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

Currently supported datasets

• Beatles
• DALI
• GuitarSet
• GTZAN genre
• Ikala
• MedleyDB Melody
• MedleyDB Pitch
• Medley-solos-DB
• ORCHSET
• RWC Classical
• RWC Jazz
• RWC Popular
• Salami
• TinySOL

For more information about these datasets see this table.

Installation

To install, simply run:

pip install mirdata

Paper

This library was presented in 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

To add datasets and code, please see CONTRIBUTING.md

Examples

Download the Orchset Dataset

import mirdata.orchset

mirdata.orchset.download()

Validate the data

import mirdata.orchset

mirdata.orchset.validate()

Load the Orchset Dataset

import mirdata.orchset

orchset_data = mirdata.orchset.load()

See what data are available for a track

import mirdata.orchset

orchset_ids = mirdata.orchset.track_ids()
orchset_data = mirdata.orchset.load()

example_track = orchset_data[orchset_ids[0]]
print(example_track)
> orchset.Track(
    track_id='Beethoven-S3-I-ex1',
    melody=F0Data(times=array([0.000e+00, 1.000e-02, 2.000e-02, ..., 1.244e+01, 1.245e+01, 1.246e+01]),
                  frequencies=array([  0.   ,   0.   ,   0.   , ..., 391.995, 391.995, 391.995]),
                  confidence=array([0, 0, 0, ..., 1, 1, 1])),
    audio_path_mono='~/mir_datasets/Orchset/audio/mono/Beethoven-S3-I-ex1.wav',
    audio_path_stereo='~/mir_datasets/Orchset/audio/stereo/Beethoven-S3-I-ex1.wav',
    composer='Beethoven',
    work='S3-I',
    excerpt='1',
    predominant_melodic_instruments=['winds', 'strings'],
    alternating_melody=True,
    contains_winds=True,
    contains_strings=True,
    contains_brass=False,
    only_strings=False,
    only_winds=False,
    only_brass=False
)

Evaluate a melody extraction algorithm on Orchset

import mir_eval
import mirdata.orchset
import numpy as np
import sox

def very_bad_melody_extractor(audio_path):
    duration = sox.file_info.duration(audio_path)
    time_stamps = np.arange(0, duration, 0.01)
    melody_f0 = np.random.uniform(low=80.0, high=800.0, size=time_stamps.shape)
    return time_stamps, melody_f0

# Evaluate on the full dataset
orchset_scores = {}
orchset_data = mirdata.orchset.load()
for track_id, track_data in orchset_data.items():
    est_times, est_freqs = very_bad_melody_extractor(track_data.audio_path_mono)

    ref_melody_data = track_data.melody
    ref_times = ref_melody_data.times
    ref_freqs = ref_melody_data.frequencies

    score = mir_eval.melody.evaluate(ref_times, ref_freqs, est_times, est_freqs)
    orchset_scores[track_id] = score

# Split the results by composer and by instrumentation
composer_scores = {}
strings_no_strings_scores = {True: {}, False: {}}
for track_id, track_data in orchset_data.items():
    if track_data.composer not in composer_scores.keys():
        composer_scores[track_data.composer] = {}

    composer_scores[track_data.composer][track_id] = orchset_scores[track_id]
    strings_no_strings_scores[track_data.contains_strings][track_id] = \
        orchset_scores[track_id]

Dataset Location

By default, all datasets tracked by this library are stored in ~/mir_datasets, (defined as MIR_DATASETS_DIR in mirdata/__init__.py). Data can alternatively be stored in another location by specifying data_home within a relevant function, e.g. mirdata.orchset.download(data_home='my_custom_path')