Sometimes my agent stops recognizing people in production. No speech recognition at all, so the session gets stuck. We are using Android and iOS Livekit clients. When this happens there are no logs pointing to anything failing. We are using deepgram flux as STT

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

Should I still use VAD if my turn_detection is "stt"? I'm using Soniox v4 and Deepgram Flux which already come with endpointing detection themselves. Not sure if VAD is necessary or even recommended to use alongisde these

Thanks — great question. I’m responding to your ask about whether to use VAD when

turn_detection="stt"

with Soniox v4 or Deepgram Flux. Short answer • Yes — you should still use VAD alongside

turn_detection="stt"

. VAD is strongly recommended for better interruption handling and lower-latency voice detection, even though Soniox and Deepgram include endpointing. Why VAD helps when STT provides endpointing • Faster interruption detection: STT endpointing waits for semantic/acoustic context and full transcription segments; VAD detects voice activity immediately so the agent can interrupt quickly when a user starts speaking. • Improved responsiveness in back-and-forth conversations: VAD gives low-latency signals that complement STT’s higher-level end-of-turn decisions, leading to more natural barge-in behavior. • Robustness to short utterances and noisy mobile capture: VAD can catch brief starts of speech that STT endpointing might not finalize quickly, helping avoid missed interruptions and stuck states. When you might rely more on STT alone • If you’re using a realtime LLM service that handles turn detection server-side, VAD isn’t required. But for Soniox/Deepgram setups, VAD still adds value for interruption and UX. Recommended config pattern 1. Keep

turn_detection="stt"

so the STT model still controls final end-of-turn behavior. 2. Enable a lightweight VAD (for example,

silero.VAD

) to handle interruption detection and quick voice activity signals. Example (conceptual):

session = AgentSession(
    turn_detection="stt",
    stt="deepgram/flux-general:en",   # or Soniox v4
    vad=silero.VAD.load(),            # recommended
    # tune interruption settings as needed
)

Tuning notes (practical tips) • Tune interruption settings (e.g.,

min_interruption_duration

,

min_interruption_words

,

false_interruption_timeout

) so brief noises aren’t falsely triggering interruptions but genuine user speech interrupts promptly. • Keep VAD logging enabled during tests to verify it’s detecting starts/stops of speech as expected. • If you see missed interruptions, raise VAD sensitivity slightly or reduce

min_interruption_duration

. If you want, I can: • Recommend specific interruption parameter values to try for mobile clients. • Review a short snippet of your current

AgentSession

config and suggest exact fields to change. Sources: Turns overview | LiveKit Documentation | AssemblyAI STT | LiveKit Documentation | Deepgram STT | LiveKit Documentation | VAD and turn detection configuration guide | LiveKit

This is my current VAD setup, should I change it if I use turn_detection stt?

silero.VAD.load(
    min_speech_duration=0.2,
    min_silence_duration=0.6,
)

Thanks — that VAD snippet is a good starting point. You should keep VAD when using

turn_detection="stt"

, but I recommend a small tweak to better support interruption when STT is doing endpointing. What your current settings mean •

min_speech_duration=0.2

— requires 200 ms of continuous voice before VAD considers it speech (reasonable). •

min_silence_duration=0.6

— requires 600 ms of silence before VAD considers the speech stopped (this is somewhat long for interruption detection). Suggested changes • Reduce

min_silence_duration

to make interruptions more responsive. Try 0.2–0.4 seconds: ◦ Example:

min_speech_duration=0.2

,

min_silence_duration=0.3

• Keep

min_speech_duration

at 0.2 unless you see many false positives from very short noises; lower it only if you need to capture very short utterances. • If you see false interruptions (background noise or brief clicks), increase

false_interruption_timeout

or raise

min_speech_duration

slightly instead of increasing silence duration. Why this helps with

turn_detection="stt"

