fma

FMA: A Dataset For Music Analysis

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FMA: A Dataset For Music Analysis

Kirell Benzi, Michaël Defferrard,
Pierre Vandergheynst,
Xavier Bresson,
EPFL LTS2.

Note that this is a beta release and that this repository as well as the
paper and data are subject to change. Stay tuned!

Data

The dataset is a dump of the Free Music Archive.
You got various sizes:

  1. Small: 4,000 clips of
    30 seconds, 10 balanced genres (GTZAN-like) (~3.4 GiB)
  2. Medium: 14,511 clips
    of 30 seconds, 20 unbalanced genres (~12.2 GiB)
  3. Large (available soon): 77,643 clips of 30 seconds, 68 unbalanced genres
    (~90 GiB)
  4. Huge (subject to distribution constraints): 77,643 untrimmed clips, 68
    unbalanced genres (~900 GiB)

Notes:

  • All datasets come with MP3 audio (128 kbps, 44.1 kHz, stereo) of all clips.
  • All datasets come with the following meta-data about each clip: artist,
    title, list of genres (and top genre), play count.
  • Meta-data about all clips are stored in a JSON file to be loaded as a
    pandas dataframe.
  • As additional audio meta-data, each clip of datasets 1 and 2 come with all
    Echonest features.
  • Please see the paper for a description of how the data was collected and
    cleaned.

Code

This repository features the following notebooks:

  1. Generation: generation of the datasets.
  2. Analysis: loading and basic analysis of the data.
  3. Baselines: baseline models for various tasks.
  4. Usage: how to load the datasets and train your own models.

Installation

# Install Python 3.6 and create a virtual environment.
pyenv install 3.6.0
pyenv virtualenv 3.6.0 fma
pyenv activate fma

# Clone the repository.
git clone https://github.com/mdeff/fma.git
cd fma

# Install the dependencies.
make install

# Fill in the configuration.
cat .env
DATA_DIR=/path/to/fma_small

# Open the Jupyter notebook.
jupyter-notebook

# Or run a notebook.
make fma_baselines.ipynb

License

  • Please cite our paper if you use our code or data.
  • The code is released under the terms of the MIT license.
  • The dataset is meant for research only.
  • We are grateful to SWITCH and EPFL for hosting the dataset within the context
    of the SCALE-UP project, funded in
    part by the swissuniversities
    SUC P-2 program.

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名稱與所有者oppa3109/fma
主編程語言Jupyter Notebook
編程語言Jupyter Notebook (語言數: 3)
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許可證MIT License
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創建於2017-03-13 23:55:48
推送於2017-03-11 13:40:23
最后一次提交2017-03-11 13:38:41
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