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.ipynb 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 genres with 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)

See the wiki (or #41) for known issues (errata).

Code

The following notebooks, scripts, and modules have been developed for the dataset.

1. usage.ipynb: shows how to load the datasets and develop, train, and test your own models with it.

2. analysis.ipynb: exploration of the metadata, data, and features. Creates the figures used in the paper.

3. baselines.ipynb: baseline models for genre recognition, both from audio and features.

4. features.py: features extraction from the audio (used to create features.csv).

5. webapi.ipynb: query the web API of the FMA. Can be used to update the dataset.

6. creation.ipynb: creation of the dataset (used to create tracks.csv and genres.csv).

7. creation.py: creation of the dataset (long-running data collection and processing).

8. utils.py: helper functions and classes.