Handle Interruptions in AI Voice Agents

Build AI Voice Agents to handle interruptions using VideoSDK. Follow this step-by-step tutorial with complete code examples.

Introduction to AI Voice Agents in handle interruptions in voice agents

What is an AI

Voice Agent

?

AI Voice Agents are sophisticated software systems designed to interact with users through voice. They can understand spoken language, process the information, and respond appropriately. These agents leverage technologies like Speech-to-Text (STT), Text-to-Speech (TTS), and Large Language Models (LLM) to facilitate seamless communication.

Why are they important for the handle interruptions in voice agents industry?

In industries where customer interaction is key, such as customer service, AI Voice Agents play a crucial role. They can handle multiple interactions simultaneously, provide 24/7 support, and improve customer satisfaction by handling interruptions gracefully, ensuring a smooth communication flow.

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 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

 using the VideoSDK framework. This agent will be capable of handling interruptions during conversations, ensuring smooth and effective communication.

Architecture and Core Concepts

High-Level Architecture Overview

The AI

Voice Agent

 processes user speech through a series of steps: capturing audio, converting it to text, processing the text to generate a response, and finally converting the response back to audio. This seamless flow is achieved using the VideoSDK framework's components, including the

Cascading pipeline in AI voice Agents

.
Diagram

Understanding Key Concepts in the VideoSDK Framework

Setting Up the Development Environment

Prerequisites

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

Step 1: Create a Virtual Environment

Create a virtual environment to manage your project 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
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

Complete Code

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 polite and efficient AI Voice Agent designed to handle interruptions gracefully during conversations. Your primary role is to assist users by providing accurate information and maintaining a smooth interaction flow, even when interruptions occur.\n\n**Persona:**\n- You are a helpful and patient customer service representative.\n\n**Capabilities:**\n- You can recognize and manage interruptions in conversations.\n- You can pause and resume tasks seamlessly.\n- You can provide information on various topics, including product details and order status.\n- You can escalate complex issues to a human representative if needed.\n\n**Constraints and Limitations:**\n- You must always acknowledge the user's interruption politely before proceeding.\n- You are not authorized to make decisions on behalf of the user or the company.\n- You must include a disclaimer that complex issues may require human intervention.\n- You cannot store or process sensitive personal information."
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, you can use the following curl command:
1curl -X POST \
2  https://api.videosdk.live/v1/meetings \
3  -H "Authorization: Bearer YOUR_API_KEY" \
4  -H "Content-Type: application/json"
5

Step 4.2: Creating the Custom Agent Class

The MyVoiceAgent class extends the Agent class provided by the VideoSDK framework. This class contains the logic for handling the agent's entry and exit actions. It uses predefined instructions to manage interruptions gracefully.
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

Step 4.3: Defining the Core Pipeline

The CascadingPipeline is crucial for processing audio data. It integrates various plugins for STT, LLM, TTS, VAD, and turn detection.
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

Step 4.4: Managing the Session and Startup Logic

The start_session function initializes the agent session and manages the connection lifecycle.
1async def start_session(context: JobContext):
2    agent = MyVoiceAgent()
3    conversation_flow = ConversationFlow(agent)
4    pipeline = CascadingPipeline(
5        stt=DeepgramSTT(model="nova-2", language="en"),
6        llm=OpenAILLM(model="gpt-4o"),
7        tts=ElevenLabsTTS(model="eleven_flash_v2_5"),
8        vad=SileroVAD(threshold=0.35),
9        turn_detector=TurnDetector(threshold=0.8)
10    )
11    session = AgentSession(
12        agent=agent,
13        pipeline=pipeline,
14        conversation_flow=conversation_flow
15    )
16    try:
17        await context.connect()
18        await session.start()
19        await asyncio.Event().wait()
20    finally:
21        await session.close()
22        await context.shutdown()
23
The make_context function sets up the room options for the agent, allowing it to join or create a meeting room.
1def make_context() -> JobContext:
2    room_options = RoomOptions(
3        name="VideoSDK Cascaded Agent",
4        playground=True
5    )
6    return JobContext(room_options=room_options)
7
Finally, the script starts the agent when executed.
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

Run the script using the following command:
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 the agent. You can test the agent's ability to handle interruptions by speaking over its responses.

Advanced Features and Customizations

Extending Functionality with Custom Tools

The VideoSDK framework allows you to extend the agent's functionality using custom tools. These tools can be integrated into the pipeline to enhance the agent's capabilities.

Exploring Other Plugins

While this tutorial uses specific plugins for STT, LLM, and TTS, the VideoSDK framework supports various other plugins. You can experiment with different plugins to optimize performance and cost.

Troubleshooting Common Issues

API Key and Authentication Errors

Ensure your API key is correctly set in the .env file. Double-check the VideoSDK documentation for authentication guidelines.

Audio Input/Output Problems

Verify that your microphone and speakers are functioning correctly. Check the agent's audio settings and permissions.

Dependency and Version Conflicts

Ensure all dependencies are installed and compatible with your Python version. Use a virtual environment to manage dependencies effectively.

Conclusion

Summary of What You've Built

In this tutorial, you've built an AI Voice Agent capable of handling interruptions using the VideoSDK framework. This agent can process speech, understand context, and respond appropriately, leveraging

AI voice Agent Sessions

 for efficient management.

Next Steps and Further Learning

To further enhance your agent, explore additional plugins and customization options provided by the VideoSDK framework. Consider integrating more complex logic and external data sources to expand the agent's capabilities. For a comprehensive understanding, refer to the

AI voice Agent core components overview

.

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