heed-wakeword 0.1.0

Tiny, custom, on-device wake-word detection. Train your own in seconds, deploy anywhere (Python / browser / mobile).

Heed Wake Word

Train your own wake word in seconds, or grab a ready-made one, then run it fully on-device. No cloud, no telemetry, no per-call fees. A wake word here is a 40 to 235 KB model that runs in Python, in the browser, and on iOS and Android.

Heed is Apache-2.0 licensed, so commercial and closed-source use are fine, with no copyleft.

Try it with no install: train your own in Colab, or run the static browser demo.

Two ways to use it

1. Train a custom word. Record a phrase a few times, or let TTS synthesize it across hundreds of voices, then train on CPU or GPU in seconds and export.
2. Use a pretrained word. The mobile demo bundles four example words (hey doc, activate x, hey jarvis, hey fetch) and an open "custom" slot for a model you train and push from the studio. hey doc and activate x are the solid ones. hey jarvis and hey fetch are quick placeholders that show off live multi-word switching. Slightly better pretrained defaults are planned.

Both paths produce the same artifact. You get an ONNX or TFLite model plus a wake.json preprocessing contract, and it runs the same way on every platform.

Where Heed fits

Tools like Picovoice (Porcupine), openWakeWord, and LiveKit are the established options today, and they are all good. Heed is for a specific gap: a fully permissive (Apache-2.0), train-your-own wake word that also runs client-side in the browser.

In practice that means you train a custom word in seconds from the studio or the CLI, with multi-speaker TTS and a cross-speaker evaluation so the model works for people other than you. The result is a sub-250 KB model that runs the same way in Python, in the browser, and on iOS and Android, as ONNX (float32 or int8) or TFLite. You can self-host the studio in Docker or train in Colab with no setup at all, and commercial and closed-source use carry no per-call fees.

Quickstart (about a minute)

pip install "heed-wakeword[ui]"   # base plus the browser studio
heed ui                            # opens http://127.0.0.1:7777

Record a few positives and negatives in the browser, press Train, then Live-test. A GPU is optional and gets used when present. If you prefer the terminal:

heed init my_phrase --phrase "hey computer"
heed train my_phrase                                  # quick, tuned to your voice
heed train my_phrase --tts-pos 400 --kokoro-pos 200   # cross-speaker, works for anyone
heed export my_phrase                                 # wake.onnx, wake.int8.onnx, wake.tflite, wake.json

The package is heed-wakeword on PyPI. You import it as heed, and the command is heed.

What you get

• Tiny and fast. small is about 10K params (41 KB), medium about 27K (108 KB), large about 60K (235 KB). INT8 is roughly 40% of that. Model inference takes 1 to 15 ms on a phone CPU.
• Many runtimes, every platform. ONNX (fp32 and INT8) and TFLite, on Python, the browser (onnxruntime-web), and React Native iOS and Android.
• A streaming preprocessor we wrote ourselves. A causal high-pass with 50/60 Hz notches feeds a 25 ms Hann window, a 512-point FFT (a power of two, so it stays fast in any language), a 40-bin log-mel, and CMN. It runs incrementally, recomputing only the frames that new audio touched, and it agrees with Python bit-for-bit in JS (CI checks this). On a phone, prep is about 15 to 20 ms per 100 ms of audio, and an energy gate skips the model during silence.
• Quality you can measure. A cross-speaker held-out eval and a cross-TTS-family eval tell you whether a model works beyond the trainer's own voice, before you ship it.
• A permissive stack. torch, numpy, scipy, soundfile, click, with optional piper-tts, kokoro-onnx, flask, and onnxruntime, all under MIT, BSD, or Apache-2.0. The models you train are yours to ship.

Deploy anywhere

The model consumes log-mel features, so any runtime reproduces the same preprocessing chain. wake.json specifies it in full, and there are reference implementations in Python (heed/audio.py) and JS (examples/*/preprocessing.js) that agree bit-for-bit.

Target	How
Python	onnxruntime on CPU. See export/README.md.
Browser	onnxruntime-web with examples/inference_browser/. Fully client-side and static-hostable on Vercel, Netlify, or GitHub Pages; ships a vercel.json.
iOS and Android	examples/inference_react_native/, with ONNX fp32/INT8 and TFLite, plus live word and runtime switching.
Other native (Flutter, Swift, Kotlin)	Run the ONNX or TFLite model, then port the preprocessing from the Python or JS reference (about 250 lines).