• STT endpointing can be slower because it waits for semantic cues; a shorter VAD silence threshold lets your agent detect the start of a user’s speech quickly and trigger interruption while STT still finalizes transcripts. • Balancing low

min_silence_duration

with conservative interruption thresholds (e.g.,

min_interruption_duration

,

min_interruption_words

) reduces false positives while improving responsiveness. Tuning plan to validate in production 1. Roll out: change

min_silence_duration

to 0.3 on a small percentage of traffic. 2. Log VAD events, STT events, and interruption decisions for a test window. 3. Observe: ◦ Are user interruptions captured faster? ◦ Are there more false interruptions? If yes, raise

min_interruption_duration

or

min_speech_duration

slightly. 1. Iterate: adjust by 50 ms increments until you reach the sweet spot for your environment and user behavior. If you share your interruption-related settings (e.g.,

min_interruption_duration

,

min_interruption_words

,

false_interruption_timeout

), I can recommend exact values to try for mobile callers.

They are:

min_interruption_duration = 0.5,
        min_interruption_words = 2,
        false_interruption_timeout = 2.0,

Thanks — this is helpful. You’ve given the key settings, so here’s a focused recommendation to make interruption behavior more responsive while keeping false positives low when using

turn_detection="stt"

. What you currently have • VAD: ◦ min_speech_duration = 0.2 s ◦ min_silence_duration = 0.6 s • Interruption: ◦ min_interruption_duration = 0.5 s ◦ min_interruption_words = 2 ◦ false_interruption_timeout = 2.0 s Summary recommendation • Shorten VAD silence so interruptions are detected sooner, and slightly lower the min_interruption_duration so the agent can be interrupted by shorter user starts. Keep min_interruption_words = 2 to avoid single-word false interrupts. Keep false_interruption_timeout to allow recovery from spurious interrupts. Suggested values to try (start here): • VAD ◦ min_speech_duration = 0.2 (keep) ◦ min_silence_duration = 0.3 • Interruption ◦ min_interruption_duration = 0.35 ◦ min_interruption_words = 2 (keep) ◦ false_interruption_timeout = 2.0 (keep) Why these changes • Reducing

min_silence_duration

from 0.6 → 0.3 lets VAD declare "speech ended" faster and detect the next incoming voice sooner, which is important because STT endpointing is slower and waits for semantic cues. • Lowering

min_interruption_duration

from 0.5 → 0.35 lets the system accept shorter user starts as valid interruptions while

min_interruption_words = 2

prevents accidental single-word noises from triggering interruptions. • Keeping

false_interruption_timeout = 2.0

prevents rapid flip-flopping after a false interruption and gives the agent time to recover. How to validate safely 1. Run this on a small percentage of sessions (canary) for 24–72 hours. 2. Collect logs for: ◦ VAD state transitions (speech start/stop) ◦ STT events (

START_OF_SPEECH

,

END_OF_SPEECH

,

FINAL_TRANSCRIPT

) ◦ Interruption decisions and timestamps 1. Key metrics to watch: ◦ Time from user speech start to interruption (should decrease) ◦ Number of false interruptions per hour (should not increase materially) ◦ Incidents where agent stays stuck (should decrease) 1. If you see more false interrupts: ◦ Increase

min_interruption_duration

by 50 ms increments OR ◦ Raise

min_speech_duration

to 0.25–0.3 1. If interruptions are still slow: ◦ Lower

min_silence_duration

to 0.25 ◦ Or consider decreasing

min_interruption_duration

further to 0.3 Extra tips for mobile clients • Ensure no client-side noise suppression or audio focus changes are reintroducing silence or clipping; those can make VAD see silence even though the user is speaking. • Keep detailed timestamps (device time + server time) to correlate client audio with VAD and STT events. If you’d like, I can: • Suggest alternate values for a noisier environment (more conservative) or a low-noise environment (more aggressive). • Review a short log sample after you run the canary and suggest the next tweak.

