Source code for espnet2.gan_tts.vits.text_encoder

# Copyright 2021 Tomoki Hayashi
#  Apache 2.0  (

"""Text encoder module in VITS.

This code is based on


import math
from typing import Tuple

import torch

from espnet.nets.pytorch_backend.conformer.encoder import Encoder
from espnet.nets.pytorch_backend.nets_utils import make_non_pad_mask

[docs]class TextEncoder(torch.nn.Module): """Text encoder module in VITS. This is a module of text encoder described in `Conditional Variational Autoencoder with Adversarial Learning for End-to-End Text-to-Speech`_. Instead of the relative positional Transformer, we use conformer architecture as the encoder module, which contains additional convolution layers. .. _`Conditional Variational Autoencoder with Adversarial Learning for End-to-End Text-to-Speech`: """ def __init__( self, vocabs: int, attention_dim: int = 192, attention_heads: int = 2, linear_units: int = 768, blocks: int = 6, positionwise_layer_type: str = "conv1d", positionwise_conv_kernel_size: int = 3, positional_encoding_layer_type: str = "rel_pos", self_attention_layer_type: str = "rel_selfattn", activation_type: str = "swish", normalize_before: bool = True, use_macaron_style: bool = False, use_conformer_conv: bool = False, conformer_kernel_size: int = 7, dropout_rate: float = 0.1, positional_dropout_rate: float = 0.0, attention_dropout_rate: float = 0.0, ): """Initialize TextEncoder module. Args: vocabs (int): Vocabulary size. attention_dim (int): Attention dimension. attention_heads (int): Number of attention heads. linear_units (int): Number of linear units of positionwise layers. blocks (int): Number of encoder blocks. positionwise_layer_type (str): Positionwise layer type. positionwise_conv_kernel_size (int): Positionwise layer's kernel size. positional_encoding_layer_type (str): Positional encoding layer type. self_attention_layer_type (str): Self-attention layer type. activation_type (str): Activation function type. normalize_before (bool): Whether to apply LayerNorm before attention. use_macaron_style (bool): Whether to use macaron style components. use_conformer_conv (bool): Whether to use conformer conv layers. conformer_kernel_size (int): Conformer's conv kernel size. dropout_rate (float): Dropout rate. positional_dropout_rate (float): Dropout rate for positional encoding. attention_dropout_rate (float): Dropout rate for attention. """ super().__init__() # store for forward self.attention_dim = attention_dim # define modules self.emb = torch.nn.Embedding(vocabs, attention_dim) torch.nn.init.normal_(self.emb.weight, 0.0, attention_dim**-0.5) self.encoder = Encoder( idim=-1, input_layer=None, attention_dim=attention_dim, attention_heads=attention_heads, linear_units=linear_units, num_blocks=blocks, dropout_rate=dropout_rate, positional_dropout_rate=positional_dropout_rate, attention_dropout_rate=attention_dropout_rate, normalize_before=normalize_before, positionwise_layer_type=positionwise_layer_type, positionwise_conv_kernel_size=positionwise_conv_kernel_size, macaron_style=use_macaron_style, pos_enc_layer_type=positional_encoding_layer_type, selfattention_layer_type=self_attention_layer_type, activation_type=activation_type, use_cnn_module=use_conformer_conv, cnn_module_kernel=conformer_kernel_size, ) self.proj = torch.nn.Conv1d(attention_dim, attention_dim * 2, 1)
[docs] def forward( self, x: torch.Tensor, x_lengths: torch.Tensor, ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]: """Calculate forward propagation. Args: x (Tensor): Input index tensor (B, T_text). x_lengths (Tensor): Length tensor (B,). Returns: Tensor: Encoded hidden representation (B, attention_dim, T_text). Tensor: Projected mean tensor (B, attention_dim, T_text). Tensor: Projected scale tensor (B, attention_dim, T_text). Tensor: Mask tensor for input tensor (B, 1, T_text). """ x = self.emb(x) * math.sqrt(self.attention_dim) x_mask = ( make_non_pad_mask(x_lengths) .to( device=x.device, dtype=x.dtype, ) .unsqueeze(1) ) # encoder assume the channel last (B, T_text, attention_dim) # but mask shape shoud be (B, 1, T_text) x, _ = self.encoder(x, x_mask) # convert the channel first (B, attention_dim, T_text) x = x.transpose(1, 2) stats = self.proj(x) * x_mask m, logs = stats.split(stats.size(1) // 2, dim=1) return x, m, logs, x_mask