# Copyright 2021 Tomoki Hayashi
# Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0)
"""HiFiGAN Residual block modules.
This code is modified from https://github.com/kan-bayashi/ParallelWaveGAN.
"""
from typing import Any, Dict, List
import torch
[docs]class ResidualBlock(torch.nn.Module):
"""Residual block module in HiFiGAN."""
def __init__(
self,
kernel_size: int = 3,
channels: int = 512,
dilations: List[int] = [1, 3, 5],
bias: bool = True,
use_additional_convs: bool = True,
nonlinear_activation: str = "LeakyReLU",
nonlinear_activation_params: Dict[str, Any] = {"negative_slope": 0.1},
):
"""Initialize ResidualBlock module.
Args:
kernel_size (int): Kernel size of dilation convolution layer.
channels (int): Number of channels for convolution layer.
dilations (List[int]): List of dilation factors.
use_additional_convs (bool): Whether to use additional convolution layers.
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.
"""
super().__init__()
self.use_additional_convs = use_additional_convs
self.convs1 = torch.nn.ModuleList()
if use_additional_convs:
self.convs2 = torch.nn.ModuleList()
assert kernel_size % 2 == 1, "Kernel size must be odd number."
for dilation in dilations:
self.convs1 += [
torch.nn.Sequential(
getattr(torch.nn, nonlinear_activation)(
**nonlinear_activation_params
),
torch.nn.Conv1d(
channels,
channels,
kernel_size,
1,
dilation=dilation,
bias=bias,
padding=(kernel_size - 1) // 2 * dilation,
),
)
]
if use_additional_convs:
self.convs2 += [
torch.nn.Sequential(
getattr(torch.nn, nonlinear_activation)(
**nonlinear_activation_params
),
torch.nn.Conv1d(
channels,
channels,
kernel_size,
1,
dilation=1,
bias=bias,
padding=(kernel_size - 1) // 2,
),
)
]
[docs] def forward(self, x: torch.Tensor) -> torch.Tensor:
"""Calculate forward propagation.
Args:
x (Tensor): Input tensor (B, channels, T).
Returns:
Tensor: Output tensor (B, channels, T).
"""
for idx in range(len(self.convs1)):
xt = self.convs1[idx](x)
if self.use_additional_convs:
xt = self.convs2[idx](xt)
x = xt + x
return x