in gemini realtime plugin, is there a clear message for signalling that a transcript is complete so i can save it to my database

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

what is the recommended way of accessing the data from these events?

Thanks — I’ll expand on recommended ways to access and use data from

user_input_transcribed

events (what part of that would you like a code example for: Python or Node.js?). Key recommendations • Listen to the

user_input_transcribed

event on the AgentSession. • Use the

is_final

flag to decide when to persist text (only save when true). • Include timestamps/metadata from the event for ordering and playback sync. • Buffer interim segments if you need continuous live display, but replace with final text when

is_final

arrives. • Emit or store a single canonical record per final segment (e.g., transcript text, start/end timestamps, speaker id) to simplify downstream queries. Minimal examples • Python:

@session.on("user_input_transcribed")
def on_transcript(event):
    print(event.transcript, event.is_final)
    if event.is_final:
        save_to_db({
            "text": event.transcript,
            "start": event.start_time,
            "end": event.end_time,
            "speaker": event.speaker_id,
        })

• Node.js/TypeScript:

session.on('user_input_transcribed', (event) => {
  console.log(event.transcript, event.isFinal);
  if (event.isFinal) {
    saveToDb({
      text: event.transcript,
      start: event.startTime,
      end: event.endTime,
      speaker: event.speakerId,
    });
  }
});

Questions to help tailor this • Which runtime do you want an expanded example for (Python or Node)? • Do you need handling for multi-speaker diarization or streaming display of interim text? Sources: Transcriber | LiveKit docs | Pipeline nodes and hooks | LiveKit docs

I am using a swift frontend but my agent is python. do i need to handle the transcripts from the agent session in python or from the frontend?

Thanks — good question. Regarding Swift frontend + Python agent: • Handle transcription events on the Python AgentSession. The

user_input_transcribed

events (with

is_final

) are emitted on the agent session — that’s the canonical place to decide when a segment is final and safe to persist. • From the agent you can: ◦ Save final transcripts to your database directly when

is_final

is true. ◦ Attach metadata (timestamps, speaker id) and produce a single canonical record per final segment. ◦ Optionally buffer interim segments if you need them for live display, but replace them with the final text when

is_final

arrives. • Forwarding to the Swift frontend: ◦ If the frontend needs live/interim text or to display finalized transcripts, send events from your Python agent to the Swift client (e.g., via your signaling channel, WebSocket, or LiveKit data messages). ◦ For UI simplicity, send interim updates for live display and send the finalized transcript (or an “update” message) when

is_final

is true so the client can replace the interim text and/or mark it persisted. Would you like a short Python snippet showing handling + forwarding, and a matching Swift example for receiving/displaying interim + final messages?

say i want to perform an analysis on the transcript that should be forwarded to the swift client in realtime. is it advised to do this from within the agent.py / livekit agent ? i.e. analyseTranscript({transcriptMetadata}) or is it better to forward the transcript to a separate server for analysis and manage sending the analysis back to the frontend on a separate path

Thanks — I’m responding to your question about whether to run realtime transcript analysis inside the Python LiveKit agent or forward transcripts to a separate analysis service for the Swift frontend. Short answer • Do lightweight, low-latency work (keyword detection, simple sentiment, realtime highlighting) in the agent. • Offload heavy, long-running, or reusable ML/NLP pipelines (large-model summarization, entity linking, batch analytics) to a separate service and return results asynchronously. • A common hybrid pattern is: agent captures transcripts, streams them immediately to the client, and simultaneously forwards segments (with IDs/metadata) to an external analyzer; the analyzer sends analysis back via LiveKit data messages or a custom text stream topic. Decision factors (quick checklist) • Latency requirement: strict → prefer agent; tolerant → external service. • CPU/memory cost: low → agent; high → external. • Scalability / reuse: need independent scaling or reuse → external. • Fault isolation: want analysis failures not to affect audio → external. • Need session context / low overhead integration → agent. Recommended implementation patterns • Agent-only (best for simple, fast analysis) a. Handle

user_input_transcribed

or

transcription_node

in

agent.py

. b. Run analysis synchronously or in a short async task. c. Publish results to frontend via data messages or a text stream topic (e.g.,

lk.analysis

). • External analyzer (best for heavy/complex work) a. Agent captures final transcripts (

