Build AI Voice Assistants for Debt Collection

Step-by-step guide to build AI voice assistants for debt collection using VideoSDK.

Introduction to AI Voice Agents in Debt Collection

What is an AI Voice Agent?

An AI Voice Agent is a software application that uses artificial intelligence to interact with users through voice commands. It processes spoken language, understands the intent, and responds accordingly. These agents are designed to simulate human conversation and can perform various tasks based on user input.

Why are they important for the Debt Collection Industry?

In the debt collection industry, AI Voice Agents can significantly enhance efficiency and customer experience. They can handle routine inquiries, provide information about payment plans, and assist with setting up automatic payments. This automation reduces the workload on human agents and ensures that customers receive timely and consistent information.

Core Components of a Voice Agent

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

What You'll Build in This Tutorial

In this tutorial, you'll learn how to build an AI Voice Assistant for debt collection using VideoSDK. We'll guide you through setting up the development environment, building the agent, and testing it in a playground environment.

Architecture and Core Concepts

High-Level Architecture Overview

The AI Voice Agent processes user speech through a series of components. Initially, the Speech-to-Text (STT) engine converts the audio into text. This text is then processed by a Large Language Model (LLM) to determine the appropriate response. Finally, the Text-to-Speech (TTS) engine converts the response back into audio, which is played back to the user.
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
12    Agent->>TTS: Convert Text to Speech
13    TTS->>User: Speak Response
14

Understanding Key Concepts in the VideoSDK Framework

  • Agent: The core class representing your bot, responsible for managing interactions.
  • CascadingPipeline: Manages the flow of audio processing from STT to LLM to TTS. Learn more about the

    Cascading pipeline in AI voice Agents

    .
  • VAD & TurnDetector: These components help the agent know when to listen and when to speak, ensuring smooth interaction. Explore the

    Turn detector for AI voice Agents

    .

Setting Up the Development Environment

Prerequisites

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

Step 1: Create a Virtual Environment

Create a virtual environment to manage your project dependencies. Run the following command in your terminal:
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

Let's start by presenting the complete code for our 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 professional and empathetic AI Voice Assistant specialized in debt collection. Your primary role is to engage with customers in a respectful and understanding manner to discuss their outstanding debts and explore payment options. You are capable of providing information about payment plans, due dates, and account balances. You can also assist customers in setting up automatic payments and remind them of upcoming due dates. However, you must always maintain a respectful tone and ensure that the customer feels heard and understood. You are not authorized to provide financial advice or make any changes to the customer's account without their explicit consent. Always remind customers to contact a financial advisor for personalized financial advice. Your interactions should comply with all relevant regulations and guidelines for debt collection practices."
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
Now, let's break down this code into smaller, manageable parts to understand its functionality.

Step 4.1: Generating a VideoSDK Meeting ID

To create a meeting ID, use the VideoSDK API. Here's an example using curl:
1curl -X POST \
2  https://api.videosdk.live/v1/rooms \
3  -H "Authorization: Bearer YOUR_API_KEY" \
4  -H "Content-Type: application/json" \
5  -d '{"name": "Debt Collection Session"}'
6

Step 4.2: Creating the Custom Agent Class

The MyVoiceAgent class extends the Agent class and defines the behavior of our voice assistant.
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
This class initializes the agent with specific instructions and defines actions when entering or exiting a session.

Step 4.3: Defining the Core Pipeline

The CascadingPipeline orchestrates the flow of audio processing.
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
Each component in the pipeline plays a crucial role:

Step 4.4: Managing the Session and Startup Logic

The start_session function manages the agent's session lifecycle.
1async def start_session(context: JobContext):
2    agent = MyVoiceAgent()
3    conversation_flow = ConversationFlow(agent)
4    pipeline = CascadingPipeline(...)
5    session = AgentSession(agent=agent, pipeline=pipeline, conversation_flow=conversation_flow)
6    try:
7        await context.connect()
8        await session.start()
9        await asyncio.Event().wait()
10    finally:
11        await session.close()
12        await context.shutdown()
13
The make_context function configures the session with RoomOptions.
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 using the WorkerJob class.
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 your AI Voice Agent, execute the script with Python:
1python main.py
2

Step 5.2: Interacting with the Agent in the Playground

After starting the agent, you'll see a playground link in the console. Open it in your browser to interact with your AI Voice Assistant.

Advanced Features and Customizations

Extending Functionality with Custom Tools

You can extend the agent's functionality by integrating custom tools using the function_tool feature. This allows you to add specific capabilities tailored to your needs.

Exploring Other Plugins

VideoSDK supports various plugins for STT, LLM, and TTS. Experiment with different combinations 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 key's validity and permissions.

Audio Input/Output Problems

Verify your microphone and speaker settings. Ensure the correct audio devices are selected in your system settings.

Dependency and Version Conflicts

Use a virtual environment to manage dependencies. Check for version compatibility issues and resolve conflicts by updating packages.

Conclusion

Summary of What You've Built

You've successfully built an AI Voice Assistant for debt collection using VideoSDK. This agent can interact with users, providing information and assistance regarding debt-related queries.

Next Steps and Further Learning

Explore additional features of VideoSDK and consider integrating more advanced AI capabilities. Continue learning by experimenting with different plugins and customizing your agent further. For a comprehensive guide, refer to the

Voice Agent Quick Start Guide

and explore the

AI voice Agent core components overview

and

AI voice Agent Sessions

. For deployment, check out the

AI voice Agent deployment

.

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