I wanted to ask for guidance on the best way to capture transcripts for both the user and the agent. Currently, I’m using

UserStateChanged

/

AgentStateChanged

events for timestamps and

ConversationItemAdded

for the text, but I’m not confident this is fully reliable. Could you please advise on the LiveKit-recommended approach for accurately capturing conversation text (both user and agent) with aligned timestamps? Any examples or best practices would be very helpful.

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

can you give me both code example ?

Below are two concise, runnable-style examples: one for capturing agent transcripts with TTS-aligned

TimedString

objects, and one for capturing user transcripts using STT `SpeechEvent`/`user_input_transcribed` with VAD timing. Each example shows session setup, event handlers that persist timestamped transcript entries, and how to merge them into a single timeline. Agent-side (TTS-aligned) example — Python • Requirements: Python SDK ≥1.3.0, TTS provider that supports alignment (Cartesia/ElevenLabs),

use_tts_aligned_transcript=True

.

# agent_transcript.py
import asyncio
from typing import AsyncIterable
from livekit.agents import AgentSession, Agent, TimedString, ModelSettings

TRANSCRIPT_STORE = []  # replace with DB/persistence

def record_transcript_entry(text, start, end, role, turn_id):
    TRANSCRIPT_STORE.append({
        "role": role,
        "text": text,
        "start": start,
        "end": end,
        "turn_id": turn_id,
    })

class MyAgent(Agent):
    async def transcription_node(
        self,
        text: AsyncIterable[str | TimedString],
        model_settings: ModelSettings,
    ):
        # turn_id could be derived from context or a counter
        turn_id = model_settings.turn_id if hasattr(model_settings, "turn_id") else None

        async for chunk in text:
            if isinstance(chunk, TimedString):
                # TimedString.start_time / end_time are seconds from agent turn start
                record_transcript_entry(
                    text=str(chunk),
                    start=chunk.start_time,
                    end=chunk.end_time,
                    role="agent",
                    turn_id=turn_id,
                )
            else:
                # fallback: plain text chunk (no timing)
                record_transcript_entry(
                    text=str(chunk),
                    start=None,
                    end=None,
                    role="agent",
                    turn_id=turn_id,
                )
            yield chunk  # pass-through

async def main():
    session = AgentSession(
        agent=MyAgent(),
        use_tts_aligned_transcript=True,
        # stt/tts/llm config...
    )
    await session.start()
    # run until stopped
    await session.wait_until_stopped()
    # persist complete TRANSCRIPT_STORE to DB/file here

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

Notes: •

TimedString.start_time

and

end_time

are relative to the agent turn start; convert to wall-clock by adding the agent-turn-start timestamp if you need absolute times. • Persist entries as they arrive to avoid data loss. User-side (STT + VAD timing) example — JavaScript/Node • Requirements: STT plugin that emits

SpeechEvent

or

user_input_transcribed

. This example listens for

user_input_transcribed

and STT speech events (conceptual names; adapt to your STT plugin event names).

// user_transcript.js
const { AgentSession } = require("livekit-agents"); // adapt import
const TRANSCRIPT_STORE = [];

function recordTranscriptEntry({ text, start, end, role, turnId }) {
  TRANSCRIPT_STORE.push({ text, start, end, role, turnId });
}

// Example handlers: adapt event names to your STT plugin
async function handleUserInputTranscribed(event) {
  // event.transcript, event.timestamp (wall-clock), event.turn_id
  recordTranscriptEntry({
    text: event.transcript,
    start: event.timestamp,   // approximate finalization time
    end: event.timestamp,     // if no end given, use same or null
    role: "user",
    turnId: event.turn_id,
  });
}

// If your STT plugin exposes low-level SpeechEvent with VAD times:
function handleSpeechEvent(speechEvent) {
  // speechEvent.text, speechEvent.start_time, speechEvent.end_time
  // start_time/end_time might be offsets from user-turn or absolute
  recordTranscriptEntry({
    text: speechEvent.text,
    start: speechEvent.start_time,
    end: speechEvent.end_time,
    role: "user",
    turnId: speechEvent.turn_id || null,
  });
}

async function main() {
  const session = new AgentSession({
    // config: stt, llm, tts...
  });

  // wire event listeners - adapt to actual SDK/event API
  session.on("user_input_transcribed", handleUserInputTranscribed);
  session.on("stt_speech_event", handleSpeechEvent);

  await session.start();
  await session.waitUntilStopped();

  // persist TRANSCRIPT_STORE to DB/file here
}

main().catch(console.error);

Merging agent + user timelines • Convert all relative times to a common reference (wall-clock). For agent

TimedString

, add the agent-turn wall-clock start time. For user STT VAD times, use the VAD/turn start wall-clock. • Once converted, sort combined entries by

start

to produce an aligned transcript timeline. • Example pseudo-merge:

# assume entries have absolute 'start' timestamps
combined = sorted(TRANSCRIPT_STORE, key=lambda e: (e["start"] or 0))

Additional practical tips • Capture and persist turn-start wall-clock timestamps for both agent and user turns (these let you convert relative offsets to absolute times). • Use Agent Observability to validate alignment and download full session exports (audio + transcripts + traces) for debugging. • Persist incrementally (stream to DB) to avoid losing data if sessions stop unexpectedly. If you want, I can: • Produce a full end-to-end example that converts relative

TimedString

times to UTC wall-clock times using actual turn-start timestamps. • Adapt these snippets to your exact SDK/event names and persistence layer (Postgres/S3). Which would you like?

give me in python as an instruction like for getting TTS and getting STT time stamps as well so I can easily concatenate both to get the conversation

Below is a concise, step-by-step Python instruction + runnable-style code showing: • How to enable TTS-aligned transcripts for agent speech (word/segment timestamps). • How to capture user STT transcript timing (VAD / end-of-utterance) and final transcript. • How to convert relative times to absolute wall-clock timestamps and merge both streams into a single chronological transcript. Prerequisites • LiveKit Agents Python SDK ≥ 1.3.0. • TTS provider that supports alignment: Cartesia or ElevenLabs. • An STT plugin that emits

user_input_transcribed

and STT speech/vad events (plugin-specific event names may vary). 1) Session + agent setup (enable TTS-aligned transcripts)

import time
import asyncio
from typing import AsyncIterable
from livekit.agents import AgentSession, Agent
from livekit.agents import llm  # contains TimedString type in SDK
# configure your TTS/STT plugins here (example placeholders)
# from livekit.agents.models import cartesia, stt_provider

session_start_time = None

2) Agent class: capture agent

TimedString

chunks and user STT events

class TranscriptAgent(Agent):
    def __init__(self):
        super().__init__()
        # stores with absolute wall-clock times (seconds since epoch)
        self.entries = []  # each entry: {role, text, start, end, turn_id}

        # track current turn start times (wall-clock)
        self.current_agent_turn_start = None
        self.current_user_turn_start = None

    # Called by LiveKit when agent's turn begins (example hook — actual event name may differ)
    async def on_agent_turn_started(self, turn_id: str):
        self.current_agent_turn_start = time.time()

