README.md

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/hugofloresgarcia/audiotools.git
pip install -e ./audiotools

install the LAC library.

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

install VampNet

git clone https://github.com/hugofloresgarcia/vampnet2.git
pip install -e ./vampnet2

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.

How the code is structured

This code was written fast to meet a publication deadline, so it can be messy and redundant at times. Currently working on cleaning it up.

├── conf         <- (conf files for training, finetuning, etc)
├── demo.py      <- (gradio UI for playing with vampnet)
├── env          <- (environment variables)
│   └── env.sh
├── models       <- (extract pretrained models)
│   ├── spotdl
│   │   ├── c2f.pth     <- (coarse2fine checkpoint)
│   │   ├── coarse.pth  <- (coarse checkpoint)
│   │   └── codec.pth    <- (codec checkpoint)
│   └── wavebeat.pth
├── README.md
├── scripts
│   ├── exp
│   │   ├── eval.py       <- (eval script)
│   │   └── train.py       <- (training/finetuning script)
│   └── utils
├── vampnet
│   ├── beats.py         <- (beat tracking logic)
│   ├── __init__.py
│   ├── interface.py     <- (high-level programmatic interface)
│   ├── mask.py
│   ├── modules
│   │   ├── activations.py 
│   │   ├── __init__.py
│   │   ├── layers.py
│   │   └── transformer.py  <- (architecture + sampling code)
│   ├── scheduler.py      
│   └── util.py

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

Fine-tuning

To fine-tune a model, see the configuration files under conf/lora/. You just need to provide a list of audio files // folders to fine-tune on, then launch the training job as usual.

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

Launching the Gradio Interface

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