---
dataset_info:
  features:
  - name: audio
    dtype: audio
  - name: title
    dtype: string
  - name: url
    dtype: string
  - name: artist
    dtype: string
  - name: album_title
    dtype: string
  - name: license
    dtype:
      class_label:
        names:
          '0': CC-BY 1.0
          '1': CC-BY 2.0
          '2': CC-BY 2.5
          '3': CC-BY 3.0
          '4': CC-BY 4.0
          '5': CC-Sampling+ 1.0
          '6': CC0 1.0
          '7': FMA Sound Recording Common Law
          '8': Free Art License
          '9': Public Domain Mark 1.0
  - name: copyright
    dtype: string
  splits:
  - name: train
    num_bytes: 6492778912.662
    num_examples: 8802
  download_size: 10506892695
  dataset_size: 6492778912.662
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
license: cc-by-4.0
task_categories:
- audio-to-audio
- audio-classification
tags:
- freemusicarchive
- freemusicarchive.org
- fma
pretty_name: Free Music Archive Commercial 16 KHz - Full
---

# FMA: A Dataset for Music Analysis

[Michaël Defferrard](https://deff.ch/), [Kirell Benzi](https://kirellbenzi.com/), [Pierre Vandergheynst](https://people.epfl.ch/pierre.vandergheynst), [Xavier Bresson](https://www.ntu.edu.sg/home/xbresson).

**International Society for Music Information Retrieval Conference (ISMIR), 2017.**

> We introduce the Free Music Archive (FMA), an open and easily accessible dataset suitable for evaluating several tasks in 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. The FMA aims to overcome this hurdle by providing 917 GiB and 343 days of Creative Commons-licensed audio from 106,574 tracks from 16,341 artists and 14,854 albums, arranged in a hierarchical taxonomy of 161 genres. It provides full-length and high-quality audio, pre-computed features, together with track- and user-level metadata, tags, and free-form text such as biographies. We here describe the dataset and how it was created, propose a train/validation/test split and three subsets, discuss some suitable MIR tasks, and evaluate some baselines for genre recognition. Code, data, and usage examples are available at https://github.com/mdeff/fma.

Paper: [arXiv:1612.01840](https://arxiv.org/abs/1612.01840) - [latex and reviews](https://github.com/mdeff/paper-fma-ismir2017)

Slides: [doi:10.5281/zenodo.1066119](https://doi.org/10.5281/zenodo.1066119)

Poster: [doi:10.5281/zenodo.1035847](https://doi.org/10.5281/zenodo.1035847)

# This Pack

This is the **full** dataset, limited only the **commercially licensed** samples comprising a total of **8,802 samples** clips of **untrimmed length** totaling **531 hours** of audio in **10.5 GB** of disk space.

# License

- The [FMA codebase](https://github.com/mdeff/fma) is released under [The MIT License](https://github.com/mdeff/fma/blob/master/LICENSE.txt).
- The FMA metadata is released under [CC-BY 4.0](https://creativecommons.org/licenses/by/4.0).
- The individual files are released under various Creative Commons family licenses, with a small amount of additional licenses. **Each file has its license attached and important details of the license enumerated.** To make it easy to use for developers and trainers, a configuration is available to limit only to commercially-usable data.

Please refer to any of the following URLs for additional details.

| Class Label | License Name | URL |
| ----------- | ------------ | --- |
| 0 | CC-BY 1.0 | https://creativecommons.org/licenses/by/1.0/ |
| 1 | CC-BY 2.0 | https://creativecommons.org/licenses/by/2.0/ |
| 2 | CC-BY 2.5 | https://creativecommons.org/licenses/by/2.5/ |
| 3 | CC-BY 3.0 | https://creativecommons.org/licenses/by/3.0/ |
| 4 | CC-BY 4.0 | https://creativecommons.org/licenses/by/4.0/ |
| 5 | CC-Sampling+ 1.0 | https://creativecommons.org/licenses/sampling+/1.0/ |
| 6 | CC0 1.0 | https://creativecommons.org/publicdomain/zero/1.0/ |
| 7 | FMA Sound Recording Common Law | https://freemusicarchive.org/Sound_Recording_Common_Law |
| 8 | Free Art License | https://artlibre.org/licence/lal/en |
| 9 | Public Domain Mark 1.0 | https://creativecommons.org/publicdomain/mark/1.0/ |

## Total Duration by License

| License | Total Duration (Percentage) |
| ------- | --------------------------- |
| CC-BY 4.0 | 377.0 hours (4.65%) |
| CC-BY 3.0 | 106.9 hours (1.32%) |
| FMA Sound Recording Common Law | 19.9 hours (0.25%) |
| CC0 1.0 | 10.5 hours (0.13%) |
| CC-BY 1.0 | 10.4 hours (0.13%) |
| Free Art License | 2.7 hours (0.03%) |
| CC-BY 2.0 | 2.5 hours (0.03%) |
| CC-Sampling+ 1.0 | 53.9 minutes (0.01%) |
| CC-BY 2.5 | 11.2 minutes (0.00%) |

# Citations

```
@inproceedings{fma_dataset,
  title = {{FMA}: A Dataset for Music Analysis},
  author = {Defferrard, Micha\"el and Benzi, Kirell and Vandergheynst, Pierre and Bresson, Xavier},
  booktitle = {18th International Society for Music Information Retrieval Conference (ISMIR)},
  year = {2017},
  archiveprefix = {arXiv},
  eprint = {1612.01840},
  url = {https://arxiv.org/abs/1612.01840},
}
```

```
@inproceedings{fma_challenge,
  title = {Learning to Recognize Musical Genre from Audio},
  subtitle = {Challenge Overview},
  author = {Defferrard, Micha\"el and Mohanty, Sharada P. and Carroll, Sean F. and Salath\'e, Marcel},
  booktitle = {The 2018 Web Conference Companion},
  year = {2018},
  publisher = {ACM Press},
  isbn = {9781450356404},
  doi = {10.1145/3184558.3192310},
  archiveprefix = {arXiv},
  eprint = {1803.05337},
  url = {https://arxiv.org/abs/1803.05337},
}
```