• VoiceFixer
  • Demo
  • Usage
    • Desktop App
    • Command line
    • Python Examples
    • Others Features
  • Materials

VoiceFixer

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.

This package provides:

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

• 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}  
 }

Demo

Please visit demo page to view what voicefixer can do.

Usage

Desktop App

First, install voicefixer via pip:

pip install voicefixer==0.1.1

You can test audio samples on your desktop by running website (powered by streamlit)

1. Clone the repo first.

git clone https://github.com/haoheliu/voicefixer.git
cd voicefixer

For windows users, please make sure you have installed WGET and added the wget command to the system path (thanks @justinjohn0306).

2. Initialize and start web page.

# Install additional web package
pip install streamlit
# Run streamlit 
streamlit run test/streamlit.py

• If you run for the first time: the web page may leave blank for several minutes for downloading models. You can checkout the terminal for downloading progresses.

• You can use this low quality speech file we provided for a test run. The page after processing will look like the following.

• For users from main land China, if you experience difficulty on downloading checkpoint. You can access them alternatively on 百度网盘 (提取密码: qis6). Please download the two checkpoints inside and place them in the following folder.
  • Place vf.ckpt inside ~/.cache/voicefixer/analysis_module/checkpoints. (The "~" represents your home directory)
  • Place model.ckpt-1490000_trimed.pt inside ~/.cache/voicefixer/synthesis_module/44100. (The "~" represents your home directory)

Command line

First, install voicefixer via pip:

pip install voicefixer==0.1.1

Process a file:

voicefixer --infile /path/to/input.wav --outfile /path/to/output.wav

Process files in a folder:

voicefixer --infolder /path/to/input --outfolder /path/to/output

Change mode (The default mode is 0):

voicefixer --infile /path/to/input.wav --outfile /path/to/output.wav --mode 1

Run all modes:

# output file saved to `/path/to/output-modeX.wav`.
voicefixer --infile /path/to/input.wav --outfile /path/to/output.wav --mode all

For more helper information please run:

voicefixer -h

Python Examples

First, install voicefixer via pip:

pip install voicefixer==0.1.1

Then run the following scripts for a test run:

git clone https://github.com/haoheliu/voicefixer.git; cd voicefixer
python3 test/test.py # test script

We expect it will give you the following output:

Initializing VoiceFixer...
Test voicefixer mode 0, Pass
Test voicefixer mode 1, Pass
Test voicefixer mode 2, Pass
Initializing 44.1kHz speech vocoder...
Test vocoder using groundtruth mel spectrogram...
Pass

test/test.py mainly contains the test of the following two APIs:

• voicefixer.restore
• vocoder.oracle

...

# TEST VOICEFIXER
## Initialize a voicefixer
print("Initializing VoiceFixer...")
voicefixer = VoiceFixer()
# Mode 0: Original Model (suggested by default)
# Mode 1: Add preprocessing module (remove higher frequency)
# Mode 2: Train mode (might work sometimes on seriously degraded real speech)
for mode in [0,1,2]:
    print("Testing mode",mode)
    voicefixer.restore(input=os.path.join(git_root,"test/utterance/original/original.flac"), # low quality .wav/.flac file
                       output=os.path.join(git_root,"test/utterance/output/output_mode_"+str(mode)+".flac"), # save file path
                       cuda=False, # GPU acceleration
                       mode=mode)
    if(mode != 2):
        check("output_mode_"+str(mode)+".flac")
    print("Pass")

# TEST VOCODER
## Initialize a vocoder
print("Initializing 44.1kHz speech vocoder...")
vocoder = Vocoder(sample_rate=44100)

### read wave (fpath) -> mel spectrogram -> vocoder -> wave -> save wave (out_path)
print("Test vocoder using groundtruth mel spectrogram...")
vocoder.oracle(fpath=os.path.join(git_root,"test/utterance/original/p360_001_mic1.flac"),
               out_path=os.path.join(git_root,"test/utterance/output/oracle.flac"),
               cuda=False) # GPU acceleration

...

You can clone this repo and try to run test.py inside the test folder.

Others Features

• How to use your own vocoder, like pre-trained HiFi-Gan?

First you need to write a following helper function with your model. Similar to the helper function in this repo: https://github.com/haoheliu/voicefixer/blob/main/voicefixer/vocoder/base.py#L35

    def convert_mel_to_wav(mel):
        """
        :param non normalized mel spectrogram: [batchsize, 1, t-steps, n_mel]
        :return: [batchsize, 1, samples]
        """
        return wav

Then pass this function to voicefixer.restore, for example:

voicefixer.restore(input="", # input wav file path
                   output="", # output wav file path
                   cuda=False, # whether to use gpu acceleration
                   mode = 0,
                   your_vocoder_func = convert_mel_to_wav)

Note:

• For compatibility, your vocoder should working on 44.1kHz wave with mel frequency bins 128.
• The input mel spectrogram to the helper function should not be normalized by the width of each mel filter.

Materials

• Voicefixer training: https://github.com/haoheliu/voicefixer_main.git
• Demo page: https://haoheliu.github.io/demopage-voicefixer/