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

Try it out!

import mirdata
import random

orchset = mirdata.Dataset('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 availalbe data

See the Examples section below for more details, or the documentation for more examples and the API reference.

Currently supported datasets

• Beatles
• Beatport EDM key
• DALI
• GiantSteps tempo
• GiantSteps key
• Groove MIDI
• GTZAN genre
• GuitarSet
• Ikala
• MAESTRO
• MedleyDB Melody
• MedleyDB Pitch
• Medley-solos-DB
• Mridangam Stroke
• ORCHSET
• RWC Classical
• RWC Jazz
• RWC Popular
• Salami
• TinySOL

For more information about these datasets see this table.

Reference

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

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

Examples

Download the Orchset Dataset

import mirdata

orchset = mirdata.Dataset('orchset')
orchset.download()

Validate the data

import mirdata

orchset = mirdata.Dataset('orchset')
orchset.validate()

Load data for a specific track

import mirdata

orchset = mirdata.Dataset('orchset')
track = orchset.track('Beethoven-S3-I-ex1')
print(track)

Load all tracks in the Orchset Dataset

import mirdata

orchset = mirdata.Dataset('orchset')
orchset_data = orchset.load_tracks()

See what data are available for a track

import mirdata

orchset = mirdata.Dataset('orchset')
orchset_ids = orchset.track_ids()
orchset_data = orchset.load_tracks()

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
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 = mirdata.Dataset('orchset')
orchset_data = orchset.load_tracks()
for track_id, track_data in orchset_data.items():
    est_times, est_freqs = very_bad_melody_extractor(track_data.audio_path_mono)

    ref_times = track_data.melody.times
    ref_freqs = track_data.melody.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.Dataset('orchset', data_home='my_custom_path')