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<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<title>Audio Transcription and Similarity Checker</title>
<style>
body {
font-family: Arial, sans-serif;
background-color: #f4f4f4;
padding: 20px;
}
.container {
max-width: 700px;
margin: 0 auto;
background: #fff;
padding: 20px;
box-shadow: 0 0 10px rgba(0, 0, 0, 0.1);
}
h1 {
text-align: center;
}
.button {
background-color: #e8b62c;
color: white;
padding: 10px 20px;
text-align: center;
cursor: pointer;
border: none;
margin-top: 10px;
display: block;
width: 100%;
}
.audio-upload {
margin-top: 20px;
text-align: center;
}
.result {
margin-top: 20px;
}
</style>
</head>
<body>
<div class="container">
<h1>Audio Transcription and Similarity Checker</h1>
<div id="original-audio" class="audio-upload">
<h2>Upload Original Audio</h2>
<input type="file" id="originalFile" accept="audio/*">
</div>
<div id="user-audio" class="audio-upload">
<h2>Upload User Audio</h2>
<input type="file" id="userFile" accept="audio/*">
</div>
<button id="transcribeButton" class="button">Perform Transcription and Testing</button>
<div id="result" class="result"></div>
</div>
<script src="https://cdn.jsdelivr.net/npm/@huggingface/transformers"></script>
<script>
const MODEL_ID = "facebook/wav2vec2-large-960h"; // Sample model, change if necessary
let processor, model;
// Load model and processor
async function loadModel() {
processor = await transformers.AutoProcessor.from_pretrained(MODEL_ID);
model = await transformers.Wav2Vec2ForCTC.from_pretrained(MODEL_ID);
}
async function transcribe(audioFile) {
const arrayBuffer = await audioFile.arrayBuffer();
const audioData = new Float32Array(arrayBuffer);
const inputValues = processor(audioData, {return_tensors: "pt", padding: true}).input_values;
const logits = await model(inputValues).logits;
const predicted_ids = logits.argmax(-1);
const transcription = processor.decode(predicted_ids, {skip_special_tokens: true});
return transcription;
}
document.getElementById("transcribeButton").addEventListener("click", async () => {
const originalFile = document.getElementById("originalFile").files[0];
const userFile = document.getElementById("userFile").files[0];
if (originalFile && userFile) {
const transcriptionOriginal = await transcribe(originalFile);
const transcriptionUser = await transcribe(userFile);
const levenshteinDistance = (a, b) => {
let dp = Array.from({length: a.length + 1}, () => Array(b.length + 1).fill(0));
for (let i = 0; i <= a.length; i++) dp[i][0] = i;
for (let j = 0; j <= b.length; j++) dp[0][j] = j;
for (let i = 1; i <= a.length; i++) {
for (let j = 1; j <= b.length; j++) {
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;
}
}
return dp[a.length][b.length];
};
const similarityScore = 1 - levenshteinDistance(transcriptionOriginal, transcriptionUser) / Math.max(transcriptionOriginal.length, transcriptionUser.length);
document.getElementById("result").innerHTML = `
<h2>Transcription Results</h2>
<p><strong>Original Transcription:</strong> ${transcriptionOriginal}</p>
<p><strong>User Transcription:</strong> ${transcriptionUser}</p>
<p><strong>Levenshtein Similarity Score:</strong> ${similarityScore.toFixed(2)}</p>
`;
} else {
alert("Please upload both audio files.");
}
});
loadModel();
</script>
</body>
</html>
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