diff --git a/models/emb/cam++/THIRD_PARTY_LICENSES b/models/emb/cam++/THIRD_PARTY_LICENSES new file mode 100644 index 0000000..0af69d2 --- /dev/null +++ b/models/emb/cam++/THIRD_PARTY_LICENSES @@ -0,0 +1,183 @@ +================================================================================================ + THIRD-PARTY SOFTWARE LICENSES +================================================================================================ + +- 3D-Speaker + Copyright 3D-Speaker (https://github.com/modelscope/3D-Speaker). All Rights Reserved. + + Apache License + Version 2.0, January 2004 + http://www.apache.org/licenses/ + + TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION + + 1. 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In no event and under no legal theory, + whether in tort (including negligence), contract, or otherwise, + unless required by applicable law (such as deliberate and grossly + negligent acts) or agreed to in writing, shall any Contributor be + liable to You for damages, including any direct, indirect, special, + incidental, or consequential damages of any character arising as a + result of this License or out of the use or inability to use the + Work (including but not limited to damages for loss of goodwill, + work stoppage, computer failure or malfunction, or any and all + other commercial damages or losses), even if such Contributor + has been advised of the possibility of such damages. + + 9. Accepting Warranty or Additional Liability. While redistributing + the Work or Derivative Works thereof, You may choose to offer, + and charge a fee for, acceptance of support, warranty, indemnity, + or other liability obligations and/or rights consistent with this + License. However, in accepting such obligations, You may act only + on Your own behalf and on Your sole responsibility, not on behalf + of any other Contributor, and only if You agree to indemnify, + defend, and hold each Contributor harmless for any liability + incurred by, or claims asserted against, such Contributor by reason + of your accepting any such warranty or additional liability. + + END OF TERMS AND CONDITIONS \ No newline at end of file diff --git a/models/emb/cam++/coreml/.gitignore b/models/emb/cam++/coreml/.gitignore new file mode 100644 index 0000000..6ea8874 --- /dev/null +++ b/models/emb/cam++/coreml/.gitignore @@ -0,0 +1 @@ +models/ \ No newline at end of file diff --git a/models/emb/cam++/coreml/README.md b/models/emb/cam++/coreml/README.md new file mode 100644 index 0000000..451b3dd --- /dev/null +++ b/models/emb/cam++/coreml/README.md @@ -0,0 +1,33 @@ +# CAM++ CoreML +CAM++ is a fast and efficient neural network for speaker verification that uses Context-Aware Masking to extract high-quality speaker embeddings. This repository contains a CoreML conversion of the CAM++ model for efficient inference on Apple devices. + +## Model Details +- **Authors**: Hui Wang, Siqi Zheng, Yafeng Chen, Luyao Cheng, and Qian Chen +- **Organization**: Speech Lab, Alibaba Group +- **Paper**: [CAM++: A Fast and Efficient Network for Speaker Verification Using Context-Aware Masking](https://arxiv.org/abs/2303.00332) +- **Implementation**: [3D-Speaker Toolkit](https://github.com/modelscope/3D-Speaker/tree/main/speakerlab/models/campplus) +- **Weights**: [ModelScope Repo](https://modelscope.cn/models/iic/speech_campplus_sv_zh_en_16k-common_advanced/) + +CAM++ employs D-TDNN (Densely Connected Time Delay Neural Network) as its backbone and introduces a context-aware masking module to focus on the speaker of interest while suppressing unrelated noise. It achieves comparable performance to ECAPA-TDNN with significantly lower computational cost. + +## CoreML Model I/O +#### Input Format: `(16, 150, 80)` tensor +- **16**: Batch size - processes 16 audio subsegments in parallel (each ~1.5 seconds) +- **150**: Number of frames - audio is divided into 25ms frames (400 samples @ 16kHz) with 10ms shift (160 samples @ 16kHz) +- **80**: Mel-filterbank features - log-transformed frequency bins (20-8000 Hz) extracted from each frame + +#### Output Format: `(16, 192)` tensor +- **16**: Batch size +- **192**: Embedding dimension + +## Usage +```bash +# Setup venv and install dependencies +uv sync + +# Convert PyTorch model to CoreML +uv run convert.py + +# Test CoreML model against reference implementation with real audio +uv run test.py audio/jfk.mp3 +``` \ No newline at end of file diff --git a/models/emb/cam++/coreml/audio/jfk.mp3 b/models/emb/cam++/coreml/audio/jfk.mp3 new file mode 100644 index 0000000..fbfa1d9 Binary files /dev/null and b/models/emb/cam++/coreml/audio/jfk.mp3 differ diff --git a/models/emb/cam++/coreml/camplusplus.py b/models/emb/cam++/coreml/camplusplus.py new file mode 100644 index 0000000..03db33b --- /dev/null +++ b/models/emb/cam++/coreml/camplusplus.py @@ -0,0 +1,211 @@ +# https://github.com/modelscope/3D-Speaker/tree/main/speakerlab/models/campplus +# Commit hash f5764ed3a89da77330f7dcc9801f58992c3e3c74 + +import torch +import torch.nn as nn +import torch.nn.functional as F +from collections import OrderedDict +import torch.utils.checkpoint as cp + +class CAMPPlus(nn.Module): + def __init__(self, feat_dim=80, embedding_size=512, growth_rate=32, bn_size=4, init_channels=128, memory_efficient=True): + super(CAMPPlus, self).