Build an AI Voice Agent for Aviation

Create an AI Voice Agent for aviation with VideoSDK. Follow our step-by-step guide with code examples and testing instructions.

Introduction to AI Voice Agents in Aviation

In recent years, AI voice agents have become increasingly prevalent across various industries, including aviation. These agents are designed to interact with users through natural language, providing information and assistance in a conversational manner. In this tutorial, we will explore how to build an AI

voice agent

 specifically tailored for the aviation industry.

What is an AI

Voice Agent

?

An AI

voice agent

 is a software application that uses artificial intelligence to understand and respond to human speech. It leverages technologies such as Speech-to-Text (STT), Large Language Models (LLM), and Text-to-Speech (TTS) to process and generate natural language responses.

Why are they important for the Aviation Industry?

In the aviation industry, AI voice agents can enhance customer service by providing real-time flight information, assisting with bookings, and offering travel advice. They can also improve operational efficiency by handling routine inquiries, allowing human staff to focus on more complex tasks.

Core Components of a

Voice Agent

  • Speech-to-Text (STT): Converts spoken language into text.
  • Large Language Model (LLM): Processes text input to generate contextually appropriate responses.
  • Text-to-Speech (TTS): Converts text responses back into spoken language.

What You’ll Build in This Tutorial

In this guide, we will build an AI

voice agent

 capable of providing aviation-related information and assistance. We will use the VideoSDK framework to implement the agent, leveraging various plugins for STT, LLM, and TTS functionalities.

Architecture and Core Concepts

High-Level Architecture Overview

The AI voice agent architecture involves several components working together to process user input and generate responses. The data flow begins with the user speaking into a microphone, which is captured and processed by the Speech-to-Text (STT) engine. The transcribed text is then passed to a Large Language Model (LLM) that generates a suitable response. Finally, the Text-to-Speech (TTS) engine converts the response back into audio for the user to hear.
Diagram

Understanding Key Concepts in the VideoSDK Framework

Agent

The Agent class represents the core of your AI voice agent. It handles interactions and manages the conversation flow.

CascadingPipeline

The

Cascading pipeline in AI voice Agents

 orchestrates the flow of audio processing through various stages: STT, LLM, and TTS.

VAD & TurnDetector

Voice

Activity Detection

 (VAD) and

Turn detector for AI voice Agents

 are crucial for determining when the agent should listen and respond, ensuring a smooth conversational experience.

Setting Up the Development Environment

Prerequisites

Before we begin, ensure you have the following:
  • Python 3.11 or higher
  • A VideoSDK account (sign up 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 API keys:
1VIDEOSDK_API_KEY=your_api_key_here
2

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

To build our AI voice agent, we will use the VideoSDK framework. Below is the complete code for the 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 an AI Voice Agent specialized in the aviation industry. Your persona is that of a knowledgeable and friendly aviation assistant. Your primary capabilities include providing real-time flight information, answering common aviation-related questions, assisting with booking and scheduling flights, and offering insights into aviation safety protocols. You can also provide updates on weather conditions affecting flights and general travel advice. However, you are not a certified aviation expert or travel agent, and you must include a disclaimer advising users to verify critical information with official sources or professionals. You should not provide personal opinions or make decisions on behalf of users. Your responses should be concise, accurate, and based on the latest available data."
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 = [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 generate a meeting ID, you can use the following curl command:
1curl -X POST "https://api.videosdk.live/v1/rooms" \
2-H "Authorization: Bearer YOUR_API_KEY" \
3-H "Content-Type: application/json" \
4-d '{"name": "Aviation Agent Room"}'
5

Step 4.2: Creating the Custom Agent Class

The MyVoiceAgent class extends the Agent class and defines the agent's behavior. It uses the agent_instructions to guide interactions. The on_enter and on_exit methods define what the agent says when a session starts and ends.

Step 4.3: Defining the Core Pipeline

The

CascadingPipeline

 is central to the agent's functionality. It processes audio input through various plugins:
  • DeepgramSTT: Converts speech to text.
  • OpenAILLM: Generates responses using a language model.
  • ElevenLabsTTS: Converts text responses back to speech.
  • SileroVAD: Detects voice activity to manage listening.
  • TurnDetector: Helps manage conversational turns.

Step 4.4: Managing the Session and Startup Logic

The start_session function initializes the agent session and starts the conversation flow. The make_context function sets up the room options, and the main block starts the job.

Running and Testing the Agent

Step 5.1: Running the Python Script

To run the agent, execute the following command:
1python main.py
2

Step 5.2: Interacting with the Agent in the Playground

After starting the agent, you will see a test URL in the console. Use this link to join the session and interact with the agent. The agent will respond to your queries based on the instructions provided.

Advanced Features and Customizations

Extending Functionality with Custom Tools

You can extend the agent's capabilities by adding custom tools using the function_tool feature, allowing for more specialized interactions.

Exploring Other Plugins

Consider exploring other plugins for STT, LLM, and TTS to enhance the agent's performance and capabilities.

Troubleshooting Common Issues

API Key and Authentication Errors

Ensure your API keys are correctly set in the .env file and that your VideoSDK account is active.

Audio Input/Output Problems

Check your microphone and speaker settings to ensure proper audio input and output.

Dependency and Version Conflicts

Ensure all dependencies are installed with compatible versions, especially when using a virtual environment.

Conclusion

Summary of What You’ve Built

In this tutorial, you have built an AI voice agent tailored for the aviation industry, capable of providing real-time information and assistance.

Next Steps and Further Learning

Explore additional features and plugins to enhance your agent's capabilities, and consider deploying it in a real-world aviation scenario. For a comprehensive understanding, refer to the

AI voice Agent core components overview

.

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