davronbekdev/pari-tts

NLP Lab Project

This is a Natural Language Processing (NLP) project with a structured codebase for data preprocessing, model training, and experimentation.

Project Structure

nlp/
├── data/
│   ├── raw/                    # Raw, unprocessed datasets
│   └── processed/              # Cleaned and preprocessed data
├── notebooks/
│   └── 01_data_preprocessing.ipynb  # Jupyter notebook for data exploration and preprocessing
├── src/
│   ├── models/                 # Model definitions and architectures
│   ├── preprocessing/          # Data preprocessing utilities
│   └── train.py               # Main training script
├── requirements.txt           # Python dependencies
└── README.md                 # This file

Setup

1. Create a virtual environment:

   python -m venv nlp-env
   source nlp-env/bin/activate  # On Windows: nlp-env\Scripts\activate
   

2. Install dependencies:

   pip install -r requirements.txt
   

3. Download NLTK data (if using NLTK):

   import nltk
   nltk.download('punkt')
   nltk.download('stopwords')
   

Usage

Data Preprocessing

1. Place your raw data files in the data/raw/ directory
2. Use the Jupyter notebook notebooks/01_data_preprocessing.ipynb for initial data exploration and preprocessing
3. Save processed data to data/processed/ directory

Model Training

Run the training script with default parameters:

python src/train.py

Or with custom parameters:

python src/train.py --epochs 20 --lr 0.0001 --batch_size 64

Directory Descriptions

• data/raw/: Store your original, unmodified datasets here
• data/processed/: Store cleaned and preprocessed data ready for training
• notebooks/: Jupyter notebooks for data exploration, visualization, and experimentation
• src/models/: Python modules containing model definitions (e.g., neural network architectures)
• src/preprocessing/: Utility functions for data cleaning, tokenization, and feature extraction
• src/train.py: Main training script with command-line interface

Getting Started

1. Add your dataset to data/raw/
2. Open notebooks/01_data_preprocessing.ipynb to explore and preprocess your data
3. Implement your model in src/models/
4. Create preprocessing utilities in src/preprocessing/
5. Run training with python src/train.py

Contributing

1. Follow PEP 8 style guidelines
2. Add docstrings to all functions and classes
3. Write unit tests for your code
4. Update this README when adding new features