V2N

This project focuses on creating a web-based application that extracts audio transcription from YouTube videos, prepares structured lecture notes, and provides a range of add-ons for an enhanced user experience. It is specifically designed for students, educators, and professionals who want to efficiently summarize video content into organized notes.

Features

1. Audio Transcription

   • Extract and process audio from YouTube videos using APIs like youtube-transcript-api or OpenAI Whisper.

   • Generate accurate and timestamped transcription.

2. Lecture Notes Preparation

   • Automatically convert the transcription into structured lecture notes.

   • Use natural language processing (NLP) to identify key points, topics, and subtopics.

   • Format the notes into headings, bullet points, and paragraphs.

3. Enable Live Editing

   • Provide an integrated editor for users to review and modify the lecture notes.

   • Add collaborative editing functionality to allow multiple users to work on the notes simultaneously.

4. Topic-based Timestamps

   • Automatically identify topics in the video and mention their corresponding timestamps in the notes.

   • Provide clickable timestamps to quickly navigate to the relevant section in the YouTube video.

5. Screenshot Integration

   • Extract and add screenshots from key moments in the video.

   • Allow users to manually select or automatically generate screenshots for visual reference in the notes.

6. User-Friendly Interface

   • Design an intuitive dashboard for users to upload or paste YouTube video links.

   • Provide options to export the notes in multiple formats (PDF, Word, Markdown).

Technologies Used

• Backend: Python (Flask or Django) for API integrations, transcription processing, and NLP.

• Frontend: React for a responsive and dynamic user interface.

• APIs and Libraries:

  • YouTube Data API for extracting video details and audio.

  • Google Speech-to-Text or Whisper for transcription.

  • Spacy or NLTK for NLP-based topic extraction.