This page contains a list of open datasets that were used to train the Magenta models.
The Bach Doodle Dataset is composed of 21.6 million harmonizations submitted from the Bach Doodle. The dataset contains both metadata about the composition (such as the country of origin and feedback), as well as a MIDI of the user-entered melody and a MIDI of the generated harmonization. The dataset contains about 6 years of total audio.
CocoChorales is a large dataset of high-quality neural renderings of generated four-part ensembles in the style of Bach chorales. It consists of 240,000 audio mixtures containing varying instrumentations with annotations for source audio, MIDI, tempo, note timing, note expression, and raw synthesis parameters.
The Expanded Groove MIDI Dataset (E-GMD) is a large dataset of human drum performances, with audio recordings annotated in MIDI. E-GMD contains 444 hours of audio from 43 drum kits and is an order of magnitude larger than similar datasets. It is also the first human-performed drum transcription dataset with annotations of velocity. It is based on our previously released Groove MIDI Dataset.
The Groove MIDI Dataset (GMD) is composed of 13.4 hours of aligned MIDI and (synthesized) audio of human-performed, tempo-aligned expressive drumming. The dataset contains 1,134 MIDI files and nearly 22,000 measures of drumming.
MAESTRO (MIDI and Audio Edited for Synchronous TRacks and Organization) is a dataset composed of about 200 hours of virtuosic piano performances captured with fine alignment (~3 ms) between note labels and audio waveforms.
A large-scale and high-quality audio dataset of annotated musical notes, containing 305,979 musical notes, each with a unique pitch, timbre, and envelope. For 1,006 instruments from commercial sample libraries, we generated four second, monophonic 16kHz audio snippets, referred to as notes, by ranging over every pitch of a standard MIDI piano, as well as five different velocities.
A collection of 50 million drawings across 345 categories, contributed by players of the game Quick, Draw!. The drawings were captured as timestamped vectors, tagged with metadata including what the player was asked to draw and in which country the player was located.