whisper.cpp

What's Changed

• ci : add GGML_NATIVE=OFF and GGML_BMI2=OFF to windows-blas by @danbev in https://github.com/ggml-org/whisper.cpp/pull/3891
• release : v1.9.1 by @danbev in https://github.com/ggml-org/whisper.cpp/pull/3892

Full Changelog: https://github.com/ggml-org/whisper.cpp/compare/v1.9.0...v1.9.1

whisper.cpp

whisper.cpp

Actions Status

License: MIT

Conan Center

npm

Stable: v1.9.1 / Roadmap

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
• OpenVINO Support
• 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:

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 |

  ...

The first run on a device is slow, since the ANE service compiles the Core ML model to some device-specific format.

Next runs are faster.

For more information about the Core ML implementation please refer to PR #566.

OpenVINO support

On platforms that support OpenVINO, the Encoder inference can be executed

on OpenVINO-supported devices including x86 CPUs and Intel GPUs (integrated & discrete).

This can result in significant speedup in encoder performance. Here are the instructions for generating the OpenVINO model and using it with whisper.cpp:

• First, setup python virtual env. and install python dependencies. Python 3.10 is recommended.

Windows:

  cd models
  python -m venv openvino_conv_env
  openvino_conv_env\Scripts\activate
  python -m pip install --upgrade pip
  pip install -r requirements-openvino.txt

Linux and macOS:

  cd models
  python3 -m venv openvino_conv_env
  source openvino_conv_env/bin/activate
  python -m pip install --upgrade pip
  pip install -r requirements-openvino.txt

• Generate an OpenVINO encoder model. For example, to generate a base.en model, use:

  python convert-whisper-to-openvino.py --model base.en

This will produce ggml-base.en-encoder-openvino.xml/.bin IR model files. It's recommended to relocate these to the same folder as ggml models, as that

is the default location that the OpenVINO extension will search at runtime.

• Build whisper.cpp with OpenVINO support:

Download OpenVINO package from release page. The recommended version to use is 2024.6.0. Ready to use Binaries of the required libraries can be found in the OpenVino Archives

After downloading & extracting package onto your development system, set up required environment by sourcing setupvars script. For example:

Linux:

  source /path/to/l_openvino_toolkit_ubuntu22_2023.0.0.10926.b4452d56304_x86_64/setupvars.sh

Windows (cmd):

  C:\Path\To\w_openvino_toolkit_windows_2023.0.0.10926.b4452d56304_x86_64\setupvars.bat

And then build the project using cmake:

  cmake -B build -DWHISPER_OPENVINO=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_ctx_init_openvino_encoder: loading OpenVINO model from 'models/ggml-base.en-encoder-openvino.xml'
  whisper_ctx_init_openvino_encoder: first run on a device may take a while ...
  whisper_openvino_init: path_model = models/ggml-base.en-encoder-openvino.xml, device = GPU, cache_dir = models/ggml-base.en-encoder-openvino-cache
  whisper_ctx_init_openvino_encoder: OpenVINO model loaded

  system_info: n_threads = 4 / 8 | AVX = 1 | AVX2 = 1 | AVX512 = 0 | FMA = 1 | NEON = 0 | ARM_FMA = 0 | F16C = 1 | FP16_VA = 0 | WASM_SIMD = 0 | BLAS = 0 | SSE3 = 1 | VSX = 0 | COREML = 0 | OPENVINO = 1 |

  ...

The first time run on an OpenVINO device is slow, since the OpenVINO framework will compile the IR (Intermediate Representation) model to a device-specific 'blob'. This device-specific blob will get

cached for the next run.

For more information about the OpenVINO implementation please refer to PR #1037.

NVIDIA GPU support

With NVIDIA cards the processing of the models is done efficiently on the GPU via cuBLAS and custom CUDA kernels.

First, make sure you have installed cuda: https://developer.nvidia.com/cuda-downloads

Now build whisper.cpp with CUDA support:

cmake -B build -DGGML_CUDA=1
cmake --build build -j --config Release

or for newer NVIDIA GPU's (RTX 5000 series):

cmake -B build -DGGML_CUDA=1 -DCMAKE_CUDA_ARCHITECTURES="86"
cmake --build build -j --config Release

Vulkan GPU support

Cross-vendor solution which allows you to accelerate workload on your GPU.

First, make sure your graphics card driver provides support for Vulkan API.

Now build whisper.cpp with Vulkan support:

cmake -B build -DGGML_VULKAN=1
cmake --build build -j --config Release

AMD ROCm GPU support

With AMD GPUs the processing can be accelerated via HIP/ROCm.

First, make sure you have installed ROCm.

Now build whisper.cpp with HIP support:

cmake -B build -DGGML_HIP=1 -DAMDGPU_TARGETS="gfx1201"
cmake --build build -j --config Release

Replace gfx1201 with your GPU architecture. You can find it with:

rocminfo | grep "gfx"

Common architectures: gfx1100 (RX 7900 XTX), gfx1101 (RX 7800 XT), gfx1201 (RX 9070 XT).

For multiple GPUs with different architectures: -DAMDGPU_TARGETS="gfx1100;gfx1201".

BLAS CPU support via OpenBLAS

Encoder processing can be accelerated on the CPU via OpenBLAS.

First, make sure you have installed openblas: https://www.openblas.net/

Now build whisper.cpp with OpenBLAS support:

cmake -B build -DGGML_BLAS=1
cmake --build build -j --config Release

Ascend NPU support

Ascend NPU provides inference acceleration via CANN and AI cores.

First, check if your Ascend NPU device is supported:

Verified devices

| Ascend NPU | Status |

|:-----------------------------:|:-------:|

| Atlas 300T A2