schism-audio/groove-midi-dataset

Groove MIDI Dataset

Quick Start

from datasets import load_dataset

# Stream to avoid downloading the entire dataset
ds = load_dataset("schismaudio/groove-midi-dataset", streaming=True)

# Or download locally
ds = load_dataset("schismaudio/groove-midi-dataset")

Dataset Description

The Groove MIDI Dataset (GMD) is a collection of 13.6 hours of aligned MIDI and synthesized audio recordings of drum performances by 10 drummers (80%+ professional). Created by Google Magenta, it contains 1,150 MIDI files covering over 22,000 measures across 18 musical styles.

Each performance was recorded on a Roland TD-11 electronic drum kit, capturing both expressive MIDI (with velocity and precise timing) and synthesized audio output. The dataset is widely used for research in drum transcription, groove modeling, humanization, and rhythmic generation.

GMD is the foundation for several derivative datasets, including E-GMD (re-rendered with 43 kits) and StemGMD (multi-kit isolated stems).

Dataset Structure

Data Fields

Field	Type	Description
midi_filename	string	Relative path to the MIDI file
audio_filename	string	Relative path to the WAV audio file (22050 Hz, mono)
drummer	string	Anonymized drummer ID (drummer1–drummer10)
session	string	Recording session (e.g., session1, eval_session)
style	string	Musical genre (18 styles)
bpm	float	Tempo in beats per minute
beat_type	string	"beat" (steady pattern) or "fill" (fill passage)
time_signature	string	Time signature (e.g., 4-4, 6-8)
duration	float	Duration in seconds
split	string	Dataset split: train, valid, or test

Data Splits

Split	Examples	Description
train	~800	Training set
valid	~200	Validation set
test	~150	Test set

Splits are defined in the info.csv metadata file.

Class Taxonomy

The dataset uses 9 canonical drum instrument classes, mapped from 22 raw MIDI pitches on the Roland TD-11:

Class	Instrument	MIDI Pitches
0	Kick	36
1	Snare (Head)	38
2	Snare (Rim)	37, 40
3	Closed Hi-Hat	42, 22
4	Open Hi-Hat	46, 26
5	Low Tom	43, 58
6	Mid Tom	47, 45
7	High Tom	50, 48
8	Ride / Crash	49, 51, 55, 57, 52

Usage Examples

Parse MIDI onsets

import pretty_midi

midi = pretty_midi.PrettyMIDI("path/to/file.mid")
for instrument in midi.instruments:
    for note in instrument.notes:
        print(f"Time: {note.start:.3f}s, Pitch: {note.pitch}, Velocity: {note.velocity}")

Load metadata

import pandas as pd

info = pd.read_csv("info.csv")
# Filter by style
rock = info[info["style"] == "rock"]
print(f"Rock tracks: {len(rock)}, Total duration: {rock['duration'].sum():.0f}s")

Dataset Creation

Source Data

All performances were recorded at Magenta's recording studio by 10 professional and semi-professional drummers playing on a Roland TD-11 electronic drum kit. Drummers performed along with a metronome at specified tempos across 18 musical styles.

Annotations

Annotations are the raw MIDI output from the electronic drum kit — no manual annotation was needed. MIDI captures exact onset times, pitches, and velocities directly from the pad triggers. Audio was synthesized by the TD-11's internal sound engine.

Known Limitations

• Synthesized audio only: Audio is the TD-11's internal sound engine output, not acoustic drums. This limits generalization to real-world acoustic drum sounds.
• Single kit: All recordings use one drum kit (Roland TD-11), so the model may overfit to this kit's timbral characteristics.
• Limited timbral diversity: To address this, consider using E-GMD (43 kits) or StemGMD (10 acoustic kits).
• Electronic pad dynamics: Velocity response differs from acoustic drums — pad triggers have fixed dynamic curves.

Related Datasets

This dataset is part of the Drum Audio Datasets collection by schismaudio. Related datasets:

• schismaudio/e-gmd — 444 hours: E-GMD re-renders GMD performances with 43 different kits
• schismaudio/stemgmd — 1,224 hours: StemGMD provides isolated per-instrument stems from 10 acoustic kits
• schismaudio/star-drums — Real-world drum recordings with accompaniment

Citation

@inproceedings{gillick2019learning,
  title={Learning to Groove with Inverse Sequence Transformations},
  author={Gillick, Jon and Roberts, Adam and Engel, Jesse and Eck, Douglas and Bamman, David},
  booktitle={International Conference on Machine Learning (ICML)},
  year={2019}
}

License

This dataset is released under the Creative Commons Attribution 4.0 International License (CC-BY 4.0).

You are free to share and adapt this dataset for any purpose, including commercial use, as long as you give appropriate credit to the original authors (Google Magenta).