Indic-Conformer model for ASR

Bhashini - The Indo-Aryan Indic-Conformer is a multilingual automatic speech recognition (ASR) model designed specifically for North-Indian languages. It is based on the Conformer large architecture, which is known for its efficiency and accuracy in processing speech signals. The model contains 115 million parameters, enabling it to effectively transcribe spoken language into text with high precision.

This ASR model has been trained on the Shrutlip dataset, a rich dataset designed to enhance automatic speech recognition capabilities in Indian languages. The model primarily supports the Odia language and has been developed by AI4Bharat, a leading research initiative focused on advancing AI-driven solutions for Indian languages.

With a batch processing setup, this model is optimized for large-scale speech-to-text tasks across general domains. It is a valuable resource for applications in speech transcription, voice-enabled interfaces, digital accessibility, and natural language processing (NLP) research. Given the increasing demand for multilingual ASR systems, this model serves as a foundational tool for improving speech technology in India’s diverse linguistic landscape.

The Indo-Aryan Indic-Conformer is open-source, and its implementation is available on GitHub, making it accessible for researchers, developers, and AI practitioners working in the domain of Indian language speech processing.

For more details about the use of model, refer to github: https://github.com/AI4Bharat/IndicTrans2/tree/main