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I am getting this logs . i am using ```min_endpoin...
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a
I am getting this logs . i am using
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min_endpointing_delay=0.2,
max_endpointing_delay=0.4,
and no turn detector and vad config are as follows "vadDetails": { "vad_provider": "silero", "min_silence_duration": 0.3, "prefix_padding_duration": 0.3, "min_speech_duration": 0.1, "max_buffered_speech": 40, "activation_threshold": 0.55 }, why im I getting high eou sometime and sometime in range of 0.9?
t
Thanks for reaching out, Harshita. I'm an AI assistant and I'm here to help. Give me a moment while I research this topic.
Thanks — I’m looking at your config and the question about seeing EOU values as high as ~0.9s even though you set: • 
min_endpointing_delay=0.2
, 
max_endpointing_delay=0.4
• Silero VAD: 
min_silence_duration=0.3
, 
prefix_padding_duration=0.3
, 
min_speech_duration=0.1
, 
max_buffered_speech=40
, 
activation_threshold=0.55
Short answer: the observed ~0.9s EOU is expected given how EOU is measured — it’s the sum of VAD silence detection + the endpointing delay chosen (min or max) + any STT/finalization or processing latency. With your settings a typical path is 0.3s (VAD silence) + 0.4s (max endpointing) = 0.7s, and STT or pipeline overhead can add ~0.2s, producing ~0.9s. Why this happens (brief): • EOU includes VAD’s required silence (
min_silence_duration
) before marking speech ended. Your 
0.3s
is already non-trivial. • The system will use the longer endpointing delay when turn confidence is low, which can be 
max_endpointing_delay = 0.4s
. • STT finalization or network/processing latency is added after endpointing and can vary per utterance. • Fluctuating VAD confidence or intermittent voice/silence can push the system to wait the longer buffer. Recommended quick fixes to reduce EOU: • Lower 
min_silence_duration
(try 0.1–0.2s) to make VAD conclude silence sooner. • Lower 
max_endpointing_delay
only if you can tolerate more aggressive cuts (e.g., 0.2–0.3s). • Check STT latency / finalization timing — if STT adds ~200ms, that will show up in EOU. • Enable EOUMetrics/logging to see the breakdown (VAD silence vs endpointing vs transcription delay). One quick diagnostic request: could you share an example EOUMetrics log (shows VAD silence, endpointing delay chosen, and transcription_delay) or a short snippet of the EOU-related log lines? That will let me tell you whether the extra ~200ms is STT processing or internal pipeline variance and give a precise next step. Sources: How does end-of-utterance detection work in conversations? | Agents Framework | livekit/livekit_composite | DeepWiki | examples/bank-ivr | livekit/livekit_composite | DeepWiki | STT Metrics | LiveKit docs | VAD Metrics | LiveKit docs
a
174852.044 DEBUG livekit.agents received user transcript {"user_transcript": "एइट क्या है?", "language": "hi-IN", "transcript_delay": 1.1323320865631104, "pid": 37665, "job_id": "AJ_85tDL6KvEj7B", "room_id": "RM_56UVPYk6ZDBg"} 174852.051 INFO livekit.agents EOU metrics {"end_of_utterance_delay": 1.13, "transcription_delay": 1.13, "pid": 37665, "job_id": "AJ_85tDL6KvEj7B", "room_id": "RM_56UVPYk6ZDBg"} 174854.258 DEBUG livekit.…echStream Sent 2000 audio chunks {"session_id": 4758121536, "pid": 37665, "job_id": "AJ_85tDL6KvEj7B", "room_id": "RM_56UVPYk6ZDBg"} 174854.273 INFO livekit.agents LLM metrics {"model_name": "gpt-4o", "model_provider": "smsclassifier3.openai.azure.com", "ttft": 1.87, "prompt_tokens": 4107, "prompt_cached_tokens": 3968, "completion_tokens": 53, "tokens_per_second": 23.88, "pid": 37665, "job_id": "AJ_85tDL6KvEj7B", "room_id": "RM_56UVPYk6ZDBg"} 174854.642 INFO livekit.agents TTS metrics {"model_name": "unknown", "model_provider": "unknown", "ttfb": 0.12172345799999107, "audio_duration": 13.0, "pid": 37665, "job_id": "AJ_85tDL6KvEj7B", "room_id": "RM_56UVPYk6ZDBg"} 174859.258 DEBUG livekit.…echStream Sent 2100 audio chunks {"session_id": 4758121536, "pid": 37665, "job_id": "AJ_85tDL6KvEj7B", "room_id": "RM_56UVPYk6ZDBg"} 174904.260 DEBUG livekit.…echStream Sent 2200 audio chunks {"session_id": 4758121536, "pid": 37665, "job_id": "AJ_85tDL6KvEj7B", "room_id": "RM_56UVPYk6ZDBg"} 174907.454 DEBUG agent The incoming text does not contain any ending phrases. {"pid": 37665, "job_id": "AJ_85tDL6KvEj7B", "room_id": "RM_56UVPYk6ZDBg"} 174908.993 DEBUG livekit.…echStream Processing VAD event: START_SPEECH {"session_id": 4758121536, "signal_type": "START_SPEECH", "pid": 37665, "job_id": "AJ_85tDL6KvEj7B", "room_id": "RM_56UVPYk6ZDBg"} 174908.994 DEBUG livekit.