分角色语音识别支持更多的模型

funasr/bin/asr_inference_launch.py

@@ -51,10 +51,10 @@ from funasr.utils.vad_utils import slice_padding_fbank
 from funasr.utils.speaker_utils import (check_audio_list,
                                         sv_preprocess,
                                         sv_chunk,
-                                        CAMPPlus,
                                         extract_feature,
                                         postprocess,
-                                        distribute_spk, ERes2Net)
+                                        distribute_spk)
+import funasr.modules.cnn as sv_module
 from funasr.build_utils.build_model_from_file import build_model_from_file
 from funasr.utils.cluster_backend import ClusterBackend
 from funasr.utils.modelscope_utils import get_cache_dir
@@ -818,11 +818,15 @@ def inference_paraformer_vad_speaker(
         format="%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s",
     )
 
-    sv_model_file = asr_model_file.replace("model.pb", "campplus_cn_common.bin")
-    if not os.path.exists(sv_model_file):
-        sv_model_file = asr_model_file.replace("model.pb", "pretrained_eres2net_aug.ckpt")
-        if not os.path.exists(sv_model_file):
-            raise FileNotFoundError("sv_model_file not found: {}".format(sv_model_file))
+    sv_model_config_path = asr_model_file.replace("model.pb", "sv_model_config.yaml")
+    if not os.path.exists(sv_model_config_path):
+        sv_model_config = {'sv_model_class': 'CAMPPlus','sv_model_file': 'campplus_cn_common.bin', 'models_config': {}}
+    else:
+        with open(sv_model_config_path, 'r') as f:
+            sv_model_config = yaml.load(f, Loader=yaml.FullLoader)
+    if sv_model_config['models_config'] is None:
+        sv_model_config['models_config'] = {}
+    sv_model_file = asr_model_file.replace("model.pb", sv_model_config['sv_model_file'])
 
     if param_dict is not None:
         hotword_list_or_file = param_dict.get('hotword')
@@ -949,14 +953,11 @@ def inference_paraformer_vad_speaker(
             ##################################
             # load sv model
             sv_model_dict = torch.load(sv_model_file)
-            print(f'load sv model params: {sv_model_file}')
-            if os.path.basename(sv_model_file) == "campplus_cn_common.bin":
-                sv_model = CAMPPlus()
-            else:
-                sv_model = ERes2Net()
+            sv_model = getattr(sv_module, sv_model_config['sv_model_class'])(**sv_model_config['models_config'])
             if ngpu > 0:
                 sv_model.cuda()
             sv_model.load_state_dict(sv_model_dict)
+            print(f'load sv model params: {sv_model_file}')
             sv_model.eval()
             cb_model = ClusterBackend()
             vad_segments = []

funasr/models/pooling/pooling_layers.py

@@ -0,0 +1,108 @@
+# Copyright 3D-Speaker (https://github.com/alibaba-damo-academy/3D-Speaker). All Rights Reserved.
+# Licensed under the Apache License, Version 2.0 (http://www.apache.org/licenses/LICENSE-2.0)
+
+""" This implementation is adapted from https://github.com/wenet-e2e/wespeaker."""
+
+import torch
+import torch.nn as nn
+
+
+class TAP(nn.Module):
+    """
+    Temporal average pooling, only first-order mean is considered
+    """
+
+    def __init__(self, **kwargs):
+        super(TAP, self).__init__()
+
+    def forward(self, x):
+        pooling_mean = x.mean(dim=-1)
+        # To be compatable with 2D input
+        pooling_mean = pooling_mean.flatten(start_dim=1)
+        return pooling_mean
+
+
+class TSDP(nn.Module):
+    """
+    Temporal standard deviation pooling, only second-order std is considered
+    """
+
+    def __init__(self, **kwargs):
+        super(TSDP, self).__init__()
+
+    def forward(self, x):
+        # The last dimension is the temporal axis
+        pooling_std = torch.sqrt(torch.var(x, dim=-1) + 1e-8)
+        pooling_std = pooling_std.flatten(start_dim=1)
+        return pooling_std
+
+
+class TSTP(nn.Module):
+    """
+    Temporal statistics pooling, concatenate mean and std, which is used in
+    x-vector
+    Comment: simple concatenation can not make full use of both statistics
+    """
+
+    def __init__(self, **kwargs):
+        super(TSTP, self).__init__()
+
+    def forward(self, x):
+        # The last dimension is the temporal axis
+        pooling_mean = x.mean(dim=-1)
+        pooling_std = torch.sqrt(torch.var(x, dim=-1) + 1e-8)
+        pooling_mean = pooling_mean.flatten(start_dim=1)
+        pooling_std = pooling_std.flatten(start_dim=1)
+
+        stats = torch.cat((pooling_mean, pooling_std), 1)
+        return stats
+
+
+class ASTP(nn.Module):
+    """ Attentive statistics pooling: Channel- and context-dependent
+        statistics pooling, first used in ECAPA_TDNN.
+    """
+
+    def __init__(self, in_dim, bottleneck_dim=128, global_context_att=False):
+        super(ASTP, self).__init__()
+        self.global_context_att = global_context_att
+
+        # Use Conv1d with stride == 1 rather than Linear, then we don't
+        # need to transpose inputs.
+        if global_context_att:
+            self.linear1 = nn.Conv1d(
+                in_dim * 3, bottleneck_dim,
+                kernel_size=1)  # equals W and b in the paper
+        else:
+            self.linear1 = nn.Conv1d(
+                in_dim, bottleneck_dim,
+                kernel_size=1)  # equals W and b in the paper
+        self.linear2 = nn.Conv1d(bottleneck_dim, in_dim,
+                                 kernel_size=1)  # equals V and k in the paper
+
+    def forward(self, x):
+        """
+        x: a 3-dimensional tensor in tdnn-based architecture (B,F,T)
+            or a 4-dimensional tensor in resnet architecture (B,C,F,T)
+            0-dim: batch-dimension, last-dim: time-dimension (frame-dimension)
+        """
+        if len(x.shape) == 4:
+            x = x.reshape(x.shape[0], x.shape[1] * x.shape[2], x.shape[3])
+        assert len(x.shape) == 3
+
+        if self.global_context_att:
+            context_mean = torch.mean(x, dim=-1, keepdim=True).expand_as(x)
+            context_std = torch.sqrt(
+                torch.var(x, dim=-1, keepdim=True) + 1e-10).expand_as(x)
+            x_in = torch.cat((x, context_mean, context_std), dim=1)
+        else:
+            x_in = x
+
+        # DON'T use ReLU here! ReLU may be hard to converge.
+        alpha = torch.tanh(
+            self.linear1(x_in))  # alpha = F.relu(self.linear1(x_in))
+        alpha = torch.softmax(self.linear2(alpha), dim=2)
+        mean = torch.sum(alpha * x, dim=2)
+        var = torch.sum(alpha * (x ** 2), dim=2) - mean ** 2
+        std = torch.sqrt(var.clamp(min=1e-10))
+        return torch.cat([mean, std], dim=1)

