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t
Thanks for reaching out, manish. I'm an AI assistant and I'm here to help. Give me a moment while I research this topic.
Hi — welcome! How can I help you today? Tell me briefly what you’re working on (app type, SDK/language, or an error you’re seeing) and any relevant IDs or screenshots if you have them.
h
How do we handle interruption while using Gemini-Realtime API
t
Thanks — I’ll explain how interruption handling works with the Gemini‑Realtime API and what you can control. Acknowledging your question: you asked how to handle interruptions while using the Gemini‑Realtime API. Key behavior (what happens automatically) • Gemini Live provides built‑in VAD-based turn detection and will automatically stop agent playback when a user starts speaking. • On interruption the agent stops speaking, conversation context is truncated to what the user heard before interruption, and the agent resumes listening to process the user’s new input. • If the detected speech is noise (no transcribed words), the agent treats it as a false interruption, waits a short timeout (default ~2s), and if nothing valid follows, resumes speaking where it left off. What you can configure • allowInterruptions: enable or disable user interruptions (default: true). • minInterruptionDuration: minimum ms of user speech to trigger an interruption (example: 500 ms). • minInterruptionWords: minimum transcribed words required to count as a valid interruption (used when STT is involved). Examples • Enable/adjust interruption behavior:
Copy code
const session = new voice.AgentSession({
  llm: new google.realtime.RealtimeModel(),
  voiceOptions: {
    allowInterruptions: true,
    minInterruptionDuration: 500,
    minInterruptionWords: 1,
  },
});
• Disable interruptions for a specific utterance:
Copy code
await session.say("Please wait while I finish", allow_interruptions=False)
• Force an interruption programmatically:
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await session.interrupt()
False interruptions • System waits a short timeout (default ~2s). If no valid speech follows, the agent resumes speaking automatically. Notes • Gemini’s built‑in turn detection takes precedence; external turn‑detection models are ignored unless you explicitly override that behavior. If you want, tell me: • Which SDK/language you’re using (JS, Python, etc.) • Whether you need examples for server or client code and I’ll provide a concrete code sample or recommend exact parameter values for your use case. Sources: Gemini Live API plugin | LiveKit docs | Turns overview | LiveKit docs | OpenAI and LiveKit partner to turn Advanced Voice into an API
h
You can provide the examples in python
t
Thanks — I’ll give concise Python examples showing: • session setup with interruption options • disabling interruptions for a specific utterance • forcing an interruption • handling false interruptions (timeout behavior) 1) Create an AgentSession with interruption options
Copy code
from livekit import voice, google

session = voice.AgentSession(
    llm=google.realtime.RealtimeModel(),
    voice_options={
        "allow_interruptions": True,
        "min_interruption_duration": 0.5,   # seconds
        "min_interruption_words": 1,
    },
)
2) Disable interruptions for a specific utterance
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# This utterance will not be interruptible by user speech
await session.say("Please let me finish this explanation.", allow_interruptions=False)
3) Force an interruption programmatically
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# Interrupt the agent immediately (e.g., triggered by some event)
await session.interrupt()
4) Handling false interruptions • The agent automatically treats brief/noisy speech as a false interruption, waits a short timeout (default ~2s), and resumes if no valid speech follows. • You can rely on the default behavior; if you need a different timeout, adjust session or voice plugin settings where available (for example, 
false_interruption_timeout
if exposed by your SDK). If you’d like, tell me which LiveKit Python SDK version you’re using or share a short snippet of your current session flow and I’ll adapt these examples to fit it exactly.
h
Copy code
session = AgentSession(
    min_interruption_words=2,
    allow_interruptions=False,
    llm=google.realtime.RealtimeModel(
        model="gemini-2.5-flash-native-audio-preview-12-2025",
        voice="leda", # "kore", #"aoede", #"zephyr", #'Puck',
        temperature=0.4,
        thinking_config=types.ThinkingConfig(
            include_thoughts=False,
        ),
),
)
this is my current implementation. anything needs to be corrected?
t
Thanks — I reviewed your snippet. It’s almost correct; a couple small fixes and recommendations: 1) Placement of interruption options • 
min_interruption_words
and 
allow_interruptions
should go inside 
voice_options
(or the SDK’s equivalent) rather than at the top level of 
AgentSession
. 2) Type/units consistency • Use seconds (float) for 
min_interruption_duration
if you set it; you currently set only 
min_interruption_words
. If you want a minimum duration, add e.g. 
min_interruption_duration=0.5
. 3) Final cleaned example
Copy code
from livekit import voice, google, types

