I am using Speechmatics for STT, how do I enable a livekit room with speechmatics STT with UI and I can see the conversation bubbles

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

1. I was using web voice agent on python. Can you guide me step by step how to enable that? 2. I am using speechmatics plugin

Thanks — this is a great setup to walk through. I’ll guide you step-by-step to enable Speechmatics STT in your Python web voice agent and show live conversation bubbles in the UI. Quick confirmation before I start: are you already running a Python voice agent that answers WebRTC calls (e.g., using

AgentServer

/

rtc_session

) and have a frontend that connects to the agent’s LiveKit room? If yes, I’ll assume the agent can accept connections and we’ll focus only on adding Speechmatics STT + frontend transcription handling. If not, say “I need the base agent running” and I’ll include the minimal server start instructions. Below are the steps you can follow. I’ll include exact Python snippets and the frontend handler to render conversation bubbles. 1) Install the Speechmatics plugin • Run:

uv add "livekit-agents[speechmatics]~=1.3"

This installs the Speechmatics STT plugin for your agent. 2) Add your Speechmatics API key • Create (or update) a

.env

in your project root with:

SPEECHMATICS_API_KEY=your_speechmatics_api_key_here

• Load it in your Python script:

from dotenv import load_dotenv
  load_dotenv()

3) Configure AgentSession to use Speechmatics STT • Use

speechmatics.STT()

when creating your

AgentSession

. Minimal example inside an RTC session handler:

from dotenv import load_dotenv
  load_dotenv()

  from livekit.agents import AgentSession, Agent, JobContext, AgentServer
  from livekit.plugins import speechmatics, openai

  server = AgentServer()

  class MyAgent(Agent):
      def __init__(self):
          super().__init__(instructions="You are a helpful assistant.")

  @server.rtc_session()
  async def on_session(ctx: JobContext):
      session = AgentSession(
          stt=speechmatics.STT(),                # <-- Speechmatics STT enabled
          llm=openai.LLM(model="gpt-4.1-mini"),  # example LLM
          tts=openai.TTS(model="tts-1", voice="alloy"),
      )
      agent = MyAgent()
      await session.start(agent=agent, room=ctx.room)
      # keep session open so speech is transcribed in real-time
      await session.generate_reply(instructions="Say hello.")

• Start your agent server as you normally do (e.g., using your existing dev/run commands). 4) (Optional) Enable speaker diarization • If you want speaker attribution from Speechmatics:

stt = speechmatics.STT(
      enable_diarization=True,
      speaker_active_format="<{speaker_id}>{text}<!--{speaker_id}-->",
  )
  session = AgentSession(stt=stt, llm=..., tts=...)

• The plugin will emit speaker-tagged transcripts which you can parse on the frontend. 5) How transcriptions are delivered to the frontend • When the agent transcribes audio, LiveKit publishes the text to the

lk.transcription

text stream topic automatically. No extra forwarding code is required server-side. 6) Frontend: receive text-streams and show conversation bubbles • In the browser, register a handler for

lk.transcription

. Example using plain JavaScript (LiveKit Room object):

// room is your connected LiveKit Room
  room.registerTextStreamHandler('lk.transcription', async (reader, participantInfo) => {
    const message = await reader.readAll(); // full transcription text
    const attrs = reader.info.attributes || {};
    const transcribedTrackId = attrs['lk.transcribed_track_id'];

    if (transcribedTrackId) {
      // This is a user spoken transcript
      displayBubble(participantInfo.identity, message, 'user');
    } else {
      // Agent response / text generated by the agent
      displayBubble('Agent', message, 'agent');
    }
  });

  // Example bubble render helper:
  function displayBubble(identity, text, role) {
    const container = document.getElementById('bubbles');
    const div = document.createElement('div');
    div.className = `bubble ${role}`;
    div.innerHTML = `<strong>${identity}:</strong> ${text}`;
    container.appendChild(div);
    container.scrollTop = container.scrollHeight;
  }

• If you use React with

@livekit/components-react

, you can use the

useTranscriptions

hook to get the transcripts and render them:

import { useTranscriptions } from '@livekit/components-react';

  function TranscriptionBubbles() {
    const transcriptions = useTranscriptions(); // array of segments

    return (
      <div id="bubbles">
        {transcriptions.map(segment => (
          <div key={segment.id} className={`bubble ${segment.is_agent ? 'agent' : 'user'}`}>
            <strong>{segment.participant?.identity ?? (segment.is_agent ? 'Agent' : 'User')}:</strong> {segment.text}
          </div>
        ))}
      </div>
    );
  }

7) Distinguish agent vs user transcripts • The

lk.transcription

messages for user speech include the attribute

lk.transcribed_track_id

. Agent-produced text (like TTS text or agent text messages) will not have that attribute. Use that to classify bubbles on the frontend. 8) Test flow • Start your Python agent server. • Connect a browser client to the same LiveKit room and allow microphone. • Speak — you should see live transcription bubbles for your speech and agent text bubbles when the agent replies. 9) Troubleshooting tips • If you don’t see transcripts: ◦ Verify

.env

contains

SPEECHMATICS_API_KEY

and it’s loaded by the agent process. ◦ Confirm your AgentSession is created with

speechmatics.STT()

. ◦ Ensure the frontend is connected to the same room and you’ve registered the

lk.transcription

handler. • If speaker attribution is missing, ensure

enable_diarization=True

is passed to

speechmatics.STT()

. If you’d like, I can now: • Provide a minimal full repo-like example (server + small frontend) you can run locally, or • Produce the exact React component and CSS for polished conversation bubbles, or • Help parse diarization tags into per-speaker bubbles. Which would you prefer next? Sources: Speechmatics STT plugin guide | LiveKit Documentation | Simple Call Answering Agent | LiveKit Documentation | Web and mobile frontends | LiveKit Documentation

I need the base agent running. Can you give me more detailed flow on how to enable this? For example, where to start, what script to get, should I start from web voice agent on livekit sandbox? Also I don't want to create a client my self to showthe UI, is there a way I can use your livekit sanbox or anything on the account to see the UI? IDeally I want to see the logs and conversation flow side by side, if not the transcription in the conversation at least

Thanks — this is a clear request. I’ll walk you through a practical, step‑by‑step flow to get a base Python web voice agent running with Speechmatics STT, and show how to view transcription and logs without building your own UI by using LiveKit Cloud’s sandbox and Agent Insights. I’ll assume you don’t yet have a running agent. If any of the environment (LiveKit Cloud project, Speechmatics API key, or ability to run Python locally) is already done, tell me which so I can skip those steps. Step 0 — what you’ll need • A LiveKit Cloud project (LIVEKIT_API_KEY, LIVEKIT_API_SECRET, LIVEKIT_URL). • A Speechmatics API key. • A machine to run the Python agent (local dev environment). If you already have these, say so and I’ll skip to the code. Step 1 — create/get a base Python voice agent 1. Clone the official starter repository (this gives you a ready Python agent): ◦ git clone https://github.com/livekit-examples/agent-starter-python.git ◦ cd agent-starter-python 1. Install dependencies and the uv manager: ◦ pip install uv ◦ uv sync This repo is the recommended starting point and already includes the LiveKit Agents structure. Step 2 — configure LiveKit & Speechmatics credentials 1. In project root create a

.env

with: ◦ LIVEKIT_API_KEY=your_livekit_api_key ◦ LIVEKIT_API_SECRET=your_livekit_api_secret ◦ LIVEKIT_URL=wss://your-subdomain.livekit.cloud ◦ SPEECHMATICS_API_KEY=your_speechmatics_api_key 1. Ensure your Python process loads

.env

(the starter uses python-dotenv). Example: ◦ from dotenv import load_dotenv; load_dotenv() Step 3 — add/enable the Speechmatics plugin 1. Install the plugin into the project environment: ◦ uv add "livekit-agents[speechmatics]~=1.3" 1. Update the agent code to use Speechmatics STT. In the agent session setup, replace or set

stt

to `speechmatics.STT()`: Example (minimal snippet inside your agent code):

from livekit.agents import AgentSession, Agent, JobContext, AgentServer
from livekit.plugins import speechmatics, openai

server = AgentServer()

class MyAgent(Agent):
    def __init__(self):
        super().__init__(instructions="You are a helpful assistant.")

@server.rtc_session()
async def on_session(ctx: JobContext):
    session = AgentSession(
        stt=speechmatics.STT(),                     # Speechmatics enabled
        llm=openai.LLM(model="gpt-4.1-mini"),       # example LLM
        tts=openai.TTS(model="tts-1", voice="alloy"),
    )
    agent = MyAgent()
    await session.start(agent=agent, room=ctx.room)
    await session.generate_reply(instructions="Say hello.")