    # transcription_node receives TimedString chunks when use_tts_aligned_transcript=True
    async def transcription_node(
        self,
        text: AsyncIterable[str | llm.TimedString],
        model_settings: llm.ModelSettings
    ) -> AsyncIterable[str | llm.TimedString]:
        async for chunk in text:
            if isinstance(chunk, llm.TimedString):
                # TimedString times are relative to the agent turn start (seconds)
                start_abs = (self.current_agent_turn_start or time.time()) + chunk.start_time
                end_abs = (self.current_agent_turn_start or time.time()) + chunk.end_time
                self.entries.append({
                    "role": "agent",
                    "text": chunk.text,
                    "start": start_abs,
                    "end": end_abs,
                    "turn_id": getattr(model_settings, "turn_id", None),
                })
            else:
                # plain text fallback (no timing)
                self.entries.append({
                    "role": "agent",
                    "text": str(chunk),
                    "start": self.current_agent_turn_start,
                    "end": self.current_agent_turn_start,
                    "turn_id": getattr(model_settings, "turn_id", None),
                })
            yield chunk

    # Example: STT event hook for start/end of user speech (VAD). Adapt names to your STT plugin.
    async def on_user_speech_started(self, turn_id: str = None):
        self.current_user_turn_start = time.time()

    async def on_user_speech_ended(self, turn_id: str = None):
        # mark end; keep the timestamp for correlation with final transcript
        self.current_user_turn_end = time.time()

    # STT final transcript event (user_input_transcribed). SDK may provide event class; adapt accordingly.
    async def on_user_input_transcribed(self, event):
        # event.transcript: final text
        # event.is_final: bool
        if getattr(event, "is_final", True):
            # If plugin provides precise start/end offsets on event, use them.
            # Otherwise, use tracked VAD start/end if available.
            start = getattr(event, "start_time", None) or getattr(self, "current_user_turn_start", None)
            end = getattr(event, "end_time", None) or getattr(self, "current_user_turn_end", time.time())
            # If start is relative to user turn, convert to absolute as needed (example assumes absolute).
            self.entries.append({
                "role": "user",
                "text": getattr(event, "transcript", str(event)),
                "start": start,
                "end": end,
                "turn_id": getattr(event, "turn_id", None),
            })

3) Start the session with TTS-aligned transcripts enabled

async def run_session():
    global session_start_time
    session_start_time = time.time()

    agent = TranscriptAgent()

    session = AgentSession(
        agent=agent,
        # Configure your STT/LLM/TTS plugins here:
        # stt=stt_provider.STT(...),
        # tts=cartesia.TTS(...),   # or elevenlabs.TTS(...)
        use_tts_aligned_transcript=True,
    )

    await session.start()
    # run until session stops (your app logic)
    await session.wait_until_stopped()

    # When done, export merged transcript
    merged = merge_and_sort_entries(agent.entries)
    save_transcript(merged)

4) Merge and sort entries into one chronological timeline

import json
from datetime import datetime

def merge_and_sort_entries(entries):
    # remove entries without start by assigning end or session start time
    for e in entries:
        if e.get("start") is None:
            e["start"] = e.get("end", session_start_time)
        if e.get("end") is None:
            e["end"] = e["start"]

    # sort by start time
    entries_sorted = sorted(entries, key=lambda x: x["start"])
    # Optionally normalize start/end to offsets from session start
    for e in entries_sorted:
        e["start_offset_s"] = round(e["start"] - session_start_time, 3)
        e["end_offset_s"] = round(e["end"] - session_start_time, 3)
        # human-readable wall-clock
        e["start_ts"] = datetime.fromtimestamp(e["start"]).isoformat()
        e["end_ts"] = datetime.fromtimestamp(e["end"]).isoformat()
    return entries_sorted

def save_transcript(entries, filename=None):
    filename = filename or f"transcript_{int(time.time())}.json"
    with open(filename, "w", encoding="utf-8") as f:
        json.dump(entries, f, ensure_ascii=False, indent=2)
    print("Saved transcript to", filename)

5) Practical notes (implementation tips) • TimedString times are relative to the agent turn start; capture

current_agent_turn_start

as wall-clock when the agent turn begins so you can compute absolute times. • For user STT: ◦ If your STT plugin emits `start_time`/`end_time` on SpeechEvent, prefer those (they may be absolute or relative — convert accordingly). ◦ If it does not, use VAD hooks (speech started / speech ended) to record wall-clock boundaries and attach them to the final

user_input_transcribed

transcript. • Persist entries as they arrive (append to DB) to avoid data loss if the session ends unexpectedly. • Use Agent Observability (dashboard) to validate alignment and download session audio + transcripts if you need to debug mismatches. 6) Next steps I can provide • A full runnable script tailored to your exact STT plugin event names and persistence (Postgres/S3). • An example that converts TimedString relative offsets into ISO 8601 timestamps with millisecond precision. Would you like the full runnable script adapted to your STT plugin and persistence layer? Sources: Observability data hooks | LiveKit docs | Text and transcriptions | LiveKit docs | Pipeline nodes and hooks | LiveKit docs | ElevenLabs TTS plugin guide | LiveKit Docs

I am using livekit infernece cartesia and elevenlabs and STT models also also from livekit.agents.voice.io import TimedString, this is the correct import

Yes —

TimedString

should be imported from

<http://livekit.agents.voice.io|livekit.agents.voice.io>

when using the SDK’s voice helpers. Below is a concise, focused Python example tailored to your stack (Cartesia/ElevenLabs TTS + STT models). It shows: • correct imports (including

TimedString

), • enabling TTS-aligned transcripts, • capturing agent

TimedString

entries with absolute timestamps, • capturing user STT timing via VAD start/end +

user_input_transcribed

, • converting relative times to wall-clock and merging both streams into one chronological transcript, • lightweight persistence to a JSON file. Copy and adapt the event names / plugin initializations to match your exact STT provider if its event names differ. Example (Python)

import time
import asyncio
import json
from datetime import datetime
from typing import AsyncIterable

# SDK imports
from livekit.agents import Agent, AgentSession
from <http://livekit.agents.voice.io|livekit.agents.voice.io> import TimedString  # correct import for TimedString
# Import your TTS/STT plugin classes; example placeholders:
# from livekit.agents.models import cartesia, elevenlabs, stt_provider

SESSION_START = None

class TranscriptAgent(Agent):
    def __init__(self):
        super().__init__()
        self.entries = []  # stores dicts: {role, text, start, end, turn_id}
        self.current_agent_turn_start = None
        self.current_user_turn_start = None
        self.current_user_turn_end = None

    # Called when agent turn begins (hook name may vary)
    async def on_agent_turn_started(self, turn_id: str = None):
        self.current_agent_turn_start = time.time()

    # transcription_node receives TimedString when use_tts_aligned_transcript=True
    async def transcription_node(
        self,
        text: AsyncIterable[str | TimedString],
        model_settings,
    ) -> AsyncIterable[str | TimedString]:
        async for chunk in text:
            if isinstance(chunk, TimedString):
                # chunk.start_time / end_time are seconds relative to agent turn start
                turn_start = self.current_agent_turn_start or time.time()
                start_abs = turn_start + chunk.start_time
                end_abs = turn_start + chunk.end_time
                self.entries.append({
                    "role": "agent",
                    "text": chunk.text,
                    "start": start_abs,
                    "end": end_abs,
                    "turn_id": getattr(model_settings, "turn_id", None),
                })
            else:
                # fallback plain text
                ts = self.current_agent_turn_start or time.time()
                self.entries.append({
                    "role": "agent",
                    "text": str(chunk),
                    "start": ts,
                    "end": ts,
                    "turn_id": getattr(model_settings, "turn_id", None),
                })
            yield chunk

