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| # -*- coding: utf-8 -*- | |
| # Copyright 2020 Tomoki Hayashi | |
| # MIT License (https://opensource.org/licenses/MIT) | |
| """Residual stack module in MelGAN.""" | |
| import torch | |
| from . import CausalConv1d | |
| class ResidualStack(torch.nn.Module): | |
| """Residual stack module introduced in MelGAN.""" | |
| def __init__(self, | |
| kernel_size=3, | |
| channels=32, | |
| dilation=1, | |
| bias=True, | |
| nonlinear_activation="LeakyReLU", | |
| nonlinear_activation_params={"negative_slope": 0.2}, | |
| pad="ReflectionPad1d", | |
| pad_params={}, | |
| use_causal_conv=False, | |
| ): | |
| """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): Hyperparameters for activation function. | |
| pad (str): Padding function module name before dilated convolution layer. | |
| pad_params (dict): Hyperparameters for padding function. | |
| use_causal_conv (bool): Whether to use causal convolution. | |
| """ | |
| super(ResidualStack, self).__init__() | |
| # defile residual stack part | |
| if not use_causal_conv: | |
| 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), | |
| ) | |
| else: | |
| self.stack = torch.nn.Sequential( | |
| getattr(torch.nn, nonlinear_activation)(**nonlinear_activation_params), | |
| CausalConv1d(channels, channels, kernel_size, dilation=dilation, | |
| bias=bias, pad=pad, pad_params=pad_params), | |
| 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) | |
| def forward(self, c): | |
| """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) | |