if end of turn detection fails, is there a way to detect if the user has been silent for more than 10 seconds and force the AI to speak?

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

this is my current set up if is_english_language: selected_turn_detector = EnglishModel() turn_detector_name = “EnglishModel” logger.info(f”🎯 Turn detector: EnglishModel selected for English”) elif turn_detector_supported: selected_turn_detector = MultilingualModel() turn_detector_name = “MultilingualModel” logger.info(f”🎯 Turn detector: MultilingualModel selected - {language_name_safe} is supported”) else: selected_turn_detector = “vad” turn_detector_name = “VAD” logger.warning(f”⚠️ Turn detector: Falling back to VAD-only detection for {language_name_safe} (language not supported by MultilingualModel)“) # For turn detector to work properly with MultilingualModel, we should use “multi” # to let Deepgram auto-detect language and pass it to the turn detector if not is_english_language and turn_detector_supported: logger.info(f”🔧 Using Deepgram ‘multi’ mode for language detection with turn detector support”) stt_language_code_for_deepgram = “multi” # Let Deepgram auto-detect and pass language to turn detector else: stt_language_code_for_deepgram = stt_language_code or “multi” if not stt_language_code: logger.error(f”❌ Deepgram language code not found for ‘{language_name_safe}’. Using ‘multi’ as fallback.“) stt_language_code = “multi” stt_language_code_for_deepgram = “multi” openai_language_code = “en” if is_english_language else None openai_detect_language = openai_language_code is None logger.info(f”🔧 Language Configuration:“) logger.info(f” • Input language: ‘{languages.language_name}’ -> normalized: ‘{language_name_safe}‘“) logger.info(f” • Deepgram STT code: ‘{stt_language_code}’ -> using: ‘{stt_language_code_for_deepgram}‘“) logger.info(f” • OpenAI STT: lang={openai_language_code}, auto_detect={openai_detect_language}“) logger.info(f” • Turn detector supported: {turn_detector_supported}“) logger.info(f”🤖 Starting agent session with models:“) logger.info(f” • STT: Deepgram nova-3-general (lang: {stt_language_code_for_deepgram}), OpenAI gpt-4o-transcribe, AssemblyAI”) logger.info(f” • LLM: Anthropic claude-3-5-sonnet-20241022, Google gemini-2.5-flash”) logger.info(f” • TTS: {voices.provider} {voices.model_name}, OpenAI gpt-4o-mini-tts, Cartesia sonic-2") # Log final STT configuration before creating agent logger.info(f”🚀 Creating Agent with final configuration:“) logger.info(f” • Deepgram language: ‘{stt_language_code_for_deepgram}’ (auto-detect mode for turn detector)“) logger.info(f” • Turn detector: {turn_detector_name}“) logger.info(f” • Language supported by turn detector: {turn_detector_supported}“) agent_session = AgentSession( # llm=openai.realtime.RealtimeModel(model=“gpt-4o-realtime-preview”), # llm=google.beta.realtime.RealtimeModel( # model=“gemini-2.0-flash-live-001", instructions=system_content, api_key=envconfig.GEMINI_API_KEY # ), # vad=ctx.proc.userdata[“vad”], # turn_detection=MultilingualModel(), # llm=[ # openai.LLM(model=“gpt-4.1-mini”), # anthropic.LLM(model=“claude-3-5-sonnet-20241022”), # ] vad=ctx.proc.userdata[“vad”], turn_detection=selected_turn_detector, min_consecutive_speech_delay=0.8, stt=STTFallbackAdapter( vad=ctx.proc.userdata[“vad”], stt=[ deepgram.STT( model=“nova-3-general”, mip_opt_out=True, filler_words=True, language=stt_language_code_for_deepgram, ), StreamAdapter( stt=openai.STT( model=“gpt-4o-transcribe”, language=openai_language_code, detect_language=openai_detect_language, ), vad=ctx.proc.userdata[“vad”], ), assemblyai.STT(), ], attempt_timeout=20.0, max_retry_per_stt=2, retry_interval=5.0, ), llm=LLMFallbackAdapter( llm=[ anthropic.LLM(model=“claude-3-5-sonnet-20241022”), google.LLM(model=“gemini-2.5-flash”, api_key=envconfig.GEMINI_API_KEY), ], attempt_timeout=45.0, max_retry_per_llm=2, retry_interval=5.0, ), tts=TTSFallbackAdapter( tts=[get_voice(voices, languages)] + [ TTSStreamAdapter( tts=openai.TTS( voice=“alloy”, model=“gpt-4o-mini-tts”, instructions=openai_instructions(languages) ) ), cartesia.TTS(voice=“97f4b8fb-f2fe-444b-bb9a-c109783a857a”, model=“sonic-2"), ], max_retry_per_tts=4, ), ) await agent_session.start( room=ctx.room, # agent=Agent( # instructions=system_content # # tts=google.TTS(credentials_info=envconfig.GOOGLE_CREDENTIALS, voice_name=“Puck”), # ), agent=Agent( instructions=system_content, ), room_input_options=RoomInputOptions(noise_cancellation=noise_cancellation.BVC(), close_on_disconnect=False), )

If you want the AI to jump in after detecting silence for more than 10 seconds, you can tweak the silence detection settings like this: • For VAD: Just update the

minSilenceDuration

parameter to 10,000 milliseconds (which is 10 seconds). Here’s how:

vad.updateOptions({
  minSilenceDuration: 10000, // 10 seconds in milliseconds
});

• For MultilingualModel: When you create the agent, set the

min_endpointing_delay

parameter to 10,000 milliseconds. Like this:

const agent = new MultimodalAgent({
  model: model,
  vad: {
    minSilenceDurationMs: 10000, // 10 seconds in milliseconds
  },
});

With these changes, the system will wait for 10 seconds of silence before it assumes you’re done talking. If you want me to walk you through this or have any other questions, just let me know!

no I mean only if the turn detector model fails (which happens sometimes) I want a fallback that checks if participant has been silent for more than 10 seconds we should end the participant turn (if and only if it is the participant turn)

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