Song-Generation-App

Generates lyrics with given constraints.

Go to the app :)

may take few minutes to load

Details of Files present in Repository

• dataset

  • The datset folder consists of .txt files which contains lyrics of some of the singers.\
  • The fromat of the txt file is
    -Song Name-
    Lyrics
    -Song Name-
    Lyrics
  • Dataset is uploaded on kaggle : Song Generation
  • All the data is collected manually from web.

• files

  • vocab_text.csv
    • contains vocabulary extracted from songs.
  • vocab_line.csv
    • contains unique count of words in line of song.
  • generate_lines.h5
    • model to generate list of number of words per line in a song.
  • generate_text.csv.h5 (Due to memory constraint, I didn't upload this file. You can get this file here from my Kaggle Notebook)

• static

  • all CSS,JS files are present here in their respective folders.

• templates

  • HTML files are present.
• app.py : python file
• Procfile : needed for Heroku to know what commands it need to run.
• requirements.txt : to specify the libraries needed to install to Heroku.
• runtime.txt : to specify specific version of python.

Details of Project

Song Generator is web-app deployed on Heroku.

• Model Training

  • First the lyrics file(.txt) is preprocessed to get required inputs (No of songs,song as list,No of words....)
  • NLTK wordnetlemmatizer is also used in preprocessing. To know more about NLTK toolkit visit NLTK.
  • In the first model the preprocessed data is taken and given to model which gives a continuous words present in the required song as output.
  • The same preprocessed data is taken and given to second model which gives a list of "Number of words" present in a line as output.
  • Finally the outputs of the 2 models is taken and the final song is produced as per the given length and lines of song.
  • I used the Tensorflow(2.3.0) ML library to do this task.To know more about TensorFlow library visit TensorFlow.
  • Output lyrics are obtained by giving a starting word in first model and starting number of word per line in second model.
  • More details are in my kaggle notebook

• Frontend

  • used an online template and made changes to HTML,CSS,JS files.

• Deployment

  • You can go through this YT playlist created by me for reference Deploying ML model on Heroku using Flask.
  • For more details visit Heroku.

Things I couldn't do because of memory constraint 😢(500MB)

• Using better pretrained models to get good results.