If I have a usecase like I need to sync my audio with transcript, what approach should I follow?

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.

what about audio?

Good question — focusing on the audio side, here’s what to do to make transcript timestamps line up with playback. Key actions for audio synchronization • Produce TTS audio with word/timestamp alignment enabled: ◦ ElevenLabs: set

sync_alignment=True

so word-level timings are returned with audio chunks. ◦ Cartesia: enable SSML/alignment features to get timing info. • Stream audio in time-ordered chunks to the client: ◦ Send audio chunks (or a continuous audio stream) in the same order and timing they were generated so playback time matches transcript timestamps. • Use the timing data to drive UI playback and highlighting: ◦ Consume

TimedString

entries (

start_time

,

end_time

in seconds relative to the agent turn) and map those to audio playback progress to highlight words/phrases. • Keep a common timebase: ◦ Align your playback clock to the agent’s turn timebase used by

TimedString

. If you buffer or delay playback, offset timestamps accordingly. • Handle interruptions and partial audio: ◦ By default transcripts are truncated to match actually spoken audio. If the agent is interrupted, stop playback and trim displayed transcript to the final timings. • If you need immediate (unsynced) transcripts: ◦ Set

sync_transcription=False

in

TextOutputOptions

(transcripts will arrive as available but won’t match exact audio timing). If you want, tell me which SDK (Python/Node/browser) and which TTS provider you’ll use and I’ll give a short code snippet showing how to consume audio chunks and

TimedString

timestamps together. , with interactive elements

so If i do this will it add more compute on the server ? because I am catching all the audio streams

Thanks — I’m responding to your question about whether enabling synced transcripts and capturing all audio streams will add server compute. Short answer • Yes — capturing and processing audio streams (for TTS/STT, VAD, alignment, recording) increases CPU and memory use; the amount scales with concurrent sessions and which processing components you run. What increases load • Running STT/TTS alignment and word-level timing. • VAD, noise suppression, or real-time audio processing. • Full-session recording vs short in-memory capture. Ways to reduce impact • Use

rtc.AudioStream

to capture short, time-ordered audio frames (lightweight) instead of heavier recording solutions like

RecorderIO

. • Only enable TTS-aligned transcripts when you need word-level timing; sentence-level alignment is cheaper. • Buffer only short snippets (e.g., 5–10s) if you don’t need full-session audio. • Rely on Opus DTX (enabled by default) to reduce bandwidth and processing during silence. • Scale horizontally: add more worker instances for large numbers of concurrent agents. If helpful, tell me: • which SDK (Python/Node/browser) you use, and • how many concurrent sessions/agents you expect —I’ll suggest a concrete capture pattern and an estimated resource strategy. Sources: Text and transcriptions | LiveKit Documentation | Self-hosted deployments | LiveKit Documentation | Capturing the First Few Seconds of a Call for Processing | Bringing Zoom's end-to-end optimizations to WebRTC | LiveKit | Bringing Zoom's end-to-end optimizations to WebRTC | LiveKit, with interactive elements

python

Great — focusing on Python. Below are concise, practical steps and a lightweight capture pattern plus compute guidance. Acknowledgement • You want Python-specific guidance to sync audio with transcripts while minimizing server compute when capturing all audio streams. Python implementation summary 1. Enable TTS-aligned transcripts ◦ Set

use_tts_aligned_transcript=True

on your

AgentSession

. 1. Choose a TTS provider that returns word timings ◦ ElevenLabs: enable

sync_alignment=True

. ◦ Cartesia: enable SSML/alignment features. 1. Consume timed transcript events ◦ Implement the

transcription_node

in your agent; it yields

TimedString

objects with

start_time

and

end_time

(seconds relative to the agent turn). Use those timestamps to drive UI highlighting against audio playback. 1. Capture audio efficiently ◦ Use

rtc.AudioStream

in

on_track_subscribed

to tap raw audio frames for short in-memory buffering/processing instead of full-session recording. ◦ For short snippets, buffer ~5–10s of audio in memory and process; avoid holding entire sessions unless necessary. Lightweight capture pattern (concept) 1. on_track_subscribed -> create an

rtc.AudioStream

for that track. 2. Collect frames into a ring buffer sized for N seconds (e.g., 5–10s). 3. When you need to transcribe or align, send buffered frames to STT/TTS pipeline, then discard or persist as needed. 4. Use `TimedString.start_time`/`end_time` and map to playback clock (apply an offset if you buffer/delay playback). Compute and scaling guidance • Yes, processing all audio streams increases CPU/memory; load scales with concurrent active sessions and enabled components (STT, VAD, noise suppression, alignment). • Example benchmark: running Silero VAD for 30 agents on a 4-core/8GB machine used ~3.8 cores and ~2.8GB memory — expect similar growth when running per-stream real-time processing. •

