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