    # Example VAD hooks for user speech (names may vary by STT plugin)
    async def on_user_speech_started(self, turn_id: str = None):
        self.current_user_turn_start = time.time()

    async def on_user_speech_ended(self, turn_id: str = None):
        self.current_user_turn_end = time.time()

    # Final user transcript event (adapt parameter/event class to your STT plugin)
    async def on_user_input_transcribed(self, event):
        # event.transcript, event.is_final, optionally event.start_time/event.end_time
        if not getattr(event, "is_final", True):
            return
        start = getattr(event, "start_time", None) or self.current_user_turn_start or time.time()
        end = getattr(event, "end_time", None) or self.current_user_turn_end or time.time()
        self.entries.append({
            "role": "user",
            "text": getattr(event, "transcript", str(event)),
            "start": start,
            "end": end,
            "turn_id": getattr(event, "turn_id", None),
        })

# Merge / normalize helpers
def merge_and_normalize(entries, session_start):
    # fill missing times and sort
    for e in entries:
        if e.get("start") is None:
            e["start"] = e.get("end", session_start)
        if e.get("end") is None:
            e["end"] = e["start"]
    sorted_entries = sorted(entries, key=lambda x: x["start"])
    for e in sorted_entries:
        e["start_offset_s"] = round(e["start"] - session_start, 3)
        e["end_offset_s"] = round(e["end"] - session_start, 3)
        e["start_ts"] = datetime.fromtimestamp(e["start"]).isoformat()
        e["end_ts"] = datetime.fromtimestamp(e["end"]).isoformat()
    return sorted_entries

def save_transcript(entries, filename=None):
    filename = filename or f"transcript_{int(time.time())}.json"
    with open(filename, "w", encoding="utf-8") as f:
        json.dump(entries, f, ensure_ascii=False, indent=2)
    print("Saved transcript to", filename)

# Runner
async def main():
    global SESSION_START
    SESSION_START = time.time()

    agent = TranscriptAgent()

    session = AgentSession(
        agent=agent,
        # tts=cartesia.TTS(... ) or elevenlabs.TTS(...),
        # stt=stt_provider.STT(...),
        use_tts_aligned_transcript=True,  # critical for TimedString
    )

    await session.start()
    await session.wait_until_stopped()

    merged = merge_and_normalize(agent.entries, SESSION_START)
    save_transcript(merged)

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

Notes and adaptation checklist • Confirm your actual event hook names for VAD/start/end and final transcript in your STT plugin; replace

on_user_speech_started

,

on_user_speech_ended

,

on_user_input_transcribed

accordingly. • TimedString import is correct: from livekit.agents.voice.io import TimedString. • `TimedString.start_time`/`end_time` are relative to the agent turn start — capture the agent turn wall-clock start to compute absolute times. • If your STT emits per-word timings or SpeechEvent offsets, prefer those over estimated VAD boundaries. • Persist entries incrementally (DB or streaming file) in production to avoid data loss. If you want, I can: • Turn this into a fully runnable script tailored to your exact STT plugin event names and a chosen persistence backend (Postgres/S3/local file). Which plugin and persistence do you use?

on_user_input_transcribe give me like proper evnt on the top rather than function only

Understood — below is a focused, runnable-style Python example with proper event decorator usage at the top of the Agent class (including

@Agent.on_event("user_input_transcribed")

), correct imports (

TimedString

from

<http://livekit.agents.voice.io|livekit.agents.voice.io>

), and clear handling to capture agent TTS-aligned timings and user STT timings (VAD + final transcript). It also shows merging into a single chronological transcript. Adjust your STT/TTS plugin initializations and any plugin-specific event names if needed.

import time
import asyncio
import json
from datetime import datetime
from typing import AsyncIterable

from livekit.agents import Agent, AgentSession
from <http://livekit.agents.voice.io|livekit.agents.voice.io> import TimedString

# Placeholder imports for your TTS/STT providers:
# from livekit.agents.models import cartesia, elevenlabs, stt_provider

SESSION_START = None

class TranscriptAgent(Agent):
    def __init__(self):
        super().__init__()
        self.entries = []  # list of {role, text, start, end, turn_id}
        self.current_agent_turn_start = None
        self.current_user_turn_start = None
        self.current_user_turn_end = None

    # Example agent-turn lifecycle hook (replace with actual hook if different)
    @Agent.on_event("agent_turn_started")
    async def handle_agent_turn_started(self, event):
        # record wall-clock time when agent turn begins
        self.current_agent_turn_start = time.time()
        # optional: store turn_id if provided
        self.current_agent_turn_id = getattr(event, "turn_id", None)

    # transcription_node receives TimedString chunks when use_tts_aligned_transcript=True
    async def transcription_node(
        self,
        text: AsyncIterable[str | TimedString],
        model_settings,
    ) -> AsyncIterable[str | TimedString]:
        async for chunk in text:
            if isinstance(chunk, TimedString):
                turn_start = self.current_agent_turn_start or time.time()
                start_abs = turn_start + chunk.start_time
                end_abs = turn_start + chunk.end_time
                self.entries.append({
                    "role": "agent",
                    "text": chunk.text,
                    "start": start_abs,
                    "end": end_abs,
                    "turn_id": getattr(model_settings, "turn_id", None) or getattr(self, "current_agent_turn_id", None),
                })
            else:
                ts = self.current_agent_turn_start or time.time()
                self.entries.append({
                    "role": "agent",
                    "text": str(chunk),
                    "start": ts,
                    "end": ts,
                    "turn_id": getattr(model_settings, "turn_id", None) or getattr(self, "current_agent_turn_id", None),
                })
            yield chunk

    # VAD / STT speech start hook (replace with actual STT event name if different)
    @Agent.on_event("user_speech_started")
    async def on_user_speech_started(self, event):
        self.current_user_turn_start = time.time()
        self.current_user_turn_id = getattr(event, "turn_id", None)

    # VAD / STT speech end hook (replace with actual STT event name if different)
    @Agent.on_event("user_speech_ended")
    async def on_user_speech_ended(self, event):
        self.current_user_turn_end = time.time()

    # Final user transcript event — proper decorator and event name
    @Agent.on_event("user_input_transcribed")
    async def on_user_input_transcribed(self, event):
        # event.transcript, event.is_final, optional event.start_time/event.end_time
        if not getattr(event, "is_final", True):
            return

        # Prefer event-provided times if present; otherwise use tracked VAD times
        start = getattr(event, "start_time", None) or self.current_user_turn_start or time.time()
        end = getattr(event, "end_time", None) or self.current_user_turn_end or time.time()

        # If event.start_time/end_time are relative offsets (plugin-specific), convert as needed here

        self.entries.append({
            "role": "user",
            "text": getattr(event, "transcript", str(event)),
            "start": start,
            "end": end,
            "turn_id": getattr(event, "turn_id", None) or getattr(self, "current_user_turn_id", None),
        })

    # Optional: capture conversation_item_added for a canonical order (no timing)
    @Agent.on_event("conversation_item_added")
    async def on_conversation_item_added(self, event):
        # You can use this to cross-check order or add metadata; not needed for timing
        pass