funasr/modules/cnn/DTDNN.py

@@ -0,0 +1,124 @@
+# Copyright 3D-Speaker (https://github.com/alibaba-damo-academy/3D-Speaker). All Rights Reserved.
+# Licensed under the Apache License, Version 2.0 (http://www.apache.org/licenses/LICENSE-2.0)
+
+from collections import OrderedDict
+
+import torch.nn.functional as F
+from torch import nn
+
+from funasr.modules.cnn.layers import DenseLayer, StatsPool, TDNNLayer, CAMDenseTDNNBlock, TransitLayer, \
+    BasicResBlock, get_nonlinear
+
+
+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 = self._make_layer(block, m_channels, num_blocks[0], stride=2)
+        self.layer2 = self._make_layer(block, m_channels, num_blocks[0], stride=2)
+
+        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 _make_layer(self, block, planes, num_blocks, stride):
+        strides = [stride] + [1] * (num_blocks - 1)
+        layers = []
+        for stride in strides:
+            layers.append(block(self.in_planes, planes, stride))
+            self.in_planes = planes * block.expansion
+        return nn.Sequential(*layers)
+
+    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 CAMPPlus(nn.Module):
+    def __init__(self,
+                 feat_dim=80,
+                 embedding_size=192,
+                 growth_rate=32,
+                 bn_size=4,
+                 init_channels=128,
+                 config_str='batchnorm-relu',
+                 memory_efficient=True,
+                 output_level='segment'):
+        super(CAMPPlus, self).__init__()
+
+        self.head = FCM(feat_dim=feat_dim)
+        channels = self.head.out_channels
+        self.output_level = output_level
+
+        self.xvector = nn.Sequential(
+            OrderedDict([
+
+                ('tdnn',
+                 TDNNLayer(channels,
+                           init_channels,
+                           5,
+                           stride=2,
+                           dilation=1,
+                           padding=-1,
+                           config_str=config_str)),
+            ]))
+        channels = init_channels
+        for i, (num_layers, kernel_size,
+                dilation) in enumerate(zip((12, 24, 16), (3, 3, 3), (1, 2, 2))):
+            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,
+                                      config_str=config_str,
+                                      memory_efficient=memory_efficient)
+            self.xvector.add_module('block%d' % (i + 1), block)
+            channels = channels + num_layers * growth_rate
+            self.xvector.add_module(
+                'transit%d' % (i + 1),
+                TransitLayer(channels,
+                             channels // 2,
+                             bias=False,
+                             config_str=config_str))
+            channels //= 2
+
+        self.xvector.add_module(
+            'out_nonlinear', get_nonlinear(config_str, channels))
+
+        if self.output_level == 'segment':
+            self.xvector.add_module('stats', StatsPool())
+            self.xvector.add_module(
+                'dense',
+                DenseLayer(
+                    channels * 2, embedding_size, config_str='batchnorm_'))
+        else:
+            assert self.output_level == 'frame', '`output_level` should be set to \'segment\' or \'frame\'. '
+
+        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)
+        if self.output_level == 'frame':
+            x = x.transpose(1, 2)
+        return x