__init__() + self.head = FCM(feat_dim=feat_dim) + channels = self.head.out_channels + + self.xvector = nn.Sequential(OrderedDict([ + ('tdnn', TDNNLayer(channels, init_channels, 5, stride=2, dilation=1, padding=-1)) + ])) + + channels = init_channels + blocks_config = [(12, 3, 1), (24, 3, 2), (16, 3, 2)] + + for i, (num_layers, kernel_size, dilation) in enumerate(blocks_config): + block = CAMDenseTDNNBlock(num_layers=num_layers, in_channels=channels, out_channels=growth_rate, bn_channels=bn_size * growth_rate, kernel_size=kernel_size, dilation=dilation, bias=False, memory_efficient=memory_efficient) + self.xvector.add_module(f'block{i+1}', block) + channels = channels + num_layers * growth_rate + + self.xvector.add_module(f'transit{i+1}', TransitLayer(channels, channels // 2, False)) + channels //= 2 + + out_nonlinear = nn.Sequential() + out_nonlinear.add_module('batchnorm', nn.BatchNorm1d(channels)) + out_nonlinear.add_module('relu', nn.ReLU(inplace=True)) + self.xvector.add_module('out_nonlinear', out_nonlinear) + + self.xvector.add_module('stats', StatsPool()) + self.xvector.add_module('dense', DenseLayer(channels * 2, embedding_size, False)) + + for m in self.modules(): + if isinstance(m, (nn.Conv1d, nn.Linear)): + nn.init.kaiming_normal_(m.weight.data) + if m.bias is not None: nn.init.zeros_(m.bias) + + def forward(self, x): + x = x.permute(0, 2, 1) # (B,T,F) => (B,F,T) + x = self.head(x) + x = self.xvector(x) + return x + +class BasicResBlock(nn.Module): + expansion = 1 + + def __init__(self, in_planes, planes, stride=1): + super(BasicResBlock, self).__init__() + self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=3, stride=(stride, 1), padding=1, bias=False) + self.bn1 = nn.BatchNorm2d(planes) + self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=1, padding=1, bias=False) + self.bn2 = nn.BatchNorm2d(planes) + + self.shortcut = nn.Sequential() + if stride != 1 or in_planes != self.expansion * planes: + self.shortcut = nn.Sequential( + nn.Conv2d(in_planes, self.expansion * planes, kernel_size=1, stride=(stride, 1), bias=False), + nn.BatchNorm2d(self.expansion * planes)) + + def forward(self, x): + out = F.relu(self.bn1(self.conv1(x))) + out = self.bn2(self.conv2(out)) + out += self.shortcut(x) + out = F.relu(out) + return out + +class FCM(nn.Module): + def __init__(self, block=BasicResBlock, num_blocks=[2, 2], m_channels=32, feat_dim=80): + super(FCM, self).__init__() + self.in_planes = m_channels + self.conv1 = nn.Conv2d(1, m_channels, kernel_size=3, stride=1, padding=1, bias=False) + self.bn1 = nn.BatchNorm2d(m_channels) + + self.layer1 = nn.Sequential( + block(self.in_planes, m_channels, stride=2), + block(m_channels, m_channels, stride=1) + ) + self.in_planes = m_channels * block.expansion + + self.layer2 = nn.Sequential( + block(self.in_planes, m_channels, stride=2), + block(m_channels, m_channels, stride=1) + ) + + self.conv2 = nn.Conv2d(m_channels, m_channels, kernel_size=3, stride=(2, 1), padding=1, bias=False) + self.bn2 = nn.BatchNorm2d(m_channels) + self.out_channels = m_channels * (feat_dim // 8) + + def forward(self, x): + x = x.unsqueeze(1) + out = F.relu(self.bn1(self.conv1(x))) + out = self.layer1(out) + out = self.layer2(out) + out = F.relu(self.bn2(self.conv2(out))) + + shape = out.shape + out = out.reshape(shape[0], shape[1]*shape[2], shape[3]) + return out + + +class StatsPool(nn.Module): + def forward(self, x, eps=1e-2): + mean = x.mean(dim=-1) + std = x.std(dim=-1, unbiased=True) if x.size(-1) > 1 else torch.zeros_like(mean) + eps + return torch.cat([mean, std], dim=-1) + + +class TDNNLayer(nn.Module): + def __init__(self, in_channels, out_channels, kernel_size, stride=1, padding=0, dilation=1, bias=False): + super(TDNNLayer, self).__init__() + if padding < 0: + assert kernel_size % 2 == 1, 'Expect equal paddings, but got even kernel size ({})'.format(kernel_size) + padding = (kernel_size - 1) // 2 * dilation + + self.linear = nn.Conv1d(in_channels, out_channels, kernel_size, stride=stride, padding=padding, dilation=dilation, bias=bias) + + self.nonlinear = nn.Sequential() + self.nonlinear.add_module('batchnorm', nn.BatchNorm1d(out_channels)) + self.nonlinear.add_module('relu', nn.ReLU(inplace=True)) + + def forward(self, x): + return self.nonlinear(self.linear(x)) + + +class CAMLayer(nn.Module): + def __init__(self, bn_channels, out_channels, kernel_size, stride, padding, dilation, bias, reduction=2): + super(CAMLayer, self).