Datasets:
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license: cc-by-4.0
task_categories:
- audio-classification
- audio-to-audio
tags:
- drum-transcription
- midi
- velocity
- electronic-drums
- groove
pretty_name: Expanded Groove MIDI Dataset (E-GMD)
size_categories:
- 10K<n<100K
configs:
- config_name: raw
data_files:
- split: train
path: data/raw/train/*.tar
- split: validation
path: data/raw/validation/*.tar
- split: test
path: data/raw/test/*.tar
- config_name: features
data_files:
- split: train
path: data/features/train/*.tar
- split: validation
path: data/features/validation/*.tar
- split: test
path: data/features/test/*.tar
---
# Expanded Groove MIDI Dataset (E-GMD)
FLAC-compressed WebDataset mirror of the [E-GMD](https://magenta.tensorflow.org/datasets/e-gmd) dataset with precomputed drum transcription features.
## Dataset Overview
| Property | Value |
|---|---|
| Samples | 45,537 (WAV + MIDI pairs) |
| Performances | 1,059 unique across 43 drum kits |
| Drummers | 10 |
| Audio | 16-bit FLAC, 44100 Hz, mono |
| Splits | train (35,217) / validation (5,031) / test (5,289) |
## Configs
| Config | Content | Est. Size |
|---|---|---|
| `raw` | FLAC audio + MIDI per sample | ~70 GB |
| `features` | Mel specs (128xT) + onset targets (26xT) + velocity targets (26xT) | ~44 GB |
## Loading
### Stream Raw Audio + MIDI
```python
from datasets import load_dataset
ds = load_dataset("schismaudio/e-gmd", name="raw", split="train", streaming=True)
for example in ds:
# audio.flac, midi.mid, metadata.json
pass
```
### Load Features
```python
ds = load_dataset("schismaudio/e-gmd", name="features", split="train")
for example in ds:
# mel_spectrogram.npy (128, T), onset_targets.npy (26, T), velocity_targets.npy (26, T), params.json
pass
```
## Feature Parameters
| Parameter | Value |
|---|---|
| Sample rate | 16000 Hz |
| Hop length | 256 |
| n_mels | 128 |
| n_classes | 26 (GM drum pitches 35-60) |
| FPS | 62.5 |
## 26-Class Drum Mapping
| Class | MIDI | Instrument |
|---|---|---|
| 0 | 35 | Acoustic Bass Drum |
| 1 | 36 | Bass Drum 1 |
| 2 | 37 | Side Stick |
| 3 | 38 | Acoustic Snare |
| 4 | 39 | Hand Clap |
| 5 | 40 | Electric Snare |
| 6 | 41 | Low Floor Tom |
| 7 | 42 | Closed Hi-Hat |
| 8 | 43 | High Floor Tom |
| 9 | 44 | Pedal Hi-Hat |
| 10 | 45 | Low Tom |
| 11 | 46 | Open Hi-Hat |
| 12 | 47 | Low-Mid Tom |
| 13 | 48 | Hi-Mid Tom |
| 14 | 49 | Crash Cymbal 1 |
| 15 | 50 | High Tom |
| 16 | 51 | Ride Cymbal 1 |
| 17 | 52 | Chinese Cymbal |
| 18 | 53 | Ride Bell |
| 19 | 54 | Tambourine |
| 20 | 55 | Splash Cymbal |
| 21 | 56 | Cowbell |
| 22 | 57 | Crash Cymbal 2 |
| 23 | 58 | Vibraslap |
| 24 | 59 | Ride Cymbal 2 |
| 25 | 60 | Hi Bongo |
**Pitch aliases:** MIDI 22 → class 7 (Closed Hi-Hat), MIDI 26 → class 11 (Open Hi-Hat)
## Citation
```bibtex
@article{callender2020improving,
title={Improving Perceptual Quality of Drum Transcription with the Expanded Groove MIDI Dataset},
author={Callender, Lee and Hawthorne, Curtis and Engel, Jesse},
journal={arXiv preprint arXiv:2004.00188},
year={2020}
}
```
## License
CC-BY-4.0
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