Towards Building Large Scale Datasets and State-of-the-Art Automatic Speech Translation Systems for 14 Indian Languages

TL;DR

This paper introduces BhasaAnuvaad, the largest speech translation dataset for Indian languages, and develops IndicSeamless, a state-of-the-art model that significantly improves translation quality across 14 languages.

Contribution

The paper presents a large-scale, diverse speech translation dataset for Indian languages and a new model that advances the state-of-the-art in speech translation performance.

Findings

BhasaAnuvaad contains over 44,000 hours of audio and 17 million aligned segments.

IndicSeamless outperforms existing models on Indian language speech translation tasks.

Open-source release of data, code, and models to foster further research.

Abstract

Speech translation for Indian languages remains a challenging task due to the scarcity of large-scale, publicly available datasets that capture the linguistic diversity and domain coverage essential for real-world applications. Existing datasets cover a fraction of Indian languages and lack the breadth needed to train robust models that generalize beyond curated benchmarks. To bridge this gap, we introduce BhasaAnuvaad, the largest speech translation dataset for Indian languages, spanning over 44 thousand hours of audio and 17 million aligned text segments across 14 Indian languages and English. Our dataset is built through a threefold methodology: (a) aggregating high-quality existing sources, (b) large-scale web crawling to ensure linguistic and domain diversity, and (c) creating synthetic data to model real-world speech disfluencies. Leveraging BhasaAnuvaad, we train IndicSeamless, a state-of-the-art speech translation model for Indian languages that performs better than existing models. Our experiments demonstrate improvements in the translation quality, setting a new standard for Indian language speech translation. We will release all the code, data and model weights in the open-source, with permissive licenses to promote accessibility and collaboration.