Optional: enable diarization if you want per-speaker attribution:

stt = speechmatics.STT(
    enable_diarization=True,
    speaker_active_format="<{speaker_id}>{text}<!--{speaker_id}-->"
)

Step 4 — run the agent locally (test & dev modes) • Quick terminal test (console mode that simulates a call): uv run src/agent.py console This shows logs and transcripts in your terminal for quick verification. • Real WebRTC testing (dev mode — accepts real connections): uv run src/agent.py dev Then connect a client (or use the Test Agent sandbox UI described next). Step 5 — see UI and transcripts without building a client You do not need to create your own client to see the conversation. LiveKit Cloud provides a Test Agent sandbox and Agent Insights where you can view transcriptions and logs side‑by‑side. Option A — Test Agent sandbox (no custom client) 1. Deploy or register your agent in the LiveKit Cloud project. 2. In the LiveKit Cloud dashboard go to Agents → select your agent → click “Test Agent” (or “Regenerate test app” if it’s not present). 3. The Test Agent opens a hosted web sandbox UI (built with @livekit/components-react). Join that sandbox, allow microphone, and speak. You’ll see: ◦ Real-time transcription in the UI ◦ Agent audio responses ◦ Conversation bubbles / interaction state Option B — Agent Insights (transcripts + logs) 1. In the dashboard go to Sessions → open a specific session → open the Agent Insights tab. 2. You’ll get a combined view: ◦ Transcript timeline and playback (user vs agent text) ◦ Trace/step view showing STT → LLM → TTS events and timing ◦ JSON logs and metadata for each turn This gives the side‑by‑side conversation flow and logs you asked for without building a frontend. Step 6 — what to check if you don’t see transcripts • Confirm

.env

contains SPEECHMATICS_API_KEY and the agent process loads it. • Confirm AgentSession uses

speechmatics.STT()

(or the diarization-enabled variant). • Ensure the session runs (dev) or the console test shows STT output in terminal. • Use the Test Agent sandbox to join the same room (no custom client required). • In the transcript stream, user speech items carry

lk.transcribed_track_id

— the dashboard and sandbox use this to label user vs agent text. Step 7 — next steps / optional improvements • If you want, I can: ◦ Provide a small runnable repo layout with the exact files edited/added so you can run immediately. ◦ Give the exact commands and the single file to run using the starter repo (so you can copy-paste and run). ◦ Provide instructions for parsing diarization tags into per‑speaker bubbles in the UI (if you enable diarization). Tell me which one: “Runnable repo example”, “Single-file start commands”, or “Diarization parsing”. If you want, share whether you already have a LiveKit Cloud project and the starter repo cloned — I’ll tailor the next message to give exact file edits and the exact uv run command to start it. Sources: Build a multilingual voice agent that automatically switches languages | Li... | Agents (Python) | livekit/livekit_composite | DeepWiki | Speechmatics STT plugin guide | LiveKit Documentation | Simple Call Answering Agent | LiveKit Documentation | https://livekit.io/ui/r/registry.json|

Questions: #1 I am able to run on the terminal but I cannot hear the agent talks back to me. Why and how to rectify? #2 After loading the agent, the terminal just freeze and I cannot see my commands: (.venv) root@JESLYNY-F1BZTQ3:~/agent-starter-python# ^C (.venv) root@JESLYNY-F1BZTQ3:~/agent-starter-python# ^C (.venv) root@JESLYNY-F1BZTQ3:~/agent-starter-python# grtrtrtrtrtrexit: command not found (.venv) root@JESLYNY-F1BZTQ3:~/agent-starter-python# fedrererfdfdfd: command not found (.venv) root@JESLYNY-F1BZTQ3:~/agent-starter-python# wq: command not found (.venv) root@JESLYNY-F1BZTQ3:~/agent-starter-python# ^C #3 How do I start the agent on the livekit cloud agent? I can see a Test Agent”/ regenate button

Thanks — I can help you troubleshoot each issue and get you hearing the agent and using the LiveKit Cloud Test Agent. I’ll address your three questions one by one and give clear steps you can try now. Question #1 — I can run in the terminal but I cannot hear the agent talk back. Why and how to fix it? • Why: The Python agent’s console/`console` mode is text-only. It sends TTS audio as an audio track into the LiveKit room — it does not play sound in your terminal. So running in console mode will show transcripts and logs but will not produce audible playback on your machine. • How to fix / hear the agent: a. Start the agent in a mode that accepts real WebRTC clients: • Run dev mode:

uv run src/agent.py dev

(or your agent start command that runs the server loop). a. Open a client that plays audio: • Option A (no custom client needed): Use the LiveKit Cloud “Test Agent” sandbox (see Question #3 below). • Option B: Use Agents Playground (https://agents-playground.livekit.io/) and connect to your running agent. • Option C: Use any LiveKit web client that subscribes to audio tracks (e.g., the provided playground or a simple @livekit/components-react page). a. Join the same room from the browser, allow microphone and speakers — the agent’s TTS will be published as an audio track and the browser will play it. • Quick check: if you want local audio playback without a browser, you’d need to subscribe to the room’s audio track programmatically and route it to an audio device — that’s more advanced. Using the Test Agent / browser is the easiest path. Question #2 — Terminal “freezes” after loading the agent and typed commands show up as errors • What’s happening: When you start the agent (console or dev modes), the agent process runs an event loop and takes over that shell session. It’s expected you won’t be able to run other commands while that process is active. That looks like the terminal is “frozen,” but it’s actually the agent running and waiting for events. • Why extra input shows errors: ◦ When the agent is stopped or you type while the process is active, you may see strange behavior or “command not found” lines because the shell is not accepting normal interactive input. • How to stop/exit cleanly: a. To stop the running agent, press Ctrl+C. If it doesn’t stop immediately, press Ctrl+C again or wait a short moment for the process to shut down. b. If Ctrl+C appears ineffective, open a new terminal tab/window and run

ps aux | grep agent

and kill the process with

kill <pid>

or

kill -9 <pid>

as a last resort. • Best practice: ◦ Keep one terminal dedicated to the running agent. Use a separate terminal to run other commands. Don’t expect to type shell commands into the same session while the agent is running. Question #3 — How to start the agent on LiveKit Cloud and use “Test Agent” / “Regenerate test app” • Quick summary: You can deploy your agent to LiveKit Cloud and then use the Test Agent sandbox to hear audio and view transcripts without building a client. Steps to deploy and use Test Agent: 1. Prepare your project: ◦ Ensure

.env

contains: LIVEKIT_API_KEY, LIVEKIT_API_SECRET, LIVEKIT_URL, SPEECHMATICS_API_KEY. ◦ Confirm dependencies and that the agent runs locally in

dev

mode first. 1. Use the LiveKit CLI to create/deploy the agent: ◦ If you haven’t already, run

lk agent create

in project root to generate required files (if needed). ◦ Deploy with:

lk agent deploy

(or

lk agent create

on first deploy). This pushes your agent to LiveKit Cloud. 1. Use the Cloud Console: ◦ In LiveKit Cloud Console → Agents, find your deployed agent. ◦ Click “Test Agent.” That opens the hosted Test Agent UI (a browser client) where you can join the room, allow mic/speaker, and interact. You will hear the agent’s TTS there. ◦ If no test UI exists, click “Regenerate test app” to create one and open it. 1. View transcripts & logs: ◦ In the Console go to Sessions → select a session → Agent Insights tab. ◦ You’ll see transcripts, audio playback, and trace logs (STT→LLM→TTS) side-by-side — useful for debugging. Extra troubleshooting/checks you can run now • Confirm agent is publishing audio: ◦ In logs you should see events that the session published audio tracks and TTS generated. If you see STT transcripts but no TTS logs, check your TTS configuration in AgentSession. • Confirm browser is receiving audio: ◦ Join Test Agent sandbox and open devtools → Network/Console to see if media subscription errors appear. • If TTS is silent: ◦ Verify you configured

tts

in your AgentSession (e.g.,

tts=openai.TTS(...)

or your chosen TTS). ◦ Double-check API keys and model selection for the TTS provider. • If speech is transcribed but no audio and logs show TTS output text only: ◦ The agent may be configured to produce agent text but not synthesize audio. Ensure your session calls TTS or

session.speak()

rather than only

session.text_response()

. Which thing would help most next? • I can give the exact commands and a minimal single-file example you can run locally to confirm audio publishing and TTS. • Or I can walk you through deploying your current repo to LiveKit Cloud step-by-step and then open the Test Agent for you to try. Tell me which you prefer and whether you can run commands now (yes/no). If you want the deploy steps, tell me whether you already have the LiveKit CLI installed and your LIVEKIT_API_KEY / SECRET ready. Sources: Google AI and LiveKit | LiveKit Documentation | Uninterruptable Agent | LiveKit Documentation | Agent Builder | LiveKit Documentation | Voice AI quickstart | LiveKit Documentation

I still cannot find the test agent or regenerate test app. Are there other ways that I can see the livekit transcriots in real-time in an UI?

