YouTube Summarization Process
1. Download & Extract
- Download YouTube Video/Thumbnail/Subtitles: The process begins by acquiring the core elements of the YouTube video: the video file itself, its thumbnail, and any available subtitles.
- Extract Frame Every 30 Seconds w/ FFMPEG: Using FFMPEG, a powerful multimedia framework, key frames are extracted at regular intervals (every 30 seconds). These frames will be crucial for visual representation and further processing.
2. Image Enhancement
Stable Diffusion XL Turbo via Huggingface API (Thumbnail & Each Frame): This stage utilizes Stable Diffusion XL Turbo, a cutting-edge AI model for image generation and enhancement, accessed through the Huggingface API. It's likely used to enhance the quality of the extracted frames and potentially the thumbnail, making them more visually appealing.
3. Summarization Loop (For each 5 minutes of video)
- Prompt LLM to summarize: A Large Language Model (LLM) is employed to generate summaries. The LLM is provided with a "prompt," likely constructed from:
- Subtitles: Text extracted from the video's subtitles.
- Large Language Model: Possibly using the LLM itself to analyze the subtitles and generate more focused prompts.
- Output as Input: The output of the LLM (the initial summary) could be fed back as input for further refinement.
4. Text-to-Speech & Video Generation
- Edge Text-to-Speech: Microsoft Edge's built-in text-to-speech engine converts the LLM-generated summaries into spoken audio (MP3).
- FFMPEG: FFMPEG is used again, this time to combine the various elements:
- MP3 (Audio): The spoken summary.
- Modified Extracted Frames: The enhanced key frames.
- Thumbnail: The (potentially enhanced) video thumbnail.
5. Output
Product: MP4 (Original Content): The final output is an MP4 video file, which is essentially a summarized version of the original YouTube video.
Overall Goal: Summarize YouTube Video
This process aims to create concise and engaging summaries of YouTube videos by combining:
- Visuals: Enhanced key frames and thumbnails.
- Audio: Spoken summaries generated from AI analysis.
- AI-powered summarization: Leveraging LLMs to extract the essence of the video content.
This approach has potential applications in creating short-form video content, educational materials, and accessibility tools.