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denoiser.py: adding code to remove model bias
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import sys | ||
sys.path.append('tacotron2') | ||
import torch | ||
from layers import STFT | ||
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class Denoiser(torch.nn.Module): | ||
""" Removes model bias from audio produced with waveglow """ | ||
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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)) | ||
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with torch.no_grad(): | ||
bias_audio = waveglow.infer(mel_input, sigma=0.0).float() | ||
bias_spec, _ = self.stft.transform(bias_audio) | ||
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self.register_buffer('bias_spec', bias_spec[:, :, 0][:, :, None]) | ||
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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 |