Metadata-Version: 2.1
Name: sxmp-mule
Version: 1.1.0
Home-page: https://github.com/PandoraMedia/music-audio-representations
Author: Matt C. McCallum
Author-email: mmccallum@pandora.com
License: GNU GPL 3.0
Project-URL: Documentation, https://github.com/PandoraMedia/music-audio-representations
Project-URL: Bug Reports, https://github.com/PandoraMedia/music-audio-representations/issues
Project-URL: Source, https://github.com/PandoraMedia/music-audio-representations
Keywords: mule audio music embeddings machine learning
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Education
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: GNU General Public License v3 (GPLv3)
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Mathematics
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Multimedia :: Sound/Audio :: Analysis
Requires-Python: >=3.8
Description-Content-Type: text/markdown
License-File: LICENSE


# MULE

The Musicset Unsupervised Large Embedding (MULE) module is your 
music-audio workhorse!

This module contains [SCOOCH](https://github.com/PandoraMedia/scooch) configurable code to run a simple 
analysis pipeline to extract audio embeddings from audio files which
may then be used for downstream music understanding purposes.

This module requires FFMpeg to read audio files, which may be 
downloaded [here](https://ffmpeg.org/download.html).

In order to create MULE embeddings, you will need a SCOOCH configuration
describing the pipeline, and the model weights. Both are licensed under 
the [CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/legalcode) license, and can be found in this [module's github repository](https://github.com/PandoraMedia/music-audio-representations).

To create embeddings for a single audio file, e.g., `test.wav` in the current
directory, you can use this module in conjunction with the provided configuration
and model weights:

```
pip install sxmp-mule
git clone https://github.com/PandoraMedia/music-audio-representations.git
cd ./music-audio-representations
mule analyze --config ./supporting_data/configs/mule_embedding.yml -i ../test.wav -o ./embedding.npy
```

For more information on this module, please check out the publication:

[*Supervised and Unsupervised Learning of Audio Representations for Music Understanding*](https://arxiv.org/abs/2210.03799), **M. C. McCallum**, F. Korzeniowski, S. Oramas, F. Gouyon, A. F. Ehmann.

