Understanding the Use of Deep Learning in Lyrics Generation

As you know, data scientists spend more time in data preprocessing than building the model as it is the most crucial step in the whole process. Hence, understanding and preparing a lyric dataset is very important for building an accurate lyric generator.

We will use Natural Language Processing techniques to do this task:

• Tokenization: The first step is to break down lyrics into individual words and tokenize them.
• Word Embeddings: We will then transform the words into numerical vectors, allowing our model to understand relationships between words.
• Text Cleaning: Finally, we will clean the text by removing punctuation, converting all the text to lowercase, and handling misspellings to enhance the quality of the data.

After data preprocessing, we need to build a deep-learning model by following these steps:

• Defining the Model Architecture: First, select a deep learning framework like TensorFlow or PyTorch. We will be using TensorFlow as it is efficient for the task at hand. Then we will design a sequence of layers, usually these layers include the ‘Embedding layer’, ‘LSTM layers’, and ‘Dense Layers’.
• Compiling the Model: The second step is to choose a suitable loss function like ‘categorical cross-entropy’ and an optimizer like ‘Adam’ to update model weights during training. After choosing them we will compile the model.
• Fitting (Training) the model: The final step is to train the model, this involves dividing the preprocessed lyric data into smaller batches, feeding the batches to the model so that it can adjust its weights during several epochs (full passes through the dataset).

Have you ever dreamt about becoming a songwriter? If yes, then your dream might come true by the end of this article. We will use Deep Learning to build an LSTM model by preprocessing the data using Natural Language Processing techniques. We will discuss more about them in the upcoming sections. In this article, you will learn about complex NLP techniques and about deep learning. So, Are you ready to build your own lyrics generator? Now, let’s dive into this.

Lyrics are more than just a sequence of words. Lyrics have hidden themes, emotions, and meanings. These hidden connections come from the careful arrangement of phrases in the lyrics. Now, let’s see an example lyric to understand it from an NLP perspective.

Never mind, I’ll find someone like you. I wish nothing but the best for you, too.”Someone Like You” by Adele

The sentiment analyzer interprets the phrase ‘Never mind, I’ll find someone like you’ as a mixture of sadness and determination. Furthermore, the sentiment analysis can also recognize the bittersweet sentence ‘I wish nothing but the best for you, too’ as showing a sense of goodwill towards the ex-partner despite the pain of separation. NLP techniques can interpret the complex emotions embedded within these lyrics.