session = voice.AgentSession(
    llm=google.realtime.RealtimeModel(
        model="gemini-2.5-flash-native-audio-preview-12-2025",
        voice="leda",
        temperature=0.4,
        thinking_config=types.ThinkingConfig(include_thoughts=False),
    ),
    voice_options={
        "allow_interruptions": False,
        "min_interruption_words": 2,
        # "min_interruption_duration": 0.5,  # optional (seconds)
    },
)
4) Behavior note • With 
allow_interruptions=False
, the model will not allow user interruptions even though 
min_interruption_words
is set — 
allow_interruptions
controls whether interruptions are permitted. If you want, tell me whether you want interruptions enabled and a preferred minimum duration (ms or seconds) and I’ll give the exact values to use.
h
need interruptions but, small words should not be interupt the agent to be pause and resume
t
Thanks — I understand: you want interruptions enabled, but ignore very short utterances so the agent isn’t paused for brief words/noise. Use 
allow_interruptions=True
and set 
min_interruption_words
(and optionally 
min_interruption_duration
) inside 
voice_options
. 
min_interruption_words
prevents interruptions unless the STT detects that many words. Also rely on the false-interruption timeout (default ~2s) to avoid resuming on noise. Corrected Python example:
Copy code
from livekit import voice, google, types

session = voice.AgentSession(
    llm=google.realtime.RealtimeModel(
        model="gemini-2.5-flash-native-audio-preview-12-2025",
        voice="leda",
        temperature=0.4,
        thinking_config=types.ThinkingConfig(include_thoughts=False),
    ),
    voice_options={
        "allow_interruptions": True,       # enable interruptions
        "min_interruption_words": 2,       # require >=2 words to count as interruption
        "min_interruption_duration": 0.5,  # optional: require >=0.5s of speech
        # "false_interruption_timeout": 2.0  # if exposed, adjust false-interruption wait (seconds)
    },
)
Notes: • With this config, single-word or very short noises won’t interrupt; only speech meeting both word and (if set) duration thresholds will. • If you observe false positives/negatives, adjust 
min_interruption_words
or 
min_interruption_duration
slightly and test in real audio conditions. Would you like a short test script to simulate user speech and validate these settings?
h
Copy code
session = AgentSession(
    llm=google.realtime.RealtimeModel(
        model="gemini-2.5-flash-native-audio-preview-12-2025",
        voice="leda", # "kore", #"aoede", #"zephyr", #'Puck',
        temperature=0.4,
        thinking_config=types.ThinkingConfig(
            include_thoughts=False,
        ),
        instructions="""
                     some instructions here....
                    """,
    ),
    voice_options={
        "allow_interruptions": True,       # enable interruptions
        "min_interruption_words": 2,       # require >=2 words to count as interruption
        "min_interruption_duration": 0.5,  # optional: require >=0.5s of speech
        # "false_interruption_timeout": 2.0  # if exposed, adjust false-interruption wait (seconds)
                    },
)
t
Thanks — your latest snippet looks correct for your goal. A few small confirmations and one optional suggestion: • Keep 
allow_interruptions=True
and set 
min_interruption_words
and 
min_interruption_duration
inside 
voice_options
— you’ve done this correctly. • The 
instructions
field belongs on the 
RealtimeModel
as you placed it. • Ensure you import the correct classes (example: 
from livekit import voice, google, types
) and create the session from 
voice.AgentSession
if your SDK requires that exact constructor. Optional tuning • If you still see brief false interruptions, raise 
min_interruption_words
to 3 or increase 
min_interruption_duration
to 0.7–1.0s. • If your SDK exposes 
false_interruption_timeout
, you can set it (e.g., 2.0s) to have the agent resume when no valid speech follows. If you want, I can: • Provide a runnable test script to simulate short vs. valid interruptions, or • Suggest exact values based on expected user speech patterns (e.g., conversational vs. short confirmations). Which would you prefer?
h
Unhandled error while getting session: AgentSession.__init__() got an unexpected keyword argument 'voice_options'
t
Thanks — that error happens because 
voice_options
is not a valid parameter in the Python SDK. Pass interruption settings directly to 
AgentSession
instead. Your model and 
instructions
stay on the 
RealtimeModel
. Try this working Python example:
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from livekit.agents import AgentSession
from livekit.plugins import google, silero, types

