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| #!/usr/bin/python3 | |
| # -*- coding: utf-8 -*- | |
| import torch | |
| import torch.nn as nn | |
| class LearnableSigmoid1d(nn.Module): | |
| def __init__(self, in_features, beta=1): | |
| super().__init__() | |
| self.beta = beta | |
| self.slope = nn.Parameter(torch.ones(in_features)) | |
| self.slope.requiresGrad = True | |
| def forward(self, x): | |
| # x shape: [batch_size, time_steps, spec_bins] | |
| return self.beta * torch.sigmoid(self.slope * x) | |
| class LearnableSigmoid2d(nn.Module): | |
| def __init__(self, in_features, beta=1): | |
| super().__init__() | |
| self.beta = beta | |
| self.slope = nn.Parameter(torch.ones(in_features, 1)) | |
| self.slope.requiresGrad = True | |
| def forward(self, x): | |
| return self.beta * torch.sigmoid(self.slope * x) | |
| def mag_pha_stft(y, n_fft, hop_size, win_size, compress_factor=1.0, center=True): | |
| hann_window = torch.hann_window(win_size).to(y.device) | |
| stft_spec = torch.stft(y, n_fft, hop_length=hop_size, win_length=win_size, window=hann_window, | |
| center=center, pad_mode='reflect', normalized=False, return_complex=True) | |
| stft_spec = torch.view_as_real(stft_spec) | |
| mag = torch.sqrt(stft_spec.pow(2).sum(-1) + 1e-9) | |
| pha = torch.atan2(stft_spec[:, :, :, 1] + 1e-10, stft_spec[:, :, :, 0] + 1e-5) | |
| # Magnitude Compression | |
| mag = torch.pow(mag, compress_factor) | |
| com = torch.stack((mag*torch.cos(pha), mag*torch.sin(pha)), dim=-1) | |
| return mag, pha, com | |
| def mag_pha_istft(mag, pha, n_fft, hop_size, win_size, compress_factor=1.0, center=True): | |
| # Magnitude Decompression | |
| mag = torch.pow(mag, (1.0/compress_factor)) | |
| com = torch.complex(mag*torch.cos(pha), mag*torch.sin(pha)) | |
| hann_window = torch.hann_window(win_size).to(com.device) | |
| wav = torch.istft(com, n_fft, hop_length=hop_size, win_length=win_size, window=hann_window, center=center) | |
| return wav | |
| if __name__ == '__main__': | |
| pass | |