Wakeword: Your Gateway to Edge AI Audio Recognition 🎤

Welcome to the Wakeword repository! This project serves as a starter guide for implementing wakeword and keyword spotting using the ESP32S3 platform. With a ready-to-go machine learning model, you can dive straight into audio command recognition.

Table of Contents

• Introduction
• Features
• Getting Started
• Installation
• Usage
• Project Structure
• Contributing
• License
• Acknowledgments
• Releases

Introduction

In today's world, voice recognition technology is becoming essential in various applications. The Wakeword project simplifies the process of integrating wakeword detection into your ESP32S3-based devices. Whether you're building a smart home assistant or a voice-controlled gadget, this project provides a solid foundation.

Features

• Ready-to-go ML Model: Pre-trained models for quick deployment.
• ESP32S3 Compatibility: Designed specifically for the ESP32S3 DevKitC-1.
• Edge AI: Run machine learning models directly on the device for low latency.
• Easy Setup: Simple instructions to get started.
• Community Support: Join a growing community of developers.

Getting Started

To begin, you need to set up your development environment. Ensure you have the following:

• ESP32S3 DevKitC-1: The hardware platform for this project.
• Arduino IDE: The preferred IDE for ESP32 development.
• Basic knowledge of C/C++: Familiarity with programming will help you customize the project.

Prerequisites

1. Install the Arduino IDE.
2. Set up the ESP32 board in the Arduino IDE.
3. Download the necessary libraries for audio processing.

Installation

To install the project, follow these steps:

1. Clone the repository:

   git clone https://github.com/techAli1996/wakeword.git

2. Navigate to the project directory:

   cd wakeword

3. Open the project in the Arduino IDE.

4. Install any required libraries listed in the README file.

Usage

After installation, you can start using the project. Follow these steps:

1. Connect your ESP32S3 to your computer.
2. Select the appropriate board and port in the Arduino IDE.
3. Upload the code to your device.
4. Open the Serial Monitor to see the output.

Testing the Model

To test the wakeword detection, you can use audio commands. The model is designed to recognize specific keywords. Speak clearly and monitor the output in the Serial Monitor.

Project Structure

The repository contains the following key directories and files:

• /src: Contains the main source code.
• /models: Pre-trained machine learning models.
• /lib: Libraries used in the project.
• README.md: This documentation file.

Contributing

We welcome contributions! If you want to improve the project, please follow these steps:

1. Fork the repository.
2. Create a new branch.
3. Make your changes.
4. Submit a pull request.

Your contributions help us improve the project and make it more useful for everyone.

License

This project is licensed under the MIT License. See the LICENSE file for more details.

Acknowledgments

• Thanks to the contributors of the ESP32 and Edge Impulse communities.
• Special thanks to the developers who created the libraries used in this project.

Releases

For the latest updates and downloadable files, visit our Releases section. Here, you can find the necessary files to download and execute for your project.

To ensure you have the latest version, always check the Releases section.

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With this guide, you are now ready to explore the world of wakeword detection using the ESP32S3. We hope you find this project useful and encourage you to share your experiences with the community. Happy coding!