how does the inference for turn detection model takes place? how do they handle concurresnt sessions? what are the recommented values for cpu and ram for handling conncurrent session for the machine where agent is running?

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

i meant how is the infenrece for turn detection model handled in case there are concurrent sessions? is there some batching implemented or all requests process serially? how is the cpu and memory contention handled?

Thanks — I’m focusing on your questions about concurrent turn-detection inference: whether requests are batched or serial, and how CPU/memory contention is handled. Short answer • Inference is handled per-session (no batching). Each AgentSession runs its own turn detector instance and inference is performed for that session independently and serially. • CPU and memory contention are managed at the infrastructure level (instance sizing and horizontal scaling), not by batching or resource-pooling inside the agent process. Details you can use • Per-session inference (no batching) ◦ Each session instantiates its own model (

MultilingualModel

) and keeps its own sliding context (the last four turns). There is no centralized batching layer that groups multiple sessions’ inferences together. ◦ Inference runs on the shared process for each session as transcriptions arrive, so requests are processed independently for each session. • CPU contention ◦ The model is CPU-only and typical per-turn inference latency is on the order of tens to a few hundred milliseconds, so concurrent sessions increase CPU load linearly. ◦ Because multiple model instances run in the same server process, CPU becomes the primary contention point when concurrency rises. ◦ To avoid throttling under sustained load, compute-optimized instances (e.g., c6i/c7i) are recommended; burstable instance types (t3/t4) can exhaust CPU credits and cause timeouts. • Memory behavior ◦ Model weights are cached/shared at the process level (not duplicated per session), so weight memory is not multiplied by sessions. ◦ Each session keeps per-session state (context windows), so overall memory usage grows roughly linearly with number of sessions, but the memory footprint per session is small (< ~500 MB model footprint overall; session state is modest). • Scaling approach ◦ There is no software-level batching or inference queueing. Scaling is achieved by provisioning sufficient CPU/RAM on agent servers and adding more agent servers when needed. The agent/job distribution assigns sessions across servers so each session is handled exactly once. ◦ Example baseline: a 4-core, 8 GB machine typically supports ~10–25 concurrent agent jobs depending on active plugins (VAD, noise cancellation, turn detector). Load tests (30 concurrent agents using Silero VAD) have shown ~3.8 cores and ~2.8 GB peak usage, indicating CPU is usually the limiting factor. If you want, I can help with either of these next steps • Estimate required CPU/RAM for your expected concurrency and which plugins you’ll run. • Suggest a simple load-test plan and the exact metrics to collect (CPU, p95/p99 inference latency, memory, VAD timeouts). • Show how to horizontally scale agent servers and what thresholds to watch for to trigger autoscaling. Which of those would be most helpful for your environment? Sources: LiveKit turn detector plugin | LiveKit Documentation | Troubleshooting Latency and Timeout Errors with Turn Detection on AWS | Updating the Turn Detector Model | LiveKit | Improving voice AI&amp;#39;s turn detection with transformers | Self-hosted deployments | LiveKit Documentation

im facing very large EOU in case of multiple concurrent sessions, I want to check whats cuasing this - stt or the turn detection, what metric / event should I consider?

Thanks — I’m responding to your question about diagnosing large End‑Of‑Utterance (EOU) delays under concurrent sessions and how to tell whether STT or the turn detector is the culprit. What to collect first (high level) • Enable and collect both the STT metrics and the EOU metrics for the same sessions so you can compare timings end-to-end. • Specifically gather: STT processing times, transcription finalization delay, turn detector inference latency, and the overall EOU delay. Key metrics/events to inspect 1. eou_metrics_collected (EOU event) ◦ end_of_utterance_delay: total time from end of user speech to detected EOU. High values here mean slow overall detection. ◦ transcription_delay: time from end of speech to final STT transcript. If this is high, STT is the likely cause. 1. STT plugin metrics (metrics_collected / STTMetrics) ◦ duration: how long each STT request took. ◦ streamed vs batch indicators and any error counts. ◦ If

