https://github.com/selmonix/otosakukws-ios
OtosakuKWS is a lightweight, on-device keyword spotting engine for iOS that detects speech commands in real time. This project relies on a CRNN CoreML model for efficient and accurate voice command recognition. 🐙🌟
https://github.com/selmonix/otosakukws-ios
audio coreml ios keyword-spotting kws machine-learning on-device real-time signal-processing speech-recognition swift wakeword
Last synced: 4 months ago
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OtosakuKWS is a lightweight, on-device keyword spotting engine for iOS that detects speech commands in real time. This project relies on a CRNN CoreML model for efficient and accurate voice command recognition. 🐙🌟
- Host: GitHub
- URL: https://github.com/selmonix/otosakukws-ios
- Owner: selmonix
- Created: 2025-06-14T21:39:24.000Z (4 months ago)
- Default Branch: main
- Last Pushed: 2025-06-22T02:10:50.000Z (4 months ago)
- Last Synced: 2025-06-22T03:23:07.637Z (4 months ago)
- Topics: audio, coreml, ios, keyword-spotting, kws, machine-learning, on-device, real-time, signal-processing, speech-recognition, swift, wakeword
- Language: Swift
- Size: 1.24 MB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# OtosakuKWS-iOS 🎤
[](https://github.com/selmonix/OtosakuKWS-iOS/releases)
Welcome to the **OtosakuKWS-iOS** repository! This project provides a lightweight, on-device keyword spotting engine for iOS. It leverages CoreML and supports real-time audio streaming. With this tool, you can implement efficient speech recognition and wake word detection in your iOS applications.
## Table of Contents
- [Features](#features)
- [Getting Started](#getting-started)
- [Prerequisites](#prerequisites)
- [Installation](#installation)
- [Usage](#usage)
- [Contributing](#contributing)
- [License](#license)
- [Acknowledgments](#acknowledgments)
## Features
- **On-Device Processing**: No need for an internet connection. All processing happens on the device.
- **Real-Time Audio Streaming**: Capture and analyze audio input in real-time.
- **CoreML Integration**: Utilize Apple's powerful machine learning framework for efficient processing.
- **Lightweight**: Designed to run smoothly on iOS devices without draining resources.
- **Keyword Spotting**: Detect specific keywords or phrases with high accuracy.
- **Customizable**: Easily modify the model to suit your specific needs.
## Getting Started
To get started with **OtosakuKWS-iOS**, follow the instructions below.
### Prerequisites
Before you begin, ensure you have the following:
- An iOS device or simulator running iOS 12.0 or later.
- Xcode 12 or later installed on your machine.
- Basic knowledge of Swift and iOS development.
### Installation
1. Clone the repository:
```bash
git clone https://github.com/selmonix/OtosakuKWS-iOS.git
cd OtosakuKWS-iOS
```
2. Open the project in Xcode:
```bash
open OtosakuKWS-iOS.xcodeproj
```
3. Build and run the project on your device or simulator.
4. For pre-built binaries, visit the [Releases](https://github.com/selmonix/OtosakuKWS-iOS/releases) section to download the latest version.
## Usage
After installation, you can start using the OtosakuKWS-iOS engine in your application.
### Basic Setup
1. Import the framework in your Swift files:
```swift
import OtosakuKWS
```
2. Initialize the keyword spotting engine:
```swift
let kwsEngine = KeywordSpottingEngine()
```
3. Start the audio stream:
```swift
kwsEngine.startAudioStream()
```
4. Set up your keyword:
```swift
kwsEngine.setKeyword("Hello")
```
5. Handle the detected keywords:
```swift
kwsEngine.onKeywordDetected = { keyword in
print("Detected keyword: \(keyword)")
}
```
### Example
Here is a simple example of how to use the engine:
```swift
import UIKit
import OtosakuKWS
class ViewController: UIViewController {
let kwsEngine = KeywordSpottingEngine()
override func viewDidLoad() {
super.viewDidLoad()
kwsEngine.onKeywordDetected = { keyword in
print("Detected keyword: \(keyword)")
}
kwsEngine.setKeyword("Hello")
kwsEngine.startAudioStream()
}
}
```
## Contributing
We welcome contributions! If you have suggestions for improvements or want to add features, please follow these steps:
1. Fork the repository.
2. Create a new branch for your feature or bug fix.
3. Make your changes.
4. Submit a pull request with a description of your changes.
## License
This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for details.
## Acknowledgments
- Thanks to the CoreML team for providing a robust framework for machine learning on iOS.
- Special thanks to the open-source community for their contributions and support.
For more details, check the [Releases](https://github.com/selmonix/OtosakuKWS-iOS/releases) section to download the latest version and get started with your implementation!