Moshood-Kausar/Yoruba_CBT_TTS

# Yoruba CBT TTS App This repository contains the Streamlit app and code used as the artifact for the paper: “Evaluating Yoruba Text-to-Speech Systems for Accessible Computer-Based Testing in Visually Impaired Learners.” It uses the facebook/mms-tts-yor model to read Yoruba CBT questions and options aloud for accessible practice. 🔗 **Live App:** yoruba-cbt-tts.streamlit.app ## Features * Yoruba Text-to-Speech using `facebook/mms-tts-yor` * Reads full CBT questions and options aloud (10) * Interactive multiple-choice interface * No Hugging Face token required (uses public model locally) ## Model Used * **Model:** `facebook/mms-tts-yor` * Loaded locally using Hugging Face `transformers` * Works without API calls or authentication ## Project Structure ``` Yoruba_CBT_TTS/ │ ├── app.py ├── questions.csv ├── generated_audio/ ├── requirements.txt └── README.md ``` * `app.py` → Main Streamlit application * `questions.csv` → WAEC-style Yoruba questions * `generated_audio/` → Stores generated audio files during runtime ## Run Locally ### 1. Clone the repository ```bash git clone github.com cd Yoruba_CBT_TTS ``` ### 2. Create and activate virtual environment **Windows (PowerShell):** ```powershell python -m venv venv .\venv\Scripts\Activate.ps1 ``` **macOS/Linux:** ```bash python -m venv venv source venv/bin/activate ``` ### 3. Install dependencies ```bash pip install -r requirements.txt ``` ### 4. Run the Streamlit app ```bash streamlit run app.py ``` --- ## Requirements Make sure `requirements.txt` includes: ``` streamlit torch transformers pandas soundfile ``` --- ## Deployment This app is deployed on **Streamlit Cloud** and works without any API keys or tokens because the model is public and downloaded locally by the `transformers` library. ## License This project is open-source and available for educational and research purposes.