Neural vocoder support
See original GitHub issueLet’s discuss neural vocoder support.
- Types
- Parallel WaveGAN (PWG), MelGAN
I can easily implement these two since I am familiar with it. Kan-bayashi has packed his repo so we can simply
pip install -U parallel_wavegan
. - WaveNet vocoder I don’t really prefer this one due to its slow inference speed.
- WaveGlow I don’t really prefer this one due to its slow training speed.
- Usage & Structure
We can add a synthesis stage to the recipe. We can provide pretrained models for users to download, and use an argument like
voc_expdir
to load the pretrained model. In addition, with PWG, kan-bayashi has also packed training code in the package, so we can provide recipes for users to train their own vocoders if they want. One example design can be likeegs/pwg/vcc2018
.
Issue Analytics
- State:
- Created 3 years ago
- Reactions:1
- Comments:6 (3 by maintainers)
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I see. I will train vocoders in
kan-bayashi/ParallelWaveGAN
and just provide pretrained model links in this repo. I will work on it next.It’s nice to integrate PWG/MelGAN. Actually, I have already discuss it with @kan-bayashi. He said he can create recipe and pre-trained model after releasing vcc2020 dataset.
For the structure, I think following is nice.
egs/vaevc/template/run.sh
egs/vaevc/<recipe>/local/download_pretrained_neuralvocoder.sh
to download pre-trained models.crank/bin/generate_wav_{pwg,melgan}.py
to generate wav file w/ pre-trained model and generated h5 in stage 5.