whisper.cpp

Fast offline speech-to-text on your own machine.

Editorially reviewedChecked Oct 2026How we review·5 visited this week

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Toolglade’s take

whisper.cpp is the go-to when you want fast, private, offline transcription baked into your own software or run from the terminal, especially on Apple Silicon where it is heavily optimized. It is best for developers and technical users; anyone wanting a ready-made app with a GUI, speaker labels and polished editing should look at a consumer transcription product instead.

Should you use whisper.cpp?

Biggest strength: Runs entirely offline and locally, so audio never leaves your device
Biggest weakness: It is a developer library/CLI, not an end-user app, so you build or integrate it yourself
Pricing: Free plan
Top alternative: Buzz · see all

Facts & pricing last checked October 2026. How we review

About whisper.cpp

whisper.cpp is a high-performance, local implementation of OpenAI's Whisper speech-to-text model. It transcribes audio quickly on CPU or GPU with no cloud involved. Developers embed it to add private, offline transcription to their own apps.

whisper.cpp is a high-performance, dependency-free implementation of OpenAI's Whisper speech-to-text model written in plain C/C++. It takes audio and produces a transcript (and can translate speech into English), running fully offline on your own device with no cloud service involved. Because the whole model runs from a single compact file in the project's 'ggml' format, it is easy to embed into other software and ships as both a command-line tool (whisper-cli) and a C-style library with bindings for many languages. It is tuned to run efficiently almost anywhere, from a Raspberry Pi or phone up to a workstation, with CPU inference plus optional acceleration via Apple's Metal/Core ML, NVIDIA CUDA, Vulkan, AMD ROCm and more. Integer quantization lets you shrink models to fit modest hardware. It is used mainly by developers adding offline transcription to apps, researchers, and power users who want private, local subtitles and notes.

Weighing your options?See how whisper.cpp compares to the alternatives.

Pros & cons

Pros

Runs entirely offline and locally, so audio never leaves your device
Plain C/C++ with no runtime dependencies, making it easy to embed and ship in other apps
Broad hardware support including CPU-only, Apple Silicon (Metal/Core ML), NVIDIA CUDA, Vulkan and AMD ROCm
Quantization and multiple model sizes let you trade accuracy for speed and memory down to tiny devices
MIT licensed with a large ecosystem of language bindings (Python, Go, Rust, Java, Swift and more)

Cons

It is a developer library/CLI, not an end-user app, so you build or integrate it yourself
The CLI expects 16 kHz WAV input, so you often need ffmpeg to convert audio first
Features like speaker diarization and word-level timestamps are marked experimental
Getting GPU/NPU acceleration working requires building from source with the right flags and toolchains