whisper.cpp for Mac

High-performance inference of OpenAI's Whisper automatic speech recognition (ASR) model: Plain C/C++ implementation without dependencies Apple Silicon first-class citizen - optimized via ARM NEON, Accelerate framework, Metal and Core ML AVX intrinsics support for x86 architectures VSX intrinsics support for POWER architectures Mixed F16 / F32 precision Integer quantization support Zero memory allocations at runtime Vulkan support Support for CPU-only inference Efficient GPU support for NVIDIA AMD ROCm GPU support AMD Ryzen AI NPU Support OpenVINO Support Huawei Ascend NPU Support Moore Threads GPU Support C-style API Voice Activity Detection (VAD)

Supported platforms: [x] Mac OS (Intel and Arm) [x] iOS [x] Android [x] Java [x] Linux / FreeBSD [x] WebAssembly [x] Windows (MSVC and MinGW) [x] Raspberry Pi [x] Docker

The entire high-level implementation of the model is contained in whisper.h and whisper.cpp. The rest of the code is part of the ggml machine learning library.

Having such a lightweight implementation of the model allows to easily integrate it in different platforms and applications. As an example, here is a video of running the model on an iPhone 13 device - fully offline, on-device: whisper.objc

https://user-images.githubusercontent.com/1991296/197385372-962a6dea-bca1-4d50-bf96-1d8c27b98c81.mp4

You can also easily make your own offline voice assistant application: command

https://user-images.githubusercontent.com/1991296/204038393-2f846eae-c255-4099-a76d-5735c25c49da.mp4

On Apple Silicon, the inference runs fully on the GPU via Metal:

https://github.com/ggml-org/whisper.cpp/assets/1991296/c82e8f86-60dc-49f2-b048-d2fdbd6b5225

Quick start

First clone the repository:

Navigate into the directory:

Then, download one of the Whisper models converted in ggml format. For example:

Now build the whisper-cli example and transcribe an audio file like this:

For a quick demo, simply run make base.en.

The command downloads the base.en model converted to custom ggml format and runs the inference on all .wav samples in the folder samples.

For detailed usage instructions, run: ./build/bin/whisper-cli -h

Note that the whisper-cli example currently runs only with 16-bit WAV files, so make sure to convert your input before running the tool. For example, you can use ffmpeg like this:

More audio samples

If you want some extra audio samples to play with, simply run:

This will download a few more audio files from Wikipedia and convert them to 16-bit WAV format via ffmpeg.

You can download and run the other models as follows:

Memory usage

POWER VSX Intrinsics

whisper.cpp supports POWER architectures and includes code which significantly speeds operation on Linux running on POWER9/10, making it capable of faster-than-realtime transcription on underclocked Raptor Talos II. Ensure you have a BLAS package installed, and replace the standard cmake setup with:

Quantization