Source code for espnet2.gan_svs.vits.pitch_predictor

# Copyright 2022 Yifeng Yu
#  Apache 2.0  (

import torch
from typeguard import typechecked

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

[docs]class Decoder(torch.nn.Module): """Pitch or Mel decoder module in VISinger 2.""" @typechecked def __init__( self, out_channels: int = 192, attention_dim: int = 192, attention_heads: int = 2, linear_units: int = 768, blocks: int = 6, pw_layer_type: str = "conv1d", pw_conv_kernel_size: int = 3, pos_enc_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, global_channels: int = -1, ): """Initialize Decoder in VISinger 2. Args: out_channels (int): The output dimension of the module. attention_dim (int): The dimension of the attention mechanism. attention_heads (int): The number of attention heads. linear_units (int): The number of units in the linear layer. blocks (int): The number of encoder blocks. pw_layer_type (str): The type of position-wise layer to use. pw_conv_kernel_size (int): The kernel size of the position-wise convolutional layer. pos_enc_layer_type (str): The type of positional encoding layer to use. self_attention_layer_type (str): The type of self-attention layer to use. activation_type (str): The type of activation function to use. normalize_before (bool): Whether to normalize the data before the position-wise layer or after. use_macaron_style (bool): Whether to use the macaron style or not. use_conformer_conv (bool): Whether to use Conformer style conv or not. conformer_kernel_size (int): The kernel size of the conformer convolutional layer. dropout_rate (float): The dropout rate to use. positional_dropout_rate (float): The positional dropout rate to use. attention_dropout_rate (float): The attention dropout rate to use. global_channels (int): The number of channels to use for global conditioning. """ super().__init__() self.prenet = torch.nn.Conv1d(attention_dim + 2, attention_dim, 3, padding=1) self.decoder = 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=pw_layer_type, positionwise_conv_kernel_size=pw_conv_kernel_size, macaron_style=use_macaron_style, pos_enc_layer_type=pos_enc_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, out_channels, 1) if global_channels > 0: self.global_conv = torch.nn.Conv1d(global_channels, attention_dim, 1)
[docs] def forward(self, x, x_lengths, g=None): """Forward pass of the Decoder. Args: x (Tensor): Input tensor (B, 2 + attention_dim, T). x_lengths (Tensor): Length tensor (B,). g (Tensor, optional): Global conditioning tensor (B, global_channels, 1). Returns: Tensor: Output tensor (B, 1, T). Tensor: Output mask (B, 1, T). """ x_mask = ( make_non_pad_mask(x_lengths) .to( device=x.device, dtype=x.dtype, ) .unsqueeze(1) ) x = self.prenet(x) * x_mask if g is not None: x = x + self.global_conv(g) x = x.transpose(1, 2) x, _ = self.decoder(x, x_mask) x = x.transpose(1, 2) x = self.proj(x) * x_mask return x, x_mask