denoiser.py

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'):
        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)).cuda()
        elif mode == 'normal':
            mel_input = torch.randn(
                (1, 80, 88)).cuda()
        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])

    def forward(self, audio, strength=0.1):
        audio_spec, audio_angles = self.stft.transform(audio.cuda().float())
        audio_spec_denoised = audio_spec.cuda() - self.bias_spec * strength
        audio_spec_denoised = torch.clamp(audio_spec_denoised, 0.0)
        audio_denoised = self.stft.inverse(audio_spec_denoised, audio_angles.cuda())
        return audio_denoised