the STT deepgram eou latency is wrong. what to do?

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

I’m talking about end_of_utterance_delay

To help you reduce the

end_of_utterance_delay

in LiveKit when using Deepgram, here are some friendly steps you can follow: 1. Adjust Deepgram’s `endpointing_ms`: Try lowering this value to shorten the silence duration before speech is finalized. For example:

from livekit.plugins import deepgram

stt = deepgram.STT(
    endpointing_ms=100,  # Adjust in milliseconds; set to 0 to disable
)

1. Modify LiveKit’s endpointing delays: You can reduce

min_endpointing_delay

and

max_endpointing_delay

in your session configuration to make things snappier:

from livekit.agents.voice import AgentSession

session = AgentSession(
    min_endpointing_delay=0.2,  # Reduced from default 0.4s
    max_endpointing_delay=1.0,  # Reduced from default 6.0s
)

1. Enable LiveKit’s turn detection model: This semantic turn detector can help adjust delays dynamically based on the flow of conversation, which is pretty neat:

from livekit.plugins import turn_detector

detector = turn_detector.TurnDetector()

session = AgentSession(
    turn_detector=detector,
    min_endpointing_delay=0.2,
    max_endpointing_delay=1.0,
)

1. Monitor and validate: Keep an eye on the

eou_metrics_collected

event so you can track and fine-tune the delay as needed:

def on_eou_metrics(metrics):
    print(f"End-of-utterance delay: {metrics.end_of_utterance_delay} seconds")

stt.on("eou_metrics_collected", on_eou_metrics)

I hope these tips help you find the right balance between responsiveness and accuracy. If you have any questions or want to explore this further, just let me know—I’m here to help! Sources: How does end-of-utterance detection work in conversations? | livekit-agents/livekit/agents/voice/agent_session.py | Improving voice AI's turn detection with transformers | Capturing metrics | LiveKit Docs