Building Vidya: An Ultra-Fast Bilingual Voice AI Tutor.
Over the past 10 days, as part of the #VoiceForBharat 10 Days of Voice Agents Challenge, I built Vidya — a real-time, bilingual (English/Hindi) AI Voice Tutor designed to make learning interactive, accessible, and human-like for students across India.
In this post, I’ll share the story of how Vidya came to life, dive into the architecture behind ultra-low latency voice agents, highlight the key features built over the 10 days, discuss the toughest challenges faced, and walk you through building your own production-ready voice agent using Murf Falcon TTS, LiveKit Agents, Deepgram STT, and Google Gemini.
1. Why Voice? Meet Vidya 🇮🇳
In India, text-based educational platforms often face a steep digital literacy and language barrier. Millions of learners feel intimidated by typing long queries or struggling through English-only user interfaces. Voice unlocks immediate, natural, and hands-free learning — allowing students to speak naturally in English, Hindi, or code-mixed Hinglish.
Vidya serves as a personal AI learning companion:
- Problem Solved: Overcomes literacy and language barriers by providing empathetic, spoken literacy and science lessons.
- Track & Target Audience: Education & Accessibility for students, young learners, and non-native English speakers across India.
- Why Voice is Essential: Voice removes UI friction. Learners can simply talk to Vidya, attempt spoken exercises, ask questions, and receive instant, warm, spoken feedback in a natural Indian accent.
2. Key Features Built Across the 10-Day Journey
Over 10 intensive days, Vidya grew from a basic echo bot into a multi-agent system equipped with tools, memory, telephony, analytics, and safety guardrails:
🎙️ 1. Ultra-Low Latency Indian Voice Powered by Murf Falcon
Using Murf Falcon TTS (livekit-murf), Vidya speaks with a natural, conversational Indian voice (Anisha). Streaming TTS with sentence tokenization (min_sentence_len=2) and text pacing delivers speech chunks with sub-second latency, giving the agent a human-like flow.
🇮🇳 2. Code-Mixed (Hinglish) & Bilingual Support
Powered by Deepgram Nova-3 STT (language="multi") and Google Gemini, Vidya fluently handles English, Hindi in Devanagari script, and code-mixed Hinglish phrases (e.g., "Namaste! Aaj hum beginner reading practice karenge.").
🧠 3. Personalized Memory for Returning Users
Vidya remembers returning students! Using a persistent profile store (user_store.py), Vidya recalls the user's name, preferred language, current learning level, and last interaction date, greeting them warmly:
"Namaste Aarav, welcome back! You were working on beginner exercises. Last seen on August 14."
🛠️ 4. Interactive Learning Tools & Scraper
Vidya is equipped with specialized function tools:
-
fetch_next_exercise: Retrieves level-appropriate practice prompts tagged with data freshness timestamps (last_updated). -
score_spoken_answer: Evaluates spoken pronunciations and answers on a 0–100 scale. -
award_learning_star: Awards virtual gold stars (🌟) to keep learners motivated. -
scrape_website: Fetches live web pages in real-time (web_scraper.py) for live context extraction.
📞 5. Outbound Telephony (SIP Calls)
Integrated with LiveKit's SIP Trunking (telephony/outbound/dial.py), Vidya can initiate active outbound phone calls to learners' mobile phones for daily study check-ins and practice sessions.
🛡️ 6. Human Escalation Engine with Explicit Consent
If a learner is stuck, frustrated, or requests a human teacher, create_escalation logs an escalation ticket (ESC-12345) and alerts support staff. Vidya follows strict privacy guardrails — asking for explicit permission before saving any personal details or submitting tickets.
📊 7. Call Analytics Dashboard
Session outcomes are tracked in call_store.py — logging call duration, agent type (browser vs. telephony), completion status (successful / failed), and success reasons (exercise_scored, star_awarded, escalated_to_human).
🔄 8. Dynamic Multi-Agent Handoffs
When a student asks a physics question (e.g., "Why does an apple fall from a tree?"), Vidya seamlessly hands off the conversation to Dr. Homi (Physics Specialist) using LiveKit's context.session.update_agent(). When physics practice ends, Dr. Homi hands the student back to Vidya for reading practice!
3. The Tough Parts: Lessons Learned
Building a real-time voice agent isn't just about linking APIs together. Here are three major hurdles faced and solved:
Challenge #1: Eliminating Awkward Conversational Latency
- The Problem: Combining STT, LLM, and TTS in sequence created a 2–3 second delay before the agent responded, ruining natural turn-taking.