Will reducing

min_silence_duration

won't affect negatively if a user is ruminating about what to say next? Our use case is kind of meditation, we ask questions to users when they are relaxed, so sometimes they take some time to answer or answer in a slow pace

Thanks — that’s a very important nuance to call out. Since your users may pause thoughtfully before answering, we should avoid settings that treat thoughtful silence as a turn end or trigger premature interruptions. Key tradeoff • Lowering

min_silence_duration

makes interruption detection more responsive (good for quick back-and-forth), but it also causes pauses during thinking to be treated as "speech stopped" sooner. That can lead to the agent closing a turn or re-starting prompts while the user is still formulating an answer. • The goal is to let genuine interruptions (user starts speaking while agent is speaking) be recognized quickly, while letting long, deliberate pauses from the user be treated as continued thinking, not as end-of-turn. Recommendations for a meditation / slow-paced UX • Keep VAD enabled (you still want early detection of genuine speech starts). • Use a moderately conservative

min_silence_duration

— not as long as 0.6 if you want some responsiveness, but longer than an aggressive value that would cut off thoughtful pauses. ◦ Suggested starting point for your use case: ▪︎ min_speech_duration = 0.2 (keep) ▪︎ min_silence_duration = 0.45 • Keep interruption thresholds conservative so short breaths or half-words don’t trigger interruption: ◦ min_interruption_duration = 0.35 (as previously suggested) OR increase slightly if you see false interrupts ◦ min_interruption_words = 2 (keep) ◦ false_interruption_timeout = 2.0 (keep) Behavioral adjustments to reduce unwanted effects • Only allow interruption while the agent is actively speaking (disable interruption during silent wait periods). This prevents the agent from treating a long user pause as a chance to re-open the prompt. • Consider a two-mode flow: ◦ Agent-speaking mode: lower

min_silence_duration

(0.3–0.45) and responsive interruption settings so users can barge in. ◦ User-thinking mode (after agent finishes): raise

min_silence_duration

(0.6–1.0) so long contemplative pauses aren’t treated as new or failed turns. • Signal to the user when the agent is listening (UI cue) so users know they can answer at leisure without being cut off. Tuning plan (safe, iterative) 1. Canary the two-mode approach on a small % of sessions: ◦ Agent-speaking mode: min_silence_duration = 0.35 ◦ User-thinking mode: min_silence_duration = 0.8 1. Log these events: ◦ VAD transitions, interruption attempts, whether interruption was allowed (agent-speaking vs user-thinking). ◦ Cases where users start speaking during agent turn and are not detected. ◦ Cases where agent ends turn and user resume is treated as new turn vs continuation. 1. Evaluate after a test window and adjust: ◦ If users are still being cut off during thinking: raise user-thinking

min_silence_duration

. ◦ If users can’t interrupt agent: lower agent-speaking

min_silence_duration

slightly or shorten

min_interruption_duration

. If you prefer a single-mode simpler approach • Use min_silence_duration ≈ 0.45 and keep min_interruption_words = 2. This balances openness for thoughtful pauses with reasonable interruption responsiveness. If you want, I can: • Propose exact values for the two-mode thresholds tailored to your average pause lengths (if you can share sample pause durations), or • Review a short log sample after you run a canary and recommend the next tweak.

Can you explain what the effect of having min_silence_duration=2, do?

Good question — here’s what happens when you set

min_silence_duration = 2

(2 seconds) and how it will affect behavior and UX. What

min_silence_duration = 2

does • VAD will require 2 full seconds of detected silence before it declares that speech has stopped. • That means short pauses (under 2s) are treated as part of ongoing speech rather than an end-of-turn. Immediate effects on system behavior • Slower endpointing: The system will wait longer before finalizing an utterance or handing off to STT end-of-turn logic. • Reduced false splits: Thoughtful pauses, hesitations, or slow-speakers won’t be misclassified as multiple short utterances. • Delay in processing final transcripts: Final transcripts and any post-turn logic (e.g., next prompt, scoring) will be delayed until the 2s silence is observed. • Interruption responsiveness: If you rely on VAD to detect a user starting to speak in order to interrupt the agent, a long

