Fine-Tuning LLMs for Conversational AI

Build an AI Voice Agent to fine-tune LLMs for conversation using VideoSDK. Follow this detailed guide with code examples.

Introduction to AI Voice Agents in Fine-Tuning LLMs for Conversation

AI Voice Agents are automated systems designed to interact with users through voice commands. They leverage technologies like Speech-to-Text (STT), Large Language Models (LLM), and Text-to-Speech (TTS) to process and respond to user inputs. These agents are crucial in the fine-tuning of LLMs for conversation, allowing developers to create more natural and human-like interactions.

What is an AI

Voice Agent

?

An AI

Voice Agent

 is a software application that uses artificial intelligence to understand and respond to voice commands. It acts as an intermediary that processes spoken language inputs, interprets them using LLMs, and generates appropriate spoken responses.

Why are They Important for the Fine-Tuning LLMs for Conversation Industry?

AI Voice Agents are pivotal in the fine-tuning process of LLMs, as they provide a practical platform for testing and refining conversational models. They help developers understand how well a model can handle real-world interactions and identify areas for improvement.

Core Components of a

Voice Agent

  • STT (Speech-to-Text): Converts spoken language into text.
  • LLM (Large Language Model): Processes the text to understand and generate human-like responses.
  • TTS (Text-to-Speech): Converts text responses back into spoken language.

What You'll Build in This Tutorial

In this tutorial, you will build an AI

Voice Agent

 capable of fine-tuning LLMs for conversational tasks using the VideoSDK framework. You will learn to set up the environment, create a custom agent, and test it in a

playground environment

.

Architecture and Core Concepts

High-Level Architecture Overview

The architecture of an AI

Voice Agent

 involves a seamless flow of data from user speech to agent response. The process begins with capturing the user's voice, converting it to text, processing it through an LLM, and finally converting the response back to speech.
Diagram

Understanding Key Concepts in the VideoSDK Framework

  • Agent: This is the core class representing your bot. It manages interactions and responses.

  • Cascading Pipeline

    : This defines the flow of audio processing, from STT to LLM to TTS.
  • VAD &

    Turn Detector

    : These components help the agent determine when to listen and when to speak, ensuring smooth interactions.

Setting Up the Development Environment

Prerequisites

To get started, ensure you have Python 3.11+ installed and a VideoSDK account. You can sign up at app.videosdk.live.

Step 1: Create a Virtual Environment

Create a virtual environment to manage your project dependencies:
1python -m venv myenv
2source myenv/bin/activate  # On Windows use `myenv\\Scripts\\activate`
3

Step 2: Install Required Packages

Install the necessary packages using pip:
1pip install videosdk-agents videosdk-plugins
2

Step 3: Configure API Keys in a .env File

Create a .env file in your project directory and add your VideoSDK API keys:
1VIDEOSDK_API_KEY=your_api_key_here
2

Building the AI Voice Agent: A Step-by-Step Guide

Let's start by presenting the complete code for our AI Voice Agent. This code will serve as the foundation for our detailed walkthrough.
1import asyncio, os
2from videosdk.agents import Agent, AgentSession, CascadingPipeline, JobContext, RoomOptions, WorkerJob, ConversationFlow
3from videosdk.plugins.silero import [SileroVAD](https://docs.videosdk.live/ai_agents/plugins/silero-vad)
4from videosdk.plugins.turn_detector import TurnDetector, pre_download_model
5from videosdk.plugins.deepgram import DeepgramSTT
6from videosdk.plugins.openai import OpenAILLM
7from videosdk.plugins.elevenlabs import ElevenLabsTTS
8from typing import AsyncIterator
9
10# Pre-downloading the Turn Detector model
11pre_download_model()
12
13agent_instructions = "You are a knowledgeable AI Voice Agent specialized in fine-tuning Large Language Models (LLMs) for conversation. Your persona is that of a friendly and insightful AI tutor who assists developers and AI enthusiasts in understanding and implementing fine-tuning techniques for conversational AI models. Your capabilities include explaining the concepts of LLM fine-tuning, providing step-by-step guidance on how to fine-tune models for specific conversational tasks, and offering best practices for optimizing model performance. You can also answer frequently asked questions about the process and suggest resources for further learning. However, you must clarify that you are not a substitute for professional AI researchers or developers and that users should verify critical information with authoritative sources. Additionally, you should not provide code execution or debugging services, but rather focus on conceptual guidance and educational support."
14
15class MyVoiceAgent(Agent):
16    def __init__(self):
17        super().__init__(instructions=agent_instructions)
18    async def on_enter(self): await self.session.say("Hello! How can I help?")
19    async def on_exit(self): await self.session.say("Goodbye!")
20
21async def start_session(context: JobContext):
22    # Create agent and conversation flow
23    agent = MyVoiceAgent()
24    conversation_flow = ConversationFlow(agent)
25
26    # Create pipeline
27    pipeline = CascadingPipeline(
28        stt=DeepgramSTT(model="nova-2", language="en"),
29        llm=OpenAILLM(model="gpt-4o"),
30        tts=ElevenLabsTTS(model="eleven_flash_v2_5"),
31        vad=SileroVAD(threshold=0.35),
32        turn_detector=TurnDetector(threshold=0.8)
33    )
34
35    session = [AgentSession](https://docs.videosdk.live/ai_agents/core-components/agent-session)(
36        agent=agent,
37        pipeline=pipeline,
38        conversation_flow=conversation_flow
39    )
40
41    try:
42        await context.connect()
43        await session.start()
44        # Keep the session running until manually terminated
45        await asyncio.Event().wait()
46    finally:
47        # Clean up resources when done
48        await session.close()
49        await context.shutdown()
50
51def make_context() -> JobContext:
52    room_options = RoomOptions(
53    #  room_id="YOUR_MEETING_ID",  # Set to join a pre-created room; omit to auto-create
54        name="VideoSDK Cascaded Agent",
55        playground=True
56    )
57
58    return JobContext(room_options=room_options)
59
60if __name__ == "__main__":
61    job = WorkerJob(entrypoint=start_session, jobctx=make_context)
62    job.start()
63

