Summarizing Videos with AI

Just today, a casual chat with a colleague sparked an idea that I couldn’t wait to try implementing after work: building an AI-powered video summarizer. The result is vid, a proof-of-concept project designed to distil video content into summaries.

The core idea behind vid is to meticulously leverage both the audio and visual streams of a video, process them with specialized AI models and then synthesize these diverse insights into a coherent, high-value summary.

The vid Pipeline: How It Works

Here’s a quick look under the hood at the workflow:

1. Audio Extraction & Transcription: The journey begins with FFmpeg, which extracts the audio track from the input video. This audio is then fed into OpenAI Whisper, a powerful speech-to-text model, generating a detailed transcript complete with timestamps. This gives vid the spoken content of the video.

2. Intelligent Frame Selection: For the visual component, OpenCV is at the heart of the process. Rather than extracting every single frame (which would be inefficient and redundant), vid processes frames to identify those with significant visual changes. A subsequent filtering step is applied to remove visually similar frames, ensuring that only truly distinct moments — “key frames” — are captured. This keeps the visual data meaningful and focused.

3. Visual Context with Gemma: Each carefully selected key frame is then sent to Gemma 3, which I’m running locally via Ollama. Gemma analyzes these images and generates precise textual descriptions of their content. This crucial step enriches the video’s understanding beyond mere spoken words, adding vital visual context that a transcript alone can’t provide.

4. Unified Summarization: Finally, the multimodal magic happens. Both the comprehensive audio transcript and the detailed visual descriptions of the key frames are combined. This rich, integrated input is then fed back into Gemma 3. With this holistic view of the video’s content and visuals, Gemma is prompted to generate the ultimate concise summary, capturing the essence of the entire video.

System Overview

For a visual walkthrough of how these components fit together, check out the system mockup:

Get Involved

This project is very much a proof of concept, but it establishes a robust foundation for more advanced video understanding and summarization systems. If you’re intrigued by the technical implementation or want to experiment with it yourself, the code is open-source and available on GitHub.

Explore vid on GitHub: https://github.com/t128n/proof-of-concept-vid