Capstone: Video Translation Tool
Automated Video Dubbing System — English to Russian
Real-World Problem Statement
Professional video dubbing is expensive, time-consuming, and inaccessible to small creators. This project builds an automated video translation pipeline that extracts audio, transcribes speech, translates to Russian, generates dubbed audio via TTS, and assembles the final video with synchronized audio.
Target Users
- Content creators expanding to Russian-speaking markets
- Educational institutions localizing course materials
- Corporate training departments
- YouTube creators automating channel localization
Project Scope
In Scope
- Audio extraction from video files (MP4, MKV, AVI)
- Speech-to-text transcription with timestamps
- Sentence grouping from word-level transcripts
- English → Russian translation (JSON structured)
- TTS synthesis with timing control
- Audio timing adjustment and smooth transitions
- Video assembly with dubbed audio
- CLI or simple web UI
- Docker deployment
Out of Scope
- Training custom models (use pre-trained/APIs)
- Real-time/live translation
- Multiple language pairs
- Speaker diarization
- Lip-sync manipulation
- Background music preservation
Timeline
Total: 10-14 days
| Phase | Duration | Activities |
|---|---|---|
| Research & Setup | 2 days | Environment, API evaluation |
| Transcription | 3 days | ASR integration, sentence grouping |
| Translation | 2-3 days | Translation API, JSON handling |
| TTS & Timing | 3-4 days | Speech synthesis, duration matching |
| Assembly & Polish | 2-3 days | Video assembly, UI, docs |
Prerequisites
- Python (intermediate), REST APIs, ffmpeg basics, Docker, Git
- Tools: Whisper/cloud ASR, OpenAI/Google Translate, Cloud TTS (ElevenLabs, Google, Azure)
- Hardware: GPU recommended (8GB+ VRAM) or cloud APIs; 16GB RAM
Pipeline Overview
INPUT VIDEO
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1. Audio Extract (ffmpeg)
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2. Transcription (ASR → word timestamps)
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3. Sentence Grouping
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4. Translation (LLM → JSON)
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5. TTS Synthesis (Russian)
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6. Timing Adjustment (speed stretch)
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7. Audio Assembly (crossfade)
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8. Video Merge
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OUTPUT VIDEO (Russian dubbed)
Key Technical Challenges
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Sentence Grouping: ASR outputs words; translation needs sentences. Use punctuation detection and pause-based segmentation.
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JSON Translation Format: Ensure consistent, parseable output from LLM with validation and retry logic.
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Timing Mismatch: Russian often differs in length from English. Use time-stretching (0.8x-1.3x safe range) while preserving natural rhythm.
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Smooth Transitions: Avoid choppy audio with crossfades, silence padding, and volume normalization.
Deliverables
1. Working Codebase
- Complete pipeline accepting video input → dubbed video output
- Docker containerized, CLI or web UI
- GitHub repo with clear commit history
2. README
- Architecture diagram, setup instructions, API configuration
- Usage examples, limitations, troubleshooting
3. Technical Write-Up (1-2 pages)
- Component choices, timing algorithm, challenges, future improvements
4. Demo Materials
- 2-3 sample videos (30-60s) with Russian dubbed output
Evaluation Rubric
Sufficient
- Pipeline works end-to-end on simple videos
- Transcription readable, translation understandable
- Audio roughly matches timing (±20%)
- Basic README with setup
Good
- Handles various formats/lengths (up to 5 min)
- Intelligent sentence grouping
- Timing within ±10%, smooth transitions
- Modular code, comprehensive docs, error handling
Excellent
- Handles edge cases (fast speech, accents, noise)
- Professional audio quality, precise sync
- Clean architecture with type hints, logging, tests
- Polished UI, novel improvements
Checkpoints
Day 3: Environment ready, ASR working, basic sentence grouping
Day 6: Translation pipeline, TTS working, end-to-end output (rough)
Day 10: Timing adjustment, smooth transitions, video assembly
Day 14: UI complete, Docker working, docs and demo ready
Test Scenarios
| Scenario | Difficulty |
|---|---|
| Clean speech, minimal noise | Easy |
| Fast speech (>150 wpm) | Medium |
| Technical vocabulary | Medium |
| Accented English | Hard |
| Background noise | Hard |
Tips for Success
- Get basic end-to-end working first, then improve each stage
- Debug with 10-30 second clips
- Log intermediate results for debugging
- Test timing early — it's the trickiest part
- Listen to output frequently
Extension Ideas
- Batch processing
- Voice cloning
- Emotion preservation
- Subtitle generation (SRT)
- Bidirectional translation
- Background audio preservation
Good luck! This project combines ASR, LLM, and TTS with audio/video engineering.