duration

or streaming stalls increase under concurrency, STT is the bottleneck. 1. Turn detector / model inference latency ◦ Per-inference latency for the turn detector (typical single inference is ~50–160 ms). If you see much higher latencies or spikes that correlate with concurrency, the turn detector (or CPU contention) is causing the EOU increase. ◦ Track p50/p95/p99 inference latency across sessions. 1. System/resource metrics ◦ Host CPU usage (%), per-core utilization, run queue length. ◦ Memory usage (process RSS) and swap activity. ◦ If CPU is saturated (high per-core usage or long runnable queue), inference will be delayed even if individual model calls are short. How to attribute delay (simple math) • For each EOU event: ◦ If transcription_delay is the majority of end_of_utterance_delay → STT is primary culprit. ◦ If transcription_delay is small but end_of_utterance_delay is large → turn detection (inference, VAD adjustments, or process contention) is likely responsible. ◦ Log both timings together so you can compute: turn_detector_delay = end_of_utterance_delay − transcription_delay. Practical traces/observability to enable • Trace spans for: audio ingestion → STT request → STT response → turn-detection inference → EOU event. • Logs that include timestamps for: speech end detection, STT finalization, model inference start/end, EOU emission. • Agent observability dashboard / trace view to inspect those spans per session. What to watch for under concurrency • Increasing STT durations or queueing in STT plugin: suggests STT backend or network throughput limits. • Rising model inference p95/p99 or CPU run queue: suggests CPU contention (multiple per-session model instances executing concurrently). • Signs of throttling on burstable VMs (e.g., t3/t4): indicates CPU credit exhaustion causing sudden latency spikes. Quick checks to run now 1. Capture a sample of eou_metrics_collected for several sessions at both low and high concurrency. 2. Compare transcription_delay vs end_of_utterance_delay per session. 3. Correlate those with host CPU utilization and model inference latency during the same interval. Immediate mitigations while you investigate • Move agents to compute‑optimized instances (e.g., c6i/c7i) if you’re on burstable VMs. • Reduce per-server concurrency temporarily to see if EOU improves (this isolates CPU contention). • If STT is the issue, consider switching STT provider/region or enabling streaming configs that reduce finalization delay. If you’d like, I can: • Provide the exact log/trace fields to capture for each span. • Suggest a short load-test script and the thresholds to monitor (CPU, p95 inference latency, transcription_delay). • Help interpret a short sample of your collected metrics (paste a few example events and I’ll point to the likely root cause). Which help would you prefer next? Sources: STT Metrics | LiveKit Documentation | LiveKit turn detector plugin | LiveKit Documentation | Troubleshooting Latency and Timeout Errors with Turn Detection on AWS | examples/frontdesk | livekit/livekit_composite | DeepWiki | Streamline troubleshooting with Agent Observability

give me the code, what sepcific events should I capture ?

Thanks — I’ll give you concise, copy‑pasteable code and a clear list of the specific events/metrics to capture so you can pinpoint whether STT or the turn detector is causing large EOU delays under concurrency. Which events/metrics to capture • eou_metrics_collected ◦ end_of_utterance_delay (total EOU delay) ◦ transcription_delay (time from speech end → STT final transcript) ◦ speech_id / session id (correlate to session) • STT metrics (metrics_collected / STTMetrics) ◦ duration (total STT processing time) ◦ audio_duration (length of audio submitted) ◦ streamed flag and any error fields • Turn-detector inference metrics / spans ◦ inference_start, inference_end (to compute per-inference latency) ◦ model confidence and chosen action (if available) • Session state events ◦ user_state_changed (user speaking/idle timestamps) ◦ agent_state_changed • Host/system metrics (for the agent server) ◦ CPU % (per-core if possible), run queue length ◦ Memory RSS and swap usage ◦ Process-level threads / open file descriptors (optional) How to attribute delays quickly • turn_detector_delay = end_of_utterance_delay − transcription_delay ◦ If transcription_delay is large → STT issue. ◦ If transcription_delay small but end_of_utterance_delay large → turn detector / CPU contention / VAD tuning. Minimal Python example you can integrate (subscribes to events and logs key fields)

import asyncio
import logging
from datetime import datetime
from livekit.agents.metrics import STTMetrics, EOUMetrics

logger = logging.getLogger("eou-debug")
logger.setLevel(<http://logging.INFO|logging.INFO>)

