FinalTake_AI

The backend and frontend code running env
Users upload multiple video takes in a single ZIP file, allowing FinalTake AI to automatically process and compare every performance.
Six-stage AI pipeline from upload to recommendation, analysing dialogue and performance across multiple takes.
AI Performance Analysis
FinalTake AI home screen showcasing an AI-powered platform for cinematic take review and performance analysis
AI Performance Dashboard. FinalTake AI ranks every take, highlights the best performance, and provides detailed scoring and recommendations.
Tech Stack used to built this project

Inspiration

Filmmakers and actors often record multiple takes of the same scene before choosing the final performance. Reviewing and comparing these takes manually can be repetitive, time-consuming, and subjective. This inspired us to build FinalTake AI, an AI-assisted platform that helps identify the strongest take and reduces the effort required for the initial review process.

What it does

FinalTake AI allows users to upload multiple video takes and automatically analyze them. It extracts audio, transcribes dialogue using Whisper AI, performs semantic analysis for script and dialogue alignment, scores the takes, and ranks them to recommend the strongest take. The results are presented through an interactive dashboard.

How we built it

We built the frontend using React and the backend using Python and FastAPI. MoviePy handles video and audio processing, while Whisper AI performs speech-to-text transcription. Sentence Transformers are used for semantic comparison of dialogue. The processed results are sent to the React frontend, where users can view scores, comparisons, rankings, and the recommended take.

Challenges we ran into

Handling multiple video files and extracting usable audio reliably was one of our major challenges. Speech transcription can vary because of background noise, accents, pronunciation, and different speaking styles. Connecting the AI processing pipeline with the frontend and ensuring that results were displayed correctly also required significant debugging.

Accomplishments that we're proud of

We built a working end-to-end MVP that takes multiple video files as input and transforms them into structured AI-assisted analysis. We are proud of integrating video processing, speech AI, semantic analysis, scoring, and an interactive results dashboard into a single application.

What we learned

We learned how to integrate AI models into a complete full-stack application rather than treating AI as an isolated component. We gained practical experience with speech-to-text, semantic similarity, media processing, REST APIs, frontend-backend integration, and presenting AI-generated results in an understandable way.

What's next for FinalTake_AI

Our next goal is to make FinalTake AI a more comprehensive multimodal performance analysis assistant. We plan to incorporate facial expressions, vocal characteristics, body language, pacing, and emotional consistency. We also aim to improve the scoring system, add project history, support larger production workflows, and eventually integrate with existing video-editing and production tools.