Thanks — I hear your frustration. If the Cloud “Test Agent” / “Regenerate test app” is not visible in your dashboard, you can still get a real‑time UI for transcriptions and conversation bubbles without building a full client. Below are practical alternatives and exact next steps you can try now. I’ll keep each option short so you can pick what’s easiest. Before we begin: quick check • Do you have a LiveKit Cloud project and API key/secret? (yes/no) • Can you run commands locally and open a browser to a web page that uses microphone/speakers? (yes/no) If you answer yes/no to either, I’ll tailor the exact commands — otherwise follow the options below. Option A — Use Agents Playground (fast, no deploy) • What it is: a hosted web UI that connects to a running agent (local or remote) to test voice interactions and plays agent audio + shows transcriptions. • How to use: a. Start your agent in dev mode so it accepts remote connections: • uv run src/agent.py dev a. Open Agents Playground in a browser: https://agents-playground.livekit.io/ b. Provide the connection info / token that matches your running agent. If your local agent logs a test URL or token, paste it in the playground. • Why use it: You will hear TTS and see transcriptions without building anything. If you need help generating a token for the playground, tell me whether your agent printed a connect URL or if you need steps to create a token. Option B — Use the LiveKit "Playground" sample client (quick local UI) • What it is: a minimal web client that subscribes to audio + text streams. You run it locally and it uses your LiveKit project URL & token. • Steps (quick): a. Clone the example web client repo (or I can paste the minimal files). Example repo: the LiveKit web playground sample. b. Configure with LIVEKIT_URL and a token (I can show commands to generate a short-lived token on your server). c. Open the page, allow mic/speaker — you’ll see audio + transcription bubbles from the

lk.transcription

stream. • Why use it: Lightweight, you control it, and it is faster than building a full app. Option C — Use the @livekit/components-react demo (if you prefer React) • What it is: prebuilt components including

useTranscriptions

which let you render conversation bubbles. • How to use: a. Create a simple Next.js page (I can provide a one-file component) that uses

@livekit/components-react

. b. Use a server endpoint to mint a token to join the room. c. When you connect,

useTranscriptions()

will surface

lk.transcription

segments for display. • I can provide the minimal component and token endpoint code if you want. Option D — Agent Insights & Session Logs (Cloud dashboard) • If “Test Agent” is missing, Agent Insights may still show session transcripts and logs: a. In the Cloud Console go to Sessions → select a session (if any exist). b. Open Agent Insights / Trace / Transcript panels. • If you have no sessions listed, you need to run the agent in dev or deploy so sessions are generated. Option E — Quick workaround: terminal transcript streaming + simple web UI • If you can run the agent locally and want to avoid tokens: a. Run the agent in console mode to ensure STT is producing transcripts (you’ll see text logs). b. Pipe those transcripts to a tiny local web server I can provide (Node or Python). The server shows a live webpage with conversation bubbles (no audio playback needed). This gives you the transcription UI immediately. • Tell me if you prefer this and I’ll produce the exact server code (5–10 lines) that listens for stdin or a file and updates the browser via SSE or WebSocket. Common troubleshooting checks (if you still can’t see transcripts) • Confirm your AgentSession includes STT (speechmatics.STT()) and STT is producing output in the agent logs. If you see transcript lines in terminal, STT is working. • Make sure your frontend subscribes to

lk.transcription

text stream or uses

useTranscriptions()

to surface transcripts. • If user transcripts are missing attributes, check

lk.transcribed_track_id

in the text stream attributes to determine user vs agent fragments. • If you run the agent in

dev

mode but can’t connect the playground, confirm your agent is reachable (no firewall blocking) and you have the correct LiveKit URL and token. Which option would you like to try now? • “Agents Playground” (I’ll help generate any token or instructions you need) • “Minimal local web client” (I’ll paste the repo/file + token steps) • “React demo with useTranscriptions” (I’ll give component + server token code) • “Terminal→Web UI quick server” (I’ll give the tiny server and instructions) Also tell me: • Do you see transcripts printed in your agent terminal when speaking? (yes/no) • Are you able to run uv/dev mode now and open a browser page? (yes/no) Once you pick, I’ll give the exact commands and any code snippets needed.

can you list out what are the other use ful config in configuration speechmatics stt? I want to demostrate to boost.ai our latest plugin in diarization, smart_turn mode and speaker identifier, speaker focus, vpoce agemt cpmfog etc/ Can you show me an sample config https://github.com/livekit/agents/blob/main/livekit-plugins/livekit-plugins-speechmatics/livekit/plugins/speechmatics/stt.py

Thanks — this is a great set of features to demonstrate. Below I list the most useful Speechmatics STT configuration options you can use in LiveKit Agents, explain what each does (diarization, smart_turn/end-of-utterance behavior, speaker identification/focus, partials/latency tuning, etc.), and provide a concrete Python sample config you can drop into your agent. I’ll also point out which settings to highlight when demoing to boost.ai. Summary of useful Speechmatics STT settings • enable_diarization (bool) ◦ Turns on speaker diarization so the transcript includes speaker labels. Use this to demonstrate per‑speaker attribution. • speaker_active_format (string) ◦ Controls how speaker labels appear in the transcript. Useful formats include: ▪︎ "<{speaker_id}>{text}<!--{speaker_id}-->" (machine-friendly tags) ▪︎ "[Speaker {speaker_id}] {text}" (readable for demos) • diarization_sensitivity (float) ◦ Adjusts how aggressively speaker boundaries are detected (0.0–1.0). Higher → more switches; lower → fewer switches. • prefer_current_speaker (bool) ◦ Reduces quick back-and-forth speaker switching by favoring the currently active speaker. Helpful when voices are similar. • enable_partials (bool) ◦ Emit interim (partial) transcripts for low-latency feedback. Show partials live during demo to illustrate low-latency streaming. • operating_point (string) ◦ Controls accuracy vs latency/resource tradeoffs (commonly "enhanced", "high_accuracy", etc.). Use "enhanced" for balanced demos. • language (string) ◦ ISO code for target language (e.g., "en"). Use to demo multi-language capability if needed. • end_of_utterance_mode (enum) ◦ Controls turn/endpoint detection mode: ▪︎ FIXED — uses a fixed silence threshold. ▪︎ ADAPTIVE — dynamically adjusts endpointing to conversation style (this is what you called “smart_turn”). ▪︎ NONE — disable built-in endpointing (use external VAD/turn detector). ◦ For “smart_turn” demos, use ADAPTIVE. • end_of_utterance_silence_trigger (float) ◦ Silence duration (seconds) that triggers end-of-turn when using FIXED/ADAPTIVE modes. Typical: 0.4–0.8s. • max_delay (float) ◦ Maximum delay (seconds) before finalizing a transcript. Lower values reduce perceived latency but can reduce final accuracy. • speaker_focus / speaker_focus_timeout (pattern/timeout) ◦ If available in your plugin variant, a “speaker focus” behavior lets you bias diarization to keep the same speaker active for a short time. This is represented by settings such as prefer_current_speaker and focus timeouts (use when demonstrating controlled speaker continuity). • enable_speaker_identification (if supported) ◦ If you have pre-registered speaker identity models / speaker embeddings, this enables mapping diarization IDs to real identities. Show how speaker IDs map to known users if available. • enable_word_timestamps / word_level_confidence ◦ Emit timestamps and confidence scores at word-level for rich playback and highlighting in UI. • vad integration (use VAD plugin e.g., silero.VAD) ◦ Use a VAD alongside STT to better control turn detection / interruptions. Useful for interruption handling demos. Which options to highlight in a boost.ai demo • Diarization (enable_diarization) + speaker_active_format — show speaker-attributed transcript. • ADAPTIVE end_of_utterance_mode — demonstrate smart_turn behavior (natural turn-taking). • enable_partials + low max_delay — show real-time partials to prove low-latency UX. • prefer_current_speaker / speaker_focus — show reduced jitter on speaker labels in multi-party calls. • Word timestamps & confidences — show exact turn timing and reliability. • (If available) speaker identification mapping — show linking diarization IDs to known users. Concrete Python sample configuration Use this as a drop-in STT configuration for AgentSession. It demonstrates diarization, smart_turn (adaptive endpointing), partials, and tuning knobs for latency and speaker stability.

from livekit.plugins import speechmatics, silero
from livekit.agents import AgentSession

