Build a Conversational AI Voice Agent

Step-by-step guide to build a conversational AI voice agent using VideoSDK. Learn to create, run, and test your own AI agent.

Introduction to AI Voice Agents in Key Differentiator of Conversational AI

AI Voice Agents have become an integral part of the conversational AI landscape, offering seamless interaction between humans and machines. These agents leverage advanced technologies such as Speech-to-Text (STT), Language Learning Models (LLM), and Text-to-Speech (TTS) to understand and respond to human queries effectively.

What is an AI

Voice Agent

?

An AI

Voice Agent

is a software application designed to interact with users through spoken language. It processes voice inputs, understands the context, and provides appropriate responses, making human-computer interaction more natural and intuitive.

Why are They Important for the Key Differentiator of Conversational AI Industry?

In the realm of conversational AI, voice agents are pivotal as they enhance user experience by enabling hands-free, voice-driven interactions. They are widely used in customer service, virtual assistants, and smart home devices, transforming the way businesses and consumers engage with technology.

Core Components of a

Voice Agent

  • Speech-to-Text (STT): Converts spoken language into text.
  • Language Learning Models (LLM): Understands and processes the text to generate meaningful responses.
  • Text-to-Speech (TTS): Converts text responses back into spoken language.

What You'll Build in This Tutorial

In this tutorial, you will build a fully functional AI

Voice Agent

using VideoSDK. This agent will be capable of explaining the key differentiators of conversational AI, providing insights and answering queries.

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 input, converting it to text, processing the text to generate a response, and finally converting this response back to speech.
Diagram

Understanding Key Concepts in the VideoSDK Framework

  • Agent: The core class representing your bot, responsible for managing interactions.
  • Cascading Pipeline in AI voice Agents

    :
    Manages the flow of audio processing through STT, LLM, and TTS.
  • VAD & TurnDetector: Components that help the agent determine when to listen and respond.

Setting Up the Development Environment

Prerequisites

To get started, ensure you have Python 3.11+ installed and a VideoSDK account. Sign up at the VideoSDK dashboard to manage your projects and obtain API keys.

Step 1: Create a Virtual Environment

1python -m venv venv
2source venv/bin/activate  # On Windows use `venv\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 key:
1VIDEOSDK_API_KEY=your_api_key_here
2

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

Here is the complete code for building your AI Voice Agent:
1import asyncio, os
2from videosdk.agents import Agent, AgentSession, CascadingPipeline, JobContext, RoomOptions, WorkerJob, ConversationFlow
3from videosdk.plugins.silero import SileroVAD
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 specializing in explaining the key differentiators of conversational AI. Your persona is that of an insightful technology guide, providing clear and concise information to users interested in understanding what sets conversational AI apart from other technologies. Your capabilities include: 1) Explaining the unique features and benefits of conversational AI, 2) Comparing conversational AI with traditional AI systems, 3) Providing examples of real-world applications of conversational AI, and 4) Answering general questions about conversational AI technology. Your constraints and limitations are: 1) You are not a human expert, so you must refrain from providing subjective opinions, 2) You must include a disclaimer that your explanations are based on current technological understanding and may evolve, 3) You cannot provide technical support or troubleshooting for specific conversational AI products or services."
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=[Silero Voice Activity Detection](https://docs.videosdk.live/ai_agents/plugins/silero-vad)(threshold=0.35),
32        turn_detector=[Turn detector for AI voice Agents](https://docs.videosdk.live/ai_agents/plugins/turn-detector)(threshold=0.8)
33    )
34
35    session = [AI voice Agent Sessions](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 create a meeting ID, use 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-d '{}'
5

Step 4.2: Creating the Custom Agent Class

The MyVoiceAgent class is the heart of your AI Voice Agent. It inherits from the Agent class and includes custom instructions that define the agent's persona and capabilities. The on_enter and on_exit methods handle the agent's initial and final interactions.

Step 4.3: Defining the Core Pipeline

The CascadingPipeline integrates various plugins to process audio data:
  • DeepgramSTT: Converts speech to text using the "nova-2" model.
  • OpenAILLM: Processes text using the "gpt-4o" model to generate responses.
  • ElevenLabsTTS: Converts text responses back to speech using the "elevenflashv2_5" model.
  • SileroVAD: Detects voice activity to manage when the agent listens.
  • TurnDetector: Determines conversation turns based on a threshold.

Step 4.4: Managing the Session and Startup Logic

The start_session function initializes the agent session, setting up the conversation flow and pipeline. The make_context function prepares the job context with room options, and the main block starts the worker job.

Running and Testing the Agent

Step 5.1: Running the Python Script

Execute the script using:
1python main.py
2

Step 5.2: Interacting with the Agent in the

AI Agent playground

Once the script runs, the console will display a playground link. Open this link in a browser to interact with your agent. Use Ctrl+C to gracefully shut down the agent when done.

Advanced Features and Customizations

Extending Functionality with Custom Tools

Enhance your agent by integrating custom tools using the function_tool concept, allowing for more specialized interactions.

Exploring Other Plugins

Experiment with different plugins for STT, LLM, and TTS to tailor the agent's capabilities to your needs.

Troubleshooting Common Issues

API Key and Authentication Errors

Ensure your API keys are correctly configured in the .env file to avoid authentication issues.

Audio Input/Output Problems

Check your microphone and speaker settings if you encounter audio issues.

Dependency and Version Conflicts

Ensure all dependencies are up-to-date and compatible with Python 3.11+.

Conclusion

Summary of What You've Built

You've successfully built a conversational AI Voice Agent capable of explaining the key differentiators of conversational AI.

Next Steps and Further Learning

Explore additional features and plugins to enhance your agent's capabilities and delve deeper into the world of conversational AI.

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