{
 "schema_version": 1,
 "generated": "2026-10-31",
 "id": "ggml-org-whisper-cpp--transcribe-audio-offline-with-whisper-cpp",
 "repo": "ggml-org/whisper.cpp",
 "repo_url": "https://github.com/ggml-org/whisper.cpp",
 "repo_slug": "ggml-org-whisper-cpp",
 "title": "Transcribe audio files offline on your own machine",
 "page_title": "How to transcribe audio files offline on your own machine with Whisper CPP",
 "summary": "Build whisper-cli, download a ggml model and turn recordings into text without any cloud service.",
 "who": "A journalist or podcaster with hours of interviews who cannot upload the audio anywhere.",
 "problem": "Cloud transcription costs money per minute and means handing private recordings to a third party.",
 "outcome": "A local transcript of each recording, produced entirely on your own hardware.",
 "steps": [
  "Run `git clone https://github.com/ggml-org/whisper.cpp.git` and cd into the folder",
  "Download a model with `sh ./models/download-ggml-model.sh base.en`",
  "Build the tool: run `cmake -B build` and then `cmake --build build -j --config Release`",
  "Convert your recording to 16-bit WAV with `ffmpeg -i input.mp3 -ar 16000 -ac 1 -c:a pcm_s16le output.wav`",
  "Transcribe it with `./build/bin/whisper-cli -f samples/jfk.wav`, swapping in your own file, and check `./build/bin/whisper-cli -h` for output options"
 ],
 "agent_prompt": "Clone ggml-org/whisper.cpp, build whisper-cli with CMake, download the base.en model, convert every MP3 in <folder> to 16 kHz mono WAV with ffmpeg and transcribe each one. Save one text file per recording next to the audio.",
 "agents": [
  "claude-code",
  "codex",
  "cursor"
 ],
 "difficulty": "Beginner",
 "time": "20 minutes",
 "basis": "readme",
 "caveat": "whisper-cli only reads 16-bit WAV, so every other format needs an ffmpeg conversion first, and larger models need several GB of RAM.",
 "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": true,
 "score": 1.0,
 "evidence": [],
 "url": "https://dedupedai.com/use-cases/ggml-org-whisper-cpp/transcribe-audio-offline-with-whisper-cpp/",
 "md": "https://dedupedai.com/use-cases/ggml-org-whisper-cpp/transcribe-audio-offline-with-whisper-cpp/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--transcribe-audio-offline-with-whisper-cpp.json",
 "api_version": "v1"
}