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Sometimes, while answering questions or even in th...
# ask-ai
m
Sometimes, while answering questions or even in the initial message, the response stops midway and does not complete the sentence.
t
Thanks for reaching out, Yashish. I'm an AI assistant and I'm here to help. Give me a moment while I research this topic.
Responses stopping or being cut off mid-sentence are typically caused by interruption detection and turn-taking mechanisms. Common reasons include: • Audio interruptions: If playback is interrupted (for example, a user speaks or another event occurs), the system truncates the response at the interruption point, preserving only the part that was played. • Turn detection: The system may prematurely detect that it should stop speaking if voice activity detection (VAD) or other turn-taking strategies signal the end too early. This is affected by configuration parameters like 
silence_duration_ms
(how quickly the system decides a turn has ended after silence) and endpointing delays. • Agent interruption configurations: The 
allow_interruptions
setting can affect whether the agent can be interrupted mid-sentence. • Technical processing: Audio buffer handling and text-to-speech (TTS) output may also contribute, especially if misconfigured or if certain TTS models previously suffered from chunking bugs (now addressed). To troubleshoot: 1. Review your VAD, 
silence_duration_ms
, and endpointing delay settings to match realistic conversation patterns. 2. Check interruption logs for events where playout was stopped unexpectedly. 3. Verify 
allow_interruptions
behavior on your agent and adjust as required. Proper tuning of these parameters can reduce unwanted response cutoffs while keeping interactions natural. Sources: livekit-plugins/livekit-plugins-aws/livekit/plugins/aws/experimental/realti... | How does end-of-utterance detection work in conversations? | OpenAI Realtime API integration guide | LiveKit Docs | pipeline-llm/interrupt_user.py | ConversationItemTruncateEvent | LiveKit Agents
m
provide its implementaion in python code
t
Hey there! Let me walk you through a friendly Python example of how LiveKit's AgentSession can help manage conversations more naturally, making sure no one gets cut off mid-sentence.
Copy code
from livekit.agents.voice import AgentSession
from livekit.plugins import silero

# Configure the agent session with interruption and turn-taking control
session = AgentSession(
    turn_detection="vad",                  # Use voice activity detection or a model
    allow_interruptions=True,               # Allow the user to interrupt the agent
    min_interruption_duration=0.5,          # Minimum time to trigger interruption (seconds)
    min_endpointing_delay=0.5,              # Wait time before considering a turn complete
    max_endpointing_delay=6.0,              # Maximum wait time for end-of-turn
    vad=silero.VAD.load()                   # Voice Activity Detection plugin
)

# Example: Say something without allowing interruption for this utterance
session.say("This message cannot be interrupted.", allow_interruptions=False)

# Manually interrupt agent (e.g., upon external event)
await session.interrupt()

# Listen to agent/session events for advanced use
@session.on("user_input_transcribed")
def on_user_input(transcript):
    print(f"User said: {transcript.transcript}")
Think of this like having a smooth, respectful conversation where everyone gets a chance to speak. You can adjust the interruption settings to suit your specific needs, making the interaction feel more natural and less robotic. Cool, right? Feel free to tweak the parameters and plugins to fit exactly what you're looking for! Sources: livekit-plugins/livekit-plugins-google/livekit/plugins/google/beta/realtime... | Turn detection and interruptions | LiveKit Docs | complex-agents/turn-taking/agent.py | Turn detection and interruptions | LiveKit Docs
m
Here i am using vad=silero.VAD.load(), turn_detection=MultilingualModel(), )
t
Hey there! Let me walk you through a super cool way to make your LiveKit agent sound more natural when it's chatting. By combining 
vad=silero.VAD.load()
and 
turn_detection=MultilingualModel()
in 
AgentSession
, you'll create an agent that listens and responds just like a human would. • Silero VAD is like having a lightning-fast speech detective that spots when someone's talking or staying quiet. • MultilingualModel is your language superhero - it understands conversation nuances across English and 13+ languages, making sure your agent doesn't jump in too quickly. Here's how you can set it up:
Copy code
from livekit.agents import AgentSession
from livekit.plugins.turn_detector.multilingual import MultilingualModel
from livekit.plugins import silero, deepgram

session = AgentSession(
    vad=silero.VAD.load(),
    turn_detection=MultilingualModel(),
    stt=deepgram.STT(model="nova-3", language="multi"),
    # ... other components such as llm, tts
)
• Grab the dependencies with: 
pip install "livekit-agents[turn-detector]"
• Don't forget to download those model weights: 
python agent.py download-files
• Pro tip: You can totally tweak 
min_endpointing_delay
and 
max_endpointing_delay
to fine-tune the conversation. Trust me, this setup is like giving your agent a communication superpower - smoother conversations, fewer awkward interruptions, and a more natural chat flow. Just make sure you've got a speech-to-text plugin to complete the magic! Sources: Turn detection and interruptions | LiveKit Docs | LiveKit turn detector plugin | LiveKit Docs | Turn detection and interruptions | LiveKit Docs | examples/voice_agents/zapier_mcp_integration.py
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