2021-07-09 05:07:36 +00:00
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import sys
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2022-03-15 17:06:25 +00:00
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from models.audio.tts.tacotron2.stft import STFT
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2021-07-09 05:07:36 +00:00
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sys.path.append('tacotron2')
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import torch
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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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