Lyrics-Generation-using-RNNs

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

Steps to implement this project :

1. Fork the repository.

2. Put lyrics_scrapper_from_GENIUS_website.py and lyrics_dataset_creator.py in the same directory.

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

My dataset used to train and get the output is => lyrics_dataset.txt

Some lyrics generated :

Love in the world

Happens each time to keep me warm

Love in the rain

And we'll stop eating food and I'll say the words that I want in this cold

I don't have to think that I want to do

I can't think of anyone, anyone else

And we gotta save ourselves

If we want it to be

I'll be taking my time

As she looks lovely in the wood

This model can be used for any type of text prediction , not being limited to only lyrics.

Scope for improvements :

1. Using a LSTM-GAN based model, it has the best potential for most coherent output.

2. Adding another layer of Birectional-LSTM and lowering the EPOCHS keeping the loss within 1.5 to 1.1 , for most meaningful text generated.