README.md

rave

Setup

Non-python dependencies

The project relies on Csound for rendering audio. Csound be downloaded from the homepage.

Project setup

The project is set up with poetry. The easiest way to install the dependencies for this project is to install poetry, and then execute the following commands inside the root folder of this project (where the pyproject.toml is located):

poetry config virtualenvs.in-project true # optional
poetry shell # initialize the virtual environment
poetry install
poetry run task test # run tests to verify that things work properly

Launching a training process

A training process is launched by running python rave/train.py.

By default, the training script uses the default configuration located in rave/configs/default.toml. This is where settings are defined, such as which feature extractors to use.

The training process renders audio files at the end of every episode so that it is possible to hear the results as the training goes on. These audio files are rendered to rave/bounces.

Testing a trained model

By default, checkpoints are saved every 100 iterations in rave/ray_results/<name-of-experiment> during training. You can use any of these checkpoints to run inference on a trained model by running rave/inference.py with the --checkpoint flag. Make sure to use the full path to the checkpoint, since the program is not very forgiving with path typos yet. Example:

python rave/inference.py --checkpoint rave/ray_results/SAC_default_2021-03-02_22-52-02/SAC_CrossAdaptiveEnv_8566d_00000_0_2021-03-02_22-52-02/checkpoint_500/checkpoint-500

It is also possible to use other sounds than the default ones when testing out the model. Check the possible flags you can set by running with the --help flag.

Inspecting results from the training process

All information regarding the training process is saved to rave/ray_results/. This information is saved in a TensorBoard-compliant format, which enables using TensorBoard to inspect the results. To do so, simply point TensorBoard to the to appropriate folder by typing this in a new terminal window:

tensorboard --logdir rave/ray_results

Note that the tensorboard command is installed with the dependencies of this project. As such, you need to activate the virtual environment beforehand with poetry shell (unless you already have tensorboard installed globally).