vad = silero.VAD.load()

session = AgentSession(
    llm=google.realtime.RealtimeModel(
        model="gemini-2.5-flash-native-audio-preview-12-2025",
        voice="leda",
        temperature=0.4,
        thinking_config=types.ThinkingConfig(include_thoughts=False),
        instructions="""
            some instructions here....
        """,
    ),
    vad=vad,
    allow_interruptions=True,
    min_interruption_words=2,
    min_interruption_duration=0.5,   # seconds
    false_interruption_timeout=2.0,
    resume_false_interruption=True,
)
Notes: • 
allow_interruptions
, 
min_interruption_words
, 
min_interruption_duration
, and 
false_interruption_timeout
must be top-level kwargs to 
AgentSession
. • Loading a VAD (e.g., 
silero.VAD
) improves responsiveness. • If you still see unexpected args, run 
print(AgentSession.__init__.__doc__)
to inspect your installed SDK’s constructor signature and tell me the SDK version if it differs — I’ll adapt the example. Sources: Turns overview | LiveKit docs | Gemini Live API plugin | LiveKit docs | OpenAI Realtime API plugin guide | LiveKit docs | Google Gemini LLM plugin guide | LiveKit docs
h
So you want me to implement the agent side VAD instead of finetuning server side automatic activity detection
t
Good question — here’s a clear answer. Short answer • You do not have to replace the server’s automatic turn detection entirely, but using an agent‑side VAD is the recommended approach for responsive, low‑latency interruption handling. Agent‑side VAD improves responsiveness and lets you tune sensitivity locally without retraining or changing server logic. Why agent‑side VAD is recommended • Lower latency: VAD on the agent process detects speech immediately (no round trips), so the agent can stop playback faster. • Tunability: You can adjust sensitivity, minimum duration, and debounce behavior to match your app’s audio environment. • Robustness: Combining agent VAD with the Gemini realtime model’s built‑in turn detection reduces false positives (agent VAD handles quick noise, model handles intent/ASR). • No model retraining: You don’t need to finetune server models or change server‑side automatic activity detection to get reliable interruption behavior. How to use both together (best practice) • Run a lightweight VAD (e.g., Silero VAD) in the agent process to detect speech starts/stops. • Pass VAD events immediately to the AgentSession (so playback can be stopped/paused quickly). • Keep server/model turn detection enabled (allow_interruptions=True) so the model finalizes turn boundaries and uses STT thresholds (
min_interruption_words
,
min_interruption_duration
,
false_interruption_timeout
) to validate interruptions. • Use 
min_interruption_words
and 
min_interruption_duration
in 
AgentSession
to ignore very short words/noise. Quick implementation notes (Python) • Load a VAD and pass it into `AgentSession`:
Copy code
from livekit.agents import AgentSession
from livekit.plugins import silero, google, types

vad = silero.VAD.load()

