mirror of https://github.com/coqui-ai/TTS.git
78 lines
2.6 KiB
Python
78 lines
2.6 KiB
Python
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 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.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, use_spectral_norm=False):
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super().__init__()
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self.sd = DiscriminatorS(use_spectral_norm=use_spectral_norm)
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self.mpd = MultiPeriodDiscriminator(use_spectral_norm=use_spectral_norm)
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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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score_sd, feats_sd = self.sd(x)
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return scores + [score_sd], feats + [feats_sd]
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