PyTorch implementation of Contrastive Learning of Musical Representations by J. Spijkervet and J.A. Burgoyne.

In this work, we introduce SimCLR to the music domain and contribute a large chain of audio data augmentations, to form a simple framework for self-supervised learning of raw waveforms of music: CLMR. We evaluate the performance of the self-supervised learned representations on the task of music classification.

• We achieve competitive results on the MagnaTagATune and Million Song Datasets relative to fully supervised training, despite only using a linear classifier on self-supervised learned representations, i.e., representations that were learned task-agnostically without any labels.
• CLMR enables efficient classification: with only 1% of the labeled data, we achieve similar scores compared to using 100% of the labeled data.
• CLMR is able to generalise to out-of-domain datasets: when training on entirely different music datasets, it is still able to perform competitively compared to fully supervised training on the target dataset.

This is the CLMR v2 implementation, for the original implementation go to the v1 branch

This repository relies on my SimCLR implementation, which can be found here and on my torchaudio-augmentations package, found here.

git clone https://github.com/spijkervet/clmr.git && cd clmr

pip3 install -r requirements.txt
# or
python3 setup.py install

The following command downloads MagnaTagATune, preprocesses it and starts self-supervised pre-training on 1 GPU (with 8 simultaneous CPU workers) and linear evaluation:

python3 preprocess.py --dataset magnatagatune

python3 main.py --dataset magnatagatune --gpus 1 --workers 8

python3 linear_evaluation.py --gpus 1 --workers 8 --checkpoint_path [path to checkpoint.pt, usually in ./runs]

Links go to download

Simply run the following command to pre-train the CLMR model on the MagnaTagATune dataset.

python main.py --dataset magnatagatune

To test a trained model, make sure to set the checkpoint_path variable in the config/config.yaml, or specify it as an argument:

python linear_evaluation.py --checkpoint_path ./clmr_checkpoint_10000.pt

The configuration of training can be found in: config/config.yaml. I personally prefer to use files instead of long strings of arguments when configuring a run. Every entry in the config file can be overrided with the corresponding flag (e.g. --max_epochs 500 if you would like to train with 500 epochs).

To view results in TensorBoard, run:

tensorboard --logdir ./runs