FMA: A Dataset for Music Analysis

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 fulllength, 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.