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TubeQuery

TubeQuery is an LLM-based model designed to fetch queries related to YouTube videos. By simply inputting a video link, you can ask questions about the video's content and receive answers.

Kaggle

Here is the publication of TubeQuery.

Overview

TubeQuery leverages advanced Natural Language Processing (NLP) techniques to analyze video content. It extracts audio, transcribes it, and then uses a language model to answer questions related to the video's content. This makes it easy to get quick answers and summaries from video lectures, tutorials, interviews, and more.

PRODUCTION_PROTOTYPE

Features

Here's a breakdown of the key features of TubeQuery:

  1. Video Analysis and Processing:

    • Accepts video links (e.g., YouTube) as input.
    • Automatically extracts audio from the provided video link.
    • Performs speech-to-text conversion using OpenAI's Whisper model to generate accurate transcripts.
    • Enables querying of the video content based on the generated transcript.

    Input Video Link

  2. Natural Language Query Support:

    • Allows users to ask questions in natural language, making it intuitive and user-friendly.
    • Provides accurate and contextually relevant answers derived directly from the video content.

    Question and Answers

  3. Transcript Summarization:

    • Offers the capability to summarize lengthy videos into concise summaries.
    • Highlights the most important key points, saving users valuable time.

    Summary

  4. Multi-Language Support:

    • Supports transcription and querying in multiple languages, depending on the language spoken in the video.

  5. Adaptable Framework:

    • Designed to work with videos from various platforms, as long as they are accessible via a public link.
    • Compatible with a wide range of video content, including educational tutorials, academic lectures, and engaging interviews.

Future Improvements

The following are planned enhancements for future versions of TubeQuery:

  1. Enhanced Accuracy:

    • Integrating more advanced AI models to further improve the accuracy of both transcription and query responses.
    • Implementing context-aware models to better understand complex or potentially ambiguous queries.

  2. Real-Time Processing:

    • Exploring the possibility of enabling live video analysis to provide real-time query responses during live events or streams.

  3. Support for Multiple Video Sources:

    • Expanding support to include private videos, YouTube playlists, and the option for users to upload custom media files.

  4. Improved Interface:

    • Developing a more interactive and user-friendly interface, potentially including features like voice input for queries and enhanced visualization of results.

  5. Advanced Analytics:

    • Incorporating advanced analytics features to provide insights into video content, such as sentiment analysis, keyword extraction, and topic detection.

  6. Integration with External Tools:

    • Aiming to integrate with popular note-taking applications, learning management systems (LMS), and collaboration platforms to enhance usability and workflow.

  7. Cloud-Based Deployment:

    • Transitioning to a cloud-based deployment to allow for scalable processing of larger datasets and to handle high traffic usage efficiently.

  8. Personalization:

    • Implementing personalization features to enable user-specific recommendations and adjustments based on individual preferences and past query history.

Tech Stack

TubeQuery is built using the following technologies:

  1. Backend:

    • Programming Language: Python
    • Framework: Utilizes standard libraries and direct implementation without relying on extensive API frameworks.

  2. Speech-to-Text:

    • Libraries: OpenAI Whisper for accurate and efficient speech-to-text conversion.

  3. Natural Language Processing:

    • Libraries/Models: Hugging Face Transformers for advanced NLP tasks and question answering.

  4. Video Processing:

    • Tools: FFMPEG is used for robust audio extraction and general video handling.