session = AgentSession(
    llm=google.realtime.RealtimeModel(...),
    vad=vad,
    allow_interruptions=True,
    min_interruption_words=2,
    min_interruption_duration=0.5,
    false_interruption_timeout=2.0,
    resume_false_interruption=True,
)
• On VAD "speech_start" events, call session.interrupt() or otherwise notify the session so playback stops immediately; the model’s STT and thresholds will confirm whether to treat it as a real interruption. Would you like a short runnable test script that feeds simulated audio to Silero VAD and demonstrates calling 
session.interrupt()
on voice start?
h
{"message": "process memory usage is high", "level": "WARNING", "name": "livekit.agents", "memory_usage_mb": 507.55859375, "memory_warn_mb": 500, "memory_limit_mb": 0, "pid": 685640, "job_id": "AJ_iUh7T7pNc4ae", "room_id": "RM_sqTH4PbvFG5W", "timestamp": "2025-12-22T120152.898383+00:00"} why this warning gets frequently
t
Thanks — I see the frequent warning showing the agent process memory at ~507 MB (threshold 500 MB). I’ll explain likely causes and give targeted steps you can try. Quick diagnosis (likely causes) • Model/plugin memory: Loading RealtimeModel + voice models and plugins (VAD, STT) can keep significant resident memory. • Long‑lived sessions or many concurrent sessions: each AgentSession and open audio buffers add memory over time. • Audio buffering: queued audio (incoming or outgoing) or large chunk sizes increases RAM. • VAD/STT implementations: native libs (e.g., Silero) or their audio buffers may allocate significant memory. • Memory leak or accumulation: objects retained in code (handlers, large transcripts, conversation history) can grow without cleanup. Immediate actions to reduce frequency • Lower concurrency per process: limit number of AgentSession instances per worker and scale horizontally. • Shorten session lifetimes: explicitly close sessions you don’t need with 
await session.close()
or the SDK equivalent. • Reduce audio buffer sizes / chunk length: use smaller frames when feeding VAD/STT. • Offload heavy plugins: only enable VAD/STT when needed, or use lighter weight VAD settings. • Increase the warn threshold if 500MB is too low for your workload (but only after confirming memory is stable). Monitoring & debugging steps 1. Confirm SDK version: 
print(livekit.__version__)
or pip freeze. Different versions change memory profiles. 2. Inspect process memory over time: run a simple script to log RSS/heap at intervals to see growth pattern. 3. Heap/profile snapshot: use tracemalloc or a native profiler to find growing allocations in Python objects. 4. Check for unclosed sessions/streams: search code paths for missing `close()`/cleanup in error paths. 5. Test with minimal config: create one session without plugins (no VAD/STT) to measure baseline memory for the model alone. Code-level mitigations • Explicitly close sessions and release plugins:
Copy code
await session.close()
del session
• Limit retained history in the AgentSession (truncate or summarize conversation state) instead of keeping full transcripts. Operational mitigations • Restart strategy: recycle workers on memory high watermark (e.g., restart at 400–450MB before hitting warning). • Autoscale: add more workers instead of increasing memory per process. • Use a lighter voice model or reduce simultaneous model instances. If you want, I can: • Provide a short memory‑profiling script to run against your process, or • Review a small reproduction (SDK version, a minimal snippet that opens sessions) and point out exact leaks. Which would you prefer, and what SDK version are you running?
h
why transcripts are unorderd while using gemini realtime
t
Thanks — I’ll explain why transcripts can appear unordered with Gemini Realtime and give concrete fixes you can apply. Acknowledging your issue: transcripts arriving out of sequence while using Gemini Realtime. Why this happens • The model streams partial (interim) and final transcription results asynchronously; interim updates may arrive before or after later final chunks. • Network jitter or parallel audio/transcription processing can deliver chunks out of order. • Transcript segments include 
segment_id
(and 
transcribed_track_id
); if you don’t buffer and reorder by these IDs, display can look scrambled. • Interim results should be treated as replaceable; showing them permanently without waiting for the 
final
flag causes apparent reordering. How to fix it (practical steps) • Only commit/display a transcript when 
lk.transcription_final == true
. Use interim results only for UI preview and replace them with the final chunk. • Buffer incoming transcript messages keyed by 
segment_id
(and 
transcribed_track_id
) and order by segment sequence before merging. • If messages include sequence numbers or timestamps, use them to sort before rendering. • Deduplicate by segment_id so interim → final replaces the same segment instead of appending another entry. • For real-time UX: show interim text in a temporary slot and swap it when the final arrives (don’t push interim into the permanent transcript list). • Use LiveKit helpers where available (for example, 
useTranscriptions
on React or the SDK’s text stream handlers) — they already handle buffering/finalization logic. Debugging checklist • Log incoming transcript events with 
segment_id
, 
transcribed_track_id
, 
lk.transcription_final
, and timestamps to confirm ordering at the transport layer. • Verify you are not concurrently appending transcripts from multiple handlers without synchronization. • Confirm whether interim messages carry earlier timestamps than later final messages (that indicates reordering during transit). If you share: • the SDK/language you’re using (Python/JS), • a short snippet of how you handle transcript events, I’ll provide a small code example to buffer/reorder and replace interim with final text. Which SDK do you want the example for? Sources: Gemini Live API plugin | LiveKit docs | Text and transcriptions | LiveKit docs
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