Speech Commands: A Dataset for Limited-Vocabulary Speech Recognition
Paper β’ 1804.03209 β’ Published
audio dict | label class label 30
classes |
|---|---|
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... | 2call |
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{"bytes":"UklGRiR9AABXQVZFZm10IBAAAAABAAEAgD4AAAB9AAACABAAZGF0YQB9AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA(...TRUNCATED) | 18record |
{"bytes":"UklGRiR9AABXQVZFZm10IBAAAAABAAEAgD4AAAB9AAACABAAZGF0YQB9AABWADIANABrAGQAWABpAI8AcgBRAHIAeg(...TRUNCATED) | 22six |
{"bytes":"UklGRiR9AABXQVZFZm10IBAAAAABAAEAgD4AAAB9AAACABAAZGF0YQB9AAAq/Tv7tPjj+p38Tf16+7b5EfkV+Vr6Sv(...TRUNCATED) | 21seven |
{"bytes":"UklGRiR9AABXQVZFZm10IBAAAAABAAEAgD4AAAB9AAACABAAZGF0YQB9AAAGAAEAGgAUAPH/4v/h//b/EQAKAPv/CQ(...TRUNCATED) | 19right |
{"bytes":"UklGRiR9AABXQVZFZm10IBAAAAABAAEAgD4AAAB9AAACABAAZGF0YQB9AAD3//7/9f/q/9z/4f/z//P/9//3/+7/8/(...TRUNCATED) | 22six |
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.
audio (WAV bytes + path), label (ClassLabel)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))
up, down, and goPreprocessing matches the MLPerf Tiny KWS reference (16 kHz, MFCC 30/20 ms, 40 mel, 10 coeff, 20β4000 Hz).