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

show me how I'd implement that similar to VADStream where I can pass in an audio stream and get events out

Thanks — I’ll show you a practical pattern you can use to get VAD-like streaming behavior from LiveKit’s turn detector. You want to feed audio and receive turn events (start / candidate end / final end) so you can segment, translate, and finalize chunks while the user is still speaking. The turn detector itself operates on STT transcripts (not raw audio), so the pattern below wires audio → VAD → STT → turn detector and exposes stream-style events you can consume. Summary of the approach • You cannot send raw audio directly into the turn detector the way you do VADStream. The turn detector needs transcript updates from an STT service. • Build a small wrapper (a “TurnDetectionStream”) that: a. Accepts incoming audio frames (or a streaming audio source). b. Forwards audio to your VAD (optional) and streaming STT provider. c. Listens for partial and final transcripts and passes them to the turn detector (via AgentSession). d. Emits stream-style events: on_turn_start, on_turn_candidate (model thinks it may be done), on_turn_final (finalized/confirmed). • Enable observability so you can inspect

eou_detection

traces for debugging. Example pattern (Python-like pseudocode) • This example shows the required pieces and event flow. Replace the STT/VAD implementations with the ones you use.

from livekit.agents import AgentSession
from livekit.plugins.turn_detector.multilingual import MultilingualModel
from livekit.plugins import silero, your_stt_plugin  # replace with your STT lib
import asyncio

class TurnDetectionStream:
    def __init__(self, stt_config, vad_config=None, on_turn_start=None,
                 on_turn_candidate=None, on_turn_final=None):
        # callbacks
        self.on_turn_start = on_turn_start
        self.on_turn_candidate = on_turn_candidate
        self.on_turn_final = on_turn_final

        # load plugins
        self.vad = silero.VAD.load() if vad_config else None
        self.stt = your_stt_plugin.STT(**stt_config)  # streaming STT instance

        # Create an AgentSession with the turn detector and STT
        self.session = AgentSession(
            stt=self.stt,
            vad=self.vad,
            turn_detection=MultilingualModel(),
            # optional tuning:
            min_endpointing_delay=0.4,
            max_endpointing_delay=2.5,
        )

        # internal state
        self.current_partial = ""
        self.turn_active = False

        # wire session events
        @self.session.on("user_started_speaking")
        def _on_start(evt=None):
            self.turn_active = True
            if self.on_turn_start:
                self.on_turn_start()

        @self.session.on("user_input_transcribed")
        def _on_transcript(evt):
            # evt may carry partial updates and is_final flag
            text = evt.transcript
            is_final = evt.is_final

            # Emit candidate events when the model signals likely EOU,
            # or when transcripts are final.
            if not self.turn_active:
                # Sometimes transcripts appear before the session emits start
                self.turn_active = True
                if self.on_turn_start:
                    self.on_turn_start()

            # Pass partial transcripts downstream for low-latency translation
            if not is_final:
                # optional: call on_turn_candidate with partial text
                if self.on_turn_candidate:
                    self.on_turn_candidate(text, confidence=getattr(evt, "confidence", None))

            if is_final:
                # The STT delivered a final chunk—treat as finalized turn piece.
                if self.on_turn_final:
                    self.on_turn_final(text)
                # Reset per-turn state; the turn_detector may still decide overall turn ended
                self.turn_active = False

        @self.session.on("user_stopped_speaking")
        def _on_stop(evt=None):
            # Emitted when combined VAD+turn-detector decide the turn ended
            if self.on_turn_final and self.turn_active:
                # If no final transcript came in for last chunk, you may want to fetch last final text
                last_final = getattr(evt, "last_final_transcript", None)
                self.on_turn_final(last_final)
            self.turn_active = False

    async def push_audio(self, audio_frame: bytes):
        """
        Push raw audio to the streaming STT (and VAD if needed).
        In an RTC environment LiveKit typically routes audio into the session automatically;
        if you're feeding custom frames, forward them to your STT API.
        """
        # Optional: run VAD to decide whether to send frames
        if self.vad:
            speaking = self.vad.process_frame(audio_frame)
            if not speaking:
                # you can avoid sending silent frames to STT to save cost
                return

