Build an AI Voice Agent with Neural TTS

Step-by-step guide to building an AI Voice Agent using Neural TTS with VideoSDK, complete with code examples.

Introduction to AI Voice Agents in Neural TTS

What is an AI

Voice Agent

?

An AI

Voice Agent

is a software application that uses artificial intelligence to interact with users through voice. These agents can understand spoken language, process the information, and respond in a conversational manner. They are designed to perform tasks such as answering questions, providing information, and assisting with various applications.

Why are they important for the neural TTS industry?

AI Voice Agents are crucial in the neural TTS industry because they leverage advanced text-to-speech technologies to deliver more natural and human-like interactions. Neural TTS systems use deep learning models to generate speech that closely mimics human intonation and rhythm, enhancing the user experience in applications such as virtual assistants, customer service bots, and accessibility tools.

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 the generated text back into spoken language.

What You'll Build in This Tutorial

In this tutorial, you will learn how to build an AI

Voice Agent

using the VideoSDK framework, integrating neural TTS capabilities to create a responsive and interactive voice application.

Architecture and Core Concepts

High-Level Architecture Overview

The architecture of an AI

Voice Agent

involves several key components working in tandem to process user input and generate responses. The process begins with capturing user speech, which is then converted to text using an STT engine. The text is processed by an LLM to generate a response, which is then converted back to speech using a TTS engine.
Diagram

Understanding Key Concepts in the VideoSDK Framework

  • Agent: The core class representing your bot, responsible for managing interactions.
  • Cascading Pipeline in AI voice Agents

    :
    The flow of audio processing, handling the sequence of STT, LLM, and TTS operations.
  • VAD & TurnDetector: Tools that help the agent determine when to listen and when to speak, ensuring smooth interactions.

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:
1python3 -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 key:
1VIDEOSDK_API_KEY=your_api_key_here
2

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

Here is the complete, runnable code block for building your AI Voice Agent:
1import asyncio, os
2from videosdk.agents import Agent, AgentSession, CascadingPipeline, JobContext, RoomOptions, WorkerJob, ConversationFlow
3from videosdk.plugins.silero import [Silero Voice Activity Detection](https://docs.videosdk.live/ai_agents/plugins/silero-vad)
4from videosdk.plugins.turn_detector import [Turn detector for AI voice Agents](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 friendly and knowledgeable AI Voice Agent specializing in providing information and assistance related to neural text-to-speech (TTS) technology. Your primary role is to educate users about neural TTS, its applications, and how it can be integrated into various systems.\n\nCapabilities:\n1. Explain the basics of neural TTS technology, including how it works and its advantages over traditional TTS systems.\n2. Provide examples of applications where neural TTS can be effectively utilized, such as in accessibility tools, virtual assistants, and customer service bots.\n3. Assist users in understanding how to implement neural TTS in their own projects, offering guidance on available tools and platforms.\n4. Answer frequently asked questions about neural TTS, including technical specifications and performance metrics.\n\nConstraints and Limitations:\n1. You are not a software developer, so you cannot provide detailed coding assistance or troubleshoot specific implementation issues.\n2. You must include a disclaimer that while you provide information on neural TTS, users should consult technical documentation or a professional for in-depth technical guidance.\n3. You cannot provide real-time support or updates on the latest neural TTS developments beyond your training data.\n4. Ensure user privacy and data security by not storing or sharing any personal information provided during interactions."
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/meetings \
2-H "Authorization: Bearer YOUR_API_KEY" \
3-H "Content-Type: application/json"
4
This will return a meeting ID that you can use to join a session.

Step 4.2: Creating the Custom Agent Class

The MyVoiceAgent class is a custom agent that inherits from the Agent class. It defines the behavior of the agent when entering and exiting a session:
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 uses the agent_instructions to guide its interactions with users.

Step 4.3: Defining the Core Pipeline

The CascadingPipeline is responsible for managing the flow of audio processing. It integrates multiple 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
Each component plays a specific role in processing and responding to user input.

Step 4.4: Managing the Session and Startup Logic

The start_session function initializes the agent session and manages its lifecycle:
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 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:
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 following command in your terminal:
1python main.py
2
This will start the agent, and you will see a playground link in the console output.

Step 5.2: Interacting with the Agent in the Playground

Use the playground link to join the session and interact with your AI Voice Agent. You can speak to the agent and receive responses based on the neural TTS capabilities.

Advanced Features and Customizations

Extending Functionality with Custom Tools

The VideoSDK framework allows you to extend your agent's functionality by integrating custom tools. These tools can be used to add specific features or handle unique tasks within your application.

Exploring Other Plugins

The framework supports various plugins for STT, LLM, and TTS. You can explore options like Cartesia for STT, Google Gemini for LLM, and Deepgram for TTS to suit your project's needs.

Troubleshooting Common Issues

API Key and Authentication Errors

Ensure that your API key is correctly configured in the .env file. Double-check the key's validity and permissions.

Audio Input/Output Problems

Check your audio device settings and ensure that your microphone and speakers are correctly configured.

Dependency and Version Conflicts

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

Conclusion

Summary of What You've Built

In this tutorial, you've built a fully functional AI Voice Agent using neural TTS technology. You've learned about the architecture, setup, and implementation of the agent using the VideoSDK framework.

Next Steps and Further Learning

Explore additional features and plugins to enhance your AI Voice Agent. Consider integrating more advanced natural language processing capabilities or experimenting with different TTS models to improve interaction quality.

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