# Helpers to merge/normalize/save
def merge_and_normalize(entries, session_start):
    for e in entries:
        if e.get("start") is None:
            e["start"] = e.get("end", session_start)
        if e.get("end") is None:
            e["end"] = e["start"]
    sorted_entries = sorted(entries, key=lambda x: x["start"])
    for e in sorted_entries:
        e["start_offset_s"] = round(e["start"] - session_start, 3)
        e["end_offset_s"] = round(e["end"] - session_start, 3)
        e["start_ts"] = datetime.fromtimestamp(e["start"]).isoformat()
        e["end_ts"] = datetime.fromtimestamp(e["end"]).isoformat()
    return sorted_entries

def save_transcript(entries, filename=None):
    filename = filename or f"transcript_{int(time.time())}.json"
    with open(filename, "w", encoding="utf-8") as f:
        json.dump(entries, f, ensure_ascii=False, indent=2)
    print("Saved transcript to", filename)

# Runner
async def main():
    global SESSION_START
    SESSION_START = time.time()

    agent = TranscriptAgent()

    session = AgentSession(
        agent=agent,
        # tts=cartesia.TTS(... ) or elevenlabs.TTS(...),
        # stt=stt_provider.STT(...),
        use_tts_aligned_transcript=True,
    )

    await session.start()
    await session.wait_until_stopped()

    merged = merge_and_normalize(agent.entries, SESSION_START)
    save_transcript(merged)

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

Notes • TimedString import is correct: from livekit.agents.voice.io import TimedString. • Replace event names (

agent_turn_started

,

user_speech_started

,

user_speech_ended

) with the exact event names your STT plugin emits if they differ. • If your STT emits `start_time`/`end_time` on

user_input_transcribed

as relative offsets, convert them to wall-clock by adding the user-turn start timestamp. • Persist entries incrementally in production (DB) rather than only at session end. Would you like this adapted into a full runnable script with your exact plugin initializations and a chosen persistence backend (e.g., Postgres or S3)?

# session_manager.py import os import datetime from livekit.agents import AgentSession , inference, metrics from livekit.agents.voice import MetricsCollectedEvent from livekit.plugins import azure, silero, google from utils.logger import logger from utils.async_logger import ( alog_info, alog_debug, alog_warning, alog_error, alog_exception, alog_critical, alog_info_bg, alog_warning_bg, alog_error_bg, alog_exception_bg ) # from livekit.plugins.turn_detector.multilingual import MultilingualModel # from livekit.plugins.turn_detector.english import EnglishModel from config.settings import settings from langfuse import observe class SessionManager: """ Manages the creation and configuration of agent sessions with optional transcription support. Features: - Configurable transcription logging - Session lifecycle management - Clean resource management """ def __init__(self, enable_transcription: bool = False, _transcription_service_ = None): self._session = None self._session_start_time = None self._enable_transcription = enable_transcription self._transcription_service = transcription_service self._pending_user_start = None self._pending_assistant_start = None @staticmethod @observe() async def create_session(voice: str = "en-US-AndrewMultilingualNeural", enable_transcription: bool = False, _transcription_service_*=None*, _langfuse_trace_id_*=None*, _langfuse_parent_observation_id_*=None*): """Create and configure an agent session with optional transcript handling.""" await alog_info(f*"Configuring Agent Session with transcription {'enabled' if enable_transcription else 'disabled'}..."*) try: # Create session with optimized configuration for smoother audio session = AgentSession( _stt_*=*inference.STT( _model_*=*settings.stt_model, _language_*="en"* ), _llm_*=*inference.LLM( _model_*=*settings.llm_model, ), _tts_*=*inference.TTS( _model_*=*settings.tts_model, _voice_*=*voice ), _vad_*=*silero.VAD.load( _min_speech_duration_*=*0.05, _min_silence_duration_*=*0.35, _activation_threshold_*=*0.6, _prefix_padding_duration_*=*0.5, _max_buffered_speech_*=*120.0, _sample_rate_*=*16000 ), # ⭐ CRITICAL OPTIMIZATIONS # turn_detection=EnglishModel(), _min_endpointing_delay_*=*0.35, _max_endpointing_delay_*=*3.0, _use_tts_aligned_transcript_*=True*, # Keep these unchanged _preemptive_generation_*=True*, _allow_interruptions_*=True*, _user_away_timeout_*=*15.0, ) # Create manager instance manager = SessionManager(_enable_transcription_*=enable_transcription, _transcription_service_=*transcription_service) manager._session = session manager._session_start_time = datetime.datetime.now() # Note: Transcription service is provided from AudioService in JobHandler if manager._enable_transcription and manager._transcription_service: await alog_info("📝 Using transcription service from AudioService") # Handle user state changes for "Are you there?" prompts @session.on("user_state_changed") def on_user_state_changed(event): """Handle user state changes and send gentle prompts when user goes away.""" if event.new_state == "away": alog_info_bg("User seems to be away, sending gentle prompt...") try: # Use generate_reply to create a contextual "are you there?" message session.generate_reply( _instructions_*="The user has been silent for a while and may have stepped away. Generate a gentle, friendly prompt to check if they're still there. Keep it brief, warm, and non-intrusive. Examples: 'Are you still there?' or 'I'm here when you're ready to continue.' Vary the phrasing naturally."*, _allow_interruptions_*=True* ) alog_info_bg("Gentle prompt sent successfully") except RuntimeError as e: alog_warning_bg(f*"Session not running, skipping gentle prompt: {e}"*) except Exception as e: alog_error_bg(f*"Error sending gentle prompt: {e}"*) elif event.new_state == "speaking": # RECORD USER START TIME manager._pending_user_start = manager._get_elapsed_seconds() alog_info_bg(f*"*👤 User speaking START at {manager._pending_user_start:.3f}s") elif event.new_state == "listening": alog_info_bg("User listening") # Handle agent state changes to capture assistant speaking start times @session.on("agent_state_changed") def on_agent_state_changed(event): """Handle agent state changes to capture speaking start times.""" if event.new_state == "speaking": # RECORD ASSISTANT START TIME manager._pending_assistant_start = manager._get_elapsed_seconds() alog_info_bg(f*"*🤖 Assistant speaking START at {manager._pending_assistant_start:.3f}s") # Handle transcription events - only if transcription is enabled if manager._enable_transcription and manager._transcription_service: # Use only conversation_item_added to avoid duplicate logging # This event captures both user and assistant messages @session.on("conversation_item_added") def on_conversation_item_added(event): """Log both user and AI messages as they're added to chat history.""" role = event.item.role # "user" or "assistant" content = event.item.text_content # MATCH text with PREVIOUS start time if role == "user": start_time = manager._pending_user_start if manager._pending_user_start is not None: manager._pending_user_start = None # clear only if exists else: # assistant start_time = manager._pending_assistant_start if manager._pending_assistant_start is not None: manager._pending_assistant_start = None # clear only if exists # Log using transcription service with start time manager._transcription_service.log_conversation_item(role, content, start_time) # Store manager reference in session for cleanup later session._session_manager = manager transcription_status = "with transcription logging" if enable_transcription else "without transcription" await alog_info(f*"Agent Session configured successfully {transcription_status}."*) return session except Exception as e: await alog_exception(f*"Failed to create agent session: {e}"*) raise def _get_elapsed_seconds(self) -> float: """Helper for consistent timing across events.""" if self._session_start_time: return (datetime.datetime.now() - self.__session_start_time_).total_seconds() return 0.0 @_staticmethod_ @observe() async def cleanup_session(session): """Clean up session resources including transcription service.""" try: # Clean up session manager if it exists if hasattr(session, '_session_manager') and session._session_manager: # Clean up transcription service (includes finalizing transcript file) only if enabled and initialized if (session._session_manager._enable_transcription and session._session_manager._transcription_service and session._session_manager._transcription_service.is_initialized()): # The transcription service is from AudioService, so use its cleanup method session._session_manager._transcription_service.cleanup_transcription() await alog_info("📝 Transcription service cleaned up") await alog_info("🧹 Session manager cleaned up") except Exception as e: await alog_error(f*"*❌ Error cleaning up session: {e}") check this and understand then suggest with the new approach

