Assume you are expert in livekit and full knowledge of google realtime model I am using google realtime model, due to me speaking english but due to my english accent is still hindi output coming out of livekit sdk in frontend is still hindi how to ensure we only get english output of user

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

can you explain in detail all the 4 steps you mentioned in. details

I'd love to walk you through a detailed explanation of the four steps to make sure your LiveKit SDK outputs only English. Let's take it step by step: 1. Set the STT language to English: Start by configuring the Speech-to-Text (STT) plugin to explicitly transcribe in English. This way, all input will be interpreted as English, no matter the speaker’s accent. For example: This setup helps the STT engine focus on processing speech as English. ◦ For Speechmatics STT:

stt = speechmatics.STT(language="en")

◦ For Wizper STT:

stt = fal.STT(language="en")

◦ For CLOVA STT:

stt = clova.STT(language="en-US")

1. Enable translation if needed: If your users might speak other languages but you want the output in English, you can use an STT plugin that supports translation. For example, with Gladia STT: `stt = gladia.STT( languages=["fr", "es", "en"], # Support multiple input languages translation_enabled=True, translation_target_languages=["en"] # Always output in English )`This ensures that even if someone speaks a different language, the transcription will be translated to English before moving forward. 2. Use English-specific models: For things like turn detection, it’s best to use models trained specifically for English. For example: ◦ Use the

EnglishModel

in the turn detector plugin:

from livekit.plugins.turn_detector.english import EnglishModel

session = AgentSession(
    turn_detection=EnglishModel(),
    stt=deepgram.STT(model="nova-3", language="en"),  # Enforce English STT
)

◦ If you want to support multiple input languages but still keep the output in English, you can use the

MultilingualModel

with English-only STT:

session = AgentSession(
    turn_detection=MultilingualModel(),
    stt=gladia.STT(language="en", translation_target_languages=["en"])
)

1. Configure TTS for English output: If your app uses Text-to-Speech (TTS) for responses, make sure the output language is set to English. For example: This keeps your agent’s vocal responses consistently in English. ◦ For ElevenLabs TTS:

tts = elevenlabs.TTS(language="en")

◦ For PlayHT TTS:

tts = playai.TTS(language="ENGLISH")

Following these steps should help you achieve consistent English output, even if your users have different accents or speak multiple languages. If you have any questions or want me to help you with any part of the implementation, just let me know — I’m here to help!

‘use client’; import { ReactNode, createContext, useContext, useEffect, useState, } from ‘react’; import { useLocalParticipant, useMaybeRoomContext, useVoiceAssistant, } from ‘@livekit/components-react’; import { Participant, RemoteParticipant, RoomEvent, type RpcInvocationData, TrackPublication, TranscriptionSegment, } from ‘livekit-client’; // import { useConnection } from ‘@/hooks/use-connection’; // Assuming this hook exists // import { useToast } from ‘@/hooks/use-toast’; // Assuming this hook exists interface Transcription { participant?: Participant; publication?: TrackPublication; segment: TranscriptionSegment; } interface AgentContextType { agent?: RemoteParticipant; displayTranscriptions: Transcription[]; } const AgentContext = createContext<AgentContextType | undefined>(undefined); export function *AgentProvider*({ children }: { children: ReactNode }) { const room = useMaybeRoomContext(); // const { shouldConnect } = useConnection(); const { agent } = useVoiceAssistant(); const { localParticipant } = useLocalParticipant(); const [rawSegments, setRawSegments] = useState<{ [_id_: string]: Transcription; }>({}); const [displayTranscriptions, setDisplayTranscriptions] = useState< Transcription[] >([]); // const { toast } = useToast(); useEffect(() => { if (!room) { return; } const updateRawSegments = ( _segments_: TranscriptionSegment[], participant?: Participant, publication?: TrackPublication, ) => { setRawSegments(prev => { const newSegments = { ...prev }; for (const segment of segments) { newSegments[segment.id] = { participant, publication, segment }; } return newSegments; }); }; room.on(RoomEvent.TranscriptionReceived, updateRawSegments); return () => { room.off(RoomEvent.TranscriptionReceived, updateRawSegments); }; }, [room]); useEffect(() => { if (localParticipant) { localParticipant.registerRpcMethod( ‘pg.toast’, async (_data_: RpcInvocationData) => { const { title, description, variant } = JSON.parse(data.payload); console.log(’Toast received:’, { description, title, variant }); // toast({ // title, // description, // variant, // }); return JSON.stringify({ shown: true }); }, ); } }, [localParticipant]); useEffect(() => { const sorted = Object.values(rawSegments).sort( (a, b) => (a.segment.firstReceivedTime ?? 0) - (b.segment.firstReceivedTime ?? 0), ); const mergedSorted = sorted.reduce((acc, current) => { if (acc.length === 0) { return [current]; } const last = _acc_[acc.length - 1]; if ( last.participant === current.participant && last.participant?.isAgent && (current.segment.firstReceivedTime ?? 0) - (last.segment.lastReceivedTime ?? 0) <= 1000 && !last.segment.id.startsWith(’status-’) && !current.segment.id.startsWith(’status-’) ) { // Merge segments from the same participant if they’re within 1 second of each other return [ ...acc.slice(0, -1), { ...current, segment: { ...current.segment, firstReceivedTime: last.segment.firstReceivedTime, // Keep the original start time id: current.segment.id, // Use the id of the latest segment text:

