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- 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 = []
- # 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)
- 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
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