high eou delay

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so Im facing high eou delay around 1.2 sec. I am using a local instance of whsiper which generated transcription in 300-350ms. then there is silero vad and the livekit turn detection model. tell me what all settings are there which I can optimised to reduce this eou latency?

Thanks — you're using local Whisper (~300–350 ms), Silero VAD, and LiveKit's turn detector and seeing ~1.2 s EOU. Below are the settings you can tune and practical recommendations to reduce EOU latency. Voice activity / silence settings (Silero VAD) • VAD frame size / hop: reduce frame length if configurable (smaller frames detect speech end sooner). Tradeoff: too small increases false positives. • VAD aggressiveness / threshold: lower detection latency by making the VAD more sensitive to speech end; watch for premature cutoffs. • Post-debounce / padding: reduce any silence padding after VAD end-of-speech (often a few hundred ms). STT/transcription timing • Whisper batching/flush: ensure Whisper streams partial transcripts with minimal buffering; reduce any internal buffering or aggregation windows. • Parallelize: return interim results quickly and mark finalization separately so turn detector can act on speech end rather than waiting for final transcript. LiveKit turn detector settings • min_endpointing_delay: lower this from the default (500 ms) toward a smaller value to allow faster endpointing when confidence is high. • max_endpointing_delay: reduce if you rarely need long waits for low-confidence cases. • confidence thresholds: raise required confidence for longer waits; conversely, lower thresholds to accept quicker endpoints (tradeoff: correctness). • model-specific timeout/latency params: if using the transformer detector, tune its aggressiveness or semantic lookahead window to reduce added delay. End-to-end timing and metrics • EOUMetrics: inspect

end_of_utterance_delay

and

transcription_delay

to see where time is spent (you already have ~300–350 ms transcription). • Correlate VAD end timestamp, STT final/interim timestamps, and turn-detector decision timestamp to identify which layer adds ~800–900 ms. Infrastructure checks • CPU / process latency: ensure VAD and turn-detector processes are not CPU-throttled; even if Whisper is local and fast, CPU contention can delay VAD/turn-detector callbacks. • I/O and thread scheduling: run VAD, STT, and turn-detector in low-latency threads/processes and avoid blocking operations between them. Practical step-by-step to try now 1. Reduce Silero VAD post-debounce/padding by ~100–200 ms and test. 2. Decrease

min_endpointing_delay

to 200–300 ms and lower max to ~2–3 s. 3. Ensure Whisper streams interim results immediately (no extra buffering). 4. Measure EOUMetrics after each change to confirm improvement. 5. If still high, profile CPU/threads for the VAD and turn-detector processes. If you want, tell me the current values for Silero padding/debounce and your LiveKit `min_endpointing_delay`/`max_endpointing_delay` and confidence thresholds and I’ll suggest exact numbers to try.

okay tell me the exact settings name which i can tweak - regarding the silero VAD, turn detection, etc. which are present in livekit

You said you have ~1.2s EOU, Whisper ~300–350ms, Silero VAD + LiveKit turn detector. Below are the exact setting names you can tweak (with short purpose and suggested ranges) to reduce EOU latency. Silero VAD (plugin / loader settings) •

min_silence_duration

— silence required to mark speech end. Default ~0.55s. Try 0.2–0.4s. •

min_speech_duration

— min speech to start a segment. Default ~0.05s. Keep 0.03–0.05s for short utterances. •

activation_threshold

— confidence threshold for speech detection. Default ~0.5. Increase in noisy environments (0.6–0.7) or lower (0.4–0.5) for earlier detection. •

prefix_padding_duration

— audio padding before speech start. Default ~0.5s. Try 0.1–0.3s to reduce latency. • (If present)

post_debounce_duration

/

suffix_padding

— silence appended after speech end. Reduce by 100–250ms. Whisper / STT streaming settings •

streaming

/

partial_result_interval

(or provider-specific buffer/window) — ensure interim/partial transcripts are emitted quickly; reduce buffer window to 100–200ms if configurable. •

batch_size

/

audio_chunk_size

— lower chunk sizes to get earlier partials (tradeoff: CPU). LiveKit turn detection / endpointing (AgentSession / TurnDetection options) •

min_endpointing_delay

— minimum delay after high-confidence end. Default 0.5s. Try 0.2–0.3s. •

max_endpointing_delay

— maximum wait for low-confidence cases. Default up to 6s. Try 1.5–2.5s. •

end_of_turn_confidence_threshold

(STT-based) — threshold for STT semantic endpointing. Default ~0.4. Try 0.25–0.35 for quicker endpoints (watch false positives). • For realtime/OpenAI plugin:

silence_duration_ms

— default 500ms; try 250–350ms. • For semantic/transformer detector:

eagerness

(or similar param) — values like `low`/`medium`/`high`; set to

high

for faster endpointing if available. Agent / session-level and plugin wiring •

turn_detection

— set to

vad

(fast VAD-only),

stt

(STT-based endpointing), or `multilingual`/transformer model. Use

vad

or

stt

for lower latency; transformer adds semantic checks and may add delay. •

vad

plugin reference — ensure LiveKit session references your tuned Silero VAD instance (no extra padding added by session layer). Observability / metrics to measure changes •

EOUMetrics.end_of_utterance_delay

— primary metric to watch. •

EOUMetrics.transcription_delay

— confirms STT time. • Correlate timestamps: VAD end → STT interim/final → turn-detector decision. Practical tuning order (apply one change at a time and measure) 1. Reduce Silero `post_debounce`/`min_silence_duration` by ~100–250ms. 2. Ensure Whisper streams partials (reduce STT buffer). 3. Set

min_endpointing_delay

= 0.2–0.3s and

max_endpointing_delay

= 1.5–2.0s. 4. If using STT endpointing, lower

end_of_turn_confidence_threshold

to ~0.3. 5. Re-measure

end_of_utterance_delay

after each step. If you want, share your current values for: •

min_silence_duration

,

prefix_padding_duration

, any `post_debounce`/`suffix_padding` in Silero; and •

min_endpointing_delay

,

max_endpointing_delay

, and

end_of_turn_confidence_threshold

in LiveKit, and I’ll recommend exact numbers to try next. Sources: Silero VAD plugin | LiveKit docs | Improving voice AI's turn detection with transformers | How does end-of-utterance detection work in conversations? | OpenAI Realtime API plugin guide | LiveKit docs | LiveKit turn detector plugin | LiveKit docs