Light-weight Online Real-time ASR: A Bit More Attention is Needed

Light-weight Online Real-time ASR: A Bit More Attention is Needed | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 5 June 2025 V1 Latest version Share on Light-weight Online Real-time ASR: A Bit More Attention is Needed Authors : Meer Muhammad Kalhoro 0009-0006-2067-4784 [email protected] and Muhammad Masab Authors Info & Affiliations https://doi.org/10.22541/au.174914695.58777421/v1 239 views 146 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract This paper introduces a unique implementation of Whisper by OpenAI, intended primarily for Telco applications. Whisper, initially released with support for 98 languages [6], represents a significant stepping stone in the world of Natural Language Processing. In this paper, we outline a hybrid model approach that enables a more diverse and cost-effective adaptation of the model for industrial deployment. We evaluate the C++ version against Whisper's original Python model, demonstrating comparable accuracy with a significantly reduced computational footprint. Our results show that the hybrid Whisper-Transformer model achieves effective real-time transcription while maintaining contextual accuracy, representing a scalable, cost-effective ASR solution for industrial applications. Supplementary Material File (academic_paper-4.pdf) Download 533.86 KB Information & Authors Information Version history V1 Version 1 05 June 2025 Copyright This work is licensed under a Creative Commons Attribution 4.0 International License Keywords artificial intelligence (ai) c++ engineering natural language processing. whisper Authors Affiliations Meer Muhammad Kalhoro 0009-0006-2067-4784 [email protected] R&D Department, VECTOR AI View all articles by this author Muhammad Masab R&D Department, VECTOR AI View all articles by this author Metrics & Citations Metrics Article Usage 239 views 146 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Meer Muhammad Kalhoro, Muhammad Masab. Light-weight Online Real-time ASR: A Bit More Attention is Needed. Authorea . 05 June 2025. DOI: https://doi.org/10.22541/au.174914695.58777421/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. 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