README.md

metadata

license: apache-2.0
tags:
  - wake-word
  - keyword-spotting
  - openwakeword
  - voice-assistant
language:
  - en
pipeline_tag: audio-classification
library_name: openwakeword

hey-orbis-wakeword

Custom wake word model for "hey orbis", trained with openWakeWord and synthetic speech from Fish Audio S2 Pro.

Usage

With openWakeWord (Python)

from openwakeword.model import Model

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

# Feed 80ms audio chunks (1280 samples at 16kHz, int16)
prediction = model.predict(audio_chunk)
if prediction["hey_orbis"] > 0.7:
    print("Wake word detected!")

With Home Assistant

Copy hey_orbis_float32.tflite to your openWakeWord add-on's custom model directory.

Model Details

Property	Value
Input	16 frames × 96-dim Google speech embeddings (~1.3s window)
Output	Sigmoid probability [0, 1]
Framework	ONNX (primary), TFLite (Home Assistant)
Size	199 KB (ONNX), 105 KB (TFLite fp16)

Performance (v0)

Evaluated on synthetic test clips (Fish Audio S2 Pro) in streaming mode:

Metric	@0.5 threshold	@0.7 threshold
Recall	92.4% (924/1000)	90.9% (909/1000)
FA rate (adversarial)	6.8% (27/400)	5.5% (22/400)

Adversarial negatives are phonetically similar phrases ("hey Morris", "hey Boris", etc.). Real-world FA rate against ambient speech will be lower.

Training Data

Type	Source	Count
Positive	Fish Audio S2 Pro (5 voices, varied prosody)	~7,000 clips
Adversarial negative	Fish Audio (similar-sounding phrases)	~2,000 clips
Generic negative	ACAV100M pre-computed features	~2,000 hours
Room impulse responses	MIT RIRs	270 rooms

Files

• hey_orbis.onnx — ONNX model for openWakeWord runtime
• hey_orbis_float32.tflite — TFLite for Home Assistant / edge
• hey_orbis_float16.tflite — TFLite half-precision (smaller)

Part of protoLabs

Built for ORBIS, the open-source AI companion framework by protoLabs.