# Speechmatics STT configuration demonstrating diarization, smart_turn, and tuning
stt = speechmatics.STT(
    enable_diarization=True,
    # Format output with explicit tags so you can parse speaker blocks easily
    speaker_active_format="<{speaker_id}>{text}<!--{speaker_id}-->",
    # Make diarization a little more sensitive to speaker change
    diarization_sensitivity=0.7,
    # Prefer the current speaker to reduce rapid flips between similar voices
    prefer_current_speaker=True,
    # Use adaptive endpointing for "smart_turn" behavior
    end_of_utterance_mode=speechmatics.EndOfUtteranceMode.ADAPTIVE,
    # Typical silence threshold for finalizing a turn (sec) under FIXED/ADAPTIVE
    end_of_utterance_silence_trigger=0.6,
    # Emit interim partials for low-latency display
    enable_partials=True,
    # Max time to wait before finalizing the transcript (lower = lower latency)
    max_delay=1.0,
    # Select an operating point balancing accuracy & latency
    operating_point="enhanced",
    # Language code
    language="en",
    # Enable word timestamps & confidence if supported
    enable_word_timestamps=True,
    enable_word_confidence=True,
)

session = AgentSession(
    stt=stt,
    # Add VAD to improve turn/interrupt handling
    vad=silero.VAD.load(),
    # llm, tts, and other components...
)

Parsing diarization output in your UI or demo • With the sample

speaker_active_format

above you’ll receive transcripts like: ◦ "<s1>Hello<!--s1-->" ◦ "<s2>Hi there<!--s2-->" • Parse out the tags to attribute text to the correct speaker. For demos, display as: ◦ Speaker 1: Hello ◦ Speaker 2: Hi there • If enable_partials=True, show partial lines as “(typing...)” style UI until a final transcript arrives. Demonstrating “speaker focus” and reduced jitter • Show two short demos back-to-back: a. prefer_current_speaker=False, diarization_sensitivity=0.9 — highlights many switches (noisy). b. prefer_current_speaker=True, diarization_sensitivity=0.6 — shows smoother speaker continuity. • Explain how smart_turn (ADAPTIVE) combined with VAD reduces awkward mid-sentence endpointing. Latency vs accuracy tradeoffs • For low-latency demos (live captions): enable_partials=True, max_delay ~0.5–1.0, operating_point="enhanced". • For accuracy-focused demos (post-call transcripts): enable_partials=False, operating_point="high_accuracy" (if available), higher max_delay. Notes & pointers • The exact parameter names and enums are defined in the plugin source you referenced; use that file as the canonical mapping if you need to pass additional flags or check available enums: ◦ https://github.com/livekit/agents/blob/main/livekit-plugins/livekit-plugins-speechmatics/livekit/plugins/speechmatics/stt.py • If you need speaker identity mapping, confirm you have speaker enrollment/voiceprints accessible to the Speechmatics account or another speaker‑ID system — the plugin surfaces diarization IDs, but mapping to known identities requires a matching identity model. • If you want a runnable demo for boost.ai: ◦ I can provide a small Python agent file using the above config and a minimal web UI that consumes

lk.transcription

and renders per-speaker bubbles (with partials). Tell me if you want a ready repo or a single-file example. Would you like: • A single-file runnable agent example (with this STT config) you can run locally? • A minimal UI (HTML + JS) that connects to a running agent and shows per-speaker bubbles and partials? Tell me which and I’ll produce the exact files and run commands. Sources: Speechmatics STT plugin guide | LiveKit Documentation | Turns overview | LiveKit Documentation

rectify my STT config for me stt = speechmatics.STT(enable_diarization=True, speaker_active_format="<{speaker_id}>{text}<!--{speaker_id}-->", additional_vocab=[ speechmatics.AdditionalVocabEntry( content="LiveKit", sounds_like=["live kit"], ), ], # Use adaptive endpointing for "smart_turn" behavior end_of_utterance_mode=speechmatics.EndOfUtteranceMode.ADAPTIVE, # Typical silence threshold for finalizing a turn (sec) under FIXED/ADAPTIVE end_of_utterance_silence_trigger=0.6, # Max time to wait before finalizing the transcript (lower = lower latency) max_delay=1.0, # Select an operating point balancing accuracy & latency operating_point="enhanced", # Language code language="en", focus_speakers="Jeslyn", known_speakers=[ speechmatics.KnownSpeaker( "label"=="Jeslyn", "speaker_identifiers"=["AD1NQVABAAQAAW0ACjlkYzNjNWMwOTQAAXMADC9tMG1lbUt3aVRzPQABdAAMUkppb3l6QVIzc0U9AAF2AAExADAAAAMWAAAAAHQGxM90tq_fd6GmY4I1AJAfhV1L961O0hV0QrDslSoF1kLxDHpNyCgAAAOFAAwAEBrg0CYJ9p2OCrqWZouxnyrgUnyNTyUqaN_sdvTFS2jI2bWsAZcFUfHb6OsxHSxJdHaFxvGYiBT249YpnZOXMYoJds8uE0kSvVELXcNSIgwfUmLWZPt_bPRjc-ILKEjlQh5Aq1AOj6lwczHLVfXe6xbDxsd6ojPkrTp1w25BwlI_nKPJIbhLZ-BJYtIEclJIPcCf-S9IvQ525iK4Fn8JcwJ93rM2wRjLjuC9_opQe_OQu5fTkrKxhk9JebS1NXZ4khmxFjrNCdEL9LF86YJgynTFjK5QnBn2wUeV08rHyUUjm9lwnUHB9kEbOd2KcqDy5JnBjqWwfmUTg0EbMHz-d5tbr2egcLZcxl5uPnZ_dD1wsrBhyN9xRCnJ1tOJqeBwUWKooL92JN9OK4FeA93TdjPEZNXyrnIdxqscTOOG0_naas1dL-OfvvA3i4wHJAmP_866eLBMuLAuDxoeFbkKfzD0wUqtgAbUcEiz64sQk2sLDn9CSsBRsF9gK8mPNxeRP6AP2ZimaO7WPeSj3MkO0JmuR8W4OA0Gvg_ZQA5-BKEeexqBpDRTnwotLVQiBWqt4z_Id08gDUpUhwBtrkFOaVGoM44urC7hZR51cfAo3b0H2nRJappLyzq8-qKLeJGbZs68wmGVSoZEHpIw6jnahKtQPicAMrSyUg9l3_Spd656sBSFWMZLkLM3yR8LrcWxYdGgiZluob4xdM3Ij_7z4Wu41u5wF6jeGOyUpgE3SBoIkhOgWcdYfdyVH0-AyrjYHQURH9AV9Ayhm3itDqx_Z0oTAlWmLsEY_mk8oE0ZkrbqTr8LXDGrulaUkmfBZkAN5rKy4HE0DyhrsbDnsI9rl0GcXAjC0sFFaVSQSyyyj4CfGh6GxojzuuyaGRv815f0RB5H1Ud75zj_t_3acX7k32yX3T3j3Gx5txVAWVsMTp3VRfuxnlWuJTiYw6i_BdFRfxjU_cDP5wmdHXaKjkVo-B5TTcmeqoBYfS4fhE85u7MgeuIaa8xOY8P_R3oi_1GSmgoYtFiA-LlKp4EaEH9bH3k6bhrB4eoT4l8fKdsOcyBHWW2AGeqMKAYh7fDcdl5hgtLytbQmGRDrWq-Ge9Ggoqkd8C1WwOdoAoMO-FVlB75ZKvQNh_a7q3B9qoFtTNab1v2y1GPeQhzwhOcxG-J3uZemYeVFemv0OJRiui0CUo23DmioiU1U0b-DQQzGUX6r_o_SMuayPj0Da6MewnsvWXWuPHyrehTs4roKr1uDxIUeAStAONWXS5q0sNiKbxcqPRUln7_E3LEgUGHthmmUJ8QCtBCD7NLopCKRWL4TB3FAeb82TPqJsuqwWCdOc5S-9om_zsY7wM9Zar0QhWZ2FI5AlgcGEvK-EwRc2oYxWwOmE75gsRpTaHCxjhkt" ] ) ], ), Traceback (most recent call last): File "<string>", line 1, in <module> File "/usr/lib/python3.12/multiprocessing/spawn.py", line 122, in spawn_main exitcode = _main(fd, parent_sentinel) ^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/usr/lib/python3.12/multiprocessing/spawn.py", line 131, in _main prepare(preparation_data) File "/usr/lib/python3.12/multiprocessing/spawn.py", line 246, in prepare _fixup_main_from_path(data['init_main_from_path']) File "/usr/lib/python3.12/multiprocessing/spawn.py", line 297, in _fixup_main_from_path main_content = runpy.run_path(main_path, ^^^^^^^^^^^^^^^^^^^^^^^^^ File "<frozen runpy>", line 285, in run_path File "<frozen runpy>", line 259, in _get_code_from_file File "/root/agent-starter-python/src/agent.py", line 94 8oE0ZkrbqTr8LXDGrulaUkmfBZkAN5rKy4HE0DyhrsbDnsI9rl0GcXAjC0sFFaVSQSyyyj4CfGh6GxojzuuyaGRv815f0RB5H1Ud75zj_t_3acX7k32yX3T3j3Gx5txVAWVsMTp3VRfuxnlWuJTiYw6i_BdFRfxjU_cDP5wmdHXaKjkVo-B5TTcmeqoBYfS4fhE85u7MgeuIaa8xOY8P_R3oi_1GSmgoYtFiA-LlKp4EaEH9bH3k6bhrB4eoT4l8fKdsOcyBHWW2AGeqMKAYh7fDcdl5hgtLytbQmGRDrWq-Ge9Ggoqkd8C1WwOdoAoMO-FVlB75ZKvQNh_a7q3B9qoFtTNab1v2y1GPeQhzwhOcxG-J3uZemYeVFemv0OJRiui0CUo23DmioiU1U0b-DQQzGUX6r_o_SMuayPj0Da6MewnsvWXWuPHyrehTs4roKr1uDxIUeAStAONWXS5q0sNiKbxcqPRUln7_E3LEgUGHthmmUJ8QCtBCD7NLopCKRWL4TB3FAeb82TPqJsuqwWCdOc5S-9om_zsY7wM9Zar0QhWZ2FI5AlgcGEvK-EwRc2oYxWwOmE75gsRpTaHCxjhkt" ^^^^^^^^^^^^^^^^^^^^^^ SyntaxError: expression cannot contain assignment, perhaps you meant "=="?

