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| import torch | |
| def denoise(noisy_wav, model, hps): | |
| norm_factor = torch.sqrt(len(noisy_wav) / torch.sum(noisy_wav ** 2.0)).to(noisy_wav.device) | |
| noisy_wav = (noisy_wav * norm_factor).unsqueeze(0) | |
| noisy_amp, noisy_pha, noisy_com = mag_pha_stft(noisy_wav, hps.n_fft, hps.hop_size, hps.win_size, hps.compress_factor) | |
| amp_g, pha_g, com_g = model(noisy_amp, noisy_pha) | |
| audio_g = mag_pha_istft(amp_g, pha_g, hps.n_fft, hps.hop_size, hps.win_size, hps.compress_factor) | |
| audio_g = audio_g / norm_factor | |
| return audio_g | |
| 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) | |
| mag = torch.abs(stft_spec) | |
| pha = torch.angle(stft_spec) | |
| # 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 |