funasr/modules/cnn/ResNet.py

@@ -0,0 +1,420 @@
+# Copyright 3D-Speaker (https://github.com/alibaba-damo-academy/3D-Speaker). All Rights Reserved.
+# Licensed under the Apache License, Version 2.0 (http://www.apache.org/licenses/LICENSE-2.0)
+
+""" Res2Net implementation is adapted from https://github.com/wenet-e2e/wespeaker.
+    ERes2Net incorporates both local and global feature fusion techniques to improve the performance. 
+    The local feature fusion (LFF) fuses the features within one single residual block to extract the local signal.
+    The global feature fusion (GFF) takes acoustic features of different scales as input to aggregate global signal.
+    ERes2Net-Large is an upgraded version of ERes2Net that uses a larger number of parameters to achieve better 
+    recognition performance. Parameters expansion, baseWidth, and scale can be modified to obtain optimal performance.
+"""
+
+import math
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+import funasr.models.pooling.pooling_layers as pooling_layers
+from funasr.modules.cnn.fusion import AFF
+
+
+class ReLU(nn.Hardtanh):
+
+    def __init__(self, inplace=False):
+        super(ReLU, self).__init__(0, 20, inplace)
+
+    def __repr__(self):
+        inplace_str = 'inplace' if self.inplace else ''
+        return self.__class__.__name__ + ' (' \
+            + inplace_str + ')'
+
+
+def conv1x1(in_planes, out_planes, stride=1):
+    "1x1 convolution without padding"
+    return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride,
+                     padding=0, bias=False)
+
+
+def conv3x3(in_planes, out_planes, stride=1):
+    "3x3 convolution with padding"
+    return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
+                     padding=1, bias=False)
+
+
+class BasicBlockERes2Net(nn.Module):
+    expansion = 2
+
+    def __init__(self, in_planes, planes, stride=1, baseWidth=32, scale=2):
+        super(BasicBlockERes2Net, self).__init__()
+        width = int(math.floor(planes * (baseWidth / 64.0)))
+        self.conv1 = conv1x1(in_planes, width * scale, stride)
+        self.bn1 = nn.BatchNorm2d(width * scale)
+        self.nums = scale
+
+        convs = []
+        bns = []
+        for i in range(self.nums):
+            convs.append(conv3x3(width, width))
+            bns.append(nn.BatchNorm2d(width))
+        self.convs = nn.ModuleList(convs)
+        self.bns = nn.ModuleList(bns)
+        self.relu = ReLU(inplace=True)
+
+        self.conv3 = conv1x1(width * scale, planes * self.expansion)
+        self.bn3 = nn.BatchNorm2d(planes * self.expansion)
+        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,
+                          bias=False),
+                nn.BatchNorm2d(self.expansion * planes))
+        self.stride = stride
+        self.width = width
+        self.scale = scale
+
+    def forward(self, x):
+        residual = x
+
+        out = self.conv1(x)
+        out = self.bn1(out)
+        out = self.relu(out)
+        spx = torch.split(out, self.width, 1)
+        for i in range(self.nums):
+            if i == 0:
+                sp = spx[i]
+            else:
+                sp = sp + spx[i]
+            sp = self.convs[i](sp)
+            sp = self.relu(self.bns[i](sp))
+            if i == 0:
+                out = sp
+            else:
+                out = torch.cat((out, sp), 1)
+
+        out = self.conv3(out)
+        out = self.bn3(out)
+
+        residual = self.shortcut(x)
+        out += residual
+        out = self.relu(out)
+
+        return out
+
+
+class BasicBlockERes2Net_diff_AFF(nn.Module):
+    expansion = 2
+
+    def __init__(self, in_planes, planes, stride=1, baseWidth=32, scale=2):
+        super(BasicBlockERes2Net_diff_AFF, self).__init__()
+        width = int(math.floor(planes * (baseWidth / 64.0)))
+        self.conv1 = conv1x1(in_planes, width * scale, stride)
+        self.bn1 = nn.BatchNorm2d(width * scale)
+        self.nums = scale
+
+        convs = []
+        fuse_models = []
+        bns = []
+        for i in range(self.nums):
+            convs.append(conv3x3(width, width))
+            bns.append(nn.BatchNorm2d(width))
+        for j in range(self.nums - 1):
+            fuse_models.append(AFF(channels=width))
+
+        self.convs = nn.ModuleList(convs)
+        self.bns = nn.ModuleList(bns)
+        self.fuse_models = nn.ModuleList(fuse_models)
+        self.relu = ReLU(inplace=True)
+
+        self.conv3 = conv1x1(width * scale, planes * self.expansion)
+        self.bn3 = nn.BatchNorm2d(planes * self.expansion)
+        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,
+                          bias=False),
+                nn.BatchNorm2d(self.expansion * planes))
+        self.stride = stride
+        self.width = width
+        self.scale = scale
+
+    def forward(self, x):
+        residual = x
+
+        out = self.conv1(x)
+        out = self.bn1(out)
+        out = self.relu(out)
+        spx = torch.split(out, self.width, 1)
+        for i in range(self.nums):
+            if i == 0:
+                sp = spx[i]
+            else:
+                sp = self.fuse_models[i - 1](sp, spx[i])
+
+            sp = self.convs[i](sp)
+            sp = self.relu(self.bns[i](sp))
+            if i == 0:
+                out = sp
+            else:
+                out = torch.cat((out, sp), 1)
+
+        out = self.conv3(out)
+        out = self.bn3(out)
+
+        residual = self.shortcut(x)
+        out += residual
+        out = self.relu(out)
+
+        return out
+
+
+class ERes2Net(nn.Module):
+    def __init__(self,
+                 block=BasicBlockERes2Net,
+                 block_fuse=BasicBlockERes2Net_diff_AFF,
+                 num_blocks=[3, 4, 6, 3],
+                 m_channels=32,
+                 feat_dim=80,
+                 embedding_size=192,
+                 pooling_func='TSTP',
+                 two_emb_layer=False):
+        super(ERes2Net, self).__init__()
+        self.in_planes = m_channels
+        self.feat_dim = feat_dim
+        self.embedding_size = embedding_size
+        self.stats_dim = int(feat_dim / 8) * m_channels * 8
+        self.two_emb_layer = two_emb_layer
+
+        self.conv1 = nn.Conv2d(1,
+                               m_channels,
+                               kernel_size=3,
+                               stride=1,
+                               padding=1,
+                               bias=False)
+        self.bn1 = nn.BatchNorm2d(m_channels)
+        self.layer1 = self._make_layer(block,
+                                       m_channels,
+                                       num_blocks[0],
+                                       stride=1)
+        self.layer2 = self._make_layer(block,
+                                       m_channels * 2,
+                                       num_blocks[1],
+                                       stride=2)
+        self.layer3 = self._make_layer(block_fuse,
+                                       m_channels * 4,
+                                       num_blocks[2],
+                                       stride=2)
+        self.layer4 = self._make_layer(block_fuse,
+                                       m_channels * 8,
+                                       num_blocks[3],
+                                       stride=2)
+
+        # Downsampling module for each layer
+        self.layer1_downsample = nn.Conv2d(m_channels * 2, m_channels * 4, kernel_size=3, stride=2, padding=1,
+                                           bias=False)
