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44 lines
1.8 KiB
44 lines
1.8 KiB
# whisper.wasm
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Inference of [OpenAI's Whisper ASR model](https://github.com/openai/whisper) inside the browser
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This example uses a WebAssembly (WASM) port of the [whisper.cpp](https://github.com/ggerganov/whisper.cpp)
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implementation of the transformer to run the inference inside a web page. The audio data does not leave your computer -
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it is processed locally on your machine. The performance is not great but you should be able to achieve x2 or x3
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real-time for the `tiny` and `base` models on a modern CPU and browser (i.e. transcribe a 60 seconds audio in about
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~20-30 seconds).
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This WASM port utilizes [WASM SIMD 128-bit intrinsics](https://emcc.zcopy.site/docs/porting/simd/) so you have to make
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sure that [your browser supports them](https://webassembly.org/roadmap/).
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The example is capable of running all models up to size `small` inclusive. Beyond that, the memory requirements and
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performance are unsatisfactory. The implementation currently support only the `Greedy` sampling strategy. Both
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transcription and translation are supported.
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Since the model data is quite big (74MB for the `tiny` model) you need to manually load the model into the web-page.
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The example supports both loading audio from a file and recording audio from the microphone. The maximum length of the
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audio is limited to 120 seconds.
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## Live demo
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Link: https://whisper.ggerganov.com
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## Build instructions
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```bash
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# build using Emscripten
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git clone https://github.com/ggerganov/whisper.cpp
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cd whisper.cpp
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mkdir build-em && cd build-em
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emcmake cmake ..
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make -j
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# copy the produced page to your HTTP path
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cp bin/whisper.wasm/index.html /path/to/html/
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cp bin/whisper.wasm/whisper.js /path/to/html/
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cp bin/libwhisper.worker.js /path/to/html/
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