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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>Bengali ASR Model Analysis Report</title>
<script src="https://cdnjs.cloudflare.com/ajax/libs/Chart.js/3.9.1/chart.min.js"></script>
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<div class="report-container">
<div class="header">
<h1>Bengali ASR Model Analysis</h1>
<p>WavLM Fine-tuning for Bengali Speech Recognition</p>
</div>
<div class="content">
<!-- Executive Summary -->
<div class="section">
<h2>Executive Summary</h2>
<p>This report analyzes a Bengali Automatic Speech Recognition (ASR) system built using Microsoft's WavLM model fine-tuned on Bengali audio data. The system processes emotion dataset audio files with a fixed Bengali transcription "১২টা বেজে গেছে" (It's past 12 o'clock).</p>
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<div class="metric-card">
<div class="metric-value">WavLM</div>
<div class="metric-label">Base Model</div>
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<div class="metric-card">
<div class="metric-value">16kHz</div>
<div class="metric-label">Sampling Rate</div>
</div>
<div class="metric-card">
<div class="metric-value">15</div>
<div class="metric-label">Training Epochs</div>
</div>
<div class="metric-card">
<div class="metric-value">CTC</div>
<div class="metric-label">Loss Function</div>
</div>
</div>
</div>
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<div class="section">
<h2>Model Architecture Analysis</h2>
<h3>WavLM Base Architecture</h3>
<div class="chart-container">
<canvas id="architectureChart"></canvas>
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<div class="info-box">
<strong>Key Architecture Features:</strong>
<ul>
<li>Pre-trained WavLM-base-plus model from Microsoft</li>
<li>Custom CTC (Connectionist Temporal Classification) head for Bengali</li>
<li>Character-based vocabulary generation</li>
<li>Wav2Vec2Processor for audio preprocessing</li>
</ul>
</div>
</div>
<!-- Training Pipeline -->
<div class="section">
<h2>Training Pipeline</h2>
<div class="pipeline-flow">
<div class="flow-step">Data Loading</div>
<div class="flow-step">Audio Processing</div>
<div class="flow-step">Augmentation</div>
<div class="flow-step">Tokenization</div>
<div class="flow-step">Model Training</div>
<div class="flow-step">Evaluation</div>
</div>
<h3>Training Configuration</h3>
<div class="chart-container">
<canvas id="trainingChart"></canvas>
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</div>
<!-- Audio Processing Analysis -->
<div class="section">
<h2>Audio Processing Analysis</h2>
<h3>Signal Processing Pipeline</h3>
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</div>
<h3>Data Augmentation Strategy</h3>
<div class="chart-container">
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</div>
<div class="warning-box">
<strong>Note:</strong> The code uses dummy noise for augmentation. In production, consider using diverse noise datasets like MUSAN for better robustness.
</div>
</div>
<!-- Vocabulary Analysis -->
<div class="section">
<h2>Vocabulary Analysis</h2>
<h3>Character Distribution</h3>
<div class="chart-container">
<canvas id="vocabChart"></canvas>
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<div class="code-analysis">
Bengali Text: "১২টা বেজে গেছে"<br>
Character Count: 14 (including spaces)<br>
Unique Characters: 10<br>
Special Tokens: [UNK], [PAD], |
</div>
</div>
<!-- Performance Metrics -->
<div class="section">
<h2>Expected Performance Metrics</h2>
<h3>Training Progress Simulation</h3>
<div class="chart-container">
<canvas id="performanceChart"></canvas>
</div>
<h3>Evaluation Metrics</h3>
<div class="chart-container">
<canvas id="metricsChart"></canvas>
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<!-- Technical Analysis -->
<div class="section">
<h2>Technical Analysis</h2>
<h3>Code Structure Breakdown</h3>
<div class="chart-container">
<canvas id="codeStructureChart"></canvas>
</div>
<h3>Key Findings</h3>
<ul>
<li><strong>Strengths:</strong> Comprehensive pipeline, proper data augmentation, modern architecture</li>
<li><strong>Limitations:</strong> Single transcription for all audio files, small vocabulary size</li>
<li><strong>Improvements:</strong> Diverse transcriptions, larger dataset, better noise augmentation</li>
</ul>
</div>
<!-- Recommendations -->
<div class="section">
<h2>Recommendations</h2>
<div class="info-box">
<h3>My Recommendations for Production Deployment:</h3>
<ul>
<li>I will expand the dataset with diverse Bengali transcriptions to improve model generalization</li>
<li>I need to implement proper train/validation/test splits for robust evaluation</li>
<li>I should use real noise datasets like MUSAN and environmental sounds for better augmentation</li>
<li>I must add pronunciation variations and accent handling to support different Bengali dialects</li>
<li>I will implement beam search decoding to achieve better transcription accuracy</li>
<li>I should consider integrating a language model for better contextual understanding</li>
</ul>
</div>
</div>
</div>
</div>
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