Final project for team Google Translate 2.0 from class SPECIAL TOPICS CSCE496 SEC 701 Fall 2020.
Lyric generation in the style of Nickelback songs.
Data is compiled by scraping Genius.com for Nickelback songs.
The script data/genius_lyrics.py accepts URLs as command line arguments and outputs the scraped lyrics in the data/output/ directory.
Each set of lyrics is stored as a JSON file containing the lyrics as well as some metadata from Genius.com.
Two methods are implemented to generate lyrics in the style of Nickelback:
- Markov processes
- Long short-term memory (LSTM)
Implementations for Markov processes exist in the markov_processes/ directory.
Implementations for LSTM exist in the lstm/ directory.
The environment project defined by lstm/environment.yaml must be created before running the script.
The Python script can be run on HCC Crane from the directory lstm/ with the command sbatch project.slurm lstm.py.
The SLURM file project.slurm requests 48 GB. This can easily be changed to 32 GB by changing line 3: #SBATCH --mem=48G to #SBATCH --mem=32G. The script has been tested successfully with 32 GB for 300 epochs.
The implementation with GloVe weights requires downloading the weights from the Stanford NLP group. The attention layer implementation is from Marco Cerliani on Stack Overflow.