LSTM neural network to generate lyrics matching artists' styles and vocabularies that may or may not make sense. 🎵✍️🧠
Using pip:
pip install -r requirements.txt
Using conda:
conda install --file requirements.txt
python demo_train.py
Use --artist flag to specify the artist to train a model for. Default is nicki_minaj.
Use --censored flag to censor explicit lyrics when printing to the terminal. Default is False.
Use --words flag to specify how many words to generate for the prediction when training. Default is 400.
python demo_train.py --artist taylor_swift --censored --words 100
python demo_predict.py
Use --artist flag to specify the artist to get predicted lyrics for. Default is nicki_minaj.
Use --censored flag to censor explicit lyrics. Default is False.
Use --words flag to specify how many words to generate. Default is 400.
python demo_predict.py --artist taylor_swift --censored --words 100
Example predicted lyrics:
Includes cross validation to evaluate model performance and hyperparameter tuning.
jupyter notebook
Lyrics were taken from AZLyrics and are organised by artists:
- Hayley Kiyoko 👩❤️💋👩
- Nicki Minaj 🐍
- Taylor Swift 👩🏼🌾
One pre-trained model and vocabulary dictionary is included per artist using the hyperparameters in the configuration file.
- Hyperparameter tuning
- Improved postprocessing (e.g. this does not currently support capitalisation of names)
- Include more artists