is_final

) and POSTs them to analysis service with

segment_id

,

speaker

, timestamps. b. Analysis service processes and returns results. c. Send results back to Swift via LiveKit data messages / custom text stream or have Swift subscribe to a WebSocket/REST callback endpoint. d. Keep result messages idempotent and include segment_id for reconciliation. • Hybrid (recommended for many apps) ◦ Stream interim text to client from agent for UI responsiveness. ◦ For final segments: agent both persists and forwards to external analyzer; when analyzer completes, agent or analyzer publishes analysis results to frontend topic

lk.analysis

. Practical tips • Always include

segment_id

,

start/end

timestamps, and

speaker_id

in messages for mapping results to UI. • Use

is_final

to decide when to persist/forward. • Send interim updates to UI but replace with final text on

is_final

. • If external analyzer will callback the client directly, design auth and deduping; otherwise publish analyzer results via LiveKit data messages so the agent maintains a single path to clients. If you want, I can: • Provide a concise Python agent snippet that forwards final segments to an external analyzer and publishes analysis results via a text stream. • Provide the matching Swift handler example for

lk.analysis

. Which of those would you like first? Sources: Gemini Live API plugin | LiveKit docs | Text and transcriptions | LiveKit docs | Pipeline nodes and hooks | LiveKit docs

what about the following: i've noticed that i get a lot of transcript completion messages, despite the user not actually having fully finished their sentence. so they may say "hello how" "are" "you" and this gets broken down into 3 transcripts. i want those 3 to be merged into 1. should this logic be implemented inside the agent.py file or elsewhere? or could i add a separate transcript_handler.py file in the src directory as a sibling file to agent.py?

You're right to merge those fragments — doing so improves UX and avoids storing many tiny segments. Responding to your question about where to implement it: Short recommendation • Implement merging in the Python agent layer, but keep the merging logic in a separate module (e.g.,

transcript_handler.py

) and import it into

agent.py

. This keeps the agent responsive while keeping code modular and testable. Why this pattern • The agent receives

user_input_transcribed

events and has the canonical `is_final`/segment metadata, so it's the right place to decide how fragments map to a final segment. • Putting logic in a separate handler file preserves separation of concerns (agent orchestration vs. transcript processing) and makes it easy to reuse, test, or move to an external service later. • Doing merging client-side (Swift) is possible but less reliable because the agent is the authority for timestamps, speaker IDs, and session context. Recommended approach (high level) • Capture incoming events in

agent.py

(

user_input_transcribed

or

transcription_node

). • Forward each event to functions in

transcript_handler.py

that: ◦ Buffer fragments per speaker/segment_id. ◦ Use heuristics to merge fragments (time gap threshold, lack of overlap, punctuation, confidence/score if available). ◦ Emit a merged final segment when either: ▪︎

is_final

true for a fragment that completes a buffered sequence, or ▪︎ a configured inactivity timeout elapses (e.g., 300–800 ms) after the last fragment. • When a merged final is ready, persist/forward it and include

merged_segment_id

,

start/end

timestamps, and original fragment IDs. Minimal design notes • Key fields to track:

speaker_id

,

segment_id

(if provided),

start_time

,

end_time

,

received_time

. • Heuristics: ◦ Merge if consecutive fragments from same speaker arrive within a short gap (configurable). ◦ Prefer

is_final

as authoritative — if any fragment in a merge is final, mark merged as final. ◦ Use punctuation or capitalization if present to help boundary detection. • Concurrency: keep per-room and per-speaker buffers; clear buffers on finalization or when room ends. • Idempotency: include original fragment IDs in merged record so duplicates are detectable. Example outline (conceptual) • src/ ◦ agent.py # registers event handlers and call into handler ◦ transcript_handler.py # buffering, merging logic, timeouts, emit function agent.py:

from transcript_handler import TranscriptMerger

merger = TranscriptMerger(on_merged=publish_merged)

@session.on("user_input_transcribed")
def on_transcript(event):
    merger.add_fragment(event)

transcript_handler.py (conceptual):

class TranscriptMerger:
    def __init__(self, on_merged, gap_threshold_ms=500):
        self.buffers = {}  # keyed by (room, speaker)
        self.on_merged = on_merged
        self.gap = gap_threshold_ms

    def add_fragment(self, event):
        key = (event.room_id, event.speaker_id)
        buf = self.buffers.setdefault(key, [])
        buf.append(event)
        # reset or start timer for inactivity-based flush
        if event.is_final:
            self.flush(key)

    def flush(self, key):
        merged = merge_fragments(self.buffers.pop(key, []))
        if merged:
            self.on_merged(merged)

