mirdata

common loaders for 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

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')

Examples

Download the Orchset Dataset

import mirdata

mirdata.orchset.download()

Load the Orchset Dataset

import mirdata

orchset_data = mirdata.orchset.load()

See what data are available for a track

import mirdata

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

example_track = orchset_data[orchset_ids[0]]
print(example_track)
> OrchsetTrack(
    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_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]

Contributing a new dataset loader

To add datasets and code, please see CONTRIBUTING.md

gabolsgabs / mirdata