mirdata 0.3.0b2

Common loaders for MIR datasets.

mirdata

common loaders for Music Information Retrieval (MIR) datasets. Find the API documentation here.

This library provides tools for working with common MIR datasets, including tools for:

• downloading datasets to a common location and format
• validating that the files for a dataset are all present
• loading annotation files to a common format, consistent with the format required by mir_eval
• parsing track level metadata for detailed evaluations

Installation

To install, simply run:

pip install mirdata

Quick example

import mirdata
import random

orchset = mirdata.initialize('orchset')
orchset.download()  # download the dataset
orchset.validate()  # validate that all the expected files are there

example_track = orchset.choice_track()  # choose a random example track
print(example_track)  # see the available data

See the documentation for more examples and the API reference.

Currently supported datasets

Supported datasets include AcousticBrainz, DALI, Guitarset, MAESTRO, TinySOL, among many others.

For the complete list of supported datasets, see the documentation

Citing

There are two ways of citing mirdata:

If you are using the library for your work, please cite the version you used as indexed at Zenodo:

DOI

If you refer to mirdata's design principles, motivation etc., please cite the following paper:

"mirdata: Software for Reproducible Usage of Datasets"
Rachel M. Bittner, Magdalena Fuentes, David Rubinstein, Andreas Jansson, Keunwoo Choi, and Thor Kell
in International Society for Music Information Retrieval (ISMIR) Conference, 2019

@inproceedings{
  bittner_fuentes_2019,
  title={mirdata: Software for Reproducible Usage of Datasets},
  author={Bittner, Rachel M and Fuentes, Magdalena and Rubinstein, David and Jansson, Andreas and Choi, Keunwoo and Kell, Thor},
  booktitle={International Society for Music Information Retrieval (ISMIR) Conference},
  year={2019}
}

Contributing a new dataset loader

We welcome contributions to this library, especially new datasets. Please see contributing for guidelines.