Michaël Defferrard, Kirell Benzi, Pierre Vandergheynst, Xavier Bresson, EPFL LTS2.

The dataset is a dump of the Free Music Archive (FMA), an interactive library of high-quality, legal audio downloads. Below the abstract from the paper.

We introduce the Free Music Archive (FMA), an open and easily accessible dataset which can be used to evaluate several tasks in music information retrieval (MIR), a field concerned with browsing, searching, and organizing large music collections. The community's growing interest in feature and end-to-end learning is however restrained by the limited availability of large audio datasets. By releasing the FMA, we hope to foster research which will improve the state-of-the-art and hopefully surpass the performance ceiling observed in e.g. genre recognition (MGR). The data is made of 106,574 tracks, 16,341 artists, 14,854 albums, arranged in a hierarchical taxonomy of 161 genres, for a total of 343 days of audio and 917 GiB, all under permissive Creative Commons licenses. It features metadata like song title, album, artist and genres; user data like play counts, favorites, and comments; free-form text like description, biography, and tags; together with full-length, high-quality audio, and some pre-computed features. We propose a train/validation/test split and three subsets: a genre-balanced set of 8,000 tracks from 8 major genres, a genre-unbalanced set of 25,000 tracks from 16 genres, and a 98 GiB version with clips trimmed to 30s. This paper describes the dataset and how it was created, proposes some tasks like music classification and annotation or recommendation, and evaluates some baselines for MGR. Code, data, and usage examples are available at https://github.com/mdeff/fma.

This is a pre-publication release. As such, this repository as well as the paper and data are subject to change. Stay tuned!

All metadata and features for all tracks are distributed in fma_metadata.zip (342 MiB). The below tables can be used with pandas or any other data analysis tool. See the paper or the usage notebook for a description.

• tracks.csv: per track metadata such as ID, title, artist, genres, tags and play counts, for all 106,574 tracks.
• genres.csv: all 163 genre IDs with their name and parent (used to infer the genre hierarchy and top-level genres).
• features.csv: common features extracted with librosa.
• echonest.csv: audio features provided by Echonest (now Spotify) for a subset of 13,129 tracks.

Then, you got various sizes of MP3-encoded audio data:

1. fma_small.zip: 8,000 tracks of 30s, 8 balanced genres (GTZAN-like) (7.2 GiB)
2. fma_medium.zip: 25,000 tracks of 30s, 16 unbalanced genres (22 GiB)
3. fma_large.zip: 106,574 tracks of 30s, 161 unbalanced genres (93 GiB)
4. fma_full.zip: 106,574 untrimmed tracks, 161 unbalanced genres (879 GiB)

The following notebooks and scripts, stored in this repository, have been developed for the dataset.

1. usage: shows how to load the datasets and develop, train and test your own models with it.
2. analysis: exploration of the metadata, data and features.
3. baselines: baseline models for genre recognition, both from audio and features.
4. features: features extraction from the audio (used to create features.csv).
5. webapi: query the web API of the FMA. Can be used to update the dataset.
6. creation: creation of the dataset (used to create tracks.csv and genres.csv).

1. Download some data, verify its integrity, and uncompress the archives.

   curl -O https://os.unil.cloud.switch.ch/fma/fma_metadata.zip
   curl -O https://os.unil.cloud.switch.ch/fma/fma_small.zip
   curl -O https://os.unil.cloud.switch.ch/fma/fma_medium.zip
   curl -O https://os.unil.cloud.switch.ch/fma/fma_large.zip
   curl -O https://os.unil.cloud.switch.ch/fma/fma_full.zip
   
   echo "f0df49ffe5f2a6008d7dc83c6915b31835dfe733  fma_metadata.zip" | sha1sum -c -
   echo "ade154f733639d52e35e32f5593efe5be76c6d70  fma_small.zip"    | sha1sum -c -
   echo "c67b69ea232021025fca9231fc1c7c1a063ab50b  fma_medium.zip"   | sha1sum -c -
   echo "497109f4dd721066b5ce5e5f250ec604dc78939e  fma_large.zip"    | sha1sum -c -
   echo "0f0ace23fbe9ba30ecb7e95f763e435ea802b8ab  fma_full.zip"     | sha1sum -c -
   
   unzip fma_metadata.zip
   unzip fma_small.zip
   unzip fma_medium.zip
   unzip fma_large.zip
   unzip fma_full.zip

