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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM62019
Volume:
10 |
4 |
April 2026
Pages:
1-10
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
, Department of Computer Science and Engineering
Sri Shakthi Institute of Engineering and Technology, India.
, Department of Computer Science and Engineering
Sri Shakthi Institute of Engineering and Technology, India.
The Transcripto AI Audio & Video Transcription Platform is designed to convert multimedia content into accurate and structured text through an intelligent and automated system. It enables users to upload audio files, video files, or provide YouTube links, which are then processed using advanced AI- based speech recognition models such as OpenAI Whisper. The platform performs preprocessing tasks including file validation and audio extraction from video using tools like FFmpeg to ensure efficient transcription. The system generates timestamped transcripts that can be edited, searched, and highlighted, improving content accessibility and usability.The platform also provides multiple export options, allowing users to download transcripts in formats such as PDF, Word, PowerPoint, TXT, and SRT. A user-friendly dashboard is included to manage transcription history, saved summaries, and exported files, enabling efficient data organization. Built using a modern tech stack with React.js for the frontend and Node.js with Express.js for the backend, the system ensures a seamless, responsive, and secure user experience. The platform reduces manual effort and improves productivity by automating transcription workflows while maintaining high accuracy. It transforms multimedia content processing into a structured and efficient task, making it useful for students, professionals, and content creators who require reliable transcription and documentation solutions.
KEYWORDS
The future development of the Transcripto AI Audio & Video Transcription Platform can focus on enhancing transcription accuracy, speed, and overall user experience by integrating more advanced AI models and intelligent features. The system can be upgraded to support real-time transcription, enabling users to convert live audio streams such as meetings, lectures, and webinars into text instantly. Additionally, incorporating multilingual transcription and translation capabilities will allow users to process content in multiple languages, making the platform more inclusive and globally accessible. Another important area of improvement is the integration of AI-based summarization and keyword extraction. By leveraging advanced Natural Language Processing techniques, the system can automatically generate concise summaries and identify important keywords from transcripts. This will help users quickly understand large volumes of content and improve productivity. Enhanced search capabilities, such as semantic search, can further improve content navigation and analysis. The platform can also expand its analytics and personalization features by providing insights such as frequently accessed content, usage patterns, and smart recommendations. Adaptive features can suggest summaries, highlights, or edits based on user behavior, making the system more intelligent and user-centric. Furthermore, Transcripto can be extended to include collaborative editing and sharing features, allowing multiple users to work on transcripts in real time. Integration with cloud storage platforms and development of mobile applications can improve accessibility and scalability, enabling users to access their data anytime and anywhere. The system can also incorporate speaker diarization to identify and separate multiple speakers in conversations, improving transcript clarity for meetings and interviews. Enhanced export features with customizable formats and styling options can further increase usability. By continuously evolving with advancements in AI and web technologies, Transcripto has the potential to become a comprehensive multimedia processing and content analysis platform, supporting a wide range of applications across education, business, research, and content creation domains.
1. Radford, A. et al. (2023). Whisper: Robust Speech Recognition via Large-Scale Weak Supervision. OpenAI Research. Available: https://openai.com 2. Jurafsky, D., & Martin, J. (2023). Speech and Language Processing (3rd Edition). Stanford University. Available: https://web.stanford.edu/~jurafsky/slp3 3. Vaswani, A. et al. (2017). Attention is All You Need. Neural Information Processing Systems (NeurIPS), 30, 5998–6008. Available: https://arxiv.org/abs/1706.03762 4. Bengio, Y., Goodfellow, I., & Courville, A. (2023). Deep Learning. MIT Press. Available: https://www.deeplearningbook.org 5. Google Research. (2022). GigaSpeech: An Evolving Dataset for Speech Recognition. Available: https://arxiv.org/abs/2106.06909 6. Microsoft. (2021). Real-Time Speech-to-Text Systems and Applications. Microsoft Research. Available: https://www.microsoft.com 7. FFmpeg Developers. (2024). FFmpeg Multimedia Framework Documentation. Available: https://ffmpeg.org 8. Node.js Foundation. (2024). Node.js Documentation. Available: https://nodejs.org 9. Express.js. (2024). Express Web Framework Guide. Available: https://expressjs.com 10. React Documentation. (2024). Building User Interfaces with React. Meta Developers. Available: https://react.dev 11. MongoDB Inc. (2024). MongoDB Database Documentation. Available: https://www.mongodb.com 12. Mozilla. (2023). Web Audio API Documentation. Available: https://developer.mozilla.org 13. W3C. (2023). Web Security and Authentication Standards. World Wide Web Consortium. Available: https://www.w3.org
Publication Date
4/30/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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Conflict of Interest
The authors declare no conflict of interest related to this research work.
Funding Information
No funding information available for this research.
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