Node.js package for Whisper speech recognition
Package: https://www.npmjs.com/package/whisper.cpp
The performance is comparable to when running whisper.cpp in the browser via WASM.
The API is currently very rudimentary: bindings/javascript/emscripten.cpp
For sample usage check tests/test-whisper.js
# load emscripten
source /path/to/emsdk/emsdk_env.sh
# clone repo
git clone https://github.com/ggerganov/whisper.cpp
cd whisper.cpp
# grab base.en model
./models/download-ggml-model.sh base.en
# prepare PCM sample for testing
ffmpeg -i samples/jfk.wav -f f32le -acodec pcm_f32le samples/jfk.pcmf32
# build
mkdir build-em && cd build-em
emcmake cmake .. && make -j
# run test
node --experimental-wasm-threads --experimental-wasm-simd ../tests/test-whisper.js
# publish npm package
make publish-npm$ node --experimental-wasm-threads --experimental-wasm-simd ../tests/test-whisper.js
whisper_model_load: loading model from 'whisper.bin'
whisper_model_load: n_vocab = 51864
whisper_model_load: n_audio_ctx = 1500
whisper_model_load: n_audio_state = 512
whisper_model_load: n_audio_head = 8
whisper_model_load: n_audio_layer = 6
whisper_model_load: n_text_ctx = 448
whisper_model_load: n_text_state = 512
whisper_model_load: n_text_head = 8
whisper_model_load: n_text_layer = 6
whisper_model_load: n_mels = 80
whisper_model_load: f16 = 1
whisper_model_load: type = 2
whisper_model_load: adding 1607 extra tokens
whisper_model_load: mem_required = 506.00 MB
whisper_model_load: ggml ctx size = 140.60 MB
whisper_model_load: memory size = 22.83 MB
whisper_model_load: model size = 140.54 MB
system_info: n_threads = 8 / 10 | AVX = 0 | AVX2 = 0 | AVX512 = 0 | NEON = 0 | F16C = 0 | FP16_VA = 0 | WASM_SIMD = 1 | BLAS = 0 |
operator(): processing 176000 samples, 11.0 sec, 8 threads, 1 processors, lang = en, task = transcribe ...
[00:00:00.000 --> 00:00:11.000] And so my fellow Americans, ask not what your country can do for you, ask what you can do for your country.
whisper_print_timings: load time = 162.37 ms
whisper_print_timings: mel time = 183.70 ms
whisper_print_timings: sample time = 4.27 ms
whisper_print_timings: encode time = 8582.63 ms / 1430.44 ms per layer
whisper_print_timings: decode time = 436.16 ms / 72.69 ms per layer
whisper_print_timings: total time = 9370.90 ms