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4abd756 4312a39 432646b bee1ba7 432646b 648dff8 1f461ff bee1ba7 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 | # whisper.wasm
Inference of [OpenAI's Whisper ASR model](https://github.com/openai/whisper) inside the browser
This example uses a WebAssembly (WASM) port of the [whisper.cpp](https://github.com/ggerganov/whisper.cpp)
implementation of the transformer to run the inference inside a web page. The audio data does not leave your computer -
it is processed locally on your machine. The performance is not great but you should be able to achieve x2 or x3
real-time for the `tiny` and `base` models on a modern CPU and browser (i.e. transcribe a 60 seconds audio in about
~20-30 seconds).
This WASM port utilizes [WASM SIMD 128-bit intrinsics](https://emcc.zcopy.site/docs/porting/simd/) so you have to make
sure that [your browser supports them](https://webassembly.org/roadmap/).
The example is capable of running all models up to size `small` inclusive. Beyond that, the memory requirements and
performance are unsatisfactory. The implementation currently support only the `Greedy` sampling strategy. Both
transcription and translation are supported.
Since the model data is quite big (74MB for the `tiny` model) you need to manually load the model into the web-page.
The example supports both loading audio from a file and recording audio from the microphone. The maximum length of the
audio is limited to 120 seconds.
## Live demo
Link: https://whisper.ggerganov.com
## Build instructions
```bash (v3.1.2)
# build using Emscripten
git clone https://github.com/ggerganov/whisper.cpp
cd whisper.cpp
mkdir build-em && cd build-em
emcmake cmake ..
make -j
# copy the produced page to your HTTP path
cp bin/whisper.wasm/* /path/to/html/
cp bin/libmain.worker.js /path/to/html/
```
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