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import torch |
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import torch.nn.functional as F |
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from torch import nn |
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from torch.nn.utils import spectral_norm, weight_norm |
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from TTS.utils.audio.torch_transforms import TorchSTFT |
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from TTS.vocoder.models.hifigan_discriminator import MultiPeriodDiscriminator |
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LRELU_SLOPE = 0.1 |
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class SpecDiscriminator(nn.Module): |
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"""docstring for Discriminator.""" |
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def __init__(self, fft_size=1024, hop_length=120, win_length=600, use_spectral_norm=False): |
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super().__init__() |
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norm_f = weight_norm if use_spectral_norm is False else spectral_norm |
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self.fft_size = fft_size |
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self.hop_length = hop_length |
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self.win_length = win_length |
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self.stft = TorchSTFT(fft_size, hop_length, win_length) |
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self.discriminators = nn.ModuleList( |
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[ |
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norm_f(nn.Conv2d(1, 32, kernel_size=(3, 9), padding=(1, 4))), |
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norm_f(nn.Conv2d(32, 32, kernel_size=(3, 9), stride=(1, 2), padding=(1, 4))), |
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norm_f(nn.Conv2d(32, 32, kernel_size=(3, 9), stride=(1, 2), padding=(1, 4))), |
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norm_f(nn.Conv2d(32, 32, kernel_size=(3, 9), stride=(1, 2), padding=(1, 4))), |
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norm_f(nn.Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))), |
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] |
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) |
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self.out = norm_f(nn.Conv2d(32, 1, 3, 1, 1)) |
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def forward(self, y): |
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fmap = [] |
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with torch.no_grad(): |
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y = y.squeeze(1) |
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y = self.stft(y) |
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y = y.unsqueeze(1) |
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for _, d in enumerate(self.discriminators): |
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y = d(y) |
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y = F.leaky_relu(y, LRELU_SLOPE) |
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fmap.append(y) |
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y = self.out(y) |
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fmap.append(y) |
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return torch.flatten(y, 1, -1), fmap |
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class MultiResSpecDiscriminator(torch.nn.Module): |
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def __init__( |
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self, fft_sizes=[1024, 2048, 512], hop_sizes=[120, 240, 50], win_lengths=[600, 1200, 240], window="hann_window" |
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): |
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super().__init__() |
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self.discriminators = nn.ModuleList( |
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[ |
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SpecDiscriminator(fft_sizes[0], hop_sizes[0], win_lengths[0], window), |
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SpecDiscriminator(fft_sizes[1], hop_sizes[1], win_lengths[1], window), |
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SpecDiscriminator(fft_sizes[2], hop_sizes[2], win_lengths[2], window), |
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] |
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) |
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def forward(self, x): |
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scores = [] |
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feats = [] |
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for d in 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 UnivnetDiscriminator(nn.Module): |
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"""Univnet 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 = MultiResSpecDiscriminator() |
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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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