Lyrebird VampNet

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

Install hooks

First install the pre-commit util:

pip install pre-commit  # with pip
brew install pre-commit  # on Mac

Then install the git hooks

pre-commit install
# check .pre-commit-config.yaml for details of hooks

Upon git commit, the pre-commit hooks will be run automatically on the stage files (i.e. added by git add)

N.B. By default, pre-commit checks only run on staged files

If you need to run it on all files:

pre-commit run --all-files

Development

Setting everything up

Run the setup script to set up your environment via:

python env/setup.py

The setup script does not require any dependencies beyond just Python. Once run, follow the instructions it prints out to create your environment file, which will be at env/env.sh.

Note that if this is a new machine, and the data is not downloaded somewhere on it already, it will ask you for a directory to download the data to.

For Github setup, if you don't have a .netrc token, create one by going to your Github profile -> Developer settings -> Personal access tokens -> Generate new token. Copy the token and keep it secret, keep it safe.

When complete, run:

source env/env.sh

Now build and launch the Docker containers:

docker compose up -d

This builds and runs a Jupyter notebook and Tensorboard in the background, which points to your TENSORBOARD_PATH env. variable.

Now, launch your development environment via:

docker compose run dev

To tear down your development environment, just do

docker compose down

Launching an experiment

Experiments are first staged by running the stage command (which corresponds to the script scripts/exp/stage.py).

stage creates a directory with a copy of all of the Git-tracked files in the root repository.stage launches a shell into said directory, so all commands are run on the copy of the original repository code. This is useful for rewinding to an old experiment and resuming it, for example. Even if the repository code changes, the snapshot in the experiment directory is unchanged from the original run, so it can be re-used.

Then, the experiment can be run via:

torchrun --nproc_per_node gpu \
  scripts/exp/train.py \
  --args.load=conf/args.yml \

The full settings are in conf/daps/train.yml.

Useful commands

Cleaning up after a run

Sometimes DDP runs fail to clear themselves out of the machine. To fix this, run

cleanup