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| # adopted from https://github.com/jik876/hifi-gan/blob/master/models.py | |
| import torch | |
| from torch import nn | |
| from torch.nn import functional as F | |
| LRELU_SLOPE = 0.1 | |
| class DiscriminatorP(torch.nn.Module): | |
| """HiFiGAN Periodic Discriminator | |
| Takes every Pth value from the input waveform and applied a stack of convoluations. | |
| Note: | |
| if `period` is 2 | |
| `waveform = [1, 2, 3, 4, 5, 6 ...] --> [1, 3, 5 ... ] --> convs -> score, feat` | |
| Args: | |
| x (Tensor): input waveform. | |
| Returns: | |
| [Tensor]: discriminator scores per sample in the batch. | |
| [List[Tensor]]: list of features from each convolutional layer. | |
| Shapes: | |
| x: [B, 1, T] | |
| """ | |
| def __init__(self, period, kernel_size=5, stride=3, use_spectral_norm=False): | |
| super().__init__() | |
| self.period = period | |
| get_padding = lambda k, d: int((k * d - d) / 2) | |
| norm_f = nn.utils.spectral_norm if use_spectral_norm else nn.utils.parametrizations.weight_norm | |
| self.convs = nn.ModuleList( | |
| [ | |
| norm_f(nn.Conv2d(1, 32, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))), | |
| norm_f(nn.Conv2d(32, 128, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))), | |
| norm_f(nn.Conv2d(128, 512, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))), | |
| norm_f(nn.Conv2d(512, 1024, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))), | |
| norm_f(nn.Conv2d(1024, 1024, (kernel_size, 1), 1, padding=(2, 0))), | |
| ] | |
| ) | |
| self.conv_post = norm_f(nn.Conv2d(1024, 1, (3, 1), 1, padding=(1, 0))) | |
| def forward(self, x): | |
| """ | |
| Args: | |
| x (Tensor): input waveform. | |
| Returns: | |
| [Tensor]: discriminator scores per sample in the batch. | |
| [List[Tensor]]: list of features from each convolutional layer. | |
| Shapes: | |
| x: [B, 1, T] | |
| """ | |
| feat = [] | |
| # 1d to 2d | |
| b, c, t = x.shape | |
| if t % self.period != 0: # pad first | |
| n_pad = self.period - (t % self.period) | |
| x = F.pad(x, (0, n_pad), "reflect") | |
| t = t + n_pad | |
| x = x.view(b, c, t // self.period, self.period) | |
| for l in self.convs: | |
| x = l(x) | |
| x = F.leaky_relu(x, LRELU_SLOPE) | |
| feat.append(x) | |
| x = self.conv_post(x) | |
| feat.append(x) | |
| x = torch.flatten(x, 1, -1) | |
| return x, feat | |
| class MultiPeriodDiscriminator(torch.nn.Module): | |
| """HiFiGAN Multi-Period Discriminator (MPD) | |
| Wrapper for the `PeriodDiscriminator` to apply it in different periods. | |
| Periods are suggested to be prime numbers to reduce the overlap between each discriminator. | |
| """ | |
| def __init__(self, use_spectral_norm=False): | |
| super().__init__() | |
| self.discriminators = nn.ModuleList( | |
| [ | |
| DiscriminatorP(2, use_spectral_norm=use_spectral_norm), | |
| DiscriminatorP(3, use_spectral_norm=use_spectral_norm), | |
| DiscriminatorP(5, use_spectral_norm=use_spectral_norm), | |
| DiscriminatorP(7, use_spectral_norm=use_spectral_norm), | |
| DiscriminatorP(11, use_spectral_norm=use_spectral_norm), | |
| ] | |
| ) | |
| def forward(self, x): | |
| """ | |
| Args: | |
| x (Tensor): input waveform. | |
| Returns: | |
| [List[Tensor]]: list of scores from each discriminator. | |
| [List[List[Tensor]]]: list of list of features from each discriminator's each convolutional layer. | |
| Shapes: | |
| x: [B, 1, T] | |
| """ | |
| scores = [] | |
| feats = [] | |
| for _, d in enumerate(self.discriminators): | |
| score, feat = d(x) | |
| scores.append(score) | |
| feats.append(feat) | |
| return scores, feats | |
| class DiscriminatorS(torch.nn.Module): | |
| """HiFiGAN Scale Discriminator. | |
| It is similar to `MelganDiscriminator` but with a specific architecture explained in the paper. | |
| Args: | |
| use_spectral_norm (bool): if `True` swith to spectral norm instead of weight norm. | |
| """ | |
| def __init__(self, use_spectral_norm=False): | |
| super().__init__() | |
| norm_f = nn.utils.spectral_norm if use_spectral_norm else nn.utils.parametrizations.weight_norm | |
| self.convs = nn.ModuleList( | |
| [ | |
| norm_f(nn.Conv1d(1, 128, 15, 1, padding=7)), | |
| norm_f(nn.Conv1d(128, 128, 41, 2, groups=4, padding=20)), | |
| norm_f(nn.Conv1d(128, 256, 41, 2, groups=16, padding=20)), | |
| norm_f(nn.Conv1d(256, 512, 41, 4, groups=16, padding=20)), | |
| norm_f(nn.Conv1d(512, 1024, 41, 4, groups=16, padding=20)), | |
| norm_f(nn.Conv1d(1024, 1024, 41, 1, groups=16, padding=20)), | |
| norm_f(nn.Conv1d(1024, 1024, 5, 1, padding=2)), | |
| ] | |
| ) | |
| self.conv_post = norm_f(nn.Conv1d(1024, 1, 3, 1, padding=1)) | |
| def forward(self, x): | |
| """ | |
| Args: | |
| x (Tensor): input waveform. | |
| Returns: | |
| Tensor: discriminator scores. | |
| List[Tensor]: list of features from the convolutiona layers. | |
| """ | |
| feat = [] | |
| for l in self.convs: | |
| x = l(x) | |
| x = F.leaky_relu(x, LRELU_SLOPE) | |
| feat.append(x) | |
| x = self.conv_post(x) | |
| feat.append(x) | |
| x = torch.flatten(x, 1, -1) | |
| return x, feat | |
| class MultiScaleDiscriminator(torch.nn.Module): | |
| """HiFiGAN Multi-Scale Discriminator. | |
| It is similar to `MultiScaleMelganDiscriminator` but specially tailored for HiFiGAN as in the paper. | |
| """ | |
| def __init__(self): | |
| super().__init__() | |
| self.discriminators = nn.ModuleList( | |
| [ | |
| DiscriminatorS(use_spectral_norm=True), | |
| DiscriminatorS(), | |
| DiscriminatorS(), | |
| ] | |
| ) | |
| self.meanpools = nn.ModuleList([nn.AvgPool1d(4, 2, padding=2), nn.AvgPool1d(4, 2, padding=2)]) | |
| 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 = [] | |
| for i, d in enumerate(self.discriminators): | |
| if i != 0: | |
| x = self.meanpools[i - 1](x) | |
| score, feat = d(x) | |
| scores.append(score) | |
| feats.append(feat) | |
| return scores, feats | |
| class HifiganDiscriminator(nn.Module): | |
| """HiFiGAN discriminator wrapping MPD and MSD.""" | |
| def __init__(self): | |
| super().__init__() | |
| self.mpd = MultiPeriodDiscriminator() | |
| self.msd = MultiScaleDiscriminator() | |
| 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_ | |