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https://github.com/mdeff/fma

FMA: A Dataset For Music Analysis
https://github.com/mdeff/fma

dataset deep-learning music-analysis music-information-retrieval open-data open-science reproducible-research

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FMA: A Dataset For Music Analysis

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README 

        
# 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 .



* Paper: [`arXiv:1612.01840`][paper] ([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)



[paper]: https://arxiv.org/abs/1612.01840

[FMA]: https://freemusicarchive.org



## Data



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.



[pandas]:   https://pandas.pydata.org/

[librosa]:  https://librosa.org/

[spotify]:  https://www.spotify.com/

[echonest]: https://web.archive.org/web/20170519050040/http://the.echonest.com/



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)



[`fma_metadata.zip`]: https://os.unil.cloud.switch.ch/fma/fma_metadata.zip

[`fma_small.zip`]:    https://os.unil.cloud.switch.ch/fma/fma_small.zip

[`fma_medium.zip`]:   https://os.unil.cloud.switch.ch/fma/fma_medium.zip

[`fma_large.zip`]:    https://os.unil.cloud.switch.ch/fma/fma_large.zip

[`fma_full.zip`]:     https://os.unil.cloud.switch.ch/fma/fma_full.zip



See the [wiki](https://github.com/mdeff/fma/wiki) (or [#41](https://github.com/mdeff/fma/issues/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](https://github.com/mdeff/fma/tree/outputs/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.



[`usage.ipynb`]:     https://nbviewer.jupyter.org/github/mdeff/fma/blob/outputs/usage.ipynb

[`analysis.ipynb`]:  https://nbviewer.jupyter.org/github/mdeff/fma/blob/outputs/analysis.ipynb

[`baselines.ipynb`]: https://nbviewer.jupyter.org/github/mdeff/fma/blob/outputs/baselines.ipynb

[`features.py`]:     features.py

[`webapi.ipynb`]:    https://nbviewer.jupyter.org/github/mdeff/fma/blob/outputs/webapi.ipynb

[`creation.ipynb`]:  https://nbviewer.jupyter.org/github/mdeff/fma/blob/outputs/creation.ipynb

[`creation.py`]:     creation.py

[`utils.py`]:        utils.py



## Usage



[![Binder](https://static.mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/mdeff/fma/outputs?urlpath=lab/tree/usage.ipynb)

  Click the binder badge to play with the code and data from your browser without installing anything.



1. Clone the repository.

    ```sh

    git clone https://github.com/mdeff/fma.git

    cd fma

    ```



1. Create a Python 3.6 environment.



    ```sh

    # with https://conda.io

    conda create -n fma python=3.6

    conda activate fma



    # with https://github.com/pyenv/pyenv

    pyenv install 3.6.0

    pyenv virtualenv 3.6.0 fma

    pyenv activate fma



    # with https://pipenv.pypa.io

    pipenv --python 3.6

    pipenv shell



    # with https://docs.python.org/3/tutorial/venv.html

    python3.6 -m venv ./env

    source ./env/bin/activate

    ```

    



1. Install dependencies.

    ```sh

    pip install --upgrade pip setuptools wheel

    pip install numpy==1.12.1  # workaround resampy's bogus setup.py

    pip install -r requirements.txt

    ```

    Note: you may need to install [ffmpeg](https://ffmpeg.org/download.html) or [graphviz](https://www.graphviz.org) depending on your usage.\

    Note: install [CUDA](https://en.wikipedia.org/wiki/CUDA) to train neural networks on GPUs (see [Tensorflow's instructions](https://www.tensorflow.org/install/)).



1. Download some data, verify its integrity, and uncompress the archives.

    ```sh

    cd data



    curl -O https://os.unil.cloud.switch.ch/fma/fma_metadata.zip

    curl -O https://os.unil.cloud.switch.ch/fma/fma_small.zip

    curl -O https://os.unil.cloud.switch.ch/fma/fma_medium.zip

    curl -O https://os.unil.cloud.switch.ch/fma/fma_large.zip

    curl -O https://os.unil.cloud.switch.ch/fma/fma_full.zip



    echo "f0df49ffe5f2a6008d7dc83c6915b31835dfe733  fma_metadata.zip" | sha1sum -c -

    echo "ade154f733639d52e35e32f5593efe5be76c6d70  fma_small.zip"    | sha1sum -c -

    echo "c67b69ea232021025fca9231fc1c7c1a063ab50b  fma_medium.zip"   | sha1sum -c -

    echo "497109f4dd721066b5ce5e5f250ec604dc78939e  fma_large.zip"    | sha1sum -c -

    echo "0f0ace23fbe9ba30ecb7e95f763e435ea802b8ab  fma_full.zip"     | sha1sum -c -



    unzip fma_metadata.zip

    unzip fma_small.zip

    unzip fma_medium.zip

    unzip fma_large.zip

    unzip fma_full.zip



    cd ..

