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| # ***************************************************************************** | |
| # Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved. | |
| # | |
| # Redistribution and use in source and binary forms, with or without | |
| # modification, are permitted provided that the following conditions are met: | |
| # * Redistributions of source code must retain the above copyright | |
| # notice, this list of conditions and the following disclaimer. | |
| # * Redistributions in binary form must reproduce the above copyright | |
| # notice, this list of conditions and the following disclaimer in the | |
| # documentation and/or other materials provided with the distribution. | |
| # * Neither the name of the NVIDIA CORPORATION nor the | |
| # names of its contributors may be used to endorse or promote products | |
| # derived from this software without specific prior written permission. | |
| # | |
| # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND | |
| # ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED | |
| # WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE | |
| # DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY | |
| # DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES | |
| # (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; | |
| # LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND | |
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| # SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. | |
| # | |
| # ***************************************************************************** | |
| import torch | |
| from common.layers import STFT | |
| class Denoiser(torch.nn.Module): | |
| """ Removes model bias from audio produced with waveglow """ | |
| def __init__(self, waveglow, filter_length=1024, n_overlap=4, | |
| win_length=1024, mode='zeros'): | |
| super(Denoiser, self).__init__() | |
| device = waveglow.upsample.weight.device | |
| dtype = waveglow.upsample.weight.dtype | |
| self.stft = STFT(filter_length=filter_length, | |
| hop_length=int(filter_length/n_overlap), | |
| win_length=win_length).to(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("Mode {} if not supported".format(mode)) | |
| with torch.no_grad(): | |
| bias_audio = waveglow.infer(mel_input, sigma=0.0).float() | |
| bias_spec, _ = self.stft.transform(bias_audio) | |
| self.register_buffer('bias_spec', bias_spec[:, :, 0][:, :, None]) | |
| def forward(self, audio, strength=0.1): | |
| audio_spec, audio_angles = self.stft.transform(audio) | |
| audio_spec_denoised = audio_spec - self.bias_spec * strength | |
| audio_spec_denoised = torch.clamp(audio_spec_denoised, 0.0) | |
| audio_denoised = self.stft.inverse(audio_spec_denoised, audio_angles) | |
| return audio_denoised | |