Step 4.1: Generating a VideoSDK Meeting ID

To interact with your AI Voice Agent, you need a meeting ID. You can generate one using the following curl command:
1curl -X POST "https://api.videosdk.live/v1/meetings" \
2-H "Authorization: Bearer YOUR_API_KEY" \
3-H "Content-Type: application/json"
4
This command will return a meeting ID that you can use to connect your agent.

Step 4.2: Creating the Custom Agent Class

The MyVoiceAgent class is where you define your agent's behavior. It inherits from the Agent class and uses the instructions provided to create a conversational persona.
1class MyVoiceAgent(Agent):
2    def __init__(self):
3        super().__init__(instructions=agent_instructions)
4    async def on_enter(self): await self.session.say("Hello! How can I help?")
5    async def on_exit(self): await self.session.say("Goodbye!")
6
This class initializes the agent with specific instructions and defines actions for when the agent session starts and ends.

Step 4.3: Defining the Core Pipeline

The CascadingPipeline is the backbone of your voice agent, managing the flow of data through various processing stages.
1pipeline = CascadingPipeline(
2    stt=DeepgramSTT(model="nova-2", language="en"),
3    llm=OpenAILLM(model="gpt-4o"),
4    tts=ElevenLabsTTS(model="eleven_flash_v2_5"),
5    vad=SileroVAD(threshold=0.35),
6    turn_detector=TurnDetector(threshold=0.8)
7)
8
This pipeline integrates STT, LLM, TTS, VAD, and a turn detector to handle the conversion and processing of voice data.

Step 4.4: Managing the Session and Startup Logic

The session management and startup logic are crucial for running your agent. The start_session function handles the lifecycle of a session.
1async def start_session(context: JobContext):
2    # Create agent and conversation flow
3    agent = MyVoiceAgent()
4    conversation_flow = ConversationFlow(agent)
5
6    # Create pipeline
7    pipeline = CascadingPipeline(
8        stt=DeepgramSTT(model="nova-2", language="en"),
9        llm=OpenAILLM(model="gpt-4o"),
10        tts=ElevenLabsTTS(model="eleven_flash_v2_5"),
11        vad=SileroVAD(threshold=0.35),
12        turn_detector=TurnDetector(threshold=0.8)
13    )
14
15    session = AgentSession(
16        agent=agent,
17        pipeline=pipeline,
18        conversation_flow=conversation_flow
19    )
20
21    try:
22        await context.connect()
23        await session.start()
24        # Keep the session running until manually terminated
25        await asyncio.Event().wait()
26    finally:
27        # Clean up resources when done
28        await session.close()
29        await context.shutdown()
30
The make_context function sets up the room options for the agent to operate in a playground environment.
1def make_context() -> JobContext:
2    room_options = RoomOptions(
3    #  room_id="YOUR_MEETING_ID",  # Set to join a pre-created room; omit to auto-create
4        name="VideoSDK Cascaded Agent",
5        playground=True
6    )
7
8    return JobContext(room_options=room_options)
9
Finally, the script entry point starts the agent job.
1if __name__ == "__main__":
2    job = WorkerJob(entrypoint=start_session, jobctx=make_context)
3    job.start()
4

Running and Testing the Agent

Step 5.1: Running the Python Script

To run your AI Voice Agent, execute the script using Python:
1python main.py
2

Step 5.2: Interacting with the Agent in the Playground

Once the script is running, you will see a playground link in the console. Open this link in your browser to interact with your agent. Speak into your microphone to test the agent's response capabilities.

Advanced Features and Customizations

Extending Functionality with Custom Tools

You can extend the functionality of your AI Voice Agent by integrating custom tools. This allows you to add new capabilities tailored to specific use cases.

Exploring Other Plugins

The VideoSDK framework supports various plugins for STT, LLM, and TTS. Explore options like Cartesia for STT, Google Gemini for LLM, and Deepgram for TTS to suit your needs.

Troubleshooting Common Issues

API Key and Authentication Errors

Ensure your API keys are correctly configured in the .env file. Double-check for any typos or missing information.

Audio Input/Output Problems

Verify your microphone and speaker settings. Ensure your system permissions allow audio access for the application.

Dependency and Version Conflicts

Use a virtual environment to manage dependencies. Check for any version conflicts and resolve them by updating or downgrading packages.

Conclusion

Summary of What You've Built

In this tutorial, you built an AI Voice Agent capable of fine-tuning LLMs for conversation. You learned about the architecture, set up the environment, and ran the agent in a playground.

Next Steps and Further Learning

Continue exploring the VideoSDK framework and experiment with different plugins and customizations. Consider diving deeper into AI concepts and refining your agent's capabilities.

Start Building With Free $20 Balance

No credit card required to start.

Want to level-up your learning? Subscribe now

Subscribe to our newsletter for more tech based insights

FAQ