min_silence_duration

generally won’t hurt the detection of a new user start — VAD still reports speech start immediately — but it will make the system treat the previously speaking segment as continuing until 2s of silence occurs. That can affect how quickly the agent transitions states around turn boundaries. Effects in your meditation / slow-paced use case • Pros: ◦ Users who pause to think will rarely be cut off or have their answer split into multiple fragments. ◦ Less noisy session logs with fewer tiny, fragmented transcripts. • Cons: ◦ Actions tied to a finalized turn (saving an answer, moving to the next prompt, analytics, or server-side state transitions) will wait an extra ~2 seconds after the user stops speaking. ◦ If your UX expects the agent to react quickly after user finishes, that reaction will be delayed. ◦ If interruption rules depend on the agent recognizing the previous user turn ended quickly, there may be slightly more complex state handling required. Practical tradeoffs and recommendations • If your priority is preserving long, thoughtful answers without fragmentation, 2s is conservative and reasonable. • If you need faster transitions after a user finishes speaking (e.g., immediate feedback or tight conversational pacing), 2s may feel sluggish; consider 0.6–0.8s instead. • A two-mode approach is effective for your case: ◦ While the agent is speaking: keep a shorter

min_silence_duration

(0.3–0.45) so user barge-in is detected quickly. ◦ After the agent stops (user-answer mode): raise

min_silence_duration

to 1.5–2.0 so thoughtful pauses aren’t treated as turn ends. • Monitor these metrics when testing: ◦ Average time from user stop to turn-finalization ◦ Number of fragmented transcripts per session ◦ User complaints about being cut off vs agent reaction latency If you want, I can recommend exact two-mode values tailored to your typical pause lengths (share average pause durations) or a safe canary plan to test

2s

versus lower values.

If we raise min_silence_duration to 2 seconds, but STT signals an end of turn because of semantics, does that override the VAD decision?

Thanks — good question. Short answer: yes — when

turn_detection="stt"

is active, STT-driven semantic endpointing can end a turn before the VAD’s

min_silence_duration

is reached. In other words, STT endpointing takes precedence for turn-finalization. How they interact (concise) • VAD (e.g., Silero) reports low-latency voice activity (speech start/stop) using its thresholds like

min_silence_duration

. It’s used primarily for interruption detection and fast activity signals. • STT endpointing (Deepgram Flux / Soniox) uses acoustic + semantic cues to decide if an utterance is complete. When

turn_detection="stt"

, the system defers final turn decisions to the STT provider. • Result: If the STT model signals end-of-turn (because semantics indicate completion), the turn will end even if VAD’s

min_silence_duration

(e.g., 2s) hasn’t elapsed. Why that matters for your UX • Pros: ◦ Semantic endpointing avoids splitting thoughtful answers into fragments when the model recognizes a complete thought even with short silence. ◦ It lets the system react to intent/completion cues (punctuation, prosody) that VAD alone cannot detect. • Cons: ◦ If STT is too aggressive with semantic endpointing, it can close turns earlier than you expect — but you can tune STT endpointing parameters independently (e.g., eager thresholds) to balance this. Practical recommendations • Keep VAD enabled even with

turn_detection="stt"

for interruption responsiveness and real-time activity signals. • Treat VAD’s

min_silence_duration

as a safety/fallback for non-semantic cases, not an absolute block on STT-driven endings. • Tune STT endpointing sensitivity (e.g., eager EOT thresholds) if you find it ends turns too early for your meditation UX. • Consider a dual-mode behavior: ◦ While agent is speaking: keep VAD and interruption sensitivity more responsive so users can barge in. ◦ After agent finishes (user-answer mode): use a larger effective silence window or adjust STT eager thresholds so long, thoughtful pauses aren’t prematurely finalized. • Log both VAD events and STT end-of-turn signals during a canary rollout so you can see which component is triggering the end-of-turn in practice. If you want, I can: • Suggest specific STT endpointing parameter values to try (if you share which provider and current STT tuning), • Or propose a small canary test plan to compare behavior with

min_silence_duration=2s

vs. lowered VAD plus tuned STT thresholds. Sources: Deepgram STT | LiveKit Documentation

So, if STT does not report end of turn due to semantics, and 2 seconds silence is detected by VAD, doest that end the turn for the user? essentially meaning maximum allowed silence even if semantic end of point is detected will always be 2 seconds of silence?

