90 lines
3.2 KiB
Python
90 lines
3.2 KiB
Python
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import torch
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from torch import nn
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from torch.nn.modules.conv import Conv1d
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from TTS.vocoder.models.hifigan_discriminator import DiscriminatorP, MultiPeriodDiscriminator
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class DiscriminatorS(torch.nn.Module):
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"""HiFiGAN Scale Discriminator. Channel sizes are different from the original HiFiGAN.
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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(Conv1d(1, 16, 15, 1, padding=7)),
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norm_f(Conv1d(16, 64, 41, 4, groups=4, padding=20)),
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norm_f(Conv1d(64, 256, 41, 4, groups=16, padding=20)),
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norm_f(Conv1d(256, 1024, 41, 4, groups=64, padding=20)),
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norm_f(Conv1d(1024, 1024, 41, 4, groups=256, padding=20)),
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norm_f(Conv1d(1024, 1024, 5, 1, padding=2)),
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]
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)
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self.conv_post = norm_f(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 = torch.nn.functional.leaky_relu(x, 0.1)
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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 VitsDiscriminator(nn.Module):
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"""VITS discriminator wrapping one Scale Discriminator and a stack of Period Discriminator.
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::
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waveform -> ScaleDiscriminator() -> scores_sd, feats_sd --> append() -> scores, feats
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|--> MultiPeriodDiscriminator() -> scores_mpd, feats_mpd ^
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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, periods=(2, 3, 5, 7, 11), use_spectral_norm=False):
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super().__init__()
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self.nets = nn.ModuleList()
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self.nets.append(DiscriminatorS(use_spectral_norm=use_spectral_norm))
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self.nets.extend([DiscriminatorP(i, use_spectral_norm=use_spectral_norm) for i in periods])
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def forward(self, x, x_hat=None):
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"""
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Args:
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x (Tensor): ground truth waveform.
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x_hat (Tensor): predicted 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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x_scores = []
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x_hat_scores = [] if x_hat is not None else None
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x_feats = []
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x_hat_feats = [] if x_hat is not None else None
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for net in self.nets:
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x_score, x_feat = net(x)
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x_scores.append(x_score)
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x_feats.append(x_feat)
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if x_hat is not None:
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x_hat_score, x_hat_feat = net(x_hat)
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x_hat_scores.append(x_hat_score)
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x_hat_feats.append(x_hat_feat)
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return x_scores, x_feats, x_hat_scores, x_hat_feats
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