Extracting Smart Video Clips with LLMs: Inside the Clips Extractor App

Introduction

In the age of information overload, finding the most relevant moments in lengthy videos can be a daunting task. Clips Extractor is an innovative application designed to solve this problem by leveraging state-of-the-art Large Language Models (LLMs) and AI-powered transcription. This blog post explores the app's goals, technical architecture, and how it uses LLMs to deliver precise, topic-based video clips.

What is Clips Extractor?

Clips Extractor enables users to extract meaningful clips from YouTube videos based on a topic of interest. Whether you're a researcher, content creator, or casual viewer, you can quickly surface the most relevant segments without manually scrubbing through hours of footage.

Key Features

  • Extract clips from YouTube video
  • Search for segments based on user-provided topics
  • Get precise timestamps and transcripts for each clip
  • Combine selected clips into a single video
  • Chrome Extension for direct YouTube integration

Technical Architecture

The application is built with a modern, scalable stack:

  • Frontend: Next.js (React, TypeScript, Tailwind CSS)
  • Backend: Python FastAPI
  • Media Processing: FFmpeg, OpenAI Whisper, GPT-4o
  • Storage: AWS S3
  • Chrome Extension: For seamless YouTube interaction

Workflow Overview

  1. Media Input: Users provide a YouTube link (auto extracted via extension)
  2. Audio Extraction: Backend uses FFmpeg to extract audio
  3. Transcription: OpenAI Whisper transcribes audio with timestamps
  4. Clip Identification: GPT-4o identifies relevant segments
  5. Clip Extraction: Backend slices video based on LLM output
  6. Delivery: Users receive downloadable clips or stream via extension

LLMs in Action: Whisper & GPT-4o

1. Transcription with Whisper

  • Model Used: whisper-1 (OpenAI)
  • Purpose: Converts spoken content to text with timestamps
  • How it works:
    • Audio is sent to the Whisper API
    • Returns detailed transcript with segment and word-level timing

2. Clip Extraction with GPT-4o

  • Model Used: gpt-4o-mini (OpenAI)
  • Purpose: Identifies transcript segments relevant to user prompt
  • How it works:
    • Prompt includes transcript and user's topic
    • Returns JSON with clip start/end times and related text

Local LLM Support

Advanced users can configure a local LLM by setting OPENAI_BASE_URL to enable private or offline processing.

Example Use Case

  1. User Input: "Find all parts where the speaker discusses climate change."
  2. Processing:
    • App transcribes video
    • GPT-4o analyzes and returns timestamps
  3. Output: Clips matching the topic with precise timestamps

Conclusion

Clips Extractor demonstrates the power of combining LLMs with traditional media processing to automate the extraction of meaningful video content. By using OpenAI's Whisper for transcription and GPT-4o for intelligent segment selection, the app delivers a seamless experience for anyone looking to quickly find and share the most important moments in any video.

Ready to try it? Check out the README for setup instructions, or install the Chrome Extension for instant YouTube integration.

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