MULTI-LINGUAL YOUTUBE SUMMARIZER

In today’s multitasking world, lengthy videos are often eschewed in favor of concise content. To address this, we propose a Multi-Lingual YouTube Video Summarizer leveraging advanced natural language processing and machine learning techniques. Utilizing HTML, CSS, and JavaScript for frontend development, and integrating models like NLTK, BERT, and audio-to-text [5] for backend processing, this system extracts content via the YouTube Data API v3 [2] to generate English summaries, thus overcoming language barriers and enhancing global accessibility. For videos lacking transcripts, the system employs sophisticated audio-to-text conversion techniques to dynamically generate them, ensuring comprehensive summarization. This solution meets the rising demand for cross-language content understanding, promoting efficient communication and knowledge acquisition. The robust technological stack ensures a dynamic, user-friendly experience, enhancing YouTube content consumption across linguistic boundaries for a diverse global audience.