Enhancing LLMs with Indian Multi-Lingual Audio Understanding for Artificial General Intelligence (AGI) Advancement

The creation of Artificial General Intelligence (AGI) includes training machines to understand human communication in the style of real-time conversations. In this paper, the shortcomings of Large Language Models (LLMs) in processing audio data of various languages and contexts, especially in linguistically diverse environments like India, are identified and pose the challenge of phonetic variations, tones, and nuanced contextual usage. With the integration of pre-trained text-based LLMs with multi-lingual and multi-task audio perception, the process includes ongoing pre-training based on audio encodings of Indian multi-lingual data. With the usage of sophisticated audio handling methodologies and multimodal data, the model is trained to provide high-quality transcriptions and resilient contextual understanding. A multi-step framework of dataset curation, preprocessing, and model training focuses on metrics like Word Error Rate (WER) and BLEU scores. Outcomes show a significant lowering of WER by 15% and an increase in BLEU scores by 20% with improved transcription accuracy and audio understanding. The paper adds to the AGI mission by creating an adaptive, culture-sensitive AI system with effective multi-lingual communication, supporting applications like voice assistants, real-time translation, and accessibility aid, while addressing the computational efficiency and diversity of data challenge.