Downloads · 30 days
0
littlebearlabs/openwakeword-features
openwakeword-features is a feature extraction model from littlebearlabs. Use it when you need embeddings to search or compare text. It is set up for openwakeword. The card lists the license as apache-2.0.
This repo mirrors the two ONNX feature-extractor models that openWakeWord uses as shared frontends for every wake-word DNN it trains:
Downloads · 30 days
0
Access
Public
Updated May 18, 2026
Repo size
2.4 MB
Likes
1
Public
Click a slice to open those files.
.onnx2.4 MB · 99%
From the Hugging Face model README
This repo mirrors the two ONNX feature-extractor models that openWakeWord uses as shared frontends for every wake-word DNN it trains:
| File | Size | Purpose |
|---|---|---|
melspectrogram.onnx | 1.1 MB | Converts 16 kHz int16 audio → 32-bin mel-spectrogram frames |
embedding_model.onnx | 1.3 MB | Google speech_embedding/1 — mel frames → 96-dim embeddings |
These are NOT trained by Little Bear Labs — they are upstream Apache-2.0 assets from the openWakeWord project, mirrored here so HuggingFace hf_hub_download callers (e.g. witness-wake) can pull them without pip / GitHub-release-API round trips.
Use this mirror if you're:
ort + the openwakeword feature pipeline directly (no Python).hf_hub_download semantics.Otherwise, prefer:
pip install openwakeword then python -c "import openwakeword; openwakeword.utils.download_models([])" — populates <site-packages>/openwakeword/resources/models/.These two graphs are the shared frontend. Pair them with any openwakeword DNN, e.g.:
littlebearlabs/hey-virgil-wake-word — Little Bear Labs's "hey virgil" detector..onnx from the openwakeword ecosystem.Apache-2.0 (inherited from openWakeWord + Google speech_embedding).
If you use this in research, cite openWakeWord:
@software{openwakeword,
author = {David Scripka},
title = {openWakeWord: A library for training open-source wake word models},
year = {2024},
url = {https://github.com/dscripka/openWakeWord}
}