| import sys | |
| sys.path.append('tacotron2') | |
| import torch | |
| from 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__() | |
| self.stft = STFT(filter_length=filter_length, | |
| hop_length=int(filter_length/n_overlap), | |
| win_length=win_length).cuda() | |
| if mode == 'zeros': | |
| mel_input = torch.zeros( | |
| (1, 80, 88), | |
| dtype=waveglow.upsample.weight.dtype, | |
| device=waveglow.upsample.weight.device) | |
| elif mode == 'normal': | |
| mel_input = torch.randn( | |
| (1, 80, 88), | |
| dtype=waveglow.upsample.weight.dtype, | |
| device=waveglow.upsample.weight.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.cuda().float()) | |
| 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 | |