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is there a way to add pre-roll statement in the gp...
# ask-ai
b
is there a way to add pre-roll statement in the gpt realtime speech to speech model that automatically greets the user when the start the convo
t
Thanks for reaching out, Harsh. I'm an AI assistant and I'm here to help. Give me a moment while I research this topic.
To add a pre-roll statement or automatic greeting in a GPT real-time speech-to-speech model, you can use the 
session.say()
method with a Text-to-Speech (TTS) plugin. This way, a warm and consistent greeting will be played right when the session starts, making the experience more engaging. Here’s a simple way to set it up: • Use the 
on_enter()
lifecycle hook in your custom agent class to trigger the greeting as soon as the session begins. For example:
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async def on_enter(self):
    await self.session.say("Hello, how can I help you today?", allow_interruptions=True)
• Make sure you have a TTS plugin configured, like OpenAI TTS or ElevenLabs TTS, so the 
say()
method works smoothly. If you don’t have a TTS plugin, another option is to use the 
generate_reply()
method with instructions to prompt the model to create a greeting. Just keep in mind that this method can be a bit less predictable since it depends on the model’s responses. Please feel free to reach out if you’d like me to walk you through this further or provide more examples—I’m here to help! Sources: Agent speech and audio | LiveKit Docs | Docs::Agents | Realtime model integrations | LiveKit Docs | livekit-plugins/livekit-plugins-aws/livekit/plugins/aws/experimental/realti... | examples/src/realtime_turn_detector.ts
b
i don't want to integrate any other model, i want to use the realtime api
t
I’m glad you asked! When using the LiveKit Realtime API alone, the 
session.say()
method for scripted greetings isn’t available since it needs a TTS plugin. But no worries—you can still create a warm, dynamic greeting using the 
generate_reply()
method to prompt the model. Here’s a little example to get you started:
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async def on_enter(self):\n    await self.session.generate_reply(\n        instructions=\"Start by greeting the user warmly: 'Hello! How can I help you today?'\",\n        allow_interruptions=True\n    )
Just keep in mind, this depends on how the model responds, so the greeting might not always match the script exactly or have precise timing. If you’re looking for more exact control over your greeting, integrating a TTS plugin would be the way to go. I’m here to help if you want some guidance on setting that up or if you have any other questions about your current setup! Sources: Realtime model integrations | LiveKit Docs | Agent speech and audio | LiveKit Docs | Docs::Agents | Agent speech and audio | LiveKit Docs | livekit-agents/livekit/agents/voice/agent_activity.py
b
can we intercept the realtime speech to speech models to return our own llm response sometimes mid convo?
t
Absolutely, you can intercept LiveKit's realtime speech-to-speech models to return custom LLM responses during a conversation. It really depends on the type of agent you're working with: • For VoicePipelineAgent: You have the option to override the 
llm_node()
method. This way, you can catch and tweak the LLM's output before it moves on to the TTS stage, letting you add your own custom responses on the fly. • For MultimodalAgent: You can take advantage of the 
on_message()
or 
on_response()
hooks to intercept and swap out the model's response with your own tailored logic. If you'd like, I’d be more than happy to provide detailed code examples or walk you through any part of this for your specific needs. Just let me know! Sources: Docs::Agents
b
yes help me with it, we are using openai gpt realtime mini model
t
To help you intercept and modify responses in the OpenAI GPT Realtime mini model using LiveKit, you can override the 
llm_node
method in your custom 
Agent
class. This approach lets you process and tweak the text output generated by the LLM before it moves on to the next step, like TTS or streaming to the client. Here’s a simple example to guide you:
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class MyAssistant(Agent):\n    async def llm_node(self, chat_ctx, tools, model_settings=None):\n        async def process_stream():\n            async with self.llm.chat(chat_ctx=chat_ctx, tools=tools) as stream:\n                async for chunk in stream:\n                    if chunk and hasattr(chunk.delta, 'content'):\n                        # Modify the LLM output here\n                        chunk.delta.content = chunk.delta.content.replace('Goodbye', 'See you later!')\n                    yield chunk\n        return process_stream()
This method gives you the flexibility to adjust the LLM’s responses dynamically during your conversation. If you have any questions or would like more examples, please don’t hesitate to ask—I’m here to help! Sources: Modifying LLM output before TTS | LiveKit Docs | livekit-plugins/livekit-plugins-openai/livekit/plugins/openai/realtime/real... | realtime/openai-realtime-pitch-shift.py
b
will it work on gemini live flash 2.5 model
t
Thanks for reaching out! I wanted to share that the 
llm_node
method isn’t directly compatible with the Gemini Live Flash 2.5 model because it’s a realtime model and doesn’t use the usual STT-LLM-TTS pipeline where 
llm_node
is typically applied. But don’t worry—you still have some great options to intercept and modify responses: • Try using `transcription_node`: This lets you adjust the transcription output, including the text generated by the LLM, before it reaches the user. • Consider text-only mode: You can set up the Gemini model with 
modalities=["TEXT"]
to turn off audio output. This way, you can intercept and tweak the text response and then send it to a separate TTS engine for audio synthesis. If you’d like, I’d be happy to provide detailed examples or walk you through the setup tailored to your needs. Just let me know! Sources: Modifying LLM output before TTS | LiveKit Docs | Pipeline nodes and hooks | LiveKit Docs | pipeline-llm/replacing_llm_output.py | Gemini Live API integration guide | LiveKit Docs | Gemini Live API integration guide | LiveKit Docs
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