Fast (GAN Based Neural) Vocoder
Todo
- Submit demo
- Support NHV
Discription
Include MelGAN, HifiGAN and Multiband-HifiGAN, maybe include NHV in the future. Developed on BiaoBei dataset, you can modify conf
and hparams.py
to fit your own dataset and model.
Usage
- Prepare data
- write path of wav data in a file, for example:
cd dataset && python3 biaobei.py
bash preprocess.sh <wav path file> <path to save processed data> dataset/audio dataset/mel
- for example:
bash preprocess.sh dataset/BZNSYP.txt processed dataset/audio dataset/mel
- write path of wav data in a file, for example:
- Train
- command:
bash train.sh \ <GPU ids> \ /path/to/audio/train \ /path/to/audio/valid \ /path/to/mel/train \ /path/to/mel/valid \ <model name> \ <if multi band> \ <if use scheduler> \ <path to configuration file>
- for example:
bash train.sh \ 0 \ dataset/audio/train \ dataset/audio/valid \ dataset/mel/train \ dataset/mel/valid \ hifigan \ 0 0 0 \ conf/hifigan/light.yaml
- Train from checkpoint
- command:
bash train.sh \ <GPU ids> \ /path/to/audio/train \ /path/to/audio/valid \ /path/to/mel/train \ /path/to/mel/valid \ <model name> \ <if multi band> \ <if use scheduler> \ <path to configuration file> \ /path/to/checkpoint \ <step of checkpoint>
- Synthesize
- command:
bash synthesize.sh \ /path/to/checkpoint \ /path/to/mel \ /path/for/saving/wav \ <model name> \ /path/to/configuration/file