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@@ -1,13 +1,16 @@
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import torch
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import torch.nn as nn
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+import torch.nn.functional as F
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try:
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- from torch.nn.utils.parametrizations import weight_norm
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+ from torch.nn.utils.parametrizations import weight_norm, spectral_norm
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except ImportError:
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- from torch.nn.utils import weight_norm
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+ from torch.nn.utils import weight_norm, spectral_norm
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from typing import List, Optional, Tuple
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from einops import rearrange
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from torchaudio.transforms import Spectrogram
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+LRELU_SLOPE = 0.1
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+
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class MultipleDiscriminator(nn.Module):
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def __init__(
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@@ -141,3 +144,87 @@ class DiscriminatorR(nn.Module):
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x += h
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return x, fmap
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+
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+
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+class MultiResSpecDiscriminator(torch.nn.Module):
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+
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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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+
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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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+
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+ return y_d_rs, y_d_gs, fmap_rs, fmap_gs
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+
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+
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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, return_complex=True)
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+
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+ # NOTE(kan-bayashi): clamp is needed to avoid nan or inf
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+ return torch.abs(x_stft).transpose(2, 1)
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+
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+
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+class SpecDiscriminator(nn.Module):
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+ """docstring for Discriminator."""
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+
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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 is 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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+
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+ self.out = norm_f(nn.Conv2d(32, 1, 3, 1, 1))
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+
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+ def forward(self, y):
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+
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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.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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+
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+ y = self.out(y)
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+ fmap.append(y)
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+
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+ return torch.flatten(y, 1, -1), fmap
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