faint-gold-41196
02/18/2026, 6:43 PMtall-airline-16756
02/18/2026, 6:43 PMtall-airline-16756
02/18/2026, 6:43 PMlanguage="multi").
• Use a responsive VAD (e.g., Silero VAD) to handle quick interruptions and improve latency for end-of-turn decisions.
• If your use case involves code‑switching (frequent language switches inside a single session), ensure your STT is configured for automatic language switching so turn detection sees accurate transcripts and prosody cues.
Example configuration (conceptual)
session = AgentSession(
turn_detection=MultilingualModel(),
stt=inference.STT(language="multi"),
vad=silero.VAD.load(),
# ... other components like llm, tts
)
A quick question to tailor the advice
• Are you primarily targeting a single Indian language (e.g., Hindi) or building for multiple languages and code-switching? That will help me suggest STT settings, VAD tuning, and any deployment tips.
If you share your primary languages and whether users will frequently switch languages mid‑call, I’ll give a short, actionable setup checklist.
Sources: LiveKit turn detector plugin | LiveKit Documentation | Improved End-of-Turn Model Cuts Voice AI Interruptions 39% | Improved End-of-Turn Model Cuts Voice AI Interruptions 39% | Sarvam STT plugin guide | LiveKit Documentation | Build a multilingual voice agent that automatically switches languages | Li...