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nagisanzeninz/greet-wakeword-v0

sourceHugging Faceapache-2.0updated 4mo agoView on Hugging Face
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Model Card

Greet Wake Word

Custom INT8 TFLite Micro wake-word detector for ESP32-S3. Fires on:
  • —hey
  • —hi
  • —hello

Metrics

MetricValue
FRR (positives)0.00%
FAR (bulk negatives, /hour)0.00
Detection threshold0.850
Model size60.0 kB
Mel features40 bins, 25 ms hop, 194 frames

Per-bucket FAR:

BucketFAR
(no buckets)—

Use with ESPHome

yaml
micro_wake_word:
  microphone: tofu_mic
  vad:
  models:
    - model: greet
      probability_cutoff: 0.850
      sliding_window_size: 5
      url: https://huggingface.co/nagisanzeninz/greet-wakeword-v0/resolve/main/greet-wakeword-v0.tflite

Full ESPHome example in `esphome.yaml`.

Training recipe

Trained with the customWakeWord pipeline:

  1. 1.~10000 synthetic positives across 3 trigger phrases (Piper + Kokoro + MeloTTS + Parler-TTS, 200+ distinct voices).
  2. 2.~~2500 hand- and LLM-curated hard-negatives covering 5 collision categories.
  3. 3.~300 hours of bulk negative audio sampled from MUSAN, DEMAND, Common Voice, AudioSet.
  4. 4.audiomentations chain: RIR convolution + additive noise + codec degradation + pitch/speed jitter.
  5. 5.microWakeWord streaming-Inception architecture, INT8 post-training quantized for tflite-micro on ESP32-S3.

See the repo README for replicability.

License

Apache 2.0. Built from open-source, permissively-licensed components only — safe to redistribute commercially.

Citation

bibtex
@misc{greet-wakeword-v0,
  title  = { greet Wake Word v0: custom keyword spotter for ESP32-S3 },
  year   = { 2026 },
  url    = { https://huggingface.co/nagisanzeninz/greet-wakeword-v0 },
  note   = { Apache 2.0. Built with the customWakeWord toolkit. }
}