-
The Solution: We enabled streaming at every tier — Deepgram Nova-3 streaming STT, Gemini Flash Lite for fast first-token generation, LiveKit's preemptive generation, and Murf Falcon's low-latency streaming TTS with sentence boundary tokenization (
min_sentence_len=2).
Challenge #2: Explicit Consent & Privacy in Spoken AI
- The Problem: Function tools automatically firing to record user profiles or create tickets without the user realizing.
- The Solution: We designed prompt-level guardrails requiring Vidya to explicitly ask out loud: "I can remember that for next time — may I save this?" or "May I share your details with a real teacher?" before invoking mutation tools.
Challenge #3: Preserving Session State During Agent Handoffs
- The Problem: Switching from Vidya to Dr. Homi mid-call could cause audio glitches or lose participant state.
-
The Solution: Leveraged LiveKit Agents SDK's native
session.update_agent()combined with WebRTC data channel events (agent_handoff) to update the Next.js frontend UI live without dropping the WebRTC room session.
4. Architecture Overview & Code Highlights
+------------------+ WebRTC Audio Stream +---------------------+
| | -------------------------> | Deepgram Nova-3 |
| Learner / UI | | Streaming STT |
| (Next.js App) | <------------------------- +----------+----------+
+--------+---------+ Real-time Audio Out |
^ v
| RTC Data Channel +---------------------+
| (State & Handoffs) | Google Gemini LLM |
| | (Flash Lite Model) |
+-------------------------------------- +----------+----------+
|
v
+---------------------+
| Murf Falcon TTS |
| (Streaming Indian) |
+---------------------+
Code Snippet: LiveKit Pipeline with Murf Falcon TTS (agent.py)
from livekit.agents import AgentSession, AgentServer, room_io
from livekit.plugins import deepgram, google, murf, silero, noise_cancellation
session = AgentSession(
stt=deepgram.STT(model="nova-3", language="multi"),
llm=google.LLM(model="gemini-3.5-flash-lite"),
tts=murf.TTS(
voice="Anisha", # Murf Falcon Indian accent voice
style="Conversation",
tokenizer=tokenize.basic.SentenceTokenizer(min_sentence_len=2),
text_pacing=True,
),
turn_detection=MultilingualModel(),
vad=ctx.proc.userdata["vad"],
preemptive_generation=True,
)
Code Snippet: Dynamic Multi-Agent Handoff (agent.py)
@function_tool
async def transfer_to_physics_specialist(self, context: RunContext, reason: str) -> str:
"""Hand off the conversation to Dr. Homi when user asks physics questions."""
logger.info("Handing off conversation to PhysicsSpecialist. Reason: %s", reason)
specialist = PhysicsSpecialist()
context.session.update_agent(specialist)
# Notify Next.js frontend UI via WebRTC data channel
payload = json.dumps({
"type": "agent_handoff",
"from_agent": "Vidya (Literacy Tutor)",
"to_agent": "Dr. Homi (Physics Specialist)",
"message": "🔄 Switched conversation to Physics Specialist (Dr. Homi)"
})
await context.room.local_participant.publish_data(payload=payload.encode("utf-8"))
return "I will connect you to our physics specialist."
5. How to Build Your Own Voice Agent (Quickstart)
Want to build your own voice AI agent? You can clone and run our open-source repository in minutes!
Step 1: Clone the Repository
git clone https://github.com/hotokeAtlast/murf-livekit-starter.git
cd murf-livekit-starter
Step 2: Set Up Backend Environment Keys
Copy backend/.env.example to backend/.env.local and fill in your keys:
LIVEKIT_URL=wss://your-livekit-project.livekit.cloud
LIVEKIT_API_KEY=your_key
LIVEKIT_API_SECRET=your_secret
MURF_API_KEY=your_murf_api_key
DEEPGRAM_API_KEY=your_deepgram_api_key
GOOGLE_API_KEY=your_google_gemini_api_key
Step 3: Run the Python Backend
cd backend
uv sync
uv run python src/agent.py dev
Step 4: Run the Next.js Frontend
In a new terminal:
cd frontend
pnpm install
pnpm dev
Open http://localhost:3000, click Connect, and start talking to your voice agent!
6. Links & Repository
- 🐙 GitHub Repository: murf-livekit-starter on GitHub
- 🔊 TTS Engine: Powered by Murf Falcon Streaming API
- ⚡ Voice Framework: Built with LiveKit Agents SDK
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