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229 lines
7.5 KiB
Python
229 lines
7.5 KiB
Python
# from https://github.com/espnet/espnet/blob/master/espnet2/gan_tts/vits/residual_coupling.py
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# Copyright 2021 Tomoki Hayashi
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# Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0)
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"""Residual affine coupling modules in VITS.
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This code is based on https://github.com/jaywalnut310/vits.
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"""
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from typing import Optional, Tuple, Union
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import torch
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from flow import FlipFlow
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from wavenet import WaveNet
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class ResidualAffineCouplingBlock(torch.nn.Module):
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"""Residual affine coupling block module.
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This is a module of residual affine coupling block, which used as "Flow" in
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`Conditional Variational Autoencoder with Adversarial Learning for End-to-End
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Text-to-Speech`_.
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.. _`Conditional Variational Autoencoder with Adversarial Learning for End-to-End
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Text-to-Speech`: https://arxiv.org/abs/2006.04558
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"""
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def __init__(
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self,
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in_channels: int = 192,
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hidden_channels: int = 192,
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flows: int = 4,
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kernel_size: int = 5,
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base_dilation: int = 1,
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layers: int = 4,
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global_channels: int = -1,
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dropout_rate: float = 0.0,
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use_weight_norm: bool = True,
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bias: bool = True,
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use_only_mean: bool = True,
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):
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"""Initilize ResidualAffineCouplingBlock module.
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Args:
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in_channels (int): Number of input channels.
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hidden_channels (int): Number of hidden channels.
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flows (int): Number of flows.
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kernel_size (int): Kernel size for WaveNet.
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base_dilation (int): Base dilation factor for WaveNet.
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layers (int): Number of layers of WaveNet.
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stacks (int): Number of stacks of WaveNet.
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global_channels (int): Number of global channels.
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dropout_rate (float): Dropout rate.
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use_weight_norm (bool): Whether to use weight normalization in WaveNet.
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bias (bool): Whether to use bias paramters in WaveNet.
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use_only_mean (bool): Whether to estimate only mean.
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"""
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super().__init__()
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self.flows = torch.nn.ModuleList()
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for i in range(flows):
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self.flows += [
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ResidualAffineCouplingLayer(
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in_channels=in_channels,
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hidden_channels=hidden_channels,
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kernel_size=kernel_size,
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base_dilation=base_dilation,
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layers=layers,
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stacks=1,
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global_channels=global_channels,
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dropout_rate=dropout_rate,
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use_weight_norm=use_weight_norm,
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bias=bias,
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use_only_mean=use_only_mean,
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)
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]
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self.flows += [FlipFlow()]
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def forward(
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self,
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x: torch.Tensor,
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x_mask: torch.Tensor,
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g: Optional[torch.Tensor] = None,
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inverse: bool = False,
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) -> torch.Tensor:
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"""Calculate forward propagation.
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Args:
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x (Tensor): Input tensor (B, in_channels, T).
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x_lengths (Tensor): Length tensor (B,).
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g (Optional[Tensor]): Global conditioning tensor (B, global_channels, 1).
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inverse (bool): Whether to inverse the flow.
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Returns:
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Tensor: Output tensor (B, in_channels, T).
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"""
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if not inverse:
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for flow in self.flows:
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x, _ = flow(x, x_mask, g=g, inverse=inverse)
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else:
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for flow in reversed(self.flows):
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x = flow(x, x_mask, g=g, inverse=inverse)
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return x
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class ResidualAffineCouplingLayer(torch.nn.Module):
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"""Residual affine coupling layer."""
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def __init__(
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self,
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in_channels: int = 192,
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hidden_channels: int = 192,
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kernel_size: int = 5,
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base_dilation: int = 1,
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layers: int = 5,
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stacks: int = 1,
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global_channels: int = -1,
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dropout_rate: float = 0.0,
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use_weight_norm: bool = True,
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bias: bool = True,
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use_only_mean: bool = True,
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):
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"""Initialzie ResidualAffineCouplingLayer module.
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Args:
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in_channels (int): Number of input channels.
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hidden_channels (int): Number of hidden channels.
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kernel_size (int): Kernel size for WaveNet.
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base_dilation (int): Base dilation factor for WaveNet.
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layers (int): Number of layers of WaveNet.
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stacks (int): Number of stacks of WaveNet.
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global_channels (int): Number of global channels.
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dropout_rate (float): Dropout rate.
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use_weight_norm (bool): Whether to use weight normalization in WaveNet.
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bias (bool): Whether to use bias paramters in WaveNet.
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use_only_mean (bool): Whether to estimate only mean.
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"""
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assert in_channels % 2 == 0, "in_channels should be divisible by 2"
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super().__init__()
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self.half_channels = in_channels // 2
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self.use_only_mean = use_only_mean
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# define modules
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self.input_conv = torch.nn.Conv1d(
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self.half_channels,
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hidden_channels,
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1,
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)
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self.encoder = WaveNet(
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in_channels=-1,
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out_channels=-1,
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kernel_size=kernel_size,
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layers=layers,
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stacks=stacks,
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base_dilation=base_dilation,
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residual_channels=hidden_channels,
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aux_channels=-1,
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gate_channels=hidden_channels * 2,
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skip_channels=hidden_channels,
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global_channels=global_channels,
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dropout_rate=dropout_rate,
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bias=bias,
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use_weight_norm=use_weight_norm,
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use_first_conv=False,
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use_last_conv=False,
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scale_residual=False,
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scale_skip_connect=True,
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)
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if use_only_mean:
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self.proj = torch.nn.Conv1d(
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hidden_channels,
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self.half_channels,
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1,
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)
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else:
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self.proj = torch.nn.Conv1d(
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hidden_channels,
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self.half_channels * 2,
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1,
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)
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self.proj.weight.data.zero_()
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self.proj.bias.data.zero_()
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def forward(
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self,
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x: torch.Tensor,
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x_mask: torch.Tensor,
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g: Optional[torch.Tensor] = None,
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inverse: bool = False,
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) -> Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]:
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"""Calculate forward propagation.
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Args:
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x (Tensor): Input tensor (B, in_channels, T).
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x_lengths (Tensor): Length tensor (B,).
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g (Optional[Tensor]): Global conditioning tensor (B, global_channels, 1).
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inverse (bool): Whether to inverse the flow.
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Returns:
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Tensor: Output tensor (B, in_channels, T).
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Tensor: Log-determinant tensor for NLL (B,) if not inverse.
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"""
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xa, xb = x.split(x.size(1) // 2, dim=1)
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h = self.input_conv(xa) * x_mask
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h = self.encoder(h, x_mask, g=g)
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stats = self.proj(h) * x_mask
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if not self.use_only_mean:
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m, logs = stats.split(stats.size(1) // 2, dim=1)
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else:
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m = stats
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logs = torch.zeros_like(m)
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if not inverse:
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xb = m + xb * torch.exp(logs) * x_mask
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x = torch.cat([xa, xb], 1)
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logdet = torch.sum(logs, [1, 2])
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return x, logdet
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else:
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xb = (xb - m) * torch.exp(-logs) * x_mask
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x = torch.cat([xa, xb], 1)
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return x
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