| import torch |
| import torch.nn.functional as F |
| import torch.nn as nn |
| from torch.nn import Conv1d, AvgPool1d, Conv2d |
| from torch.nn.utils import weight_norm, spectral_norm |
|
|
| from .utils import get_padding |
|
|
| LRELU_SLOPE = 0.1 |
|
|
| def stft(x, fft_size, hop_size, win_length, window): |
| """Perform STFT and convert to magnitude spectrogram. |
| Args: |
| x (Tensor): Input signal tensor (B, T). |
| fft_size (int): FFT size. |
| hop_size (int): Hop size. |
| win_length (int): Window length. |
| window (str): Window function type. |
| Returns: |
| Tensor: Magnitude spectrogram (B, #frames, fft_size // 2 + 1). |
| """ |
| x_stft = torch.stft(x, fft_size, hop_size, win_length, window, |
| return_complex=True) |
| real = x_stft[..., 0] |
| imag = x_stft[..., 1] |
|
|
| return torch.abs(x_stft).transpose(2, 1) |
|
|
| class SpecDiscriminator(nn.Module): |
| """docstring for Discriminator.""" |
|
|
| def __init__(self, fft_size=1024, shift_size=120, win_length=600, window="hann_window", use_spectral_norm=False): |
| super(SpecDiscriminator, self).__init__() |
| norm_f = weight_norm if use_spectral_norm == False else spectral_norm |
| self.fft_size = fft_size |
| self.shift_size = shift_size |
| self.win_length = win_length |
| self.window = getattr(torch, window)(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 = [] |
| y = y.squeeze(1) |
| y = stft(y, self.fft_size, self.shift_size, self.win_length, self.window.to(y.get_device())) |
| y = y.unsqueeze(1) |
| for i, 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(MultiResSpecDiscriminator, self).__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, y, y_hat): |
| y_d_rs = [] |
| y_d_gs = [] |
| fmap_rs = [] |
| fmap_gs = [] |
| for i, d in enumerate(self.discriminators): |
| y_d_r, fmap_r = d(y) |
| y_d_g, fmap_g = d(y_hat) |
| y_d_rs.append(y_d_r) |
| fmap_rs.append(fmap_r) |
| y_d_gs.append(y_d_g) |
| fmap_gs.append(fmap_g) |
|
|
| return y_d_rs, y_d_gs, fmap_rs, fmap_gs |
|
|
|
|
| class DiscriminatorP(torch.nn.Module): |
| def __init__(self, period, kernel_size=5, stride=3, use_spectral_norm=False): |
| super(DiscriminatorP, self).__init__() |
| self.period = period |
| norm_f = weight_norm if use_spectral_norm == False else spectral_norm |
| self.convs = nn.ModuleList([ |
| norm_f(Conv2d(1, 32, (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0))), |
| norm_f(Conv2d(32, 128, (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0))), |
| norm_f(Conv2d(128, 512, (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0))), |
| norm_f(Conv2d(512, 1024, (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0))), |
| norm_f(Conv2d(1024, 1024, (kernel_size, 1), 1, padding=(2, 0))), |
| ]) |
| self.conv_post = norm_f(Conv2d(1024, 1, (3, 1), 1, padding=(1, 0))) |
|
|
| def forward(self, x): |
| fmap = [] |
|
|
| |
| b, c, t = x.shape |
| if t % self.period != 0: |
| 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) |
| fmap.append(x) |
| x = self.conv_post(x) |
| fmap.append(x) |
| x = torch.flatten(x, 1, -1) |
|
|
| return x, fmap |
|
|
|
|
| class MultiPeriodDiscriminator(torch.nn.Module): |
| def __init__(self): |
| super(MultiPeriodDiscriminator, self).__init__() |
| self.discriminators = nn.ModuleList([ |
| DiscriminatorP(2), |
| DiscriminatorP(3), |
| DiscriminatorP(5), |
| DiscriminatorP(7), |
| DiscriminatorP(11), |
| ]) |
|
|
| def forward(self, y, y_hat): |
| y_d_rs = [] |
| y_d_gs = [] |
| fmap_rs = [] |
| fmap_gs = [] |
| for i, d in enumerate(self.discriminators): |
| y_d_r, fmap_r = d(y) |
| y_d_g, fmap_g = d(y_hat) |
| y_d_rs.append(y_d_r) |
| fmap_rs.append(fmap_r) |
| y_d_gs.append(y_d_g) |
| fmap_gs.append(fmap_g) |
|
|
| return y_d_rs, y_d_gs, fmap_rs, fmap_gs |
| |
| class WavLMDiscriminator(nn.Module): |
| """docstring for Discriminator.""" |
|
|
| def __init__(self, slm_hidden=768, |
| slm_layers=13, |
| initial_channel=64, |
| use_spectral_norm=False): |
| super(WavLMDiscriminator, self).__init__() |
| norm_f = weight_norm if use_spectral_norm == False else spectral_norm |
| self.pre = norm_f(Conv1d(slm_hidden * slm_layers, initial_channel, 1, 1, padding=0)) |
| |
| self.convs = nn.ModuleList([ |
| norm_f(nn.Conv1d(initial_channel, initial_channel * 2, kernel_size=5, padding=2)), |
| norm_f(nn.Conv1d(initial_channel * 2, initial_channel * 4, kernel_size=5, padding=2)), |
| norm_f(nn.Conv1d(initial_channel * 4, initial_channel * 4, 5, 1, padding=2)), |
| ]) |
|
|
| self.conv_post = norm_f(Conv1d(initial_channel * 4, 1, 3, 1, padding=1)) |
| |
| def forward(self, x): |
| x = self.pre(x) |
| |
| fmap = [] |
| for l in self.convs: |
| x = l(x) |
| x = F.leaky_relu(x, LRELU_SLOPE) |
| fmap.append(x) |
| x = self.conv_post(x) |
| x = torch.flatten(x, 1, -1) |
|
|
| return x |
