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})`
"""
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