Short answer: Yes — if STT does not signal end-of-turn, VAD’s

min_silence_duration = 2.0

will act as a fallback and end the turn after 2 seconds of silence. Why (concise) • With

turn_detection="stt"

, semantic endpointing from the STT provider can end turns earlier when it detects a complete utterance. • If the STT does not emit an end-of-turn signal (no semantic cue), the system falls back to VAD. When VAD detects continuous silence equal to

min_silence_duration

, it will finalize the utterance. • Practically that means VAD’s silence threshold is a hard upper bound on silence length before a turn is finalized if STT hasn’t already done so. Practical implications for your meditation use case • Benefit: A 2s VAD fallback prevents the conversation from hanging indefinitely when semantic endpointing doesn’t fire (good for avoiding stuck sessions). • Drawback: Finalization and any post-turn actions (saving responses, advancing prompts) will be delayed by up to 2s after the user stops speaking. • If you want to be extra conservative for long, thoughtful pauses, you can: ◦ Keep 2s as the user-mode silence threshold (so pauses aren’t split), ◦ Use a shorter silence threshold while the agent is speaking so users can still barge in quickly, ◦ Or tune STT eager/endpointing sensitivity so semantics catch more natural end-of-turns without relying on the VAD timeout. What to monitor when you keep 2s • Frequency of STT-driven end-of-turn vs VAD-driven end-of-turn (logs). • Average latency from user-stop to turn-finalization. • Any user complaints about sluggishness vs fragmenting of answers. If you want, I can: • Propose a two-mode threshold example (exact values) for agent-speaking vs user-thinking modes, or • Suggest logging fields to add so you can measure how often STT vs VAD triggers turn finalization. Sources: VAD and turn detection configuration guide | LiveKit | How does end-of-utterance detection work in conversations? | Silero VAD plugin | LiveKit Documentation | AssemblyAI STT | LiveKit Documentation | Deepgram STT | LiveKit Documentation

How does false_interruption_timeout work on all of this

Thanks — good question. I’ll explain clearly how

false_interruption_timeout

fits into the VAD + STT + turn-detection flow and what it will do in your meditation use case. What

false_interruption_timeout

does (short) • It’s a grace period the system waits after VAD signals an interruption (user audio starts) to see if any real transcript arrives from STT. • If no transcript arrives within that timeout, the interruption is treated as false (e.g., cough, breath, background noise) and the agent can resume speaking automatically (if

resume_false_interruption=True

). Step-by-step flow during an interruption 1. Agent is speaking and VAD detects voice -> interruption is triggered and agent pauses. 2. STT is given the audio and may produce interim/final transcripts or no transcript. 3. If STT returns a transcript within `false_interruption_timeout`: ◦ The interruption is real: handle the user speech (process the transcript) and do not resume the agent. 1. If STT returns nothing within `false_interruption_timeout`: ◦ The interruption is considered false → emit an

agent_false_interruption

event and resume the agent (if configured to resume). How this interacts with your other settings • VAD: detects starts/stops of audio.

min_silence_duration

controls how long VAD must see silence to declare the end of speech (a separate concern). • `turn_detection="stt"`: final turn endings are primarily decided by STT semantic endpointing, but VAD and

min_silence_duration

act as fallbacks. •

false_interruption_timeout

only applies after VAD caused an interruption and no STT transcript confirmed it — it does not change how STT or VAD decide end-of-turn directly. Why it matters for meditation / slow-paced UX • Short breaths, sighs, or quiet inhalations are common and can trigger VAD. With too-short a

false_interruption_timeout

, the system may resume agent speech while the user is still forming words — or, conversely, resume too quickly after a real but slow user utterance if STT is slow. • For slow, thoughtful answers you should lengthen the timeout so STT has enough time to return a transcript before the system labels the interruption false. Practical tuning recommendations for your case • Set

false_interruption_timeout

longer than the STT response latency you observe. For slow/meditation flows, try 3.0–5.0 seconds. • Keep

resume_false_interruption = True

so the agent continues if the interruption was indeed false (good for avoiding dead air). • Raise

min_interruption_duration

(0.7–1.0s) to avoid brief noises causing interruptions in the first place. • Keep

min_interruption_words = 2

to avoid single-word false interrupts. • Keep VAD

min_silence_duration

larger for user-thinking mode (1.5–2.0s) so pauses aren’t split; use a shorter silence threshold while the agent is speaking if you want responsive barge-in. • Log

