Source code for espnet2.gan_tts.melgan.residual_stack

# Copyright 2021 Tomoki Hayashi
#  Apache 2.0  (

"""Residual stack module in MelGAN.

This code is modified from


from typing import Any, Dict

import torch

[docs]class ResidualStack(torch.nn.Module): """Residual stack module introduced in MelGAN.""" def __init__( self, kernel_size: int = 3, channels: int = 32, dilation: int = 1, bias: bool = True, nonlinear_activation: str = "LeakyReLU", nonlinear_activation_params: Dict[str, Any] = {"negative_slope": 0.2}, pad: str = "ReflectionPad1d", pad_params: Dict[str, Any] = {}, ): """Initialize ResidualStack module. Args: kernel_size (int): Kernel size of dilation convolution layer. channels (int): Number of channels of convolution layers. dilation (int): Dilation factor. bias (bool): Whether to add bias parameter in convolution layers. nonlinear_activation (str): Activation function module name. nonlinear_activation_params (Dict[str, Any]): Hyperparameters for activation function. pad (str): Padding function module name before dilated convolution layer. pad_params (Dict[str, Any]): Hyperparameters for padding function. """ super().__init__() # defile residual stack part assert (kernel_size - 1) % 2 == 0, "Not support even number kernel size." self.stack = torch.nn.Sequential( getattr(torch.nn, nonlinear_activation)(**nonlinear_activation_params), getattr(torch.nn, pad)((kernel_size - 1) // 2 * dilation, **pad_params), torch.nn.Conv1d( channels, channels, kernel_size, dilation=dilation, bias=bias ), getattr(torch.nn, nonlinear_activation)(**nonlinear_activation_params), torch.nn.Conv1d(channels, channels, 1, bias=bias), ) # defile extra layer for skip connection self.skip_layer = torch.nn.Conv1d(channels, channels, 1, bias=bias)
[docs] def forward(self, c: torch.Tensor) -> torch.Tensor: """Calculate forward propagation. Args: c (Tensor): Input tensor (B, channels, T). Returns: Tensor: Output tensor (B, chennels, T). """ return self.stack(c) + self.skip_layer(c)