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| # 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
|
|