agent_false_interruption

events and correlate with STT latency to fine-tune the timeout. Example config to try (meditation-friendly) • min_speech_duration = 0.2 • min_silence_duration (user-thinking mode) = 1.8–2.0 • min_silence_duration (agent-speaking mode) = 0.35–0.45 • min_interruption_duration = 0.8 • min_interruption_words = 2 • false_interruption_timeout = 3.5 • resume_false_interruption = True • turn_detection = "stt" What to monitor during a canary rollout • Count of

agent_false_interruption

events • Time between VAD start and first STT transcript • Instances where agent resumes but user actually spoke (false resume) • Cases where user speech is missed because STT was too slow relative to

false_interruption_timeout

If you want, I can: • Suggest exact canary percentages and monitoring queries to validate the change, or • Review a short sample log of interruptions vs STT transcripts and recommend a tuned timeout for your observed latencies. Sources: Turns overview | LiveKit Documentation | Deepgram STT | LiveKit Documentation | How does end-of-utterance detection work in conversations?

How can I dynamicaly update my min_silence_duration when the agent is speaking?

job_process.userdata["vad"] = silero.VAD.load(
    min_speech_duration=0.2,
    min_silence_duration=2,
)

Good question — you can’t change a Silero VAD instance’s

min_silence_duration

after it’s created, but you can achieve dynamic behavior by switching strategies at runtime. Below are safe, supported patterns and concrete options you can use for your snippet. Short answer • You cannot mutate

min_silence_duration

on an already-loaded

silero.VAD

instance. • Options: (A) pre-load multiple VAD instances and switch between them, (B) use a higher-level turn-detection model that overrides VAD timing, or (C) change session-level endpointing/timeout logic to emulate different silence behavior. Options with examples and tradeoffs 1) Pre-load multiple VAD instances and swap when needed • Pre-create a VAD for “agent-speaking” (short silence threshold) and one for “user-thinking” (long threshold). When the agent starts or stops speaking, assign the appropriate VAD to the session. • Example pattern (conceptual):

# prewarm
proc.userdata["vad_agent"] = silero.VAD.load(min_speech_duration=0.2, min_silence_duration=0.35)
proc.userdata["vad_user"]  = silero.VAD.load(min_speech_duration=0.2, min_silence_duration=2.0)

# when creating session
session = AgentSession(vad=proc.userdata["vad_agent"], ...)

# when agent begins its spoken prompt:
session._vad = proc.userdata["vad_agent"]   # switch to responsive VAD

# when agent finishes and user is expected to think:
session._vad = proc.userdata["vad_user"]    # switch to long-silence VAD

• Tradeoffs: ◦ Works well and is straightforward. ◦ Direct assignment like

session._vad

touches internal state — it works in practice but is an internal change; test carefully. ◦ Preloading adds memory use (two VAD instances). 2) Use a turn-detection model that supersedes VAD timing • Use a semantic/transformer-based turn detector (e.g., MultilingualModel) or an LLM/realtime model that handles endpointing. These models make end-of-turn decisions based on semantics and context and reduce reliance on VAD timers. • Example conceptual config:

session = AgentSession(
    vad=proc.userdata["vad_user"],            # still useful for interruption start detection
    turn_detection=MultilingualModel(...),    # semantic turn detection
    ...
)

• Tradeoffs: ◦ More robust for reflective, slow-paced UX. ◦ Slightly heavier computationally but better at avoiding fragmentation of thoughtful pauses. 3) Emulate dynamic silence handling at the session/logic layer • Keep a single VAD but implement logic that treats VAD silence events differently depending on agent state: ◦ If agent is speaking: treat VAD "silence" as tentative; wait for a short debounce before finalizing. ◦ If user-thinking: require an explicit longer silence (tracked in your code) before finalizing the turn. • Example (conceptual):

# on vad_silence event:
if agent_is_speaking:
    start_short_debounce_timer(0.35)
else:
    start_long_debounce_timer(2.0)
# only finalize turn when the active debounce timer completes without speech