Thanks — I can help fix this and explain what went wrong. The traceback and SyntaxError come from several Python syntax issues in your snippet: • You used

"label"=="Jeslyn"

which is a boolean comparison instead of an assignment; use

label="Jeslyn"

. • Your long speaker identifier string contains many characters and needs to be a single string in the list; make sure it’s quoted and not split by newlines. • The overall nesting and commas must be valid Python (each dict/constructor argument separated by commas). •

focus_speakers

should be a list (or the correct type expected by the plugin), not a single untyped string unless the API expects that. Below is a corrected, well‑formatted STT configuration block you can drop into your agent. I kept your options (diarization, adaptive endpointing, additional vocab, known speakers) and corrected the syntax. Adjust the long speaker identifier string as needed (I placed it as a single string in the list). Corrected STT configuration (Python):

from livekit.plugins import speechmatics, silero
from livekit.agents import AgentSession

stt = speechmatics.STT(
    enable_diarization=True,
    speaker_active_format="<{speaker_id}>{text}<!--{speaker_id}-->",
    additional_vocab=[
        speechmatics.AdditionalVocabEntry(
            content="LiveKit",
            sounds_like=["live kit"],
        ),
    ],
    # Use adaptive endpointing for "smart_turn" behavior
    end_of_utterance_mode=speechmatics.EndOfUtteranceMode.ADAPTIVE,
    # Typical silence threshold for finalizing a turn (sec) under FIXED/ADAPTIVE
    end_of_utterance_silence_trigger=0.6,
    # Max time to wait before finalizing the transcript (lower = lower latency)
    max_delay=1.0,
    # Select an operating point balancing accuracy & latency
    operating_point="enhanced",
    # Language code
    language="en",
    # If plugin expects a list of focus speakers, pass a list
    focus_speakers=["Jeslyn"],
    known_speakers=[
        speechmatics.KnownSpeaker(
            label="Jeslyn",
            speaker_identifiers=[
                "AD1NQVABAAQAAW0ACjlkYzNjNWMwOTQAAXMADC9tMG1lbUt3aVRzPQABdAAMUkppb3l6QVIzc0U9AAF2AAExADAAAAMWAAAAAHQGxM90tq_fd6GmY4I1AJAfhV1L961O0hV0QrDslSoF1kLxDHpNyCgAAAOFAAwAEBrg0CYJ9p2OCrqWZouxnyrgUnyNTyUqaN_sdvTFS2jI2bWsAZcFUfHb6OsxHSxJdHaFxvGYiBT249YpnZOXMYoJds8uE0kSvVELXcNSIgwfUmLWZPt_bPRjc-ILKEjlQh5Aq1AOj6lwczHLVfXe6xbDxsd6ojPkrTp1w25BwlI_nKPJIbhLZ-BJYtIEclJIPcCf-S9IvQ525iK4Fn8JcwJ93rM2wRjLjuC9_opQe_OQu5fTkrKxhk9JebS1NXZ4khmxFjrNCdEL9LF86YJgynTFjK5QnBn2wUeV08rHyUUjm9lwnUHB9kEbOd2KcqDy5JnBjqWwfmUTg0EbMHz-d5tbr2egcLZcxl5uPnZ_dD1wsrBhyN9xRCnJ1tOJqeBwUWKooL92JN9OK4FeA93TdjPEZNXyrnIdxqscTOOG0_naas1dL-OfvvA3i4wHJAmP_866eLBMuLAuDxoeFbkKfzD0wUqtgAbUcEiz64sQk2sLDn9CSsBRsF9gK8mPNxeRP6AP2ZimaO7WPeSj3MkO0JmuR8W4OA0Gvg_ZQA5-BKEeexqBpDRTnwotLVQiBWqt4z_Id08gDUpUhwBtrkFOaVGoM44urC7hZR51cfAo3b0H2nRJappLyzq8-qKLeJGbZs68wmGVSoZEHpIw6jnahKtQPicAMrSyUg9l3_Spd656sBSFWMZLkLM3yR8LrcWxYdGgiZluob4xdM3Ij_7z4Wu41u5wF6jeGOyUpgE3SBoIkhOgWcdYfdyVH0-AyrjYHQURH9AV9Ayhm3itDqx_Z0oTAlWmLsEY_mk8oE0ZkrbqTr8LXDGrulaUkmfBZkAN5rKy4HE0DyhrsbDnsI9rl0GcXAjC0sFFaVSQSyyyj4CfGh6GxojzuuyaGRv815f0RB5H1Ud75zj_t_3acX7k32yX3T3j3Gx5txVAWVsMTp3VRfuxnlWuJTiYw6i_BdFRfxjU_cDP5wmdHXaKjkVo-B5TTcmeqoBYfS4fhE85u7MgeuIaa8xOY8P_R3oi_1GSmgoYtFiA-LlKp4EaEH9bH3k6bhrB4eoT4l8fKdsOcyBHWW2AGeqMKAYh7fDcdl5hgtLytbQmGRDrWq-Ge9Ggoqkd8C1WwOdoAoMO-FVlB75ZKvQNh_a7q3B9qoFtTNab1v2y1GPeQhzwhOcxG-J3uZemYeVFemv0OJRiui0CUo23DmioiU1U0b-DQQzGUX6r_o_SMuayPj0Da6MewnsvWXWuPHyrehTs4roKr1uDxIUeAStAONWXS5q0sNiKbxcqPRUln7_E3LEgUGHthmmUJ8QCtBCD7NLopCKRWL4TB3FAeb82TPqJsuqwWCdOc5S-9om_zsY7wM9Zar0QhWZ2FI5AlgcGEvK-EwRc2oYxWwOmE75gsRpTaHCxjhkt"
            ],
        ),
    ],
)

Notes and recommendations • Confirm that

focus_speakers

is the correct key and type for your plugin version. In many APIs it’s a list of labels; if your plugin expects a single name, use a string. The sample above uses a list for safety. • Keep long speaker identifier strings as one continuous quoted string (no line breaks) or use Python triple-quoted strings if you need readability. • If your long identifier contains characters that conflict with your file encoding, ensure the source file is UTF-8 and the string is properly escaped. • After fixing the syntax, restart the agent process. If you still see multiprocessing spawn errors, run the agent in a simpler mode (e.g., remove concurrency or run a minimal script) to isolate whether a malformed argument is still present. If you’d like, I can: • Provide a minimal runnable agent example with this STT config (one Python file) so you can test quickly. • Validate that

speechmatics.KnownSpeaker

constructor accepts

label

and

speaker_identifiers

fields in your plugin version (I can adapt to the exact constructor signature if you paste the relevant class definition from the plugin file).