+        self.layer2_downsample = nn.Conv2d(m_channels * 4, m_channels * 8, kernel_size=3, padding=1, stride=2,
+                                           bias=False)
+        self.layer3_downsample = nn.Conv2d(m_channels * 8, m_channels * 16, kernel_size=3, padding=1, stride=2,
+                                           bias=False)
+
+        # Bottom-up fusion module
+        self.fuse_mode12 = AFF(channels=m_channels * 4)
+        self.fuse_mode123 = AFF(channels=m_channels * 8)
+        self.fuse_mode1234 = AFF(channels=m_channels * 16)
+
+        self.n_stats = 1 if pooling_func == 'TAP' or pooling_func == "TSDP" else 2
+        self.pool = getattr(pooling_layers, pooling_func)(
+            in_dim=self.stats_dim * block.expansion)
+        self.seg_1 = nn.Linear(self.stats_dim * block.expansion * self.n_stats,
+                               embedding_size)
+        if self.two_emb_layer:
+            self.seg_bn_1 = nn.BatchNorm1d(embedding_size, affine=False)
+            self.seg_2 = nn.Linear(embedding_size, embedding_size)
+        else:
+            self.seg_bn_1 = nn.Identity()
+            self.seg_2 = nn.Identity()
+
+    def _make_layer(self, block, planes, num_blocks, stride):
+        strides = [stride] + [1] * (num_blocks - 1)
+        layers = []
+        for stride in strides:
+            layers.append(block(self.in_planes, planes, stride))
+            self.in_planes = planes * block.expansion
+        return nn.Sequential(*layers)
+
+    def forward(self, x):
+        x = x.permute(0, 2, 1)  # (B,T,F) => (B,F,T)
+        x = x.unsqueeze_(1)
+        out = F.relu(self.bn1(self.conv1(x)))
+        out1 = self.layer1(out)
+        out2 = self.layer2(out1)
+        out1_downsample = self.layer1_downsample(out1)
+        fuse_out12 = self.fuse_mode12(out2, out1_downsample)
+        out3 = self.layer3(out2)
+        fuse_out12_downsample = self.layer2_downsample(fuse_out12)
+        fuse_out123 = self.fuse_mode123(out3, fuse_out12_downsample)
+        out4 = self.layer4(out3)
+        fuse_out123_downsample = self.layer3_downsample(fuse_out123)
+        fuse_out1234 = self.fuse_mode1234(out4, fuse_out123_downsample)
+        stats = self.pool(fuse_out1234)
+
+        embed_a = self.seg_1(stats)
+        if self.two_emb_layer:
+            out = F.relu(embed_a)
+            out = self.seg_bn_1(out)
+            embed_b = self.seg_2(out)
+            return embed_b
+        else:
+            return embed_a
+
+
+class BasicBlockRes2Net(nn.Module):
+    expansion = 2
+
+    def __init__(self, in_planes, planes, stride=1, baseWidth=32, scale=2):
+        super(BasicBlockRes2Net, self).__init__()
+        width = int(math.floor(planes * (baseWidth / 64.0)))
+        self.conv1 = conv1x1(in_planes, width * scale, stride)
+        self.bn1 = nn.BatchNorm2d(width * scale)
+        self.nums = scale - 1
+        convs = []
+        bns = []
+        for i in range(self.nums):
+            convs.append(conv3x3(width, width))
+            bns.append(nn.BatchNorm2d(width))
+        self.convs = nn.ModuleList(convs)
+        self.bns = nn.ModuleList(bns)
+        self.relu = ReLU(inplace=True)
+
+        self.conv3 = conv1x1(width * scale, planes * self.expansion)
+        self.bn3 = nn.BatchNorm2d(planes * self.expansion)
+        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,
+                          bias=False),
+                nn.BatchNorm2d(self.expansion * planes))
+        self.stride = stride
+        self.width = width
+        self.scale = scale
+
+    def forward(self, x):
+        residual = x
+
+        out = self.conv1(x)
+        out = self.bn1(out)
+        out = self.relu(out)
+        spx = torch.split(out, self.width, 1)
+        for i in range(self.nums):
+            if i == 0:
+                sp = spx[i]
+            else:
+                sp = sp + spx[i]
+            sp = self.convs[i](sp)
+            sp = self.relu(self.bns[i](sp))
+            if i == 0:
+                out = sp
+            else:
+                out = torch.cat((out, sp), 1)
+
+        out = torch.cat((out, spx[self.nums]), 1)
+
+        out = self.conv3(out)
+        out = self.bn3(out)
+
+        residual = self.shortcut(x)
+        out += residual
+        out = self.relu(out)
+
+        return out
+
+
+class Res2Net(nn.Module):
+    def __init__(self,
+                 block=BasicBlockRes2Net,
+                 num_blocks=[3, 4, 6, 3],
+                 m_channels=32,
+                 feat_dim=80,
+                 embedding_size=192,
+                 pooling_func='TSTP',
+                 two_emb_layer=False):
+        super(Res2Net, self).__init__()
+        self.in_planes = m_channels
+        self.feat_dim = feat_dim
+        self.embedding_size = embedding_size
+        self.stats_dim = int(feat_dim / 8) * m_channels * 8
+        self.two_emb_layer = two_emb_layer
+
+        self.conv1 = nn.Conv2d(1,
+                               m_channels,
+                               kernel_size=3,
+                               stride=1,
+                               padding=1,
+                               bias=False)
+        self.bn1 = nn.BatchNorm2d(m_channels)
+        self.layer1 = self._make_layer(block,
+                                       m_channels,
+                                       num_blocks[0],
+                                       stride=1)
+        self.layer2 = self._make_layer(block,
+                                       m_channels * 2,
+                                       num_blocks[1],
+                                       stride=2)
+        self.layer3 = self._make_layer(block,
+                                       m_channels * 4,
+                                       num_blocks[2],
+                                       stride=2)
+        self.layer4 = self._make_layer(block,
+                                       m_channels * 8,
+                                       num_blocks[3],
+                                       stride=2)
+
+        self.n_stats = 1 if pooling_func == 'TAP' or pooling_func == "TSDP" else 2
+        self.pool = getattr(pooling_layers, pooling_func)(
+            in_dim=self.stats_dim * block.expansion)
+        self.seg_1 = nn.Linear(self.stats_dim * block.expansion * self.n_stats,
+                               embedding_size)
+        if self.two_emb_layer:
+            self.seg_bn_1 = nn.BatchNorm1d(embedding_size, affine=False)
+            self.seg_2 = nn.Linear(embedding_size, embedding_size)
+        else:
+            self.seg_bn_1 = nn.Identity()
+            self.seg_2 = nn.Identity()
+
+    def _make_layer(self, block, planes, num_blocks, stride):
+        strides = [stride] + [1] * (num_blocks - 1)
+        layers = []
+        for stride in strides:
+            layers.append(block(self.in_planes, planes, stride))
+            self.in_planes = planes * block.expansion
+        return nn.Sequential(*layers)
+
+    def forward(self, x):
+        x = x.permute(0, 2, 1)  # (B,T,F) => (B,F,T)
+
+        x = x.unsqueeze_(1)
+        out = F.relu(self.bn1(self.conv1(x)))
+        out = self.layer1(out)
+        out = self.layer2(out)
+        out = self.layer3(out)
+        out = self.layer4(out)
+
+        stats = self.pool(out)
+
+        embed_a = self.seg_1(stats)
+        if self.two_emb_layer:
+            out = F.relu(embed_a)
+            out = self.seg_bn_1(out)
+            embed_b = self.seg_2(out)
+            return embed_b
+        else:
+            return embed_a

