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Artificial Intelligence · Major Project
Automatic Video Chaptering
Transcribes a video and splits it into titled chapters by topic
3-4 weeks build
Overview
Takes a lecture or talk, transcribes it with timestamps, and finds where the topic changes by measuring embedding similarity between neighbouring windows of transcript. Each segment then gets a short generated title, and the result is a clickable chapter list that jumps the player to the right moment.
What makes it stand up
- 01Timestamped transcription with Whisper
- 02Topic-shift detection from sliding-window embedding similarity
- 03Tunable segment length and boundary sensitivity
- 04Generated chapter titles from each segment's key content
- 05Boundary accuracy measured against hand-marked chapters
Module breakdown
- 01
Transcription
Audio extraction and Whisper with word timestamps.
- 02
Windowing
Overlapping transcript windows embedded as vectors.
- 03
Segmentation
Similarity dips smoothed and picked as boundaries.
- 04
Titling
Keyphrase and summary-based chapter titles.
- 05
Player
React video player with a clickable chapter list.
After this, you will be able to
- Explain how embedding similarity signals a topic change
- Defend how you evaluated chapter boundaries
- Demo chaptering a lecture video end to end
Technology stack
- Python
- Whisper
- Sentence Transformers
- FastAPI
- React
- Level
- Major Project
- Domain
- Artificial Intelligence
- Build time
- 3-4 weeks
- Difficulty
You receive
- Complete, commented source code
- 60-90 page project report (IEEE format)
- Editable presentation deck
- Architecture, ER and UML diagrams
- Local setup and deployment guide
- Sample dataset or seed data
- Viva question bank with answers
- One 45-minute walkthrough call
- 30 days of doubt-clearing support