hey-adi-wakeword

Custom-trained "Hey ADI" wake word model for the OpenWakeWord framework. Built to trigger the ADI Genesis offline voice assistant pipeline at theLAB.

Wake phrase: "hey addie"

📖 Build writeup: How this wake word was trained

🎥 Demo video:

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Files

Both files are functionally equivalent — pick the runtime that fits your deployment target. ONNX for desktop/server, TFLite for edge/embedded.

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Pipeline

This wake word is the front door to a fully offline voice stack:

OpenWakeWord ("hey addie") → Parakeet STT → Ollama (advanced-data-intelligence:e4b) → OmniVoice TTS (adi_ref voice)

No cloud dependencies — wake detection through response generation runs entirely on local hardware.

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Usage

ONNX (onnxruntime)

from openwakeword.model import Model

model = Model(
    wakeword_models=["hey_addie.onnx"],
    inference_framework="onnx",
)

# frame = 16kHz, 16-bit mono PCM audio chunk (1280 samples / 80ms)
prediction = model.predict(frame)
score = prediction["hey_addie"]

if score > 0.5:
    print("Wake word detected")

TFLite (tflite-runtime)

from openwakeword.model import Model

model = Model(
    wakeword_models=["hey_addie.tflite"],
    inference_framework="tflite",
)

prediction = model.predict(frame)
score = prediction["hey_addie"]

Audio requirements

• Sample rate: 16 kHz
• Channels: mono
• Format: 16-bit PCM
• Frame size: 1280 samples (80 ms) per predict() call

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Threshold tuning

Tune to your microphone and room acoustics.

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Intended use

On-device wake word detection for offline voice assistants. Designed for the ADI Genesis stack but works with any OpenWakeWord-compatible application.

Limitations

• Trained for the English phrase "hey addie" — will not generalize to other phrases.
• Performance varies with microphone quality, background noise, and distance.
• Single-phrase model; pair with additional wake word models if you need multiple triggers.

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Built in theLAB — Learning. Algorithms. Breakthroughs. 🔬