ET-AI/om_lang

Oromo language i18n # I18n framework for the Oromo language Embarking on the development of an Internationalization (I18n) framework tailored for the Oromo language requires a deep dive into linguistic nuances such as phonetics, syntax, and semantics. It's crucial to examine existing frameworks for insights and collaborate among linguists, software developers, and cultural experts to create a system that respects the language's uniqueness. Integrating machine learning with Oromo language datasets improves accuracy. An open-source community ensures ongoing improvement. User feedback and iterative testing refine the framework. Comprehensive documentation on GitHub and other open-source platforms facilitates implementation for widespread adoption. Consideration of unstructured datasets, including text, images, video, and audio, ensures a holistic approach for a culturally sensitive I18n framework for Oromo. Here are the recommended steps: **Step 1: Export and Organize Translation Content** - Export translated content to an Excel file by each team member. - Combine all Excel files, organizing data with columns for different languages (e.g., English and Oromo). - Prepare Po files for each target language and Commit on GitHub for both i10n and i18n. - Contribute to the i18n community (e.g., Django, Transifex) for internationalization from the GitHub repository. **Step 2: Create MongoDB Database Schema** - Identify key components (message keys, language codes, translations) for the NoSQL (e.g., MongoDB) database schema. - Use MongoDB import procedures to load CSV data into the database. - Test the i10n and i18n implementation with MongoDB in your application.