Speech Commands: A Dataset for Limited-Vocabulary Speech Recognition
Paper • 1804.03209 • Published
Datasets:
audio dict | label class label 30
classes |
|---|---|
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... | 14play |
{
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... | 2call |
{"bytes":"UklGRiT6AABXQVZFZm10IBAAAAABAAEAgD4AAAB9AAACABAAZGF0YQD6AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA(...TRUNCATED) | 3close |
{"bytes":"UklGRiT6AABXQVZFZm10IBAAAAABAAEAgD4AAAB9AAACABAAZGF0YQD6AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA(...TRUNCATED) | 2call |
{"bytes":"UklGRiT6AABXQVZFZm10IBAAAAABAAEAgD4AAAB9AAACABAAZGF0YQD6AAD6//r/+v/6//v///8CAAUABgAFAAcABw(...TRUNCATED) | 11no |
{"bytes":"UklGRiT6AABXQVZFZm10IBAAAAABAAEAgD4AAAB9AAACABAAZGF0YQD6AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA(...TRUNCATED) | 18search |
{"bytes":"UklGRiT6AABXQVZFZm10IBAAAAABAAEAgD4AAAB9AAACABAAZGF0YQD6AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA(...TRUNCATED) | 22start |
{"bytes":"UklGRiT6AABXQVZFZm10IBAAAAABAAEAgD4AAAB9AAACABAAZGF0YQD6AAD+//7/TwBsABUAYwB7ADkAjACKAGUAfQ(...TRUNCATED) | 21six |
{"bytes":"UklGRiT6AABXQVZFZm10IBAAAAABAAEAgD4AAAB9AAACABAAZGF0YQD6AAD+/67/u/8mABL/+/+E/+7+8f9P/8X/Kf(...TRUNCATED) | 19set_a_timer |
{"bytes":"UklGRiT6AABXQVZFZm10IBAAAAABAAEAgD4AAAB9AAACABAAZGF0YQD6AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA(...TRUNCATED) | 22start |
End of preview. Expand in Data Studio
KWS Smart-Device Commands (20-class)
Merged, length-normalized keyword-spotting dataset for on-device voice control (Nordic nRF5340 + Edge Impulse). Every clip is 16 kHz mono, fixed 2 s, RMS-leveled and silence-padded. 1,447 clips per class (28,940 total). Real CC-BY audio only — no TTS.
- Classes (30): answer, back, call, close, eight, five, four, help, left, next, nine, no, one, open, play, previous, record, right, search, set_a_timer, seven, six, start, stop, three, two, volume_down, volume_up, yes, zero
- Features:
audio(WAV bytes + path),label(ClassLabel) - Splits: stratified train/test (80/20)
- License: CC-BY 4.0
Usage
from datasets import load_dataset, Audio
import soundfile as sf
import io
ds = load_dataset("snowballlab/30-keywords")
print(ds)
print(ds["train"].features["label"].names)
# Decode one clip with soundfile (no torch needed):
ex = ds["train"][0]
y, sr = sf.read(io.BytesIO(ex["audio"]["bytes"]))
print(y.shape, sr, ds["train"].features["label"].int2str(ex["label"]))
# Or, if you have torchcodec installed:
# ds = ds.cast_column("audio", Audio(sampling_rate=16000))
Sources & attribution
- Google Speech Commands v0.02 — CC-BY 4.0 (Warden, 2018, arXiv:1804.03209)
- Multilingual Spoken Words Corpus (English) — CC-BY 4.0 (MLCommons)
- Timers and Such v1.0 — CC-BY 4.0 (Lugosch et al., 2021)
volume_up/volume_downreconstructed by concatenating CC-BY components (MSWC "volume" + GSC "up"/"down").
Preprocessing matches the MLPerf Tiny KWS reference (16 kHz, MFCC 30/20 ms, 40 mel, 10 coeff, 20–4000 Hz).
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