/** - @file opus_types.h - @brief Opus reference implementation types -*/ -#ifndef OPUS_TYPES_H -#define OPUS_TYPES_H - -#define opus_int int /* used for counters etc; at least 16 bits */ -#define opus_int64 long long -#define opus_int8 signed char - -#define opus_uint unsigned int /* used for counters etc; at least 16 bits */ -#define opus_uint64 unsigned long long -#define opus_uint8 unsigned char - -/* Use the real stdint.h if it's there (taken from Paul Hsieh's pstdint.h) */ -#if (defined(__STDC__) && __STDC__ && defined(__STDC_VERSION__) && __STDC_VERSION__ >= 199901L) || (defined(__GNUC__) && (defined(_STDINT_H) || defined(_STDINT_H_)) || defined (HAVE_STDINT_H)) -#include -# undef opus_int64 -# undef opus_int8 -# undef opus_uint64 -# undef opus_uint8 - typedef int8_t opus_int8; - typedef uint8_t opus_uint8; - typedef int16_t opus_int16; - typedef uint16_t opus_uint16; - typedef int32_t opus_int32; - typedef uint32_t opus_uint32; - typedef int64_t opus_int64; - typedef uint64_t opus_uint64; -#elif defined(_WIN32) - -# if defined(__CYGWIN__) -# include <_G_config.h> - typedef _G_int32_t opus_int32; - typedef _G_uint32_t opus_uint32; - typedef _G_int16 opus_int16; - typedef _G_uint16 opus_uint16; -# elif defined(__MINGW32__) - typedef short opus_int16; - typedef unsigned short opus_uint16; - typedef int opus_int32; - typedef unsigned int opus_uint32; -# elif defined(__MWERKS__) - typedef int opus_int32; - typedef unsigned int opus_uint32; - typedef short opus_int16; - typedef unsigned short opus_uint16; -# else - /* MSVC/Borland */ - typedef __int32 opus_int32; - typedef unsigned __int32 opus_uint32; - typedef __int16 opus_int16; - typedef unsigned __int16 opus_uint16; -# endif - -#elif defined(__MACOS__) - -# include - typedef SInt16 opus_int16; - typedef UInt16 opus_uint16; - typedef SInt32 opus_int32; - typedef UInt32 opus_uint32; - -#elif (defined(__APPLE__) && defined(__MACH__)) /* MacOS X Framework build */ - -# include - typedef int16_t opus_int16; - typedef u_int16_t opus_uint16; - typedef int32_t opus_int32; - typedef u_int32_t opus_uint32; - -#elif defined(__BEOS__) - - /* Be */ -# include - typedef int16 opus_int16; - typedef u_int16 opus_uint16; - typedef int32_t opus_int32; - typedef u_int32_t opus_uint32; - -#elif defined (__EMX__) - - /* OS/2 GCC */ - typedef short opus_int16; - typedef unsigned short opus_uint16; - typedef int opus_int32; - typedef unsigned int opus_uint32; - -#elif defined (DJGPP) - - /* DJGPP */ - typedef short opus_int16; - typedef unsigned short opus_uint16; - typedef int opus_int32; - typedef unsigned int opus_uint32; - -#elif defined(R5900) - - /* PS2 EE */ - typedef int opus_int32; - typedef unsigned opus_uint32; - typedef short opus_int16; - typedef unsigned short opus_uint16; - -#elif defined(__SYMBIAN32__) - - /* Symbian GCC */ - typedef signed short opus_int16; - typedef unsigned short opus_uint16; - typedef signed int opus_int32; - typedef unsigned int opus_uint32; - -#elif defined(CONFIG_TI_C54X) || defined (CONFIG_TI_C55X) - - typedef short opus_int16; - typedef