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import sys
import torch
from .utils.stft import STFT
class Denoiser(torch.nn.Module):
""" Removes model bias from audio produced with waveglow """
def __init__(self, melgan, filter_length=1024, n_overlap=4,
win_length=1024, mode='zeros',device='cpu'):
super(Denoiser, self).__init__()
self.stft = STFT(filter_length=filter_length,
hop_length=int(filter_length/n_overlap),
win_length=win_length,device=device).to(device)
if mode == 'zeros':
mel_input = torch.zeros(
(1, 80, 88)).to(device)
elif mode == 'normal':
mel_input = torch.randn(
(1, 80, 88)).to(device)
else:
raise Exception("Mode {} if not supported".format(mode))
with torch.no_grad():
bias_audio = melgan.inference(mel_input).float() # [B, 1, T]
bias_spec, _ = self.stft.transform(bias_audio.squeeze(0))
self.register_buffer('bias_spec', bias_spec[:, :, 0][:, :, None])
self.device = device
self.to(device)
def forward(self, audio, strength=0.1):
audio_spec, audio_angles = self.stft.transform(audio.to(self.device).float())
audio_spec_denoised = audio_spec.to(self.device) - self.bias_spec * strength
audio_spec_denoised = torch.clamp(audio_spec_denoised, 0.0)
audio_denoised = self.stft.inverse(audio_spec_denoised, audio_angles.to(self.device))
return audio_denoised |