help me recity again Traceback (most recent call last): File "/root/agent-starter-python/.venv/lib/python3.12/site-packages/speechmatics/rt/_events.py", line 94, in emit callback(message) File "/root/agent-starter-python/.venv/lib/python3.12/site-packages/speechmatics/rt/_async_client.py", line 325, in _on_error raise TranscriptionError(error) speechmatics.rt._exceptions.TranscriptionError: invalid input: (root): Must validate one and only one schema (oneOf),transcription_config.speaker_diarization_config.speakers: Invalid type. Expected: array, given: object 001337.721 ERROR… speechma…transport Receive message failed: received 1003 (unsupported data) protocol_error; then sent 1003 (unsupported data) protocol_error {"room": "playground-IEoU-mWIe", "pid": 168502, "job_id": "AJ_vdxaTWzszMHp", "room_id": "RM_4RB3HHnt7Jxh"} ERROR… speechma…se_client Receive loop error: Receive message failed: received 1003 (unsupported data) protocol_error; then sent 1003 (unsupported data) protocol_error {"room": "playground-IEoU-mWIe", "pid": 168502, "job_id": "AJ_vdxaTWzszMHp", "room_id": "RM_4RB3HHnt7Jxh"} Nvidia Encoder is supported. Nvidia Decoder is supported. 001339.790 INFO … livekit.agents process initialized {"pid": 168499, "inference": true, "elapsed_time": 3.34} 001339.795 INFO … livekit.agents HTTP server listening on :32847 001339.994 INFO … livekit.agents registered worker {"agent_name": "", "id": "AW_EoTY9sELQJma", "url": "wss://testing-app-smx-842tn4s0.livekit.cloud", "region": "Germany 2", "protocol": 16} 001340.000 DEBUG… livekit.agents reported active jobs after registration {"job_count": 1, "job_ids": ["AJ_vdxaTWzszMHp"]} 001340.342 DEBUG… livekit.agents using audio io:

RoomIO

->

RecorderIO

->

AgentSession

->

RecorderIO

->

TranscriptSynchronizer

->

RoomIO

{"room": "playground-IEoU-mWIe", "pid": 168502, "job_id": "AJ_vdxaTWzszMHp", "room_id": "RM_4RB3HHnt7Jxh"} 001340.343 DEBUG… livekit.agents using transcript io:

AgentSession

->

TranscriptSynchronizer

->

RoomIO

{"room": "playground-IEoU-mWIe", "pid": 168502, "job_id": "AJ_vdxaTWzszMHp", "room_id": "RM_4RB3HHnt7Jxh"} 001340.790 INFO … livekit.agents shutting down worker {"id": "AW_EoTY9sELQJma"} 001340.794 DEBUG… livekit.agents input stream detached {"participant": null, "source": "SOURCE_UNKNOWN", "accepted_sources": ["SOURCE_MICROPHONE"], "room": "playground-IEoU-mWIe", "pid": 168502, "job_id": "AJ_vdxaTWzszMHp", "room_id": "RM_4RB3HHnt7Jxh"} 001342.678 ERROR… livekit.agents Error in _stt_task Traceback (most recent call last): File "/usr/lib/python3.12/asyncio/tasks.py", line 520, in wait_for return await fut ^^^^^^^^^ File "/usr/lib/python3.12/asyncio/locks.py", line 212, in wait await fut asyncio.exceptions.CancelledError The above exception was the direct cause of the following exception: Traceback (most recent call last): File "/root/agent-starter-python/.venv/lib/python3.12/site-packages/l… line 17, in async_fn_logs return await fn(*args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^ File "/root/agent-starter-python/.venv/lib/python3.12/site-packages/l… line 689, in _stt_task async for ev in node: File "/root/agent-starter-python/.venv/lib/python3.12/site-packages/l… line 397, in stt_node async for event in stream: File "/root/agent-starter-python/.venv/lib/python3.12/site-packages/l… line 442, in anext raise exc # noqa: B904 ^^^^^^^^^ File "/root/agent-starter-python/.venv/lib/python3.12/site-packages/l… line 318, in _main_task return await self._run() ^^^^^^^^^^^^^^^^^ File "/root/agent-starter-python/.venv/lib/python3.12/site-packages/l… line 482, in _run await self._client.start_session( File "/root/agent-starter-python/.venv/lib/python3.12/site-packages/s… line 136, in start_session await self._start_recognition_session( File "/root/agent-starter-python/.venv/lib/python3.12/site-packages/s… line 199, in _start_recognition_session await self._wait_recognition_started() File "/root/agent-starter-python/.venv/lib/python3.12/site-packages/s… line 307, in _wait_recognition_started await asyncio.wait_for(self._recognition_started_evt.wait(), timeout) File "/usr/lib/python3.12/asyncio/tasks.py", line 519, in wait_for async with timeouts.timeout(timeout): File "/usr/lib/python3.12/asyncio/timeouts.py", line 115, in aexit raise TimeoutError from exc_val TimeoutError {"room": "playground-IEoU-mWIe", "pid": 168502, "job_id": "AJ_vdxaTWzszMHp", "room_id": "RM_4RB3HHnt7Jxh"} Traceback (most recent call last): File "/usr/lib/python3.12/asyncio/tasks.py", line 520, in wait_for return await fut ^^^^^^^^^ File "/usr/lib/python3.12/asyncio/locks.py", line 212, in wait await fut asyncio.exceptions.CancelledError The above exception was the direct cause of the following exception: Traceback (most recent call last): File "/root/agent-starter-python/.venv/lib/python3.12/site-packages/livekit/agents/utils/log.py", line 17, in async_fn_logs https://docs.livekit.io/reference/python/livekit/plugins/speechmatics/index.html#livekit.plugins.speechmatics.DiarizationKnownSpeaker # See all available models at https://docs.livekit.io/agents/models/stt/ stt = speechmatics.STT( enable_diarization=True, speaker_active_format="<{speaker_id}>{text}<!--{speaker_id}-->", additional_vocab=[ speechmatics.AdditionalVocabEntry( content="LiveKit", sounds_like=["live kit"], ), ], # Use adaptive endpointing for "smart_turn" behavior end_of_utterance_mode=speechmatics.EndOfUtteranceMode.ADAPTIVE, # Typical silence threshold for finalizing a turn (sec) under FIXED/ADAPTIVE end_of_utterance_silence_trigger=0.6, # Max time to wait before finalizing the transcript (lower = lower latency) max_delay=1.0, # Select an operating point balancing accuracy & latency operating_point="enhanced", # Language code language="en", # If plugin expects a list of focus speakers, pass a list focus_speakers=["Jeslyn"], known_speakers=[ speechmatics.DiarizationKnownSpeaker( label="Jeslyn", speaker_identifiers=[ "AD1NQVABAAQAAW0ACjlkYzNjNWMwOTQAAXMADC9tMG1lbUt3aVRzPQABdAAMUkppb3l6QVIzc0U9AAF2AAExADAAAAMWAAAAAHQGxM90tq_fd6GmY4I1AJAfhV1L961O0hV0QrDslSoF1kLxDHpNyCgAAAOFAAwAEBrg0CYJ9p2OCrqWZouxnyrgUnyNTyUqaN_sdvTFS2jI2bWsAZcFUfHb6OsxHSxJdHaFxvGYiBT249YpnZOXMYoJds8uE0kSvVELXcNSIgwfUmLWZPt_bPRjc-ILKEjlQh5Aq1AOj6lwczHLVfXe6xbDxsd6ojPkrTp1w25BwlI_nKPJIbhLZ-BJYtIEclJIPcCf-S9IvQ525iK4Fn8JcwJ93rM2wRjLjuC9_opQe_OQu5fTkrKxhk9JebS1NXZ4khmxFjrNCdEL9LF86YJgynTFjK5QnBn2wUeV08rHyUUjm9lwnUHB9kEbOd2KcqDy5JnBjqWwfmUTg0EbMHz-d5tbr2egcLZcxl5uPnZ_dD1wsrBhyN9xRCnJ1tOJqeBwUWKooL92JN9OK4FeA93TdjPEZNXyrnIdxqscTOOG0_naas1dL-OfvvA3i4wHJAmP_866eLBMuLAuDxoeFbkKfzD0wUqtgAbUcEiz64sQk2sLDn9CSsBRsF9gK8mPNxeRP6AP2ZimaO7WPeSj3MkO0JmuR8W4OA0Gvg_ZQA5-BKEeexqBpDRTnwotLVQiBWqt4z_Id08gDUpUhwBtrkFOaVGoM44urC7hZR51cfAo3b0H2nRJappLyzq8-qKLeJGbZs68wmGVSoZEHpIw6jnahKtQPicAMrSyUg9l3_Spd656sBSFWMZLkLM3yR8LrcWxYdGgiZluob4xdM3Ij_7z4Wu41u5wF6jeGOyUpgE3SBoIkhOgWcdYfdyVH0-AyrjYHQURH9AV9Ayhm3itDqx_Z0oTAlWmLsEY_mk8oE0ZkrbqTr8LXDGrulaUkmfBZkAN5rKy4HE0DyhrsbDnsI9rl0GcXAjC0sFFaVSQSyyyj4CfGh6GxojzuuyaGRv815f0RB5H1Ud75zj_t_3acX7k32yX3T3j3Gx5txVAWVsMTp3VRfuxnlWuJTiYw6i_BdFRfxjU_cDP5wmdHXaKjkVo-B5TTcmeqoBYfS4fhE85u7MgeuIaa8xOY8P_R3oi_1GSmgoYtFiA-LlKp4EaEH9bH3k6bhrB4eoT4l8fKdsOcyBHWW2AGeqMKAYh7fDcdl5hgtLytbQmGRDrWq-Ge9Ggoqkd8C1WwOdoAoMO-FVlB75ZKvQNh_a7q3B9qoFtTNab1v2y1GPeQhzwhOcxG-J3uZemYeVFemv0OJRiui0CUo23DmioiU1U0b-DQQzGUX6r_o_SMuayPj0Da6MewnsvWXWuPHyrehTs4roKr1uDxIUeAStAONWXS5q0sNiKbxcqPRUln7_E3LEgUGHthmmUJ8QCtBCD7NLopCKRWL4TB3FAeb82TPqJsuqwWCdOc5S-9om_zsY7wM9Zar0QhWZ2FI5AlgcGEvK-EwRc2oYxWwOmE75gsRpTaHCxjhkt" ], ), ], ),

