199 lines
6.4 KiB
Python
199 lines
6.4 KiB
Python
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import torch
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from torch import nn
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# from TTS.utils.audio.torch_transforms import TorchSTFT
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from TTS.encoder.models.base_encoder import BaseEncoder
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class SELayer(nn.Module):
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def __init__(self, channel, reduction=8):
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super(SELayer, self).__init__()
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self.avg_pool = nn.AdaptiveAvgPool2d(1)
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self.fc = nn.Sequential(
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nn.Linear(channel, channel // reduction),
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nn.ReLU(inplace=True),
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nn.Linear(channel // reduction, channel),
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nn.Sigmoid(),
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)
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def forward(self, x):
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b, c, _, _ = x.size()
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y = self.avg_pool(x).view(b, c)
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y = self.fc(y).view(b, c, 1, 1)
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return x * y
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class SEBasicBlock(nn.Module):
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expansion = 1
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def __init__(self, inplanes, planes, stride=1, downsample=None, reduction=8):
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super(SEBasicBlock, self).__init__()
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self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
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self.bn1 = nn.BatchNorm2d(planes)
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self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, padding=1, bias=False)
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self.bn2 = nn.BatchNorm2d(planes)
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self.relu = nn.ReLU(inplace=True)
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self.se = SELayer(planes, reduction)
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self.downsample = downsample
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self.stride = stride
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def forward(self, x):
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residual = x
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out = self.conv1(x)
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out = self.relu(out)
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out = self.bn1(out)
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out = self.conv2(out)
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out = self.bn2(out)
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out = self.se(out)
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if self.downsample is not None:
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residual = self.downsample(x)
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out += residual
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out = self.relu(out)
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return out
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class ResNetSpeakerEncoder(BaseEncoder):
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"""Implementation of the model H/ASP without batch normalization in speaker embedding. This model was proposed in: https://arxiv.org/abs/2009.14153
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Adapted from: https://github.com/clovaai/voxceleb_trainer
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"""
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# pylint: disable=W0102
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def __init__(
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self,
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input_dim=64,
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proj_dim=512,
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layers=[3, 4, 6, 3],
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num_filters=[32, 64, 128, 256],
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encoder_type="ASP",
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log_input=False,
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use_torch_spec=False,
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audio_config=None,
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):
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super(ResNetSpeakerEncoder, self).__init__()
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self.encoder_type = encoder_type
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self.input_dim = input_dim
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self.log_input = log_input
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self.use_torch_spec = use_torch_spec
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self.audio_config = audio_config
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self.proj_dim = proj_dim
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self.conv1 = nn.Conv2d(1, num_filters[0], kernel_size=3, stride=1, padding=1)
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self.relu = nn.ReLU(inplace=True)
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self.bn1 = nn.BatchNorm2d(num_filters[0])
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self.inplanes = num_filters[0]
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self.layer1 = self.create_layer(SEBasicBlock, num_filters[0], layers[0])
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self.layer2 = self.create_layer(SEBasicBlock, num_filters[1], layers[1], stride=(2, 2))
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self.layer3 = self.create_layer(SEBasicBlock, num_filters[2], layers[2], stride=(2, 2))
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self.layer4 = self.create_layer(SEBasicBlock, num_filters[3], layers[3], stride=(2, 2))
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self.instancenorm = nn.InstanceNorm1d(input_dim)
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if self.use_torch_spec:
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self.torch_spec = self.get_torch_mel_spectrogram_class(audio_config)
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else:
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self.torch_spec = None
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outmap_size = int(self.input_dim / 8)
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self.attention = nn.Sequential(
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nn.Conv1d(num_filters[3] * outmap_size, 128, kernel_size=1),
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nn.ReLU(),
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nn.BatchNorm1d(128),
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nn.Conv1d(128, num_filters[3] * outmap_size, kernel_size=1),
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nn.Softmax(dim=2),
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)
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if self.encoder_type == "SAP":
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out_dim = num_filters[3] * outmap_size
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elif self.encoder_type == "ASP":
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out_dim = num_filters[3] * outmap_size * 2
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else:
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raise ValueError("Undefined encoder")
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self.fc = nn.Linear(out_dim, proj_dim)
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self._init_layers()
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def _init_layers(self):
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for m in self.modules():
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if isinstance(m, nn.Conv2d):
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nn.init.kaiming_normal_(m.weight, mode="fan_out", nonlinearity="relu")
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elif isinstance(m, nn.BatchNorm2d):
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nn.init.constant_(m.weight, 1)
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nn.init.constant_(m.bias, 0)
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def create_layer(self, block, planes, blocks, stride=1):
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downsample = None
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if stride != 1 or self.inplanes != planes * block.expansion:
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downsample = nn.Sequential(
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nn.Conv2d(self.inplanes, planes * block.expansion, kernel_size=1, stride=stride, bias=False),
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nn.BatchNorm2d(planes * block.expansion),
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)
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layers = []
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layers.append(block(self.inplanes, planes, stride, downsample))
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self.inplanes = planes * block.expansion
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for _ in range(1, blocks):
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layers.append(block(self.inplanes, planes))
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return nn.Sequential(*layers)
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# pylint: disable=R0201
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def new_parameter(self, *size):
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out = nn.Parameter(torch.FloatTensor(*size))
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nn.init.xavier_normal_(out)
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return out
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def forward(self, x, l2_norm=False):
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"""Forward pass of the model.
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Args:
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x (Tensor): Raw waveform signal or spectrogram frames. If input is a waveform, `torch_spec` must be `True`
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to compute the spectrogram on-the-fly.
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l2_norm (bool): Whether to L2-normalize the outputs.
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Shapes:
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- x: :math:`(N, 1, T_{in})` or :math:`(N, D_{spec}, T_{in})`
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"""
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x.squeeze_(1)
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# if you torch spec compute it otherwise use the mel spec computed by the AP
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if self.use_torch_spec:
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x = self.torch_spec(x)
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if self.log_input:
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x = (x + 1e-6).log()
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x = self.instancenorm(x).unsqueeze(1)
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x = self.conv1(x)
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x = self.relu(x)
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x = self.bn1(x)
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x = self.layer1(x)
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x = self.layer2(x)
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x = self.layer3(x)
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x = self.layer4(x)
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x = x.reshape(x.size()[0], -1, x.size()[-1])
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w = self.attention(x)
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if self.encoder_type == "SAP":
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x = torch.sum(x * w, dim=2)
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elif self.encoder_type == "ASP":
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mu = torch.sum(x * w, dim=2)
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sg = torch.sqrt((torch.sum((x**2) * w, dim=2) - mu**2).clamp(min=1e-5))
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x = torch.cat((mu, sg), 1)
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x = x.view(x.size()[0], -1)
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x = self.fc(x)
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if l2_norm:
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x = torch.nn.functional.normalize(x, p=2, dim=1)
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return x
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