whisper.cpp
High-performance inference of OpenAI's Whisper automatic speech recognition (ASR) model:
Supported platforms:
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:
This is the beginning of the README. Read the rest at the original. Read the full README ↗
