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| """Residual stack module in MelGAN."""
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|
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| import torch
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| from . import CausalConv1d
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| class ResidualStack(torch.nn.Module):
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| """Residual stack module introduced in MelGAN."""
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| def __init__(self,
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| kernel_size=3,
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| channels=32,
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| dilation=1,
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| bias=True,
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| nonlinear_activation="LeakyReLU",
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| nonlinear_activation_params={"negative_slope": 0.2},
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| pad="ReflectionPad1d",
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| pad_params={},
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| use_causal_conv=False,
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| ):
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| """Initialize ResidualStack module.
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| Args:
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| kernel_size (int): Kernel size of dilation convolution layer.
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| channels (int): Number of channels of convolution layers.
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| dilation (int): Dilation factor.
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| bias (bool): Whether to add bias parameter in convolution layers.
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| nonlinear_activation (str): Activation function module name.
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| nonlinear_activation_params (dict): Hyperparameters for activation function.
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| pad (str): Padding function module name before dilated convolution layer.
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| pad_params (dict): Hyperparameters for padding function.
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| use_causal_conv (bool): Whether to use causal convolution.
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|
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| """
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| super(ResidualStack, self).__init__()
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| if not use_causal_conv:
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| assert (kernel_size - 1) % 2 == 0, "Not support even number kernel size."
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| self.stack = torch.nn.Sequential(
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| getattr(torch.nn, nonlinear_activation)(**nonlinear_activation_params),
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| getattr(torch.nn, pad)((kernel_size - 1) // 2 * dilation, **pad_params),
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| torch.nn.Conv1d(channels, channels, kernel_size, dilation=dilation, bias=bias),
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| getattr(torch.nn, nonlinear_activation)(**nonlinear_activation_params),
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| torch.nn.Conv1d(channels, channels, 1, bias=bias),
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| )
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| else:
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| self.stack = torch.nn.Sequential(
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| getattr(torch.nn, nonlinear_activation)(**nonlinear_activation_params),
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| CausalConv1d(channels, channels, kernel_size, dilation=dilation,
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| bias=bias, pad=pad, pad_params=pad_params),
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| getattr(torch.nn, nonlinear_activation)(**nonlinear_activation_params),
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| torch.nn.Conv1d(channels, channels, 1, bias=bias),
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| )
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| self.skip_layer = torch.nn.Conv1d(channels, channels, 1, bias=bias)
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| def forward(self, c):
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| """Calculate forward propagation.
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| Args:
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| c (Tensor): Input tensor (B, channels, T).
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| Returns:
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| Tensor: Output tensor (B, chennels, T).
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| """
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| return self.stack(c) + self.skip_layer(c)
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|
