Whisper vs whisper.cpp
Which should you use? A side-by-side look at what each one does, who it suits, what it costs, how hard it is to set up, and where each falls short.
Open source · MIT
Open-source speech-to-text model that transcribes and translates audio in many languages.
Open source · MIT
Fast C/C++ port of OpenAI's Whisper speech-to-text model, runs locally on CPU or GPU.
Our verdict
Whisper is OpenAI's speech-to-text model, used from the command line or Python to transcribe and translate audio in many languages. whisper.cpp is a C/C++ port of that same model, built to run locally with few dependencies on CPUs, GPUs, and modest hardware, and it is aimed at developers embedding offline transcription into native apps. Both are free and MIT-licensed, so the choice mostly comes down to your language stack and hardware.
Pick Whisper if you are a Python developer or a non-developer comfortable with the command line who wants multilingual transcription and translation with the original model.
Pick whisper.cpp if you need offline transcription inside a C or C++ app, or want to run it efficiently on your own hardware such as Apple Silicon or a Raspberry Pi.
| Key difference | Whisper | whisper.cpp |
|---|---|---|
| Type | AI speech recognition model, used via command line or Python | Local inference library written in C/C++ with no external dependencies |
| Best at | Multilingual transcription and translation to English in one model | Running efficiently on CPU, GPU, or constrained hardware, offline |
| Hardware needs | Larger models need roughly 5 to 10 GB of VRAM | Quantization options reduce model size and memory use |
| Audio input | Works with common audio files via ffmpeg | Command-line example only accepts 16-bit WAV, so conversion is often needed |
| Setup | Install with a package manager; needs ffmpeg and sometimes Rust | Clone and build from source; needs CMake and terminal comfort |
Written with AI from both products' catalogue entries · updated 2026-10-29
Who each one is for
Choose Whisper if you need…
- Developers who want to add transcription of audio files to a Python project
- People who need to transcribe recordings such as interviews or lectures locally
- Projects needing multilingual transcription with one model
- Users who want English-only transcription with the smaller .en models
Choose whisper.cpp if you need…
- Developers who want to embed offline speech-to-text in a C, C++, or other native app
- People on Apple Silicon who want GPU or Neural Engine accelerated transcription
- Users who need to transcribe audio privately on their own computer
- Projects targeting constrained or embedded hardware such as Raspberry Pi
Side-by-side
| Whisper | whisper.cpp | |
|---|---|---|
| Type | AI model / agent | Library |
| Price | Free and open source | Free and open source |
| Licence | MIT | MIT |
| GitHub stars | 112k | 54.9k |
| Setup difficulty | Intermediate | Intermediate |
| How you get it | Install with a package manager | Clone and build from source |
| Main language | Python | C++ |
| Last code update | 2 months ago | 7 days ago |
What each one is
Whisper
Whisper is a speech recognition model from OpenAI that turns spoken audio into written text. It was trained on a large set of varied audio and can also translate non-English speech into English and identify which language is being spoken. You can use it from a command line or from Python code.
whisper.cpp
whisper.cpp is a C/C++ implementation of OpenAI's Whisper automatic speech recognition model, which turns spoken audio into text. It is written to run with no external dependencies and can work offline on the device. People use it to add transcription to their own apps or to transcribe audio on their own machine without sending it to a cloud service.
Best for
Whisper
- Developers who want to add transcription of audio files to a Python project
- People who need to transcribe recordings such as interviews or lectures locally
- Projects needing multilingual transcription with one model
- Users who want English-only transcription with the smaller .en models
whisper.cpp
- Developers who want to embed offline speech-to-text in a C, C++, or other native app
- People on Apple Silicon who want GPU or Neural Engine accelerated transcription
- Users who need to transcribe audio privately on their own computer
- Projects targeting constrained or embedded hardware such as Raspberry Pi
Strengths
Whisper
- Handles many languages and can translate speech to English in one model
- Offers several model sizes so you can trade speed against accuracy
- Released under the MIT License, which permits broad reuse
whisper.cpp
- No external dependencies in the core implementation, which eases integration into many platforms
- Supports a wide range of hardware including CPU, Metal, CUDA-based NVIDIA GPUs, Vulkan, and Core ML
- Includes quantization options that reduce model size and memory use
Not ideal for
Whisper
- Users without a suitable computer, since larger models need several GB of video memory (VRAM), per the README's table
- Anyone needing translation with the turbo model, which the README says is not trained for translation
- People wanting a point-and-click app, since the README describes command-line and Python usage only
whisper.cpp
- Non-technical users who want a ready-made app with a graphical interface, since the README centers on command-line usage and building from source
- Anyone who needs audio in formats other than 16-bit WAV without first converting it, because the CLI example only accepts 16-bit WAV
- Teams needing a hosted transcription API, since this is a local inference library rather than a service
Trade-offs
Whisper
- Larger models need roughly 5 to 10 GB of VRAM, so modest hardware may struggle
- Accuracy varies widely by language, according to the README
- Requires ffmpeg and sometimes Rust for installation
whisper.cpp
- Command-line example only accepts 16-bit WAV, so audio often needs conversion first
- Building and choosing models requires comfort with CMake and the terminal
- Core ML and some accelerator paths require extra setup and platform-specific steps
Getting started
Whisper
Install the latest release
pip install -U openai-whisperInstall ffmpeg on Ubuntu or Debian
sudo apt update && sudo apt install ffmpegwhisper.cpp
Clone the repository
git clone https://github.com/ggml-org/whisper.cpp.gitDownload the base.en model
sh ./models/download-ggml-model.sh base.enFrequently asked questions
Should I use Whisper or whisper.cpp?
Whisper is OpenAI's speech-to-text model, used from the command line or Python to transcribe and translate audio in many languages. whisper.cpp is a C/C++ port of that same model, built to run locally with few dependencies on CPUs, GPUs, and modest hardware, and it is aimed at developers embedding offline transcription into native apps. Both are free and MIT-licensed, so the choice mostly comes down to your language stack and hardware. Pick Whisper if you are a Python developer or a non-developer comfortable with the command line who wants multilingual transcription and translation with the original model. Pick whisper.cpp if you need offline transcription inside a C or C++ app, or want to run it efficiently on your own hardware such as Apple Silicon or a Raspberry Pi.
What is the difference between Whisper and whisper.cpp?
Whisper: Open-source speech-to-text model that transcribes and translates audio in many languages. whisper.cpp: Fast C/C++ port of OpenAI's Whisper speech-to-text model, runs locally on CPU or GPU. Whisper suits developers who want to add transcription of audio files to a python project, while whisper.cpp suits developers who want to embed offline speech-to-text in a c, c++, or other native app.
Are Whisper and whisper.cpp free?
Whisper: Free and open source. whisper.cpp: Free and open source.
Is Whisper or whisper.cpp more popular?
Whisper has more GitHub stars (112k) than whisper.cpp (54.9k). Popularity isn't the same as fit — check which one matches your use case above.
What are other alternatives to Whisper and whisper.cpp?
Other options people compare them with include faster-whisper, Nano-vLLM, FunASR, ts-pattern.
Keep comparing
Built from our catalogue entries for both products: open-source details come from each project's README (AI-assisted, refreshed weekly) and GitHub data; AI-tool details are editorial. Spotted something wrong? Let us know from either product's page.