Deployment needs none of the training dependencies. A 3 MB runtime and your sub-250 KB model cover it.

Install

pip install heed-wakeword              # core: train and the model
pip install "heed-wakeword[ui]"        # plus the browser studio (Flask)
pip install "heed-wakeword[tts]"       # plus piper-tts, then: heed download-tts
pip install "heed-wakeword[kokoro]"    # plus kokoro-onnx, then: heed download-kokoro
pip install "heed-wakeword[export]"    # plus onnx and onnxruntime (export, verify)
pip install "heed-wakeword[all]"       # everything
heed doctor                            # check torch, onnxruntime, and TTS
heed smoke                             # synthetic end-to-end self-test, no mic

Self-host the studio (Docker)

Run the browser studio in a container, with no local Python setup:

docker compose up      # builds the image, then serves http://127.0.0.1:7777

Recordings and trained models persist in ./workspace. The image is CPU-only, which is fine for training a tiny model; for GPU training, run heed natively. See Dockerfile.

Recording good data

This is the biggest lever on quality.

• Positives. 8 to 30 recordings of the phrase. Vary your prosody, distance from the mic, and room. Variety beats raw count.
• Negatives. Distractor phrases in your own voice ("good morning", "the weather is nice") make precious hard negatives. Add similar-sounding phrases (for "hey doc", add "hey John") so the model learns the boundary.
• Cross-speaker. Turn on TTS (--tts-pos, --kokoro-pos) to synthesize the phrase across hundreds of voices, so the model is not tied to you. Confirm with the cross-speaker eval before you ship.

CLI reference

heed ui              [--host 127.0.0.1] [--port 7777] [--workspace DIR]
heed init            <name> --phrase "..."
heed record          <name> --kind {positive|negative} --count N
heed download-tts / download-kokoro
heed train           <name> [--epochs N] [--tts-pos N] [--kokoro-pos N]
                            [--target-fpr X] [--model-size {small|medium|large}] ...
heed test            <name> <audio.wav>
heed listen          <name>
heed eval            <name> [--positive-dir P] [--negative-dir N]
heed cross-tts-test  <name>
heed export          <name>
heed smoke / doctor

Run heed <cmd> --help for the full options.

Design, in one paragraph

Log-mel spectrograms (40 bins, a 25 ms window, a 10 ms hop, a 512-point FFT) feed a small depthwise-separable 1D CNN over time, with a stride-2 stem, a few DS-conv blocks, a global average pool, and a linear head. Training builds a per-user set from a handful of real positives, signal-processing augmentation (a VTLP-style speaker warp, reverb, noise, gain), and optional multi-speaker TTS, with a speaker-prototype regularizer that discourages sensitivity to the trainer's own voice. The high-pass is causal and state-retaining, so the exact same filtering streams chunk by chunk on-device, and the STFT is computed incrementally so only the frames that new audio touched get recomputed. The threshold is calibrated to a target false-positive rate, and inference is a sliding window with an RMS and voice-band energy gate in front of the model. See notes/ for the design rationale and a comparison with prior work.

GPU and CPU

Training auto-detects CUDA and uses it when present, otherwise it runs on CPU. The model is small, so CPU training works fine and is only a little slower. Model inference is CPU-only by design, because the model is far too small for GPU offload to beat the data-transfer cost. The one place a GPU pays off is TTS synthesis during training. See the install notes for the optional onnxruntime-gpu swap.

Documentation

Deeper guides for each tool live in docs/: the studio UI, export and deploy, Colab, Docker, mobile, and browser and JS.

Roadmap

Everything below works today: custom training from the studio or the CLI, on GPU or CPU; multi-speaker TTS augmentation and a cross-speaker evaluation; ONNX and TFLite export with verified numerical equivalence; inference in the browser and on iOS and Android, with live multi-word switching; a zero-install Colab trainer; a static client-side browser demo; and a Docker image for the studio.

A few directions are interesting for later: a curated pack of speaker-independent phrases, more reference preprocessing ports, folding the preprocessing into the model graph so raw audio goes straight in, or embedded targets like TFLite-Micro. None of these are promised. This is a v0.1, and what runs today is the real scope.

License

Apache-2.0. You can use Heed commercially, in closed-source products, with no obligation to open your own code. Keep the license and NOTICE file. The license includes a patent grant. Every dependency is MIT, BSD, or Apache-2.0.