whisper.cpp

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.

Lipflow is an open-source, cross-platform silent lip-reading dictation tool that uses a webcam to turn mouthed words into text pasted at the cursor. It runs locally on macOS, Windows, and Linux: face landmarks are tracked live, 96×96 grayscale mouth crops are sampled at 25 fps, and an Auto-AVSR visual-speech-recognition pipeline combines a 3D-convolutional ResNet front end, Conformer encoder, Transformer decoder, CTC scoring, and a subword language model with beam search. Users can train the language model and lip reader on their phrasing and face, import local Wispr Flow dictation history, and optionally apply cleanup through offline rules, an on-device Qwen model, Claude, Codex CLI, or Ollama. Whisper mode combines lip and audio input with an audio-visual model while the hotkey is held. The repository is MIT-licensed, although its LRS3-trained model weights are identified as for non-commercial research use.

Voicebox is a local-first, open-source AI voice studio developed in the jamiepine/voicebox repository. It combines voice cloning, text-to-speech, speech-to-text dictation, voice profiles, audio effects, and agent voice output in a desktop application. It generates speech through seven switchable TTS engines, including Qwen3-TTS, Qwen CustomVoice, LuxTTS, Chatterbox, HumeAI TADA, and Kokoro, and supports voice cloning from reference audio, preset voices, multilingual generation, automatic text chunking, crossfading, and multi-track story editing. Whisper-based transcription powers global dictation, in-app recording, and the transcription API; an optional local Qwen3 LLM can refine dictation or rewrite text according to a voice profile's persona. The application exposes REST endpoints for speech generation, agent speech, transcription, and profile management, plus a built-in MCP server with tools for speaking, transcribing, and browsing captures and profiles. It runs models and stores voice data locally, using Tauri with a React/TypeScript frontend and FastAPI backend; platform backends include MLX on Apple Silicon and PyTorch on CUDA, ROCm, XPU, DirectML, or CPU. The repository is licensed under the MIT License and provides macOS, Windows, and Docker distribution, with Linux build-from-source instructions.

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.

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.