i am running agentsession with these parameters, i want to ensure i have best quality interruption handling, end of sentence/turn detection and voice activity detection. AgentSession( vad=silero.VAD.load(), stt=deepgram.STT(model="nova-3", language="multi", interim_results=False), # en-US, llm=groq.LLM(model="meta-llama/llama-4-scout-17b-16e-instruct", temperature=0.2), tts=deepgram.TTS(model="aura-2-andromeda-en",sample_rate=24000), ) Is this configuration correct?

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Can u provide me with both, also should turn_detection="vad" be used or multilingual?

Thanks — I’ll give you both: (A) import/install & download steps for the turn detector, and (B) recommended numeric values and when to use them. Also: use

turn_detection=MultilingualModel()

(not

"vad"

) for best end-of-turn and interruption handling. A) Install / import / download 1. Install (Python)

uv add "livekit-agents[turn-detector,silero]~=1.3"

1. Imports in your agent script

from livekit.agents import AgentSession
from livekit.plugins.turn_detector.multilingual import MultilingualModel
from livekit.plugins import silero
# your STT/LLM/TTS imports (Deepgram, Groq, etc.)

1. Download model weights (run once before starting)

uv run agent.py download-files

If you use a custom runner, ensure a

download-files

command runs the agent’s downloader (the quickstart shows exact scripts). B) Recommended configuration and numeric tuning Use this minimal recommended session plus tuned thresholds:

session = AgentSession(
  vad=silero.VAD.load(),
  stt=deepgram.STT(model="nova-3", language="multi", interim_results=True),
  llm=groq.LLM(model="meta-llama/llama-4-scout-17b-16e-instruct", temperature=0.2),
  tts=deepgram.TTS(model="aura-2-andromeda-en", sample_rate=24000),
  turn_detection=MultilingualModel(),
  preemptive_generation=True,
  # endpointing
  min_endpointing_delay=0.5,    # respond quickly when model is confident
  max_endpointing_delay=6.0,    # wait longer if model predicts continuation
  # interruption tuning
  allow_interruptions=True,
  min_interruption_duration=0.5,  # avoid false interrupts from short noises
  min_interruption_words=1,       # require at least one recognized word
)

When to change these values • Noisy environments: increase

min_interruption_duration

to 0.8–1.0s and consider noise suppression upstream. • Fast back-and-forth dialogs (short utterances): lower

min_endpointing_delay

to 0.3s to reduce latency, but test for false cuts. • Very long user monologues: increase

max_endpointing_delay

(8–10s) to avoid premature cutoffs. • Multilingual conversations: keep

language="multi"

and use

MultilingualModel()

. Why choose

MultilingualModel()

over

"vad"

• `vad`: uses silence only — simple and low-resource but causes false cuts or long waits. • `MultilingualModel()`: combines VAD + interim STT + context to predict end-of-turn — far better for natural conversational flow and interruption handling. Use

"vad"

only when STT/interim results are unavailable or you must minimize resources. If you want, I can: • Suggest exact threshold values tailored to sample audio (typical utterance length and SNR), or • Provide the exact

download-files

script contents and where model weights are stored. Which would you prefer? Sources: Turns overview | LiveKit docs | examples/voice_agents | livekit/livekit_composite | DeepWiki | LiveKit turn detector plugin | LiveKit docs | Voice AI quickstart | LiveKit docs

yes - Suggest exact threshold values tailored to sample audio (typical utterance length and SNR)

Thanks — to give precise thresholds I need two quick samples about your audio environment: 1. Typical user utterance length (average spoken turn): short (0.5–2s), medium (2–6s), or long (6–20s)? 2. Typical SNR / noise level: quiet (SNR > 20 dB), moderate (10–20 dB), or noisy (< 10 dB)? Share those two items and I’ll return exact values for: • min_endpointing_delay / max_endpointing_delay • min_interruption_duration / min_interruption_words • interim buffer / preemptive generation timing suggestions • any recommended noise-suppression or AGC settings.