This is a pruned version of the original VoiceFixer implementation here.
- Finish initial working version
- Work on Vocoder - Current candidate: iSTFTNet
For this repository, we will be adopting the Nvidia NeMo's manifest format.
# {'input_filepath': <rel. path to noisy audio file>, 'target_filepath': <rel. path to clean audio file>, ... other params}
{"input_filepath": "noisy/0/AuXX152x/0_0.wav", "target_filepath": "clean/0/0_0.wav", "speaker_id": "103-1240", "gender": "f"}
{"input_filepath": "noisy/0/AuXX152x/0_1.wav", "target_filepath": "clean/0/0_1.wav", "speaker_id": "103-1240", "gender": "f"}- Update/add the configs in the config folder for your own dataset and experiment
- Run the following command when running locally:
python3 train.py @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}
}
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