What to emit to frontend • Send merged transcripts via a text stream or data message (topic like

lk.transcript.merged

) including: ◦

text

,

merged_segment_id

,

original_fragment_ids

,

start

,

end

,

speaker_id

,

is_final

. If you want, I can: • Provide a concrete Python implementation of

TranscriptMerger

with a timeout-based flush and simple heuristics. • Provide the Swift handler example for receiving merged transcripts. Which would you like first?

for the transcripts in gemini realtime, is there any notion of the transcript order (for both the agent and user) so we can ensure transcripts are always ordered correctly, even if received in a strange order

Thanks — I’m responding to your question about transcript ordering for Gemini Realtime (both agent and user) and how to ensure transcripts stay in correct sequence even if messages arrive out of order. Short answer • Use the stream’s sequence semantics plus metadata (segment IDs and timestamps) to order transcripts. Rely on

is_final

(or

lk.transcription_final

) to know when a segment is authoritative and replace any interim for that segment. Track per-speaker/per-segment buffers and sort by timestamps or segment_id when assembling the timeline. What to use (concrete signals) • segment_id (or

lk.segment_id

on text streams) — group interim/final updates for the same utterance. • finality flag (

is_final

in agent events,

lk.transcription_final

on text streams) — treat final as authoritative. • start/end timestamps (or timed tokens /

TimedString

when available) — order segments precisely and merge fragments. • speaker_id / track_id — separate streams per speaker so you don’t intermix turns. Recommended implementation patterns • Agent-side canonical ordering: ◦ Capture

user_input_transcribed

events in the agent (or override

transcription_node

for richer timing). ◦ Buffer fragments keyed by (room, speaker, segment_id). ◦ On each fragment: update buffer, reset inactivity timer for that key. ◦ If

is_final

arrives, flush that segment as final (replace any interim). ◦ If inactivity timeout elapses for the buffer, flush as final candidate (optionally mark as provisional). ◦ Persist and/or forward merged final with

segment_id

,

start

,

end

,

speaker_id

, and original fragment IDs. • Frontend-side ordering (Swift): ◦ Register a text stream handler for

lk.transcription

and

lk.analysis

(if using). ◦ Use the

reader.info.attributes

(

lk.transcription_final

,

lk.segment_id

, etc.) to group messages. ◦ Keep a per-segment buffer of interim messages; when final arrives, swap in final and render/persist. ◦ If messages arrive out of order, use timestamps or segment_id sequence to reorder before display. Example flow (conceptual) 1. Agent emits interim updates with same

segment_id

. 2. Swift receives interim updates and displays them (buffered). 3. Agent emits final update with

lk.transcription_final=true

for that

segment_id

. 4. Swift replaces interim with final and marks persisted/complete. Practical ordering rules • Always group by

segment_id

first. • Within a segment, use server-provided timestamps (`start_time`/`end_time` or TimedString) to order tokens if needed. • If

segment_id

is missing in a context, fall back to (speaker_id + monotonic sequence or arrival timestamp) but prefer segment_id when available. • Include

received_time

when you store events so you can detect reordering and reconcile later. Edge cases & mitigations • Duplicate or delayed final arrives after another segment started: use timestamps and segment_id to place it correctly; if it overlaps, consider merging or flagging overlap for manual review. • Lost final: set a conservative inactivity timeout (e.g., 300–800 ms) to flush buffered fragments as a candidate final. • Multi-speaker turns: maintain per-speaker buffers to avoid cross-speaker concatenation. Agent vs frontend responsibility • Agent is the canonical source of truth for finality, timestamps, and segment grouping. Do ordering/merging in the agent if you need guaranteed, server-side canonical transcripts (recommended for persistence and analysis). • Frontend should implement buffering and replacement logic so UX remains smooth despite network reordering. • Best practice: perform canonical merging/persistence in the agent, and stream interim + final to the frontend. This gives you a single authoritative record and a responsive UI. If you’d like, I can: • Provide a compact Python

TranscriptMerger

implementation that buffers by (room, speaker, segment_id), uses a timeout, and flushes on

is_final

. • Provide the Swift handler code that registers a text stream handler, buffers interim messages by

segment_id

, and applies final replacements. Which snippet would you like first? Sources: Text and transcriptions | LiveKit docs