High Accuracy Speech Recognition with AI Voice Agents

Build a high accuracy speech recognition AI Voice Agent with VideoSDK. Follow our step-by-step guide with complete code examples.

Introduction to AI Voice Agents in High Accuracy Speech Recognition

What is an AI

Voice Agent

?

An AI

Voice Agent

 is a sophisticated software application designed to interact with users through voice commands. These agents leverage technologies like speech-to-text (STT), natural language processing (NLP), and text-to-speech (TTS) to understand and respond to user queries. They are often used in applications ranging from virtual assistants to customer service bots.

Why are they important for the high accuracy speech recognition industry?

AI Voice Agents play a crucial role in industries where precision in understanding spoken language is paramount. In healthcare, for instance, they can assist in scheduling appointments or providing information about symptoms, ensuring that users receive accurate and timely responses. High accuracy speech recognition is vital in these contexts to avoid misunderstandings that could lead to incorrect advice or actions.

Core Components of a

Voice Agent

  • Speech-to-Text (STT): Converts spoken language into written text.
  • Large Language Models (LLM): Processes and understands the text to generate appropriate responses.
  • Text-to-Speech (TTS): Converts text responses back into spoken language, allowing for seamless interaction.

What You'll Build in This Tutorial

In this tutorial, you will learn how to build a high accuracy speech recognition AI

Voice Agent

 using the VideoSDK framework. We will guide you through setting up the development environment, building the agent, and testing it in a real-world scenario.

Architecture and Core Concepts

High-Level Architecture Overview

The AI

Voice Agent

 processes user speech through a series of steps: capturing audio, converting it to text, understanding the query, generating a response, and finally converting it back to audio for the user. This flow ensures that the agent can interact naturally and efficiently with users.
Diagram

Understanding Key Concepts in the VideoSDK Framework

  • Agent: The core class representing your bot, responsible for managing the interaction flow.

  • Cascading Pipeline in AI voice Agents

    :     Manages the sequence of processing steps from STT to LLM to TTS, ensuring smooth data flow.

  • VAD & TurnDetector

    :     These components help the agent determine when to listen and when to speak, ensuring efficient interaction without interruptions.

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 your project 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-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 keys:
1VIDEOSDK_API_KEY=your_api_key_here
2

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

Below is the complete, runnable 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 [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 [OpenAI LLM Plugin for voice agent](https://docs.videosdk.live/ai_agents/plugins/llm/openai)
7from videosdk.plugins.elevenlabs import ElevenLabsTTS
8from typing import AsyncIterator
9
10pre_download_model()
11
12agent_instructions = "You are a highly accurate speech recognition AI Voice Agent designed to assist users in various domains with precision and efficiency. Your primary persona is that of a 'helpful healthcare assistant'. Your capabilities include: 1) Accurately transcribing spoken language into text with high precision. 2) Answering questions related to common health symptoms and providing general advice. 3) Assisting users in scheduling medical appointments by understanding their spoken requests. 4) Providing information on healthcare services and facilities. However, you must adhere to the following constraints and limitations: 1) You are not a licensed medical professional, and you must always include a disclaimer advising users to consult a healthcare provider for medical advice. 2) You should not store or share any personal health information. 3) Your responses should be based on publicly available information and should not include personal opinions or unverified data. 4) You must ensure user privacy and data security at all times."
13
14class MyVoiceAgent(Agent):
15    def __init__(self):
16        super().__init__(instructions=agent_instructions)
17    async def on_enter(self): await self.session.say("Hello! How can I help?")
18    async def on_exit(self): await self.session.say("Goodbye!")
19
20async def start_session(context: JobContext):
21    agent = MyVoiceAgent()
22    conversation_flow = ConversationFlow(agent)
23
24    pipeline = CascadingPipeline(
25        stt=DeepgramSTT(model="nova-2", language="en"),
26        llm=OpenAILLM(model="gpt-4o"),
27        tts=ElevenLabsTTS(model="eleven_flash_v2_5"),
28        vad=SileroVAD(threshold=0.35),
29        turn_detector=TurnDetector(threshold=0.8)
30    )
31
32    session = [AI voice Agent Sessions](https://docs.videosdk.live/ai_agents/core-components/agent-session)(
33        agent=agent,
34        pipeline=pipeline,
35        conversation_flow=conversation_flow
36    )
37
38    try:
39        await context.connect()
40        await session.start()
41        await asyncio.Event().wait()
42    finally:
43        await session.close()
44        await context.shutdown()
45
46def make_context() -> JobContext:
47    room_options = RoomOptions(
48        name="VideoSDK Cascaded Agent",
49        playground=True
50    )
51
52    return JobContext(room_options=room_options)
53
54if __name__ == "__main__":
55    job = WorkerJob(entrypoint=start_session, jobctx=make_context)
56    job.start()
57