funasr/modules/cnn/ResNet_aug.py

@@ -0,0 +1,273 @@
+# Copyright 3D-Speaker (https://github.com/alibaba-damo-academy/3D-Speaker). All Rights Reserved.
+# Licensed under the Apache License, Version 2.0 (http://www.apache.org/licenses/LICENSE-2.0)
+
+""" Res2Net implementation is adapted from https://github.com/wenet-e2e/wespeaker.
+    ERes2Net incorporates both local and global feature fusion techniques to improve the performance. 
+    The local feature fusion (LFF) fuses the features within one single residual block to extract the local signal.
+    The global feature fusion (GFF) takes acoustic features of different scales as input to aggregate global signal.
+    ERes2Net-Large is an upgraded version of ERes2Net that uses a larger number of parameters to achieve better 
+    recognition performance. Parameters expansion, baseWidth, and scale can be modified to obtain optimal performance.
+"""
+
+import math
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+import funasr.models.pooling.pooling_layers as pooling_layers
+from funasr.modules.cnn.fusion import AFF
+
+
+class ReLU(nn.Hardtanh):
+
+    def __init__(self, inplace=False):
+        super(ReLU, self).__init__(0, 20, inplace)
+
+    def __repr__(self):
+        inplace_str = 'inplace' if self.inplace else ''
+        return self.__class__.__name__ + ' (' \
+            + inplace_str + ')'
+
+
+def conv1x1(in_planes, out_planes, stride=1):
+    "1x1 convolution without padding"
+    return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride,
+                     padding=0, bias=False)
+
+
+def conv3x3(in_planes, out_planes, stride=1):
+    "3x3 convolution with padding"
+    return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
+                     padding=1, bias=False)
+
+
+class BasicBlockERes2Net(nn.Module):
+    expansion = 4
+
+    def __init__(self, in_planes, planes, stride=1, baseWidth=24, scale=3):
+        super(BasicBlockERes2Net, self).__init__()
+        width = int(math.floor(planes * (baseWidth / 64.0)))
+        self.conv1 = conv1x1(in_planes, width * scale, stride)
+        self.bn1 = nn.BatchNorm2d(width * scale)
+        self.nums = scale
+
+        convs = []
+        bns = []
+        for i in range(self.nums):
+            convs.append(conv3x3(width, width))
+            bns.append(nn.BatchNorm2d(width))
+        self.convs = nn.ModuleList(convs)
+        self.bns = nn.ModuleList(bns)
+        self.relu = ReLU(inplace=True)
+
+        self.conv3 = conv1x1(width * scale, planes * self.expansion)
+        self.bn3 = nn.BatchNorm2d(planes * self.expansion)
+        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,
+                          bias=False),
+                nn.BatchNorm2d(self.expansion * planes))
+        self.stride = stride
+        self.width = width
+        self.scale = scale
+
+    def forward(self, x):
+        residual = x
+
+        out = self.conv1(x)
+        out = self.bn1(out)
+        out = self.relu(out)
+        spx = torch.split(out, self.width, 1)
+        for i in range(self.nums):
+            if i == 0:
+                sp = spx[i]
+            else:
+                sp = sp + spx[i]
+            sp = self.convs[i](sp)
+            sp = self.relu(self.bns[i](sp))
+            if i == 0:
+                out = sp
+            else:
+                out = torch.cat((out, sp), 1)
+
+        out = self.conv3(out)
+        out = self.bn3(out)
+
+        residual = self.shortcut(x)
+        out += residual
+        out = self.relu(out)
+
+        return out
+
+
+class BasicBlockERes2Net_diff_AFF(nn.Module):
+    expansion = 4
+
+    def __init__(self, in_planes, planes, stride=1, baseWidth=24, scale=3):
+        super(BasicBlockERes2Net_diff_AFF, self).__init__()
+        width = int(math.floor(planes * (baseWidth / 64.0)))
+        self.conv1 = conv1x1(in_planes, width * scale, stride)
+        self.bn1 = nn.BatchNorm2d(width * scale)
+
+        self.nums = scale
+
+        convs = []
+        fuse_models = []
+        bns = []
+        for i in range(self.nums):
+            convs.append(conv3x3(width, width))
+            bns.append(nn.BatchNorm2d(width))
+        for j in range(self.nums - 1):
+            fuse_models.append(AFF(channels=width))
+
+        self.convs = nn.ModuleList(convs)
+        self.bns = nn.ModuleList(bns)
+        self.fuse_models = nn.ModuleList(fuse_models)
+        self.relu = ReLU(inplace=True)
+
+        self.conv3 = conv1x1(width * scale, planes * self.expansion)
+        self.bn3 = nn.BatchNorm2d(planes * self.expansion)
+        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,
+                          bias=False),
+                nn.BatchNorm2d(self.expansion * planes))
+        self.stride = stride
+        self.width = width
+        self.scale = scale
+
+    def forward(self, x):
+        residual = x
+
+        out = self.conv1(x)
+        out = self.bn1(out)
+        out = self.relu(out)
+        spx = torch.split(out, self.width, 1)
+        for i in range(self.nums):
+            if i == 0:
+                sp = spx[i]
+            else:
+                sp = self.fuse_models[i - 1](sp, spx[i])
+
+            sp = self.convs[i](sp)
+            sp = self.relu(self.bns[i](sp))
+            if i == 0:
+                out = sp
+            else:
+                out = torch.cat((out, sp), 1)
+
+        out = self.conv3(out)
+        out = self.bn3(out)
+
+        residual = self.shortcut(x)
+        out += residual
+        out = self.relu(out)
+
+        return out
+
+
+class ERes2NetAug(nn.Module):
+    def __init__(self,
+                 block=BasicBlockERes2Net,
+                 block_fuse=BasicBlockERes2Net_diff_AFF,
+                 num_blocks=[3, 4, 6, 3],
+                 m_channels=64,
+                 feat_dim=80,
+                 embedding_size=192,
+                 pooling_func='TSTP',
+                 two_emb_layer=False):
+        super(ERes2NetAug, self).__init__()
+        self.in_planes = m_channels
+        self.feat_dim = feat_dim
+        self.embedding_size = embedding_size
+        self.stats_dim = int(feat_dim / 8) * m_channels * 8
+        self.two_emb_layer = two_emb_layer
+
+        self.conv1 = nn.Conv2d(1,
+                               m_channels,
+                               kernel_size=3,
+                               stride=1,
+                               padding=1,
+                               bias=False)
+        self.bn1 = nn.BatchNorm2d(m_channels)
+        self.layer1 = self._make_layer(block,
+                                       m_channels,
+                                       num_blocks[0],
+                                       stride=1)
+        self.layer2 = self._make_layer(block,
+                                       m_channels * 2,
+                                       num_blocks[1],
+                                       stride=2)
+        self.layer3 = self._make_layer(block_fuse,
+                                       m_channels * 4,
+                                       num_blocks[2],
+                                       stride=2)
+        self.layer4 = self._make_layer(block_fuse,
+                                       m_channels * 8,
+                                       num_blocks[3],
+                                       stride=2)
+
+        self.layer1_downsample = nn.Conv2d(m_channels * 4, m_channels * 8, kernel_size=3, padding=1, stride=2,
+                                           bias=False)
+        self.layer2_downsample = nn.Conv2d(m_channels * 8, m_channels * 16, kernel_size=3, padding=1, stride=2,
+                                           bias=False)
+        self.layer3_downsample = nn.Conv2d(m_channels * 16, m_channels * 32, kernel_size=3, padding=1, stride=2,
+                                           bias=False)
+        self.fuse_mode12 = AFF(channels=m_channels * 8)
+        self.fuse_mode123 = AFF(channels=m_channels * 16)
+        self.fuse_mode1234 = AFF(channels=m_channels * 32)
+
+        self.n_stats = 1 if pooling_func == 'TAP' or pooling_func == "TSDP" else 2
+        self.pool = getattr(pooling_layers, pooling_func)(
+            in_dim=self.stats_dim * block.expansion)
+        self.seg_1 = nn.Linear(self.stats_dim * block.expansion * self.n_stats,
+                               embedding_size)
+        if self.two_emb_layer:
+            self.seg_bn_1 = nn.BatchNorm1d(embedding_size, affine=False)
+            self.seg_2 = nn.Linear(embedding_size, embedding_size)
+        else:
+            self.seg_bn_1 = nn.Identity()
+            self.seg_2 = nn.Identity()
+
+    def _make_layer(self, block, planes, num_blocks, stride):
+        strides = [stride] + [1] * (num_blocks - 1)
+        layers = []
+        for stride in strides:
+            layers.append(block(self.in_planes, planes, stride))
+            self.in_planes = planes * block.expansion
+        return nn.Sequential(*layers)
+
+    def forward(self, x):
+        x = x.permute(0, 2, 1)  # (B,T,F) => (B,F,T)
+
+        x = x.unsqueeze_(1)
+        out = F.relu(self.bn1(self.conv1(x)))
+        out1 = self.layer1(out)
+        out2 = self.layer2(out1)
+        out1_downsample = self.layer1_downsample(out1)
+        fuse_out12 = self.fuse_mode12(out2, out1_downsample)
+        out3 = self.layer3(out2)
+        fuse_out12_downsample = self.layer2_downsample(fuse_out12)
+        fuse_out123 = self.fuse_mode123(out3, fuse_out12_downsample)
+        out4 = self.layer4(out3)
+        fuse_out123_downsample = self.layer3_downsample(fuse_out123)
+        fuse_out1234 = self.fuse_mode1234(out4, fuse_out123_downsample)
+        stats = self.pool(fuse_out1234)
+
+        embed_a = self.seg_1(stats)
+        if self.two_emb_layer:
+            out = F.relu(embed_a)
+            out = self.seg_bn_1(out)
+            embed_b = self.seg_2(out)
+            return embed_b
+        else:
+            return embed_a

