Transforming Words into Music: A Deep Dive into the Lyrics Generator App

Ever faced writer’s block while writing song lyrics? The Lyrics Generator app, built using Streamlit and powered by a pre-trained TensorFlow model, can help you overcome that. Simply provide a few starting words, and the app will generate new lyrics. Let’s explore how it works and how this tool can benefit lyricists, musicians, and hobbyists alike.

Features of the Lyrics Generator App

This simple yet effective app comes with a host of features designed to make lyric creation easy:

Seed Text Input: Enter a few words, and let the model predict what comes next.

Word Count Control: You can specify how many words you want to generate.

Real-Time Output: Hit “Generate” and watch as the lyrics form before your eyes.

Entertaining and Unexpected Results: The app ensures a unique outcome each time, thanks to the probabilistic nature of the model’s predictions.

How It Works: Under the Hood

At its core, the Lyrics Generator uses natural language processing (NLP) models to predict the next word in a sequence based on the words you input.

1. Input and Tokenization
When you enter your seed text, the app converts your input into a numerical format using tokenization.

seed_text = st.text_input("Enter the first words for your song")
next_words = st.number_input("Enter the number of words for your song", min_value=0, format="%d")

if st.button("Generate"):
for _ in range(next_words):
token_list = tokenizer.texts_to_sequences([seed_text])[0]
token_list = pad_sequences([token_list], maxlen=19, padding='pre')

Here, the app tokenizes the seed text and pads the sequence to a fixed length of 19 words. This padding is essential for keeping the input consistent with the model’s expected format.

2. Model Prediction
The app’s TensorFlow model predicts the probabilities of the next word based on the current input sequence.

predicted_probs = model.predict(token_list)[0]
predicted = np.random.choice([x for x in range(len(predicted_probs))], p = predicted_probs)
The model’s prediction step uses a probabilistic approach, giving more variety in the generated lyrics. Rather than picking the highest probability word every time, it allows for randomness.

3. Word Generation
Based on the model’s output, the app selects the next word and appends it to the existing seed text.

output_word = ' '
for word, index in tokenizer.word_index.items():
if index == predicted:
output_word = word
break
seed_text += ' ' + output_word

The process is repeated for the number of words specified by the user, generating a new lyric sequence. The app dynamically updates and displays the lyrics as they are generated.

Use Cases
This app has various practical and fun uses, including:

For Songwriters and Lyricists: Whether you're stuck on the next line or just need inspiration, this app can generate fresh ideas to get you going.

For Musicians: Use the app to create lyrics that fit your music, or simply to experiment with different lyrical patterns.

For Creative Fun: Want to create random, quirky lyrics for entertainment? This app is perfect for sparking unexpected creativity.

For Learning NLP: This project demonstrates real-world applications of text processing, tokenization, sequence modeling, and predictive generation in NLP.

How to Use the App
Enter the starting words of your song into the input box.
Choose the number of words you want the model to generate.
Hit the Generate button and see the magic unfold!

st.write(f"The generated lyrics is given below and it has {next_words} words (not as grammatical as modern day lyricists but still!!😁). Have fun!")
st.write(seed_text)

Technical Details

Framework: The app is built using Streamlit, a fast and easy-to-use framework for creating Python-based apps.
Model: The model is a pre-trained TensorFlow model that predicts the next word in a sequence using a probabilistic approach.
Tokenization: The Keras tokenizer is used for converting words into numerical sequences that the model can understand.
Sequence Padding: Input sequences are padded to ensure consistent input length for the model.
Real-time Output: The results are generated dynamically based on user input.

Conclusion

The Lyrics Generator app is a powerful example of how NLP models can be applied creatively in the music industry. It offers users an easy way to generate new lyrics, providing both practical support for songwriters and a fun tool for hobbyists. With its simple interface and interactive features, it makes lyric generation accessible to everyone.

Ready to start generating lyrics? Explore the app today and let the AI surprise you with its musical creativity!
Lyrics Generator