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Jot is an Apache-2.0-licensed macOS menu-bar dictation app created by Ammaar Reshi. Holding a configurable hotkey records speech, sends it directly from the Mac to Gemini's specialist transcription model, removes filler words and applies corrections such as changes of mind, then inserts the polished text at the cursor. It supports hands-free recording, cancellation, tone matching, a custom dictionary, and a History window. Audio is written to disk from the start of capture, with crash recovery and offline retry queuing. The insertion pipeline tries the macOS Accessibility API first, then a clipboard-restoring paste, and finally offers a manual paste when focus changes; an optional tone pass is guarded by validation and falls back to the raw transcript. Dictations and retry data are stored in a local SQLite-based history store. The app requires macOS 14+, a Gemini API key, microphone permission, and Accessibility permission, and is explicitly described as not an officially supported Google product.

BetterVoice is an experimental, open-source macOS menu-bar app for local voice dictation with screen context. It transcribes speech locally and captures the full display whenever the user circles an area with the pointer, marking each capture with a blue pointer trail and pulse while preserving multiple references in order. The app inserts the transcript into the selected text field when macOS permits it and pastes captured images when both speech and screen context are available. It supports quick notes and long explanations, configurable microphones, dictation languages, circle-detection thresholds, and keyboard shortcuts. English uses a local Parakeet model by default, while other languages use a separately downloaded multilingual model; an optional English-only grammar-cleanup feature runs locally through quantized ONNX weights.

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.

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.