This project generates new lyrics which did'nt exist before of your favourite artists , using Recurrent-Neural-Networks (RNNs) from a data-set created by web scrapping The GENIUS website using its API.
After vectorizing the text and creating training examples and targets , a four layer model was used. The Four layers used are :
1. Embedding layer -> The input layer. A trainable lookup table that will map the numbers of each character to a vector with embedding_dim dimensions
2. GRU layer -> A type of RNN with size units=rnn_units (Birectional-LSTM could also be used here.)
3. Dense layer -> The output layer, with vocab_size outputs and 'RELU' as the activation fuction
4. Dropout layer -> Benifits regularisation and prevents overfitting
The final prediction loop for text generation works as follows
3. Head over to https://genius.com/api-clients/new , create your GENIUS account and then generate a client api and get your 'CLIENT ACCESS TOKEN'.
4. Install 'lyricsgenius' module in your local terminal. Execute lyrics_scrapper_from_GENIUS_website.py for each individual artist , to fetch their songs from The GENIUS website in a JSON file. (number of songs , title , popularity and be mentioned). Repeat execution for many artists.
5. Execute lyrics_dataset_creator.py to parse the JSON files which selects just the 'lyrics' key. It also creates the dataset named 'lyrics_dataset.txt' after doing some data-cleansing.
6. Run the Lyrics_Generation.ipynb notebook (preferably in Google Collab with GPU support for faster execution) , uploading the 'lyrics_dataset.txt' file , getting the new lyrics being generated.
7. Tweak the hyper-parameters according to "YOUR" best results