__init__() + self.linear_local = nn.Conv1d(bn_channels, out_channels, kernel_size, stride=stride, padding=padding, dilation=dilation, bias=bias) + self.linear1 = nn.Conv1d(bn_channels, bn_channels // reduction, 1) + self.relu = nn.ReLU(inplace=True) + self.linear2 = nn.Conv1d(bn_channels // reduction, out_channels, 1) + self.sigmoid = nn.Sigmoid() + + def forward(self, x): + y = self.linear_local(x) + context = x.mean(-1, keepdim=True) + self.seg_pooling(x) + return y * self.sigmoid(self.linear2(self.relu(self.linear1(context)))) + + def seg_pooling(self, x, seg_len=100, stype='avg'): + seg = F.avg_pool1d(x, kernel_size=seg_len, stride=seg_len, ceil_mode=True) if stype == 'avg' else F.max_pool1d(x, kernel_size=seg_len, stride=seg_len, ceil_mode=True) + return seg.unsqueeze(-1).expand(*seg.shape, seg_len).reshape(*seg.shape[:-1], -1)[..., :x.shape[-1]] + + +class CAMDenseTDNNLayer(nn.Module): + def __init__(self, in_channels, out_channels, bn_channels, kernel_size, stride=1, dilation=1, bias=False, memory_efficient=False): + super(CAMDenseTDNNLayer, self).__init__() + assert kernel_size % 2 == 1, 'Expect equal paddings, but got even kernel size ({})'.format(kernel_size) + self.memory_efficient = memory_efficient + + self.nonlinear1 = nn.Sequential() + self.nonlinear1.add_module('batchnorm', nn.BatchNorm1d(in_channels)) + self.nonlinear1.add_module('relu', nn.ReLU(inplace=True)) + + self.linear1 = nn.Conv1d(in_channels, bn_channels, 1, bias=False) + + self.nonlinear2 = nn.Sequential() + self.nonlinear2.add_module('batchnorm', nn.BatchNorm1d(bn_channels)) + self.nonlinear2.add_module('relu', nn.ReLU(inplace=True)) + + padding = (kernel_size - 1) // 2 * dilation + self.cam_layer = CAMLayer(bn_channels, out_channels, kernel_size, stride, padding, dilation, bias) + + def forward(self, x): + if self.training and self.memory_efficient: + x = cp.checkpoint(lambda x: self.linear1(self.nonlinear1(x)), x) + else: + x = self.linear1(self.nonlinear1(x)) + return self.cam_layer(self.nonlinear2(x)) + + +class CAMDenseTDNNBlock(nn.ModuleList): + def __init__(self, num_layers, in_channels, out_channels, bn_channels, kernel_size, stride=1, dilation=1, bias=False, memory_efficient=False): + super(CAMDenseTDNNBlock, self).__init__() + for i in range(num_layers): + layer = CAMDenseTDNNLayer(in_channels + i * out_channels, out_channels, bn_channels, kernel_size, stride, dilation, bias, memory_efficient) + self.add_module(f'tdnnd{i+1}', layer) + + def forward(self, x): + for layer in self: + x = torch.cat([x, layer(x)], dim=1) + return x + + +class TransitLayer(nn.Module): + def __init__(self, in_channels, out_channels, bias=True): + super(TransitLayer, self).__init__() + self.nonlinear = nn.Sequential() + self.nonlinear.add_module('batchnorm', nn.BatchNorm1d(in_channels)) + self.nonlinear.add_module('relu', nn.ReLU(inplace=True)) + self.linear = nn.Conv1d(in_channels, out_channels, 1, bias=bias) + + def forward(self, x): + return self.linear(self.nonlinear(x)) + + +class DenseLayer(nn.Module): + def __init__(self, in_channels, out_channels, bias=False): + super(DenseLayer, self).__init__() + self.linear = nn.Conv1d(in_channels, out_channels, 1, bias=bias) + self.nonlinear = nn.Sequential() + self.nonlinear.add_module('batchnorm', nn.BatchNorm1d(out_channels, affine=False)) + self.nonlinear.add_module('relu', nn.ReLU(inplace=True)) + + def forward(self, x): + return self.nonlinear(self.linear(x if len(x.shape) == 3 else x.unsqueeze(-1)).squeeze(-1) if len(x.shape) == 2 else self.linear(x)) \ No newline at end of file diff --git a/models/emb/cam++/coreml/camplusplus_coreml.py b/models/emb/cam++/coreml/camplusplus_coreml.py new file mode 100644 index 0000000..873c8f2 --- /dev/null +++ b/models/emb/cam++/coreml/camplusplus_coreml.py @@ -0,0 +1,221 @@ +# https://github.com/modelscope/3D-Speaker/tree/main/speakerlab/models/campplus +# CoreML friendly version + +import torch +import torch.nn as nn +import torch.nn.functional as F +from collections import OrderedDict + +class CAMPPlusCoreML(nn.Module): + def __init__(self, feat_dim=80, embedding_size=512, growth_rate=32, bn_size=4, init_channels=128, memory_efficient=False): + super(CAMPPlusCoreML, self).