# Vakyansh: ASR Toolkit for Low Resource Indic languages

arXiv: https://arxiv.org/abs/2203.16512 · HTML: https://mlsanity.com/paper/2203.16512/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 18 references that are in the map.
- First posted on arXiv 30 Mar 2022.

## Builds on (most cited first, 6 of 6)

- [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://mlsanity.com/paper/2006.11477/) (2020)
- [ALBERT: A Lite BERT for Self-supervised Learning of Language Representations](https://mlsanity.com/paper/1909.11942/) (2019)
- [fairseq: A Fast, Extensible Toolkit for Sequence Modeling](https://mlsanity.com/paper/1904.01038/) (2019)
- [Multilingual and code-switching ASR challenges for low resource Indian languages](https://mlsanity.com/paper/2104.00235/) (2021)
- [CLSRIL-23: Cross Lingual Speech Representations for Indic Languages](https://mlsanity.com/paper/2107.07402/) (2021)
- [Automatic Speech Recognition in Sanskrit: A New Speech Corpus and Modelling Insights](https://mlsanity.com/paper/2106.05852/) (2021)

## Built on by (most cited first, 7 of 7)

- [Dialect Matters: Cross-Lingual ASR Transfer for Low-Resource Indic Language Varieties](https://mlsanity.com/paper/2601.04373/) (2026)
- [Benchmarking Automatic Speech Recognition for Indian Languages in Agricultural Contexts](https://mlsanity.com/paper/2602.03868/) (2026)
- [BhashaSutra: A Task-Centric Unified Survey of Indian NLP Datasets, Corpora, and Resources](https://mlsanity.com/paper/2604.18423/) (2026)
- [Temporally Aligning Long Audio Interviews with Questions: A Case Study in Multimodal Data Integration](https://mlsanity.com/paper/2310.06702/) (2023)
- [Rethinking Indic AI from a Lens of Cultural Heritage Preservation](https://mlsanity.com/paper/2607.06544/) (2026)
- [Comparative Analysis of Multilingual Pre-trained Models for Nepali Automatic Speech Recognition](https://mlsanity.com/paper/2608.12327/) (2026)
- [Model-Agnostic and Language-Agnostic Voice Pipeline Improvement for the Agriculture Domain](https://mlsanity.com/paper/2609.20504/) (2026)

## Abstract (arXiv)

We present Vakyansh, an end to end toolkit for Speech Recognition in Indic languages. India is home to almost 121 languages and around 125 crore speakers. Yet most of the languages are low resource in terms of data and pretrained models. Through Vakyansh, we introduce automatic data pipelines for data creation, model training, model evaluation and deployment. We create 14,000 hours of speech data in 23 Indic languages and train wav2vec 2.0 based pretrained models. These pretrained models are then finetuned to create state of the art speech recognition models for 18 Indic languages which are followed by language models and punctuation restoration models. We open source all these resources with a mission that this will inspire the speech community to develop speech first applications using our ASR models in Indic languages.