Port of OpenAI's Whisper model in C/C++
Hi HN, OpenAI recently released a model for automatic speech recognition called Whisper [0]. I decided to reimplement the inference of the model from scratch using C/C++. To achieve this I implemented a minimalistic tensor library in C and ported the high-level architecture of the model in C++. The entire code is less than 8000 lines of code and is contained in just 2 source files without any third-party dependencies. The Github project is here: https://github.com/ggerganov/whisper.cpp With this implementation I can very easily build and run the model - “make…
In plain words
Whisper.cpp is a C/C++ implementation of OpenAI's Whisper automatic speech recognition model. It enables developers to run speech-to-text inference with minimal dependencies—just two source files totaling under 8,000 lines of code. The project supports multiple platforms including iPhones, Raspberry Pi, and web browsers via WebAssembly, using CPU-only inference with optimizations for various processor architectures.
written from the facts on this page · September 2026
From the sources
In the maker’s words, at launch
Hi HN, OpenAI recently released a model for automatic speech recognition called Whisper [0]. I decided to reimplement the inference of the model from scratch using C/C++. To achieve this I implemented a minimalistic tensor library in C and ported the high-level architecture of the model in C++. The entire code is less than 8000 lines of code and is contained in just 2 source files without any third-party dependencies. The Github project is here: https://github.com/ggerganov/whisper.cpp With this implementation I can very easily build and run the model - “make base.en”. It also allows me to run it on a wide range of devices. For example, I have provided examples of running the model on an iPhone, Raspberry Pi 4 and even in a web page via WebAssembly! The implementation runs fully on the CPU and utilizes FP16, AVX intrinsics on x86 architectures and NEON + Accelerate framework on Apple Silicon. The latter is especially efficient and I observe that the inference is about 2-3 times faster compared to the current PyTorch implementation provided by OpenAI when running it on my MacBook M1 Pro. The WASM port utilizes SIMD 128-bit intrinsics - a feature supported in some modern web browsers [1]. I am very happy with the performance that I observe on Apple Silicon devices. I didn’t expect that the Accelerate framework [2] (i.e. CBLAS) offers such a dramatic performance boost for matrix multiplications so I was very pleasantly surprised! To enable the framework in your C/C++ projects, all you have to do is add `-framework Accelerate` to your clang command-line flags. This entire exercise of implementing the Whisper model was very interesting to me and helped me understand a lot about how the transformer architecture works. I also got a lot of positive feedback from people finding and using my project. We brainstormed on a lot of interesting tools that can potentially be created with this library (such as speech-to-text plugin for Vim, RPi4 voice assistant, WASM chat bot, etc). If interested, checkout the “Examples” section and the “Show and tell” discussions for some ideas! Would love to know what you think about this project and about your experience with using the Accelerate framework in any of your projects. Cheers! [0] https://github.com/openai/whisper [1] https://chromestatus.com/feature/6533147810332672 [2] https://developer.apple.com/documentation/accelerate
Does a similar job
all alternatives →- WPWindows port of OpenAI's Whisper automatic speech recognition model2023 · github.com · ▲43
This project is a Windows port of the whisper.cpp implementation: https://github.com/ggerganov/whisper.cpp Which in turn is a C++ port of OpenAI's Whisper automatic speech recognition (ASR) model: https://github.com/openai/whisper The implementation has no dependencies, usually much faster than realtime, and should hopefully work on most Windows computers in the world.
- WOWhispering – Open-source, local-first dictation you can trust2025 · github.com · ▲591
Hey HN! Braden here, creator of Whispering, an open-source speech-to-text app. I really like dictation. For years, I relied on transcription tools that were almost good, but they were all closed-source. Even a lot of them that claimed to be “local” or “on-device” were still black boxes that left me wondering where my audio really went. So I built Whispering. It’s open-source, local-first, and most importantly, transparent with your data. Your data is stored locally on your device, and your audio goes directly from your machine to a local provider (Whisper C++, Speaches, etc.) or your chosen…
- MOMoonshine Open-Weights STT models – higher accuracy than WhisperLargev3Feb 2026 · github.com · ▲316
I wanted to share our new speech to text model, and the library to use them effectively. We're a small startup (six people, sub-$100k monthly GPU budget) so I'm proud of the work the team has done to create streaming STT models with lower word-error rates than OpenAI's largest Whisper model. Admittedly Large v3 is a couple of years old, but we're near the top the HF OpenASR leaderboard, even up against Nvidia's Parakeet family. Anyway, I'd love to get feedback on the models and software, and hear about what people might build with it.
- IMI made a free transcription service powered by Whisper AI2022 · freesubtitles.ai · ▲224
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