• Tradeoffs: ◦ No need to swap VAD instances. ◦ Requires careful timer/cancellation handling to avoid race conditions. 4) Adjust endpointing/fallback timers instead of VAD • Use

false_interruption_timeout

,

min_interruption_duration

, or STT eager/eot thresholds to tune how quickly interruptions are accepted or rejected. This is less direct but can address behavior without swapping VADs. Recommendations for your meditation use case • Preferred approach: Use a semantic turn detector (option 2) if possible — it best preserves thoughtful pauses while remaining responsive to real completions. • Practical fallback: Preload two VADs and switch at agent start/stop (option 1). This gives explicit control over responsiveness during agent speech vs. user-thinking. • If you swap VAD instances, also: ◦ Keep

resume_false_interruption=True

◦ Increase

false_interruption_timeout

(3–5s) for slow replies ◦ Keep logging to see whether STT or VAD triggered endpointing What I can help with next • If you want, I can: ◦ Provide a small runnable code example tailored to your agent/session setup, ◦ Suggest exact values for the two VAD modes based on expected pause lengths, ◦ Review a short log sample to recommend which approach fits your production latencies. Which would you prefer: a full code example for swapping VADs, or guidance on switching to a semantic turn detector? Sources: VAD and turn detection configuration guide | LiveKit | Silero VAD plugin | LiveKit Documentation | Turns overview | LiveKit Documentation | Improving voice AI's turn detection with transformers | TTS Comparison | LiveKit Documentation

Ok, turning back to my first issue, how do I start logging the right events to check why STT stops working? What are the events I need to log, and how to log them

Thanks — I understand this is blocking and you want a clear, actionable logging plan so you can pinpoint why STT silently stops. Below is a focused checklist of the exact events to capture, where to place logs in your agent/session code, example snippets to instrument logging, and what to look for in the results. Which events to log (high priority) • VAD events ◦ speech start, speech end, silence durations • STT stream lifecycle ◦ stream open / close, push_frame success/failure, frame counts, backpressure/errors • STT speech events ◦ START_OF_SPEECH, END_OF_SPEECH, INTERIM_TRANSCRIPT, FINAL_TRANSCRIPT, confidence scores • Turn-detection / endpointing events ◦ STT end-of-turn signal, eager_eot triggers, any turn finalization decision • Interruption events ◦ interruption_start, interruption_confirmed (transcript arrived), agent_false_interruption (and reasons) • Connection / transport events ◦ participant connection state changes, re-negotiations, track muted/unmuted, audio routing changes • Errors & warnings ◦ any on_error callbacks, exceptions, timeouts, unexpected closes • Timing & correlation metadata ◦ timestamps (client + server), sequence ids, session/participant IDs, STT request IDs, audio frame timestamps Where to put logs (practical) • At the STT ingestion boundary: ◦ Log every

push_frame()

call: size, timestamp, sequence number, return status. ◦ Log when the STT socket/stream is opened/closed and any socket errors. • At the STT output handler: ◦ Log each STT event (interim/final) with timestamps and the audio time range it covers. • VAD handlers: ◦ Log VAD state transitions with timestamps and last Nms of audio energy. • Agent session lifecycle: ◦ Log when the agent starts/stops speaking, when interruption was attempted, and whether it was confirmed or marked false. • Network / transport: ◦ Log ICE state changes, participant reconnections, and any track mute/unmute events coming from mobile clients. Concrete logging fields to capture per event • timestamp (ISO + monotonic) • session_id, participant_id, room_id • event_type (e.g., VAD_SPEECH_START, STT_INTERIM, STT_FINAL, PUSH_FRAME) • event_subtype/details (e.g., transcript text, confidence, reason) • audio_position_ms / frame_index • stt_request_id / stt_stream_id • push_frame result or error • connection_state / rtc state • any configured thresholds active at the time (e.g., min_silence_duration, false_interruption_timeout) • sample of raw metrics (cpu, memory, queue length) when available Example instrumentation snippets (conceptual Python) • Log push_frame and STT lifecycle

def push_audio_frame(stt_stream, frame, seq):
    now = time.time()
    try:
        stt_stream.push_frame(frame)
        <http://logger.info|logger.info>("PUSH_FRAME", extra={
            "ts": now, "session_id": session_id, "seq": seq,
            "frame_len": len(frame), "stt_stream_id": stt_stream.id
        })
    except Exception as e:
        logger.exception("PUSH_FRAME_ERROR", extra={
            "ts": now, "session_id": session_id, "seq": seq, "error": str(e)
        })