   If you get any error while decompressing the archives (especially with the Windows and macOS system unzippers), please try 7zip. That is probably an unsupported compression issue.

2. Optionally, use pyenv to install Python 3.6 and create a virtual environment.

   pyenv install 3.6.0
   pyenv virtualenv 3.6.0 fma
   pyenv activate fma

3. Clone the repository.

   git clone https://github.com/mdeff/fma.git
   cd fma

4. Install the Python dependencies from requirements.txt. Depending on your usage, you may need to install ffmpeg or graphviz. Install CUDA if you want to train neural networks on GPUs (see Tensorflow's instructions).

   make install

5. Fill in the configuration.

   cat .env
   AUDIO_DIR=/path/to/audio
   FMA_KEY=IFIUSETHEAPI

6. Open Jupyter or run a notebook.

   jupyter-notebook
   make baselines.ipynb

• A Music Information Retrieval Dataset, Made With FMA, freemusicarchive.org, 2017-05-22.
• Pre-publication release announced, twitter.com, 2017-05-09.
• FMA: A Dataset For Music Analysis, tensorflow.blog, 2017-03-14.
• Beta release discussed, twitter.com, 2017-02-08.
• FMA Data Set for Researchers Released, freemusicarchive.org, 2016-12-15.

Dataset lists

• https://github.com/caesar0301/awesome-public-datasets
• https://archive.ics.uci.edu/ml/datasets/FMA:+A+Dataset+For+Music+Analysis
• http://deeplearning.net/datasets
• http://www.audiocontentanalysis.org/data-sets
• https://teachingmir.wikispaces.com/Datasets
• https://en.wikipedia.org/wiki/List_of_datasets_for_machine_learning_research
• https://cloudlab.atlassian.net/wiki/display/datasets/FMA:+A+Dataset+For+Music+Analysis

• 2017-05-09 pre-publication release

  • paper: arXiv:1612.01840v2
  • code: git tag rc1
  • fma_metadata.zip sha1: f0df49ffe5f2a6008d7dc83c6915b31835dfe733
  • fma_small.zip sha1: ade154f733639d52e35e32f5593efe5be76c6d70
  • fma_medium.zip sha1: c67b69ea232021025fca9231fc1c7c1a063ab50b
  • fma_large.zip sha1: 497109f4dd721066b5ce5e5f250ec604dc78939e
  • fma_full.zip sha1: 0f0ace23fbe9ba30ecb7e95f763e435ea802b8ab

• 2016-12-06 beta release

  • paper: arXiv:1612.01840v1
  • code: git tag beta
  • fma_small.zip sha1: e731a5d56a5625f7b7f770923ee32922374e2cbf
  • fma_medium.zip sha1: fe23d6f2a400821ed1271ded6bcd530b7a8ea551

Please open an issue or a pull request if you want to contribute. Let's try to keep this repository the central place around the dataset! Links to resources related to the dataset are welcome. I hope the community will like it and that we can keep it lively by evolving it toward people's needs.

• Please cite our paper if you use our code or data.
• The code in this repository is released under the terms of the MIT license.
• The metadata is released under the terms of the Creative Commons Attribution 4.0 International License (CC BY 4.0).
• We do not hold the copyright on the audio and distribute it under the terms of the license chosen by the artist.
• The dataset is meant for research purposes.
• We are grateful to SWITCH and EPFL for hosting the dataset within the context of the SCALE-UP project, funded in part by the swissuniversities SUC P-2 program.