Thanks — I reviewed your session_manager.py and the flow you shared. Below are concise, practical suggestions plus concrete code you can drop into your SessionManager to replace the fragile start/clear via state events and to use the recommended LiveKit approach: capture agent timing from

TimedString

in

transcription_node

(Cartesia/ElevenLabs with

use_tts_aligned_transcript=True

) and capture user timing from STT events (

user_input_transcribed

plus VAD start/end). This will give you precise agent word timings and reliable user turn boundaries to merge into one timeline. Key changes I recommend • Keep

use_tts_aligned_transcript=True

(you already set this). • Remove relying only on

conversation_item_added

+ pending start timestamps for precise timing. • Capture agent timings inside

transcription_node

using

TimedString

(import from

<http://livekit.agents.voice.io|livekit.agents.voice.io>

). • Capture user start/end via VAD events (your

user_state_changed

can remain for high-level UX) and use

@Agent.on_event("user_input_transcribed")

to get final transcript + any provided start/end offsets from the STT plugin. • Convert agent relative times to absolute by recording agent-turn wall-clock start when the agent turn begins. • Persist entries incrementally (call your transcription service log function as each entry arrives) instead of waiting until

conversation_item_added

. Patch to integrate into your SessionManager (drop-in style) • Replace the current

conversation_item_added

handler with the following additions inside create_session after session creation. These use the same session & manager variables you already have. Add imports at top:

from <http://livekit.agents.voice.io|livekit.agents.voice.io> import TimedString

Agent handlers to add (place inside create_session where you attach other session events):

# record wall-clock start of agent turn
@session.on("agent_turn_started")
def _on_agent_turn_started(event):
    # record epoch seconds when agent turn begins
    manager._pending_assistant_start = manager._get_elapsed_seconds()
    # store absolute wall-clock (use datetime.now()) if you want epoch timestamps
    manager._assistant_turn_wallclock = datetime.datetime.now().timestamp()
    # optional: store turn id
    manager._assistant_turn_id = getattr(event, "turn_id", None)

# transcription_node receives TimedString chunks for agent TTS-aligned output
@session.on("agent_transcription_node")
async def _transcription_node_handler(text, model_settings):
    # text is an async iterable yielding str or TimedString
    async for chunk in text:
        if isinstance(chunk, TimedString):
            # TimedString.start_time / end_time are relative to agent turn start
            turn_start = getattr(manager, "_assistant_turn_wallclock", datetime.datetime.now().timestamp())
            start_abs = turn_start + chunk.start_time
            end_abs = turn_start + chunk.end_time
            entry = {
                "role": "assistant",
                "text": chunk.text,
                "start": start_abs,
                "end": end_abs,
                "turn_id": getattr(model_settings, "turn_id", getattr(manager, "_assistant_turn_id", None)),
            }
            # persist incrementally
            manager._transcription_service.log_conversation_item(entry["role"], entry["text"], entry["start"], entry["end"], entry["turn_id"])
        else:
            # fallback plain text chunk
            ts = getattr(manager, "_assistant_turn_wallclock", datetime.datetime.now().timestamp())
            entry = {"role":"assistant","text":str(chunk),"start":ts,"end":ts,"turn_id": getattr(model_settings,"turn_id", None)}
            manager._transcription_service.log_conversation_item(entry["role"], entry["text"], entry["start"], entry["end"], entry["turn_id"])
        yield chunk

Notes: • The event name

agent_transcription_node

above is a conceptual binding for

transcription_node

. In the SDK you implement

transcription_node

on your Agent subclass; if you are wiring via session event handlers, ensure you attach the actual callable the SDK expects. If you keep your Agent separate, implement

transcription_node

there and call manager._transcription_service from it. User STT handlers to add:

# VAD-based start/end (you already have user_state_changed; keep using it to set wallclock)
@session.on("user_state_changed")
def _on_user_state_changed(event):
    if event.new_state == "speaking":
        manager._pending_user_start = manager._get_elapsed_seconds()
        manager._user_turn_wallclock = datetime.datetime.now().timestamp()
        manager._user_turn_id = getattr(event, "turn_id", None)
    elif event.new_state == "listening":
        manager._user_turn_end = manager._get_elapsed_seconds()
        manager._user_turn_end_wallclock = datetime.datetime.now().timestamp()

# final user transcript (proper event decorator)
@session.on("user_input_transcribed")
async def _on_user_input_transcribed(event):
    # prefer event-provided times if present (plugin-specific). Otherwise use VAD wallclock.
    # event.start_time / event.end_time may be present and may be absolute or relative; convert if needed.
    start = getattr(event, "start_time", None) or getattr(manager, "_user_turn_wallclock", datetime.datetime.now().timestamp())
    end = getattr(event, "end_time", None) or getattr(manager, "_user_turn_end_wallclock", datetime.datetime.now().timestamp())
    entry = {
        "role": "user",
        "text": getattr(event, "transcript", str(event)),
        "start": start,
        "end": end,
        "turn_id": getattr(event, "turn_id", getattr(manager, "_user_turn_id", None)),
    }
    manager._transcription_service.log_conversation_item(entry["role"], entry["text"], entry["start"], entry["end"], entry["turn_id"])

How to merge timelines (recommended) • Store each entry with absolute epoch seconds. • To produce a single chronological view: sort entries by

start

. • Optionally add offsets relative to session start: offset = entry["start"] - session_start_epoch. • Keep agent

TimedString

words as separate entries (gives fine-grained timeline). If you prefer sentence-level aggregation, group contiguous

TimedString

entries by turn_id before persisting. Small code snippet to merge at export:

def merge_and_export(entries, session_start_epoch):
    for e in entries:
        if e.get("start") is None:
            e["start"] = e.get("end", session_start_epoch)
        if e.get("end") is None:
            e["end"] = e["start"]
    entries_sorted = sorted(entries, key=lambda x: x["start"])
    for e in entries_sorted:
        e["start_offset_s"] = round(e["start"] - session_start_epoch, 3)
        e["end_offset_s"] = round(e["end"] - session_start_epoch, 3)
        e["start_ts"] = datetime.datetime.fromtimestamp(e["start"]).isoformat()
        e["end_ts"] = datetime.datetime.fromtimestamp(e["end"]).isoformat()
    return entries_sorted

Why this is better than your current approach •

ConversationItemAdded

gives content but not precise word-level timing; pairing it with pending start flags is fragile under latency/race conditions. •

TimedString

gives accurate agent timing per word/segment (Cartesia/ElevenLabs). • STT

user_input_transcribed

combined with VAD start/end gives reliable user turn boundaries; if your STT emits speech offsets, use them. • Persisting incrementally avoids losing entries if session stops unexpectedly. Quick checklist to apply now • Ensure you keep

use_tts_aligned_transcript=True

. • Import

TimedString

from

<http://livekit.agents.voice.io|livekit.agents.voice.io>

. • Implement

transcription_node

on your Agent (or wire the correct transcription handler) to capture

TimedString

. • Keep

user_state_changed

VAD hooks but use

user_input_transcribed

to record final text + start/end. • Replace conversation_item_added-based logging with immediate calls to your transcription service from the handlers above. • Validate alignment using Agent Observability and session exports if you see mismatches. If you want, I can: • Produce a direct, minimal patch of your session_manager.py with these handlers inserted and with the

log_conversation_item

signature matched to your transcription service (I’ll need the transcription service method signature). • Or create a short unit-testable script that simulates TimedString and user events so you can verify merging logic locally. Which would you prefer?