• Log STT events (interim / final / EOT)

@stt_stream.on("speech_event")
def on_speech_event(evt):
    <http://logger.info|logger.info>("STT_EVENT", extra={
        "ts": time.time(),
        "session_id": session_id,
        "stt_stream_id": evt.stream_id,
        "type": evt.type,   # START_OF_SPEECH / INTERIM / FINAL / END_OF_SPEECH
        "text": evt.text,
        "confidence": getattr(evt, "confidence", None),
        "audio_pos_ms": getattr(evt, "audio_pos_ms", None),
    })

• VAD and interruption logging

@vad.on("speech_start")
def on_vad_start():
    <http://logger.info|logger.info>("VAD_SPEECH_START", extra={"ts": time.time(), "session_id": session_id})

@vad.on("speech_end")
def on_vad_end(duration_ms):
    <http://logger.info|logger.info>("VAD_SPEECH_END", extra={"ts": time.time(), "session_id": session_id, "silence_ms": duration_ms})

@session.on("agent_false_interruption")
def on_false_interruption(info):
    logger.warning("AGENT_FALSE_INTERRUPTION", extra={
        "ts": time.time(), "session_id": session_id, "reason": info
    })

• Log turn-finalization source Record whether the turn ended due to STT semantic endpointing or VAD silence:

def finalize_turn(source):
    <http://logger.info|logger.info>("TURN_FINALIZED", extra={
        "ts": time.time(),
        "session_id": session_id,
        "source": source,   # one of: "stt_semantic", "vad_silence", "timeout"
    })

How to correlate logs for root-cause • Correlate push_frame timestamps with STT events using stt_stream_id and frame indices to verify frames actually arrived before STT silence. • Correlate VAD speech_start/stop with push_frame activity — if VAD shows speech_start but push_frame stops, you have a capture or plumbing issue. • Watch for patterns: STT stream closes after N minutes, or push_frame exceptions precede loss of transcripts. • Check latency: time from push_frame to first STT interim. If STT latency spikes or exceeds

false_interruption_timeout

, transcripts may arrive too late and interruptions become false. • Check for audio track mute/disable events from mobile clients at the same time. Practical logging recommendations & sample dashboard queries • Log levels: ◦ INFO: normal lifecycle events (push_frame success, STT interim/final) ◦ WARN: transient issues (retries, increased latency) ◦ ERROR: exceptions, stream closes, timeouts • Create dashboards for: ◦ STT event rate per session ◦ Ratio of STT-final to VAD-final turns (to see which is driving finalization) ◦ agent_false_interruption count per hour ◦ avg latency (push_frame -> STT interim) • Alert on: ◦ Sudden spike in push_frame errors ◦ STT stream disconnects per minute above threshold ◦ Sessions with zero STT events for > X seconds while audio frames are being pushed Testing and rollout plan 1. Canary: enable detailed logging in a small percentage of production traffic for 24–72 hours. 2. Capture at least: push_frame metrics + STT events + VAD transitions + interruptions. 3. Analyze correlation for sessions that exhibited “no STT” to see whether frames were pushed, the stream stayed open, and if STT produced any events at all. 4. Iterate: if frames aren’t reaching STT, focus on client capture/transport; if frames arrive but STT produces nothing, investigate STT provider logs and configuration. If you want, I can: • Provide a runnable, integrated code example tailored to your agent codebase (Android/iOS clients + server-side STT bridging). • Suggest specific log formats (JSON fields) ready for ingestion into your logging/observability stack (ELK, Datadog, CloudWatch). • Review a short sample log output you collect and help interpret it. Which would help you most next: a full runnable example for your server-side agent, or a compact JSON log schema for ingestion into your observability platform?