Build an AI Voice Agent for Metrics

Implement an AI Voice Agent for voice AI metrics with VideoSDK, covering setup, architecture, and testing.

Introduction to AI Voice Agents in Voice AI Measuring Metrics

What is an AI

Voice Agent

?

An AI

Voice Agent

 is a sophisticated software application designed to interact with users through voice commands. It processes spoken language, interprets the intent, and responds in a human-like manner. These agents utilize advanced technologies such as Speech-to-Text (STT), Language Learning Models (LLM), and Text-to-Speech (TTS) to facilitate seamless communication.

Why are they important for the voice AI measuring metrics industry?

AI Voice Agents play a crucial role in the voice AI industry by providing insights into user interactions, measuring performance metrics like accuracy, latency, and engagement. They enable businesses to refine their voice AI systems, ensuring better user experiences and improved service delivery.

Core Components of a

Voice Agent

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

What You'll Build in This Tutorial

In this tutorial, you'll learn how to build an AI

Voice Agent

 using the VideoSDK framework. This agent will focus on measuring metrics in voice AI systems, providing insights and guidance on improving these metrics.

Architecture and Core Concepts

High-Level Architecture Overview

The AI

Voice Agent

 operates by capturing user speech, processing it through a series of components, and generating a response. The data flow begins with capturing audio, converting it to text, interpreting the text, and finally synthesizing a vocal response.
Diagram

Understanding Key Concepts in the VideoSDK Framework

  • Agent: The core class representing your bot, handling interactions.
  • CascadingPipeline: Manages the flow of audio processing from STT to LLM to TTS.
  • VAD & TurnDetector: These components help the agent determine when to listen and when to respond, ensuring smooth interaction.

Setting Up the Development Environment

Prerequisites

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

Step 1: Create a Virtual Environment

Create a virtual environment to manage dependencies:
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 keys:
1VIDEOSDK_API_KEY=your_api_key_here
2

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

Here is the complete code for the AI Voice Agent:
1import asyncio, os
2from videosdk.agents import Agent, [AgentSession](https://docs.videosdk.live/ai_agents/core-components/agent-session), [CascadingPipeline](https://docs.videosdk.live/ai_agents/core-components/cascading-pipeline), 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](https://docs.videosdk.live/ai_agents/plugins/turn-detector), 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 specialized AI Voice Agent focused on 'voice ai measuring metrics'. Your persona is that of a 'data-driven analytics consultant'. Your primary capabilities include: 1) Explaining various metrics used in voice AI systems, such as accuracy, latency, and user engagement. 2) Providing insights on how to measure and improve these metrics. 3) Offering guidance on tools and methodologies for effective metric analysis. Your constraints are: 1) You are not a software developer and cannot provide code-level support. 2) You must include a disclaimer that your advice is for informational purposes only and should be validated by a professional in the field. 3) You cannot access real-time data or perform live analysis."
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(
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 generate a meeting ID, use the following curl command:
1curl -X POST https://api.videosdk.live/v1/meetings -H "Authorization: Bearer YOUR_API_KEY"
2

Step 4.2: Creating the Custom Agent Class

The MyVoiceAgent class extends the Agent class, where you define the agent's behavior. It uses a set of instructions to guide its responses and interactions.

Step 4.3: Defining the Core Pipeline

The

Cascading Pipeline in AI voice Agents

 is crucial for processing audio data. It integrates STT, LLM, TTS, VAD, and TurnDetector plugins to manage the conversation flow effectively.

Step 4.4: Managing the Session and Startup Logic

The start_session function initializes the agent and the conversation flow, while make_context sets up the room options. The main block runs the agent, allowing it to interact with users.

Running and Testing the Agent

Step 5.1: Running the Python Script

Execute the script with:
1python main.py
2

Step 5.2: Interacting with the Agent in the Playground

Once the script is running, a playground link will be available in the console. Use this link to join the session and interact with the agent.

Advanced Features and Customizations

Extending Functionality with Custom Tools

You can extend the agent's functionality by integrating custom tools using the function_tool concept, allowing for more specialized interactions.

Exploring Other Plugins

Consider exploring other STT, LLM, and TTS plugins to enhance the agent's capabilities, such as Cartesia for STT or Google Gemini for LLM.

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 audio device settings and permissions if you encounter issues with sound input or output.

Dependency and Version Conflicts

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

Conclusion

Summary of What You've Built

You've successfully built an AI Voice Agent capable of measuring and providing insights on voice AI metrics using the VideoSDK framework.

Next Steps and Further Learning

Explore additional plugins and customize your agent further to enhance its capabilities and adapt it to different use cases. For a comprehensive understanding, refer to the

AI voice Agent core components overview

.

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