I’ve retrieved the conversation history (from session logs) containing all the user and agent messages with timestamps(created_at). What’s the best way to sync this session history with the audio recording?

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

can you give an example code

I'd love to share an example code snippet that helps you record audio and enable TTS-aligned transcription, so you can sync your session history beautifully with audio recordings. Let’s walk through it together: 1. Record Audio via Room Composite Egress

from livekit import api

async def start_audio_recording():
    req = api.RoomCompositeEgressRequest(
        room_name="example-room",
        audio_only=True,
        file_outputs=[api.EncodedFileOutput(
            file_type=api.EncodedFileType.OGG,
            filepath="recordings/session.ogg",
            s3=api.S3Upload(
                bucket="my-bucket",
                region="us-west-2",
                access_key="my-access-key",
                secret="my-secret-key",
            ),
        )],
    )
    lkapi = api.LiveKitAPI()
    await lkapi.egress.start_room_composite_egress(req)
    await lkapi.aclose()

2. Enable TTS-Aligned Transcription

from livekit import AgentSession

session = AgentSession(
    stt=my_stt,
    llm=my_llm,
    tts=my_tts,
    use_tts_aligned_transcript=True,  # This enables word/sentence-level timestamps
)

3. Access Timed Transcripts

async def transcription_node(self, text, model_settings):
    async for chunk in text:
        if isinstance(chunk, TimedString):
            print(f"[{chunk.start_time:.2f}s - {chunk.end_time:.2f}s] {chunk.text}")
        yield chunk

4. Export Session History

async def save_session_history():
    import json
    from datetime import datetime

    filename = f"/tmp/transcript_{session.room_name}_{datetime.now().strftime('%Y%m%d_%H%M%S')}.json"
    with open(filename, 'w') as f:
        json.dump(session.history.to_dict(), f, indent=2)

Please feel free to reach out if you'd like me to clarify any part or provide more examples—I’m here to help!

now to map the timedString to session history?

To map

TimedString

objects to session history, you can use the timestamps provided by

TimedString

(e.g.,

start_time

and

end_time

) and align them with the

created_at

timestamps in the session history. Here's a friendly guide on how you can do it: 1. Extract TimedString Data: You can use the

transcription_node

method to capture

TimedString

objects along with their timing information. Here’s a little snippet to help you out:`async def transcription_node(self, text, model_settings): async for chunk in text: if isinstance(chunk, TimedString): print(f"[{chunk.start_time:.2f}s - {chunk.end_time:.2f}s] {chunk.text}") # Store or process the timing data here yield chunk` 2. Retrieve Session History: Next, you’ll want to access the session history using

session.history.to_dict()

. This will give you all the messages along with their

created_at

timestamps. Here’s how you can do that:`history = session.history.to_dict() for item in history['items']: print(f"[{item['created_at']}] {item['role']}: {item['text_content']}")` 3. Align TimedString with Session History: Finally, you can compare the

start_time

and

end_time

of each

TimedString

with the

created_at

timestamps in the session history. A simple matching algorithm can help find the closest timestamps. Here’s an example:`for item in history['items']: for timed_string in timed_strings: # List of TimedString objects if abs(timed_string.start_time - item['created_at']) < threshold: print(f"Matched: {item['text_content']} with {timed_string.text}")` If you have any questions or want me to walk you through any part of this, please don’t hesitate to ask. I’m here to help!