unsigned short opus_uint16; - typedef long opus_int32; - typedef unsigned long opus_uint32; - -#elif defined(CONFIG_TI_C6X) - - typedef short opus_int16; - typedef unsigned short opus_uint16; - typedef int opus_int32; - typedef unsigned int opus_uint32; - -#else - - /* Give up, take a reasonable guess */ - typedef short opus_int16; - typedef unsigned short opus_uint16; - typedef int opus_int32; - typedef unsigned int opus_uint32; - -#endif - -#endif /* OPUS_TYPES_H */ diff --git a/go/teamspeak/.opus/install/x86_64/lib/libopus.a b/go/teamspeak/.opus/install/x86_64/lib/libopus.a deleted file mode 100644 index c68c3f3..0000000 Binary files a/go/teamspeak/.opus/install/x86_64/lib/libopus.a and /dev/null differ diff --git a/go/teamspeak/contract.go b/go/teamspeak/contract.go new file mode 100644 index 0000000..fb5754c --- /dev/null +++ b/go/teamspeak/contract.go @@ -0,0 +1,153 @@ +package teamspeak + +// bridge-contract.md —— Go ↔ Kotlin 跨语言契约的单一事实来源。 +// +// 为什么需要这个文件： +// gomobile 不能导出 []string / []*T / Go error，所以复杂数据全部走 JSON 字符串。 +// 代价是**编译器无法校验字段名**：Go 侧把 `channelId` 写成 `channelID`， +// Kotlin 侧不会编译失败，只会在运行时静默拿到默认值（"" / 0 / false）， +// 而 Kotlin 的 `Json { ignoreUnknownKeys = true }` 让这个失效模式更隐蔽。 +// +// 边界上的三种数据类型（按保护强度递减，文档必须写清属于哪一类）： +// +// A 类｜gomobile 导出 struct（TextMsg / PokeEvent / Channel / Client） +// 字段是编译期检查的，Go 侧改名 → Kotlin 侧编译失败。**不需要**常量保护。 +// +// B 类｜JSON 字符串（channelJSON / clientJSON / serverInfoJSON / ...） +// 完全无编译期保护，就是本文件存在的理由。字段名集中声明在下方常量里， +// contract_test.go 断言常量与 struct tag 永远一致——改 tag 会让测试变红。 +// +// C 类｜广告过但无消费方的契约（channelDetailedJSON / clientDetailedInfoJSON / +// initialSyncJSON） +// 已被 GetInitialSyncJSON 等导出，但 Kotlin 侧当前不解析。 +// 声明字段名是为了钉住形状，避免「文档说有、实际对不上」。 +// 若将来要消费，先跑 contract_test.go 确认字段名。 +// +// 字段名的运行时事实仍是 bridge.go / kotlin_api.go 中的 `json:"..."` tag +// （encoding/json 只认 tag）；本文件是声明，测试是保证两者不漂移的机制。 + +// BridgeContractVersion 跨语言契约版本。 +// +// 递增规则：任何**不兼容**变更（字段改名、类型变更、字段删除）都必须 +1。 +// 只新增 Optional 字段（Kotlin 侧有默认值）不需要 +1。 +// 递增后必须同步更新： +// - android/.../TSBridge.kt 的 EXPECTED_CONTRACT_VERSION +// - docs/bridge-contract.md +const BridgeContractVersion = "1" + +// ─── B 类：channelJSON ⇄ ChannelInfo ────────────────────────── +// 生产方 GetChannelsJSON()；消费方 Repository.fetchChannels()。 +const ( + FieldChannelID = "id" + FieldChannelName = "name" + FieldChannelParentID = "parentId" + FieldChannelDescription = "description" + FieldChannelIsPassword = "isPassword" +) + +// ─── B 类：clientJSON ⇄ ClientInfo ──────────────────────────── +// 生产方 GetClientsJSON()；消费方 Repository.fetchClients()。 +const ( + FieldClientID = "id" + FieldClientNickname = "nickname" + FieldClientUID = "uid" + FieldClientChannelID = "channelId" + FieldClientServerGroups = "serverGroups" + FieldClientIsSelf = "isSelf" +) + +// ─── B 类：serverInfoJSON ⇄ ServerInfo ──────────────────────── +// 生产方 GetServerInfoJSON()；消费方 Repository / ServerViewModel。 +const ( + FieldServerName = "name" + FieldServerWelcomeMessage = "welcomeMessage" + FieldServerMaxClients = "maxClients" + FieldServerClientsOnline = "clientsOnline" + FieldServerChannelsOnline = "channelsOnline" + FieldServerUptime = "uptime" + FieldServerVersion = "version" + FieldServerPlatform = "platform" + FieldServerCreated = "created" + FieldServerIconID = "iconId" + FieldServerDefaultServerGroup = "defaultServerGroup" + FieldServerDefaultChannelGroup = "defaultChannelGroup" +) + +// ─── B 类：DownloadFileBytesJSON ⇄ TSBridge.downloadFileBytes ─ +// 生产方 DownloadFileBytesJSON()；消费方 FileDownloadManager。 +// 失败时返回 "{}"（而非空串），消费方以 data 为空判定失败。 +const ( + FieldFileData = "data" + FieldFileSize = "size" +) + +// ─── C 类：channelDetailedJSON（GetChannelsDetailedJSON）────── +// 当前无 Kotlin 消费方。字段名在此钉住形状。 +const ( + FieldChannelDetailTopic = "topic" + FieldChannelDetailOrder = "order" + FieldChannelDetailCodec = "codec" + FieldChannelDetailCodecQuality = "codecQuality" + FieldChannelDetailNeededTalkPower = "neededTalkPower" + FieldChannelDetailMaxClients = "maxClients" + FieldChannelDetailMaxFamilyClients = "maxFamilyClients" + FieldChannelDetailIsMaxClientsUnlimited = "isMaxClientsUnlimited" + FieldChannelDetailIsMaxFamilyClientsUnlimited = "isMaxFamilyClientsUnlimited" + FieldChannelDetailIsPermanent = "isPermanent" + FieldChannelDetailIsSemiPermanent = "isSemiPermanent" + FieldChannelDetailIsDefault = "isDefault" + FieldChannelDetailIsOrdered = "isOrdered" + FieldChannelDetailIconID = "iconId" + FieldChannelDetailNeededModifyPower = "neededModifyPower" +) + +// ─── C 类：clientDetailedInfoJSON（GetClientDetailInfoJSON）─── +// 当前无 Kotlin 消费方。 +const ( + FieldClientDetailType = "type" + FieldClientDetailAway = "away" + FieldClientDetailAwayMessage = "awayMessage" + FieldClientDetailInputMuted = "inputMuted" + FieldClientDetailOutputMuted = "outputMuted" + FieldClientDetailPlatform = "platform" + FieldClientDetailVersion = "version" + FieldClientDetailIP = "ip" + FieldClientDetailCreated = "created" + FieldClientDetailLastConnected = "lastConnected" + FieldClientDetailTotalConnections = "totalConnections" + FieldClientDetailDescription = "description" + FieldClientDetailIconID = "iconId" +) + +// ─── C 类：initialSyncJSON（GetInitialSyncJSON）──────────────── +// 一次性返回全部初始数据的聚合契约。当前无 Kotlin 消费方 +// （Repository.performInitialSync 走的是三个独立 getter 串行请求）。 +const ( + FieldSyncChannels = "channels" + FieldSyncClients = "clients" + FieldSyncSelfID = "selfId" + FieldSyncSelfChannelID = "selfChannelId" + FieldSyncServer = "server" +) + +// 消息 targetMode 取值。 +// +// ⚠️ 收发能力不对称（重要，别再读成三种都支持）： +// +// TargetModePrivate(1) 仅接收。发送侧未实现——TSBridge.sendTextMessage 只实现 mode=2， +// 其余返回"暂不支持该消息类型"。SDK 的 SendTextMessage 走的也是 +// mode=2 路径，私聊回复需要另找 API。 +// TargetModeChannel(2) 收发均支持。 +// TargetModeServer(3) 仅接收。 +const ( + TargetModePrivate = 1 + TargetModeChannel = 2 + TargetModeServer = 3 +) + +// GetContractVersion 返回当前 bridge 契约版本。 +// Kotlin 侧在建立连接前调用并与本地期望值比对。 +// gomobile 不支持返回 error，版本不匹配由 Kotlin 侧抛错处理。 +func GetContractVersion() string { + return BridgeContractVersion +} diff --git a/go/teamspeak/contract_golden_test.go b/go/teamspeak/contract_golden_test.go new file mode 100644 index 0000000..aad29e0 --- /dev/null +++ b/go/teamspeak/contract_golden_test.go @@ -0,0 +1,169 @@ +package teamspeak + +import ( + "encoding/json" + "os" + "path/filepath" + "reflect" + "sort" + "testing" +) + +// goldenDir 是 Go 与 Kotlin **共用**的契约样本目录。 +// +// 这不是普通测试夹具，而是跨语言契约的共享事实载体： +// - Go 侧（本文件）：断言样本的字段集合与 struct tag 完全一致 +// - Kotlin 侧（BridgeContractTest）：断言同一样本能被数据类正确反序列化 +// +// 于是字段漂移的传播链被钉死： +// 改 Go tag → 本文件失败 → 必须更新样本 → Kotlin 测试随之失败。 +// 任何一环被跳过都会留下一个红灯，不存在「静默降级」的路径。 +const goldenDir = "../../android/app/src/test/resources/contract" + +func readGolden(t *testing.T, name string) []byte { + t.Helper() + path := filepath.Join(goldenDir, name) + raw, err := os.ReadFile(path) + if err != nil { + t.Fatalf("读取契约样本失败 %s: %v\n"+ + "（该目录是 Go 与 Kotlin 共用的契约样本，缺失即契约失守）", path, err) + } + return raw +} + +// keySetsEqual 比较两组字段名是否完全相同（顺序无关）。 +func keySetsEqual(a, b []string) bool { + if len(a) != len(b) { + return false + } + sort.Strings(a) + sort.Strings(b) + for i := range a { + if a[i] != b[i] { + return false + } + } + return true +} + +// assertGoldenMatchesType 断言样本 JSON 的字段集合 == struct 的 tag 集合。 +// +// 两个方向都查： +// - 样本多出字段 → 样本过期，或有人改了 tag 却没同步 +// - 样本缺少字段 → struct 新增了字段，必须补进样本（否则 Kotlin 侧拿默认值） +func assertGoldenMatchesType(t *testing.T, sampleName string, raw []byte, target any, objectIsArray bool) { + t.Helper() + + typ := reflect.TypeOf(target) + var keys []string + + if objectIsArray { + var items []map[string]json.RawMessage + if err := json.Unmarshal(raw, &items); err != nil { + t.Fatalf("%s 不是合法 JSON 数组: %v", sampleName, err) + } + if len(items) == 0 { + t.Fatalf("%s 样本为空数组，无法校验契约", sampleName) + } + for k := range items[0] { + keys = append(keys, k) + } + } else { + var item map[string]json.RawMessage + if err := json.Unmarshal(raw, &item); err != nil { + t.Fatalf("%s 不是合法 JSON 对象: %v", sampleName, err) + } + for k := range item { + keys = append(keys, k) + } + } + + want := make([]string, 0, typ.NumField()) + for i := 0; i < typ.NumField(); i++ { + if name, ok := jsonFieldNames(typ)[typ.Field(i).Name]; ok { + want = append(want, name) + } + } + + if !keySetsEqual(keys, want) { + sort.Strings(keys) + sort.Strings(want) + t.Errorf("%s 契约漂移：\n 样本字段 = %v\n struct tag = %v\n"+ + " 两者必须一致——样本是 Kotlin 侧的唯一解析依据。", + sampleName, keys, want) + } +} + +func TestGolden_Channels(t *testing.T) { + assertGoldenMatchesType(t, "channels.json", readGolden(t, "channels.json"), channelJSON{}, true) +} + +func TestGolden_Clients(t *testing.T) { + assertGoldenMatchesType(t, "clients.json", readGolden(t, "clients.json"), clientJSON{}, true) +} + +func TestGolden_ServerInfo(t *testing.T) { + assertGoldenMatchesType(t, "server_info.json", readGolden(t, "server_info.json"), serverInfoJSON{}, false) +} + +// TestGolden_ChannelsRoundTrip 样本必须能被 Go 自己解析回结构体， +// 且关键字段值正确——保证样本描述的是「Go 真的会发出来的东西」。 +func TestGolden_ChannelsRoundTrip(t *testing.T) { + var got []channelJSON + if err := json.Unmarshal(readGolden(t, "channels.json"), &got); err != nil { + t.Fatalf("unmarshal: %v", err) + } + if len(got) != 2 { + t.Fatalf("期望 2 个频道，实际 %d", len(got)) + } + if got[0].ID != "1" || got[0].Name != "大厅" || got[0].ParentID != "0" { + t.Errorf("频道[0] 解析异常: %+v", got[0]) + } + if !got[1].IsPassword { + t.Errorf("频道[1] 应为加密频道: %+v", got[1]) + } +} + +func TestGolden_ClientsRoundTrip(t *testing.T) { + var got []clientJSON + if err := json.Unmarshal(readGolden(t, "clients.json"), &got); err != nil { + t.Fatalf("unmarshal: %v", err) + } + if len(got) != 3 { + t.Fatalf("期望 3 个客户端，实际 %d", len(got)) + } + if !got[0].IsSelf || got[0].UID != "abc123==" || got[0].ChannelID != "1" { + t.Errorf("客户端[0] 解析异常: %+v", got[0]) + } + if len(got[2].ServerGroups) != 2 { + t.Errorf("客户端[2] 应有 2 个服务器组: %+v", got[2].ServerGroups) + } +} + +// TestGolden_ServerInfoTypes 钉住 Kotlin 侧按 String 解析的字段。 +// uptime/created 在 Go 侧是 string（数值被格式化过）， +// 若改成数值类型，Kotlin 的 String 字段会静默变空。 +func TestGolden_ServerInfoTypes(t *testing.T) { + var got serverInfoJSON + if err := json.Unmarshal(readGolden(t, "server_info.json"), &got); err != nil { + t.Fatalf("unmarshal: %v", err) + } + if got.Name != "My Server" || got.ClientsOnline != 3 || got.MaxClients != 32 { + t.Errorf("服务器信息解析异常: %+v", got) + } + if got.Uptime != "3600" || got.Created != "1700000000" { + t.Errorf("uptime/created 必须保持字符串形态: uptime=%q created=%q", got.Uptime, got.Created) + } + + typ := reflect.TypeOf(serverInfoJSON{}) + for _, name := range []string{"Uptime", "Created", "IconID"} { + f, ok := typ.FieldByName(name) + if !ok { + t.Fatalf("serverInfoJSON 缺少字段 %s", name) + } + if f.Type.Kind() != reflect.String { + t.Errorf("serverInfoJSON.%s 必须是 string（Kotlin ServerInfo 按 String 解析），实际 %s", + name, f.Type.Kind()) + } + } +} diff --git a/go/teamspeak/contract_test.go b/go/teamspeak/contract_test.go new file mode 100644 index 0000000..a16fb4b --- /dev/null +++ b/go/teamspeak/contract_test.go @@ -0,0 +1,245 @@ +package teamspeak + +import ( + "encoding/json" + "reflect" + "sort" + "testing" +) + +// jsonFieldNames 通过反射取出 struct 上实际生效的 JSON 字段名。 +// encoding/json 只认 tag，所以这是运行时的事实来源。 +// 无 tag 的字段退化为 Go 字段名（与 encoding/json 行为一致）。 +func jsonFieldNames(t reflect.Type) map[string]string { + if t.Kind() == reflect.Ptr { + t = t.Elem() + } + out := make(map[string]string, t.NumField()) + for i := 0; i < t.NumField(); i++ { + f := t.Field(i) + name := f.Name + if tag, ok := f.Tag.Lookup("json"); ok { + name = tag + // 去掉 ",omitempty" 之类的选项 + for j := 0; j < len(name); j++ { + if name[j] == ',' { + name = name[:j] + break + } + } + } + out[f.Name] = name + } + return out +} + +// assertFields 断言「契约常量」与「struct 实际 tag」完全一致。 +// declared 的 key 是 Go 字段名，value 是 contract.go 里的常量值。 +// +// 这正是文档所说「改动 bridge.go 任一字段名会导致测试失败」的机制。 +func assertFields(t *testing.T, typeName string, sample any, declared map[string]string) { + t.Helper() + + actual := jsonFieldNames(reflect.TypeOf(sample)) + + // 反向索引：tag 名 → Go 字段名，用于给出可读的报错 + actualByTag := make(map[string]string, len(actual)) + for goName, tagName := range actual { + actualByTag[tagName] = goName + } + + for goName, wantTag := range declared { + gotTag, exists := actual[goName] + if !exists { + t.Errorf("%s: 契约声明了字段 %q，但 struct 中没有该字段（契约已过期）", typeName, goName) + continue + } + if gotTag != wantTag { + t.Errorf("%s.