whisper.cpp
High-performance C/C++ port of OpenAI's Whisper model for offline speech-to-text inference.
Run fast, local speech recognition across devices with zero runtime memory allocations.
whisper.cpp is a high-performance C/C++ implementation of OpenAI's Whisper speech recognition model. It allows you to run automatic speech recognition locally without heavy Python dependencies. You should use it if you need fast, private, offline audio transcription embedded into C/C++ or native applications.
What it is
This tool provides lightweight inference for Whisper models using plain C/C++ and the ggml machine learning library. It supports hardware acceleration across Apple Silicon, NVIDIA GPUs, Vulkan, OpenVINO, and CPU architectures via SIMD intrinsics. You can transcribe WAV files, perform voice activity detection, and run integer-quantized models for lower memory usage.
Who it's for
It suits software developers, audio tool builders, and system integrators who want local, fast speech-to-text without Python runtime overhead. It is less suitable for non-technical creators who expect a graphical user interface out of the box.
Key features
First-Class Apple Silicon Support
Uses ARM NEON, Core ML, Accelerate framework, and Metal for optimized execution on macOS and iOS.
Integer Model Quantization
Reduces memory consumption and disk space requirements by quantizing ggml models down to 5-bit precision.
Broad Hardware Backend Acceleration
Supports NVIDIA GPUs, AMD ROCm, OpenVINO, Vulkan, and AVX or VSX SIMD instruction sets.
Zero Runtime Memory Allocation
Avoids memory overhead during execution to maintain predictable execution performance.
Up and running in 3 steps
Our honest verdict
What's great
- Runs completely offline without external network calls or cloud API costs.
- Has no mandatory external dependencies beyond a C/C++ compiler and CMake.
- Supports a wide variety of hardware acceleration backends including Core ML, Metal, CUDA, and Vulkan.
- Includes bindings and examples for iOS, Android, WebAssembly, Java, and command-line usage.
Where it falls short
- Requires command-line builds or compilation steps to set up and integrate.
- Accepts only 16-bit WAV files directly in the example CLI, requiring prior FFmpeg conversion for other formats.
- Demands hardware resource allocation and local system memory depending on the Whisper model size chosen.
Pricing & license
whisper.cpp is completely free and open source under the MIT licence. There are no paid hosted tiers offered by the project, so your only cost is the hardware infrastructure you run it on.
Open remote AI-training roles
876 open remote AI-training roles, updated November 15, 2026; most pay $20–$170 an hour. Zekai lists them free; the application goes to the employer’s platform.
Alternatives
These reviewed AI tools do the same job instantly — from our scored directory.
Use it, or have us set it up
FAQ
Do I need Python to run whisper.cpp?
No, the core inference engine runs entirely in C/C++ without Python runtime dependencies.
What audio formats does the command-line interface accept?
The included whisper-cli tool specifically requires 16-bit WAV audio files.
