218 lines
7.1 KiB
Python
218 lines
7.1 KiB
Python
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# adopted from https://github.com/jik876/hifi-gan/blob/master/models.py
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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.parametrizations.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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# 1d to 2d
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b, c, t = x.shape
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if t % self.period != 0: # pad first
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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.parametrizations.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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