voicefixer 0.0.7

This package is written for the restoration of degraded speech

VoiceFixer

This package provides:

• A pretrained 44.1k universal speaker-independent neural vocoder.
• A pretrained Voicefixer, which is build based on neural vocoder.

Voicefixer aims at the restoration of human speech regardless how serious its degraded. It can handle noise, reveberation, low resolution (2kHz~44.1kHz) and clipping (0.1-1.0 threshold) effect within one model.

Demo

Please visit demo page to view what voicefixer can do.

Usage

• Basic example:

# Will automatically download model parameters.
from voicefixer import VoiceFixer
from voicefixer import Vocoder

# Initialize model
voicefixer = VoiceFixer()
# Speech restoration
voicefixer.restore(input="", # input wav file path
                   output="", # output wav file path
                   cuda=False, # whether to use gpu acceleration
                   mode = 0) # You can try out mode 0, 1 to find out the best result

# Universal speaker independent vocoder
vocoder = Vocoder(sample_rate=44100) # Only 44100 sampling rate is supported.

# Convert mel spectrogram to waveform
wave = vocoder.forward(mel=mel_spec) # This forward function is used in the following oracle function.

# Test vocoder using the mel spectrogram of 'fpath', save output to file out_path
vocoder.oracle(fpath="", # input wav file path
               out_path="") # output wav file path

Materials

• Voicefixer training: https://github.com/haoheliu/voicefixer_main.git
• Demo page: https://haoheliu.github.io/demopage-voicefixer/
• If you found this repo helpful, please consider citing

@misc{liu2021voicefixer,
title={VoiceFixer: Toward General Speech Restoration With Neural Vocoder},
author={Haohe Liu and Qiuqiang Kong and Qiao Tian and Yan Zhao and DeLiang Wang and Chuanzeng Huang and Yuxuan Wang},
year={2021},
eprint={2109.13731},
archivePrefix={arXiv},
primaryClass={cs.SD}
}