funasr/modules/cnn/__init__.py

@@ -0,0 +1,3 @@
+from .DTDNN import CAMPPlus
+from .ResNet import ERes2Net
+from .ResNet_aug import ERes2NetAug

funasr/modules/cnn/fusion.py

@@ -0,0 +1,29 @@
+# Copyright 3D-Speaker (https://github.com/alibaba-damo-academy/3D-Speaker). All Rights Reserved.
+# Licensed under the Apache License, Version 2.0 (http://www.apache.org/licenses/LICENSE-2.0)
+
+import torch
+import torch.nn as nn
+
+
+class AFF(nn.Module):
+
+    def __init__(self, channels=64, r=4):
+        super(AFF, self).__init__()
+        inter_channels = int(channels // r)
+
+        self.local_att = nn.Sequential(
+            nn.Conv2d(channels * 2, inter_channels, kernel_size=1, stride=1, padding=0),
+            nn.BatchNorm2d(inter_channels),
+            nn.SiLU(inplace=True),
+            nn.Conv2d(inter_channels, channels, kernel_size=1, stride=1, padding=0),
+            nn.BatchNorm2d(channels),
+        )
+
+    def forward(self, x, ds_y):
+        xa = torch.cat((x, ds_y), dim=1)
+        x_att = self.local_att(xa)
+        x_att = 1.0 + torch.tanh(x_att)
+        xo = torch.mul(x, x_att) + torch.mul(ds_y, 2.0-x_att)
+
+        return xo
+