__init__() + self.head = FCM(feat_dim=feat_dim) + channels = self.head.out_channels + + self.xvector = nn.Sequential(OrderedDict([ + ('tdnn', TDNNLayer(channels, init_channels, 5, stride=2, dilation=1, padding=-1)) + ])) + + channels = init_channels + blocks_config = [(12, 3, 1), (24, 3, 2), (16, 3, 2)] + + for i, (num_layers, kernel_size, dilation) in enumerate(blocks_config): + block = CAMDenseTDNNBlock(num_layers=num_layers, in_channels=channels, out_channels=growth_rate, + bn_channels=bn_size * growth_rate, kernel_size=kernel_size, + dilation=dilation, bias=False, memory_efficient=False) + self.xvector.add_module(f'block{i+1}', block) + channels = channels + num_layers * growth_rate + + self.xvector.add_module(f'transit{i+1}', TransitLayer(channels, channels // 2, False)) + channels //= 2 + + out_nonlinear = nn.Sequential() + out_nonlinear.add_module('batchnorm', nn.BatchNorm1d(channels)) + out_nonlinear.add_module('relu', nn.ReLU(inplace=True)) + self.xvector.add_module('out_nonlinear', out_nonlinear) + + self.xvector.add_module('stats', StatsPool()) + self.xvector.add_module('dense', DenseLayer(channels * 2, embedding_size, False)) + + for m in self.modules(): + if isinstance(m, (nn.Conv1d, nn.Linear)): + nn.init.kaiming_normal_(m.weight.data) + if m.bias is not None: + nn.init.zeros_(m.bias) + + def forward(self, x): + x = x.permute(0, 2, 1) # (B,T,F) => (B,F,T) + x = self.head(x) + x = self.xvector(x) + return x + +class BasicResBlock(nn.Module): + expansion = 1 + + def __init__(self, in_planes, planes, stride=1): + super(BasicResBlock, self).__init__() + self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=3, stride=(stride, 1), padding=1, bias=False) + self.bn1 = nn.BatchNorm2d(planes) + self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=1, padding=1, bias=False) + self.bn2 = nn.BatchNorm2d(planes) + + self.shortcut = nn.Sequential() + if stride != 1 or in_planes != self.expansion * planes: + self.shortcut = nn.Sequential( + nn.Conv2d(in_planes, self.expansion * planes, kernel_size=1, stride=(stride, 1), bias=False), + nn.BatchNorm2d(self.expansion * planes)) + + def forward(self, x): + out = F.relu(self.bn1(self.conv1(x))) + out = self.bn2(self.conv2(out)) + out = out + self.shortcut(x) + out = F.relu(out) + return out + +class FCM(nn.Module): + def __init__(self, block=BasicResBlock, num_blocks=[2, 2], m_channels=32, feat_dim=80): + super(FCM, self).__init__() + self.in_planes = m_channels + self.conv1 = nn.Conv2d(1, m_channels, kernel_size=3, stride=1, padding=1, bias=False) + self.bn1 = nn.BatchNorm2d(m_channels) + + self.layer1 = nn.Sequential( + block(self.in_planes, m_channels, stride=2), + block(m_channels, m_channels, stride=1) + ) + self.in_planes = m_channels * block.expansion + + self.layer2 = nn.Sequential( + block(self.in_planes, m_channels, stride=2), + block(m_channels, m_channels, stride=1) + ) + + self.conv2 = nn.Conv2d(m_channels, m_channels, kernel_size=3, stride=(2, 1), padding=1, bias=False) + self.bn2 = nn.BatchNorm2d(m_channels) + self.out_channels = m_channels * (feat_dim // 8) + + def forward(self, x): + x = x.unsqueeze(1) + out = F.relu(self.bn1(self.conv1(x))) + out = self.layer1(out) + out = self.layer2(out) + out = F.relu(self.bn2(self.conv2(out))) + + shape = out.shape + out = out.reshape(shape[0], shape[1]*shape[2], shape[3]) + return out + + +class StatsPool(nn.Module): + def forward(self, x): + # CoreML-friendly implementation (conditional operations (if x.size(-1) > 1) aren't traced well) + mean = x.mean(dim=-1) + # Compute std with epsilon for stability + var = x.var(dim=-1, unbiased=False) + std = torch.sqrt(var + 1e-5) + return torch.cat([mean, std], dim=-1) + + +class TDNNLayer(nn.Module): + def __init__(self, in_channels, out_channels, kernel_size, stride=1, padding=0, dilation=1, bias=False): + super(TDNNLayer, self).__init__() + if padding < 0: + assert kernel_size % 2 == 1, 'Expect equal paddings, but got even kernel size ({})'.format(kernel_size) + padding = (kernel_size - 1) // 2 * dilation + + self.linear = nn.Conv1d(in_channels, out_channels, kernel_size, stride=stride, padding=padding, dilation=dilation, bias=bias) + + self.nonlinear = nn.Sequential() + self.nonlinear.add_module('batchnorm', nn.BatchNorm1d(out_channels)) + self.nonlinear.add_module('relu', nn.ReLU(inplace=True)) + + def forward(self, x): + return self.nonlinear(self.linear(x)) + + +class CAMLayer(nn.Module): + def __init__(self, bn_channels, out_channels, kernel_size, stride, padding, dilation, bias, reduction=2): + super(CAMLayer, self).__init__() + self.linear_local = nn.Conv1d(bn_channels, out_channels, kernel_size, stride=stride, padding=padding, dilation=dilation, bias=bias) + self.linear1 = nn.Conv1d(bn_channels, bn_channels // reduction, 1) + self.relu = nn.ReLU(inplace=True) + self.linear2 = nn.Conv1d(bn_channels // reduction, out_channels, 1) + self.sigmoid = nn.Sigmoid() + + def forward(self, x): + y = self.linear_local(x) + # Simplified context computation for CoreML + # Using 2x the mean to approximate the original context + pooling + context = 2.0 * x.mean(-1, keepdim=True) + + attention = self.sigmoid(self.linear2(self.relu(self.linear1(context)))) + return y * attention + + +class CAMDenseTDNNLayer(nn.Module): + def __init__(self, in_channels, out_channels, bn_channels, kernel_size, stride=1, dilation=1, bias=False, memory_efficient=False): + super(CAMDenseTDNNLayer, self).