…echStream Speech started {"session_id": 4758121536, "pid": 37665, "job_id": "AJ_85tDL6KvEj7B", "room_id": "RM_56UVPYk6ZDBg"} 174909.258 DEBUG livekit.…echStream Sent 2300 audio chunks {"session_id": 4758121536, "pid": 37665, "job_id": "AJ_85tDL6KvEj7B", "room_id": "RM_56UVPYk6ZDBg"} 174914.258 DEBUG livekit.…echStream Sent 2400 audio chunks {"session_id": 4758121536, "pid": 37665, "job_id": "AJ_85tDL6KvEj7B", "room_id": "RM_56UVPYk6ZDBg"} 174919.259 DEBUG livekit.…echStream Sent 2500 audio chunks {"session_id": 4758121536, "pid": 37665, "job_id": "AJ_85tDL6KvEj7B", "room_id": "RM_56UVPYk6ZDBg"} 174924.259 DEBUG livekit.…echStream Sent 2600 audio chunks {"session_id": 4758121536, "pid": 37665, "job_id": "AJ_85tDL6KvEj7B", "room_id": "RM_56UVPYk6ZDBg"} 174929.261 DEBUG livekit.…echStream Sent 2700 audio chunks {"session_id": 4758121536, "pid": 37665, "job_id": "AJ_85tDL6KvEj7B", "room_id": "RM_56UVPYk6ZDBg"} 174931.988 DEBUG livekit.…echStream Processing VAD event: END_SPEECH {"session_id": 4758121536, "signal_type": "END_SPEECH", "pid": 37665, "job_id": "AJ_85tDL6KvEj7B", "room_id": "RM_56UVPYk6ZDBg"} 174931.989 DEBUG livekit.…echStream Speech ended, flush triggered {"session_id": 4758121536, "pid": 37665, "job_id": "AJ_85tDL6KvEj7B", "room_id": "RM_56UVPYk6ZDBg"} 174932.008 DEBUG livekit.…echStream VAD triggered flush, sending flush message {"session_id": 4758121536, "pid": 37665, "job_id": "AJ_85tDL6KvEj7B", "room_id": "RM_56UVPYk6ZDBg"} 174932.629 DEBUG livekit.…echStream Transcript processed successfully {"session_id": 4758121536, "text_length": 218, "language": "hi-IN", "request_id": "20260102_25b6122c-8319-41dd-9d6a-a5ff2221fd0c", "confidence": 0.0, "pid": 37665, "job_id": "AJ_85tDL6KvEj7B", "room_id": "RM_56UVPYk6ZDBg"} 174932.631 INFO livekit.agents STT metrics {"model_name": "saarika:v2.5", "model_provider": "Sarvam", "audio_duration": 23.39, "pid": 37665, "job_id": "AJ_85tDL6KvEj7B", "room_id": "RM_56UVPYk6ZDBg"} DEBUG livekit.agents received user transcript {"user_transcript": "मेरे पास कोई प्रेफर्ड टाइम नहीं है अभी के लिए लेकिन आप सुन सकती है ना मुझे हाँ मैं बोलूँ कि मेरे पास कोई प्रेफर्ड टाइम नहीं है आप एक काम करिए आप मेरा कॉल पैक तीन तारीख दिसंबर ट्वेंटी ट्वेंटी फाइव को शेड्यूल कर सकती हूँ", "language": "hi-IN", "transcript_delay": 1.5190582275390625, "pid": 37665, "job_id": "AJ_85tDL6KvEj7B", "room_id": "RM_56UVPYk6ZDBg"} 174932.633 INFO livekit.agents EOU metrics {"end_of_utterance_delay": 1.52, "transcription_delay": 1.52, "pid": 37665, "job_id": "AJ_85tDL6KvEj7B", "room_id": "RM_56UVPYk6ZDBg"}
t
Thanks — I reviewed your logs and config. You're seeing EOU values of ~1.13s and ~1.52s; here's why and what to try next. Why this is happening • EOU is the sum of VAD silence detection + endpointing delay chosen (min or max) + STT/transcription finalization and pipeline overhead. • Your Silero VAD requires 
min_silence_duration = 0.3s
, and you set 
max_endpointing_delay = 0.4s
. That already totals ~0.7s before STT/processing. • The logs show 
transcript_delay
≈ 1.13s and 1.52s in EOUMetrics, which means STT finalization and processing added ~0.4–0.8s on top of VAD+endpointing for those turns. The STT metrics show Sarvam processing 23s of audio and the earlier short transcript had transcription_delay ~1.13s — this aligns with STT being the dominant extra latency. • The system will pick the longer endpointing delay when confidence is lower or VAD is unstable, which increases totals on some utterances. Concrete actions to reduce EOU • Lower 
min_silence_duration
to 0.1–0.2s to reduce the VAD wait time. • Reduce 
max_endpointing_delay
(e.g., 0.2–0.3s) if you can accept more aggressive cutoffs. • Investigate STT finalization latency: check Sarvam provider logs/metrics for per-request processing time; if STT adds ~200–800ms, that will show up in EOUMetrics. • If available, enable preemptive generation or realtime turn detection from STT/LLM so response work can start before full finalization. • Turn on or share detailed EOUMetrics breakdown (VAD silence, endpointing_delay_used, transcription_delay) for multiple samples so we can confirm where most time is spent. Quick diagnostic request Could you share the EOUMetrics that include the breakdown fields (VAD silence, endpointing_delay_used, transcription_delay) for a couple of the slow examples? With that I can tell you whether to focus on STT tuning, VAD settings, or endpointing thresholds.
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