Can i get the llm output before pass it to the tts? If yes give me example

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

I'm using only stt and llm so the output need to be send to the participants using send data when the output finalized

If you want to send LLM output to participants using

sendpack

while working with just STT and LLM, you can make good use of LiveKit's

TextStreamWriter

feature. Here's a simple way to do it:

async function sendLlmOutputToParticipants(llmOutput, participantId) {
    const stream = await room.localParticipant.streamText({
        destinationIdentities: [participantId], // Optional, leave empty to send to all participants
        topic: "llm-output", // Optional topic for filtering
    });

    try {
        await stream.write(llmOutput); // Send the LLM output
    } finally {
        await stream.close(); // Make sure to close the stream
    }
}

Here are a few key things to keep in mind: • Create a Text Stream Writer: Use

streamText()

to get a

TextStreamWriter

instance that lets you send text data. • Write LLM Output: Call the

write()

method to send the LLM's text output to your participants. • Close the Stream: Don’t forget to close the stream with

close()

once you’re done sending. • Optional Parameters: You can specify who gets the message using

destinationIdentities

(or leave it empty to reach everyone) and use

topic

to label the data stream. This approach is a smooth way to share text-based LLM output without needing TTS. If you have any questions or want me to walk you through it, just let me know—I’m here to help!

But the case is simple the text that will be passed to the tts i want to do console log to see it

Hey there! If you want to log the text you’re sending to the TTS for debugging, it’s pretty straightforward. You can just use

console.log

right before the text goes to the TTS service. Here’s a quick example:

const text = "This is the text to be synthesized"; 
console.log(`Text to synthesize: ${text}`); // Logs the text to the console

If you’re working with a streaming API, you can log the text as it’s being passed along like this:

const stream = tts.stream();

// Push text to stream and log it
stream.pushText(text);
console.log(`Stream text: ${text}`);

// End the stream
stream.flush();
stream.endInput();

For something a bit more advanced, you might want to wrap the TTS functionality in a custom class that logs the text for you. Here’s how you could do that:

class LoggingTTS {
  constructor(tts) {
    this.tts = tts;
  }

  async synthesize(text) {
    console.log(`Synthesizing text: ${text}`);
    return this.tts.synthesize(text);
  }

  stream() {
    const stream = this.tts.stream();
    const originalPushText = stream.pushText.bind(stream);

    stream.pushText = (text) => {
      console.log(`Streaming text: ${text}`);
      return originalPushText(text);
    };

    return stream;
  }
}

// Usage
const loggingTTS = new LoggingTTS(tts);
const text = "This is the text to be synthesized";
const audioStream = loggingTTS.synthesize(text);

These are some handy ways to keep an eye on the text before it gets processed by your TTS engine. If you have any questions or want to dive deeper, just let me know—I’m here to help!

I'm using only stt and llm i want when i speak the output from llm will be in console log

Hey there! If you want to log the LLM output to the console while using just STT and LLM, you can easily do this by overriding the

llm_node

method in your agent class. Here’s a simple example to get you started:

import logging
from livekit.agents import Agent
from livekit.plugins import deepgram, openai

logger = logging.getLogger("llm-logger")
logger.setLevel(<http://logging.INFO|logging.INFO>)

class LoggingAgent(Agent):
    def __init__(self):
        super().__init__(
            instructions="You are a helpful assistant.",
            stt=deepgram.STT(),
            llm=openai.LLM(model="gpt-4o"),
        )

    async def llm_node(self, chat_ctx, tools, model_settings=None):
        async def process_stream():
            async with self.llm.chat(chat_ctx=chat_ctx, tools=tools, tool_choice=None) as stream:
                async for chunk in stream:
                    if chunk is None:
                        continue

                    content = getattr(chunk.delta, 'content', None) if hasattr(chunk, 'delta') else str(chunk)
                    if content is None:
                        yield chunk
                        continue

                    # Log the LLM output
                    <http://logger.info|logger.info>(f"LLM Output: {content}")
                    print(f"LLM Output: {content}")

                    yield chunk

        return process_stream()

Here’s a quick rundown: • Custom Agent Class: We create a

LoggingAgent

that builds on the base

Agent

and sets up STT and LLM. • Logging LLM Output: By overriding

llm_node

, you catch the LLM’s output stream and log each piece right to your console. • Real-Time Feedback: You’ll see the LLM output printed out as it’s generated, so you can keep track instantly. This way, you get all your LLM outputs logged neatly while working with only STT and LLM. If you want to chat more or need a hand with anything else, just let me know—I’m here to help!