Contrastive Audio-Language Learning for Music

Ilaria Manco^1,2, Emmanouil Benetos¹, Elio Quinton², Gyorgy Fazekas^1
1 Queen Mary University of London, ² Universal Music Group

This repository is the official implementation of Contrastive Audio-Language Learning for Music, accepted to ISMIR 2022.

In this work we introduced MusCALL, a framework for Contrastive Audio-Language Learning in the music domain. Our approach consists of a dual-encoder architecture that learns the alignment between pairs of music audio and descriptive sentences, producing multimodal embeddings that can be used for text-to-audio and audio-to-text retrieval out-of-the-box. Thanks to this property, MusCALL can be transferred to virtually any task that can be cast as text-based retrieval in a zero-shot fashion.

We provide code for training and evaluation of MusCALL on audio-text cross-modal retrieval and two zero-shot classification tasks (auto-tagging and genre classification).

Setup

Create a fresh virtual environment:

python -m venv venv 
source venv/bin/activate

Then, clone the repository and install the dependencies:

git clone https://www.github.com/ilaria-manco/muscall 
cd muscall 
pip install -r requirements.txt
pip install -e .

Preparing the dataset

MusCALL is trained on a multimodal dataset of (audio, text) pairs.

Annotations should be provided in JSON format and must include the following fields:

audio_id: the unique identifier for each audio track in the dataset

caption : a string with the textual description of the audio track

audio_path: path to the audio track, relative to the root audio directory

One JSON file per split must be provided and stored in the data/datasets directory, following this structure:

dataset_name
├── audio            
│   ├── track_1.npy
│   ├── track_2.npy
|   └── ...
├── dataset_train.json    
├── dataset_val.json    
└── dataset_test.json

An illustrative example of the dataset is provided in data/datasets/audiocaption/.

Training MusCALL

Dataset, model and training configurations are set in the respective yaml files in configs. You can also pass some options via the CLI, overwriting the arguments in the config files. For more details on the CLI options, please refer to the training script.

To train the model with the default configs, simply run

cd scripts/
python train.py 

This will generate a model_id and create a new folder in save/experiments/ where the output will be saved.

If you wish to resume training from a saved checkpoint, run this command:

python train.py --experiment_id <model_id> 

Evaluating MusCALL

Once trained, you can evaluate MusCALL on the cross-modal retrieval task:

python evaluate.py <model_id> retrieval

or, in the zero-shot transfer setting, on an arbitrary music classification task.

In our zero-shot evaluation, we include:

• mtt: auto-tagging on the MagnaTagATune Dataset
• gtzan: music genre classification on the GTZAN dataset

python evaluate.py <model_id> zeroshot <dataset_name>

You'll need to download the datasets inside the datasets/ folder and preprocess them before running the zeroshot evaluation.

Cite

If you use the code in this repo, please consider citing our work:

@inproceedings{manco2022,
  title={Contrastive Audio-Language Learning for Music}, 
  author={Manco, Ilaria and Benetos, Emmanouil and Quinton, Elio and Fazekas, György},
  booktitle={Proceedings of the 23rd International Society for Music Information Retrieval Conference (ISMIR)}, 
  year={2022},
}

License

This repository is released under the GNU General Public License v3.0 license. Please see the LICENSE file for more details.

Some of the code is adapted from the following repos:

• CLIP by @openai
• x-clip by @lucidrains

Contact

If you have any questions, please get in touch: [email protected].