N'Ko-Bambara Multilingual System Architecture Plan

This document outlines the comprehensive architecture for implementing a modular multilingual system that processes N'Ko and Bambara languages alongside English and French. The system leverages the RobotsMali/bam-asr-early dataset as its foundation and implements a five-layer modular architecture supporting bidirectional translation across all language pairs. ### Dataset Characteristics - **Total Duration**: 37.41 hours of audio - **Total Samples**: 38,769 (Train: 37,306 | Test: 1,463) - **Language Pairs**: Bambara ↔ French with aligned transcriptions - **Audio Quality**: Variable duration (0.42–54.6 seconds) ### Dataset Subsets 1. **Oza's Bambara-ASR**: ~29 hours (primary ASR training data) 2. **Jeli-ASR-RMAI**: ~3.5 hours (conversational speech) 3. **oza-tts-mali-pense**: ~4 hours (TTS-optimized recordings) 4. **Reading-tutor-data**: ~1 hour (educational content) The system follows a five-layer modular architecture designed for scalability and language-specific optimization: ### Layer A: Speech Processing Layer - **ASR Module**: wav2vec 2.0-based speech recognition - **TTS Module**: Neural speech synthesis - **Audio Preprocessing**: Noise reduction, normalization, segmentation