{
  "slug": "mlx-whisper",
  "name": "MLX Whisper",
  "aliases": ["mlx-whisper", "mlx_whisper"],
  "vendor": { "name": "Apple (MLX contributors)", "slug": "apple", "website": "https://github.com/ml-explore" },
  "homepage_url": "https://github.com/ml-explore/mlx-examples/tree/main/whisper",
  "docs_url": "https://pypi.org/project/mlx-whisper/",
  "repo_url": "https://github.com/ml-explore/mlx-examples",
  "license_url": "https://github.com/ml-explore/mlx-examples/blob/main/LICENSE",
  "product_type": "library",
  "product_status": "active",
  "summary": "Port of OpenAI's Whisper speech-recognition models to MLX, offering a CLI and Python API for local transcription on Apple Silicon",
  "description": "MLX Whisper is the Whisper example from Apple's `mlx-examples` repository packaged as a PyPI library (`mlx-whisper`, MIT license). It runs OpenAI's open Whisper speech-recognition models on Apple Silicon through MLX rather than PyTorch, using pre-converted weights hosted in the `mlx-community` organization on the Hugging Face Hub or models you convert yourself.\n\nThe `mlx_whisper` command transcribes an audio file, or audio piped on standard input, into text, SRT, VTT or JSON output, with options for model choice, language and translation. The Python `transcribe` function returns segments and can produce word-level timestamps. ffmpeg must be installed for audio decoding. It is a convenient choice for researchers and developers who want fast local transcription of lectures, interviews or recordings without sending audio to a cloud service.\n\nLimits are those of Whisper itself (accuracy varies by language and audio quality, no built-in speaker diarization) plus the Apple Silicon requirement; it is a focused example package rather than a full transcription product.",
  "capabilities": ["Local transcription of audio files or stdin streams", "Multiple output formats including text, SRT, VTT and JSON", "Word-level timestamps", "Choice of Whisper model sizes from the Hugging Face Hub", "Python API alongside the command-line tool"],
  "workflows": ["Transcribe recorded lectures or meetings on a Mac", "Batch-generate subtitles for a video archive", "Embed speech-to-text in a local Python pipeline"],
  "advantages": ["No cloud dependency; audio stays on the machine", "Simple pip install and single command", "MIT license"],
  "limitations": ["Apple Silicon only", "No speaker diarization", "Requires ffmpeg and model downloads", "Maintained as an example package rather than a standalone product"],
  "prerequisites": ["Mac with Apple Silicon", "Python 3.8 or newer", "ffmpeg"],
  "audiences": ["Researchers transcribing interviews and lectures", "Developers adding local speech-to-text to Mac tools"],
  "alternatives": ["whisper", "mlx"],
  "categories": [{ "slug": "apple-silicon-ml", "primary": true }, { "slug": "speech-vision-multimodal" }],
  "tags": ["apple-silicon", "macos", "speech-recognition", "transcription", "open-source", "python", "cli"],
  "use_cases": ["run-local-models", "transcribe-audio"],
  "operating_systems": ["macos"],
  "execution_modes": ["local"],
  "interfaces": ["cli", "library"],
  "deployment_models": ["desktop"],
  "integrations": ["MLX", "Hugging Face Hub", "ffmpeg"],
  "data_formats": ["MP3", "WAV", "M4A (via ffmpeg)", "TXT", "SRT", "VTT", "JSON"],
  "languages": ["Python"],
  "source_model": "open-source",
  "license_spdx": "MIT",
  "pricing_model": ["open-source", "free"],
  "self_hostable": true,
  "sources": [
    { "url": "https://github.com/ml-explore/mlx-examples/tree/main/whisper", "type": "official_site", "title": "mlx-examples/whisper README", "supports": ["name", "summary", "capabilities", "prerequisites"], "checked_at": "2026-11-02" },
    { "url": "https://pypi.org/project/mlx-whisper/", "type": "package_registry", "title": "mlx-whisper on PyPI", "supports": ["product_status", "prerequisites", "license_spdx"], "checked_at": "2026-11-02" },
    { "url": "https://github.com/ml-explore/mlx-examples/blob/main/LICENSE", "type": "license_page", "supports": ["license_spdx", "source_model"], "checked_at": "2026-11-02" }
  ],
  "confidence": "high",
  "discovery_method": "editorial-research",
  "depth": "standard",
  "editorial_status": "reviewed",
  "visibility": "public",
  "featured": false,
  "author": "batch-C"
}