# Handlers assume your AgentSession exposes .stt and emits these events
def on_stt_metrics(metrics: STTMetrics):
    # Important fields to capture
    <http://logger.info|logger.info>(
        "STT_METRIC ts=%s session=%s duration=%.3fs audio_duration=%.3fs streamed=%s error=%s",
        datetime.fromtimestamp(metrics.timestamp).isoformat(),
        getattr(metrics, "session_id", "unknown"),
        metrics.duration,
        getattr(metrics, "audio_duration", 0.0),
        metrics.streamed,
        metrics.error,
    )

def on_eou_metrics(metrics: EOUMetrics):
    # Key fields to capture for EOU diagnosis
    <http://logger.info|logger.info>(
        "EOU_METRIC ts=%s session=%s end_of_utterance_delay=%.3fs transcription_delay=%.3fs speech_id=%s",
        datetime.fromtimestamp(metrics.timestamp).isoformat(),
        getattr(metrics, "session_id", "unknown"),
        metrics.end_of_utterance_delay,
        metrics.transcription_delay,
        metrics.speech_id,
    )
    # Compute inferred turn detector contribution
    td_delay = metrics.end_of_utterance_delay - metrics.transcription_delay
    <http://logger.info|logger.info>("INFERRED_TURN_DETECTOR_DELAY session=%s td_delay=%.3fs",
                getattr(metrics, "session_id", "unknown"), td_delay)

# Example wiring into your session setup
async def attach_debug_handlers(session):
    # subscribe to STT metrics
    session.stt.on("metrics_collected", lambda m: asyncio.create_task(async_wrap(on_stt_metrics, m)))
    # subscribe to EOU metrics
    session.stt.on("eou_metrics_collected", lambda m: asyncio.create_task(async_wrap(on_eou_metrics, m)))

async def async_wrap(fn, *args, **kwargs):
    try:
        return fn(*args, **kwargs)
    except Exception:
        logger.exception("error in metrics handler")

What to log alongside those events • Timestamp (ISO), session/room id, speech_id, user id, host CPU/mem snapshot (at same timestamp). • If you have traces, record spans for: audio_end → STT_request → STT_response → turn_detector_inference → eou_emitted. Suggested short investigation steps 1. Collect ~100 EOU events at low concurrency and ~100 at your target high concurrency. 2. Compare median/p95/p99 of: ◦ transcription_delay (STT) ◦ inferred turn_detector_delay (end_of_utterance_delay − transcription_delay) ◦ host CPU p95/p99 during those windows 1. If transcription_delay rises with concurrency → focus on STT (network, provider throughput, streaming vs finalization). 2. If inferred turn_detector_delay rises while transcription_delay stays low → focus on model inference / CPU contention (move to compute-optimized instances or reduce per-server concurrency). If you want, I can: • Produce a small script that fetches these events from your logs or tracing backend and produces a short report (p50/p95/p99 and correlation with CPU). • Help interpret a few sample log lines you capture (paste 5–10 lines and I’ll point to the likely root cause). Which would you prefer next? Sources: LiveKit turn detector plugin | LiveKit Documentation | Improved End-of-Turn Model Cuts Voice AI Interruptions 39% | Turns overview | LiveKit Documentation | AssemblyAI plugin guide | LiveKit Documentation | Troubleshooting Latency and Timeout Errors with Turn Detection on AWS