Introduction to AI Voice Agents in how to build an ai voice agent
What is an AI Voice Agent?
An AI Voice Agent is a software application designed to interact with users through voice commands. It processes spoken language, interprets the intent, and responds appropriately, often mimicking human-like conversations. These agents leverage technologies such as Speech-to-Text (STT), Text-to-Speech (TTS), and Language Learning Models (LLM) to facilitate seamless communication.
Why are they important for the how to build an ai voice agent industry?
AI Voice Agents are crucial in various industries, providing customer support, automating routine tasks, and enhancing user experiences. In the context of building AI voice agents, they serve as practical examples to understand the integration of multiple AI technologies.
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 text responses back into spoken language.
What You'll Build in This Tutorial
In this tutorial, you will build a fully functional AI Voice Agent using the VideoSDK framework. This agent will guide users on how to build AI voice agents, providing step-by-step instructions and answering common questions. For a comprehensive overview, refer to the
Voice Agent Quick Start Guide
.Architecture and Core Concepts
High-Level Architecture Overview
The AI Voice Agent architecture involves a data flow that starts with user speech, which is converted to text using STT. The text is then processed by an LLM to generate a response, which is converted back to speech using TTS. This cycle repeats as the agent interacts with the user.
Understanding Key Concepts in the VideoSDK Framework
- Agent: The core class representing your bot, handling interactions and responses.
- CascadingPipeline: Manages the flow of audio processing, linking STT, LLM, and TTS components. Learn more about the
Cascading pipeline in AI voice Agents
. - VAD & TurnDetector: These components help the agent determine when to listen and when to speak, ensuring smooth interaction. Explore the
Turn detector for AI voice Agents
for more details.
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
To avoid conflicts, create a virtual environment:
bash
python -m venv voice-agent-env
source voice-agent-env/bin/activate # On Windows use `voice-agent-env\Scripts\activate`
Step 2: Install Required Packages
Install the necessary packages using pip:
bash
pip install videosdk
pip install python-dotenvStep 3: Configure API Keys in a .env file
Create a
.env file in your project directory and add your VideoSDK API keys:
VIDEOSDK_API_KEY=your_api_key_hereBuilding the AI Voice Agent: A Step-by-Step Guide
Here is the complete, runnable code for the 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 an AI Voice Agent specialized in guiding users on 'how to build an AI voice agent'. Your persona is that of a knowledgeable and friendly tech mentor. Your primary capabilities include providing step-by-step instructions, offering tips on best practices, and suggesting tools and frameworks for building AI voice agents. You can also answer common questions related to AI voice agent development. However, you must clarify that you are not a substitute for professional software development training and recommend consulting with experienced developers for complex issues. Always encourage users to test their implementations thoroughly before deployment."
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()
63Step 4.1: Generating a VideoSDK Meeting ID
To interact with the AI Voice Agent, you need a meeting ID. You can generate one using the VideoSDK API:
bash
curl -X POST "https://api.videosdk.live/v1/meetings" \
-H "Authorization: Bearer YOUR_API_KEY" \
-H "Content-Type: application/json"Step 4.2: Creating the Custom Agent Class
The
MyVoiceAgent class extends the Agent class, defining the agent's behavior:
python
class MyVoiceAgent(Agent):
def __init__(self):
super().__init__(instructions=agent_instructions)
async def on_enter(self): await self.session.say("Hello! How can I help?")
async def on_exit(self): await self.session.say("Goodbye!")
This class initializes the agent with specific instructions and defines actions upon entering and exiting a session.Step 4.3: Defining the Core Pipeline
The
CascadingPipeline orchestrates the flow of audio processing:
python
pipeline = CascadingPipeline(
stt=DeepgramSTT(model="nova-2", language="en"),
llm=OpenAILLM(model="gpt-4o"),
tts=ElevenLabsTTS(model="eleven_flash_v2_5"),
vad=SileroVAD(threshold=0.35),
turn_detector=TurnDetector(threshold=0.8)
)
Each component in the pipeline has a specific role, from converting speech to text, processing the text, and converting the response back to speech. For more information on the TTS component, check out the ElevenLabs TTS Plugin for voice agent
.Step 4.4: Managing the Session and Startup Logic
The
start_session function manages the session lifecycle:
```python
async def start_session(context: JobContext):1# Create agent and conversation flow
2agent = MyVoiceAgent()
3conversation_flow = ConversationFlow(agent)
4
5# Create pipeline
6pipeline = CascadingPipeline(
7 stt=DeepgramSTT(model="nova-2", language="en"),
8 llm=OpenAILLM(model="gpt-4o"),
9 tts=ElevenLabsTTS(model="eleven_flash_v2_5"),
10 vad=SileroVAD(threshold=0.35),
11 turn_detector=TurnDetector(threshold=0.8)
12)
13
14session = AgentSession(
15 agent=agent,
16 pipeline=pipeline,
17 conversation_flow=conversation_flow
18)
19
20try:
21 await context.connect()
22 await session.start()
23 # Keep the session running until manually terminated
24 await asyncio.Event().wait()
25finally:
26 # Clean up resources when done
27 await session.close()
28 await context.shutdown()1This function sets up the agent, pipeline, and conversation flow, and handles the connection and cleanup processes. For a deeper understanding of the session management, refer to the [AI voice Agent Sessions](https://docs.videosdk.live/ai_agents/core-components/agent-session).
2
3## Running and Testing the Agent
4
5### Step 5.1: Running the Python Script
6To run the agent, execute the Python script:
7bash
python main.py
```
Step 5.2: Interacting with the Agent in the Playground
Once the script is running, a playground link will be displayed in the console. Use this link to join the session and interact with the agent. You can test the agent's responses and functionality.
Advanced Features and Customizations
Extending Functionality with Custom Tools
The VideoSDK framework allows you to extend the agent's capabilities by integrating custom tools, enhancing the agent's functionality.
Exploring Other Plugins
Beyond the default plugins, you can explore other STT, LLM, and TTS options to better suit your specific needs. For instance, consider using the
Deepgram STT Plugin for voice agent
for advanced speech-to-text capabilities or theSilero Voice Activity Detection
for improved voice activity detection.Troubleshooting Common Issues
API Key and Authentication Errors
Ensure your API keys are correctly set in the
.env file and that you have the necessary permissions.Audio Input/Output Problems
Check your microphone and speaker settings, and ensure they are configured correctly.
Dependency and Version Conflicts
Ensure all dependencies are installed with compatible versions as specified in the documentation.
Conclusion
Summary of What You've Built
You have successfully built an AI Voice Agent using the VideoSDK framework, capable of interacting with users and providing guidance on building AI voice agents. For a complete understanding of the components involved, review the
AI voice Agent core components overview
.Next Steps and Further Learning
Explore additional plugins and customizations to enhance your agent's capabilities. Continue learning about AI technologies to build more sophisticated voice agents.
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