mirror of https://github.com/coqui-ai/TTS.git
273 lines
9.0 KiB
Python
273 lines
9.0 KiB
Python
import numpy as np
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import torch
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from torch import nn
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from TTS.utils.audio import TorchSTFT
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from TTS.utils.io import load_fsspec
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import torchaudio
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class PreEmphasis(nn.Module):
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def __init__(self, coefficient=0.97):
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super().__init__()
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self.coefficient = coefficient
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self.register_buffer("filter", torch.FloatTensor([-self.coefficient, 1.0]).unsqueeze(0).unsqueeze(0))
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def forward(self, x):
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assert len(x.size()) == 2
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x = torch.nn.functional.pad(x.unsqueeze(1), (1, 0), "reflect")
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return torch.nn.functional.conv1d(x, self.filter).squeeze(1)
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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(nn.Module):
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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.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 = torch.nn.Sequential(
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PreEmphasis(audio_config["preemphasis"]),
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# TorchSTFT(
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# n_fft=audio_config["fft_size"],
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# hop_length=audio_config["hop_length"],
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# win_length=audio_config["win_length"],
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# sample_rate=audio_config["sample_rate"],
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# window="hamming_window",
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# mel_fmin=0.0,
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# mel_fmax=None,
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# use_htk=True,
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# do_amp_to_db=False,
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# n_mels=audio_config["num_mels"],
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# power=2.0,
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# use_mel=True,
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# mel_norm=None,
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# )
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torchaudio.transforms.MelSpectrogram(
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sample_rate=audio_config["sample_rate"],
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n_fft=audio_config["fft_size"],
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win_length=audio_config["win_length"],
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hop_length=audio_config["hop_length"],
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window_fn=torch.hamming_window,
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n_mels=audio_config["num_mels"],
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)
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)
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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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with torch.no_grad():
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with torch.cuda.amp.autocast(enabled=False):
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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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else:
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x = x.transpose(1, 2)
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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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@torch.no_grad()
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def compute_embedding(self, x, num_frames=250, num_eval=10, return_mean=True):
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"""
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Generate embeddings for a batch of utterances
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x: 1xTxD
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"""
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# map to the waveform size
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if self.use_torch_spec:
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num_frames = num_frames * self.audio_config["hop_length"]
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max_len = x.shape[1]
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if max_len < num_frames:
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num_frames = max_len
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offsets = np.linspace(0, max_len - num_frames, num=num_eval)
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frames_batch = []
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for offset in offsets:
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offset = int(offset)
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end_offset = int(offset + num_frames)
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frames = x[:, offset:end_offset]
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frames_batch.append(frames)
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frames_batch = torch.cat(frames_batch, dim=0)
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embeddings = self.forward(frames_batch, l2_norm=True)
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if return_mean:
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embeddings = torch.mean(embeddings, dim=0, keepdim=True)
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return embeddings
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def load_checkpoint(self, config: dict, checkpoint_path: str, eval: bool = False, use_cuda: bool = False):
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state = load_fsspec(checkpoint_path, map_location=torch.device("cpu"))
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self.load_state_dict(state["model"])
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if use_cuda:
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self.cuda()
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if eval:
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self.eval()
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assert not self.training
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