#On-device
Showing 15 of 15 repositories tagged #on-device, ranked by stars
DeepSpeech is an open source embedded (offline, on-device) speech-to-text engine which can run in real time on devices ranging from a Raspberry Pi 4 to high power GPU servers.
Lightning-Fast, On-Device, Multilingual TTS — running natively via ONNX.
The free AI already on your Mac. CLI tool, OpenAI-compatible server, and interactive chat — all on-device via Apple Intelligence. No API keys, no cloud, no downloads.
Local-first healthcare AI: clinical NER & HIPAA PII de-identification that runs 100% on-device. 1,000+ medical models, 12 languages, Apple MLX + Python, no cloud, no patient data leaving your network. Apache-2.0
Your on-phone / mobile AI Agent / Claw, capable of operating terminals and performing a wide range of tasks in the Android world || 你的手机 AI 代理，她可以操作终端，也可以完成 Android 世界的广泛任务
AI speech toolkit for Apple Silicon — ASR, TTS, speech-to-speech, VAD, and diarization powered by MLX and CoreML
AubAI brings you on-device gen-AI capabilities, including offline text generation and more, directly within your app.
电子鹦鹉 / Toy Language Model
Precision genomics for everyone, everywhere. Powered by private AI.
Eris is a private AI chat application that runs entirely on your device using Apple's MLX framework. Named after the dwarf planet that challenged our understanding of the solar system, Eris challenges the notion that AI must live in the cloud.
On-device speech SDK for Android — ASR, TTS, VAD, and noise cancellation powered by ONNX Runtime with Qualcomm NNAPI acceleration
Fully on-device on-screen translator for macOS — region capture + live overlay, powered by Vision OCR and Apple Translation
On-device AI for Expo apps. Run language models locally. No API keys, no cloud, just native intelligence.
Voice dictation that stays on your device. Open-source, local-first, and built for people who actually care about privacy.
LiveTalk is a unified, high-performance talking head generation system that combines the power of LivePortrait and MuseTalk open-source repositories. The PyTorch models from these projects have been ported to ONNX format and optimized for CoreML to enable efficient on-device inference in Unity.