        # Send frame to STT streaming endpoint
        await self.stt.send_audio_frame(audio_frame)

    async def close(self):
        await self.session.close()
        await self.stt.close()
        # cleanup

How this maps to your goal (translate & finalize while user still speaking) • Partial transcripts: use

on_turn_candidate

to translate partial chunks for low-latency UX. Mark them as provisional. • Model candidate event: when the turn detector’s confidence indicates semantic completeness, emit a candidate-final event (you can implement this by checking confidence fields in

user_input_transcribed

or inspect

eou_detection

traces). Treat the chunk as “finalized” for translation and display, but keep the UI able to update if later corrections arrive. • Finalization: when STT emits

is_final

or

user_stopped_speaking

fires (combined signal), treat the chunk as fully finalized and lock it in. Tuning and reliability tips • Use STT that supports frequent partial updates (low-latency partial transcripts). Faster partials = lower latency segmentation. • Choose conservative thresholds if false-positive finalization is costly. You can require both: turn_detector_confidence > X AND brief silence (VAD) before finalizing. • Keep a rollback/update path: if a later final transcript changes text, update the translation and UI. This is common with streaming STT. • Enable Agent Observability /

eou_detection

traces to inspect why the model fired; use traces to tune thresholds. Questions to help me give code you can drop in • Which STT provider/plugin are you using (or planning to use)? That determines how to push audio frames and listen for partials. • Are you running in an RTC context (LiveKit server will route audio) or a custom audio pipeline where you’ll call

push_audio(frame)

yourself? • Do you want the wrapper to emit three event types (start / candidate / final) or fewer? If you tell me your STT provider and runtime (RTC vs custom frames), I’ll provide a concrete code snippet you can run (with STT send/receive calls and how to read model confidence fields). Sources: Using a transformer to improve end of turn detection | LiveKit | Turns overview | LiveKit Documentation | LiveKit turn detector plugin | LiveKit Documentation | Using a transformer to improve end of turn detection | LiveKit, with interactive elements

ok, just write the function to take in a transcript then

Thanks — I’ll provide a focused function you can drop into your code that accepts transcript updates and uses the LiveKit turn detection model semantics to decide when a chunk should be treated as a completed turn. This function follows the AgentSession / MultilingualModel pattern: it expects streaming transcript updates (partial and final) and emits three callbacks you can use to segment, translate, and finalize chunks. Notes from the environment • The turn detector operates on transcripts (text) and emits confidence-based decisions; it does not consume raw audio. • The typical integration is via an AgentSession with

turn_detection=MultilingualModel()

and an STT plugin that yields partial/final transcript events. Provided function • Signature: process_transcript(session, transcript, is_final=False, confidence=None) • Behavior: ◦ Feeds transcript updates into the session’s turn-detection flow by simulating how streaming STT would present partial and final results. ◦ Uses turn-detector related signals exposed via session events to decide candidate and final turns. ◦ Calls callbacks: on_candidate(transcript, confidence), on_final(transcript) Example implementation (Python-like pseudocode):

# Assumes:
# - AgentSession is created elsewhere with turn_detection=MultilingualModel() and stt plugin
# - session exposes an API to ingest transcript updates or the application routes STT updates to session events
# - We provide simple local logic to decide candidate vs final if session-level signals are not directly available.

def process_transcript(session, transcript: str, is_final: bool = False, confidence: float = None,
                       on_candidate=None, on_final=None, candidate_confidence_threshold: float = 0.75):
    """
    Process a streaming transcript update and decide whether to emit:
      - on_candidate: an early candidate chunk (low-latency translation)
      - on_final: a finalized chunk (final STT or model-confirmed turn end)

