FMA: A Dataset For Music Analysis
Michaël Defferrard, Kirell Benzi, Pierre Vandergheynst, Xavier Bresson.
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(latex and reviews) - Slides:
doi:10.5281/zenodo.1066119 - Poster:
doi:10.5281/zenodo.1035847
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.
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).
Code
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.analysis.ipynb: exploration of the metadata, data, and features. Creates the figures used in the paper.baselines.ipynb: baseline models for genre recognition, both from audio and features.features.py: features extraction from the audio (used to createfeatures.csv).webapi.ipynb: query the web API of the FMA. Can be used to update the dataset.creation.ipynb: creation of the dataset (used to createtracks.csvandgenres.csv).creation.py: creation of the dataset (long-running data collection and processing).utils.py: helper functions and classes.
Usage
-
Clone the repository.
git clone https://github.com/mdeff/fma.git cd fma -
Create a Python 3.6 environment.
# 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
-
Install dependencies.
pip install --upgrade pip setuptools wheel pip install numpy==1.12.1 # workaround resampy's bogus setup.py pip install -r requirements.txtNote: you may need to install ffmpeg or graphviz depending on your usage.
Note: install CUDA to train neural networks on GPUs (see Tensorflow's instructions). -
Download some data, verify its integrity, and uncompress the archives.
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 if decompression errors. It might be an unsupported compression issue.
-
Fill a
.envconfiguration 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 -
Open Jupyter or run a notebook.
jupyter notebook make usage.ipynb
Impact, coverage, and resources
100+ research papers
Full list on Google Scholar. Some picks below.
- Zero-shot Learning for Audio-based Music Classification and Tagging
- One deep music representation to rule them all? A comparative analysis of different representation learning strategies
- Deep Learning for Audio-Based Music Classification and Tagging: Teaching Computers to Distinguish Rock from Bach
- Learning Discrete Structures for Graph Neural Networks
- A context encoder for audio inpainting
- OpenMIC-2018: An Open Data-set for Multiple Instrument Recognition
- Detecting Music Genre Using Extreme Gradient Boosting
- Transfer Learning of Artist Group Factors to Musical Genre Classification
- Learning to Recognize Musical Genre from Audio: Challenge Overview
- Representation Learning of Music Using Artist Labels
2 derived works
~10 posts
- Music Genre Classification With TensorFlow, Towards Data Science, 2020-08-11.
- Music Genre Classification: Transformers vs Recurrent Neural Networks, Towards Data Science, 2020-06-14.
- Using CNNs and RNNs for Music Genre Recognition, Towards Data Science, 2018-12-13.
- Over 1.5 TB’s of Labeled Audio Datasets, Towards Data Science, 2018-11-13.
- Discovering Descriptive Music Genres Using K-Means Clustering, Medium, 2018-04-09.
- 25 Open Datasets for Deep Learning Every Data Scientist Must Work With, Analytics Vidhya, 2018-03-29.
- Learning Music Genres, Medium, 2017-12-13.
- music2vec: Generating Vector Embeddings for Genre-Classification Task, Medium, 2017-11-28.
- A Music Information Retrieval Dataset, Made With FMA, freemusicarchive.org, 2017-05-22.
- Pre-publication release announced, twitter.com, 2017-05-09.
- FMA: A Dataset For Music Analysis, tensorflow.blog, 2017-03-14.
- Beta release discussed, twitter.com, 2017-02-08.
- FMA Data Set for Researchers Released, freemusicarchive.org, 2016-12-15.
5 events
- Summer Workshop by the Haverford Digital Scholarship Library, 2020-07.
- Genre recognition challenge at the Web Conference, Lyon, 2018-04.
- Slides presented at the Data Jam days, Lausanne, 2017-11-24.
- Poster presented at ISMIR 2017, Suzhou, 2017-10-24.
- Slides for the Open Science in Practice summer school at EPFL, 2017-09-29.
~10 dataset lists
- https://github.com/caesar0301/awesome-public-datasets
- https://archive.ics.uci.edu/ml/datasets/FMA:+A+Dataset+For+Music+Analysis
- http://deeplearning.net/datasets
- http://www.audiocontentanalysis.org/data-sets
- https://github.com/ismir/mir-datasets
- https://teachingmir.wikispaces.com/Datasets
- https://en.wikipedia.org/wiki/List_of_datasets_for_machine_learning_research
- https://loc.gov/item/2018655052
- https://cloudlab.atlassian.net/wiki/display/datasets/FMA:+A+Dataset+For+Music+Analysis
- https://www.datasetlist.com
- https://data-flair.training/blogs/deep-learning-project-ideas
Contributing
Contribute by opening an issue or a pull request. Let this repository be a hub around the dataset!
History
2017-05-09 pre-publication release
- paper: arXiv:1612.01840v2
- code: git tag rc1
fma_metadata.zipsha1:f0df49ffe5f2a6008d7dc83c6915b31835dfe733fma_small.zipsha1:ade154f733639d52e35e32f5593efe5be76c6d70fma_medium.zipsha1:c67b69ea232021025fca9231fc1c7c1a063ab50bfma_large.zipsha1:497109f4dd721066b5ce5e5f250ec604dc78939efma_full.zipsha1:0f0ace23fbe9ba30ecb7e95f763e435ea802b8ab- known issues: see #41
2016-12-06 beta release
- paper: arXiv:1612.01840v1
- code: git tag beta
fma_small.zipsha1:e731a5d56a5625f7b7f770923ee32922374e2cbffma_medium.zipsha1: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.
- The metadata is released under the Creative Commons Attribution 4.0 International License (CC BY 4.0).
- 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.
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