    ```



    Note: try [7zip](https://www.7-zip.org) if decompression errors.

    It might be an [unsupported compression issue](https://github.com/mdeff/fma/issues/5).



1. Fill a `.env` configuration file (at repository's root) with the following content.

    ```

    AUDIO_DIR=./data/fma_small/  # the path to a decompressed fma_*.zip

    FMA_KEY=MYKEY  # only if you want to query the freemusicarchive.org API

    ```



1. Open Jupyter or run a notebook.

    ```sh

    jupyter notebook

    make usage.ipynb

    ```



## Impact, coverage, and resources



100+ research papers



Full list on [Google Scholar](https://scholar.google.com/scholar?cites=13646959466952873682,13785796238335741238,7544459641098681164,5736399534855095976).

Some picks below.



* [Zero-shot Learning for Audio-based Music Classification and Tagging](https://arxiv.org/abs/1907.02670)

* [One deep music representation to rule them all? A comparative analysis of different representation learning strategies](https://doi.org/10.1007/s00521-019-04076-1)

* [Deep Learning for Audio-Based Music Classification and Tagging: Teaching Computers to Distinguish Rock from Bach](https://sci-hub.tw/10.1109/MSP.2018.2874383)

* [Learning Discrete Structures for Graph Neural Networks](https://arxiv.org/abs/1903.11960)

* [A context encoder for audio inpainting](https://arxiv.org/abs/1810.12138)

* [OpenMIC-2018: An Open Data-set for Multiple Instrument Recognition](https://archives.ismir.net/ismir2018/paper/000248.pdf)

* [Detecting Music Genre Using Extreme Gradient Boosting](https://doi.org/10.1145/3184558.3191822)

* [Transfer Learning of Artist Group Factors to Musical Genre Classification](https://doi.org/10.1145/3184558.3191823)

* [Learning to Recognize Musical Genre from Audio: Challenge Overview](https://arxiv.org/abs/1803.05337)

* [Representation Learning of Music Using Artist Labels](https://arxiv.org/abs/1710.06648)



2 derived works



* [OpenMIC-2018: An Open Data-set for Multiple Instrument Recognition](https://github.com/cosmir/openmic-2018)

* [ConvNet features](https://github.com/keunwoochoi/FMA_convnet_features) from [Transfer learning for music classification and regression tasks](https://arxiv.org/abs/1703.09179)



~10 posts



* [Music Genre Classification With TensorFlow](https://towardsdatascience.com/music-genre-classification-with-tensorflow-3de38f0d4dbb), Towards Data Science, 2020-08-11.

* [Music Genre Classification: Transformers vs Recurrent Neural Networks](https://towardsdatascience.com/music-genre-classification-transformers-vs-recurrent-neural-networks-631751a71c58), Towards Data Science, 2020-06-14.

* [Using CNNs and RNNs for Music Genre Recognition](https://towardsdatascience.com/using-cnns-and-rnns-for-music-genre-recognition-2435fb2ed6af), Towards Data Science, 2018-12-13.

* [Over 1.5 TB’s of Labeled Audio Datasets](https://towardsdatascience.com/a-data-lakes-worth-of-audio-datasets-b45b88cd4ad), Towards Data Science, 2018-11-13.

* [Discovering Descriptive Music Genres Using K-Means Clustering](https://medium.com/latinxinai/discovering-descriptive-music-genres-using-k-means-clustering-d19bdea5e443), Medium, 2018-04-09.

* [25 Open Datasets for Deep Learning Every Data Scientist Must Work With](https://www.analyticsvidhya.com/blog/2018/03/comprehensive-collection-deep-learning-datasets/), Analytics Vidhya, 2018-03-29.

* [Learning Music Genres](https://medium.com/@diegoagher/learning-music-genres-5ab1cabadfed), Medium, 2017-12-13.