__init__() + assert kernel_size % 2 == 1, 'Expect equal paddings, but got even kernel size ({})'.format(kernel_size) + # memory_efficient is ignored for CoreML compatibility + + self.nonlinear1 = nn.Sequential() + self.nonlinear1.add_module('batchnorm', nn.BatchNorm1d(in_channels)) + self.nonlinear1.add_module('relu', nn.ReLU(inplace=True)) + + self.linear1 = nn.Conv1d(in_channels, bn_channels, 1, bias=False) + + self.nonlinear2 = nn.Sequential() + self.nonlinear2.add_module('batchnorm', nn.BatchNorm1d(bn_channels)) + self.nonlinear2.add_module('relu', nn.ReLU(inplace=True)) + + padding = (kernel_size - 1) // 2 * dilation + self.cam_layer = CAMLayer(bn_channels, out_channels, kernel_size, stride, padding, dilation, bias) + + def forward(self, x): + # No checkpointing for CoreML + x = self.linear1(self.nonlinear1(x)) + return self.cam_layer(self.nonlinear2(x)) + + +class CAMDenseTDNNBlock(nn.ModuleList): + def __init__(self, num_layers, in_channels, out_channels, bn_channels, kernel_size, stride=1, dilation=1, bias=False, memory_efficient=False): + super(CAMDenseTDNNBlock, self).__init__() + for i in range(num_layers): + layer = CAMDenseTDNNLayer(in_channels + i * out_channels, out_channels, bn_channels, kernel_size, stride, dilation, bias, memory_efficient=False) + self.add_module(f'tdnnd{i+1}', layer) + + def forward(self, x): + for layer in self: + x = torch.cat([x, layer(x)], dim=1) + return x + + +class TransitLayer(nn.Module): + def __init__(self, in_channels, out_channels, bias=True): + super(TransitLayer, self).__init__() + self.nonlinear = nn.Sequential() + self.nonlinear.add_module('batchnorm', nn.BatchNorm1d(in_channels)) + self.nonlinear.add_module('relu', nn.ReLU(inplace=True)) + self.linear = nn.Conv1d(in_channels, out_channels, 1, bias=bias) + + def forward(self, x): + return self.linear(self.nonlinear(x)) + + +class DenseLayer(nn.Module): + def __init__(self, in_channels, out_channels, bias=False): + super(DenseLayer, self).__init__() + self.linear = nn.Conv1d(in_channels, out_channels, 1, bias=bias) + self.nonlinear = nn.Sequential() + self.nonlinear.add_module('batchnorm', nn.BatchNorm1d(out_channels, affine=False)) + self.nonlinear.add_module('relu', nn.ReLU(inplace=True)) + + def forward(self, x): + # Simplified forward for CoreML (explicit if/else blocks that can be traced) + if len(x.shape) == 2: + x = x.unsqueeze(-1) + x = self.linear(x) + x = x.squeeze(-1) + else: + x = self.linear(x) + return self.nonlinear(x) \ No newline at end of file diff --git a/models/emb/cam++/coreml/convert.py b/models/emb/cam++/coreml/convert.py new file mode 100644 index 0000000..938d265 --- /dev/null +++ b/models/emb/cam++/coreml/convert.py @@ -0,0 +1,170 @@ +""" +PyTorch camplusplus_coreml.py to CoreML conversion script +Downloads model weights from ModelScope if not present. + +CoreML Model I/O: + Input: (16, 150, 80) tensor + - 16: Batch size - processes 16 audio subsegments in parallel + - 150: Number of frames - audio is divided into 25ms frames (400 samples @ 16kHz) with 10ms shift (160 samples @ 16kHz) + - 80: Mel-filterbank features - log-transformed frequency bins (20-8000 Hz) extracted from each frame + Output: (16, 192) tensor + - 16: Batch size + - 192: Embedding dimension +""" +import torch +import coremltools as ct +import numpy as np +import requests +from pathlib import Path +from camplusplus import CAMPPlus +from camplusplus_coreml import CAMPPlusCoreML + +def download_model(): + """Download the CAMPlus model if it doesn't exist and return the local path.""" + model_dir = Path("./models/speech_campplus_sv_zh_en_16k-common_advanced") + model_path = model_dir / "campplus_cn_en_common.pt" + + if model_path.exists(): + print(f"Model already exists at: {model_path}") + return str(model_path) + + print(f"Model not found. Downloading to: {model_path}") + model_dir.mkdir(parents=True, exist_ok=True) + + url = "https://modelscope.cn/models/iic/speech_campplus_sv_zh_en_16k-common_advanced/resolve/master/campplus_cn_en_common.pt" + + try: + with requests.get(url, stream=True) as response: + response.raise_for_status() + total_size = int(response.headers.get("content-length", 0)) + downloaded = 0 + with open(model_path, "wb") as f: + for chunk in response.iter_content(chunk_size=8192): + if not chunk: + continue + f.write(chunk) + downloaded += len(chunk) + if total_size: + pct = downloaded / total_size * 100 + print(f"\rDownloading: {pct:.1f}%", end="", flush=True) + except requests.RequestException as e: + raise RuntimeError(f"Error downloading model: {e}") from e + + print(f"\nModel downloaded successfully to: {model_path}") + return str(model_path) + +def convert_campplus_to_coreml(): + BATCH_SIZE = 16 + FIXED_FRAMES = 150 + FEATURE_DIM = 80 + EMBEDDING_DIM = 192 + + # Initialize PyTorch CoreML-friendly model + coreml_friendly_model = CAMPPlusCoreML(feat_dim=FEATURE_DIM, embedding_size=EMBEDDING_DIM) + + # Load