Ethiopian Text-to-Speech Project

🚀 Project Overview

A groundbreaking initiative leveraging deep learning to create natural-sounding speech in Ethiopian languages.

✨ Key Features

• Custom TTS model trained on Ethiopian language audio datasets
• GPU acceleration support for enhanced performance
• Comprehensive setup and training documentation
• Flexible configuration for various deployment scenarios

🛠️ Technology Stack

📊 Performance Metrics

💻 Installation

git clone https://github.com/dawit3228/Ethiopa-text-to-speech.git

cd Ethiopa-text-to-speech

python3 -m venv name

source name/bin/activate

pip install -r requirements.txt

pip install TTS

sudo apt-get install espeak

pip install -e .

🚀 Training the Model

CUDA_VISIBLE_DEVICES="0" python3 TTS/bin/train_tacotron.py --config_path TTS/tts/configs/ljspeech_tacotron2_dynamic_conv_attn.json

python3 TTS/bin/train_tacotron.py --config_path TTS/tts/configs/ljspeech_tacotron2_dynamic_conv_attn.json

🛠️ Technologies Used

🏋️ Challenges and Solutions

• Data Scarcity

  Limited availability of high-quality Ethiopian language audio datasets.

  Solution: Collaborated with local linguists and volunteers to create a custom dataset.

🔮 Future Improvements

• Support for additional Ethiopian languages and dialects
• Integration with popular voice assistants and smart devices
• Development of a user-friendly web interface for easy text-to-speech conversion

🎉 Conclusion

This project demonstrates our commitment to advancing language technology and our expertise in applying cutting-edge AI techniques to solve unique challenges. By bridging the gap between written text and spoken word in Ethiopian languages, we're not only pushing technological boundaries but also preserving and promoting linguistic diversity.