53.5k stars · MIT · v1.9.3 (2026-08-20) · Track this in Scout
Whisper in C++ — fast on plain hardware and excellent on Apple Silicon, with no Python and no CUDA.
▶Repo detailsthe review · specs · pros & cons · install
What it is
OpenAI's Whisper model rewritten in C++ so it runs fast on ordinary hardware. It is unusually fast on Apple computers. It is one program and one model file. No Python setup and no CUDA (special graphics-card software).
What it is good for. In Edition 2 I recommended whisperX and said clearly that it wants a GPU. This is the version that runs on an ordinary Mac tonight. The transcript is the most reusable thing a channel produces. It is the caption file, which raises watch time and is the minimum for accessibility. It is the source text for the Russian subtitles that Edition 4 argued for. And published on a website as an article, it is the only way a YouTube video ever earns Google traffic. Record once, publish in three places.
- No Python, no CUDA, no tangle of dependencies. Build it once and forget it.
- Fast enough on a Mac that transcribing becomes normal, not an event.
- MIT licence, and released steadily. Version 1.9.3 came out on 20 August.
- It overlaps with whisperX (Ed. 2 #4). whisperX is still better at word-level timing and at telling speakers apart. Pick one. Do not keep both.
- Russian accuracy is clearly lower than English. A human has to check it before you publish anything in Russian.
- The repository moved from
ggerganov/whisper.cpptoggml-org/whisper.cpp. Older guides point at the old address.
The same text also feeds textstat (Ed.
git clone https://github.com/ggml-org/whisper.cpp cd whisper.cpp cmake -B build && cmake --build build -j --config Release sh ./models/download-ggml-model.sh large-v3-turbo ./build/bin/whisper-cli -m models/ggml-large-v3-turbo.bin -f lesson.wav -osrt -l ru
