VideoSummarizer AI with Amazon Bedrock

Watching long videos wastes time. Our tool gives instant summaries.

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About the Project

Inspiration

The inspiration for this project came from the increasing amount of video-based content available online and the difficulty of quickly extracting useful information from long videos. I wanted to build a system that could automatically summarize video content so users can understand the main idea without watching the entire video. This project also allowed me to explore how different AI components can be combined into a complete end-to-end pipeline.

What I Learned

Through this project, I gained hands-on experience with:

  • Processing video files and extracting audio
  • Converting speech to text using automatic speech recognition
  • Working with AI models for natural language understanding and summarization
  • Structuring a clean and maintainable project repository
  • Handling real-world issues such as model access permissions and dependency management

I also learned how to debug integration issues between different libraries and services, and how to design a project that remains flexible and extensible.

How I Built the Project

The project was built as a multi-stage pipeline:

  1. A video file is provided as input from the assets folder.
  2. The audio is extracted from the video using a video processing library.
  3. The extracted audio is converted into text using a speech-to-text model.
  4. The resulting transcript is passed to a summarization module to generate a concise summary.
  5. The output is displayed to the user.

The project is organized into separate folders for source code, assets, and documentation to keep the structure clear and professional. The system is designed so that users can easily test it with different videos by adding new files to the assets folder.

Challenges Faced

One of the main challenges was handling external model access and permissions, especially when working with cloud-based AI services. Debugging these issues required careful analysis of error messages and understanding how account-level permissions work. Another challenge was ensuring that all components of the pipeline worked smoothly together, from video processing to text summarization. Overcoming these challenges helped me develop stronger problem-solving and debugging skills.

Overall, this project was a valuable learning experience that strengthened my understanding of applied AI systems and real-world software development workflows.

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