Enhancing En-X Translation: A Chrome Extension-Based Approach to Indic Language Models

Language translation is the lifeblood of any communication that crosses linguistic boundaries. Recent trends in the domain of neural machine translation (NMT) are already superior to the old traditions. In such circumstances, the works done by Prahwini et al. (2024) and Vandan Mujadia et al. (2024) highlight the application of NMT for resource-constrained Indian languages. In view of many challenges like parallel corpus scarcity, we present a real-time adaptable translation model that works on the Fairseq framework. It provides high-accuracy translations for Assamese, Gujarati, Kannada, Bengali, Telugu, Tamil, Malayalam, Marathi, Hindi, Oriya, and Punjabi, thereby aiding in accessibility as well as communication. KEYWORDS: Machine Translation (MT), Neural Machine Translation (NMT), Indian Languages, Fairseq, Translation, Models, Parallel Corpora, Real-time Translation, Indic Languages, BLEU Scores, API (Application Programming Interface), Cross-Lingual Translation, Language Processing, Grammatical Structures, Morphology, Language Accessibility, Translation Evaluation, Under-resourced Languages, Chrome Extension, Speech-to-Text (STT), Text-to-Speech (TTS).