Lyrics Classification and Generation Project

This project focuses on the preprocessing, classification, and generation of song lyrics using machine learning techniques. It includes steps for data cleaning, tokenization, training a classification model, and training a generation model to create new lyrics.

Datasets

The project utilizes several datasets, each containing a different number of songs per artist:

• best_10songs_perartist.csv
• best_20songs_perartist.csv
• best_50songs_perartist.csv
• best_100songs_perartist.csv
• best_200songs_perartist.csv

Setup Instructions

1. Clone the Repository:

   git clone 
   cd lyrics-analysis-generation
   

2. Create and Activate Virtual Environment:

   python3 -m venv venv
   source venv/bin/activate  
   # On Windows: venv\Scripts\activate
   

Data Preprocessing

Tokenization

Tokenize the lyrics using the provided Token_vector.ipynb notebook to prepare the data for further processing.

Text Cleaning

Clean the lyrics data using the text_cleanning.ipynb notebook to remove unnecessary characters and ensure uniform formatting.

Classification Model

Train a model to classify song genres based on their lyrics using the classifier.ipynb notebook. This involves feature extraction, model training, and evaluation.

Lyrics Generation Model

Train a model to generate new song lyrics in the style of a specific artist using the generator.ipynb notebook or the generator.py script. The model is trained to predict the next word in a sequence given the previous words.