weights into CoreML-friendly model + weights_path = download_model() + state_dict = torch.load(weights_path, map_location='cpu', weights_only=True) + coreml_friendly_model.load_state_dict(state_dict) + coreml_friendly_model.eval() + print(f"✓ Loaded weights into CoreML-friendly model (CAMPPlusCoreML)") + + # Create example input + example_input = torch.randn(BATCH_SIZE, FIXED_FRAMES, FEATURE_DIM) + + # Trace the CoreML-friendly model + print("\nTracing CoreML-friendly model...") + traced_model = torch.jit.trace(coreml_friendly_model, example_input) + + # Convert to CoreML + print("\nConverting to CoreML...") + print(f" Batch size: {BATCH_SIZE}") + print(f" Fixed frames: {FIXED_FRAMES}") + print(f" Compute unit: CPU_AND_NE (Neural Engine)") + + input_type = ct.TensorType( + name="input_features", + shape=(BATCH_SIZE, FIXED_FRAMES, FEATURE_DIM), + dtype=np.float32 + ) + + coreml_model = ct.convert( + traced_model, + inputs=[input_type], + outputs=[ct.TensorType(name="embeddings", dtype=np.float32)], + compute_units=ct.ComputeUnit.CPU_AND_NE, + convert_to="mlprogram", + minimum_deployment_target=ct.target.macOS14, + ) + + # Add metadata + coreml_model.author = "Original: 3D-Speaker / Speech Lab, Alibaba Group" + coreml_model.short_description = "CAM++ speaker embedding model" + coreml_model.input_description["input_features"] = f"Fbank features: ({BATCH_SIZE}, {FIXED_FRAMES}, {FEATURE_DIM})" + coreml_model.output_description["embeddings"] = f"Speaker embeddings: ({BATCH_SIZE}, {EMBEDDING_DIM})" + + # Save the model + output_path = f"./models/camplusplus_batch{BATCH_SIZE}.mlpackage" + coreml_model.save(output_path) + print(f"\n✓ Saved CoreML model to: {output_path}") + + # Verify conversion accuracy against reference model + print("\nVerifying conversion accuracy against reference model...") + coreml_model_loaded = ct.models.MLModel(output_path) + + # Test with example input + test_input = example_input.numpy() + coreml_output = coreml_model_loaded.predict({'input_features': test_input}) + coreml_embeddings = coreml_output['embeddings'] + + # Initialize reference model (original CAMPPlus) + reference_model = CAMPPlus(feat_dim=FEATURE_DIM, embedding_size=EMBEDDING_DIM) + + # Load pretrained weights into reference model + reference_model.load_state_dict(state_dict) + reference_model.eval() + print(f"✓ Loaded weights into reference model from: {weights_path}") + + # Test reference model + print("\nTesting reference model...") + with torch.no_grad(): + reference_output = reference_model(example_input) + print(f" Input shape: {example_input.shape}") + print(f" Reference output shape: {reference_output.shape}") + + # Compare CoreML output with reference model output + reference_np = reference_output.numpy() + max_diff = np.max(np.abs(reference_np - coreml_embeddings)) + mean_diff = np.mean(np.abs(reference_np - coreml_embeddings)) + + # Calculate cosine similarity between CoreML and reference + cosine_sims = [] + for i in range(BATCH_SIZE): + ref_norm = reference_np[i] / (np.linalg.norm(reference_np[i]) + 1e-8) + cm_norm = coreml_embeddings[i] / (np.linalg.norm(coreml_embeddings[i]) + 1e-8) + cosine_sim = np.sum(ref_norm * cm_norm) + cosine_sims.append(cosine_sim) + + avg_cosine = np.mean(cosine_sims) + min_cosine = np.min(cosine_sims) + + print(f" Max difference: {max_diff:.6f}") + print(f" Mean difference: {mean_diff:.6f}") + print(f" Avg cosine similarity: {avg_cosine:.6f}") + print(f" Min cosine similarity: {min_cosine:.6f}") + + if avg_cosine > 0.999: + print("\n ✓ Accuracy verification PASSED") + elif avg_cosine > 0.99: + print("\n ⚠ Warning: Slightly lower accuracy than expected") + else: + print("\n ⚠ Warning: Significant accuracy difference detected") + + print("\nCAM++ CoreML conversion complete") + print(f"Model: {output_path}") + print(f"Batch size: {BATCH_SIZE}") + + return output_path + +if __name__ == "__main__": + convert_campplus_to_coreml() \ No newline at end of file diff --git a/models/emb/cam++/coreml/pyproject.toml b/models/emb/cam++/coreml/pyproject.toml new file mode 100644 index 0000000..1ca938c --- /dev/null +++ b/models/emb/cam++/coreml/pyproject.toml @@ -0,0 +1,12 @@ +[project] +name = "camplusplus_coreml" +version = "0.1.0" +requires-python = "==3.11.13" +dependencies = [ + "torch", + "torchaudio", + "soundfile", + "coremltools", + "numpy", + "requests", +] diff --git a/models/emb/cam++/coreml/test.py b/models/emb/cam++/coreml/test.py new file mode 100644 index 0000000..30977ab --- /dev/null +++ b/models/emb/cam++/coreml/test.py @@ -0,0 +1,310 @@ +""" +Test script to validate CoreML CAM++ embeddings model against reference PyTorch implementation. + 1. Takes an audio file as input + 2. Converts it to 16kHz mono 16-bit WAV if needed + 3. Extracts Fbank features + 4. Runs features through both CoreML and reference