whisper.cpp
Build a local speech-recognition CLI or application integration with explicit audio-format and memory requirements.
- Starting price
- Free local software
- Free access
- No hosted usage allowance promised
- Billing unit
- Local runtime; own hardware
- Platform
- Windows, macOS, Linux and embedded targets
- Verified
- 21 Oct 2026
Overview
What you provide
- Provide compatible model weights and a supported audio file to the CLI or application binding.
What you get
- whisper.cpp computes local speech-recognition text with runtime options for timestamps and integration.
Choosing this tool
whisper.cpp suits developers embedding speech recognition or automating a controlled CLI pipeline. Compare the original Whisper Python implementation for a PyTorch route, or faster-whisper for a CTranslate2-based Python integration. The runtime is a distinct integration product from the original model; choosing it means owning build, audio conversion and deployment details.
A decision worked through
The README lists the base model at about 142 MiB on disk and roughly 388 MB of memory, versus around 2.9 GiB and 3.9 GB for large. Those are project reference figures, not a measurement on your device. Test an appropriate model and convert the sample audio before designing concurrency.
whisper.cpp alternatives for local speech inference
Compare three local speech-recognition implementations: C/C++ whisper.cpp, the original PyTorch Whisper package and faster-whisper using CTranslate2. Match language, model, audio preparation and deployment backend before measuring speed.
| Product | Price / unit | Free allowance | Input → output | Key limit |
|---|---|---|---|---|
| whisper.cppRead profileOfficial source ↗ | MIT C/C++ runtime | No hosted minute quota | Audio becomes local recognition text. | Build, weights and audio conversion. |
| Whisper PythonOfficial source ↗ | MIT reference implementation | Own PyTorch inference | Audio becomes recognition text. | Python, PyTorch and FFmpeg setup. |
| faster-whisperOfficial source ↗ | MIT Python implementation | Own CTranslate2 runtime | Audio becomes local transcripts. | Match compute type and GPU libraries. |
Setup & workflow
- Build the documented CLI with the intended acceleration backend.
- Download a model and convert a short recording into the expected audio format.
- Measure and review the sample before adding bindings or concurrent jobs.
Requirements & installation
- Build with CMake, download a compatible model and prepare a 16-bit WAV sample for the basic CLI.
Limits & review
- The basic whisper-cli example expects 16-bit WAV input; the README shows FFmpeg conversion. GPU and NPU backends require their own build configuration, and model reference memory is not a concurrency guarantee.
Your part
- Inspect transcription errors and measure latency on target hardware before product integration.
Sources & verification3
This profile is based on official sources, not a hands-on product test.
Vendor descriptions and demos document advertised features. Editorial guidance is based on these sources.
- Official whisper.cpp setup and memory tablegithub.com
- Whisper Python official implementationgithub.com
- faster-whisper official implementationgithub.com
