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I see this log in production { “message”: “EO...
# ask-ai
c
I see this log in production { “message”: “EOU metrics”, “level”: “INFO”, “name”: “livekit.agents”, “model_name”: “multilingual”, “model_provider”: “livekit”, “end_of_utterance_delay”: 17.69, “transcription_delay”: 17.56, “pid”: 146058, “job_id”: “…”, “room_id”: “…, “timestamp”: “2026-03-12T060745.199400+00:00" } Why is end_of_utterance_delay so large. It happened multiple times. The language is English
t
Thanks for reaching out, Abhishek. I'm an AI assistant and I'm here to help. Give me a moment while I research this topic.
Thanks — I looked at the log you shared (EOU = 17.69s, transcription_delay = 17.56s) and I understand why you’re concerned. Below I’ll explain the likely causes and give targeted next steps you can take to find and fix the source of the long delay. What this metric means • 
end_of_utterance_delay
measures the time between when a user stops speaking and when the system decides the utterance is complete and triggers the agent to respond. 17.69 seconds is far above normal and indicates a significant delay in the EOU pipeline. Most likely causes (based on your matching transcription_delay) • STT finalization delay: The transcription took ~17.56s to finalize, so EOU decision was delayed waiting for the transcript. When transcription finalization is delayed, EOU is delayed too. • VAD/endpointing behavior: Long or soft pauses, low volume, or noisy audio can prevent VAD from detecting silence, causing the system to wait much longer before ending the turn. • Model inference / CPU throttling: The turn-detection model or other inference tasks can be slow if the host is CPU constrained (e.g., burstable instances exhausted of credits). That can push inference from milliseconds to seconds. • Fallback/timeout misconfiguration: A misconfigured or very long fallback timeout for EOU (or using 
end_of_utterance_mode
that defers to slower mechanisms) can produce long waits. • Backpressure / high load: If agent processing is queued or stalled (concurrency limits, blocked tasks), EOU evaluation can be delayed. How to triage quickly 1. Confirm correlation across events ◦ Check other logs around the same timestamp (same room_id/job_id) to see whether STT plugin events, VAD events, or model inference start/finish entries line up with the 17.56s transcription delay. 1. Inspect STT plugin behavior ◦ Look for STT finalization events and any warnings/errors from the STT plugin used for this room. If Speechmatics (or another STT) is configured, verify its 
end_of_utterance_silence_trigger
and ensure the plugin is releasing final transcripts quickly. 1. Verify deployment CPU/latency ◦ Check host CPU usage and scheduling at the time of the incident. If you’re using burstable instances (t3/t4), check CPU credit exhaustion. Also check for long GC pauses or blocked threads that might slow inference. 1. Check VAD / audio quality ◦ Pull the audio for the affected sessions and listen for long pauses, very soft speech, or background noise that could confuse VAD. If VAD thresholds are too aggressive, soft speech may be treated as ongoing speech. 1. Review EOU configuration ◦ Confirm 
end_of_utterance_mode
and any fallback/max endpointing timeout values. A too-large timeout will let long delays occur before forcing an EOU. Immediate mitigations you can apply • Reduce fallback/max endpointing timeouts to a reasonable value (for interactive use, 0.5–2s silence is typical). • If underprovisioned, move to a non-burstable instance class or increase CPU capacity to eliminate inference stalls. • Tune VAD sensitivity or apply basic pre-processing (gain/AGC) if audio volume is low. • If STT is the bottleneck, try enabling faster STT mode (if available) or a different STT configuration that finalizes transcripts earlier. What I can help you with next • If you want, share the room_id and a couple of timestamps for affected logs and I’ll point to the exact log events to inspect (STT plugin events, model start/finish, VAD events). • I can also walk you step-by-step through checking host CPU metrics and relevant configuration fields (STT silence trigger, end_of_utterance_mode, timeouts) if you tell me where these settings live in your deployment. If you prefer a prioritized checklist to run in your environment, tell me which items you want first (logs, audio samples, or host metrics) and I’ll give the exact commands/log queries to use. Sources: How does end-of-utterance detection work in conversations? | Troubleshooting Latency and Timeout Errors with Turn Detection on AWS | Speechmatics STT plugin guide | LiveKit Documentation | Improving voice AI's turn detection with transformers | Improved End-of-Turn Model Cuts Voice AI Interruptions 39%
c
I see too many STT logs before it. I’m using turn_detection=“stt” in the setup. Should I use vad or EnglishModal or MultilingualModel?
