Inference of OpenAI's Whisper ASR model inside the browser

This example uses a WebAssembly (WASM) port of the 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 so you have to make sure that your browser supports them.

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.

Link: https://whisper.ggerganov.com

# build using Emscripten
git clone https://github.com/ggerganov/whisper.cpp
cd whisper.cpp
mkdir build-em && cd build-em
emcmake cmake ..
make -j

The example can then be started by running a local HTTP server:

python3 examples/server.py

And then opening a browser to the following URL: http://localhost:8000/whisper.wasm

To run the example in a different server, you need to copy the following files to the server's HTTP path:

# copy the produced page to your HTTP path
cp bin/whisper.wasm/*    /path/to/html/
cp bin/libmain.worker.js /path/to/html/