MAESTRO (MIDI and Audio Edited for Synchronous TRacks and Organization) is a dataset composed of over 200 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 200 hours of paired audio and MIDI recordings from ten years of International Piano-e-Competition. The MIDI data includes key strike velocities and sustain/sostenuto/una corda 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.

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 provided as a zip file containing the MIDI and WAV files as well as metadata in CSV and JSON formats. A MIDI-only archive of the dataset is also available.

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.

V2.0.0

In this update we added another year of competition performances and preserved sostenuto (CC 66) and una corda (CC 67) messages in MIDI files, in addition to sustain pedal (CC 64) present since V1.0.0.

Crucially, this version has a new train/validation/test split, which is not compatible with V1.0.0.

maestro-v2.0.0.zip

Size: 103GB (122GB uncompressed)
SHA256: 572c6054e8d2c7219aa4df9a29357da0f9789524c11fa38cef7d4bd8542c93f0

maestro-v2.0.0-midi.zip

Size: 57MB (85MB uncompressed)
SHA256: ec2cc9d94886c6b376db1eaa2b8ad1ce62ff9f0a28b3744782b13163295dadf3

Metadata files as separate downloads:

Certain statistics of the dataset:

Split Performances Duration (hours) Size (GB) Notes (millions)
Train 967 161.3 97.7 5.73
Validation 137 19.4 11.8 0.64
Test 178 20.5 12.4 0.76
Total 1282 201.2 121.8 7.13

V1.0.0

This is the original release of the dataset, which was used to produce all results in the MAESTRO paper.

maestro-v1.0.0.zip

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

maestro-v1.0.0-midi.zip

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

Metadata files:

Statistics:

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

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."
  In International Conference on Learning Representations, 2019.

You can also use the following BibTeX entry:

@inproceedings{
  hawthorne2018enabling,
  title={Enabling Factorized Piano Music Modeling and Generation with the {MAESTRO} Dataset},
  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},
  booktitle={International Conference on Learning Representations},
  year={2019},
  url={https://openreview.net/forum?id=r1lYRjC9F7},
}

Please also make sure to specify which version of the dataset you are using.