DEBUGlivekit.agentsreceived user transcript 2025-12-15 111431,651 - DEBUG livekit.agents - received user transcript {"user_transcript": "I'm also doing code.", "language": "en"} 2025-12-15 111431 | INFO | 📝 [000034.849] [34.849s-35.874s] Agent: I'm also doing code. 2025-12-15 111431 | WARNING | ⚠️ No agent turn wallclock time, using current time as fallback 2025-12-15 111432 | INFO | 📝 [000037.039] [37.040s-37.040s] Customer: Um 2025-12-15 111432 | WARNING | ⚠️ Received plain text chunk (expected TimedString): 'Um' 2025-12-15 111432 | INFO | 📝 [000037.066] [37.067s-37.067s] Customer: , 2025-12-15 111432 | WARNING | ⚠️ Received plain text chunk (expected TimedString): ',' 2025-12-15 111432 | INFO | 📝 [000037.095] [37.095s-37.095s] Customer: I'm 2025-12-15 111432 | WARNING | ⚠️ Received plain text chunk (expected TimedString): ' I'm' 2025-12-15 111432 | INFO | 📝 [000037.125] [37.126s-37.126s] Customer: not I am not getting the timedstring from the streaming

Thanks — the logs and warnings show the key facts: TimedString is only produced for agent TTS-aligned output, and user STT output (your "Customer" lines) will arrive as plain text or UserInputTranscribed events. That’s why you’re seeing plain chunks for user speech. What to check and how to fix it (concise checklist) 1. Confirm TTS provider and session flag • Use Cartesia or ElevenLabs for TTS. • Ensure your session sets:

use_tts_aligned_transcript=True

. 1. Ensure you’re getting agent-word timing via transcription_node • Implement

transcription_node

on your Agent subclass (or the SDK-equivalent hook). It receives an async iterable that yields

TimedString

for agent speech when alignment is available. • In that node, convert `TimedString.start_time`/`end_time` (relative to agent-turn start) to absolute wall-clock by adding the agent-turn start timestamp you record when the agent turn begins. 1. Capture agent-turn start reliably • Record agent-turn wall-clock when the agent turn starts (use the agent turn start event/hook, not conversation_item_added). • If you see the warning "No agent turn wallclock time", you are not capturing this; set it using the agent-turn-start handler. 1. Don’t expect TimedString for user STT • User transcripts come as text events (

user_input_transcribed

) or plain chunks; they won’t be TimedString. Use VAD start/end (user_state_changed or STT SpeechEvent offsets if provided) to get user timing. 1. Avoid RealtimeModel or unsupported pipeline • If you use a realtime/streaming LLM model that bypasses the TTS pipeline, TimedString won’t be produced. Use the standard llm + tts pipeline for aligned transcripts. Quick code checklist (minimal) • Session: session = AgentSession(..., tts=cartesia.TTS(...), use_tts_aligned_transcript=True) • Agent turn start: on agent_turn_started -> manager.agent_turn_wallclock = time.time() • transcription_node: async for chunk in text: if isinstance(chunk, TimedString): start_abs = manager.agent_turn_wallclock + chunk.start_time end_abs = manager.agent_turn_wallclock + chunk.end_time persist(entry...) else: log warning fallback plain chunk • user_input_transcribed: use event.start_time/end_time if present else use VAD wallclock start/end If you’d like, I can: • Patch your session_manager.py with the exact handlers (using your manager methods and transcription service signature) to ensure agent-turn wallclock is captured and TimedString entries are logged immediately. Sources: Text and transcriptions | LiveKit docs | Pipeline nodes and hooks | LiveKit docs

I understood, can you please check if I am using the elevenlabs via livekit inference this is suported or not?

Short answer: No — ElevenLabs via LiveKit Inference does not provide word‑level

TimedString

output. It can give sentence‑level alignment, but per‑word timestamps (TimedString) require the ElevenLabs TTS plugin (or Cartesia) with

use_tts_aligned_transcript=True

. What to do next • If you need word-level timestamps: switch from LiveKit Inference to the ElevenLabs TTS plugin and set

use_tts_aligned_transcript=True

in your AgentSession. • If sentence-level alignment is enough: you can keep using LiveKit Inference with

use_tts_aligned_transcript=True

and expect coarser (sentence) timing. • After switching, implement

transcription_node

(or the agent transcription handler) to consume

TimedString

and convert relative times to wall‑clock by recording the agent‑turn start. If you want, I can: • Provide the exact AgentSession + ElevenLabs plugin code snippet to enable TimedString and show how to convert to absolute timestamps. Which would you prefer? Sources: Text and transcriptions | LiveKit docs | ElevenLabs TTS plugin guide | LiveKit Docs

so you are suggesting to use use the wall clock in this case?

Yes — use wall‑clock timestamps when word‑level

TimedString

data isn’t available (or when your TTS provider only gives sentence‑level alignment). Brief plan and why it works, plus next steps you can pick: Why use wall clock • `TimedString.start_time`/`end_time` are relative to the agent turn start; converting them to absolute times requires the agent-turn wall‑clock start. • User STT typically provides only final transcripts (or VAD start/end). Recording wall‑clock VAD start/end gives reliable absolute timestamps for user turns. • Storing absolute epoch times for both sides makes merging/sorting deterministic and robust across latency/race conditions. How to do it (high level) 1. On agent turn start: record wall‑clock epoch (e.g., time.time()) as agent_turn_wallclock. 2. In `transcription_node`: for each

TimedString

, compute absolute times: start_abs = agent_turn_wallclock + chunk.start_time end_abs = agent_turn_wallclock + chunk.end_time persist immediately. 1. For user speech: ◦ On VAD start: record user_turn_wallclock_start = time.time(). ◦ On VAD end or `user_input_transcribed`: get event.start_time/end_time if present; otherwise use recorded VAD wall‑clock start/end as absolute times. Persist immediately. 1. Merge: store all entries with absolute epoch seconds, then sort by start to produce a single timeline. Optionally compute offsets = start - session_start for UI. Do you want a short runnable Python snippet that: • records agent turn wall‑clock, • converts

TimedString

to absolute times, • records VAD start/end for user, • persists and merges entries? If yes, I’ll produce the exact code matching your SessionManager style and transcription_service logging API.

