--- title: free-music-archive-full canonical_url: "https://www.modelscope.cn/datasets/benjamin-paine/free-music-archive-full" md_url: "https://www.modelscope.cn/datasets/benjamin-paine/free-music-archive-full.md" repository: benjamin-paine/free-music-archive-full last_updated: 2025-03-18 license: "Apache License 2.0" storage_size: "488 GB" downloads: 101702 stars: 0 --- # free-music-archive-full > free-music-archive-full - benjamin-paine 在 ModelScope 开源的数据集。FMA: A Dataset for Music Analysis benjamin-paine/free-music-archive-full 是 ModelScope 魔搭社区上的数据集，存储大小 488 GB，采用 Apache License 2.0 许可。 - **Repository**: benjamin-paine/free-music-archive-full - **License**: Apache License 2.0 - **Storage size**: 488 GB - **Downloads**: 101702 - **Stars**: 0 - **Last updated**: 2025-03-18 Source: https://www.modelscope.cn/datasets/benjamin-paine/free-music-archive-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, comprising a total of **106,199** clips of **untrimmed length** over **16** *unbalanced* genres totaling **8,104 hours** of audio. Packed as Parquet files, this dataset is 593 GB in size, roughly a 34% size saving over the original ZIP file. ## Repack Notes - 173 files were unreadable by `libsndfile / libmpg123`, these were removed. - 202 files had licenses that were unclear on whether or not they permitted redistribution, or the full license text was unavailable. These were removed. - Many of the remaining files had mixed or inconsistent encoding. To homogenize the dataset, all audio was re-encoded using `libmpg123`. # 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-BY-NC 2.0 | https://creativecommons.org/licenses/by-nc/2.0/ | | 6 | CC-BY-NC 2.1 | https://creativecommons.org/licenses/by-nc/2.1/ | | 7 | CC-BY-NC 2.5 | https://creativecommons.org/licenses/by-nc/2.5/ | | 8 | CC-BY-NC 3.0 | https://creativecommons.org/licenses/by-nc/3.0/ | | 9 | CC-BY-NC 4.0 | https://creativecommons.org/licenses/by-nc/4.0/ | | 10 | CC-BY-NC-ND 2.0 | https://creativecommons.org/licenses/by-nc-nd/2.0/ | | 11 | CC-BY-NC-ND 2.1 | https://creativecommons.org/licenses/by-nc-nd/2.1/ | | 12 | CC-BY-NC-ND 2.5 | https://creativecommons.org/licenses/by-nc-nd/2.5/ | | 13 | CC-BY-NC-ND 3.0 | https://creativecommons.org/licenses/by-nc-nd/3.0/ | | 14 | CC-BY-NC-ND 4.0 | https://creativecommons.org/licenses/by-nc-nd/4.0/ | | 15 | CC-BY-NC-SA 2.0 | https://creativecommons.org/licenses/by-nc-sa/2.0/ | | 16 | CC-BY-NC-SA 2.1 | https://creativecommons.org/licenses/by-nc-sa/2.1/ | | 17 | CC-BY-NC-SA 2.5 | https://creativecommons.org/licenses/by-nc-sa/2.5/ | | 18 | CC-BY-NC-SA 3.0 | https://creativecommons.org/licenses/by-nc-sa/3.0/ | | 19 | CC-BY-NC-SA 4.0 | https://creativecommons.org/licenses/by-nc-sa/4.0/ | | 20 | CC-BY-ND 2.0 | https://creativecommons.org/licenses/by-nd/2.0/ | | 21 | CC-BY-ND 2.5 | https://creativecommons.org/licenses/by-nd/2.5/ | | 22 | CC-BY-ND 3.0 | https://creativecommons.org/licenses/by-nd/3.0/ | | 23 | CC-BY-ND 4.0 | https://creativecommons.org/licenses/by-nd/4.0/ | | 24 | CC-BY-SA 2.0 | https://creativecommons.org/licenses/by-sa/2.0/ | | 25 | CC-BY-SA 2.5 | https://creativecommons.org/licenses/by-sa/2.5/ | | 26 | CC-BY-SA 3.0 | https://creativecommons.org/licenses/by-sa/3.0/ | | 27 | CC-BY-SA 4.0 | https://creativecommons.org/licenses/by-sa/4.0/ | | 28 | CC-NC-Sampling+ 1.0 | https://creativecommons.org/licenses/nc-sampling+/1.0/ | | 29 | CC-Sampling+ 1.0 | https://creativecommons.org/licenses/sampling+/1.0/ | | 30 | FMA Sound Recording Common Law | https://freemusicarchive.org/Sound_Recording_Common_Law | | 31 | Free Art License | https://artlibre.org/licence/lal/en | | 32 | Free Music Philosophy (FMP) | https://irdial.com/free_and_easy.htm | ## Total Duration by License | License | Total Duration (Percentage) | | ------- | --------------------------- | | CC-BY-NC-SA 3.0 | 2768.3 hours (34.16%) | | CC-BY-NC-ND 3.0 | 2296.4 hours (28.34%) | | CC-BY-NC-ND 4.0 | 1018.4 hours (12.57%) | | CC-BY-NC-SA 4.0 | 533.2 hours (6.58%) | | CC-BY 4.0 | 377.0 hours (4.65%) | | CC-BY-NC 3.0 | 288.9 hours (3.56%) | | CC-BY-NC 4.0 | 232.6 hours (2.87%) | | CC-BY 3.0 | 106.9 hours (1.32%) | | CC-BY-SA 4.0 | 99.4 hours (1.23%) | | CC-BY-SA 3.0 | 79.7 hours (0.98%) | | CC-BY-NC-SA 2.0 | 65.1 hours (0.80%) | | CC-BY-NC-ND 2.0 | 56.2 hours (0.69%) | | CC-BY-ND 3.0 | 36.8 hours (0.45%) | | CC-BY-ND 4.0 | 25.0 hours (0.31%) | | CC-BY-NC-ND 2.5 | 24.2 hours (0.30%) | | FMA Sound Recording Common Law | 19.9 hours (0.25%) | | CC-BY-NC-SA 2.5 | 18.0 hours (0.22%) | | CC-BY-NC 2.5 | 13.3 hours (0.16%) | | CC0 1.0 | 10.5 hours (0.13%) | | CC-BY 1.0 | 10.4 hours (0.13%) | | Free Music Philosophy (FMP) | 4.4 hours (0.05%) | | Free Art License | 2.7 hours (0.03%) | | CC-BY 2.0 | 2.5 hours (0.03%) | | CC-BY-NC 2.1 | 2.4 hours (0.03%) | | CC-BY-NC-SA 2.1 | 2.3 hours (0.03%) | | CC-BY-SA 2.0 | 1.9 hours (0.02%) | | CC-BY-NC 2.0 | 1.6 hours (0.02%) | | CC-BY-ND 2.5 | 1.6 hours (0.02%) | | CC-NC-Sampling+ 1.0 | 1.4 hours (0.02%) | | CC-BY-NC-ND 2.1 | 65.0 minutes (0.01%) | | CC-Sampling+ 1.0 | 53.9 minutes (0.01%) | | CC-BY-SA 2.5 | 31.8 minutes (0.01%) | | CC-BY-ND 2.0 | 29.7 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}, } ```