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import torch |
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from torch import nn |
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from torch.nn import functional as F |
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LRELU_SLOPE = 0.1 |
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class DiscriminatorP(torch.nn.Module): |
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"""HiFiGAN Periodic Discriminator |
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Takes every Pth value from the input waveform and applied a stack of convoluations. |
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Note: |
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if `period` is 2 |
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`waveform = [1, 2, 3, 4, 5, 6 ...] --> [1, 3, 5 ... ] --> convs -> score, feat` |
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Args: |
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x (Tensor): input waveform. |
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Returns: |
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[Tensor]: discriminator scores per sample in the batch. |
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[List[Tensor]]: list of features from each convolutional layer. |
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Shapes: |
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x: [B, 1, T] |
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""" |
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def __init__(self, period, kernel_size=5, stride=3, use_spectral_norm=False): |
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super().__init__() |
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self.period = period |
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get_padding = lambda k, d: int((k * d - d) / 2) |
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norm_f = nn.utils.spectral_norm if use_spectral_norm else nn.utils.weight_norm |
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self.convs = nn.ModuleList( |
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[ |
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norm_f(nn.Conv2d(1, 32, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))), |
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norm_f(nn.Conv2d(32, 128, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))), |
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norm_f(nn.Conv2d(128, 512, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))), |
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norm_f(nn.Conv2d(512, 1024, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))), |
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norm_f(nn.Conv2d(1024, 1024, (kernel_size, 1), 1, padding=(2, 0))), |
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] |
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) |
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self.conv_post = norm_f(nn.Conv2d(1024, 1, (3, 1), 1, padding=(1, 0))) |
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def forward(self, x): |
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""" |
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Args: |
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x (Tensor): input waveform. |
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Returns: |
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[Tensor]: discriminator scores per sample in the batch. |
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[List[Tensor]]: list of features from each convolutional layer. |
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Shapes: |
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x: [B, 1, T] |
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""" |
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feat = [] |
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b, c, t = x.shape |
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if t % self.period != 0: |
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n_pad = self.period - (t % self.period) |
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x = F.pad(x, (0, n_pad), "reflect") |
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t = t + n_pad |
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x = x.view(b, c, t // self.period, self.period) |
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for l in self.convs: |
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x = l(x) |
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x = F.leaky_relu(x, LRELU_SLOPE) |
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feat.append(x) |
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x = self.conv_post(x) |
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feat.append(x) |
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x = torch.flatten(x, 1, -1) |
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return x, feat |
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class MultiPeriodDiscriminator(torch.nn.Module): |
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"""HiFiGAN Multi-Period Discriminator (MPD) |
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Wrapper for the `PeriodDiscriminator` to apply it in different periods. |
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Periods are suggested to be prime numbers to reduce the overlap between each discriminator. |
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""" |
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def __init__(self, use_spectral_norm=False): |
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super().__init__() |
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self.discriminators = nn.ModuleList( |
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[ |
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DiscriminatorP(2, use_spectral_norm=use_spectral_norm), |
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DiscriminatorP(3, use_spectral_norm=use_spectral_norm), |
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DiscriminatorP(5, use_spectral_norm=use_spectral_norm), |
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DiscriminatorP(7, use_spectral_norm=use_spectral_norm), |
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DiscriminatorP(11, use_spectral_norm=use_spectral_norm), |
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] |
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) |
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def forward(self, x): |
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""" |
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Args: |
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x (Tensor): input waveform. |
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Returns: |
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[List[Tensor]]: list of scores from each discriminator. |
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[List[List[Tensor]]]: list of list of features from each discriminator's each convolutional layer. |
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Shapes: |
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x: [B, 1, T] |
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""" |
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scores = [] |
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feats = [] |
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for _, d in enumerate(self.discriminators): |
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score, feat = d(x) |
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scores.append(score) |
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feats.append(feat) |
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return scores, feats |
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class DiscriminatorS(torch.nn.Module): |
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"""HiFiGAN Scale Discriminator. |
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It is similar to `MelganDiscriminator` but with a specific architecture explained in the paper. |
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Args: |
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use_spectral_norm (bool): if `True` swith to spectral norm instead of weight norm. |
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""" |
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def __init__(self, use_spectral_norm=False): |
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super().__init__() |
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norm_f = nn.utils.spectral_norm if use_spectral_norm else nn.utils.weight_norm |
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self.convs = nn.ModuleList( |
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[ |
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norm_f(nn.Conv1d(1, 128, 15, 1, padding=7)), |
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norm_f(nn.Conv1d(128, 128, 41, 2, groups=4, padding=20)), |
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norm_f(nn.Conv1d(128, 256, 41, 2, groups=16, padding=20)), |
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norm_f(nn.Conv1d(256, 512, 41, 4, groups=16, padding=20)), |
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norm_f(nn.Conv1d(512, 1024, 41, 4, groups=16, padding=20)), |
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norm_f(nn.Conv1d(1024, 1024, 41, 1, groups=16, padding=20)), |
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norm_f(nn.Conv1d(1024, 1024, 5, 1, padding=2)), |
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] |
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) |
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self.conv_post = norm_f(nn.Conv1d(1024, 1, 3, 1, padding=1)) |
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def forward(self, x): |
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""" |
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Args: |
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x (Tensor): input waveform. |
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Returns: |
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Tensor: discriminator scores. |
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List[Tensor]: list of features from the convolutiona layers. |
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""" |
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feat = [] |
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for l in self.convs: |
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x = l(x) |
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x = F.leaky_relu(x, LRELU_SLOPE) |
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feat.append(x) |
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x = self.conv_post(x) |
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feat.append(x) |
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x = torch.flatten(x, 1, -1) |
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return x, feat |
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class MultiScaleDiscriminator(torch.nn.Module): |
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"""HiFiGAN Multi-Scale Discriminator. |
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It is similar to `MultiScaleMelganDiscriminator` but specially tailored for HiFiGAN as in the paper. |
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""" |
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def __init__(self): |
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super().__init__() |
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self.discriminators = nn.ModuleList( |
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[ |
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DiscriminatorS(use_spectral_norm=True), |
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DiscriminatorS(), |
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DiscriminatorS(), |
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] |
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) |
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self.meanpools = nn.ModuleList([nn.AvgPool1d(4, 2, padding=2), nn.AvgPool1d(4, 2, padding=2)]) |
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def forward(self, x): |
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""" |
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Args: |
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x (Tensor): input waveform. |
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Returns: |
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List[Tensor]: discriminator scores. |
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List[List[Tensor]]: list of list of features from each layers of each discriminator. |
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""" |
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scores = [] |
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feats = [] |
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for i, d in enumerate(self.discriminators): |
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if i != 0: |
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x = self.meanpools[i - 1](x) |
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score, feat = d(x) |
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scores.append(score) |
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feats.append(feat) |
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return scores, feats |
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class HifiganDiscriminator(nn.Module): |
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"""HiFiGAN discriminator wrapping MPD and MSD.""" |
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def __init__(self): |
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super().__init__() |
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self.mpd = MultiPeriodDiscriminator() |
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self.msd = MultiScaleDiscriminator() |
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def forward(self, x): |
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""" |
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Args: |
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x (Tensor): input waveform. |
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Returns: |
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List[Tensor]: discriminator scores. |
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List[List[Tensor]]: list of list of features from each layers of each discriminator. |
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""" |
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scores, feats = self.mpd(x) |
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scores_, feats_ = self.msd(x) |
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return scores + scores_, feats + feats_ |
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