| import torch |
| import torch.nn.functional as F |
| from torch import nn |
| from torch.nn.utils import spectral_norm |
| from torch.nn.utils.parametrizations import weight_norm |
|
|
| from src.utils.TTS.utils.audio.torch_transforms import TorchSTFT |
| from src.utils.TTS.vocoder.models.hifigan_discriminator import MultiPeriodDiscriminator |
|
|
| LRELU_SLOPE = 0.1 |
|
|
|
|
| class SpecDiscriminator(nn.Module): |
| """docstring for Discriminator.""" |
|
|
| def __init__(self, fft_size=1024, hop_length=120, win_length=600, use_spectral_norm=False): |
| super().__init__() |
| norm_f = weight_norm if use_spectral_norm is False else spectral_norm |
| self.fft_size = fft_size |
| self.hop_length = hop_length |
| self.win_length = win_length |
| self.stft = TorchSTFT(fft_size, hop_length, win_length) |
| self.discriminators = nn.ModuleList( |
| [ |
| norm_f(nn.Conv2d(1, 32, kernel_size=(3, 9), padding=(1, 4))), |
| norm_f(nn.Conv2d(32, 32, kernel_size=(3, 9), stride=(1, 2), padding=(1, 4))), |
| norm_f(nn.Conv2d(32, 32, kernel_size=(3, 9), stride=(1, 2), padding=(1, 4))), |
| norm_f(nn.Conv2d(32, 32, kernel_size=(3, 9), stride=(1, 2), padding=(1, 4))), |
| norm_f(nn.Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))), |
| ] |
| ) |
|
|
| self.out = norm_f(nn.Conv2d(32, 1, 3, 1, 1)) |
|
|
| def forward(self, y): |
| fmap = [] |
| with torch.no_grad(): |
| y = y.squeeze(1) |
| y = self.stft(y) |
| y = y.unsqueeze(1) |
| for _, d in enumerate(self.discriminators): |
| y = d(y) |
| y = F.leaky_relu(y, LRELU_SLOPE) |
| fmap.append(y) |
|
|
| y = self.out(y) |
| fmap.append(y) |
|
|
| return torch.flatten(y, 1, -1), fmap |
|
|
|
|
| class MultiResSpecDiscriminator(torch.nn.Module): |
| def __init__( |
| self, fft_sizes=[1024, 2048, 512], hop_sizes=[120, 240, 50], win_lengths=[600, 1200, 240], window="hann_window" |
| ): |
| super().__init__() |
| self.discriminators = nn.ModuleList( |
| [ |
| SpecDiscriminator(fft_sizes[0], hop_sizes[0], win_lengths[0], window), |
| SpecDiscriminator(fft_sizes[1], hop_sizes[1], win_lengths[1], window), |
| SpecDiscriminator(fft_sizes[2], hop_sizes[2], win_lengths[2], window), |
| ] |
| ) |
|
|
| def forward(self, x): |
| scores = [] |
| feats = [] |
| for d in self.discriminators: |
| score, feat = d(x) |
| scores.append(score) |
| feats.append(feat) |
|
|
| return scores, feats |
|
|
|
|
| class UnivnetDiscriminator(nn.Module): |
| """Univnet discriminator wrapping MPD and MSD.""" |
|
|
| def __init__(self): |
| super().__init__() |
| self.mpd = MultiPeriodDiscriminator() |
| self.msd = MultiResSpecDiscriminator() |
|
|
| def forward(self, x): |
| """ |
| Args: |
| x (Tensor): input waveform. |
| |
| Returns: |
| List[Tensor]: discriminator scores. |
| List[List[Tensor]]: list of list of features from each layers of each discriminator. |
| """ |
| scores, feats = self.mpd(x) |
| scores_, feats_ = self.msd(x) |
| return scores + scores_, feats + feats_ |
|
|
