Life Recorder

quill is a fully local, ultra-minimalist macOS meeting recorder and transcriber developed by digimata. It runs as a single Swift menu-bar binary and uses macOS Core Audio process taps to capture microphone and system audio as separate CAF tracks without a virtual audio device or kernel extension. After recording stops, quill transcribes each track on the device, shifts the results by their start offsets, and merges the timestamped segments into a speaker-tagged transcript. The separate microphone and system tracks provide two-party diarization without a speaker-identification model. Each session produces the audio tracks, metadata, canonical JSON and Markdown transcripts, and a transcription log in a recordings directory. The default on-device transcription engine is Parakeet TDT 0.6B v2 through FluidAudio's Core ML port. It requires macOS 15 or later, supports optional launch-at-login operation, and can resume unfinished transcription jobs after relaunch. A WhisperKit fallback or re-transcription engine is planned.

OpenWhispr is an open-source, cross-platform desktop voice-to-text application for macOS, Windows, and Linux. A global hotkey captures speech and inserts the resulting text at the cursor in another application; dictation can use local Whisper or NVIDIA Parakeet speech-to-text engines, where audio remains on the device, or cloud providers through user-supplied keys. It also supports dictation translation, AI-agent commands, meeting transcription with speaker diarization and voice fingerprinting, audio and video transcription, and searchable notes with semantic search and optional cloud sync. The application is built with Electron, React, TypeScript, SQLite, whisper.cpp, and sherpa-onnx, and provides an API and MCP server for programmatic access to notes and transcriptions. The repository describes it as having no data collection or telemetry and distributes installers for the three supported desktop platforms. It is licensed under the MIT license.

whisper.cpp is a C/C++ implementation of OpenAI's Whisper automatic speech-recognition model for speech-to-text inference. It is designed for offline, on-device use and supports CPU-only inference, GPU and NPU acceleration, integer quantization, mixed F16/F32 precision, voice activity detection, and a C-style API. The implementation targets macOS, iOS, Android, Linux, Windows, WebAssembly, Raspberry Pi, Docker, and other platforms, with optimizations including ARM NEON, Apple's Accelerate and Metal frameworks, x86 AVX, POWER VSX, Vulkan, CUDA-related NVIDIA support, ROCm, OpenVINO, and other backends. The repository includes the whisper-cli example, which transcribes 16-bit WAV audio using Whisper models converted to ggml format; its zero-dependency implementation is intended for integration into applications such as on-device captioning tools.

Qwen Scribe is a private, local transcription and system-wide dictation application for Apple Silicon Macs. It runs Qwen3-ASR through mlx-qwen3-asr on the Mac's Metal GPU, keeping audio and transcripts on the computer without an account, API key, or cloud service. Users can drag audio or video into a local web interface, transcribe with automatic or forced language selection, vocabulary hints, word timestamps, and SRT export, and search or export local transcript history. A menu-bar helper provides push-to-talk dictation in text fields, displaying listening and transcription states in a non-focus-stealing HUD before pasting the result at the cursor. The project requires macOS 14 or newer, Apple Silicon, Python 3.12 or newer, and ffmpeg for non-WAV audio and video. It offers Qwen3-ASR 1.7B and 0.6B model options; model weights are downloaded separately and require roughly 3.4 GB or 1.2 GB of unified memory respectively, in addition to application overhead. It is distributed as a downloadable beta Mac app or can be built from source; the repository describes the project as version 0.2.1-beta.1.