Build an AI Voice Agent with Mozilla DeepSpeech

Implement an AI Voice Agent using Mozilla DeepSpeech and VideoSDK. Follow this step-by-step guide with code examples.

Introduction to AI Voice Agents in Mozilla DeepSpeech

What is an AI

Voice Agent

?

AI Voice Agents are sophisticated systems designed to interact with users through voice commands. They leverage advanced technologies such as speech-to-text (STT), natural language processing, and text-to-speech (TTS) to understand and respond to user queries. These agents can perform a variety of tasks, from simple information retrieval to complex decision-making processes.

Why are they important for the Mozilla DeepSpeech industry?

In the context of Mozilla DeepSpeech, AI Voice Agents play a crucial role in making speech recognition technology accessible and user-friendly. They enable seamless integration of voice interfaces in applications, enhancing user experience and accessibility. Use cases include virtual assistants, customer service bots, and interactive voice response systems.

Core Components of a

Voice Agent

  • STT (Speech-to-Text): Converts spoken language into text.
  • LLM (Large Language Model): Processes and understands the text to 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 Mozilla DeepSpeech and VideoSDK. The agent will be capable of understanding and responding to user queries in real-time.

Architecture and Core Concepts

High-Level Architecture Overview

The AI

Voice Agent

 processes user input through a series of steps. It starts with capturing user speech, converting it to text using STT, processing the text with an LLM, and finally converting the response back to speech using TTS.
Diagram

Understanding Key Concepts in the VideoSDK Framework

Setting Up the Development Environment

Prerequisites

  • Python 3.11+
  • VideoSDK Account (sign up at app.videosdk.live)

Step 1: Create a Virtual Environment

To avoid conflicts between dependencies, create a virtual environment:
1python -m venv myenv
2source myenv/bin/activate  # On Windows use `myenv\Scripts\activate`
3

Step 2: Install Required Packages

Install the necessary packages using pip:
1pip install videosdk-agents videosdk-plugins-silero videosdk-plugins-turn-detector videosdk-plugins-deepgram videosdk-plugins-openai videosdk-plugins-elevenlabs
2

Step 3: Configure API Keys in a .env file

Create a .env file in your project directory and add your API keys:
1VIDEOSDK_API_KEY=your_api_key_here
2

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

Here’s the complete 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 a knowledgeable AI Voice Agent specializing in speech-to-text conversion using Mozilla DeepSpeech. Your primary role is to assist users in understanding and implementing Mozilla DeepSpeech for various applications. You can provide detailed explanations, answer technical questions, and guide users through setup and troubleshooting processes. However, you are not a substitute for professional technical support and should advise users to consult official documentation or technical experts for complex issues. You must ensure that users are aware of the limitations of Mozilla DeepSpeech, such as language support and accuracy variations, and encourage them to test the system thoroughly in their specific use cases."
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=[OpenAI LLM Plugin for voice agent](https://docs.videosdk.live/ai_agents/plugins/llm/openai)(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, use the following curl command:
1curl -X POST "https://api.videosdk.live/v1/rooms" \
2-H "Authorization: Bearer YOUR_API_KEY" \
3-H "Content-Type: application/json" \
4-d '{"name":"My Meeting"}'
5

Step 4.2: Creating the Custom Agent Class

The MyVoiceAgent class extends the Agent class and defines the agent's behavior. The on_enter and on_exit methods handle the agent's greetings and farewells, ensuring a smooth interaction flow.

Step 4.3: Defining the Core Pipeline

The CascadingPipeline is the backbone of the agent, connecting various plugins:
  • DeepgramSTT: Converts speech to text.
  • OpenAILLM: Processes text to generate intelligent responses.
  • ElevenLabsTTS: Converts text back to speech.
  • SileroVAD & TurnDetector: Manage voice

    activity detection

     and turn-taking.

Step 4.4: Managing the Session and Startup Logic

The start_session function initializes the agent session and pipeline, starting the agent's interaction loop. The make_context function sets up the room options for the VideoSDK session, and the main block runs the agent.

Running and Testing the Agent

Step 5.1: Running the Python Script

Execute the script using:
1python main.py
2

Step 5.2: Interacting with the Agent in the

AI Agent playground

Once the script is running, use the playground link provided in the console to interact with your agent. This allows you to test the agent's capabilities in a controlled environment.

Advanced Features and Customizations

Extending Functionality with Custom Tools

You can extend your agent's functionality by integrating custom tools using the function_tool concept, allowing for more specialized interactions.

Exploring Other Plugins

Explore other STT/LLM/TTS options to suit your application's needs, such as Google Gemini or Deepgram for cost-effective solutions.

Troubleshooting Common Issues

API Key and Authentication Errors

Ensure your API keys are correctly configured in the .env file and that your VideoSDK account is active.

Audio Input/Output Problems

Check your microphone and speaker settings, and ensure the correct devices are selected.

Dependency and Version Conflicts

Ensure all dependencies are installed with compatible versions, and consider using a virtual environment to manage them.

Conclusion

Summary of What You’ve Built

You have successfully built an AI Voice Agent using Mozilla DeepSpeech and VideoSDK, capable of real-time interaction and response.

Next Steps and Further Learning

Explore additional features and plugins to enhance your agent's capabilities, and consider deploying it in real-world applications for further testing and improvement.

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