Papers
arxiv:2603.28714

VAANI: Capturing the language landscape for an inclusive digital India

Published on Mar 31
Authors:
,
,
,
,
,
,
,
,
,
,
,
,
,
,
,
,
,
,
,
,

Abstract

The VAANI project creates a large-scale multi-modal dataset representing India's linguistic diversity with diverse audio, image, and transcription data across 112 languages from 165 districts.

AI-generated summary

Project VAANI is an initiative to create an India-representative multi-modal dataset that comprehensively maps India's linguistic diversity, starting with 165 districts across the country in its first two phases. Speech data is collected through a carefully structured process that uses image-based prompts to encourage spontaneous responses. Images are captured through a separate process that encompasses a broad range of topics, gathered from both within and across districts. The collected data undergoes a rigorous multi-stage quality evaluation, including both automated and manual checks to ensure highest possible standards in audio quality and transcription accuracy. Following this thorough validation, we have open-sourced around 289K images, approximately 31,270 hours of audio recordings, and around 2,067 hours of transcribed speech, encompassing 112 languages from 165 districts from 31 States and Union territories. Notably, significant of these languages are being represented for the first time in a dataset of this scale, making the VAANI project a groundbreaking effort in preserving and promoting linguistic inclusivity. This data can be instrumental in building inclusive speech models for India, and in advancing research and development across speech, image, and multimodal applications.

Community

Sign up or log in to comment

Get this paper in your agent:

hf papers read 2603.28714
Don't have the latest CLI?
curl -LsSf https://hf.co/cli/install.sh | bash

Models citing this paper 10

Browse 10 models citing this paper

Datasets citing this paper 2

Spaces citing this paper 3

Collections including this paper 0

No Collection including this paper

Add this paper to a collection to link it from this page.