Transforming Cricket Commentary into Summaries: Generating Text Highlights with Whisper, Rapid API, and Generative AI
Author(s): 1. Kandra Aashritha, 2. Adoni Manish, 3. Bhaskar Das, 4. Reddymani Yashwanth, 5. Sandela Sai Karthik
Authors Affiliations:
- Student, Department of CSE(DS), Hyderabad Institute of Technology and Management, Hyderabad, India
- Student, Department of CSE(DS), Hyderabad Institute of Technology and Management, Hyderabad, India
- Associate Professsor, Department of CSE(DS), Hyderabad Institute of Technology and Management, Hyderabad, India
- Student, Department of CSE(DS), Hyderabad Institute of Technology and Management, Hyderabad, India
- Student, Department of CSE(DS), Hyderabad Institute of Technology and Management, Hyderabad, India
Smart Highlights: Automated Summarization of Cricket Videos introduces an innovative approach to generating concise, text-based summaries from cricket video footage, providing fans and content creators with quick, detailed overviews of key match events. This project addresses the challenge of efficiently processing lengthy cricket matches to highlight pivotal moments and player performances. The solution starts by extracting the audio commentary from cricket video footage and preprocessing it to reduce background noise, isolating the commentatorβs voice for clarity. The cleaned audio is transcribed into text using Open AIβs Whisper model, accurately capturing the match narrative, including scores, player names, and event details. Complementing this transcription, match data is retrieved using Rapid API, which provides official scorecard details such as player statistics, scores, and other critical metrics. Finally, Googleβs generative AI, integrated via the google. generative ai library on the Vertex AI platform, processes the commentary and scorecard data to produce structured, readable summaries. These summaries highlight pivotal moments like boundaries, wickets, and standout player performances. This automated summarization offers a valuable tool for sports media agencies, social media platforms, and cricket enthusiasts who seek quick, insightful match highlights. By combining audio processing, data retrieval, and generative AI, Smart Highlights provides an efficient, scalable solution for transforming cricket matches into concise, accessible summaries, enhancing the speed and quality of sports media consumption.
Kandra Aashritha, Adoni Manish, Bhaskar Das, Reddymani Yashwanth, Sandela Sai Karthik(2025); International Journal for Innovative Research in Multidisciplinary Field, ISSN(O): 2455-0620, Vol-11, Issue-2, Pp.127-136. Available on β https://www.ijirmf.com/
- Wayesa, F., Leranso, M., Asefa, G., & Kedir, A. (2023). Pattern-based hybrid book recommendation system using semantic relationships. Scientific Reports, 13(1), 3693.
- Ayyaz, S., Qamar, U., & Nawaz, R. (2018). HCF-CRS: A Hybrid Content based Fuzzy Conformal Recommender System for providing recommendations with confidence. PloS one, 13(10), e0204849.
- Dang, C. N., Moreno-GarcΓa, M. N., & Prieta, F. D. L. (2021). An approach to integrating sentiment analysis into recommender systems. Sensors, 21(16), 5666.
- Uta, M., Felfernig, A., Le, V. M., Tran, T. N. T., Garber, D., Lubos, S., & Burgstaller, T. (2024). Knowledge-based recommender systems: overview and research directions. Frontiers in big Data, 7, 1304439.
- Zhong, S. T., Huang, L., Wang, C. D., Lai, J. H., & Philip, S. Y. (2020). An autoencoder framework with attention mechanism for cross-domain recommendation. IEEE Transactions on Cybernetics, 52(6), 5229-5241.
- Zhang, Q., Lu, J., Wu, D., & Zhang, G. (2018). A cross-domain recommender system with kernel-induced knowledge transfer for overlapping entities. IEEE transactions on neural networks and learning systems, 30(7), 1998-2012.
- Liang, R., Zhang, Q., Wang, J., & Lu, J. (2022). A hierarchical attention network for cross-domain group recommendation. IEEE transactions on neural networks and learning systems, 35(3), 3859-3873.
- Chen, J., Wang, C., Wang, J., Ying, X., & Wang, X. (2017). Learning the personalized intransitive preferences of images. IEEE Transactions on Image Processing, 26(9), 4139-4153.
- Hao, P., Zhang, G., Martinez, L., & Lu, J. (2017). Regularizing knowledge transfer in recommendation with tag-inferred correlation. IEEE transactions on cybernetics, 49(1), 83-96.
- Liao, W., Zhang, Q., Yuan, B., Zhang, G., & Lu, J. (2022). Heterogeneous multidomain recommender system through adversarial learning. IEEE Transactions on Neural Networks and Learning Systems, 34(11), 8965-8977.
- Xiong, W., & Zhang, Y. (2023). An intelligent film recommender system based on emotional analysis. PeerJ Computer Science, 9, e1243.
- Cui, P., Yin, B., & Xu, B. (2023). The application of social recommendation algorithm integrating attention model in movie recommendation. Scientific Reports, 13(1), 16938.
- The Movie Database. (n.d.). Popular movies. Retrieved from https://api.themoviedb.org/3/movie/popular
- (n.d.). *Goodreads API*. Retrieved from https://www.goodreads.com/api
- Lubos, S., Felfernig, A., & Tautschnig, M. (2023). An overview of video recommender systems: state-of-the-art and research issues. Frontiers in big Data, 6, 1281614.
- FernΓ‘ndez, D., Formoso, V., Cacheda, F., & Carneiro, V. (2019). High Order Profile Expansion to tackle the new user problem on recommender systems. PloS one, 14(11), e0224555.