%s: JSON 字段名漂移：契约常量=%q，实际 tag=%q\n"+ + " 改 tag 就必须同步 contract.go 与 docs/bridge-contract.md，并考虑递增 BridgeContractVersion", + typeName, goName, wantTag, gotTag) + } + } + + // 反向检查：struct 中新增了字段却没登记进契约 + var missing []string + for goName, tagName := range actual { + if _, ok := declared[goName]; !ok { + missing = append(missing, goName+"(json:"+tagName+")") + } + } + if len(missing) > 0 { + sort.Strings(missing) + t.Errorf("%s: struct 存在未登记进 contract.go 的字段：%v\n"+ + " 新增字段需要在 contract.go 中声明，否则 Kotlin 侧的静默失效模式无人看守", + typeName, missing) + } +} + +// ─── B 类契约：有 Kotlin 消费方，必须逐字段对齐 ──────────────── + +func TestChannelJSON_ContractStable(t *testing.T) { + assertFields(t, "channelJSON", channelJSON{}, map[string]string{ + "ID": FieldChannelID, + "Name": FieldChannelName, + "ParentID": FieldChannelParentID, + "Description": FieldChannelDescription, + "IsPassword": FieldChannelIsPassword, + }) +} + +func TestClientJSON_ContractStable(t *testing.T) { + assertFields(t, "clientJSON", clientJSON{}, map[string]string{ + "ID": FieldClientID, + "Nickname": FieldClientNickname, + "UID": FieldClientUID, + "ChannelID": FieldClientChannelID, + "ServerGroups": FieldClientServerGroups, + "IsSelf": FieldClientIsSelf, + }) +} + +func TestServerInfoJSON_ContractStable(t *testing.T) { + assertFields(t, "serverInfoJSON", serverInfoJSON{}, map[string]string{ + "Name": FieldServerName, + "WelcomeMessage": FieldServerWelcomeMessage, + "MaxClients": FieldServerMaxClients, + "ClientsOnline": FieldServerClientsOnline, + "ChannelsOnline": FieldServerChannelsOnline, + "Uptime": FieldServerUptime, + "Version": FieldServerVersion, + "Platform": FieldServerPlatform, + "Created": FieldServerCreated, + "IconID": FieldServerIconID, + "DefaultServerGroup": FieldServerDefaultServerGroup, + "DefaultChannelGroup": FieldServerDefaultChannelGroup, + }) +} + +// ─── C 类契约：当前无消费方，但形状必须可验证 ────────────────── + +func TestChannelDetailedJSON_ContractStable(t *testing.T) { + // channelDetailedJSON 内嵌了 channelJSON 的五个字段（id/name/parentId/description/isPassword）， + // 这里只登记它自己的字段，公共字段由 TestChannelJSON_ContractStable 覆盖。 + assertFields(t, "channelDetailedJSON", channelDetailedJSON{}, map[string]string{ + "ID": FieldChannelID, + "Name": FieldChannelName, + "ParentID": FieldChannelParentID, + "Topic": FieldChannelDetailTopic, + "Order": FieldChannelDetailOrder, + "Codec": FieldChannelDetailCodec, + "CodecQuality": FieldChannelDetailCodecQuality, + "NeededTalkPower": FieldChannelDetailNeededTalkPower, + "MaxClients": FieldChannelDetailMaxClients, + "MaxFamilyClients": FieldChannelDetailMaxFamilyClients, + "IsMaxClientsUnlimited": FieldChannelDetailIsMaxClientsUnlimited, + "IsMaxFamilyClientsUnlimited": FieldChannelDetailIsMaxFamilyClientsUnlimited, + "IsPermanent": FieldChannelDetailIsPermanent, + "IsSemiPermanent": FieldChannelDetailIsSemiPermanent, + "IsDefault": FieldChannelDetailIsDefault, + "IsPassword": FieldChannelIsPassword, + "IsOrdered": FieldChannelDetailIsOrdered, + "IconID": FieldChannelDetailIconID, + "NeededModifyPower": FieldChannelDetailNeededModifyPower, + }) +} + +func