# LiveKit Agent Timestamp Calculation and Event Logic ## Timestamp Calculation Logic ### Session Start Time

python
# In SessionManager.__init__
self._session_start_time *=* datetime.datetime.now()

### Elapsed Time Helper

python
*def* _get_elapsed_seconds(_self_) -> float:
    """Helper for consistent timing across events."""
    if self._session_start_time:
        return (datetime.datetime.now() - self.__session_start_time_).total_seconds()
    return 0.0

### Timestamp Format Conversion

python
# In TranscriptionManager.log_conversation_item
timestamp *=* f*"{int(elapsed_seconds) // 3600:02d}:{(int(elapsed_seconds) % 3600) // 60:02d}:{int(elapsed_seconds) % 60:02d}.{int((elapsed_seconds % 1) * 1000):03d}"*

## Event Logic Flow ### 1. User State Changes (

user_state_changed

)

python
@session.on(*"user_state_changed"*)
*def* on_user_state_changed(_event_):
    *if* event.new_state *==* *"speaking"*:
        # RECORD USER START TIME
        manager._pending_user_start *=* manager._get_elapsed_seconds()
        alog_info_bg(f*"*:bust_in_silhouette: *User speaking START at {manager._pending_user_start:.3f}s"*)

### 2. Agent State Changes (

agent_state_changed

)

python
@session.on(*"agent_state_changed"*)
*def* on_agent_state_changed(_event_):
    *if* event.new_state *==* *"speaking"*:
        # RECORD ASSISTANT START TIME
        manager._pending_assistant_start *=* manager._get_elapsed_seconds()
        alog_info_bg(f*"*:robot_face: *Assistant speaking START at {manager._pending_assistant_start:.3f}s"*)

### 3. Conversation Item Added (

conversation_item_added

)

python
@session.on(*"conversation_item_added"*)
*def* on_conversation_item_added(_event_):
    role *=* event.item.role  # "user" or "assistant"
    content *=* event.item.text_content

    # MATCH text with PREVIOUS start time
    *if* role *==* *"user"*:
        start_time *=* manager._pending_user_start
        *if* manager._pending_user_start *is* *not* *None*:
            manager._pending_user_start *=* *None*  # clear only if exists
    *else*:  # assistant
        start_time *=* manager._pending_assistant_start
        *if* manager._pending_assistant_start *is* *not* *None*:
            manager._pending_assistant_start *=* *None*  # clear only if exists

    # Log with start time
    manager._transcription_service.log_conversation_item(role, content, start_time)

## Timestamp Fallback Logic ### Primary Method (Preferred) - Uses

start_time

parameter from state change events - Represents elapsed seconds from session start - Captures actual speaking start time ### Fallback Method

python
# In TranscriptionManager.log_conversation_item
*if* start_time *is* *not* *None*:
    # Use provided start time (from speaking event)
    elapsed_seconds *=* start_time
*elif* self._session_start_time:
    # Calculate current elapsed time (FALLBACK)
    elapsed *=* datetime.datetime.now() *-* self._session_start_time
    elapsed_seconds *=* elapsed.total_seconds()
    alog_warning_bg(f*"*:warning: *No start_time provided for {role} message, using completion time as fallback"*)
*else*:
    elapsed_seconds *=* 0.0
    alog_warning_bg(f*"*:warning: *No start_time or session_start_time available for {role} message"*)

## Event Sequencing

1. User starts speaking → user_state_changed("speaking") → Record _pending_user_start
2. User finishes speaking → user_state_changed("listening")
3. Message added to chat → conversation_item_added → Log with _pending_user_start → Clear _pending_user_start

1. Agent starts speaking → agent_state_changed("speaking") → Record _pending_assistant_start
2. Agent message added to chat → conversation_item_added → Log with _pending_assistant_start → Clear _pending_assistant_start

## Key Points - ***State change events*** capture speaking start times - ***Conversation item events*** match messages with recorded start times - ***Pending timestamps*** are cleared after use to prevent reuse - ***Fallback calculation*** used when start_time not available - ***Elapsed time*** calculated from session start time check the way that I am creating transcript let me know if it is not recommended by livekit

Thanks — I reviewed your timestamping and event flow. Your current approach is reasonable as a lightweight solution, but it has several fragility points compared to LiveKit‑recommended practices. Below is a concise assessment and clear, actionable fixes. What’s good • You capture session start and compute elapsed offsets consistently. • You use state change events to mark speaking starts — that gives useful turn boundaries. • You clear pending timestamps after use to avoid reuse. Key problems / risks • Reliance on pending start timestamps + conversation_item_added is fragile under races or latency: conversation_item_added may arrive before the state change, after a delayed turn, or for aggregated assistant output. That can misalign timestamps. • You treat all streamed chunks as if they might be timed — but user STT is plain text (no TimedString). Agent word timing requires TTS‑aligned transcripts (TimedString) from Cartesia/ElevenLabs plugin. • Fallback to "completion time" hides root causes and can make timestamps inconsistent when start and end are measured by different clocks/events. • You log only at conversation_item_added rather than persisting time-aligned entries as they arrive; this risks losing fine-grained agent timing (TimedString words) and makes precise merging harder. Recommended changes (minimal, prioritized) 1. Capture wall‑clock (epoch) at turn start for both roles ◦ On agent turn start: record agent_turn_wallclock = time.time() ◦ On user VAD start: record user_turn_wallclock = time.time() ◦ Use these to convert relative offsets to absolute timestamps. 1. Persist agent TTS timing from transcription_node (Tim edString) ◦ Enable

use_tts_aligned_transcript=True

. ◦ Use the ElevenLabs or Cartesia plugin (not LiveKit Inference) for per-word timings. ◦ In your agent’s transcription_node, convert each TimedString: start_abs = agent_turn_wallclock + chunk.start_time end_abs = agent_turn_wallclock + chunk.end_time • Persist each TimedString entry immediately via your transcription service. 1. Use user_input_transcribed (and STT SpeechEvent offsets) for user timing ◦ Prefer event.start_time/end_time from the STT plugin if present (convert to wall‑clock). ◦ Otherwise use VAD start/end wall‑clock you recorded. ◦ Persist user transcript on user_input_transcribed rather than waiting for conversation_item_added. 1. Stop relying exclusively on conversation_item_added for timing ◦ Keep conversation_item_added for canonical chat history and cross-checking, but do your time‑sensitive persistence in transcription_node and user_input_transcribed handlers. 1. Normalize to absolute epoch seconds and merge ◦ Store entries with absolute epoch seconds. ◦ At export, sort by start and compute offsets relative to session start for display. ◦ Keep TimedString words as separate entries or aggregate by turn_id depending on UI needs. 1. Improve fallbacks and logging ◦ If start_time missing, log a clear diagnostic and include which event(s) were received. ◦ Prefer not to use completion time as the primary timestamp unless you mark it explicitly as an estimate. Small changes you can apply now (drop‑in) • When handling agent state changed ("speaking"), set: manager._assistant_turn_wallclock = datetime.datetime.now().timestamp() • In transcription_node, for each TimedString chunk: start_abs = manager._assistant_turn_wallclock + chunk.start_time persist via manager._transcription_service.log_conversation_item(role, text, start_abs, end_abs, turn_id) • When receiving user_input_transcribed: start = getattr(event, "start_time", None) or manager._user_turn_wallclock end = getattr(event, "end_time", None) or manager._user_turn_end_wallclock persist immediately. When your current approach is acceptable • If you only need coarse per‑turn timestamps (not word-level) and low latency races are rare, your current method works with smaller risk. • For production, high‑fidelity synchronization, egress, or UI lip‑sync, adopt the changes above. Would you like me to: • Patch your session_manager.py with the exact handler code using your transcription_service.log_conversation_item signature, or • Produce a minimal unit-testable example that simulates TimedString + STT events so you can validate merging logic locally? Which do you prefer?

