Regression Testing for Voice Bots

Implement regression testing for voice bots with AI Voice Agents. Step-by-step tutorial with code examples.

Introduction to AI Voice Agents in Regression Testing for Voice Bots

What is an AI Voice Agent?

An AI Voice Agent is a software program that interacts with users through voice commands, understanding spoken language, processing it, and responding appropriately. In the context of regression testing for voice bots, these agents can automate and streamline testing processes, ensuring that voice bots perform consistently after updates or changes.

Why are they important for the regression testing for voice bots industry?

AI Voice Agents are crucial in regression testing as they help identify issues that may arise from recent code changes. They simulate real user interactions, allowing developers to catch bugs and ensure the voice bot's functionality remains intact. This is essential for maintaining a high-quality user experience.

Core Components of a Voice Agent

  • STT (Speech-to-Text): Converts spoken language into text.
  • LLM (Large Language Model): Processes the text to understand intent 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 assist in regression testing for voice bots, leveraging components like STT, LLM, and TTS to simulate and test voice interactions. For a detailed setup, refer to the

Voice Agent Quick Start Guide

.

Architecture and Core Concepts

High-Level Architecture Overview

The architecture of an AI Voice Agent involves several components that work together to process user speech and generate responses. The process begins with capturing audio input, converting it to text, processing the text to understand the user's intent, generating a text response, and finally converting this response back to speech.
1sequenceDiagram
2    participant User
3    participant Agent
4    participant STT
5    participant LLM
6    participant TTS
7    User->>Agent: Speak
8    Agent->>STT: Convert Speech to Text
9    STT->>Agent: Text
10    Agent->>LLM: Process Text
11    LLM->>Agent: Response Text
12    Agent->>TTS: Convert Text to Speech
13    TTS->>User: Speak
14

Understanding Key Concepts in the VideoSDK Framework

Setting Up the Development Environment

Prerequisites

To follow this tutorial, you need Python 3.11+ and a VideoSDK account, which you can create at app.videosdk.live.

Step 1: Create a Virtual Environment

First, create a virtual environment to manage your project's 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
2pip install python-dotenv
3

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

Below is the complete code for the AI Voice Agent. We'll break it down into smaller sections to explain each part.
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 software testing assistant specializing in regression testing for voice bots. Your primary role is to assist developers and QA engineers by providing insights, best practices, and guidance on conducting effective regression testing for voice bots. You can explain the importance of regression testing, outline the steps involved, and suggest tools and frameworks that can be used. However, you are not a substitute for a professional software tester and should always recommend consulting with a qualified testing expert for comprehensive testing strategies. You should also remind users to consider the specific requirements and constraints of their voice bot applications when planning their testing approach."
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, replacing YOUR_API_KEY with your actual VideoSDK API key:
1curl -X POST "https://api.videosdk.live/v1/rooms" -H "Authorization: YOUR_API_KEY"
2

Step 4.2: Creating the Custom Agent Class

The MyVoiceAgent class is where you define the behavior of your voice agent. It inherits from the Agent class and uses the provided agent_instructions to guide its interactions.
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 a crucial part of the agent, defining how audio is processed through various stages. It includes plugins for STT, LLM, TTS, VAD, and Turn Detection. For more information on the plugins, check out the

Deepgram STT Plugin for voice agent

,

OpenAI LLM Plugin for voice agent

, and

ElevenLabs TTS Plugin for voice agent

.
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, setting up the conversation flow and pipeline. It handles the connection and ensures the session runs until manually terminated. For more interactive testing, you can use the

AI Agent playground

.
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 JobContext, which includes room options for the session.
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 main block starts the job, initializing the session and running the agent.
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 the agent, execute the following command in your terminal:
1python main.py
2

Step 5.2: Interacting with the Agent in the Playground

Once the script is running, you will find a playground link in the console. Use this link to join the session and interact with the agent. Speak into your microphone, and the agent will respond based on its programmed instructions.

Advanced Features and Customizations

Extending Functionality with Custom Tools

The VideoSDK framework allows you to extend your agent's functionality by integrating custom tools. This can include additional plugins or external APIs 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 options. Explore different plugins to find the best fit for your needs.

Troubleshooting Common Issues

API Key and Authentication Errors

Ensure your API keys are correctly set in the .env file. Double-check for typos or missing credentials.

Audio Input/Output Problems

Verify that your microphone and speakers are properly configured and recognized by your system.

Dependency and Version Conflicts

Ensure all dependencies are installed with compatible versions. Use a virtual environment to avoid conflicts.

Conclusion

Summary of What You've Built

In this tutorial, you built an AI Voice Agent using the VideoSDK framework, capable of assisting in regression testing for voice bots.

Next Steps and Further Learning

Explore more advanced features of the VideoSDK framework and consider integrating additional plugins to enhance your agent's capabilities. For more insights, review the

AI voice Agent Sessions

.

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