MAESTRO (MIDI and Audio Edited for Synchronous TRacks and Organization) is a dataset composed of over 172 hours of virtuosic piano performances captured with fine alignment (~3 ms) between note labels and audio waveforms.

Contents

Dataset

We partnered with organizers of the International Piano-e-Competition for the raw data used in this dataset. During each installment of the competition virtuoso pianists perform on Yamaha Disklaviers which, in addition to being concert-quality acoustic grand pianos, utilize an integrated high-precision MIDI capture and playback system. Recorded MIDI data is of sufficient fidelity to allow the audition stage of the competition to be judged remotely by listening to contestant performances reproduced over the wire on another Disklavier instrument.

The dataset contains over a week of paired audio and MIDI recordings from nine years of International Piano-e-Competition. The MIDI data includes key strike velocities and sustain pedal positions. Audio and MIDI files are aligned with ∼3 ms accuracy and sliced to individual musical pieces, which are annotated with composer, title, and year of performance. Uncompressed audio is of CD quality or higher (44.1–48 kHz 16-bit PCM stereo).

A train/validation/test split configuration is also proposed, so that the same composition, even if performed by multiple contestants, does not appear in multiple subsets. Repertoire is mostly classical, including composers from the 17th to early 20th century.

Split Performances Compositions (approx.) Duration (hours) Size (GB) Notes (millions)
Train 954 295 140.1 83.6 5.06
Validation 105 60 15.3 9.1 0.54
Test 125 75 16.9 10.1 0.57
Total 1184 430 172.3 102.8 6.18


For more information about how the dataset was created and several applications of it, please see the paper where it was introduced: Enabling Factorized Piano Music Modeling and Generation with the MAESTRO Dataset.

For an example application of the dataset, see our blog post on Wave2Midi2Wave.

Download

MAESTRO is available as a zip file containing the MIDI and WAV files as well as metadata in CSV and JSON formats.

maestro-v1.0.0.zip

Size: 87GB (103GB uncompressed)
SHA256: 97471232457147d5bffa72db8c4897166ba52afd4a64197004b806c2ec85ad27

A MIDI-only version of the dataset is also available.

maestro-v1.0.0-midi.zip

Size: 45MB (67MB uncompressed)
SHA256: f620f9e1eceaab8beea10617599add2e9c83234199b550382a2f603098ae7135

The metadata files have the following fields for every MIDI/WAV pair:

Field Description
canonical_composer Composer of the piece. We have attempted to standardize on a single spelling for a given name.
canonical_title Title of the piece. Not guaranteed to be standardized to a single representation.
split Suggested train/validation/test split.
year Year of performance.
midi_filename MIDI filename.
audio_filename WAV filename.
duration Duration in seconds, based on the MIDI file.


The metadata files are also available as separate downloads:

License

The dataset is made available by Google LLC under a Creative Commons Attribution Non-Commercial Share-Alike 4.0 (CC BY-NC-SA 4.0) license.

How to Cite

If you use the MAESTRO dataset in your work, please cite the paper where it was introduced:

Curtis Hawthorne, Andriy Stasyuk, Adam Roberts, Ian Simon, Cheng-Zhi Anna Huang,
  Sander Dieleman, Erich Elsen, Jesse Engel, and Douglas Eck. "Enabling
  Factorized Piano Music Modeling and Generation with the MAESTRO Dataset."
  arXiv preprint arXiv:1810.12247, 2018.

You can also use the following BibTeX entry:

@misc{maestro2018,
    Author = {Curtis Hawthorne and Andriy Stasyuk and Adam Roberts and Ian Simon
              and Cheng-Zhi Anna Huang and Sander Dieleman and Erich Elsen and
              Jesse Engel and Douglas Eck},
    Title = {Enabling Factorized Piano Music Modeling and Generation with the
             MAESTRO Dataset},
    Journal = {arXiv preprint arXiv:1810.12247},
    Year = {2018},
}