${last.segment.text} ${_current_.segment.text}

, }, }, ]; } else { return [...acc, current]; } }, [] as Transcription[]); setDisplayTranscriptions(mergedSorted); }, [rawSegments]); // useEffect(() => { // if (shouldConnect) { // setRawSegments({}); // setDisplayTranscriptions([]); // } // }, [shouldConnect]); return ( <AgentContext.Provider value={{ agent, displayTranscriptions }}> {children} </AgentContext.Provider> ); } export function *useAgent*() { const context = useContext(AgentContext); if (context === undefined) { throw new Error(‘useAgent must be used within an AgentProvider’); } return context; } this is Frontend code [N/A|PID:82459] 2025-09-18 153008 INFO Conversation item added from user. Interrupted: False [N/A|PID:82459] 2025-09-18 153008 INFO text content: क्या मैं हिंदी में बात कर सकता हूं [AJ_8W4oQLuordSM|PID:82459] 2025-09-18 153008 INFO Conversation item added from user. Interrupted: False 2025-09-18 153008,007 - INFO uplevel-livekit-worker - Conversation item added from user. Interrupted: False {“job_id”: “AJ_8W4oQLuordSM”, “pid”: 82459} [AJ_8W4oQLuordSM|PID:82459] 2025-09-18 153008 INFO text content: क्या मैं हिंदी में बात कर सकता हूं 2025-09-18 153008,008 - INFO uplevel-livekit-worker - text content: क्या मैं हिंदी में बात कर सकता हूं {“j this is log from backend Can you help me I am using deepgram, can you help me how to output translated output of STT

Hi there! I wanted to share that Deepgram STT doesn’t support direct real-time translation to English just yet. But don’t worry, there are a couple of great ways you can still get this done: 1. Option 1: Use Deepgram for transcription and an external service for translation: You can take the transcribed text from Deepgram and send it over to a translation service like OpenAI's GPT for real-time translation. Here’s a little example to guide you:

from livekit.plugins import openai

llm = openai.LLM()

async def process_transcript(event):
    if event.type == stt.SpeechEventType.FINAL_TRANSCRIPT:
        transcription = event.alternatives[0].text
        prompt = f"Translate the following text into English: {transcription}"
        stream = llm.chat(prompt)

        translated_text = ""
        async for chunk in stream:
            content = chunk.choices[0].delta.content
            if content:
                translated_text += content
        print(f"Translated: {translated_text}")

2. Option 2: Use Gladia STT for native translation: If you’re looking for a more seamless, native real-time translation, you might want to try the Gladia STT plugin. It supports direct translation, and here’s how you can set it up:

from livekit.plugins import gladia

stt_provider = gladia.STT(
    translation_enabled=True,
    languages=["es", "fr"],  # Input languages
    translation_target_languages=["en"],  # Output to English
)

Please feel free to reach out if you’d like any help implementing these options or if you have any other questions—I’m here to support you! Sources: examples/other/translation/multi-user-translator.py | Deepgram STT integration guide | LiveKit Docs | Deepgram integration guide | LiveKit Docs | Deepgram STT integration guide | LiveKit Docs | server/main.py

we are showing what user is speaking as caption in realtime to user on screen

I'd love to help you display real-time captions of what the user is speaking on the screen! Here's a friendly step-by-step approach you can follow: 1. Use Deepgram for transcription: Start by configuring Deepgram STT to transcribe the user's speech in real-time. For example:

from livekit.plugins import deepgram

stt_provider = deepgram.STT(language="en")  # Set the language to English

2. Stream transcription results to the frontend: You'll want to listen for transcription events (like

RoomEvent.TranscriptionReceived

) and update the captions on the screen accordingly. Here's a simple example:

useEffect(() => {
  if (!room) {
    return;
  }
  const updateCaptions = (segments) => {
    setCaptions((prev) => [...prev, ...segments]);
  };
  room.on(RoomEvent.TranscriptionReceived, updateCaptions);

  return () => {
    room.off(RoomEvent.TranscriptionReceived, updateCaptions);
  };
}, [room]);

3. Translate captions if needed: If you want the captions always in English, no matter the spoken language, you can forward the transcription to a translation service like OpenAI GPT or Gladia STT with translation enabled. This way, everyone can follow along easily! 4. Render captions on the screen: Use a React state to keep track of captions and render them in your UI. Here's how you might do it:

return (
  <div>
    {captions.map((caption, index) => (
      <p key={index}>{caption.text}</p>
    ))}
  </div>
);

This setup should help you show real-time captions smoothly on the screen. If you have any questions or need a hand with the implementation, please don’t hesitate to reach out—I’m here to support you every step of the way!

is there easy way?

Let's make displaying real-time captions a bit easier for you by using a Speech-to-Text (STT) plugin that can handle transcription and translation right away, without extra steps. Here’s a simple way to do it: 1. Try Gladia STT: Gladia STT is great because it supports real-time transcription and translation all in one. You can set it up to transcribe and translate directly into English like this:

from livekit.plugins import gladia

stt_provider = gladia.STT(
    translation_enabled=True,  # Enable translation
    languages=["auto"],  # Automatically detect input language
    translation_target_languages=["en"]  # Output in English
)

2. Stream captions straight to your frontend: Simply listen for transcription events (like

RoomEvent.TranscriptionReceived

) and update the captions on screen right away—no extra translation steps needed. This way, everything happens smoothly in one step, making your workflow simpler and cutting down on the need for extra services. If you'd like, I’m here to help you get this set up—just let me know!