whisper.cpp

whisper.cpp 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 Ascend NPU Support Moore Threads GPU Support C-style API Voice Activity Detection (VAD) Supported platforms: Mac OS (Intel and Arm) iOS Android Java Linux / FreeBSD WebAssembly Windows (MSVC and MinGW) Raspberry Pi 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 whisper-iphone-13-mini-2.mp4 You can also easily make your own offline voice assistant application: command command-0.mp4 On Apple Silicon, the inference runs fully on the GPU via Metal: metal-base-1.mp4 Quick start First clone the repository: git clone https://github.com/ggml-org/whisper.cpp.git Navigate into the directory: cd whisper.cpp Then, download one of the Whisper models converted in ggml format. For example: sh ./models/download-ggml-model.sh base.en Now build the whisper-cli example and transcribe an audio file like this: # build the project cmake -B build cmake --build build -j --config Release # transcribe an audio file ./build/bin/whisper-cli -f samples/jfk.wav 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: ffmpeg -i input.mp3 -ar 16000 -ac 1 -c:a pcm_s16le output.wav More audio samples If you want some extra audio samples to play with, simply run: make -j samples 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: make -j tiny.en make -j tiny make -j base.en make -j base make -j small.en make -j small make -j medium.en make -j medium make -j large-v1 make -j large-v2 make -j large-v3 make -j large-v3-turbo Memory usage Model Disk Mem tiny 75 MiB ~273 MB base 142 MiB ~388 MB small 466 MiB ~852 MB medium 1.5 GiB ~2.1 GB large 2.9 GiB ~3.9 GB 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: # build with GGML_BLAS defined cmake -B build -DGGML_BLAS=1 cmake --build build -j --config Release ./build/bin/whisper-cli [ .. etc .. ] Quantization whisper.cpp supports integer quantization of the Whisper ggml models. Quantized models require less memory and disk space and depending on the hardware can be processed more efficiently. Here are the steps for creating and using a quantized model: # quantize a model with Q5_0 method cmake -B build cmake --build build -j --config Release ./build/bin/quantize models/ggml-base.en.bin models/ggml-base.en-q5_0.bin q5_0 # run the examples as usual, specifying the quantized model file ./build/bin/whisper-cli -m models/ggml-base.en-q5_0.bin ./samples/gb0.wav Core ML support On Apple Silicon devices, the Encoder inference can be executed on the Apple Neural Engine (ANE) via Core ML. This can result in significant speed-up - more than x3 faster compared with CPU-only execution. Here are the instructions for generating a Core ML model and using it with whisper.cpp: Install Python dependencies needed for the creation of the Core ML model: pip install ane_transformers pip install openai-whisper pip install coremltools To ensure coremltools operates correctly, please confirm that Xcode is installed and execute xcode-select --install to install the command-line tools. Python 3.11 is recommended. MacOS Sonoma (version 14) or newer is recommended, as older versions of MacOS might experience issues with transcription hallucination. [OPTIONAL] It is recommended to utilize a Python version management system, such as Miniconda for this step: To create an environment, use: conda create -n py311-whisper python=3.11 -y To activate the environment, use: conda activate py311-whisper Generate a Core ML model. For example, to generate a base.en model, use: ./models/generate-coreml-model.sh base.en This will generate the folder models/ggml-base.en-encoder.mlmodelc Build whisper.cpp with Core ML support: # using CMake cmake -B build -DWHISPER_COREML=1 cmake --build build -j --config Release Run the examples as usual. For example: $ ./build/bin/whisper-cli -m models/ggml-base.en.bin -f samples/jfk.wav ... whisper_init_state: loading Core ML model from 'models/ggml-base.en-encoder.mlmodelc' whisper_init_state: first run on a device may take a while ... whisper_init_state: Core ML model loaded system_info: n_threads = 4 / 10 | AVX = 0 | AVX2 = 0 | AVX512 = 0 | FMA = 0 | NEON = 1 | ARM_FMA = 1 | F16C = 0 | FP16_VA = 1 | WASM_SIMD = 0 | BLAS = 1 | SSE3 = 0 | VSX = 0 | COREML = 1 | ... T