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index.html
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<html>
<head>
<meta charset="utf-8">
<script src="https://code.jquery.com/jquery-3.5.1.slim.min.js" integrity="sha384-DfXdz2htPH0lsSSs5nCTpuj/zy4C+OGpamoFVy38MVBnE+IbbVYUew+OrCXaRkfj" crossorigin="anonymous"></script>
<script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs/dist/tf.min.js"></script>
<link rel="stylesheet" href="https://stackpath.bootstrapcdn.com/bootstrap/4.5.0/css/bootstrap.min.css" integrity="sha384-9aIt2nRpC12Uk9gS9baDl411NQApFmC26EwAOH8WgZl5MYYxFfc+NcPb1dKGj7Sk" crossorigin="anonymous">
<title>Speech Recognition in browser</title>
<script>
const WIDTH = 800;
const HEIGHT = 100;
const fftSize = 256;
const fftResult = Math.floor(fftSize/2)+1;
const enDic = ['down', 'go', 'left', 'no', 'right', 'stop', 'up', 'yes'];
let spectroOffset = 0;
let modelEn = null;
let captureStream = null;
let source = null;
let analyser = null;
let audioCtx = null;
let stop = true;
const blockSize = 128/4;
const maxLength = 11;
let silenceCount = 0;
let buffer = [];
let interval = null;
let timeFromLastReco = 0;
function fillBuffer()
{
buffer=[];
for(i=0;i < maxLength * blockSize;i++)
{
buffer.push(new Array(fftResult).fill(0));
}
}
async function loadModel()
{
modelEn = await tf.loadGraphModel('http://localhost:3000/quantized_model_en/model.json');
}
async function predictWord()
{
console.log("buffer:", buffer);
let newBufferEn = [];
for(i=buffer.length;i>0;)
{
let newBlock = [];
for(j=0;j<blockSize;j++)
{
if(buffer[i-1-j])
{
newBlock.unshift(buffer[i-1-j]);
}
else {
newBlock.unshift(new Array(fftResult).fill(0));
}
}
if(newBufferEn.length>=maxLength) {break;}
newBufferEn.unshift(newBlock);
i-=blockSize/2;
}
let tensorEn = tf.tensor(newBufferEn).expandDims(-1);
tensorEn = tf.where(tf.isInf(tensorEn), tf.zerosLike(tensorEn), tensorEn);
console.log(tensorEn);
let predictionEn = await modelEn.executeAsync(tensorEn.expandDims(0));
predictionEn = predictionEn.squeeze();
const predictionArrayEn = await predictionEn.array();
const wordEn = await predictionEn.argMax(-1).array();
console.log(predictionArrayEn, wordEn, predictionArrayEn[wordEn], enDic[wordEn]);
if(predictionArrayEn[wordEn]>0.6)
{
$('#recognitionResult').append($('<p></p>').html("en: " + enDic[wordEn] + " " + predictionArrayEn[wordEn]));
}
}
function catchData()
{
const spectrum = new Float32Array(analyser.frequencyBinCount);
analyser.getFloatFrequencyData(spectrum);
let arrayData = Array.from(spectrum);
arrayData.push(arrayData[arrayData.length-1]);
buffer.push(arrayData);
const volume = (arrayData[0] + arrayData[1] + arrayData[2] + arrayData[3])/4;
if(volume < -60)
{
//console.log("Silence skip:", volume);
silenceCount++;
if(silenceCount>10)
{
timeFromLastReco=0;
}
return;
}
silenceCount=0;
timeFromLastReco++;
if(buffer.length > (maxLength* blockSize) && (timeFromLastReco>30))
{
timeFromLastReco=0;
buffer = buffer.slice(buffer.length - (maxLength*blockSize), buffer.length);
predictWord();
}
}
function catchDataFile()
{
const spectrum = new Float32Array(analyser.frequencyBinCount);
analyser.getFloatFrequencyData(spectrum);
let arrayData = Array.from(spectrum);
arrayData.push(arrayData[arrayData.length-1]);
buffer.push(arrayData);
}
function catchDataRec()
{
interval = setInterval(function(){ catchData() }, 8);
}
function catchDataRecFile()
{
interval = setInterval(function(){ catchDataFile() }, 8);
}
async function drawSignal()
{
if(stop) return;
let bufferLength = analyser.frequencyBinCount;
let dataArray = new Uint8Array(bufferLength);
let canvas = document.getElementById('oscilo');
let canvasCtx = canvas.getContext('2d');
let drawVisual = requestAnimationFrame(drawSignal);
analyser.getByteTimeDomainData(dataArray);
canvasCtx.fillStyle = 'rgb(200, 200, 200)';
canvasCtx.fillRect(0, 0, WIDTH, HEIGHT);
canvasCtx.lineWidth = 2;
canvasCtx.strokeStyle = 'rgb(0, 0, 0)';
canvasCtx.beginPath();
let sliceWidth = WIDTH * 1.0 / bufferLength;
let x = 0;
for(let i = 0; i < bufferLength; i++) {
let v = dataArray[i] / 128.0;
let y = v * HEIGHT/2;
if(i === 0) {
canvasCtx.moveTo(x, y);
} else {
canvasCtx.lineTo(x, y);
}
x += sliceWidth;
}
canvasCtx.lineTo(canvas.width, canvas.height/2);
canvasCtx.stroke();
}
async function drawSpect()
{
if(stop) return;
const spectrum = new Uint8Array(analyser.frequencyBinCount);
