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import torch |
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from .layers import STFT |
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class Denoiser(torch.nn.Module): |
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""" Removes model bias from audio produced with waveglow """ |
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def __init__(self, waveglow, filter_length=1024, n_overlap=4, |
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win_length=1024, mode='zeros'): |
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super(Denoiser, self).__init__() |
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self.stft = STFT(filter_length=filter_length, |
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hop_length=int(filter_length/n_overlap), |
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win_length=win_length).cuda() |
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if mode == 'zeros': |
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mel_input = torch.zeros( |
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(1, 80, 88), |
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dtype=waveglow.upsample.weight.dtype, |
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device=waveglow.upsample.weight.device) |
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elif mode == 'normal': |
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mel_input = torch.randn( |
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(1, 80, 88), |
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dtype=waveglow.upsample.weight.dtype, |
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device=waveglow.upsample.weight.device) |
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else: |
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raise Exception("Mode {} if not supported".format(mode)) |
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with torch.no_grad(): |
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bias_audio = waveglow.infer(mel_input, sigma=0.0).float() |
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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): |
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audio_spec, audio_angles = self.stft.transform(audio.cuda().float()) |
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audio_spec_denoised = audio_spec - self.bias_spec * strength |
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audio_spec_denoised = torch.clamp(audio_spec_denoised, 0.0) |
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audio_denoised = self.stft.inverse(audio_spec_denoised, audio_angles) |
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return audio_denoised |
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