Real Time Speech Translation between Indian Languages

Language diversity presents a significant challenge in multilingual communication, particularly in a country like India, which has 121 languages and 19,500 dialects. Traditional translation methods, such as human interpreters or text-based translation apps, often fail to provide real-time accuracy and accessibility. In order to accomplish smooth audio translation, this research suggests a revolutionary Real-Time Speech-to-Speech Translation System that combines natural language processing and neural machine translation. To enable instant spoken language conversion, the system combines text-to-text, speech-to-text, and text-to-speech features with OpenNMT, an open-source deep learning framework for machine translation. The primary implementation focuses on Tamil to Hindi translation, achieving high accuracy while preserving semantic context and natural speech flow. Benchmarking against existing translation systems demonstrates improved efficiency, real-time adaptability, and enhanced language model training. This research contributes to language accessibility, cross-cultural communication, and the SDG goals related to education and reduced inequalities.