How can I force the STT tasks to finish if the user keeps in silence? Currently I'm using a timer which runs

await self.session.generate_reply(user_input="")

. But this approach deletes the STT related events which I need for business logic.

Thanks for reaching out, Tarek-Asis. I'm an AI assistant and I'm here to help. Give me a moment while I research this topic.

But I'm also using VAD and MultiLengual Turn Detection model

Thanks — you're asking how to force STT to finish on silence without losing STT events while using VAD and the Multilingual turn-detection model. Short answer: do NOT inject empty input. Either let the Multilingual model + VAD handle end-of-turn by tuning its timing, or switch to manual turn control and call commit_user_turn() from your silence timer — that preserves STT events. Two practical options: • Keep Multilingual + VAD active and tune detection parameters (e.g., increase max/threshold delays or min silence) so the model waits through short pauses before ending the turn. This avoids manual intervention and preserves all STT events. • Use manual turn control when you need explicit control: a. Set turn_detection="manual" on the session. b. Continue using VAD for UI/voice activity feedback only. c. When your silence timer fires, call await self.session.commit_user_turn() (not generate_reply("")) — this commits the current transcription and preserves STT events for your business logic. d. Only stop audio/input after commit_user_turn() to ensure all buffered STT data is processed. Which would you prefer — tuning the Multilingual model or switching to manual control? Tell me your SDK (Python/JS), session class, and the silence timeout you use and I’ll give a concrete code snippet. Sources: Turns overview | LiveKit Documentation | Turn Detection for Voice Agents: VAD, Endpointing, and Model-Based Detectio... | Using a transformer to improve end of turn detection | LiveKit | Voice Agent Architecture: STT, LLM, and TTS Pipelines Explained | LiveKit | Turn Detection for Voice Agents: VAD, Endpointing, and Model-Based Detectio..., with interactive elements

Could I get empty responses tuning the Multilingual model?

Good question — yes, overly aggressive tuning of the Multilingual turn-detection model can increase the chance of empty agent replies if the model closes the user turn on silence before meaningful transcript content is recognized. What can cause empty responses • If the model’s max/min delays or silence thresholds are too short, it may decide the turn is complete while only transcribing filler/noise or nothing yet, producing an empty user input for the downstream agent. • If the LLM/response generator runs on very small or partial transcripts, it may produce no useful reply. How to avoid empty responses • Increase the model’s patience: raise

max_delay

or the silence timeout so the detector waits through short pauses and filler (e.g., “uh”, “mm”) before ending the turn. • Require a minimum transcript length or semantic confidence before committing a turn. Only allow the turn to complete if STT produced N characters/words or confidence > threshold. • Use VAD as a gate: require a sustained VAD silence window (e.g., 300–700 ms) plus the Multilingual model’s semantic end-of-turn signal. • Post-commit validation: after the Multilingual model signals end-of-turn, check the accumulated transcript; if it’s empty or below threshold, either extend waiting or trigger a re-prompt/clarification instead of calling the agent. • If you need deterministic control, switch to manual turn control and call

commit_user_turn()

only when your combined rules (VAD silence + transcript length/confidence) are satisfied. If you tell me your SDK (Python/JS), which parameters you can set (e.g.,

min_delay

,

max_delay

), and your current silence timeout, I’ll provide a concrete tuning example or code snippet. Which would you like? , with interactive elements

I'm using Python and I need that if the user keeps in silence for more than 8 seconds, our LLM gets an empty response

I don't have the answer you're looking for. You could also try asking your question: • in one of the other Slack channels or • to https://deepwiki.com/livekit/livekit_composite which is trained on all LiveKit source code If you find the answer, please post it here to help others!