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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.

Steno is an open-source, privacy-first desktop AI notepad and meeting notetaker for Windows and macOS, intended for confidential conversations. It records microphone and system audio, provides live or post-meeting transcription with speaker labels, generates summaries, and supports natural-language queries across individual notes or the meeting library. Its local mode uses on-device AI models; on Apple Silicon, live transcription uses Parakeet TDT v3 through MLX, while Whisper supports post-stop transcription in 99 languages. On macOS 14.4 and later, native Core Audio Tap captures both sides of virtual meetings without a virtual audio cable and with selectable microphone input. Steno can detect meeting start and end events, offer to start or summarize recordings, append recordings to existing notes, provide global recording shortcuts, and include user notes in generated summaries. Notes, transcripts, and summaries are stored as editable Markdown, with one-way synchronization to an Obsidian vault and report templates for structured outputs. The project states that local recordings, transcripts, and summaries remain on the device and have no cloud usage limits; users can alternatively configure hosted models through OpenAI, Anthropic, AWS Bedrock, or a custom API endpoint.

Life Recorder is an open-source native iPhone recorder with a private Mac receiver. The iPhone captures roughly one-minute AAC chunks while the screen is locked or other apps are in use, then uploads them over an authenticated HTTPS connection. The Mac receiver runs whisper.cpp locally, removes common stage-direction markers and repetitive hallucinated noise, and appends the results to one continuous Markdown transcript with hourly capture markers; audio is deleted after durable receipt and successful transcription. It requires an iPhone development build installed with Xcode, a macOS system running Python, ffmpeg, whisper-cli, and a downloaded GGML Whisper model. Setup creates a bearer token and self-signed TLS certificate, uses certificate pinning, and stores the transcript in a private runtime directory. The default connection is over the local network; cellular use requires a private VPN. The project is licensed under MIT.

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.