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17. AI Video Subtitle and Dubbing System

Authors
Affiliations
Birmingham City University
Sunway College Kathmandu

Objective

Automatically generates subtitles and dubbed audio in multiple languages for videos using speech recognition, translation, and text-to-speech.

System Architecture

[Mermaid diagram - flowchart showing core components and data flow]

[3-5 sentence description of architecture]

Technical Approach

Key Components

Pipeline / Data Flow

[Detailed description of request → processing → response flow]

Complexity Analysis

MetricComplexityNotes
Model sizeASR: 500M-2B, TTS: 100M-1B per language[implications]
Time complexityO(video_duration)[notes]
Space complexity~3-10GB[notes]
Latency target<1x video duration (real-time)[real-time vs. batch]
Throughput target10-100 videos/day[per GPU/instance]

Pros & Cons

Pros

Cons

Trade-offs

[1-2 paragraphs discussing key technical trade-offs]

Real-World Applications

Where This Pattern Appears

Production Considerations

[2-3 paragraphs on scaling, failure modes, monitoring, cost]

References & Citations

Citation 1: Architecture & Design

Title: [Paper/Blog Title on AI Video Subtitle and Dubbing System Architecture]

Citation 2: Performance & Benchmarks

Title: [Performance Benchmarks for AI Video Subtitle and Dubbing System]

Citation 3: Implementation Details

Title: [Implementation Details and Trade-offs]

Citation 4: Real-World Deployment

Title: [Production Deployment Insights]

Reproducibility Checklist