https://github.com/techali1996/wakeword
ESP32S3 Wakeword/Keyword Spotting starter project with ready to go ML model
https://github.com/techali1996/wakeword
asr data-collection keyword-spotting ovos python speech-api speech-recognition stt tensorflow tts vad voice-activity-detection wake-word wakeword
Last synced: about 1 month ago
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ESP32S3 Wakeword/Keyword Spotting starter project with ready to go ML model
- Host: GitHub
- URL: https://github.com/techali1996/wakeword
- Owner: techAli1996
- License: apache-2.0
- Created: 2025-04-15T06:46:21.000Z (6 months ago)
- Default Branch: main
- Last Pushed: 2025-08-20T04:29:28.000Z (about 2 months ago)
- Last Synced: 2025-08-20T06:23:24.057Z (about 2 months ago)
- Topics: asr, data-collection, keyword-spotting, ovos, python, speech-api, speech-recognition, stt, tensorflow, tts, vad, voice-activity-detection, wake-word, wakeword
- Language: C
- Size: 4.68 MB
- Stars: 2
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# 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](#introduction)
- [Features](#features)
- [Getting Started](#getting-started)
- [Installation](#installation)
- [Usage](#usage)
- [Project Structure](#project-structure)
- [Contributing](#contributing)
- [License](#license)
- [Acknowledgments](#acknowledgments)
- [Releases](#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:
```bash
git clone https://github.com/techAli1996/wakeword.git
```
2. Navigate to the project directory:
```bash
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](https://github.com/techAli1996/wakeword/releases). 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](https://github.com/techAli1996/wakeword/releases).
---
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!