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snac-vocos
hertz-pj/snac-vocos
snac-vocos is a machine learning model from hertz-pj. Use it for the machine learning task on the model card, and read the license before you ship it in a product.
A trainer for SNAC (Multi-Scale Neural Audio Codec) has replaced the decoder with Vocos.
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Public
Updated Oct 28, 2024
Repo size
1.7 GB
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.ckpt1.7 GB · 100%
At a glance
- Access
- Public
- Created
- Oct 28, 2024
- Updated
- Oct 28, 2024
- SHA
- 5a02594b
Model card
From the Hugging Face model README
SNAC-Vocos
A trainer for SNAC (Multi-Scale Neural Audio Codec) has replaced the decoder with Vocos.
Installation
Suggested python>=3.9.
Clone the repository:
git clone https://github.com/hertz-pj/SNAC-Vocos
cd SNAC-Vocos
Install packages:
pip install -r requirements.txt
Infer
Refer to the infer.py for inference instructions and usage examples.
Available Models
| Model name | Huggingface | Corpus | Domain |
|---|---|---|---|
| snac_vocos_16khz_hop200_scale8421_1kh | 🤗 | 1k hours | Speech(Mandarin/English) |
Training
1、Prepare a filelist of audio files for the training and validation set, e.g. train.list.
2、Fill a config file, e.g. snac_vocos.yaml. The main parameters to pay attention to are batch_size, filelist_path, save_dir, and device.
3、Start training
python train.py fit --config ./configs/snac_vocos.yaml
TODO
- Release code
- Release a checkpoint trained with 1k hours of speech(Mandarin/English).
- Demo page.
Acknowledgements
This implementation uses parts of the code from the following Github repos:
- Created
- Oct 28, 2024
- Updated
- Oct 28, 2024