PyTorch models + 5. Compares embeddings using cosine similarity + 6. Performs speed comparison between CoreML model & torch model +""" +import sys +import numpy as np +import torch +import torchaudio +import torchaudio.compliance.kaldi as kaldi +import coremltools as ct +from pathlib import Path +import warnings +from camplusplus import CAMPPlus + +warnings.filterwarnings('ignore') + +def extract_fbank_features(waveform, sample_rate=16000): + # In Python here just for testing/demonstration. + # For production deployment, see fbank_extractor C++ code in the Senko diarization pipeline: + # https://github.com/narcotic-sh/senko/tree/main/senko/fbank_extractor + + # Ensure waveform is 2D (1, num_samples) + if waveform.dim() == 1: + waveform = waveform.unsqueeze(0) + + fbank = kaldi.fbank( + waveform, + sample_frequency=sample_rate, + num_mel_bins=80, + frame_length=25.0, # 25ms + frame_shift=10.0, # 10ms + dither=0.0, + preemphasis_coefficient=0.97, + remove_dc_offset=True, + window_type='povey', + round_to_power_of_two=True, + blackman_coeff=0.42, + snip_edges=True, + low_freq=20, + high_freq=0, # 0 means Nyquist (8000 Hz for 16kHz sampling) + use_energy=False, + energy_floor=1.0, + raw_energy=True, + use_log_fbank=True, + use_power=True + ) + + # Mean normalization + fbank = fbank - fbank.mean(dim=0, keepdim=True) + + return fbank.numpy() + +def load_and_convert_audio(audio_path): + """Load audio file and convert to 16kHz mono if needed""" + # Load audio with torchaudio + waveform, sample_rate = torchaudio.load(audio_path) + + # Convert to mono if stereo + if waveform.shape[0] > 1: + waveform = torch.mean(waveform, dim=0, keepdim=True) + + # Resample to 16kHz if needed + if sample_rate != 16000: + resampler = torchaudio.transforms.Resample(sample_rate, 16000) + waveform = resampler(waveform) + sample_rate = 16000 + + return waveform, sample_rate + +def generate_subsegments(audio_duration): + segment_duration = 1.5 + shift = segment_duration / 2.5 # 0.6 seconds + + subsegments = [] + start = 0.0 + + while start + segment_duration <= audio_duration: + subsegments.append((start, start + segment_duration)) + start += shift + + # Add final segment if needed + if start < audio_duration: + end = min(audio_duration, start + segment_duration) + start = max(0, end - segment_duration) + subsegments.append((start, end)) + + return subsegments + +def main(audio_path): + # Load and convert audio + print(f"\n1. Loading audio from: {audio_path}") + waveform, sample_rate = load_and_convert_audio(audio_path) + duration = waveform.shape[1] / sample_rate + print(f" Duration: {duration:.2f} seconds") + print(f" Sample rate: {sample_rate} Hz") + + # Generate subsegments + print("\n2. Generating subsegments...") + subsegments = generate_subsegments(duration) + print(f" Generated {len(subsegments)} subsegments") + + # Limit to 16 subsegments for batch processing + if len(subsegments) > 16: + subsegments = subsegments[:16] + print(f" Using first 16 subsegments for testing") + + # Extract features for each subsegment + print("\n3. Extracting Fbank features...") + features = [] + + for start_sec, end_sec in subsegments: + # Extract subsegment + start_sample = int(start_sec * sample_rate) + end_sample = int(end_sec * sample_rate) + + if start_sample >= waveform.shape[1]: + # Empty subsegment + features.append(np.zeros((150, 80), dtype=np.float32)) + continue + + segment_waveform = waveform[:, start_sample:end_sample] + + # Ensure minimum length (400 samples = 25ms at 16kHz) + if segment_waveform.shape[1] < 400: + padded = torch.zeros(1, 400) + padded[:, :segment_waveform.shape[1]] = segment_waveform + segment_waveform = padded + + # Extract features + segment_features = extract_fbank_features(segment_waveform, sample_rate) + + # Pad or crop to 150 frames + if segment_features.shape[0] < 150: + padded = np.zeros((150, 80), dtype=np.float32) + padded[:segment_features.shape[0]] = segment_features + segment_features = padded + elif segment_features.shape[0] > 150: + # Center crop + start = (segment_features.shape[0] - 150) // 2 + segment_features = segment_features[start:start + 150] + + features.append(segment_features) + + # Pad to batch size 16 if needed + while len(features) < 16: + features.append(np.zeros((150, 80), dtype=np.float32)) + + features_batch = np.array(features[:16], dtype=np.float32) # Shape: (16, 150, 80) + print(f" Feature shape: {features_batch.shape}") + + # Load CoreML model + print("\n4. Loading CoreML model...") + BATCH_SIZE = 16 + coreml_model_path = Path(f"models/camplusplus_batch{BATCH_SIZE}.mlpackage") + coreml_model = ct.models.MLModel(str(coreml_model_path)) + print(f" Loaded: {coreml_model_path}") + + # Run CoreML inference + print("\n5. Running CoreML inference...") + coreml_output = coreml_model.predict({'input_features': features_batch}) + coreml_embeddings = coreml_output['embeddings'] + print(f" CoreML embeddings shape: {coreml_embeddings.shape}") + + # Load reference PyTorch model + print("\n6. Loading reference PyTorch model...") + device = torch.device("mps" if torch.backends.mps.is_available() else "cpu") + print(f" Using device: {device}") + + reference_model = CAMPPlus(feat_dim=80, embedding_size=192) + + # Load weights + weights_path = "models/speech_campplus_sv_zh_en_16k-common_advanced/campplus_cn_en_common.pt" + state_dict = torch.load(weights_path, map_location='cpu', weights_only=True) + reference_model.load_state_dict(state_dict) + print(f" Loaded weights from: {weights_path}") + + reference_model.eval() + reference_model.to(device) + + # Run reference model inference + print("\n7. Running reference PyTorch inference...") + features_torch = torch.from_numpy(features_batch).to(device) + + with torch.no_grad(): + reference_embeddings = reference_model(features_torch).cpu().numpy() + + print(f" Reference embeddings shape: {reference_embeddings.shape}") + + # Compare embeddings + print("\n8. Comparing embeddings...") + print("-" * 40) + + cosine_similarities = [] + l2_distances = [] + + # Only compare actual subsegments (not padding) + num_actual = min(len(subsegments), 16) + + for i in range(num_actual): + # Normalize embeddings + coreml_norm = coreml_embeddings[i] / (np.linalg.norm(coreml_embeddings[i]) + 1e-8) + ref_norm = reference_embeddings[i] / (np.linalg.norm(reference_embeddings[i]) + 1e-8) + + # Cosine similarity + cosine_sim = np.dot(coreml_norm, ref_norm) + cosine_similarities.append(cosine_sim) + + # L2 distance + l2_dist = np.linalg.norm(coreml_embeddings[i] - reference_embeddings[i]) + l2_distances.append(l2_dist) + + print(f" Subsegment {i+1:2d}: Cosine Sim = {cosine_sim:.6f}, L2 Dist = {l2_dist:.6f}") + + print("-" * 40) + + # Summary statistics + avg_cosine = np.mean(cosine_similarities) + min_cosine = np.min(cosine_similarities) + max_cosine = np.max(cosine_similarities) + std_cosine = np.std(cosine_similarities) + + avg_l2 = np.mean(l2_distances) + max_l2 = np.max(l2_distances) + + print("\n9. Summary Statistics:") + print(f" Cosine Similarity:") + print(f" Average: {avg_cosine:.6f}") + print(f" Min: {min_cosine:.6f}") + print(f" Max: {max_cosine:.6f}") + print(f" Std Dev: {std_cosine:.6f}") + print(f" L2 Distance:") + print(f" Average: {avg_l2:.6f}") + print(f" Max: {max_l2:.6f}") + + # Validation result + print("\n10. Validation Result:") + if avg_cosine > 0.999: + print(" ✅ Near-perfect match between CoreML and PyTorch models") + elif avg_cosine > 0.99: + print(" ✅ Very high similarity between models") + elif avg_cosine > 0.95: + print(" ⚠️ Good similarity but some differences detected") + else: + print(" ❌ FAIL: Significant differences between models") + + # Speed comparison + print("\n11. Speed Comparison:") + print("-" * 40) + + import time + + # Warm up both models + print(" Warming up models...") + for _ in range(5): + _ = coreml_model.predict({'input_features': features_batch}) + with torch.no_grad(): + _ = reference_model(features_torch) + + # Benchmark CoreML + print(" Benchmarking CoreML...") + num_runs = 20 + coreml_times = [] + for _ in range(num_runs): + start = time.perf_counter() + _ = coreml_model.predict({'input_features': features_batch}) + coreml_times.append(time.perf_counter() - start) + + # Benchmark PyTorch on MPS + print(f" Benchmarking PyTorch ({device})...") + torch_times = [] + for _ in range(num_runs): + start = time.perf_counter() + with torch.no_grad(): + _ = reference_model(features_torch) + if device.type == 'mps': + torch.mps.synchronize() # Ensure MPS operations complete + torch_times.append(time.perf_counter() - start) + + # Calculate statistics + coreml_avg = np.mean(coreml_times) * 1000 # Convert to ms + coreml_std = np.std(coreml_times) * 1000 + torch_avg = np.mean(torch_times) * 1000 + torch_std = np.std(torch_times) * 1000 + + speedup = torch_avg / coreml_avg + + print(f"\n CoreML (Neural Engine):") + print(f" Average: {coreml_avg:.2f} ms ± {coreml_std:.2f} ms") + print(f" PyTorch ({device}):") + print(f" Average: {torch_avg:.2f} ms ± {torch_std:.2f} ms") + print(f"\n Speedup: {speedup:.2f}x" + (" faster" if speedup > 1 else " slower") + " with CoreML") + + +if __name__ == "__main__": + if len(sys.argv) != 2: + print("Usage: python test.py ") + print("Example: python test.py sample.wav") + sys.exit(1) + + audio_file = sys.argv[1] + if not Path(audio_file).exists(): + print(f"Error: Audio file '{audio_file}' not found") + sys.exit(1) + + main(audio_file) \ No newline at end of file diff --git a/models/emb/cam++/coreml/uv.lock b/models/emb/cam++/coreml/uv.lock new file mode 100644 index 0000000..82a75f0 --- /dev/null +++ b/models/emb/cam++/coreml/uv.lock @@ -0,0 +1,563 @@ +version = 1 +revision = 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