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| ### Code modified from Rafael Valle's implementation https://github.com/NVIDIA/waveglow/blob/5bc2a53e20b3b533362f974cfa1ea0267ae1c2b1/denoiser.py | |
| """Waveglow style denoiser can be used to remove the artifacts from the HiFiGAN generated audio.""" | |
| import torch | |
| class Denoiser(torch.nn.Module): | |
| """Removes model bias from audio produced with waveglow""" | |
| def __init__(self, vocoder, filter_length=1024, n_overlap=4, win_length=1024, mode="zeros"): | |
| super().__init__() | |
| self.filter_length = filter_length | |
| self.hop_length = int(filter_length / n_overlap) | |
| self.win_length = win_length | |
| dtype, device = next(vocoder.parameters()).dtype, next(vocoder.parameters()).device | |
| self.device = device | |
| if mode == "zeros": | |
| mel_input = torch.zeros((1, 80, 88), dtype=dtype, device=device) | |
| elif mode == "normal": | |
| mel_input = torch.randn((1, 80, 88), dtype=dtype, device=device) | |
| else: | |
| raise Exception(f"Mode {mode} if not supported") | |
| def stft_fn(audio, n_fft, hop_length, win_length, window): | |
| spec = torch.stft( | |
| audio, | |
| n_fft=n_fft, | |
| hop_length=hop_length, | |
| win_length=win_length, | |
| window=window, | |
| return_complex=True, | |
| ) | |
| spec = torch.view_as_real(spec) | |
| return torch.sqrt(spec.pow(2).sum(-1)), torch.atan2(spec[..., -1], spec[..., 0]) | |
| self.stft = lambda x : stft_fn( | |
| audio=x, | |
| n_fft=self.filter_length, | |
| hop_length=self.hop_length, | |
| win_length=self.win_length, | |
| window=torch.hann_window(self.win_length, device=device) | |
| ) | |
| self.istft = lambda x, y: torch.istft( | |
| torch.complex(x * torch.cos(y), x * torch.sin(y)), | |
| n_fft=self.filter_length, | |
| hop_length=self.hop_length, | |
| win_length=self.win_length, | |
| window=torch.hann_window(self.win_length, device=device), | |
| ) | |
| with torch.no_grad(): | |
| bias_audio = vocoder(mel_input).float().squeeze(0) | |
| bias_spec, _ = self.stft(bias_audio) | |
| self.register_buffer("bias_spec", bias_spec[:, :, 0][:, :, None]) | |
| def forward(self, audio, strength=0.0005): | |
| audio_spec, audio_angles = self.stft(audio) | |
| audio_spec_denoised = audio_spec - self.bias_spec.to(audio.device) * strength | |
| audio_spec_denoised = torch.clamp(audio_spec_denoised, 0.0) | |
| audio_denoised = self.istft(audio_spec_denoised, audio_angles) | |
| return audio_denoised |