Meta-learning musical style in few-shot fashion using MAML.
Code repository for a Stanford CS 330 (Deep Multi-task and Meta-Learning) course project.
Dependencies can be easily installed by replicating the provided Conda environment. Execute
conda env create -f environment.yml
in the base directory to create this environment. Note that this environment was used on Windows10.
The environment can be activated using conda activate cs330.
Datasets have not been included due to their size. See the following links for download of the Lakh Midi Dataset and the Maestro Dataset (both used for this project).
https://salu133445.github.io/lakh-pianoroll-dataset/dataset
https://magenta.tensorflow.org/datasets/maestro
Raw datasets should be extracted to the ./data/raw directory.
This should create the folder structure: ./data/raw/lpd/lpd_cleansed/ where the contents of the lpd_cleansed
See the preprocessing scripts in ./preprocessing to convert the raw data into data usable for this project.
The ./dataset directory also includes the dataset classes for creating token embeddings for use with the language models.