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Update index.html
Browse files- index.html +161 -41
index.html
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<meta charset="UTF-8">
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<meta name="viewport" content="width=device-width, initial-scale=1.0">
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<title>Audio Transcription and Similarity Checker</title>
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<style>
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body {
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font-family:
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background-color: #f4f4f4;
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padding: 20px;
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}
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.container {
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max-width:
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margin: 0 auto;
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background: #fff;
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padding:
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}
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h1 {
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text-align: center;
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}
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.button {
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background-color: #
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color: white;
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padding: 10px 20px;
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text-align: center;
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cursor: pointer;
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border: none;
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display: block;
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width: 100%;
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}
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text-align: center;
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}
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.result {
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margin-top: 20px;
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}
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</style>
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</head>
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<body>
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<div class="container">
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<h1>Audio Transcription and Similarity Checker</h1>
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<h2>Upload Original Audio</h2>
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<input type="file" id="originalFile" accept="audio/*">
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</div>
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<div id="user-audio" class="audio-
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<h2>Upload User Audio</h2>
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<input type="file" id="userFile" accept="audio/*">
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</div>
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<button id="transcribeButton" class="button">Perform Transcription and Testing</button>
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<div id="result" class="result"></div>
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</div>
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@@ -62,6 +121,8 @@
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<script>
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const MODEL_ID = "facebook/wav2vec2-large-960h"; // Sample model, change if necessary
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let processor, model;
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// Load model and processor
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async function loadModel() {
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model = await transformers.Wav2Vec2ForCTC.from_pretrained(MODEL_ID);
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}
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const audioData = new Float32Array(arrayBuffer);
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const inputValues = processor(audioData, {return_tensors: "pt", padding: true}).input_values;
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const logits = await model(inputValues).logits;
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const predicted_ids = logits.argmax(-1);
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}
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document.getElementById("transcribeButton").addEventListener("click", async () => {
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}
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} else {
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alert("Please upload both audio files.");
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}
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});
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loadModel();
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</script>
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</body>
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</html>
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<meta charset="UTF-8">
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<meta name="viewport" content="width=device-width, initial-scale=1.0">
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<title>Audio Transcription and Similarity Checker</title>
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<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.15.3/css/all.min.css">
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<style>
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body {
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font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif;
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background-color: #f4f4f4;
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padding: 20px;
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}
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.container {
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max-width: 800px;
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margin: 0 auto;
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background: #fff;
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padding: 30px;
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border-radius: 10px;
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box-shadow: 0 4px 8px rgba(0, 0, 0, 0.1);
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}
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h1 {
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text-align: center;
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margin-bottom: 30px;
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}
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.audio-section {
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text-align: center;
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margin-bottom: 20px;
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}
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input[type="file"] {
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display: none;
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}
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.upload-btn {
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background-color: #007bff;
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color: white;
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padding: 10px 20px;
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cursor: pointer;
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border-radius: 5px;
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margin: 10px;
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border: none;
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display: inline-block;
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}
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.button {
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background-color: #28a745;
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color: white;
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padding: 10px 20px;
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cursor: pointer;
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border: none;
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border-radius: 5px;
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margin-top: 20px;
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display: block;
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width: 100%;
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font-size: 16px;
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}
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.button:hover {
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background-color: #218838;
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}
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#progress-bar {
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width: 0;
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height: 20px;
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background-color: #4caf50;
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text-align: center;
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line-height: 20px;
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color: white;
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border-radius: 5px;
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display: none;
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}
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#progress-container {
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width: 100%;
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background-color: #ddd;
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border-radius: 5px;
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margin-top: 20px;
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}
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.result {
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margin-top: 20px;
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}
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.recorder {
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cursor: pointer;
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background-color: #dc3545;
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color: white;
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padding: 10px 20px;
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border-radius: 50%;
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font-size: 24px;
