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Skiza-App: Speech Emotion Recognition for Kenyan Swahili

Welcome to Skiza-App! This project focuses on developing and deploying a model to recognize emotions from speech, specifically tailored for Kenyan Swahili. The model leverages advanced machine learning techniques and is deployed using Streamlit for the web interface and user interaction.

Table of Contents

1. Installation
2. Usage
3. Features
4. Model Evaluation
5. Deployment
6. Contributing
7. License

Installation

To get started with Skiza-App, follow these steps to set up your environment and install the necessary dependencies:

1. Clone the Repository

git clone https://github.com/estherdatascientist/Swahili-Speech-Emotion-Recognition-System.git
cd Swahili-Speech-Emotion-Recognition-System

2. Create a Virtual Environment

It’s recommended to use a virtual environment to manage dependencies:

python -m venv venv
source venv/bin/activate  # On Windows use `venv\Scripts\activate`

3. Install Dependencies

Install the required Python packages using pip:

pip install -r requirements.txt

4. Download and Prepare Data

Ensure you have the Swahili speech dataset. Update the data_dir path in the configuration files to point to your dataset location.

5. Install Streamlit

If not included in requirements.txt, you may need to install Streamlit separately:

pip install streamlit

Usage

1. Training the Model

To train the model, open and run the Jupyter notebook main.ipynb. This notebook will load the dataset, preprocess the audio files, extract features, and train various models. The best-performing model (Stacking Model with KNN as the meta-learner) will be saved in the models directory.

jupyter notebook main.ipynb

2. Running the Streamlit App Locally

To start the Streamlit app locally, use:

streamlit run app.py

This will open a new browser tab with the Streamlit app, allowing you to upload audio files and get emotion predictions.

3. Accessing the Web Version

You can also access the Skiza-App online at the following URL:

• Skiza-AI Web App

This web version provides the same functionality as the local app, allowing you to upload audio files, folders, or long audio files for emotion analysis.

Features

• Emotion Recognition: Detects emotions from audio clips in Kenyan Swahili.
• Real-time Predictions: Provides immediate feedback through the Streamlit interface.
• User-friendly Interface: Streamlit provides a simple and intuitive web interface for uploading and analyzing audio files.
• Model Evaluation: Includes model evaluation metrics such as accuracy, confusion matrix, and ROC curves.

Model Evaluation

The Stacking Model with KNN as the meta-learner achieved an accuracy of 83%, making it the best performer among the tested models. For details on model performance and evaluation, refer to the evaluation module in the codebase.

Deployment

Skiza-App is deployed using:

• Streamlit: For building the web interface and allowing users to upload and analyze audio files.
• Streamlit Server: Manages the web app environment and user interactions.

Contributing

Contributions are welcome! If you would like to contribute to this project, please follow these steps:

1. Fork the repository.
2. Create a feature branch (git checkout -b feature/YourFeature).
3. Commit your changes (git commit -am 'Add new feature').
4. Push to the branch (git push origin feature/YourFeature).
5. Open a pull request.

Please make sure to adhere to the coding standards and include tests for new features.

Acknowledgments

Special thanks to the data collection participants and the following contributors for their valuable input and support:

• Doreen Wanjiru
• Gregory Mikuro
• Samuel Hellen
• Ian Korir

License

This project is licensed under the GNU General Public License (GPL). See the LICENSE file for details.

Thank you for using and contributing to Skiza-App!

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