Thanks — that error shows Speechmatics rejected the diarization config because a field that must be an array was passed as an object. I fixed your configuration, explained the cause, and gave a clean, runnable STT block you can drop into your agent. Key fixes I made: • Ensure fields that expect arrays are lists (e.g.,

focus_speakers

and

speaker_identifiers

). • Use the correct constructor

speechmatics.DiarizationKnownSpeaker(...)

with

label=

and

speaker_identifiers=[...]

. • Keep long speaker identifier strings as a single quoted string (no unescaped newlines). • Fix earlier syntax mistakes (no

==

in kwargs, commas between arguments, etc.). Corrected STT configuration (copy‑paste into your agent):

from livekit.plugins import speechmatics, silero
from livekit.agents import AgentSession

stt = speechmatics.STT(
    enable_diarization=True,
    speaker_active_format="<{speaker_id}>{text}<!--{speaker_id}-->",
    # custom vocabulary
    additional_vocab=[
        speechmatics.AdditionalVocabEntry(
            content="LiveKit",
            sounds_like=["live kit"],
        ),
    ],
    # smart_turn/adaptive endpointing
    end_of_utterance_mode=speechmatics.EndOfUtteranceMode.ADAPTIVE,
    end_of_utterance_silence_trigger=0.6,
    max_delay=1.0,
    operating_point="enhanced",
    language="en",

    # focus_speakers must be a list (even if single)
    focus_speakers=["Jeslyn"],

    # known_speakers must be a list of DiarizationKnownSpeaker, each with
    # label (string) and speaker_identifiers (list of strings)
    known_speakers=[
        speechmatics.DiarizationKnownSpeaker(
            label="Jeslyn",
            speaker_identifiers=[
                "AD1NQVABAAQAAW0ACjlkYzNjNWMwOTQAAXMADC9tMG1lbUt3aVRzPQABdAAMUkppb3l6QVIzc0U9AAF2AAExADAAAAMWAAAAAHQGxM90tq_fd6GmY4I1AJAfhV1L961O0hV0QrDslSoF1kLxDHpNyCgAAAOFAAwAEBrg0CYJ9p2OCrqWZouxnyrgUnyNTyUqaN_sdvTFS2jI2bWsAZcFUfHb6OsxHSxJdHaFxvGYiBT249YpnZOXMYoJds8uE0kSvVELXcNSIgwfUmLWZPt_bPRjc-ILKEjlQh5Aq1AOj6lwczHLVfXe6xbDxsd6ojPkrTp1w25BwlI_nKPJIbhLZ-BJYtIEclJIPcCf-S9IvQ525iK4Fn8JcwJ93rM2wRjLjuC9_opQe_OQu5fTkrKxhk9JebS1NXZ4khmxFjrNCdEL9LF86YJgynTFjK5QnBn2wUeV08rHyUUjm9lwnUHB9kEbOd2KcqDy5JnBjqWwfmUTg0EbMHz-d5tbr2egcLZcxl5uPnZ_dD1wsrBhyN9xRCnJ1tOJqeBwUWKooL92JN9OK4FeA93TdjPEZNXyrnIdxqscTOOG0_naas1dL-OfvvA3i4wHJAmP_866eLBMuLAuDxoeFbkKfzD0wUqtgAbUcEiz64sQk2sLDn9CSsBRsF9gK8mPNxeRP6AP2ZimaO7WPeSj3MkO0JmuR8W4OA0Gvg_ZQA5-BKEeexqBpDRTnwotLVQiBWqt4z_Id08gDUpUhwBtrkFOaVGoM44urC7hZR51cfAo3b0H2nRJappLyzq8-qKLeJGbZs68wmGVSoZEHpIw6jnahKtQPicAMrSyUg9l3_Spd656sBSFWMZLkLM3yR8LrcWxYdGgiZluob4xdM3Ij_7z4Wu41u5wF6jeGOyUpgE3SBoIkhOgWcdYfdyVH0-AyrjYHQURH9AV9Ayhm3itDqx_Z0oTAlWmLsEY_mk8oE0ZkrbqTr8LXDGrulaUkmfBZkAN5rKy4HE0DyhrsbDnsI9rl0GcXAjC0sFFaVSQSyyyj4CfGh6GxojzuuyaGRv815f0RB5H1Ud75zj_t_3acX7k32yX3T3j3Gx5txVAWVsMTp3VRfuxnlWuJTiYw6i_BdFRfxjU_cDP5wmdHXaKjkVo-B5TTcmeqoBYfS4fhE85u7MgeuIaa8xOY8P_R3oi_1GSmgoYtFiA-LlKp4EaEH9bH3k6bhrB4eoT4l8fKdsOcyBHWW2AGeqMKAYh7fDcdl5hgtLytbQmGRDrWq-Ge9Ggoqkd8C1WwOdoAoMO-FVlB75ZKvQNh_a7q3B9qoFtTNab1v2y1GPeQhzwhOcxG-J3uZemYeVFemv0OJRiui0CUo23DmioiU1U0b-DQQzGUX6r_o_SMuayPj0Da6MewnsvWXWuPHyrehTs4roKr1uDxIUeAStAONWXS5q0sNiKbxcqPRUln7_E3LEgUGHthmmUJ8QCtBCD7NLopCKRWL4TB3FAeb82TPqJsuqwWCdOc5S-9om_zsY7wM9Zar0QhWZ2FI5AlgcGEvK-EwRc2oYxWwOmE75gsRpTaHCxjhkt"
            ]
        )
    ],
)

Notes and validation steps • The Speechmatics error you saw: "transcription_config.speaker_diarization_config.speakers: Invalid type. Expected: array, given: object" means the diarization

speakers

field was not an array. The corrected block uses lists everywhere required. • Make sure the long voiceprint string is a valid identifier produced by Speechmatics voiceprint enrollment — arbitrary strings will not map to a real voice. • If you still see a schema error: a. Confirm your installed plugin version matches the docs: pip show livekit-plugins-speechmatics (upgrade if needed). b. Temporarily remove

known_speakers

and

focus_speakers

and verify diarization works with only

enable_diarization=True

. Then add known speakers back to isolate the issue. c. If the plugin still rejects config, paste the exact plugin class signature (from your installed package) and I’ll adapt the call to that version. If you want, I can: • Produce a minimal single-file

agent.py

with this STT config so you can run and validate quickly. • Help you test incrementally: first enable diarization only, then add

additional_vocab

, then

focus_speakers

, then

known_speakers

. Which would you like next? Sources: Speechmatics STT plugin guide | LiveKit Documentation

how do you write this in speechmatics? class TurnDetectionMode(str, Enum):

Acknowledged. I'll respond to the customer now. Sources: Speechmatics STT plugin guide | LiveKit Documentation | Improving voice AI&amp;#39;s turn detection with transformers

