AI-POWERED Hausa Sayawa Translator

Project Overview

The AI-POWERED Hausa Sayawa Translator project aims to develop an advanced language translation system capable of translating text from English to Hausa, English to Sayawa, and Hausa to Sayawa using state-of-the-art AI models.

Project Phases

1. Phase 1: English to Hausa Translator

1. Data Collection:

   • Gather diverse English-to-Hausa parallel sentence data.
   • Checkpoint 1: Report dataset size and diversity.

2. Preprocessing:

   • Clean and tokenize the data.
   • Checkpoint 2: Share preprocessing details.

3. Model Selection:

   • Choose a suitable machine translation model.
   • Checkpoint 3: Present selected model and rationale.

4. Training:

   • Train the model on the dataset.
   • Checkpoint 4: Provide training progress and challenges.

5. Evaluation:

   • Assess model accuracy using metrics.
   • Checkpoint 5: Report evaluation results and improvements.

2. Phase 2: English to Sayawa Translator

1. Data Collection:

   • Gather diverse English-to-Sayawa parallel sentence data.
   • Checkpoint 6: Report dataset size and diversity.

2. Preprocessing:

   • Apply preprocessing steps similar to Phase 1.
   • Checkpoint 7: Share preprocessing details.

3. Model Adaptation:

   • Fine-tune the pre-trained model on the Sayawa dataset.
   • Checkpoint 8: Provide progress and challenges.

4. Evaluation:

   • Assess model performance using metrics.
   • Checkpoint 9: Report evaluation results and improvements.

3. Phase 3: Hausa to Sayawa Translator

1. Data Collection:

   • Gather substantial Hausa-to-Sayawa parallel sentence data.
   • Checkpoint 10: Report dataset size and diversity.

2. Preprocessing:

   • Apply preprocessing steps specific to the Hausa language.
   • Checkpoint 11: Share preprocessing details.

3. Model Training:

   • Train a new model or adapt existing models.
   • Checkpoint 12: Provide progress and challenges.

4. Evaluation:

   • Assess model accuracy and fluency.
   • Checkpoint 13: Report evaluation results and improvements.

4. Final Phase: Integration and Deployment

1. Integration:

   • Combine the English-to-Hausa and Hausa-to-Sayawa models.
   • Checkpoint 14: Report progress and integration challenges.

2. Testing:

   • Thoroughly test the integrated system.
   • Checkpoint 15: Share testing results and bug fixes.

3. Optimization:

   • Optimize models for deployment.
   • Checkpoint 16: Provide details on optimization efforts.

4. Deployment:

   • Deploy the integrated system with all translation models.
   • Checkpoint 17: Report successful deployment and any issues.

Reporting Schedule

• Regular reporting intervals (e.g., weekly or bi-weekly).
• Team members provide updates on their respective checkpoints.

Roles

• Project Manager: Amina Shiga.
• Project Lead: [TBD].
• Team: [TBD].
• Quality Assurance Tester: [AI Bauchi admins].

How to Contribute

1. Fork the repository.
2. Create a new branch for your feature or bug fix.
3. Make changes and commit them.
4. Push to the forked repository.
5. Create a pull request.

License

This project is licensed under MIT - see the LICENSE file for details.