EryxLabs/swahili-tts

Swahili Text-to-Speech using XTTS-v2 speaker conditioning # Eryx Labs - Swahili TTS Natural Swahili Text-to-Speech using XTTS-v2 fine-tuning. ## Overview This project creates a Swahili TTS system by fine-tuning XTTS-v2 on Mozilla Common Voice Swahili data. Combined with our Swahili LLM, it enables complete voice-enabled conversational AI in Swahili. ## Features - Natural Swahili voice synthesis - Voice cloning capabilities - ~10 hours of training data from Common Voice - Interactive synthesis mode ## Quick Start ### Prerequisites - Python 3.10+ - HuggingFace account (for Common Voice access) - GPU recommended (NVIDIA or Apple Silicon) ### Installation ```bash cd swahili-tts # Create virtual environment python -m venv venv source venv/bin/activate # Install dependencies pip install -r requirements.txt # Set HuggingFace token (for Common Voice) export HF_TOKEN='your-token-here' ``` ### Run the Pipeline ```bash # Step 1: Download Swahili voice data python scripts/01_download_data.py # Step 2: Preprocess audio python scripts/02_preprocess.py # Step 3: Fine-tune XTTS-v2 python scripts/03_train.py # Step 4: Synthesize speech python scripts/04_synthesize.py ``` ### Interactive Mode ```bash python scripts/04_synthesize.py --interactive ``` ## Project Structure ``` swahili-tts/ ├── config.yaml # Configuration ├── requirements.txt # Dependencies ├── README.md ├── scripts/ │ ├── 01_download_data.py # Download Common Voice │ ├── 02_preprocess.py # Audio preprocessing │ ├── 03_train.py # Fine-tune XTTS-v2 │ └── 04_synthesize.py # Generate speech ├── data/ │ ├── raw/ # Downloaded audio │ └── processed/ # Training-ready data ├── checkpoints/ # Model checkpoints └── models/ # Exported models ``` ## Data Source **Mozilla Common Voice Swahili** - ~15 hours of validated Swahili speech - ~6,000+ voice recordings - Multiple speakers - Natural conversational style ## Model Details | Parameter | Value | |----------- …