rtc.AudioStream

is lightweight compared to `RecorderIO`; prefer it for per-track, low-overhead capture. • Rely on Opus DTX (enabled by default) — it reduces bandwidth/processing during silence. • For high concurrency, scale horizontally (add worker instances) and/or limit per-instance processing (short buffers, offload heavy STT jobs to dedicated workers). Next step offer • If you share expected concurrency and whether you’ll run VAD/STT inline or offload, I’ll give a concrete Python snippet for

on_track_subscribed

+ an estimated resource plan., with interactive elements

so my doubt is for comleted audio I need to buffer all right in the memory then onluy I would get the complete synchornised audio right

Short answer: No — you don't have to buffer the entire session in memory to get synced audio+transcript. You can stream and align incrementally. How to do it in Python (concept + recommendations) • Stream audio frames with

rtc.AudioStream

in

on_track_subscribed

and keep a small rolling buffer (5–10s) rather than full-session buffering. • Produce/receive TTS audio with word timings (enable

use_tts_aligned_transcript=True

on

AgentSession

and

sync_alignment=True

for ElevenLabs / enable Cartesia alignment). • As audio is generated/streamed, consume

TimedString

objects from

transcription_node

(they include `start_time`/`end_time` relative to the agent turn). Map those timestamps to your playback clock. • If you delay playback (buffering/encoding), apply the same offset to

TimedString

timestamps so UI highlights remain aligned. • For long responses or final, high-quality audio files: stream for real-time UX and concurrently write audio to disk (or object storage) in chunks; once file is complete you can produce a final pass if you need absolute-precision alignment. • Use incremental commits: process and emit transcript highlights as soon as matching audio chunks are available; only keep longer-term storage for post-processing or archival. Practical pattern 1. on_track_subscribed -> create

rtc.AudioStream

. 2. Push frames into a ring buffer (5–10s). 3. Stream frames to your playback client as they arrive. 4. When

TimedString

events arrive, use their times + your playback offset to highlight words in real time. 5. Optionally persist audio chunks to disk/cloud in the background for any later reprocessing. When full in-memory buffering is necessary • You only need full in-memory buffering if you must perform a single offline pass that requires the entire raw session immediately (rare for live UX). Otherwise, chunked streaming + background persistence is far more efficient. If you want, I can: • Provide a short Python snippet showing

on_track_subscribed

using

rtc.AudioStream

+ a ring buffer and consuming

TimedString

events, or • Sketch a storage/worker architecture for high concurrency. Which would you prefer?, with interactive elements

no I am asking then how I would get the cimplete audio

Good question — here are the practical ways to get the complete audio (while keeping transcripts synced), and when to use each. Options to obtain complete audio • Stream-to-disk/chunks (recommended): write audio chunks to disk or object storage as they arrive. This gives you a complete file after the session without holding the whole session in memory. Continue streaming playback and use

TimedString

timestamps (with any playback offset) to highlight words in real time. • RecorderIO/full-session recording: use

RecorderIO

if you need a server-side single-file recording of the entire session immediately. This uses more memory/IO than streaming chunks. • In-memory full buffer (only if necessary): buffer the entire session in RAM if you require a single, immediate in-memory audio blob for post-processing — not recommended at scale. How to keep transcript alignment while getting the complete file 1. Enable TTS-aligned transcripts (

use_tts_aligned_transcript=True

) so you receive

TimedString

events with `start_time`/`end_time`. 2. Stream audio frames via

rtc.AudioStream

in

on_track_subscribed

and: ◦ Write frames to a rolling file/append to an on-disk temp file or upload chunks to object storage. ◦ Also push frames to the client for live playback. 1. Use the same timebase for playback and

TimedString

timestamps. If you buffer/delay playback by X seconds, apply the same offset to timestamps when highlighting. 2. After the session ends (or recording completes), you’ll have a complete audio file assembled from chunks or RecorderIO output that matches the transcript timings you already used during playback. When to choose which approach • Low memory + real-time UX: stream chunks to disk/cloud + real-time mapping with

TimedString

. • Need immediate single file server-side: RecorderIO or assemble chunks into a file at end. • High concurrency: avoid full in-memory buffering; prefer chunked streaming and background assembly. If you want, I can provide a short Python snippet showing: • streaming frames to disk in

on_track_subscribed

with a ring buffer, and • consuming

TimedString

events to drive UI highlights with offsets. Which snippet would help you most? , with interactive elements