funasr/modules/cnn/layers.py

@@ -0,0 +1,254 @@
+# Copyright 3D-Speaker (https://github.com/alibaba-damo-academy/3D-Speaker). All Rights Reserved.
+# Licensed under the Apache License, Version 2.0 (http://www.apache.org/licenses/LICENSE-2.0)
+
+import torch
+import torch.nn.functional as F
+import torch.utils.checkpoint as cp
+from torch import nn
+
+
+def get_nonlinear(config_str, channels):
+    nonlinear = nn.Sequential()
+    for name in config_str.split('-'):
+        if name == 'relu':
+            nonlinear.add_module('relu', nn.ReLU(inplace=True))
+        elif name == 'prelu':
+            nonlinear.add_module('prelu', nn.PReLU(channels))
+        elif name == 'batchnorm':
+            nonlinear.add_module('batchnorm', nn.BatchNorm1d(channels))
+        elif name == 'batchnorm_':
+            nonlinear.add_module('batchnorm',
+                                 nn.BatchNorm1d(channels, affine=False))
+        else:
+            raise ValueError('Unexpected module ({}).'.format(name))
+    return nonlinear
+
+
+def statistics_pooling(x, dim=-1, keepdim=False, unbiased=True, eps=1e-2):
+    mean = x.mean(dim=dim)
+    std = x.std(dim=dim, unbiased=unbiased)
+    stats = torch.cat([mean, std], dim=-1)
+    if keepdim:
+        stats = stats.unsqueeze(dim=dim)
+    return stats
+
+
+class StatsPool(nn.Module):
+    def forward(self, x):
+        return statistics_pooling(x)
+
+
+class TDNNLayer(nn.Module):
+    def __init__(self,
+                 in_channels,
+                 out_channels,
+                 kernel_size,
+                 stride=1,
+                 padding=0,
+                 dilation=1,
+                 bias=False,
+                 config_str='batchnorm-relu'):
+        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 = get_nonlinear(config_str, out_channels)
+
+    def forward(self, x):
+        x = self.linear(x)
+        x = self.nonlinear(x)
+        return 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)
+        context = self.relu(self.linear1(context))
+        m = self.sigmoid(self.linear2(context))
+        return y * m
+
+    def seg_pooling(self, x, seg_len=100, stype='avg'):
+        if stype == 'avg':
+            seg = F.avg_pool1d(x, kernel_size=seg_len, stride=seg_len, ceil_mode=True)
+        elif stype == 'max':
+            seg = F.max_pool1d(x, kernel_size=seg_len, stride=seg_len, ceil_mode=True)
+        else:
+            raise ValueError('Wrong segment pooling type.')
+        shape = seg.shape
+        seg = seg.unsqueeze(-1).expand(*shape, seg_len).reshape(*shape[:-1], -1)
+        seg = seg[..., :x.shape[-1]]
+        return seg
+
+
+class CAMDenseTDNNLayer(nn.Module):
+    def __init__(self,
+                 in_channels,
+                 out_channels,
+                 bn_channels,
+                 kernel_size,
+                 stride=1,
+                 dilation=1,
+                 bias=False,
+                 config_str='batchnorm-relu',
+                 memory_efficient=False):
+        super(CAMDenseTDNNLayer, self).__init__()
+        assert kernel_size % 2 == 1, 'Expect equal paddings, but got even kernel size ({})'.format(
+            kernel_size)
+        padding = (kernel_size - 1) // 2 * dilation
+        self.memory_efficient = memory_efficient
+        self.nonlinear1 = get_nonlinear(config_str, in_channels)
+        self.linear1 = nn.Conv1d(in_channels, bn_channels, 1, bias=False)
+        self.nonlinear2 = get_nonlinear(config_str, bn_channels)
+        self.cam_layer = CAMLayer(bn_channels,
+                                  out_channels,
+                                  kernel_size,
+                                  stride=stride,
+                                  padding=padding,
+                                  dilation=dilation,
+                                  bias=bias)
+
+    def bn_function(self, x):
+        return self.linear1(self.nonlinear1(x))
+
+    def forward(self, x):
+        if self.training and self.memory_efficient:
+            x = cp.checkpoint(self.bn_function, x)
+        else:
+            x = self.bn_function(x)
+        x = self.cam_layer(self.nonlinear2(x))
+        return x
+
+
+class CAMDenseTDNNBlock(nn.ModuleList):
+    def __init__(self,
+                 num_layers,
+                 in_channels,
+                 out_channels,
+                 bn_channels,
+                 kernel_size,
+                 stride=1,
+                 dilation=1,
+                 bias=False,
+                 config_str='batchnorm-relu',
+                 memory_efficient=False):
+        super(CAMDenseTDNNBlock, self).__init__()
+        for i in range(num_layers):
+            layer = CAMDenseTDNNLayer(in_channels=in_channels + i * out_channels,
+                                      out_channels=out_channels,
+                                      bn_channels=bn_channels,
+                                      kernel_size=kernel_size,
+                                      stride=stride,
+                                      dilation=dilation,
+                                      bias=bias,
+                                      config_str=config_str,
+                                      memory_efficient=memory_efficient)
+            self.add_module('tdnnd%d' % (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,
+                 config_str='batchnorm-relu'):
+        super(TransitLayer, self).__init__()
+        self.nonlinear = get_nonlinear(config_str, in_channels)
+        self.linear = nn.Conv1d(in_channels, out_channels, 1, bias=bias)
+
+    def forward(self, x):
+        x = self.nonlinear(x)
+        x = self.linear(x)
+        return x
+
+
+class DenseLayer(nn.Module):
+    def __init__(self,
+                 in_channels,
+                 out_channels,
+                 bias=False,
+                 config_str='batchnorm-relu'):
+        super(DenseLayer, self).__init__()
+        self.linear = nn.Conv1d(in_channels, out_channels, 1, bias=bias)
+        self.nonlinear = get_nonlinear(config_str, out_channels)
+
+    def forward(self, x):
+        if len(x.shape) == 2:
+            x = self.linear(x.unsqueeze(dim=-1)).squeeze(dim=-1)
+        else:
+            x = self.linear(x)
+        x = self.nonlinear(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

funasr/utils/speaker_utils.py

@@ -1,25 +1,18 @@
 # Copyright (c) Alibaba, Inc. and its affiliates.
 """ Some implementations are adapted from https://github.com/yuyq96/D-TDNN
 """
-import math
-
-import torch
-import torch.nn.functional as F
-import torch.utils.checkpoint as cp
-from torch import nn
 
 import io
-import os
-from typing import Any, Dict, List, Union
+from typing import Union
 
-import numpy as np
 import librosa as sf
+import numpy as np
 import torch
-import torchaudio
-import logging
-from funasr.utils.modelscope_file import File
-from collections import OrderedDict
+import torch.nn.functional as F
 import torchaudio.compliance.kaldi as Kaldi
+from torch import nn
+
+from funasr.utils.modelscope_file import File
 
 
 def check_audio_list(audio: list):
@@ -104,230 +97,6 @@ def sv_chunk(vad_segments: list, fs = 16000) -> list:
     return segs
 
 
-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 = self._make_layer(
-            block, m_channels, num_blocks[0], stride=2)
-        self.layer2 = self._make_layer(
-            block, m_channels, num_blocks[0], stride=2)
-
-        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 _make_layer(self, block, planes, num_blocks, stride):
-        strides = [stride] + [1] * (num_blocks - 1)
-        layers = []
-        for stride in strides:
-            layers.append(block(self.in_planes, planes, stride))
-            self.in_planes = planes * block.expansion
-        return nn.Sequential(*layers)
-
-    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 CAMPPlus(nn.Module):
-
-    def __init__(self,
-                 feat_dim=80,
-                 embedding_size=192,
-                 growth_rate=32,
-                 bn_size=4,
-                 init_channels=128,
-                 config_str='batchnorm-relu',
-                 memory_efficient=True,
-                 output_level='segment'):
-        super(CAMPPlus, self).__init__()
-
-        self.head = FCM(feat_dim=feat_dim)
-        channels = self.head.out_channels
-        self.output_level = output_level
-
-        self.xvector = nn.Sequential(
-            OrderedDict([
-                ('tdnn',
-                 TDNNLayer(
-                     channels,
-                     init_channels,
-                     5,
-                     stride=2,
-                     dilation=1,
-                     padding=-1,
-                     config_str=config_str)),
-            ]))
-        channels = init_channels
-        for i, (num_layers, kernel_size, dilation) in enumerate(
-                zip((12, 24, 16), (3, 3, 3), (1, 2, 2))):
-            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,
-                config_str=config_str,
-                memory_efficient=memory_efficient)
-            self.xvector.add_module('block%d' % (i + 1), block)
-            channels = channels + num_layers * growth_rate
-            self.xvector.add_module(
-                'transit%d' % (i + 1),
-                TransitLayer(
-                    channels, channels // 2, bias=False,
-                    config_str=config_str))
-            channels //= 2
-
-        self.xvector.add_module('out_nonlinear',
-                                get_nonlinear(config_str, channels))
-
-        if self.output_level == 'segment':
-            self.xvector.add_module('stats', StatsPool())
-            self.xvector.add_module(
-                'dense',
-                DenseLayer(
-                    channels * 2, embedding_size, config_str='batchnorm_'))
-        else:
-            assert self.output_level == 'frame', '`output_level` should be set to \'segment\' or \'frame\'. '
-
-        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)
-        if self.output_level == 'frame':
-            x = x.transpose(1, 2)
-        return x
-
-
-def get_nonlinear(config_str, channels):
-    nonlinear = nn.Sequential()
-    for name in config_str.split('-'):
-        if name == 'relu':
-            nonlinear.add_module('relu', nn.ReLU(inplace=True))
-        elif name == 'prelu':
-            nonlinear.add_module('prelu', nn.PReLU(channels))
-        elif name == 'batchnorm':
-            nonlinear.add_module('batchnorm', nn.BatchNorm1d(channels))
-        elif name == 'batchnorm_':
-            nonlinear.add_module('batchnorm',
-                                 nn.BatchNorm1d(channels, affine=False))
-        else:
-            raise ValueError('Unexpected module ({}).'.format(name))
-    return nonlinear
-
-
-def statistics_pooling(x, dim=-1, keepdim=False, unbiased=True, eps=1e-2):
-    mean = x.mean(dim=dim)
-    std = x.std(dim=dim, unbiased=unbiased)
-    stats = torch.cat([mean, std], dim=-1)
-    if keepdim:
-        stats = stats.unsqueeze(dim=dim)
-    return stats
-
-
-class StatsPool(nn.Module):
-
-    def forward(self, x):
-        return statistics_pooling(x)
-
-
-class TDNNLayer(nn.Module):
-
-    def __init__(self,
-                 in_channels,
-                 out_channels,
-                 kernel_size,
-                 stride=1,
-                 padding=0,
-                 dilation=1,
-                 bias=False,
-                 config_str='batchnorm-relu'):
-        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 = get_nonlinear(config_str, out_channels)
-
-    def forward(self, x):
-        x = self.linear(x)
-        x = self.nonlinear(x)
-        return x
-
-
 def extract_feature(audio):
     features = []
     for au in audio:
@@ -387,116 +156,6 @@ class CAMLayer(nn.Module):
         return seg
 
