From bb719cf2376f934bda5a0fabbff896c0956f13aa Mon Sep 17 00:00:00 2001
From: hamzaq2000 <mhamzaqayyum@icloud.com>
Date: Sun, 21 Sep 2025 17:43:43 -0700
Subject: [PATCH 1/4] CAM++ CoreML conversion & test scripts

---
 models/emb/cam++/coreml/.gitignore            |   1 +
 models/emb/cam++/coreml/README.md             |  33 +
 models/emb/cam++/coreml/audio/jfk.mp3         | Bin 0 -> 76447 bytes
 models/emb/cam++/coreml/camplusplus.py        | 210 +++++++
 models/emb/cam++/coreml/camplusplus_coreml.py | 221 +++++++
 models/emb/cam++/coreml/convert.py            | 170 ++++++
 models/emb/cam++/coreml/pyproject.toml        |  12 +
 models/emb/cam++/coreml/test.py               | 306 ++++++++++
 models/emb/cam++/coreml/uv.lock               | 563 ++++++++++++++++++
 9 files changed, 1516 insertions(+)
 create mode 100644 models/emb/cam++/coreml/.gitignore
 create mode 100644 models/emb/cam++/coreml/README.md
 create mode 100644 models/emb/cam++/coreml/audio/jfk.mp3
 create mode 100644 models/emb/cam++/coreml/camplusplus.py
 create mode 100644 models/emb/cam++/coreml/camplusplus_coreml.py
 create mode 100644 models/emb/cam++/coreml/convert.py
 create mode 100644 models/emb/cam++/coreml/pyproject.toml
 create mode 100644 models/emb/cam++/coreml/test.py
 create mode 100644 models/emb/cam++/coreml/uv.lock

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
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diff --git a/models/emb/cam++/coreml/camplusplus.py b/models/emb/cam++/coreml/camplusplus.py
new file mode 100644
index 0000000..3869d15
--- /dev/null
+++ b/models/emb/cam++/coreml/camplusplus.py
@@ -0,0 +1,210 @@
+# https://github.com/modelscope/3D-Speaker/tree/main/speakerlab/models/campplus
+
+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..893d2b7
--- /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 = "./models/camplusplus_batch16.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..4dbd526
--- /dev/null
+++ b/models/emb/cam++/coreml/test.py
@@ -0,0 +1,306 @@
+#!/usr/bin/env python3
+"""
+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):
+    # 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...")
+    coreml_model_path = Path("models/camplusplus_batch16.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 <audio_file>")
+        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
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new file mode 100644
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From 00efbf04964cc790b971cb0bcc5e244f6a186511 Mon Sep 17 00:00:00 2001
From: hamzaq2000 <mhamzaqayyum@icloud.com>
Date: Mon, 22 Sep 2025 13:14:54 -0700
Subject: [PATCH 2/4] Use BATCH_SIZE variable in model file name

---
 models/emb/cam++/coreml/convert.py | 2 +-
 models/emb/cam++/coreml/test.py    | 3 ++-
 2 files changed, 3 insertions(+), 2 deletions(-)

diff --git a/models/emb/cam++/coreml/convert.py b/models/emb/cam++/coreml/convert.py
index 893d2b7..938d265 100644
--- a/models/emb/cam++/coreml/convert.py
+++ b/models/emb/cam++/coreml/convert.py
@@ -104,7 +104,7 @@ def convert_campplus_to_coreml():
     coreml_model.output_description["embeddings"] = f"Speaker embeddings: ({BATCH_SIZE}, {EMBEDDING_DIM})"
 
     # Save the model
-    output_path = "./models/camplusplus_batch16.mlpackage"
+    output_path = f"./models/camplusplus_batch{BATCH_SIZE}.mlpackage"
     coreml_model.save(output_path)
     print(f"\n✓ Saved CoreML model to: {output_path}")
 
diff --git a/models/emb/cam++/coreml/test.py b/models/emb/cam++/coreml/test.py
index 4dbd526..561c3e7 100644
--- a/models/emb/cam++/coreml/test.py
+++ b/models/emb/cam++/coreml/test.py
@@ -152,7 +152,8 @@ def main(audio_path):
 
     # Load CoreML model
     print("\n4. Loading CoreML model...")
-    coreml_model_path = Path("models/camplusplus_batch16.mlpackage")
+    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}")
 

From 1f138006ede9b5728bb9437ebccc4118e258c87f Mon Sep 17 00:00:00 2001
From: hamzaq2000 <mhamzaqayyum@icloud.com>
Date: Tue, 23 Sep 2025 08:31:05 -0700
Subject: [PATCH 3/4] added CAM++ 3D-Speaker commit hash + link Senko C++
 fbank_extractor

---
 models/emb/cam++/coreml/camplusplus.py | 23 ++++++++++++-----------
 models/emb/cam++/coreml/test.py        |  5 ++++-
 2 files changed, 16 insertions(+), 12 deletions(-)

diff --git a/models/emb/cam++/coreml/camplusplus.py b/models/emb/cam++/coreml/camplusplus.py
index 3869d15..03db33b 100644
--- a/models/emb/cam++/coreml/camplusplus.py
+++ b/models/emb/cam++/coreml/camplusplus.py
@@ -1,4 +1,5 @@
 # https://github.com/modelscope/3D-Speaker/tree/main/speakerlab/models/campplus
+# Commit hash f5764ed3a89da77330f7dcc9801f58992c3e3c74
 
 import torch
 import torch.nn as nn
@@ -15,15 +16,15 @@ def __init__(self, feat_dim=80, embedding_size=512, growth_rate=32, bn_size=4, i
        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
 
@@ -31,7 +32,7 @@ def __init__(self, feat_dim=80, embedding_size=512, growth_rate=32, bn_size=4, i
        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))
 
@@ -116,9 +117,9 @@ def __init__(self, in_channels, out_channels, kernel_size, stride=1, padding=0,
         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))
@@ -131,7 +132,7 @@ 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.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()
@@ -151,17 +152,17 @@ def __init__(self, in_channels, out_channels, bn_channels, kernel_size, stride=1
         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)
 
diff --git a/models/emb/cam++/coreml/test.py b/models/emb/cam++/coreml/test.py
index 561c3e7..30977ab 100644
--- a/models/emb/cam++/coreml/test.py
+++ b/models/emb/cam++/coreml/test.py
@@ -1,4 +1,3 @@
-#!/usr/bin/env python3
 """
 Test script to validate CoreML CAM++ embeddings model against reference PyTorch implementation.
     1. Takes an audio file as input
@@ -21,6 +20,10 @@
 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)

From cf295a54f4f146cb1db24e09562029c63347ce66 Mon Sep 17 00:00:00 2001
From: hamzaq2000 <mhamzaqayyum@icloud.com>
Date: Tue, 23 Sep 2025 12:06:57 -0700
Subject: [PATCH 4/4] add 3D-Speaker license text

---
 models/emb/cam++/THIRD_PARTY_LICENSES | 183 ++++++++++++++++++++++++++
 1 file changed, 183 insertions(+)
 create mode 100644 models/emb/cam++/THIRD_PARTY_LICENSES

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. Definitions.
+
+      "License" shall mean the terms and conditions for use, reproduction,
+      and distribution as defined by Sections 1 through 9 of this document.
+
+      "Licensor" shall mean the copyright owner or entity authorized by
+      the copyright owner that is granting the License.
+
+      "Legal Entity" shall mean the union of the acting entity and all
+      other entities that control, are controlled by, or are under common
+      control with that entity. For the purposes of this definition,
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