# Copyright 2021 Tomoki Hayashi
# Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0)
"""Residual stack module in MelGAN.
This code is modified from https://github.com/kan-bayashi/ParallelWaveGAN.
"""
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)