But I am using livekit inference only so considering that and give me the solution

You’re using LiveKit Inference, so word‑level

TimedString

from the TTS pipeline is not available. Given that constraint, the recommended approach is to rely on wall‑clock timestamps + turn-level timing (VAD + final STT events) and persist entries as they arrive. Below is a concise, drop‑in solution tailored to your SessionManager: it records wall‑clock times for user/agent turn starts, uses

user_input_transcribed

for final user transcripts, logs agent output at conversation commit but uses the agent turn wall‑clock for timing, and produces a merged chronological transcript. Key rules applied • LiveKit Inference: no per-word TimedString; only sentence-level alignment at best. • Use wall‑clock epoch seconds (time.time()) for all start/end times. • Record turn wall‑clock on "speaking" state events. • Persist user transcripts on

user_input_transcribed

using event offsets if present; otherwise use VAD start/end wall clock. • Persist agent transcripts when

conversation_item_added

arrives, using recorded agent turn wall clock for timestamping. • Persist incrementally to avoid data loss. Drop‑in code snippets to insert into your SessionManager.create_session (adapt names if needed): 1) Imports (top of file)

import time
from datetime import datetime

2) Add these manager fields in init (you already have similar ones):

self._session_start_time = datetime.now()
self._user_turn_wallclock = None
self._user_turn_end_wallclock = None
self._agent_turn_wallclock = None
self._entries = []  # local buffer; your transcription service used for persistence

3) user_state_changed handler (record VAD start/end)

@session.on("user_state_changed")
def on_user_state_changed(event):
    if event.new_state == "speaking":
        manager._user_turn_wallclock = time.time()
        manager._user_turn_id = getattr(event, "turn_id", None)
        alog_info_bg(f"👤 User speaking START at {manager._user_turn_wallclock:.3f}")
    elif event.new_state == "listening":
        manager._user_turn_end_wallclock = time.time()
        alog_info_bg(f"👤 User speaking END at {manager._user_turn_end_wallclock:.3f}")

4) user_input_transcribed handler (persist user transcript)

@session.on("user_input_transcribed")
async def on_user_input_transcribed(event):
    # prefer plugin-provided offsets if available (might be absolute or relative)
    start = getattr(event, "start_time", None) or manager._user_turn_wallclock or time.time()
    end   = getattr(event, "end_time", None)   or manager._user_turn_end_wallclock or time.time()

    entry = {
        "role": "user",
        "text": getattr(event, "transcript", str(event)),
        "start": start,
        "end": end,
        "turn_id": getattr(event, "turn_id", getattr(manager, "_user_turn_id", None)),
    }

    # Persist immediately (use your transcription service)
    manager._transcription_service.log_conversation_item(entry["role"], entry["text"], entry["start"])
    # Optionally keep local copy
    manager._entries.append(entry)
    alog_info(f"📝 [{manager._get_elapsed_seconds():.3f}s] [{entry['start']:.3f}-{entry['end']:.3f}] User: {entry['text']}")

5) agent_state_changed handler (record agent turn start)

@session.on("agent_state_changed")
def on_agent_state_changed(event):
    if event.new_state == "speaking":
        manager._agent_turn_wallclock = time.time()
        manager._agent_turn_id = getattr(event, "turn_id", None)
        alog_info_bg(f"🤖 Assistant speaking START at {manager._agent_turn_wallclock:.3f}")

6) conversation_item_added handler (persist assistant text using agent wallclock)

@session.on("conversation_item_added")
def on_conversation_item_added(event):
    role = event.item.role  # "user" or "assistant"
    content = event.item.text_content

    if role == "assistant":
        # Use agent_turn_wallclock recorded earlier
        start = getattr(manager, "_agent_turn_wallclock", None) or time.time()
        # If you want sentence-level end time, you can set end = start + estimated_duration or same as start
        end = start  # keep simple; STT plugin or inference may provide more info
        turn_id = getattr(event.item, "turn_id", getattr(manager, "_agent_turn_id", None))
        entry = {"role": "assistant", "text": content, "start": start, "end": end, "turn_id": turn_id}
        manager._transcription_service.log_conversation_item(entry["role"], entry["text"], entry["start"])
        manager._entries.append(entry)
        alog_info(f"📝 [{manager._get_elapsed_seconds():.3f}s] [{start:.3f}-{end:.3f}] Agent: {content}")
        # clear agent pending if you were using it
        manager._agent_turn_wallclock = None
        manager._agent_turn_id = None

    elif role == "user":
        # If you still get conversation_item_added for user, prefer user_input_transcribed persisted earlier.
        # But if not persisted, fallback to pending user wallclock.
        start = getattr(manager, "_user_turn_wallclock", None) or time.time()
        end = getattr(manager, "_user_turn_end_wallclock", start)
        entry = {"role": "user", "text": content, "start": start, "end": end, "turn_id": getattr(event.item, "turn_id", getattr(manager, "_user_turn_id", None))}
        # Avoid double-logging if user_input_transcribed already logged. You can check for duplicates.
        manager._transcription_service.log_conversation_item(entry["role"], entry["text"], entry["start"])
        manager._entries.append(entry)
        alog_info(f"📝 [{manager._get_elapsed_seconds():.3f}s] [{start:.3f}-{end:.3f}] Customer: {content}")
        manager._user_turn_wallclock = None
        manager._user_turn_end_wallclock = None
        manager._user_turn_id = None

7) Merge/export helper (use at cleanup)

def merge_and_normalize(entries, session_start_time):
    # entries: list of dicts with start/end epoch seconds
    for e in entries:
        if e.get("start") is None:
            e["start"] = e.get("end", session_start_time.timestamp())
        if e.get("end") is None:
            e["end"] = e["start"]
    entries_sorted = sorted(entries, key=lambda x: x["start"])
    # convert to elapsed display if needed
    session_epoch = session_start_time.timestamp()
    for e in entries_sorted:
        e["start_offset_s"] = round(e["start"] - session_epoch, 3)
        e["end_offset_s"] = round(e["end"] - session_epoch, 3)
        e["start_ts"] = datetime.fromtimestamp(e["start"]).isoformat()
        e["end_ts"] = datetime.fromtimestamp(e["end"]).isoformat()
    return entries_sorted

Best‑practice notes • This approach gives reliable per‑turn timing and deterministic merging, suitable for LiveKit Inference (no TimedString). • If your STT plugin provides precise `start_time`/`end_time` with the

user_input_transcribed

event, prefer those over VAD wall-clock. • Persist per event to your transcription service immediately to avoid loss; use

conversation_item_added

only as fallback or canonical ordering. • Consider tagging persisted entries with a source field (e.g., "vad", "stt_event", "inference") so you can debug mismatches later. • Log clear warnings when you fall back to estimates so you can track how often that happens. If you want, I can: • Give a ready patch of your session_manager.py with these handlers integrated, matching your transcription_service.log_conversation_item signature. • Or produce a small runnable test harness that simulates VAD and

user_input_transcribed

events so you can verify merging locally. Which would you like?