 
-class CAMDenseTDNNLayer(nn.Module):
-
-    def __init__(self,
-                 in_channels,
-                 out_channels,
-                 bn_channels,
-                 kernel_size,
-                 stride=1,
-                 dilation=1,
-                 bias=False,
-                 config_str='batchnorm-relu',
-                 memory_efficient=False):
-        super(CAMDenseTDNNLayer, self).__init__()
-        assert kernel_size % 2 == 1, 'Expect equal paddings, but got even kernel size ({})'.format(
-            kernel_size)
-        padding = (kernel_size - 1) // 2 * dilation
-        self.memory_efficient = memory_efficient
-        self.nonlinear1 = get_nonlinear(config_str, in_channels)
-        self.linear1 = nn.Conv1d(in_channels, bn_channels, 1, bias=False)
-        self.nonlinear2 = get_nonlinear(config_str, bn_channels)
-        self.cam_layer = CAMLayer(
-            bn_channels,
-            out_channels,
-            kernel_size,
-            stride=stride,
-            padding=padding,
-            dilation=dilation,
-            bias=bias)
-
-    def bn_function(self, x):
-        return self.linear1(self.nonlinear1(x))
-
-    def forward(self, x):
-        if self.training and self.memory_efficient:
-            x = cp.checkpoint(self.bn_function, x)
-        else:
-            x = self.bn_function(x)
-        x = self.cam_layer(self.nonlinear2(x))
-        return x
-
-
-class CAMDenseTDNNBlock(nn.ModuleList):
-
-    def __init__(self,
-                 num_layers,
-                 in_channels,
-                 out_channels,
-                 bn_channels,
-                 kernel_size,
-                 stride=1,
-                 dilation=1,
-                 bias=False,
-                 config_str='batchnorm-relu',
-                 memory_efficient=False):
-        super(CAMDenseTDNNBlock, self).__init__()
-        for i in range(num_layers):
-            layer = CAMDenseTDNNLayer(
-                in_channels=in_channels + i * out_channels,
-                out_channels=out_channels,
-                bn_channels=bn_channels,
-                kernel_size=kernel_size,
-                stride=stride,
-                dilation=dilation,
-                bias=bias,
-                config_str=config_str,
-                memory_efficient=memory_efficient)
-            self.add_module('tdnnd%d' % (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,
-                 config_str='batchnorm-relu'):
-        super(TransitLayer, self).__init__()
-        self.nonlinear = get_nonlinear(config_str, in_channels)
-        self.linear = nn.Conv1d(in_channels, out_channels, 1, bias=bias)
-
-    def forward(self, x):
-        x = self.nonlinear(x)
-        x = self.linear(x)
-        return x
-
-
-class DenseLayer(nn.Module):
-
-    def __init__(self,
-                 in_channels,
-                 out_channels,
-                 bias=False,
-                 config_str='batchnorm-relu'):
-        super(DenseLayer, self).__init__()
-        self.linear = nn.Conv1d(in_channels, out_channels, 1, bias=bias)
-        self.nonlinear = get_nonlinear(config_str, out_channels)
-
-    def forward(self, x):
-        if len(x.shape) == 2:
-            x = self.linear(x.unsqueeze(dim=-1)).squeeze(dim=-1)
-        else:
-            x = self.linear(x)
-        x = self.nonlinear(x)
-        return x
-
 def postprocess(segments: list, vad_segments: list,
                 labels: np.ndarray, embeddings: np.ndarray) -> list:
     assert len(segments) == len(labels)
@@ -592,300 +251,3 @@ def distribute_spk(sentence_list, sd_time_list):
         d['spk'] = sentence_spk
         sd_sentence_list.append(d)
     return sd_sentence_list
-
-
-class AFF(nn.Module):
-
-    def __init__(self, channels=64, r=4):
-        super(AFF, self).__init__()
-        inter_channels = int(channels // r)
-
-        self.local_att = nn.Sequential(
-            nn.Conv2d(channels * 2, inter_channels, kernel_size=1, stride=1, padding=0),
-            nn.BatchNorm2d(inter_channels),
-            nn.SiLU(inplace=True),
-            nn.Conv2d(inter_channels, channels, kernel_size=1, stride=1, padding=0),
-            nn.BatchNorm2d(channels),
-        )
-
-    def forward(self, x, ds_y):
-        xa = torch.cat((x, ds_y), dim=1)
-        x_att = self.local_att(xa)
-        x_att = 1.0 + torch.tanh(x_att)
-        xo = torch.mul(x, x_att) + torch.mul(ds_y, 2.0 - x_att)
-
-        return xo
-
-
-class TSTP(nn.Module):
-    """
-    Temporal statistics pooling, concatenate mean and std, which is used in
-    x-vector
-    Comment: simple concatenation can not make full use of both statistics
-    """
-
-    def __init__(self, **kwargs):
-        super(TSTP, self).__init__()
-
-    def forward(self, x):
-        # The last dimension is the temporal axis
-        pooling_mean = x.mean(dim=-1)
-        pooling_std = torch.sqrt(torch.var(x, dim=-1) + 1e-8)
-        pooling_mean = pooling_mean.flatten(start_dim=1)
-        pooling_std = pooling_std.flatten(start_dim=1)
-
-        stats = torch.cat((pooling_mean, pooling_std), 1)
-        return stats
-
-
-class ReLU(nn.Hardtanh):
-
-    def __init__(self, inplace=False):
-        super(ReLU, self).__init__(0, 20, inplace)
-
-    def __repr__(self):
-        inplace_str = 'inplace' if self.inplace else ''
-        return self.__class__.__name__ + ' (' \
-            + inplace_str + ')'
-
-
-def conv1x1(in_planes, out_planes, stride=1):
-    "1x1 convolution without padding"
-    return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride,
-                     padding=0, bias=False)
-
-
-def conv3x3(in_planes, out_planes, stride=1):
-    "3x3 convolution with padding"
-    return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
-                     padding=1, bias=False)
-
-
-class BasicBlockERes2Net(nn.Module):
-    expansion = 4
-
-    def __init__(self, in_planes, planes, stride=1, baseWidth=24, scale=3):
-        super(BasicBlockERes2Net, self).__init__()
-        width = int(math.floor(planes * (baseWidth / 64.0)))
-        self.conv1 = conv1x1(in_planes, width * scale, stride)
-        self.bn1 = nn.BatchNorm2d(width * scale)
-        self.nums = scale
-
-        convs = []
-        bns = []
-        for i in range(self.nums):
-            convs.append(conv3x3(width, width))
-            bns.append(nn.BatchNorm2d(width))
-        self.convs = nn.ModuleList(convs)
-        self.bns = nn.ModuleList(bns)
-        self.relu = ReLU(inplace=True)
-
-        self.conv3 = conv1x1(width * scale, planes * self.expansion)
-        self.bn3 = nn.BatchNorm2d(planes * self.expansion)
-        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,
-                          bias=False),
-                nn.BatchNorm2d(self.expansion * planes))
-        self.stride = stride
-        self.width = width
-        self.scale = scale
-
-    def forward(self, x):
-        residual = x
-
-        out = self.conv1(x)
-        out = self.bn1(out)
-        out = self.relu(out)
-        spx = torch.split(out, self.width, 1)
-        for i in range(self.nums):
-            if i == 0:
-                sp = spx[i]
-            else:
-                sp = sp + spx[i]
-            sp = self.convs[i](sp)
-            sp = self.relu(self.bns[i](sp))
-            if i == 0:
-                out = sp
-            else:
-                out = torch.cat((out, sp), 1)
-
-        out = self.conv3(out)
-        out = self.bn3(out)
-
-        residual = self.shortcut(x)
-        out += residual
-        out = self.relu(out)
-
-        return out
-
-
-class BasicBlockERes2Net_diff_AFF(nn.Module):
-    expansion = 4
-
-    def __init__(self, in_planes, planes, stride=1, baseWidth=24, scale=3):
-        super(BasicBlockERes2Net_diff_AFF, self).__init__()
-        width = int(math.floor(planes * (baseWidth / 64.0)))
-        self.conv1 = conv1x1(in_planes, width * scale, stride)
-        self.bn1 = nn.BatchNorm2d(width * scale)
-
-        self.nums = scale
-
-        convs = []
-        fuse_models = []
-        bns = []
-        for i in range(self.nums):
-            convs.append(conv3x3(width, width))
-            bns.append(nn.BatchNorm2d(width))
-        for j in range(self.nums - 1):
-            fuse_models.append(AFF(channels=width))
-
-        self.convs = nn.ModuleList(convs)
-        self.bns = nn.ModuleList(bns)
-        self.fuse_models = nn.ModuleList(fuse_models)
-        self.relu = ReLU(inplace=True)
-
-        self.conv3 = conv1x1(width * scale, planes * self.expansion)
-        self.bn3 = nn.BatchNorm2d(planes * self.expansion)
-        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,
-                          bias=False),
-                nn.BatchNorm2d(self.expansion * planes))
-        self.stride = stride
-        self.width = width
-        self.scale = scale
-
-    def forward(self, x):
-        residual = x
-
-        out = self.conv1(x)
-        out = self.bn1(out)
-        out = self.relu(out)
-        spx = torch.split(out, self.width, 1)
-        for i in range(self.nums):
-            if i == 0:
-                sp = spx[i]
-            else:
-                sp = self.fuse_models[i - 1](sp, spx[i])
-
-            sp = self.convs[i](sp)
-            sp = self.relu(self.bns[i](sp))
-            if i == 0:
-                out = sp
-            else:
-                out = torch.cat((out, sp), 1)
-
-        out = self.conv3(out)
-        out = self.bn3(out)
-
-        residual = self.shortcut(x)
-        out += residual
-        out = self.relu(out)
-
-        return out
-
-
-class ERes2Net(nn.Module):
-    def __init__(self,
-                 block=BasicBlockERes2Net,
-                 block_fuse=BasicBlockERes2Net_diff_AFF,
-                 num_blocks=[3, 4, 6, 3],
-                 m_channels=64,
-                 feat_dim=80,
-                 embedding_size=192,
-                 pooling_func='TSTP',
-                 two_emb_layer=False):
-        super(ERes2Net, self).__init__()
-        self.in_planes = m_channels
-        self.feat_dim = feat_dim
-        self.embedding_size = embedding_size
-        self.stats_dim = int(feat_dim / 8) * m_channels * 8
-        self.two_emb_layer = two_emb_layer
-
-        self.conv1 = nn.Conv2d(1,
-                               m_channels,
-                               kernel_size=3,
-                               stride=1,
-                               padding=1,
-                               bias=False)
-        self.bn1 = nn.BatchNorm2d(m_channels)
-        self.layer1 = self._make_layer(block,
-                                       m_channels,
-                                       num_blocks[0],
-                                       stride=1)
-        self.layer2 = self._make_layer(block,
-                                       m_channels * 2,
-                                       num_blocks[1],
-                                       stride=2)
-        self.layer3 = self._make_layer(block_fuse,
-                                       m_channels * 4,
-                                       num_blocks[2],
-                                       stride=2)
-        self.layer4 = self._make_layer(block_fuse,
-                                       m_channels * 8,
-                                       num_blocks[3],
-                                       stride=2)
-
-        self.layer1_downsample = nn.Conv2d(m_channels * 4, m_channels * 8, kernel_size=3, padding=1, stride=2,
-                                           bias=False)
-        self.layer2_downsample = nn.Conv2d(m_channels * 8, m_channels * 16, kernel_size=3, padding=1, stride=2,
-                                           bias=False)
-        self.layer3_downsample = nn.Conv2d(m_channels * 16, m_channels * 32, kernel_size=3, padding=1, stride=2,
-                                           bias=False)
-        self.fuse_mode12 = AFF(channels=m_channels * 8)
-        self.fuse_mode123 = AFF(channels=m_channels * 16)
-        self.fuse_mode1234 = AFF(channels=m_channels * 32)
-
-        self.n_stats = 1 if pooling_func == 'TAP' or pooling_func == "TSDP" else 2
-        self.pool = TSTP(in_dim=self.stats_dim * block.expansion)
-        self.seg_1 = nn.Linear(self.stats_dim * block.expansion * self.n_stats,
-                               embedding_size)
-        if self.two_emb_layer:
-            self.seg_bn_1 = nn.BatchNorm1d(embedding_size, affine=False)
-            self.seg_2 = nn.Linear(embedding_size, embedding_size)
-        else:
-            self.seg_bn_1 = nn.Identity()
-            self.seg_2 = nn.Identity()
-
-    def _make_layer(self, block, planes, num_blocks, stride):
-        strides = [stride] + [1] * (num_blocks - 1)
-        layers = []
-        for stride in strides:
-            layers.append(block(self.in_planes, planes, stride))
-            self.in_planes = planes * block.expansion
-        return nn.Sequential(*layers)
-
-    def forward(self, x):
-        x = x.permute(0, 2, 1)  # (B,T,F) => (B,F,T)
-
-        x = x.unsqueeze_(1)
-        out = F.relu(self.bn1(self.conv1(x)))
-        out1 = self.layer1(out)
-        out2 = self.layer2(out1)
-        out1_downsample = self.layer1_downsample(out1)
-        fuse_out12 = self.fuse_mode12(out2, out1_downsample)
-        out3 = self.layer3(out2)
-        fuse_out12_downsample = self.layer2_downsample(fuse_out12)
-        fuse_out123 = self.fuse_mode123(out3, fuse_out12_downsample)
-        out4 = self.layer4(out3)
-        fuse_out123_downsample = self.layer3_downsample(fuse_out123)
-        fuse_out1234 = self.fuse_mode1234(out4, fuse_out123_downsample)
-        stats = self.pool(fuse_out1234)
-
-        embed_a = self.seg_1(stats)
-        if self.two_emb_layer:
-            out = F.relu(embed_a)
-            out = self.seg_bn_1(out)
-            embed_b = self.seg_2(out)
-            return embed_b
-        else:
-            return embed_a