edgeimpulse/Hey-Edge
Audio Classification • Updated
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Synthetic, augmented audio for training a small wake-word / keyword-spotting model. Generated with piper_tts and local audio augmentation.
| Label | Samples |
|---|---|
background_noise |
200 |
hey_edge |
378 |
unknown |
1071 |
hey_edge — the target wake phrase and close variants.unknown — near-miss and unrelated short phrases.background_noise — synthetic background noise.| Property | Value |
|---|---|
| Format | WAV |
| Channels | Mono |
| Sample rate | 16000 Hz |
| Clip length | 2.0 seconds |
Audio is organised into one sub-folder per class so the Hugging Face dataset
viewer infers the label column automatically:
audio/
train/
hey_edge/ hey_edge.<id>.wav ...
unknown/ unknown.<id>.wav ...
background_noise/ background_noise.<id>.wav ...
test/
hey_edge/ ...
unknown/ ...
background_noise/ ...
edge_impulse_metadata.csv
hf_metadata.csv
selected_voices.csv
dataset_summary.json
from datasets import load_dataset, Audio
ds = load_dataset("edgeimpulse/Hey-Edge")
ds = ds.cast_column("audio", Audio(sampling_rate=16000))
print(ds)
Filenames follow the Edge Impulse label-prefix convention
(hey_edge.<id>.wav) so they upload directly:
edge-impulse-uploader --category training audio/train/**/*.wav
edge-impulse-uploader --category testing audio/test/**/*.wav
Synthetic TTS is a bootstrap, not a production benchmark. Add real device and environment recordings before deploying a wake-word product.
CC BY 4.0. Verify that your use of the generated synthetic speech complies with the terms of the voice models and tools used to create it.