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:
fma_small.zip: 8,000 tracks of 30s, 8 balanced genres (GTZAN-like) (7.2 GiB)
fma_medium.zip: 25,000 tracks of 30s, 16 unbalanced genres (22 GiB)
fma_large.zip: 106,574 tracks of 30s, 161 unbalanced genres (93 GiB)
fma_full.zip: 106,574 untrimmed tracks, 161 unbalanced genres (879 GiB)
See the wiki (or #41) for known issues (errata).
The following notebooks, scripts, and modules have been developed for the dataset.
usage.ipynb: shows how to load the datasets and develop, train, and test your own models with it.
baselines.ipynb: baseline models for genre recognition, both from audio and features.
features.py: features extraction from the audio (used to create
creation.ipynb: creation of the dataset (used to create
creation.py: creation of the dataset (long-running data collection and processing).
utils.py: helper functions and classes.