    Parameters:
    - session: AgentSession configured with MultilingualModel() for turn detection.
               The session will already be receiving STT partials in a normal flow.
               If the session exposes EOU decisions/trace, they will be used when available.
    - transcript: latest partial or final transcript text
    - is_final: whether this transcript is a final STT result
    - confidence: optional confidence score from STT or model for this transcript
    - on_candidate: callback signature on_candidate(text, confidence)
    - on_final: callback signature on_final(text)
    - candidate_confidence_threshold: confidence above which we treat a partial as a candidate-final

    Returns: None (invokes callbacks as appropriate)
    """

    # 1) If STT says this is final, always emit on_final
    if is_final:
        if on_final:
            on_final(transcript)
        return

    # 2) Try to use session-level EOU detection if observable traces are available.
    #    Many AgentSession integrations log eou_detection traces. If session provides
    #    a way to query the latest EOU decision or confidence, use it.
    eou_info = None
    try:
        # This is a safe, defensive attempt to read an EOU decision if the session exposes it.
        # Concrete sessions may expose eou_detection via observability or attributes.
        eou_info = getattr(session, "latest_eou_decision", None)
    except Exception:
        eou_info = None

    if eou_info:
        # eou_info is expected to be a dict with keys like {'likely_done': bool, 'confidence': float}
        likely_done = eou_info.get("likely_done")
        eou_conf = eou_info.get("confidence")
        if likely_done:
            # Model believes the semantic turn is complete — treat as candidate final
            if on_candidate:
                on_candidate(transcript, eou_conf)
            # Optionally choose to immediately finalize depending on confidence
            if eou_conf is not None and eou_conf > 0.95:
                if on_final:
                    on_final(transcript)
            return

    # 3) Fallback heuristic when no session EOU info:
    #    Use STT confidence (if available) or simple punctuation heuristics to detect completeness.
    if confidence is not None:
        if confidence >= candidate_confidence_threshold:
            if on_candidate:
                on_candidate(transcript, confidence)
            # Conservative: do not immediately finalize unless extremely high confidence
            if confidence >= 0.98 and on_final:
                on_final(transcript)
            return

    # 4) Text heuristics: punctuation or sentence-ending tokens often imply a completed thought.
    #    This is a conservative fallback only.
    trimmed = transcript.strip()
    if len(trimmed) > 0 and trimmed[-1] in {'.', '?', '!'}:
        # treat as candidate-final
        if on_candidate:
            on_candidate(transcript, confidence)
        return

    # 5) Default: emit no event yet (still streaming / incomplete)
    return

How to wire this into your pipeline • Normal flow: streaming STT sends partials → call process_transcript(session, partial_text, is_final=False, confidence=...) for each partial. • When the STT sends a final chunk, call process_transcript(..., is_final=True) to finalize. • If AgentSession exposes

latest_eou_decision

(or similar observable trace for

eou_detection

), process_transcript will use it to make model-driven decisions. If not available, the function falls back to confidence and punctuation heuristics. • Callbacks: ◦ on_candidate: translate and display as provisional; include metadata marking it provisional. ◦ on_final: mark chunk as finalized and lock translation. Tuning and next steps • If your session exposes richer EOU traces (e.g.,

{'likely_done': True, 'confidence': 0.xx}

), adapt the attribute access inside the function to read the real field names and use them directly. • Lower the

candidate_confidence_threshold

to be more eager; raise it to be conservative. • Always keep a rollback/update path: streaming STT often revises earlier partials; update/replace translations when final arrives. If you want, I can: • Provide a version tied to your STT provider (showing the specific partial/final callbacks). • Add an adaptive threshold that uses recent EOU false-positive history to adjust sensitivity. Which would you prefer? , with interactive elements

no, I want a function that takes in transcripts and then outputs the eou_detection, use the livekit turn detection model, the code should be portable to a file with all required imports

I don't have the answer you're looking for. You could also try asking your question: • in one of the other Slack channels or • to https://deepwiki.com/livekit/livekit_composite which is trained on all LiveKit source code If you find the answer, please post it here to help others!

yes you do, use your first reply