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| # adapted from https://github.com/rishikksh20/HiFi-GAN/blob/main/denoiser.py | |
| # MIT License | |
| # Copyright (c) 2020 Rishikesh | |
| # Permission is hereby granted, free of charge, to any person obtaining a copy | |
| # of this software and associated documentation files (the "Software"), to deal | |
| # in the Software without restriction, including without limitation the rights | |
| # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell | |
| # copies of the Software, and to permit persons to whom the Software is | |
| # furnished to do so, subject to the following conditions: | |
| # The above copyright notice and this permission notice shall be included in all | |
| # copies or substantial portions of the Software. | |
| # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR | |
| # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, | |
| # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE | |
| # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER | |
| # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, | |
| # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE | |
| # SOFTWARE. | |
| import torch | |
| import torch.nn as nn | |
| import torchaudio | |
| class Denoiser(nn.Module): | |
| """ Removes model bias from audio produced with hifigan """ | |
| def __init__(self, hifigan, filter_length=1024, n_overlap=4, | |
| win_length=1024, mode='zeros', **infer_kw): | |
| super().__init__() | |
| w = next(p for name, p in hifigan.named_parameters() | |
| if name.endswith('.weight')) | |
| # self.stft = STFT(filter_length=filter_length, | |
| # hop_length=int(filter_length/n_overlap), | |
| # win_length=win_length).to(w.device) | |
| self.stft = torchaudio.transforms.Spectrogram(filter_length, | |
| hop_length=int(filter_length/n_overlap), | |
| win_length=win_length, power=None).to(w.device) | |
| self.istft = torchaudio.transforms.InverseSpectrogram(filter_length, | |
| hop_length=int(filter_length/n_overlap), | |
| win_length=win_length).to(w.device) | |
| mel_init = {'zeros': torch.zeros, 'normal': torch.randn}[mode] | |
| mel_input = mel_init((1, 80, 88), dtype=w.dtype, device=w.device) | |
| with torch.no_grad(): | |
| bias_audio = hifigan(mel_input, **infer_kw).float() | |
| if len(bias_audio.size()) > 2: | |
| bias_audio = bias_audio.squeeze(0) | |
| elif len(bias_audio.size()) < 2: | |
| bias_audio = bias_audio.unsqueeze(0) | |
| assert len(bias_audio.size()) == 2 | |
| bias_spec = self.stft(bias_audio).abs() | |
| self.register_buffer('bias_spec', bias_spec[:, :, 0][:, :, None]) | |
| def forward(self, audio, strength=0.1): | |
| audio_spec = self.stft(audio.float()) | |
| audio_spec_mag, audio_spec_phase = audio_spec.abs(), audio_spec.angle() | |
| audio_spec_denoised = audio_spec_mag - self.bias_spec * strength | |
| audio_spec_denoised = torch.clamp(audio_spec_denoised, 0.0) | |
| audio_denoised = self.istft(audio_spec_denoised*torch.exp(1j*audio_spec_phase)) | |
| return audio_denoised | |