Step 4.1: Generating a VideoSDK Meeting ID

To interact with your AI Voice Agent, you need a meeting ID. Use the following curl command to generate one:
1curl -X POST "https://api.videosdk.live/v1/meetings" \
2-H "Authorization: YOUR_API_KEY" \
3-H "Content-Type: application/json"
4

Step 4.2: Creating the Custom Agent Class

The MyVoiceAgent class extends the Agent class, providing custom behavior for when the agent enters or exits a session. This customization allows the agent to greet users and say goodbye, enhancing user interaction.

Step 4.3: Defining the Core Pipeline

The CascadingPipeline is a crucial component that defines the flow of data through various processing stages:
  • STT (DeepgramSTT): Converts speech to text using the Deepgram API.
  • LLM (OpenAILLM): Processes the text and generates responses using OpenAI's GPT-4.
  • TTS (ElevenLabsTTS): Converts text responses back to speech.
  • VAD (SileroVAD): Detects when the user is speaking to trigger the STT process.
  • TurnDetector: Manages conversational turns, ensuring the agent responds at the right time.

Step 4.4: Managing the Session and Startup Logic

The start_session function initializes the agent, pipeline, and session, managing the entire interaction lifecycle. The make_context function sets up the environment, including room options for testing. Finally, the if __name__ == "__main__": block ensures the agent starts correctly when the script is run.

Running and Testing the Agent

Step 5.1: Running the Python Script

To start your AI Voice Agent, run the script using:
1python main.py
2

Step 5.2: Interacting with the Agent in the Playground

Once the script is running, you will see a link to the VideoSDK playground in the console. Click the link to join the session and interact with your agent. You can speak to the agent and see how it responds in real-time.

Advanced Features and Customizations

Extending Functionality with Custom Tools

The VideoSDK framework allows you to create custom tools to extend your agent's capabilities. By defining new function_tool classes, you can add specialized processing or data handling features.

Exploring Other Plugins

While this tutorial uses specific plugins, VideoSDK supports various STT, LLM, and TTS options. Explore the documentation to find plugins that best suit your needs.

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 in your VideoSDK account.

Audio Input/Output Problems

Check your microphone and speaker settings to ensure they are configured correctly. Test with different devices if issues persist.

Dependency and Version Conflicts

Ensure all packages are up-to-date and compatible with Python 3.11+. Use a virtual environment to avoid conflicts with other projects.

Conclusion

Summary of What You've Built

In this tutorial, you built a high accuracy speech recognition AI Voice Agent using VideoSDK. You learned how to set up the environment, build the agent, and test it in a real-world scenario.

Next Steps and Further Learning

Explore additional features and plugins offered by VideoSDK to enhance your agent's capabilities. Consider integrating with other APIs to expand the agent's functionality.

Start Building With Free $20 Balance

No credit card required to start.

Want to level-up your learning? Subscribe now

Subscribe to our newsletter for more tech based insights

FAQ