Deep-Learning-Project

Attribute a song to an author or a genre

Setup

Our project depends on Hedwig, which is designed for Python 3.6 and PyTorch 0.4. PyTorch recommends Anaconda for managing your environment. We'd recommend creating a custom environment as follows:

$ conda create --f environment.yml

Code depends on data from NLTK (e.g., stopwords) so you'll have to download them. Run:

$ python src/nltk_download.py

Datasets

Download the Reuters, word2vec embeddings from hedwig-data.

$ git clone https://github.com/j-cahill/hedwig.git
$ git clone https://git.uwaterloo.ca/jimmylin/hedwig-data.git

After cloning the hedwig-data repo, you need to unzip the embeddings and run the preprocessing script:

cd hedwig-data/embeddings/word2vec 
gzip -d GoogleNews-vectors-negative300.bin.gz 
python bin2txt.py GoogleNews-vectors-negative300.bin GoogleNews-vectors-negative300.txt

Create Lyrics Dataset

Download 380,000 lyrics from metrolyrics and 55,000+ song lyrics from Kaggle. Run

$ python src/data_preprocessing.py

To impute the missing genre values using the Genius API. Then run

$ python src/lyrics_preprocessor.py
$ python src/lyrics_preprocessor_artists.py

to perform basic data cleanup and create train/test/dev splits for both genre and artist.

Folder Structure

It is imperative for the code to run that the repo be structured in the following way

Deep-Learning-Project   
│
|---hedwig
│   
└---hedwig-data
    │----LyricsGenre
    |	train.tsv
    |	test.tsv
    |	dev.tsv
    │----LyricsArtist
    |	train.tsv
    |	test.tsv
	|	dev.tsv

Other files and folder may be present as well but this structure must be observed.

Model Training

Genre

Train a BERT model with the following command, testing will immediately follow

$ cd hedwig/
$ python -m models.bert --dataset LyricsGenre --model bert-base-uncased --max-seq-length 256 --batch-size 16 --lr 2e-5 --epochs 2

Artist

$ cd hedwig/
$ python -m models.bert --dataset LyricsArtist --model bert-base-uncased --max-seq-length 256 --batch-size 16 --lr 2e-5 --epochs 20

Models are saved to hedwig/model_checkpoints

naotominakawa / deep-learning-project-2019 Goto Github PK