t
Thanks — I see you’re using 
turn_detection="stt"
and want to reduce STT noise and long EOU delays. Short answer: switch off pure 
stt
-based endpointing and use either VAD or the Multilingual turn detector. My recommendation and why below. Recommendation (best balance) • Use the MultilingualModel together with a VAD (e.g., Silero VAD). ◦ Why: it uses both acoustic signals and semantic/contextual cues to decide turns, so it reduces unnecessary incremental STT calls while improving accuracy over plain VAD. It will cut down the many STT logs you’re seeing and reduce false long EOU waits caused by relying only on STT finalization. When to pick VAD-only • Use 
turn_detection="vad"
if you want the simplest, lowest-STT-usage option and you can tolerate more false endpoints on natural pauses. VAD endpointing triggers on silence and generates far fewer STT events, but it has no semantic understanding and may cut off speakers who pause to think. Why NOT use 
turn_detection="stt"
• 
stt
produces many incremental transcripts and relies on STT finalization for EOU. That causes high STT log volume and ties EOU delay to transcription latency (which you observed). Why NOT use 
EnglishModel
• 
EnglishModel
is superseded by 
MultilingualModel
. Use 
MultilingualModel
even for English — it performs better and supports more scenarios. Suggested configuration to try 1. Turn on multilingual detector + VAD: ◦ turn_detection = MultilingualModel() ◦ vad = silero.VAD.load() ◦ keep STT configured, but it will be used less aggressively for endpointing 1. Tune endpointing timeouts: ◦ min_endpointing_delay ≈ 0.5s ◦ max_endpointing_delay ≈ 2–3s ◦ These keep responsiveness while allowing the turn detector to use context. 1. Monitor after change: ◦ Watch STT logs and EOU/transcription delays for the same rooms to confirm STT log volume drops and EOU improves. If you want, I can: • Provide the exact code snippet or config lines to switch to MultilingualModel + Silero VAD for your agent setup. • Help pick timeout values based on whether your users commonly pause to think or speak continuously. Which would you like next: the config snippet or help picking endpointing timeout values? Sources: LiveKit turn detector plugin | LiveKit Documentation | Updating the Turn Detector Model | LiveKit | Turns overview | LiveKit Documentation
c
I see this warning also when i deploy in production None of PyTorch, TensorFlow >= 2.0, or Flax have been found. Models won’t be available and only tokenizers, configuration and file/data utilities can be used
t
Thanks — I see the deployment warning: "None of PyTorch, TensorFlow >= 2.0, or Flax have been found. Models won’t be available and only tokenizers, configuration and file/data utilities can be used." I’ll explain what that means, the impact, and clear next steps to resolve it so your MultilingualModel or other local turn-detection models will be available in production. What the warning means • The runtime cannot find any supported ML backends (PyTorch, TensorFlow >= 2.0, or Flax). • As a result, model weights cannot be loaded or run locally; only tokenizers and config files will work. Any features that require model inference (local turn detectors, semantic detectors, or on-host model-based processing) will be unavailable. Impact on your setup • If you switch from 
turn_detection="stt"
to a model-based detector like 
MultilingualModel
, the detector will fail to run locally without an ML backend. • You will still be able to use VAD-based detection (Silero VAD) because VAD is typically lightweight and may not require the same frameworks, but semantic/context-aware turn detection will not work until a framework is installed. • This likely explains why you saw model-related stalls or behavior if the runtime attempted to load a model and could not. How to fix it (pick one of these options) • Install PyTorch (recommended for most deployments) a. Choose a CPU or GPU build depending on your host. b. Example (CPU-only) install: • Python pip:
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pip install torch --index-url <https://download.pytorch.org/whl/cpu>
• Or with a specific supported version for your Python/cUDA if using GPU. Follow PyTorch’s install selector at https://pytorch.org/. 1. Restart your agent processes so the environment picks up the new package. • Or install TensorFlow >= 2.0 ◦ CPU-only:
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pip install "tensorflow>=2.0"
• If you use GPU, install the matching GPU build for your CUDA drivers. • Or install Flax (JAX/Flax) if you prefer that stack: ◦ Install JAX + Flax according to hardware (CPU/GPU) instructions from https://github.com/google/jax. ◦ Then pip install flax. Notes & recommendations • Use PyTorch unless you have a specific need for TensorFlow/Flax — it’s generally well supported by open-weight models used for turn detection. • If you run in containers, add the framework install to your Dockerfile to ensure reproducible deployments. • If you use GPU instances, ensure CUDA and matching framework versions are installed. Framework mismatch is a common source of failures. • After installing, verify by running a small Python check:
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import torch
  print(torch.__version__)
Or for TensorFlow:
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import tensorflow as tf
  print(tf.__version__)
Fallbacks if you can’t install a framework immediately • Continue with 
turn_detection="vad"
to reduce STT noise while you provision frameworks. • Keep 
turn_detection="stt"
but tune STT endpointing/timeouts as a short-term mitigation. • Use an external (remote) model inference endpoint if you have one, so you don’t need on-host frameworks. If you want, I can: • Provide the exact Dockerfile snippet or pip lines tailored to your environment (CPU vs GPU, OS) to install PyTorch or TensorFlow. • Walk through verifying the framework is available in a deployed container and confirming models load successfully. • Give the minimal config to switch to MultilingualModel once the framework is present. Which environment are you running in (Docker image base, CPU or GPU host, and Linux distribution)? I’ll give the exact install commands and a verification snippet you can drop into your deployment.
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