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30 classes
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14one
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2call
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3close
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2call
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11next
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18record
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22six
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21seven
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19right
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22six
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KWS Smart-Device Commands (30-class)

Merged, length-normalized keyword-spotting dataset for on-device voice control (Nordic nRF5340 + Edge Impulse). Every clip is 16 kHz mono, fixed 1 s, RMS-leveled and silence-padded. 1,000 clips per class. Real CC-BY audio only β€” no TTS.

  • Classes (30): answer, back, call, close, down, eight, five, four, go, help, left, next, nine, no, one, open, play, previous, record, right, search, seven, six, start, stop, three, two, 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-v2")
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 β€” CC-BY 4.0 (Warden, 2018, arXiv:1804.03209), including up, down, and go
  • Multilingual Spoken Words Corpus (English) β€” CC-BY 4.0 (MLCommons)

Preprocessing matches the MLPerf Tiny KWS reference (16 kHz, MFCC 30/20 ms, 40 mel, 10 coeff, 20–4000 Hz).

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Paper for snowballlab/30-keywords-v2