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import torch |
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import torch.nn.functional as F |
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import torch.nn as nn |
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from torch.nn import Conv1d, AvgPool1d, Conv2d |
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from torch.nn.utils import weight_norm, spectral_norm |
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from .utils import get_padding |
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LRELU_SLOPE = 0.1 |
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def stft(x, fft_size, hop_size, win_length, window): |
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"""Perform STFT and convert to magnitude spectrogram. |
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Args: |
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x (Tensor): Input signal tensor (B, T). |
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fft_size (int): FFT size. |
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hop_size (int): Hop size. |
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win_length (int): Window length. |
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window (str): Window function type. |
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Returns: |
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Tensor: Magnitude spectrogram (B, #frames, fft_size // 2 + 1). |
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""" |
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x_stft = torch.stft(x, fft_size, hop_size, win_length, window, |
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return_complex=True) |
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real = x_stft[..., 0] |
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imag = x_stft[..., 1] |
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return torch.abs(x_stft).transpose(2, 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, shift_size=120, win_length=600, window="hann_window", use_spectral_norm=False): |
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super(SpecDiscriminator, self).__init__() |
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norm_f = weight_norm if use_spectral_norm == False else spectral_norm |
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self.fft_size = fft_size |
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self.shift_size = shift_size |
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self.win_length = win_length |
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self.window = getattr(torch, window)(win_length) |
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self.discriminators = nn.ModuleList([ |
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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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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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y = y.squeeze(1) |
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y = stft(y, self.fft_size, self.shift_size, self.win_length, self.window.to(y.get_device())) |
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y = y.unsqueeze(1) |
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for i, 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__(self, |
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fft_sizes=[1024, 2048, 512], |
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hop_sizes=[120, 240, 50], |
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win_lengths=[600, 1200, 240], |
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window="hann_window"): |
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super(MultiResSpecDiscriminator, self).__init__() |
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self.discriminators = nn.ModuleList([ |
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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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def forward(self, y, y_hat): |
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y_d_rs = [] |
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y_d_gs = [] |
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fmap_rs = [] |
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fmap_gs = [] |
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for i, d in enumerate(self.discriminators): |
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y_d_r, fmap_r = d(y) |
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y_d_g, fmap_g = d(y_hat) |
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y_d_rs.append(y_d_r) |
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fmap_rs.append(fmap_r) |
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y_d_gs.append(y_d_g) |
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fmap_gs.append(fmap_g) |
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return y_d_rs, y_d_gs, fmap_rs, fmap_gs |
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class DiscriminatorP(torch.nn.Module): |
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def __init__(self, period, kernel_size=5, stride=3, use_spectral_norm=False): |
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super(DiscriminatorP, self).__init__() |
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self.period = period |
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norm_f = weight_norm if use_spectral_norm == False else spectral_norm |
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self.convs = nn.ModuleList([ |
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norm_f(Conv2d(1, 32, (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0))), |
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norm_f(Conv2d(32, 128, (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0))), |
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norm_f(Conv2d(128, 512, (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0))), |
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norm_f(Conv2d(512, 1024, (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0))), |
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norm_f(Conv2d(1024, 1024, (kernel_size, 1), 1, padding=(2, 0))), |
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]) |
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self.conv_post = norm_f(Conv2d(1024, 1, (3, 1), 1, padding=(1, 0))) |
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def forward(self, x): |
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fmap = [] |
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b, c, t = x.shape |
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if t % self.period != 0: |
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n_pad = self.period - (t % self.period) |
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x = F.pad(x, (0, n_pad), "reflect") |
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t = t + n_pad |
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x = x.view(b, c, t // self.period, self.period) |
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for l in self.convs: |
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x = l(x) |
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x = F.leaky_relu(x, LRELU_SLOPE) |
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fmap.append(x) |
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x = self.conv_post(x) |
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fmap.append(x) |
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x = torch.flatten(x, 1, -1) |
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return x, fmap |
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class MultiPeriodDiscriminator(torch.nn.Module): |
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def __init__(self): |
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super(MultiPeriodDiscriminator, self).__init__() |
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self.discriminators = nn.ModuleList([ |
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DiscriminatorP(2), |
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DiscriminatorP(3), |
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DiscriminatorP(5), |
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DiscriminatorP(7), |
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DiscriminatorP(11), |
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]) |
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def forward(self, y, y_hat): |
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y_d_rs = [] |
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y_d_gs = [] |
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fmap_rs = [] |
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fmap_gs = [] |
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for i, d in enumerate(self.discriminators): |
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y_d_r, fmap_r = d(y) |
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y_d_g, fmap_g = d(y_hat) |
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y_d_rs.append(y_d_r) |
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fmap_rs.append(fmap_r) |
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y_d_gs.append(y_d_g) |
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fmap_gs.append(fmap_g) |
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return y_d_rs, y_d_gs, fmap_rs, fmap_gs |
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class WavLMDiscriminator(nn.Module): |
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"""docstring for Discriminator.""" |
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def __init__(self, slm_hidden=768, |
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slm_layers=13, |
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initial_channel=64, |
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use_spectral_norm=False): |
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super(WavLMDiscriminator, self).__init__() |
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norm_f = weight_norm if use_spectral_norm == False else spectral_norm |
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self.pre = norm_f(Conv1d(slm_hidden * slm_layers, initial_channel, 1, 1, padding=0)) |
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self.convs = nn.ModuleList([ |
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norm_f(nn.Conv1d(initial_channel, initial_channel * 2, kernel_size=5, padding=2)), |
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norm_f(nn.Conv1d(initial_channel * 2, initial_channel * 4, kernel_size=5, padding=2)), |
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norm_f(nn.Conv1d(initial_channel * 4, initial_channel * 4, 5, 1, padding=2)), |
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]) |
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self.conv_post = norm_f(Conv1d(initial_channel * 4, 1, 3, 1, padding=1)) |
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def forward(self, x): |
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x = self.pre(x) |
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fmap = [] |
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for l in self.convs: |
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x = l(x) |
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x = F.leaky_relu(x, LRELU_SLOPE) |
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fmap.append(x) |
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x = self.conv_post(x) |
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x = torch.flatten(x, 1, -1) |
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return x |
