README.md

🇰🇪 LughaGen Modular Engine

LughaGen is a high-performance, modular data preprocessing and normalization engine designed to build high-quality parallel corpora for low-resource Kenyan and regional languages.

The pipeline dynamically loads, normalizes, cleans, and partitions raw parallel datasets (CSV, Excel, Parquet, JSONL) into stratified train, validation, and test splits ready for Machine Learning, Neural Machine Translation (NMT), and LLM tokenizer training.

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🚀 Key Features

• Dynamic Translation Unpacking: Automatically detects and unpacks complex nested structures (like Hugging Face translation dictionary layouts).
• Self-Healing Schema Parsing: If a dataset lacks a metadata.json file or has corrupted headers, the engine dynamically falls back to parsing folder-name patterns (e.g., english-kamba ➔ eng to kam) using a pre-configured language alias matrix.
• De-duplication & Sanitization: Cleans formatting anomalies, stripping invisible artifacts (such as Windows Byte Order Marks and trailing web spaces).
• Stratified Splitting: Handles balanced train/val/test dataset partitioning while maintaining strict index mapping integrity.

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📊 Consolidated Corpus Statistics

The pipeline successfully unified 6,190,120 parallel sentences across several key regional language pairs:

Language Pair	Total Records	Unique Domains	Avg Source Length (Chars)	Description
eng-kik	2,200,684	1	~47.9	English ↔ Kikuyu (Gĩkũyũ)
eng-kam	1,679,956	1	~43.9	English ↔ Kamba (Kĩkamba)
luo-eng	1,470,655	1	~52.0	Dholuo ↔ English
kam-swa	223,350	1	~49.4	Kĩkamba ↔ Swahili (Kiswahili)
kik-swa	219,495	1	~49.2	Kikuyu ↔ Swahili
swa-eng	203,321	1	~48.9	Swahili ↔ English
swa-kal	131,960	1	~50.5	Swahili ↔ Kalenjin
eng-luo	40,422	1	~130.7	English ↔ Dholuo
kam-kik	18,710	1	~47.1	Kĩkamba ↔ Kikuyu
swa-shg	1,567	1	~50.6	Swahili ↔ Sheng

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📂 Repository Structure

├── datasets/
│   ├── raw/                      # Raw language asset directories (Ignored by Git)
│   │   ├── english-kamba/
│   │   ├── english-kikuyu/
│   │   └── ...
│   └── processed/                # Normalized & partitioned outputs
│       ├── dataset_statistics.csv # Summary of current build (Tracked)
│       ├── train.csv             # (Ignored by Git)
│       ├── validation.csv        # (Ignored by Git)
│       └── test.csv              # (Ignored by Git)
├── scripts/                      # Core processing architecture
│   ├── merge_datasets.py         # Main execution runner
│   ├── normalizer.py             # Smart schema mapper & format decoder
│   ├── cleaner.py                # Text deduplication and cleaning rules
│   ├── splitter.py               # Train/Val/Test partitioning logic
│   ├── dataset_loader.py         # Multi-format I/O loader
│   ├── pipeline_config.py        # System-wide path configurations
│   └── statistics.py             # Analytics generation tool
├── .gitignore
├── requirements.txt
└── README.md