TestInitialSyncJSON_ContractStable(t *testing.T) { + assertFields(t, "initialSyncJSON", initialSyncJSON{}, map[string]string{ + "Channels": FieldSyncChannels, + "Clients": FieldSyncClients, + "SelfID": FieldSyncSelfID, + "SelfChannelID": FieldSyncSelfChannelID, + "Server": FieldSyncServer, + }) +} + +// ─── 往返测试：真实样本 → JSON → 字段名断言 ──────────────────── +// +// 上面的反射测试保证「常量 == tag」，这里保证「tag == 实际序列化输出」， +// 两者合起来才能覆盖 encoding/json 的全部行为（如 omitempty、内嵌 struct）。 + +func TestChannelJSON_RoundTrip(t *testing.T) { + in := channelJSON{ + ID: "42", + Name: "大厅", + ParentID: "0", + Description: "desc", + IsPassword: true, + } + raw, err := json.Marshal(in) + if err != nil { + t.Fatalf("marshal: %v", err) + } + + var asMap map[string]json.RawMessage + if err := json.Unmarshal(raw, &asMap); err != nil { + t.Fatalf("unmarshal to map: %v", err) + } + + want := []string{ + FieldChannelID, FieldChannelName, FieldChannelParentID, + FieldChannelDescription, FieldChannelIsPassword, + } + for _, k := range want { + if _, ok := asMap[k]; !ok { + t.Errorf("序列化结果缺少字段 %q；实际 JSON=%s", k, raw) + } + } + + // 值必须真的能往返（父频道 ID 是字符串而非数字，Kotlin 侧按 String 解析） + var back channelJSON + if err := json.Unmarshal(raw, &back); err != nil { + t.Fatalf("unmarshal back: %v", err) + } + if back != in { + t.Errorf("往返后数据不一致：in=%+v out=%+v", in, back) + } +} + +// TestChannelJSON_IDIsStringType 钉住一个曾经的真实失效模式： +// Kotlin 的 ChannelInfo.id / parentId 是 String，Go 侧若改成数值类型， +// Kotlin 会在运行时抛序列化异常（而不是编译失败）。 +func TestChannelJSON_IDIsStringType(t *testing.T) { + typ := reflect.TypeOf(channelJSON{}) + for _, fieldName := range []string{"ID", "ParentID"} { + f, ok := typ.FieldByName(fieldName) + if !ok { + t.Fatalf("channelJSON 缺少字段 %s", fieldName) + } + if f.Type.Kind() != reflect.String { + t.Errorf("channelJSON.%s 必须是 string（Kotlin ChannelInfo 按 String 解析），实际为 %s", + fieldName, f.Type.Kind()) + } + } +} + +// TestClientJSON_ChannelIDIsStringType 同上：ClientInfo.channelId 是 String。 +func TestClientJSON_ChannelIDIsStringType(t *testing.T) { + f, ok := reflect.TypeOf(clientJSON{}).FieldByName("ChannelID") + if !ok { + t.Fatal("clientJSON 缺少 ChannelID 字段") + } + if f.Type.Kind() != reflect.String { + t.Errorf("clientJSON.ChannelID 必须是 string（Kotlin ClientInfo.channelId 按 String 解析），实际为 %s", + f.Type.Kind()) + } +} + +// TestContractVersion_NonEmpty 版本号是对外承诺，不能为空。 +func TestContractVersion_NonEmpty(t *testing.T) { + if GetContractVersion() == "" { + t.Fatal("BridgeContractVersion 不能为空——Kotlin 启动时会校验它") + } + if GetContractVersion() != BridgeContractVersion { + t.Fatalf("GetContractVersion()=%q 与常量 %q 不一致", GetContractVersion(), BridgeContractVersion) + } +} + +// TestTargetModeValues 发送/接收能力不对称依赖这三个数值稳定。 +func TestTargetModeValues(t *testing.T) { + if TargetModePrivate != 1 || TargetModeChannel != 2 || TargetModeServer != 3 { + t.Fatalf("targetMode 取值被改动：private=%d channel=%d server=%d（必须为 1/2/3）", + TargetModePrivate, TargetModeChannel, TargetModeServer) + } +}