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"""Residual stack module in MelGAN.""" |
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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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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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