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display: inline-block;
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margin-top: 20px;
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}
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</style>
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</head>
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<body>
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<div class="container">
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<h1>Audio Transcription and Similarity Checker</h1>
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<div id="original-audio" class="audio-section">
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<h2>Upload Original Audio</h2>
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<label class="upload-btn" for="originalFile">Choose Audio File</label>
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<input type="file" id="originalFile" accept="audio/*">
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<div id="originalRecorder" class="recorder">
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<i class="fas fa-microphone"></i>
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</div>
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</div>
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<div id="user-audio" class="audio-section">
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<h2>Upload User Audio</h2>
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<label class="upload-btn" for="userFile">Choose Audio File</label>
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<input type="file" id="userFile" accept="audio/*">
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<div id="userRecorder" class="recorder">
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<i class="fas fa-microphone"></i>
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</div>
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</div>
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<button id="transcribeButton" class="button">Perform Transcription and Testing</button>
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<div id="progress-container">
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<div id="progress-bar">0%</div>
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</div>
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<div id="result" class="result"></div>
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</div>
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<script>
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const MODEL_ID = "facebook/wav2vec2-large-960h"; // Sample model, change if necessary
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let processor, model;
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let originalAudioBlob = null;
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let userAudioBlob = null;
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// Load model and processor
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async function loadModel() {
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model = await transformers.Wav2Vec2ForCTC.from_pretrained(MODEL_ID);
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}
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// Simulate progress bar loading
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function updateProgressBar(percentComplete) {
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const progressBar = document.getElementById("progress-bar");
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progressBar.style.width = percentComplete + "%";
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progressBar.innerHTML = percentComplete + "%";
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if (percentComplete === 100) {
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setTimeout(() => {
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progressBar.style.display = "none";
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progressBar.style.width = "0%";
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}, 500);
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} else {
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progressBar.style.display = "block";
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}
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}
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async function transcribe(audioBlob) {
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const arrayBuffer = await audioBlob.arrayBuffer();
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const audioData = new Float32Array(arrayBuffer);
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const inputValues = processor(audioData, {return_tensors: "pt", padding: true}).input_values;
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const logits = await model(inputValues).logits;
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const predicted_ids = logits.argmax(-1);
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}
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document.getElementById("transcribeButton").addEventListener("click", async () => {
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if (!originalAudioBlob || !userAudioBlob) {
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alert("Please upload or record both audio files.");
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return;
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}
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updateProgressBar(0);
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let percentComplete = 0;
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const progressInterval = setInterval(() => {
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percentComplete += 10;
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updateProgressBar(percentComplete);
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if (percentComplete >= 100) clearInterval(progressInterval);
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}, 200);
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const transcriptionOriginal = await transcribe(originalAudioBlob);
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const transcriptionUser = await transcribe(userAudioBlob);
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clearInterval(progressInterval);
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updateProgressBar(100);
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const levenshteinDistance = (a, b) => {
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let dp = Array.from({length: a.length + 1}, () => Array(b.length + 1).fill(0));
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for (let i = 0; i <= a.length; i++) dp[i][0] = i;
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for (let j = 0; j <= b.length; j++) dp[0][j] = j;
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for (let i = 1; i <= a.length; i++) {
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for (let j = 1; j <= b.length; j++) {
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dp[i][j] = a[i - 1] === b[j - 1] ? dp[i - 1][j - 1] : Math.min(dp[i - 1][j], dp[i][j - 1], dp[i - 1][j - 1]) + 1;
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}
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}
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return dp[a.length][b.length];
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};
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const similarityScore = 1 - levenshteinDistance(transcriptionOriginal, transcriptionUser) / Math.max(transcriptionOriginal.length, transcriptionUser.length);
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document.getElementById("result").innerHTML = `
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<h2>Transcription Results</h2>
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<p><strong>Original Transcription:</strong> ${transcriptionOriginal}</p>
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<p><strong>User Transcription:</strong> ${transcriptionUser}</p>
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<p><strong>Levenshtein Similarity Score:</strong> ${similarityScore.toFixed(2)}</p>
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`;
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});
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// Initialize model
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loadModel();
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// Handle voice recording (using browser APIs)
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const recordAudio = () => {
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return new Promise(async resolve => {
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const stream = await navigator.mediaDevices.getUserMedia({ audio: true });
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const mediaRecorder = new MediaRecorder(stream);
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const audioChunks = [];
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mediaRecorder.addEventListener("dataavailable", event => {
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audioChunks.push(event.data);
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});
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mediaRecorder.addEventListener("stop", () => {
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const audioBlob = new Blob(audioChunks);
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resolve(audioBlob);
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});
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mediaRecorder.start();
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setTimeout(() => {
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mediaRecorder.stop();
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}, 3000); // Record for 3 seconds
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});
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};
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document.getElementById("originalRecorder").addEventListener("click", async () => {
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originalAudioBlob = await recordAudio();
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alert("Original audio recorded!");
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});
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document.getElementById("userRecorder").addEventListener("click", async () => {
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userAudioBlob = await recordAudio();
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alert("User audio recorded!");
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});
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</script>
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</body>
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</html>
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