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Privacy-first AI meeting note taker for Windows and macOS. Free, open source (MIT). On-device Whisper/Parakeet transcription and speaker diarization, no meeting bot. Open-source alternative to Granola, Otter and Fireflies. https://meetily.ai
See, hear, and interact on your local devices.
MNN: A blazing-fast, lightweight inference engine battle-tested by Alibaba, powering high-performance on-device LLMs and Edge AI.
Production ready toolkit to run AI locally
Translate darknet to tensorflow. Load trained weights, retrain/fine-tune using tensorflow, export constant graph def to mobile devices
On-device wake word detection powered by deep learning
Local speech-to-text for macOS on-device AI, fully private, optional cloud
NobodyWho is an inference engine that lets you run LLMs locally and efficiently on any device.
Thin, unified, C++-flavored wrappers for the CUDA APIs
Android Input Method Editor (IME) based on Whisper
CAI NEURAL API - Pascal based deep learning neural network API optimized for AVX, AVX2 and AVX512 instruction sets plus OpenCL capable devices including AMD, Intel and NVIDIA.
TFLite Support is a toolkit that helps users to develop ML and deploy TFLite models onto mobile / ioT devices.
On-device Vision Model - a fully offline, free vision pipeline that gives text-only models (e.g. DeepSeek) image understanding and multilingual OCR through an OpenAI-compatible local API. Only tokens are needed: no API key, no subscription, no cloud cost. Recommended for native speakers of Chinese, English, Japanese and Korean.
Accelerated tensor operations and dynamic neural networks based on reverse mode automatic differentiation for every device that can run Swift - from watchOS to Linux
Profile your Tensorflow Lite model on-device
An example app that demos how to use TFLite to do automatic speech recognition on-device
Lightweight on-device keyword spotting engine for iOS using CoreML and real-time audio streaming.
OtosakuStreamingASR-iOS is a real-time speech recognition engine for iOS, built with Swift and Core ML. It uses a fast and lightweight streaming Conformer model optimized for on-device inference. Designed for developers who need efficient audio transcription on mobile.
Ceci n'est pas une pomme - Deep Learning on mobile device