Source code for espnet2.gan_tts.vits.posterior_encoder

# Copyright 2021 Tomoki Hayashi
#  Apache 2.0  (

"""Posterior encoder module in VITS.

This code is based on


from typing import Optional, Tuple

import torch

from espnet2.gan_tts.wavenet import WaveNet
from espnet2.gan_tts.wavenet.residual_block import Conv1d
from espnet.nets.pytorch_backend.nets_utils import make_non_pad_mask

[docs]class PosteriorEncoder(torch.nn.Module): """Posterior encoder module in VITS. This is a module of posterior encoder described in `Conditional Variational Autoencoder with Adversarial Learning for End-to-End Text-to-Speech`_. .. _`Conditional Variational Autoencoder with Adversarial Learning for End-to-End Text-to-Speech`: """ def __init__( self, in_channels: int = 513, out_channels: int = 192, hidden_channels: int = 192, kernel_size: int = 5, layers: int = 16, stacks: int = 1, base_dilation: int = 1, global_channels: int = -1, dropout_rate: float = 0.0, bias: bool = True, use_weight_norm: bool = True, ): """Initilialize PosteriorEncoder module. Args: in_channels (int): Number of input channels. out_channels (int): Number of output channels. hidden_channels (int): Number of hidden channels. kernel_size (int): Kernel size in WaveNet. layers (int): Number of layers of WaveNet. stacks (int): Number of repeat stacking of WaveNet. base_dilation (int): Base dilation factor. global_channels (int): Number of global conditioning channels. dropout_rate (float): Dropout rate. bias (bool): Whether to use bias parameters in conv. use_weight_norm (bool): Whether to apply weight norm. """ super().__init__() # define modules self.input_conv = Conv1d(in_channels, hidden_channels, 1) self.encoder = WaveNet( in_channels=-1, out_channels=-1, kernel_size=kernel_size, layers=layers, stacks=stacks, base_dilation=base_dilation, residual_channels=hidden_channels, aux_channels=-1, gate_channels=hidden_channels * 2, skip_channels=hidden_channels, global_channels=global_channels, dropout_rate=dropout_rate, bias=bias, use_weight_norm=use_weight_norm, use_first_conv=False, use_last_conv=False, scale_residual=False, scale_skip_connect=True, ) self.proj = Conv1d(hidden_channels, out_channels * 2, 1)
[docs] def forward( self, x: torch.Tensor, x_lengths: torch.Tensor, g: Optional[torch.Tensor] = None ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]: """Calculate forward propagation. Args: x (Tensor): Input tensor (B, in_channels, T_feats). x_lengths (Tensor): Length tensor (B,). g (Optional[Tensor]): Global conditioning tensor (B, global_channels, 1). Returns: Tensor: Encoded hidden representation tensor (B, out_channels, T_feats). Tensor: Projected mean tensor (B, out_channels, T_feats). Tensor: Projected scale tensor (B, out_channels, T_feats). Tensor: Mask tensor for input tensor (B, 1, T_feats). """ x_mask = ( make_non_pad_mask(x_lengths) .unsqueeze(1) .to( dtype=x.dtype, device=x.device, ) ) x = self.input_conv(x) * x_mask x = self.encoder(x, x_mask, g=g) stats = self.proj(x) * x_mask m, logs = stats.split(stats.size(1) // 2, dim=1) z = (m + torch.randn_like(m) * torch.exp(logs)) * x_mask return z, m, logs, x_mask