{"id":25340909,"url":"https://github.com/403errors/tubequery","last_synced_at":"2026-04-12T00:46:59.971Z","repository":{"id":277252171,"uuid":"897565782","full_name":"403errors/TubeQuery","owner":"403errors","description":"TubeQuery is a LLM based model, fetching all the queries related to your video. 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By simply inputting a video link, you can ask questions about the video's content and receive answers.\n\n[![Kaggle](https://img.shields.io/badge/Kaggle-View%20on%20Kaggle-blue?logo=kaggle)](https://www.kaggle.com/code/sitama/tubequery)\n\nHere is the [publication](https://app.readytensor.ai/publications/sw0uANUAdEXE) of TubeQuery. \n\n## Overview\n\nTubeQuery 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.\n\n![PRODUCTION_PROTOTYPE](imgs/PRODUCTION%20PROTOTYPE%20OF%20TUBEQUERY.png)\n\n## Features\n\nHere's a breakdown of the key features of TubeQuery:\n\n1. **Video Analysis and Processing:**\n   - Accepts video links (e.g., YouTube) as input.\n   - Automatically extracts audio from the provided video link.\n   - Performs speech-to-text conversion using OpenAI's Whisper model to generate accurate transcripts.\n   - Enables querying of the video content based on the generated transcript.\n\n   ![Input Video Link](imgs/input_video_link.png)\n\n2. **Natural Language Query Support:**\n   - Allows users to ask questions in natural language, making it intuitive and user-friendly.\n   - Provides accurate and contextually relevant answers derived directly from the video content.\n\n   ![Question and Answers](imgs/question_answers.png)\n\n3. **Transcript Summarization:**\n   - Offers the capability to summarize lengthy videos into concise summaries.\n   - Highlights the most important key points, saving users valuable time.\n\n   ![Summary](imgs/summary.png)\n\n4. **Multi-Language Support:**\n   - Supports transcription and querying in multiple languages, depending on the language spoken in the video.\n\n5. **Adaptable Framework:**\n   - Designed to work with videos from various platforms, as long as they are accessible via a public link.\n   - Compatible with a wide range of video content, including educational tutorials, academic lectures, and engaging interviews.\n\n---\n\n## Future Improvements\n\nThe following are planned enhancements for future versions of TubeQuery:\n\n1. **Enhanced Accuracy:**\n   - Integrating more advanced AI models to further improve the accuracy of both transcription and query responses.\n   - Implementing context-aware models to better understand complex or potentially ambiguous queries.\n\n2. **Real-Time Processing:**\n   - Exploring the possibility of enabling live video analysis to provide real-time query responses during live events or streams.\n\n3. **Support for Multiple Video Sources:**\n   - Expanding support to include private videos, YouTube playlists, and the option for users to upload custom media files.\n\n4. **Improved Interface:**\n   - Developing a more interactive and user-friendly interface, potentially including features like voice input for queries and enhanced visualization of results.\n\n5. **Advanced Analytics:**\n   - Incorporating advanced analytics features to provide insights into video content, such as sentiment analysis, keyword extraction, and topic detection.\n\n6. **Integration with External Tools:**\n   - Aiming to integrate with popular note-taking applications, learning management systems (LMS), and collaboration platforms to enhance usability and workflow.\n\n7. **Cloud-Based Deployment:**\n   - Transitioning to a cloud-based deployment to allow for scalable processing of larger datasets and to handle high traffic usage efficiently.\n\n8. **Personalization:**\n   - Implementing personalization features to enable user-specific recommendations and adjustments based on individual preferences and past query history.\n\n---\n\n## Tech Stack\n\nTubeQuery is built using the following technologies:\n\n1. **Backend:**\n   - **Programming Language**: Python\n   - **Framework**:  Utilizes standard libraries and direct implementation without relying on extensive API frameworks.\n\n2. **Speech-to-Text:**\n   - **Libraries**: OpenAI Whisper for accurate and efficient speech-to-text conversion.\n\n3. **Natural Language Processing:**\n   - **Libraries/Models**: Hugging Face Transformers for advanced NLP tasks and question answering.\n\n4. **Video Processing:**\n   - **Tools**: FFMPEG is used for robust audio extraction and general video handling.\n","funding_links":[],"categories":[],"sub_categories":[],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2F403errors%2Ftubequery","html_url":"https://awesome.ecosyste.ms/projects/github.com%2F403errors%2Ftubequery","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2F403errors%2Ftubequery/lists"}