Lyrics Generation Model using GRU

This repository contains code for a text generation model implemented in a Jupyter notebook (lyricsGenerator.ipynb). The model utilizes Gated Recurrent Units (GRU) to generate lyrics based on patterns learned from a dataset of song lyrics.

Dataset

The dataset used in this project comprises song lyrics sourced from a diverse range of artists and genres. The dataset can be accessed from Kaggle.

Due to the computational reasons only a small portion of original dataset from kaggle has been utilized in this project.

Dependencies

This project utilizes various Python libraries for tasks such as data analysis, model building, and text generation:

- Pandas
- Matplotlib
- TextBlob
- WordCloud
- Scikit-learn
- NLTK
- TensorFlow

Make sure to have these libraries installed before running the code.

Usage

1. Clone the repository to your local machine.
2. Ensure you have all dependencies installed.
3. Open and run the lyricsGenerator.ipynb notebook in Jupyter environment.
4. Follow the instructions within the notebook to analyze the dataset, preprocess the data, build the model, train it, and generate lyrics.
5. You can change the model parameters to improve the performance.

Acknowledgments

This project was inspired by the following resources:

1. Text Generation with GRU: Medium Article
2. Lyrics Generator RNN: Kaggle

License

This project is licensed under the MIT License - see the LICENSE file for details.

For any questions or issues, feel free to contact me.