{
 "schema_version": 1,
 "generated": "2026-10-31",
 "id": "ggml-org-whisper-cpp--speed-up-whisper-on-apple-silicon-with-core-ml",
 "repo": "ggml-org/whisper.cpp",
 "repo_url": "https://github.com/ggml-org/whisper.cpp",
 "repo_slug": "ggml-org-whisper-cpp",
 "title": "Speed up transcription on a Mac with the Neural Engine",
 "page_title": "How to speed up transcription on a Mac with the Neural Engine with Whisper CPP",
 "summary": "Generate a Core ML encoder so Whisper CPP runs the encoder on the Apple Neural Engine, more than 3x faster than CPU only.",
 "who": "A Mac user who transcribes often and wants the fastest local setup the hardware allows.",
 "problem": "CPU only transcription on a laptop is slow for long recordings and keeps the fans spinning.",
 "outcome": "whisper-cli loading a Core ML encoder and transcribing noticeably faster on Apple Silicon.",
 "steps": [
  "Install the Python packages: `pip install ane_transformers`, `pip install openai-whisper` and `pip install coremltools`",
  "Make sure Xcode is installed and run `xcode-select --install` for the command line tools",
  "Generate the encoder with `./models/generate-coreml-model.sh base.en`, which creates models/ggml-base.en-encoder.mlmodelc",
  "Build with Core ML: `cmake -B build -DWHISPER_COREML=1` then `cmake --build build -j --config Release`",
  "Run `./build/bin/whisper-cli -m models/ggml-base.en.bin -f samples/jfk.wav` and look for the Core ML model loaded line"
 ],
 "agent_prompt": "On my Apple Silicon Mac, set up ggml-org/whisper.cpp with Core ML support: install the Python requirements, generate the Core ML encoder for base.en, rebuild with WHISPER_COREML=1 and transcribe samples/jfk.wav. Tell me how long it took compared with the CPU build.",
 "agents": [
  "claude-code",
  "codex",
  "cursor"
 ],
 "difficulty": "Intermediate",
 "time": "45 minutes",
 "basis": "readme",
 "caveat": "The first run on a device is slow while the Neural Engine compiles the model, and the README recommends macOS Sonoma or newer to avoid transcription hallucination.",
 "category": "Content",
 "tags": [
  "Content"
 ],
 "lang": "c++",
 "requirements": [
  "A C/C++ toolchain and CMake to build from source",
  "ffmpeg to convert audio to 16-bit WAV, since whisper-cli reads 16-bit WAV files",
  "Disk and RAM for the model you pick: about 142 MiB and 388 MB for base, up to 2.9 GiB and 3.9 GB for large",
  "Optional: Xcode command line tools and Python 3.11 for Core ML on Apple Silicon"
 ],
 "risk": "none",
 "seen_in_the_wild": false,
 "mentioned_publicly": false,
 "risks": [],
 "best": false,
 "score": 0.8,
 "evidence": [],
 "url": "https://dedupedai.com/use-cases/ggml-org-whisper-cpp/speed-up-whisper-on-apple-silicon-with-core-ml/",
 "md": "https://dedupedai.com/use-cases/ggml-org-whisper-cpp/speed-up-whisper-on-apple-silicon-with-core-ml/index.md",
 "disclaimer": "Deduped lists open-source repositories that belong to other people. We do not own, maintain, audit, or vouch for any of them, and a repo appearing here is not an endorsement or a safety check. The use cases, steps, and prompts are written by AI and may be incomplete, outdated, or just wrong. Where a use case is marked \"Seen in the wild\", we link to public posts where someone described something similar, but we have not verified those posts and the use case itself is still our AI's own write-up. Use all of this at your own risk. Only run code, commands, or agents on systems and accounts you are authorized to use, and read the code and every command before you run it, especially anything that touches credentials, files, or the network. Nothing here is professional, security, legal, or financial advice. All trademarks and project names belong to their respective owners, and their use here does not imply affiliation. Content is provided as is, without warranties of any kind. Found a mistake, a safety problem, or something you want removed? Email hello@dedupedai.com and we will look at it promptly.",
 "api": "https://dedupedai.com/api/v1/usecases/ggml-org-whisper-cpp--speed-up-whisper-on-apple-silicon-with-core-ml.json",
 "api_version": "v1"
}