|
import torch |
|
from torch import nn |
|
|
|
|
|
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 |
|
""" |
|
|
|
|
|
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) |
|
|
|
|
|
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 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 |
|
|
