is there a way to integrate this turn detection model into livekit cascaded agents? https://github.com/pipecat-ai/smart-turn

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

embed inside agent runtime. We're using realtime transcript --> LLM --> TTS at the moment. I want turn signals to influence whether the agent speaks/turn control

Thanks — I’m responding about embedding Pipecat

smart-turn

inside your agent runtime so its turn signals control whether the agent speaks. I can guide you, but I need a few specifics to produce a concrete integration pattern and code sketches: 1. Which runtime/SDK are you using: Python or Node.js? 2. LiveKit Agents package/version you have installed. 3. How will you invoke

smart-turn

in-process: ◦ imported Python module, ◦ subprocess per utterance, ◦ or an async in-process function called on each STT partial/final? 1. Do you use streaming partial transcripts or only final transcripts? Do you have word-level timestamps? 2. Exact desired agent behaviour when smart-turn signals end-of-user-turn: ◦ immediately cancel agent TTS and speak, ◦ wait N ms for confirmation, ◦ or queue reply and start only after VAD confirms silence? 1. Latency/CPU constraints (e.g., must decisions be <100 ms?) 2. Do you want to replace LiveKit’s built-in turn detector or run alongside it and feed your signal into LiveKit’s turn control? 3. Any constraints on bundling model weights or licensing concerns? Answering these lets me provide: • an integration pattern for STT → smart-turn → LLM → TTS, • example call/invocation code for your runtime, • recommended confidence thresholds and debounce logic, • how to wire the turn decision into AgentSession turn handling (Python/Node code sketch). Sources: LiveKit turn detector plugin | LiveKit Documentation | Using a transformer to improve end of turn detection | LiveKit | Turns overview | LiveKit Documentation, with interactive elements

python, livekit-agents 1.4 it should call on each STT partial and final. I want to replace livekits turn detector

Thanks — I’m responding to your request to embed Pipecat’s

smart-turn

inside a Python LiveKit agent (livekit-agents v1.4), call it on every STT partial and final, and replace LiveKit’s built-in turn detector so its signals control agent speaking/turn-taking. I can provide a concrete integration pattern and code sketch. A couple quick clarifying points before I produce the sample: • Will you import

smart-turn

as a Python module in-process (yes/no)? • Do you have a preferred threshold or debounce behavior for when the agent should start speaking on an end-of-turn signal (e.g., immediate on signal, or wait 100–300 ms to avoid false positives)? If “yes” to in-process import and you’re okay with a short debounce, I’ll return a focused Python example showing: 1. A custom

TurnDetector

class that wraps

smart-turn

and exposes

on_partial(text, meta)

/

on_final(text, meta)

methods. 2. How to register that detector in

AgentSession(turn_handling=...)

to replace the built-in detector. 3. How to call it from the STT callback (partials + finals), convert model outputs to a confidence score, and use a debounce/threshold. 4. How to map the detector decision into agent actions (cancel TTS, yield/start speaking) via the AgentSession turn APIs. Tell me whether you will import

smart-turn

in-process and whether you prefer immediate or slightly debounced turn starts, and I’ll post the code example. , with interactive elements

yes, import smart-turn as python module, immediate on signal

Thanks — I’ll show a focused Python pattern you can drop into your livekit-agents v1.4 setup to embed

smart-turn

as an in-process turn detector, call it on every STT partial/final, and have its immediate end-of-turn signal control agent speaking/turn-taking. Key idea: implement a custom turn detector object with `on_partial`/`on_final` methods that the STT callback calls. When

smart-turn

signals end-of-turn, call the AgentSession turn APIs to immediately start/cancel TTS or claim the floor. Example sketch (concise):

