126 lines
3.5 KiB
JavaScript
126 lines
3.5 KiB
JavaScript
// Copyright (c) 2023-2024 Xiaomi Corporation (authors: Fangjun Kuang)
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const sherpa_onnx = require('sherpa-onnx');
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function createRecognizer() {
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// Please download test files from
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// https://github.com/k2-fsa/sherpa-onnx/releases/tag/asr-models
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const config = {
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'modelConfig': {
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'whisper': {
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'encoder': './sherpa-onnx-whisper-tiny.en/tiny.en-encoder.int8.onnx',
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'decoder': './sherpa-onnx-whisper-tiny.en/tiny.en-decoder.int8.onnx',
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'tailPaddings': 2000,
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},
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'tokens': './sherpa-onnx-whisper-tiny.en/tiny.en-tokens.txt',
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'debug': 0,
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}
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};
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return sherpa_onnx.createOfflineRecognizer(config);
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}
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function createVad() {
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// please download silero_vad.onnx from
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// https://github.com/k2-fsa/sherpa-onnx/releases/download/asr-models/silero_vad.onnx
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const config = {
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sileroVad: {
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model: './silero_vad.onnx',
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threshold: 0.5,
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minSpeechDuration: 0.25,
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minSilenceDuration: 0.5,
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maxSpeechDuration: 5,
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windowSize: 512,
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},
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sampleRate: 16000,
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debug: true,
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numThreads: 1,
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bufferSizeInSeconds: 60,
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};
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return sherpa_onnx.createVad(config);
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}
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const recognizer = createRecognizer();
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const vad = createVad();
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// please download ./Obama.wav from
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// https://github.com/k2-fsa/sherpa-onnx/releases/tag/asr-models
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const waveFilename = './Obama.wav';
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const wave = sherpa_onnx.readWave(waveFilename);
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if (wave.sampleRate != recognizer.config.featConfig.sampleRate) {
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throw new Error(
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'Expected sample rate: ${recognizer.config.featConfig.sampleRate}. Given: ${wave.sampleRate}');
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}
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console.log('Started')
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let start = Date.now();
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const windowSize = vad.config.sileroVad.windowSize;
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for (let i = 0; i < wave.samples.length; i += windowSize) {
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const thisWindow = wave.samples.subarray(i, i + windowSize);
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vad.acceptWaveform(thisWindow);
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while (!vad.isEmpty()) {
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const segment = vad.front();
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vad.pop();
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let start_time = segment.start / wave.sampleRate;
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let end_time = start_time + segment.samples.length / wave.sampleRate;
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start_time = start_time.toFixed(2);
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end_time = end_time.toFixed(2);
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const stream = recognizer.createStream();
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stream.acceptWaveform(wave.sampleRate, segment.samples);
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recognizer.decode(stream);
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const r = recognizer.getResult(stream);
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if (r.text.length > 0) {
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const text = r.text.toLowerCase().trim();
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console.log(`${start_time} -- ${end_time}: ${text}`);
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}
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stream.free();
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}
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}
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vad.flush();
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while (!vad.isEmpty()) {
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const segment = vad.front();
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vad.pop();
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let start_time = segment.start / wave.sampleRate;
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let end_time = start_time + segment.samples.length / wave.sampleRate;
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start_time = start_time.toFixed(2);
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end_time = end_time.toFixed(2);
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const stream = recognizer.createStream();
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stream.acceptWaveform(wave.sampleRate, segment.samples);
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recognizer.decode(stream);
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const r = recognizer.getResult(stream);
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if (r.text.length > 0) {
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const text = r.text.toLowerCase().trim();
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console.log(`${start_time} -- ${end_time}: ${text}`);
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}
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}
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let stop = Date.now();
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console.log('Done')
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const elapsed_seconds = (stop - start) / 1000;
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const duration = wave.samples.length / wave.sampleRate;
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const real_time_factor = elapsed_seconds / duration;
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console.log('Wave duration', duration.toFixed(3), 'seconds')
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console.log('Elapsed', elapsed_seconds.toFixed(3), 'seconds')
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console.log(
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`RTF = ${elapsed_seconds.toFixed(3)}/${duration.toFixed(3)} =`,
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real_time_factor.toFixed(3))
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vad.free();
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recognizer.free();
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