forked from mrq/DL-Art-School
43 lines
1.6 KiB
Python
43 lines
1.6 KiB
Python
import sys
|
|
|
|
from models.tacotron2.stft import STFT
|
|
|
|
sys.path.append('tacotron2')
|
|
import torch
|
|
|
|
|
|
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
|