In a rustic as culturally numerous and linguistically wealthy as India, constructing inclusive AI begins with gathering consultant, high-quality datasets. That’s the imaginative and prescient behind Venture Vaani—a large-scale, open-source initiative led by ARTPARK, IISc Bengaluru, and Google, aiming to offer voice to each Indian language and dialect.
The formidable objective? To gather 150,000+ hours of speech and 15,000+ hours of transcriptions from 1 million folks throughout 773 districts of India.
As one of many key distributors for this nationwide mission, Shaip performed a pivotal function in curating spontaneous speech information, transcription, and metadata assortment—laying the groundwork for equitable voice applied sciences that actually characterize the true India.
The Imaginative and prescient Behind Venture Vaani
Venture Vaani is designed to bridge the AI inclusion hole by creating the largest multimodal, multilingual, open-source dataset in India. This information is foundational for growing correct speech recognition, translation, and generative AI techniques in native Indian languages—a lot of that are underrepresented in world tech ecosystems.
The long-term imaginative and prescient is to energy impactful functions in:
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Healthcare – Voice-based telemedicine -
Schooling – Vernacular studying platforms -
Governance – Conversational interfaces for citizen companies -
Accessibility – Voice instruments for differently-abled customers -
Catastrophe response – Actual-time communication in native dialects
How Shaip Helped Construct India’s Largest Open-Supply Speech Dataset for Venture Vaani
Shaip was entrusted with the gathering of 8,000 hours of spontaneous speech and 800 hours of manually verified transcriptions. Our accountability spanned speaker onboarding, audio seize, metadata tagging, transcription coordination, and high quality management.
8,000 hours of spontaneous audio information
800 hours of high-quality guide transcriptions
Recordings from 400+ native audio system per district, representing numerous age teams, genders, and dialects
80 districts, lined
Picture-based prompting to make sure pure, contextual speech
Right here’s what made our method distinctive:
District-Stage Variety
We sourced recordings from 80 districts unfold throughout states like Bihar, Uttar Pradesh, Karnataka, West Bengal, and Maharashtra. Every district contributed 100 hours of audio information, making certain regional steadiness. We engaged native audio system, making certain illustration of regional accents and dialects typically ignored in mainstream AI datasets.
Linguistic & Demographic Illustration
We sourced recordings from 80 districts unfold throughout states like Bihar, Uttar Pradesh, Karnataka, West Bengal, and Maharashtra. Every district contributed 100 hours of audio information, making certain regional steadiness. We engaged native audio system, making certain illustration of regional accents and dialects typically ignored in mainstream AI datasets.
Picture-Prompted Speech
To stimulate spontaneous and pure vocabulary, contributors have been proven 45–90 pictures per session and requested to explain them. Members have been prompted utilizing numerous pictures—starting from cultural symbols to on a regular basis objects—to elicit pure, spontaneous responses of their native language. This ensured recordings mirrored real-world, contextual speech—important for coaching superior NLP techniques.
Excessive-High quality Transcription Requirements
Solely 10% of speech information was transcribed—amounting to 800 hours. Transcriptions have been carried out by native linguists inside a 20–50 km radius of the speaker, making certain familiarity with dialects and nuances. A second-layer examine ensured <5% phrase error price (WER).
Strict High quality Assurance
Audio information needed to meet a excessive bar: no background noise, echoes, cellphone vibrations, or distortions. Audio was recorded in quiet, echo-free environments. Recordsdata underwent rigorous evaluation to satisfy tips for speech readability, noise ranges, metadata accuracy, and speaker verification. Metadata tagging needed to be correct throughout all information, and all recordings have been checked for speaker and placement alignment.
Challenges We Solved
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Distant logistics – Managing groups throughout 80 districts -
Speaker range – Onboarding 32,000+ verified audio system in distant places -
Cultural sensitivity – Respecting native customs and dialects -
Knowledge integrity – Assembly high quality and compliance requirements -
High quality Management – throughout a number of linguistic and cultural contexts
Our success got here all the way down to meticulous planning, technology-driven validation, and partnerships with native groups who understood the cultural nuances of every area.
Influence and Functions
Shaip’s contribution has not solely accelerated the progress of Venture Vaani but additionally set the inspiration for inclusive AI in India. The curated speech dataset is already getting used to construct and fine-tune AI fashions for:
- Vernacular voice assistants
- Regional translation engines
- Accessible communication instruments for the visually impaired
- AI-driven edtech platforms for rural college students
- Rural telemedicine
- Voice-based citizen companies
- Actual-time translation and transcription
Conclusion
Venture Vaani is a daring step towards inclusive, accessible AI—and Shaip is honored to play a foundational function. Shaip’s work on Venture Vaani reaffirms our dedication to constructing moral, inclusive AI techniques rooted in range and illustration. With over 8,000 hours of speech collected and 800 hours transcribed, we’re proud to have performed an element in one in all India’s most visionary digital inclusion initiatives.
As Venture Vaani continues towards its bigger objective of 150,000+ hours of knowledge, we stand able to help the following frontier of AI innovation that speaks to—and for—each Indian.
Need to associate with us to construct AI that understands the true world? www.shaip.com
