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Zero
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# -*- coding: utf-8 -*-
"""
References:
- https://github.com/jik876/hifi-gan
- https://github.com/kan-bayashi/ParallelWaveGAN
"""
import torch
class Conv1d(torch.nn.Conv1d):
"""
Conv1d module with customized initialization.
"""
def __init__(self, *args, **kwargs):
super(Conv1d, self).__init__(*args, **kwargs)
def reset_parameters(self):
torch.nn.init.kaiming_normal_(self.weight, nonlinearity="relu")
if self.bias is not None:
torch.nn.init.constant_(self.bias, 0.0)
class Conv1d1x1(Conv1d):
"""
1x1 Conv1d with customized initialization.
"""
def __init__(self, in_channels, out_channels, bias):
super(Conv1d1x1, self).__init__(in_channels, out_channels, kernel_size=1, padding=0, dilation=1, bias=bias)
class HiFiGANResidualBlock(torch.nn.Module):
"""Residual block module in HiFiGAN."""
def __init__(self,
kernel_size=3,
channels=512,
dilations=(1, 3, 5),
bias=True,
use_additional_convs=True,
nonlinear_activation="LeakyReLU",
nonlinear_activation_params={"negative_slope": 0.1}, ):
"""
Initialize HiFiGANResidualBlock 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): 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, ), )]
def forward(self, x):
"""
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
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