---
license: cc-by-4.0
pretty_name: Expanded Groove MIDI Dataset
task_categories:
- audio-classification
size_categories:
- 10K<n<100K
tags:
- format:audiofolder
- modality:audio
- modality:text
- library:datasets
- library:mlcroissant
- midi
- drums
- percussion
- drum-transcription
- music-information-retrieval
- automatic-drum-transcription
---

# Expanded Groove MIDI Dataset (E-GMD)

This repository mirrors version 1.0.0 of Google's Expanded Groove MIDI Dataset
(E-GMD) for access through the Hugging Face Hub.

E-GMD is a large dataset of human drum performances with audio recordings
annotated in MIDI. It contains 444.5 hours of audio from 43 drum kits, with the
same train, validation, and test split definitions as the original Groove MIDI
Dataset.

## Quick Start

```python
from datasets import load_dataset

ds = load_dataset("schism-audio/e-gmd", split="train", streaming=True)
first = next(iter(ds))
print(first["audio"], first["midi_path"], first["split"])
```

## Repository Layout

The original archive contains some session folders with more than 10,000 files,
which exceeds Hugging Face Hub's per-folder repository limit. This mirror keeps
the original filenames and original drummer/session paths, but stages files under
split and kit directories:

```text
audio/{split}/{kit_slug}/{original_drummer/session/path}.wav
midi/{split}/{kit_slug}/{original_drummer/session/path}.midi
metadata.csv
metadata/{split}.csv
metadata/all.csv
e-gmd-v1.0.0.csv
```

The root `metadata.csv` follows the AudioFolder convention and contains one row
per WAV file. The `file_name` column points at the audio file, and the remaining
columns preserve E-GMD metadata plus the paired MIDI path.

The `metadata/*.csv` files are retained from the original mirror and add these
path columns:

- `file_name`: audio path relative to the split folder
- `audio_path`: audio path relative to the repository root
- `midi_path`: paired MIDI path relative to the repository root
- `original_audio_filename`: original archive audio path
- `original_midi_filename`: original archive MIDI path

## Loading Audio With Metadata

The default dataset is loadable with `datasets` through the AudioFolder
convention:

```python
from datasets import load_dataset

train = load_dataset("schism-audio/e-gmd", split="train", streaming=True)
print(train[0]["audio"], train[0]["midi_path"])
```

The official train/test/validation split is preserved in the `split` column.
The paired MIDI file for each row is available through `midi_path`.

## Splits

| Split | Unique sequences | Total sequences | Duration |
|---|---:|---:|---:|
| Train | 819 | 35,217 | 341.4 hours |
| Test | 123 | 5,289 | 50.9 hours |
| Validation | 117 | 5,031 | 52.2 hours |
| Total | 1,059 | 45,537 | 444.5 hours |

## License

The dataset is made available by Google LLC under the Creative Commons
Attribution 4.0 International license (CC BY 4.0).

## Source

- Official Magenta page: https://magenta.tensorflow.org/datasets/e-gmd
- Zenodo record: https://zenodo.org/records/4300943
- Version: 1.0.0
- Original full archive SHA256:
  `7d9a264fb4c9eabd9fec09d5f8e333192f529b1a1b845d170279a977ac436053`

## Citation

If you use this dataset, cite the original E-GMD paper and specify version
1.0.0:

```bibtex
@misc{callender2020improving,
    title={Improving Perceptual Quality of Drum Transcription with the Expanded Groove MIDI Dataset},
    author={Lee Callender and Curtis Hawthorne and Jesse Engel},
    year={2020},
    eprint={2004.00188},
    archivePrefix={arXiv},
    primaryClass={cs.SD}
}
```