On-device wake word detection powered by deep learning
Alexa replacement and controller for Echo Dot 2nd Generation device.
This is a list of interesting papers and projects about TinyML.
Turn an Amazon Echo Dot or Echo Show into a fully local Home Assistant voice satellite, media player, display and Bluetooth proxy. No Amazon, no cloud.
Open firmware for the Echo Show 5, 1st and 2nd gen: Alpine Linux instead of Android, no Alexa. A local Home Assistant voice satellite with its own touch screen, on-device wake word, SSH and A/B over-the-air updates.
OpenClaw voice assistant app for Android - Wake word activation & system assistant integration
Your AI assistant, built for the agentic era. Open source and local: talk to it, and it runs your agents, your coding CLIs, your browser and your apps. Windows, macOS, Linux.
A multi-model Muse: personal AI voice assistant on ESP32 - wake word or knob, any OpenAI-compatible brain (STT/chat/TTS mix-and-match), streaming replies, out of the box. Built on muse-gadget-sdk.
Dashie for Android — the family dashboard and Home Assistant kiosk app. AGPL-3.0.
wake word engine benchmark framework
A lightweight, wake word detection engine. Train custom, high-accuracy models with minimal effort.
Linux firmware for the Echo Dot (2nd gen): no Fire OS, no Alexa. A local Home Assistant voice satellite with all seven mics, Bluetooth speaker mode, signed A/B updates and SSH.
Offline wake word & keyword spotting engine. One ONNX model detects multiple keywords. ONNX/TFLite, model <130KB, inference <10ms. Runs on Android, ESP32, Linux, Web, and Home Assistant (Wyoming protocol server + HA add-on). No cloud, no data usage.
Create any wake word, deploy on any device and operating system.
Wake word detection with custom phrases without model training
A custom "Hey Donut" wake word for Home Assistant — 62 KB, runs on-device, zero false accepts per hour. Includes the full training pipeline so you can teach your assistant any name. CC0.
GPT powered personal voice assistant, immitates GLaDOS from Portal.
Optimized ESP32-S3/P4 fully offline speech to text and text to speech system for Micropython and AtomVM, featuring a finetuned STT model for noisy environments, plus a data generation/field capture/model finetuning framework. Includes high performance 4/8bit int kernels for Xtensa/RiscV PIE vector units
A lightweight, offline-capable voice assistant that listens for a wake phrase, converts speech to text, runs local or API-based intent handling, and replies with natural-sounding TTS. Built for quick local deployment, it supports configurable wake words, microphone I/O, and easy integration with custom actions or LLMs.
Use Porcupine wake word to trigger a voice assistant pipeline in Home-Assistant
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