* [music2vec: Generating Vector Embeddings for Genre-Classification Task](https://medium.com/@rajatheb/music2vec-generating-vector-embedding-for-genre-classification-task-411187a20820), Medium, 2017-11-28.

* [A Music Information Retrieval Dataset, Made With FMA](https://web.archive.org/web/20190907182116/http://freemusicarchive.org/member/cheyenne_h/blog/A_Music_Information_Retrieval_Dataset_Made_With_FMA), freemusicarchive.org, 2017-05-22.

* [Pre-publication release announced](https://twitter.com/m_deff/status/861985446116589569), twitter.com, 2017-05-09.

* [FMA: A Dataset For Music Analysis](https://tensorflow.blog/2017/03/14/fma-a-dataset-for-music-analysis), tensorflow.blog, 2017-03-14.

* [Beta release discussed](https://twitter.com/YadFaeq/status/829406463286063104), twitter.com, 2017-02-08.

* [FMA Data Set for Researchers Released](https://web.archive.org/web/20190826112752/http://freemusicarchive.org/member/cheyenne_h/blog/FMA_Dataset_for_Researchers), freemusicarchive.org, 2016-12-15.



5 events



* [Summer Workshop](https://hcdigitalscholarship.github.io/audio-files) by the [Haverford Digital Scholarship Library](https://www.haverford.edu/library/digital-scholarship), 2020-07.

* [Genre recognition challenge](https://www.crowdai.org/challenges/www-2018-challenge-learning-to-recognize-musical-genre) at the [Web Conference](https://www2018.thewebconf.org/program/challenges-track/), Lyon, 2018-04.

* [Slides](https://doi.org/10.5281/zenodo.1066119) presented at the [Data Jam days](http://datajamdays.org), Lausanne, 2017-11-24.

* [Poster](https://doi.org/10.5281/zenodo.1035847) presented at [ISMIR 2017](https://ismir2017.ismir.net), Suzhou, 2017-10-24.

* [Slides](https://doi.org/10.5281/zenodo.999353) for the [Open Science in Practice](https://osip2017.epfl.ch) summer school at EPFL, 2017-09-29.



~10 dataset lists



* 

* 

* 

* 

* 

* 

* 

* 

* 

* 

* 



## Contributing



Contribute by opening an [issue](https://github.com/mdeff/fma/issues) or a [pull request](https://github.com/mdeff/fma/pulls).

Let this repository be a hub around the dataset!



## History



**2017-05-09 pre-publication release**

* paper: [arXiv:1612.01840v2](https://arxiv.org/abs/1612.01840v2)

* code: [git tag rc1](https://github.com/mdeff/fma/releases/tag/rc1)

* `fma_metadata.zip` sha1: `f0df49ffe5f2a6008d7dc83c6915b31835dfe733`

* `fma_small.zip`    sha1: `ade154f733639d52e35e32f5593efe5be76c6d70`

* `fma_medium.zip`   sha1: `c67b69ea232021025fca9231fc1c7c1a063ab50b`

* `fma_large.zip`    sha1: `497109f4dd721066b5ce5e5f250ec604dc78939e`

* `fma_full.zip`     sha1: `0f0ace23fbe9ba30ecb7e95f763e435ea802b8ab`

* known issues: see [#41](https://github.com/mdeff/fma/issues/41)



**2016-12-06 beta release**

* paper: [arXiv:1612.01840v1](https://arxiv.org/abs/1612.01840v1)

* code: [git tag beta](https://github.com/mdeff/fma/releases/tag/beta)

* `fma_small.zip`  sha1: `e731a5d56a5625f7b7f770923ee32922374e2cbf`

* `fma_medium.zip` sha1: `fe23d6f2a400821ed1271ded6bcd530b7a8ea551`



## Acknowledgments and Licenses



We are grateful to the [Swiss Data Science Center] ([EPFL] and [ETHZ]) for hosting the dataset.



Please cite our work if you use our code or data.



```

@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},

}

```



* The code in this repository is released under the [MIT license](LICENSE.txt).

* The metadata is released under the [Creative Commons Attribution 4.0 International License (CC BY 4.0)][ccby40].

* We do not hold the copyright on the audio and distribute it under the license chosen by the artist.

* The dataset is meant for research purposes.



[ccby40]: https://creativecommons.org/licenses/by/4.0

[Swiss Data Science Center]: https://datascience.ch/collaboration-and-partnerships

[EPFL]: https://www.epfl.ch

[ETHZ]: https://www.ethz.ch