analyser.getByteFrequencyData(spectrum);
const spectroCanvas = document.getElementById('spectrogram');
const spectroContext = spectroCanvas.getContext('2d');
requestAnimationFrame(drawSpect);
const slice = spectroContext.getImageData(0, spectroOffset, spectroCanvas.width, 1);
for (let i = 0; i < spectrum.length; i++) {
slice.data[4 * i + 0] = spectrum[i] // R
slice.data[4 * i + 1] = spectrum[i] // G
slice.data[4 * i + 2] = spectrum[i] // B
slice.data[4 * i + 3] = 255 // A
}
spectroContext.putImageData(slice, 0, spectroOffset);
spectroOffset += 1;
spectroOffset %= spectroCanvas.height;
}
async function startAudioCapture()
{
console.log("startAudioCapture");
stop = false;
let constraints = {audio: {channelCount:1, echoCancellation:true, noiseSuppression:true, sampleRate:16000}, video: false};
try {
captureStream = await navigator.mediaDevices.getUserMedia(constraints);
console.log("captureStream: ", captureStream);
console.log("getAudioTracks: ", captureStream.getAudioTracks());
const audio_track = captureStream.getAudioTracks()[0];
console.log("Use mic: ", audio_track.label);
audioCtx = new (window.AudioContext || window.webkitAudioContext)({"sampleRate":16000});
console.log("audioCtx.sampleRate:", audioCtx.sampleRate);
analyser = audioCtx.createAnalyser();
source = audioCtx.createMediaStreamSource(captureStream);
source.connect(analyser);
analyser.fftSize = fftSize;
analyser.smoothingTimeConstant = 0;
console.log("frequencyBinCount: ", analyser.frequencyBinCount)
drawSignal();
drawSpect();
catchDataRec();
} catch(err) {
console.error("Error: " + err);
}
}
$(document).ready(function() {
loadModel();
fillBuffer();
$("#startAudioCapture").click(function() {
startAudioCapture();
});
$(".fileToWord").click(function() {
const filename=$(this).attr('file');
const sr = $(this).attr('sr');
fillBuffer();
stop=false;
audioCtx = new (window.AudioContext || window.webkitAudioContext)({"sampleRate":sr});
source = audioCtx.createBufferSource();
analyser = audioCtx.createAnalyser();
source.connect(analyser);
source.onended = function(){
clearInterval(interval);
source.stop(0);
stop=true;
predictWord();
};
analyser.fftSize = fftSize;
analyser.smoothingTimeConstant = 0;
console.log("frequencyBinCount: ", analyser.frequencyBinCount);
let request = new XMLHttpRequest();
request.open('GET', filename, true);
request.responseType = 'arraybuffer';
request.onload = function() {
let audioData = request.response;
audioCtx.decodeAudioData(audioData, function(buffer) {
source.buffer = buffer;
source.connect(audioCtx.destination);
source.loop = false;
console.log(audioCtx.sampleRate);
const spectrum = new Uint8Array(analyser.frequencyBinCount);
analyser.getByteFrequencyData(spectrum);
const spectroCanvas = document.getElementById('spectrogram');
console.log(spectrum.length)
spectroCanvas.width = spectrum.length;
spectroCanvas.height = 200;
source.start(0);
catchDataRecFile();
drawSignal();
drawSpect();
},
function(e){"Error with decoding audio data" + e.err});
}
request.send();
});
$("#stopAudioCapture").click(function() {
console.log("stopAudioCapture");
stop=true;
clearInterval(interval);
if(audioCtx!=null) audioCtx.close();
if(captureStream!=null) captureStream.getAudioTracks().forEach(function(track) {if (track.readyState == 'live') {track.stop();}});
});
});
</script>
</head>
<body>
<div class="container">
<div class="jumbotron">
<h1>Speech Recognition in browser</h1>
<div class="align-center">
<a id="startAudioCapture" class="btn btn-primary">Share mic</a>
<a id="stopAudioCapture" class="btn btn-primary">Stop mic</a>
<a class="fileToWord btn btn-primary" file="clips/00f0204f_nohash_0.wav" sr="16000">File to word (down)</a>
<a class="fileToWord btn btn-primary" file="clips/0b40aa8e_nohash_0.wav" sr="16000">File to word (yes)</a>
<a class="fileToWord btn btn-primary" file="clips/1c6e5447_nohash_0.wav" sr="16000">File to word (no)</a>
<a class="fileToWord btn btn-primary" file="clips/2a89ad5c_nohash_0.wav" sr="16000">File to word (left)</a>
<a class="fileToWord btn btn-primary" file="clips/0a9f9af7_nohash_0.wav" sr="16000">File to word (go)</a>
</div>
<hr>
<canvas id="oscilo" width="800" height="100"></canvas>
<canvas id="spectrogram" width="1024" height="400"></canvas>
<h2>Recognition results</h2>
<p id="recognitionResult"></p>
</div>
</div>
</body>
</html>