Building a Multilingual Voice AI Pipeline with Bhashini for Public Welfare Surveys
A real-time speech pipeline supporting 14 Indian languages using Bhashini — enabling automated voice surveys with under 2-second latency.
Platform
Web & Telephony
Duration
2 Months
<2s
Pipeline latency
14
Languages supported
0
Human translators needed
Project overview
Demonstrated that building production-grade multilingual voice systems for India doesn't require expensive commercial APIs or compromising on language coverage.
Platform
Web & Telephony
Duration
2 Months
Type
AI & Voice
Stack
8 technologies
The challenge
A public welfare program needed to conduct large-scale citizen surveys across multiple Indian states. The existing system only supported English, which meant the majority of the target population couldn't participate.
English-only surveys excluded the majority of the target population
Human translators created bottlenecks and couldn't scale beyond a few hundred calls per day
Commercial speech APIs lacked reliable support for most Indian regional languages
Per-minute pricing from commercial providers made large-scale deployment financially unviable
No unified pipeline existed that could handle speech recognition, translation, and synthesis in a single flow
What we set out to do
- 01
Build a unified STT → Translation → TTS pipeline supporting 14 Indian languages
- 02
Achieve end-to-end voice pipeline latency under 2 seconds
- 03
Integrate with Bhashini for sovereign, cost-effective speech services
- 04
Enable citizens to complete surveys entirely in their native language without human intervention
- 05
Pre-generate survey audio assets at scale using async queue processing
How we solved it
Bhashini API Integration
Integrated with Bhashini's Dhruva inference API for ASR, Neural Machine Translation, and TTS.
Key decision
Bhashini over Google Cloud Speech / AWS Transcribe
Result
Coverage for 14 Indian languages. Significantly more cost-effective at scale.
Automated Translation in the Pipeline
Automatic translation between any supported language pair using Bhashini's NMT service.
Key decision
Automated NMT over manual/human translation workflows
Result
Zero human translators needed. Full round-trip translation handled automatically.
Real-Time STT → NMT → TTS Pipeline
Three-stage pipeline with per-stage latency tracking and language-aware routing.
Key decision
Three-stage pipeline with per-stage latency tracking
Result
End-to-end pipeline latency under 2 seconds.
Pre-Generation & Dynamic Variables
Static survey content pre-generated via BullMQ queues. Dynamic content uses variable placeholders.
Key decision
Queue-based pre-generation + real-time synthesis for dynamic variables
Result
Zero TTS latency for static content. Personalized audio without sacrificing speed.
Measurable impact
<2s
End-to-end pipeline latency
14
Indian languages supported
0
Human translators needed
0ms
TTS latency for pre-generated survey audio
Tech stack
What we learned
This project demonstrated that building production-grade multilingual voice systems for India doesn't require expensive commercial APIs or compromising on language coverage.
- 01
Bhashini provides viable, production-ready speech AI for Indian languages — but requires careful model routing
- 02
Pre-generating static audio via queues eliminates TTS latency for known content
- 03
Dynamic variable support makes personalized, multilingual audio feasible at scale
- 04
Error isolation across pipeline stages is critical when depending on external APIs
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