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I have implemented STT-> LLM -> TTS pipeline...
# ask-ai
l
I have implemented STT-> LLM -> TTS pipeline. with silero VAD and turn_detection=EnglishModel(), while user turn my user provide long input with small pauses in between. but before user completed the user turn agent start llm request. how to overcome this
✅ 1
t
Thanks for reaching out, Rajadurai. I'm an AI assistant and I'm here to help. Give me a moment while I research this topic.
Thanks — I understand you’re seeing the agent start the LLM request while a user is still speaking with short pauses. I’ll help you stop those premature triggers. Two quick questions so I can recommend the best fix: 1. Are you using the built-in `MultilingualModel()`/`EnglishModel()` turn detector, or only Silero VAD for endpointing? 2. What are your current VAD/turn-handling settings (e.g., 
min_silence_duration
, 
min_endpointing_delay
)? If you want an immediate checklist to try, do these: • Increase silence threshold in Silero VAD ◦ Raise 
min_silence_duration
(default ~0.55s) to 0.9–1.5s so short pauses aren’t treated as end-of-speech. • Add endpointing delay ◦ Increase 
min_endpointing_delay
(default ~0.5s) to 0.8–1.5s so the system waits a bit after silence before triggering. • Use the model-based detector with conservative settings ◦ Keep the `MultilingualModel()`/`EnglishModel()` enabled but pair it with longer VAD silence and endpointing delay so semantic predictions don’t fire on short pauses. • Apply confidence gating (if available) ◦ Ignore turn-detection predictions below a confidence threshold so low-confidence "end" guesses don’t trigger the LLM. • If you need real-time but robust behavior, combine VAD + model ◦ Let Silero handle raw silence detection with longer silence durations, while the turn model confirms end-of-turn before LLM call. Example configuration to try:
Copy code
session = AgentSession(
    vad=silero.VAD.load(min_silence_duration=1.0),
    turn_handling=TurnHandlingOptions(
        turn_detection=MultilingualModel(),
        endpointing={"min_delay": 1.0},
    ),
    ...
)
If you tell me which detector you’re using and your current values for 
min_silence_duration
and endpointing delay, I’ll suggest exact numbers and one or two targeted tweaks to try next. Sources: Turns overview | LiveKit Documentation | Turn Detection for Voice Agents: VAD, Endpointing, and Model-Based Detectio... | Silero VAD plugin | LiveKit Documentation | Build Your First AI Voice Agent in Python: Complete Tutorial | LiveKit, with interactive elements
l
session = AgentSession( stt=stt, llm=llm, tts=tts, #turn_detection=EnglishModel(), turn_handling=TurnHandlingOptions( turn_detection=EnglishModel(), interruption={ "mode": "vad", }, ), vad=ctx.proc.userdata["vad"], preemptive_generation=True, userdata=verification_userdata, false_interruption_timeout=1.0, resume_false_interruption=True, ) my session config
t
Thanks — I see your session config and what you’re trying to stop: premature LLM requests when the user has short pauses. Good info. Try these targeted changes (small, incremental): 1. Increase VAD silence threshold ◦ Set 
vad = silero.VAD.load(min_silence_duration=1.0)
(or 1.2–1.5s) so short pauses don’t mark speech end. 1. Add / increase endpointing delay in turn handling ◦ Use 
turn_handling=TurnHandlingOptions(..., endpointing={"min_delay": 1.0})
to wait after silence before confirming end-of-turn. 1. Adjust false interruption / resume timing ◦ Keep 
false_interruption_timeout=1.0
but consider increasing to 1.2–1.5s if users often pause longer. ◦ 
resume_false_interruption=True
is good — it lets user continue the same turn if resumed. 1. Make preemptive generation conservative ◦ If 
preemptive_generation=True
is causing early LLM calls, try 
False
or enable it only after you raise silence/endpointing thresholds. 1. Keep model-based detection but require stronger evidence ◦ Continue 
turn_detection=EnglishModel()
but pair it with longer VAD silence and endpointing so semantic predictions don’t fire on brief pauses. Example to try:
Copy code
session = AgentSession(
    stt=stt,
    llm=llm,
    tts=tts,
    turn_handling=TurnHandlingOptions(
        turn_detection=EnglishModel(),
        endpointing={"min_delay": 1.0},
        interruption={"mode": "vad"},
    ),
    vad=silero.VAD.load(min_silence_duration=1.2),
    preemptive_generation=False,
    false_interruption_timeout=1.2,
    resume_false_interruption=True,
    userdata=verification_userdata,
)
If that still cuts off users, tell me: • how long typical pauses are (avg ms), • whether premature triggers happen before or after silence detection, and • whether 
preemptive_generation
produced partial LLM requests (yes/no). I’ll recommend tuning values based on those specifics. , with interactive elements
l
how to update this parameter in between the session
t
Thanks — I looked at your session config. Short answer: You cannot update VAD or turn-detection/endpointing parameters while an AgentSession is already running. VAD settings (like 
min_silence_duration
) and 
TurnHandlingOptions
(e.g., 
endpointing
delays, 
turn_detection
) must be set when you create the session. To change them you need to recreate the AgentSession with the new values. What you can do instead: • Use dynamic endpointing (enable at session creation) so delays adapt automatically to the conversation. • Tune parameters at startup (higher 
min_silence_duration
, larger 
endpointing
min_delay
) and disable 
preemptive_generation
if you want to avoid premature LLM calls. • Keep 
resume_false_interruption=True
and raise 
false_interruption_timeout
to allow users to continue after brief pauses without restarting the turn. If you want, I can: • Suggest exact startup values based on typical pause length (tell me the average pause length), or • Provide a code snippet showing how to enable dynamic endpointing and restart the session with new settings. Which would you like? Sources: Silero VAD plugin | LiveKit Documentation | LiveKit turn detector plugin | LiveKit Documentation | Adaptive interruption handling | LiveKit Documentation | Turns overview | LiveKit Documentation, with interactive elements
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