VampNet

This repository contains recipes for training generative music models on top of the Lyrebird Audio Codec.

Setting up

install AudioTools

git clone https://github.com/descriptinc/audiotools.git
pip install -e ./audiotools

install the Descript Audio Codec.

git clone https://github.com/descriptinc/dac.git
pip install -e ./dac

install VampNet

git clone https://github.com/hugofloresgarcia/vampnet.git
pip install -e ./vampnet

A note on argbind

This repository relies on argbind to manage CLIs and config files. Config files are stored in the conf/ folder.

Getting the Pretrained Models

Download the pretrained models from this link. Then, extract the models to the models/ folder.

Usage

First, you'll want to set up your environment

source ./env/env.sh

Staging a Run

Staging a run makes a copy of all the git-tracked files in the codebase and saves them to a folder for reproducibility. You can then run the training script from the staged folder.

stage --name my_run --run_dir /path/to/staging/folder

Training a model

python scripts/exp/train.py --args.load conf/vampnet.yml --save_path /path/to/checkpoints

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.

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:

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:

python  scripts/exp/train.py --args.load conf/<fine_tune_name>/c2f.yml

launch the interface:

python  demo.py --args.load conf/generated/<fine_tune_name>/interface.yml

Launching the Gradio Interface

python demo.py --args.load conf/interface/spotdl.yml --Interface.device cuda