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| title: "VampNet: Music Generation with Masked Transformers" | |
| emoji: 🤖 | |
| colorFrom: gray | |
| colorTo: gray | |
| sdk: gradio | |
| sdk_version: 3.36.1 | |
| app_file: app.py | |
| pinned: false | |
| # VampNet | |
| This repository contains recipes for training generative music models on top of the Descript Audio Codec. | |
| ## try `unloop` | |
| you can try vampnet in a co-creative looper called unloop. see this link: https://github.com/hugofloresgarcia/unloop | |
| # Setting up | |
| Requires Python 3.9 or later. | |
| install VampNet | |
| ```bash | |
| git clone https://github.com/hugofloresgarcia/vampnet.git | |
| pip install -e ./vampnet | |
| ``` | |
| ## A note on argbind | |
| This repository relies on [argbind](https://github.com/pseeth/argbind) to manage CLIs and config files. | |
| Config files are stored in the `conf/` folder. | |
| ## Getting the Pretrained Models | |
| ### Licensing for Pretrained Models: | |
| The weights for the models are licensed [`CC BY-NC-SA 4.0`](https://creativecommons.org/licenses/by-nc-sa/4.0/deed.ml). Likewise, any VampNet models fine-tuned on the pretrained models are also licensed [`CC BY-NC-SA 4.0`](https://creativecommons.org/licenses/by-nc-sa/4.0/deed.ml). | |
| Download the pretrained models from [this link](https://zenodo.org/record/8136629). Then, extract the models to the `models/` folder. | |
| # Usage | |
| ## Launching the Gradio Interface | |
| You can launch a gradio UI to play with vampnet. | |
| ```bash | |
| python app.py --args.load conf/interface.yml --Interface.device cuda | |
| ``` | |
| # Training / Fine-tuning | |
| ## Training a model | |
| To train a model, run the following script: | |
| ```bash | |
| python scripts/exp/train.py --args.load conf/vampnet.yml --save_path /path/to/checkpoints | |
| ``` | |
| You can edit `conf/vampnet.yml` to change the dataset paths or any training hyperparameters. | |
| For coarse2fine models, you can use `conf/c2f.yml` as a starting configuration. | |
| See `python scripts/exp/train.py -h` for a list of options. | |
| ## Fine-tuning | |
| To fine-tune a model, use the script in `scripts/exp/fine_tune.py` to generate 3 configuration files: `c2f.yml`, `coarse.yml`, and `interface.yml`. | |
| The first two are used to fine-tune the coarse and fine models, respectively. The last one is used to launch the gradio interface. | |
| ```bash | |
| python scripts/exp/fine_tune.py "/path/to/audio1.mp3 /path/to/audio2/ /path/to/audio3.wav" <fine_tune_name> | |
| ``` | |
| This will create a folder under `conf/<fine_tune_name>/` with the 3 configuration files. | |
| The save_paths will be set to `runs/<fine_tune_name>/coarse` and `runs/<fine_tune_name>/c2f`. | |
| launch the coarse job: | |
| ```bash | |
| python scripts/exp/train.py --args.load conf/<fine_tune_name>/coarse.yml | |
| ``` | |
| this will save the coarse model to `runs/<fine_tune_name>/coarse/ckpt/best/`. | |
| launch the c2f job: | |
| ```bash | |
| python scripts/exp/train.py --args.load conf/<fine_tune_name>/c2f.yml | |
| ``` | |
| launch the interface: | |
| ```bash | |
| python demo.py --args.load conf/generated/<fine_tune_name>/interface.yml | |
| ``` | |