# example_turn_integration.py
from livekit.agents import AgentSession, inference, TurnHandlingOptions
from livekit.agents.turns import BaseTurnDetector  # adapter base (pseudo-name - use actual SDK base if different)
from livekit.agents.session import AgentSessionTurnController  # pseudo import for APIs to control turns
import smart_turn  # pipecat-ai smart-turn python module
import asyncio
import time

class SmartTurnDetector(BaseTurnDetector):
    def __init__(self, model_path=None, threshold=0.5):
        # initialize smart-turn model per its API
        self.model = smart_turn.load_model(model_path)  # adapt to smart-turn loader
        self.threshold = threshold
        self._last_signal = 0

    async def on_partial(self, partial_text, metadata=None):
        # call smart-turn on partial transcript
        score = self.model.predict_partial(partial_text, metadata=metadata)  # adapt to actual API
        if score >= self.threshold:
            await self._signal_end_of_turn(score)

    async def on_final(self, final_text, metadata=None):
        score = self.model.predict_final(final_text, metadata=metadata)  # adapt to actual API
        if score >= self.threshold:
            await self._signal_end_of_turn(score)

    async def _signal_end_of_turn(self, score):
        # immediate-on-signal behavior: call AgentSession turn control to let agent speak.
        # Use debounce to avoid double-firing within short window
        now = time.time()
        if now - self._last_signal < 0.05:  # tiny debounce (50ms)
            return
        self._last_signal = now

        # The AgentSession exposes turn control; obtain controller from session context.
        # This snippet assumes a global or injected reference to the active AgentSession or its controller:
        if hasattr(self, "turn_controller"):
            await self.turn_controller.request_agent_turn(immediate=True, reason="smart-turn")
        else:
            # fallback: set an event or callback the AgentSession listens to
            if hasattr(self, "on_end_of_turn_callback"):
                await self.on_end_of_turn_callback(score)

# --- wiring into AgentSession and STT callbacks ---

async def run_agent():
    # create turn detector instance
    detector = SmartTurnDetector(model_path="path/to/weights", threshold=0.5)

    # build AgentSession with your STT/LLM/TTS
    session = AgentSession(
        stt=inference.STT(language="en", realtime=True),
        llm=inference.LLM(...),
        tts=inference.TTS(...),
        turn_handling=TurnHandlingOptions(
            # register a custom detector object (replace built-in)
            turn_detection=detector,
            # ensure session will honor external detector decisions
            external_turn_detector=True,  # pseudo-flag; adapt to SDK config
        ),
    )

    # provide detector access to session's turn controller if available
    if hasattr(session, "turn_controller"):
        detector.turn_controller = session.turn_controller
    else:
        # register a callback the session consumes to start agent speak
        detector.on_end_of_turn_callback = session.trigger_agent_turn  # adapt to actual API

    # STT callback wiring: call detector on partials and finals
    async def stt_callback(partial_text=None, final_text=None, metadata=None):
        if partial_text:
            await detector.on_partial(partial_text, metadata=metadata)
        if final_text:
            await detector.on_final(final_text, metadata=metadata)

    # attach stt_callback to your STT stream (SDK-specific)
    session.stt.on_transcript(stt_callback)

    # start session (connect to LiveKit room etc.)
    await session.start()

if __name__ == "__main__":
    asyncio.run(run_agent())

Notes and practical tips • Adapt function names: replace `predict_partial`/`predict_final`, loader, and any `turn_controller`/`trigger_agent_turn` calls to the exact smart-turn and livekit-agents APIs. The snippet shows integration points and minimal debounce (50ms). • Immediate behavior: when smart-turn signals, call the agent turn API to cancel or preempt TTS and start agent response immediately. • Latency: call smart-turn on each partial; keep the model loaded in memory to meet low-latency needs. The integration avoids subprocess calls to keep response fast. • Avoid double-handling: unregister or disable LiveKit built-in detector (we used

turn_handling.turn_detection=detector

/

external_turn_detector=True

placeholder) — configure the AgentSession to accept your detector as the authoritative source. • Confidence threshold: tune

threshold

to balance false positives/negatives. With immediate-on-signal you may prefer a slightly higher threshold if you see spurious triggers. If you’d like, I can: • Convert this sketch into a fully runnable code example using the exact livekit-agents v1.4 class/method names (I’ll need the precise SDK import names you see in your environment), or • Provide a recommended threshold and debounce tuning steps based on observed false-positive rate. Which would you prefer? , with interactive elements