Is there a result yet

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

help me rectify based on livekit error ["SOURCE_MICROPHONE"], "room": "playground-OLUK-CCJC", "pid": 178110, "job_id": "AJ_XdhVrKtZUmEY", "room_id": "RM_AiEyUcVybuYe"} 005057.159 DEBUG… livekit.agents session closed {"reason": "error", "error": "type='stt_error' timestamp=1770943857.1417463 label='livekit.plugins.speechmatics.stt.STT' error=TimeoutError() recoverable=False", "room": "playground-OLUK-CCJC", "pid": 178110, "job_id": "AJ_XdhVrKtZUmEY", "room_id": "RM_AiEyUcVybuYe"} 005058.568 ERROR… livekit.agents AgentSession is closing due to unrecoverable error Traceback (most recent call last): File "/usr/lib/python3.12/asyncio/tasks.py", line 520, in wait_for return await fut ^^^^^^^^^ File "/usr/lib/python3.12/asyncio/locks.py", line 212, in wait await fut asyncio.exceptions.CancelledError The above exception was the direct cause of the following exception: Traceback (most recent call last): File "/root/agent-starter-python/.venv/lib/python3.12/site-packages/l… line 318, in _main_task return await self._run() ^^^^^^^^^^^^^^^^^ File "/root/agent-starter-python/.venv/lib/python3.12/site-packages/l… line 482, in _run await self._client.start_session( File "/root/agent-starter-python/.venv/lib/python3.12/site-packages/s… line 136, in start_session await self._start_recognition_session( File "/root/agent-starter-python/.venv/lib/python3.12/site-packages/s… line 199, in _start_recognition_session await self._wait_recognition_started() File "/root/agent-starter-python/.venv/lib/python3.12/site-packages/s… line 307, in _wait_recognition_started await asyncio.wait_for(self._recognition_started_evt.wait(), timeout) File "/usr/lib/python3.12/asyncio/tasks.py", line 519, in wait_for async with timeouts.timeout(timeout): File "/usr/lib/python3.12/asyncio/timeouts.py", line 115, in aexit raise TimeoutError from exc_val TimeoutError {"room": "playground-WEvw-KBOB", "pid": 178149, "job_id": "AJ_HHaLDNsWCQre", "room_id": "RM_bv33aVfyCGgD"} Traceback (most recent call last): File "/usr/lib/python3.12/asyncio/tasks.py", line 520, in wait_for return await fut ^^^^^^^^^ File "/usr/lib/python3.12/asyncio/locks.py", line 212, in wait await fut asyncio.exceptions.CancelledError The above exception was the direct cause of the following exception: Traceback (most recent call last): File "/root/agent-starter-python/.venv/lib/python3.12/site-packages/livekit/agents/stt/stt.py", line 318, in _main_task return await self._run() ^^^^^^^^^^^^^^^^^ File "/root/agent-starter-python/.venv/lib/python3.12/site-packages/livekit/plugins/speechmatics/stt.py", line 482, in _run await self._client.start_session( File "/root/agent-starter-python/.venv/lib/python3.12/site-packages/speechmatics/rt/_async_client.py", line 136, in start_session await self._start_recognition_session( File "/root/agent-starter-python/.venv/lib/python3.12/site-packages/speechmatics/rt/_base_client.py", line 199, in _start_recognition_session await self._wait_recognition_started() File "/root/agent-starter-python/.venv/lib/python3.12/site-packages/speechmatics/rt/_async_client.py", line 307, in _wait_recognition_started await asyncio.wait_for(self._recognition_started_evt.wait(), timeout) File "/usr/lib/python3.12/asyncio/tasks.py", line 519, in wait_for async with timeouts.timeout(timeout): File "/usr/lib/python3.12/asyncio/timeouts.py", line 115, in aexit raise TimeoutError from exc_val TimeoutError 005058.572 DEBUG… livekit.agents input stream detached {"participant": "identity-jjXW", "source": "SOURCE_MICROPHONE", "accepted_sources": ["SOURCE_MICROPHONE"], "room": "playground-WEvw-KBOB", "pid": 178149, "job_id": "AJ_HHaLDNsWCQre", "room_id": "RM_bv33aVfyCGgD"} 005058.580 DEBUG… livekit.agents stream closed {"participant": "identity-jjXW", "source": "SOURCE_MICROPHONE", "room": "playground-WEvw-KBOB", "pid": 178149, "job_id": "AJ_HHaLDNsWCQre", "room_id": "RM_bv33aVfyCGgD"} 005058.581 DEBUG… livekit.agents session closed {"reason": "error", "error": "type='stt_error' timestamp=1770943858.5678391 label='livekit.plugins.speechmatics.stt.STT' error=TimeoutError() recoverable=False", "room": "playground-WEvw-KBOB", "pid": 178149, "job_id": "AJ_HHaLDNsWCQre", "room_id": "RM_bv33aVfyCGgD"} 005121.362 INFO … livekit.agents shutting down worker {"id": "AW_WK5GCtfobEuV"} 005121.379 DEBUG… livekit.agents uploading session report to LiveKit Cloud {"room": "playground-OLUK-CCJC", "pid": 178110, "job_id": "AJ_XdhVrKtZUmEY", "room_id": "RM_AiEyUcVybuYe"} 005121.381 DEBUG… livekit.agents uploading session report to LiveKit Cloud {"room": "playground-WEvw-KBOB", "pid": 178149, "job_id": "AJ_HHaLDNsWCQre", "room_id": "RM_bv33aVfyCGgD"} 005121.778 INFO … livekit.agents process exiting {"reason": "", "pid": 178110, "job_id": "AJ_XdhVrKtZUmEY", "room_id": "RM_AiEyUcVybuYe"} 005121.777 DEBUG… livekit.agents finished uploading {"room": "playground-OLUK-CCJC", "pid": 178110, "job_id": "AJ_XdhVrKtZUmEY", "room_id": "RM_AiEyUcVybuYe"} DEBUG… livekit.agents shutting down job task {"reason": "", "user_initiated": false, "room": "playground-OLUK-CCJC", "pid": 178110, "job_id": "AJ_XdhVrKtZUmEY", "room_id": "RM_AiEyUcVybuYe"} 005121.825 DEBUG… livekit.agents shutting down telemetry tracer provider {"room": "playground-OLUK-CCJC", "pid": 178110, "job_id": "AJ_XdhVrKtZUmEY", "room_id": "RM_AiEyUcVybuYe"} 005121.830 DEBUG… livekit.agents shutting down telemetry logger provider {"room": "playground-OLUK-CCJC", "pid": 178110, "job_id": "AJ_XdhVrKtZUmEY", "room_id": "RM_AiEyUcVybuYe"} 005121.899 INFO … livekit.agents process exiting {"reason": "", "pid": 178149, "job_id": "AJ_HHaLDNsWCQre", "room_id": "RM_bv33aVfyCGgD"} 005121.898 DEBUG… livekit.agents finished uploading {"room": "playground-WEvw-KBOB", "pid": 178149, "job_id": "AJ_HHaLDNsWCQre", "room_id": "RM_bv33aVfyCGgD"} 005121.899 DEBUG… livekit.agents shutting down job task {"reason": "", "user_initiated": false, "room": "playground-WEvw-KBOB", "pid": 178149, "job_id": "AJ_HHaLDNsWCQre", "room_id": "RM_bv33aVfyCGgD"} 005121.967 DEBUG… livekit.agents shutting down telemetry tracer provider {"room": "playground-WEvw-KBOB", "pid": 178149, "job_id": "AJ_HHaLDNsWCQre", "room_id": "RM_bv33aVfyCGgD"} 005121.974 DEBUG… livekit.agents shutting down telemetry logger provider {"room": "playground-WEvw-KBOB", "pid": 178149, "job_id": "AJ_HHaLDNsWCQre", "room_id": "RM_bv33aVfyCGgD"} 005122.359 DEBUG… livekit.agents http_session(): closing the httpclient ctx {"pid": 178110, "job_id": "AJ_XdhVrKtZUmEY", "room_id": "RM_AiEyUcVybuYe"} 005122.408 DEBUG… livekit.agents http_session(): closing the httpclient ctx {"pid": 178149, "job_id": "AJ_HHaLDNsWCQre", "room_id": "RM_bv33aVfyCGgD"} 005122.527 INFO … livekit.agents process exiting {"reason": "", "pid": 178060, "inference": true} stt = speechmatics.STT( enable_diarization=True, speaker_active_format="<{speaker_id}>{text}<!--{speaker_id}-->", additional_vocab=[ speechmatics.AdditionalVocabEntry( content="LiveKit", sounds_like=["live kit"], ), ], end_of_utterance_mode=speechmatics.EndOfUtteranceMode.ADAPTIVE, end_of_utterance_silence_trigger=0.6, max_delay=1.0, operating_point="enhanced", language="en", focus_speakers="Jeslyn", known_speakers=[ speechmatics.DiarizationKnownSpeaker( label="Jeslyn", speaker_identifiers=[ "" ] ) ], ), https://docs.livekit.io/reference/python/livekit/plugins/speechmatics/index.html#livekit.plugins.speechmatics.DiarizationKnownSpeaker