Low Latency Voice Agents: A Complete Guide

Build low latency AI voice agents with VideoSDK. Step-by-step tutorial with code and testing.

Introduction to AI Voice Agents in Low Latency Voice Agents

What is an AI

Voice Agent

?

AI Voice Agents are software programs designed to interact with users through voice commands. They utilize technologies like Speech-to-Text (STT), Language Learning Models (LLM), and Text-to-Speech (TTS) to process and respond to user inputs. These agents are increasingly used in various industries to automate customer service, provide real-time assistance, and enhance user experience.

Why are they important for the Low Latency Voice Agents Industry?

In industries where quick response times are crucial, such as customer support and real-time data analysis, low latency voice agents play a vital role. They ensure that user interactions are seamless and efficient, reducing the time between a user's query and the agent's response. This efficiency can significantly enhance customer satisfaction and operational productivity.

Core Components of a

Voice Agent

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

What You'll Build in This Tutorial

In this tutorial, you'll learn how to build a low latency

voice agent

 using the VideoSDK framework. We'll cover everything from setting up your development environment to deploying a fully functional

voice agent

.

Architecture and Core Concepts

High-Level Architecture Overview

The architecture of a low latency

voice agent

 involves several key components working together. The process begins with capturing user speech, which is then converted to text using STT. This text is processed by an LLM to generate a response, which is finally converted back to speech using TTS.
Diagram

Understanding Key Concepts in the VideoSDK Framework

  • Agent: The core class representing your bot in the VideoSDK framework. It handles the interaction logic and lifecycle of the voice agent.
  • CascadingPipeline: This defines the flow of audio processing, orchestrating the STT, LLM, and TTS components to work in sequence. For more details, refer to the

    Cascading pipeline in AI voice Agents

    .
  • VAD & TurnDetector: Voice

    Activity Detection

     (VAD) and Turn Detection are critical for determining when the agent should listen or speak, ensuring smooth interaction. You can learn more about the

    Turn detector for AI voice Agents

    .

Setting Up the Development Environment

Prerequisites

  • Python 3.11+: Ensure you have Python installed on your machine.
  • VideoSDK Account: Sign up at app.videosdk.live to access API keys and manage your projects.

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

To build your AI voice agent, we'll start with a complete code overview and then break it down into manageable parts.
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 low latency voice agent designed to assist users in real-time with minimal delay. Your primary role is to act as a helpful customer service representative for a tech company. You are capable of answering questions about product features, troubleshooting common issues, and providing guidance on software updates. You can also escalate complex issues to human support agents when necessary. However, you must operate within the constraints of not providing any personal opinions or making decisions on behalf of the user. Additionally, you must include a disclaimer that technical advice should be verified with official documentation or a human expert. Your responses should be concise, accurate, and delivered with a friendly tone to ensure a positive user experience."
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 interact with your voice agent, you'll need a meeting ID. You can generate one using the VideoSDK API:
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 is where you define the behavior of your voice agent. It inherits from the Agent class and implements methods like on_enter and on_exit to manage interactions when a user joins or leaves the session.

Step 4.3: Defining the Core Pipeline

The CascadingPipeline is central to processing audio data. It orchestrates the STT, LLM, and TTS plugins to convert user speech into text, generate a response, and convert it back to speech. For a comprehensive understanding, see the

AI voice Agent core components overview

.
  • STT: Uses DeepgramSTT to transcribe speech.
  • LLM: Utilizes OpenAILLM to process and generate responses.
  • TTS: Employs ElevenLabsTTS to synthesize speech from text.
  • VAD & TurnDetector: Ensure the agent listens and responds at the right times.

Step 4.4: Managing the Session and Startup Logic

The start_session function initializes the agent and its components, while make_context sets up the environment for the session. The main block at the end of the script starts the agent using these configurations.

Running and Testing the Agent

Step 5.1: Running the Python Script

To start your voice agent, run the Python script:
1python main.py
2

Step 5.2: Interacting with the Agent in the Playground

Once the script is running, you'll receive a playground URL in the console. Use this link to join the session and interact with your agent. You can test its capabilities by asking questions or requesting assistance.

Advanced Features and Customizations

Extending Functionality with Custom Tools

The VideoSDK framework allows you to extend your agent's capabilities by integrating custom tools. This can include additional plugins or custom logic to handle specific tasks.

Exploring Other Plugins

While this tutorial uses specific plugins for STT, LLM, and TTS, you can explore alternatives to suit your needs. Options like Cartesia for STT or Google Gemini for LLM offer different features and performance characteristics.

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 entries.

Audio Input/Output Problems

Verify that your microphone and speaker settings are correctly configured and that your system permissions allow audio access.

Dependency and Version Conflicts

Use a virtual environment to manage dependencies and avoid conflicts. Check the compatibility of installed packages if you encounter errors.

Conclusion

Summary of What You've Built

You've successfully built a low latency voice agent using the VideoSDK framework. This agent can process speech in real-time, providing quick and accurate responses.

Next Steps and Further Learning

Explore additional features and plugins to enhance your agent's capabilities. Consider integrating with other APIs or services to expand its functionality.

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