How to Build a Lyrics Generator with OpenAI Jukebox and Python

Posted on by Panther

OpenAI Jukebox

Generating lyrics for songs has always been a challenging task. It requires creativity, understanding of music, and the ability to craft meaningful lyrics. However, with advancements in natural language processing and machine learning, we can now leverage powerful models like OpenAI Jukebox to automatically generate lyrics for songs.

In this tutorial, we will explore how to build a lyrics generator using OpenAI Jukebox and Python. We will cover the following steps:

  1. Setting up the environment
  2. Installing the necessary libraries
  3. Collecting lyrics dataset
  4. Preprocessing the dataset
  5. Training a lyrics generator model with OpenAI Jukebox
  6. Generating lyrics with the trained model

Let’s get started!

1. Setting up the environment

Before we begin, make sure you have Python installed on your machine. You can download and install Python from the official website: https://www.python.org.

Additionally, it’s a good practice to create a virtual environment for our project to keep the dependencies isolated. You can create a virtual environment using venv module that comes bundled with Python. Open your terminal or command prompt and execute the following command:

python3 -m venv lyrics-generator

This command will create a new directory named lyrics-generator which contains the necessary files for our virtual environment.

Next, activate the virtual environment by running the appropriate command based on your operating system:

  • On macOS and Linux:
    bash
    source lyrics-generator/bin/activate
  • On Windows:
    bash
    lyrics-generatorScriptsactivate

Your terminal prompt should now change to indicate that you are working inside the virtual environment.

2. Installing the necessary libraries

Once the virtual environment is activated, we can proceed to install the necessary libraries. In this tutorial, we will be using the transformers library, which provides a high-level API to use various state-of-the-art language models, including OpenAI Jukebox.

To install the transformers library, run the following command:

pip install transformers

This command will download and install the transformers library along with its dependencies.

3. Collecting lyrics dataset

To train our lyrics generator model, we need a large dataset of song lyrics. There are several options to obtain such a dataset. One approach is to use a pre-existing dataset available on platforms like Kaggle or GitHub. Another approach is to scrape lyrics from websites that provide song lyrics like Genius or MetroLyrics.

In this tutorial, we will use the LyricsGenius library to scrape lyrics from Genius. This library provides a simple way to access the Genius API and fetch lyrics for various songs.

To install LyricsGenius, run the following command:

pip install lyricsgenius

Once installed, we need to obtain an access token to use the Genius API. Follow these steps to get an access token:

  1. Go to the Genius API Client Management page.
  2. Sign in with your Genius account or create a new account if you don’t have one.
  3. Click on “New API Client” and fill in the required information.
  4. Once the API client is created, you will see your access token.

Make sure to keep your access token safe and private. We will use it in the next step.

4. Preprocessing the dataset

Now that we have obtained an access token, we can start scraping lyrics using the LyricsGenius library. Create a new Python script (e.g., scrape_lyrics.py) and import the necessary modules:

import lyricsgenius

# Replace 'YOUR_ACCESS_TOKEN' with your actual access token
genius = lyricsgenius.Genius('YOUR_ACCESS_TOKEN')

Next, let’s define a function to scrape lyrics for a given artist:

def scrape_artist_lyrics(artist_name, num_songs):
    artist = genius.search_artist(artist_name, max_songs=num_songs, sort="popularity")
    artist.save_lyrics()
    print(f'{len(artist.songs)} songs by {artist.name} saved!')

In the scrape_artist_lyrics function, we use the search_artist method to search for the given artist and fetch their top songs. The max_songs parameter specifies the maximum number of songs to fetch, and the sort parameter determines the sorting criterion (e.g., by popularity).

We then call the save_lyrics method to save the lyrics for the artist’s songs to a local file. Finally, we print the number of songs saved.

To use the function, call it with the artist name and the desired number of songs to scrape:

scrape_artist_lyrics('Ed Sheeran', 50)

You can replace 'Ed Sheeran' with your preferred artist and adjust the number of songs accordingly.

Once you run the script, you should see a message indicating the number of songs saved. The lyrics are saved in a file named artist.txt in the current directory.

Repeat this step for multiple artists to collect a diverse dataset of lyrics. Make sure to save the lyrics for each artist in a separate text file.

5. Training a lyrics generator model with OpenAI Jukebox

In this step, we will use the collected dataset of lyrics to train a lyrics generator model using OpenAI Jukebox.

To train the model, we need to preprocess the dataset and convert it into the format expected by OpenAI Jukebox. OpenAI provides a Python package called jukebox that simplifies the preprocessing and training process.

Install the jukebox package by running the following command:

pip install jukebox

Once installed, create a new Python script (e.g., train_lyrics_generator.py) and import the necessary modules:

import torch
from jukebox.train import jukebox

Next, let’s set up the training configuration:

config = {
    'name': 'lyrics-generator',
    'prompt': '###LYRICS###',
    'length': 512,
    'bs': 3,
    'do_train': True,
    'train_folder': 'data/lyrics',
    'val_folder': 'data/lyrics',
    'ckpt_folder': 'checkpoint/lyrics'
}

In the configuration, we specify the training name, the prompt to indicate the start of lyrics generation, the desired length of generated lyrics, batch size (bs), training mode (do_train), and the folders for training, validation, and checkpoints.

We also need to define the paths for the package:

paths = {
    'mconf': 'jukebox/train/example_configs/5b_lyrics',
    'audio': 'data/lyrics_audio',
    'models': 'models/lyrics_models'
}

These paths specify the model configuration file, folder for audio files, and folder for pre-trained models.

Now, let’s define a function to train the lyrics generator model:

def train_lyrics_generator():
    model = jukebox(config, paths)
    model.train(torch.device('cuda'))

In the train_lyrics_generator function, we create an instance of the jukebox model using the provided configuration and paths. We then call the train method to start the training process on a CUDA device.

To start the training, simply call the function:

train_lyrics_generator()

This will start the training process and save the trained model checkpoints in the specified checkpoint folder.

Note: Training a lyrics generator model with OpenAI Jukebox requires significant computational resources. Make sure you have access to a machine with a GPU and sufficient memory to handle the training process.

6. Generating lyrics with the trained model

Once we have trained the lyrics generator model, we can use it to generate lyrics for songs.

Create a new Python script (e.g., generate_lyrics.py) and import the necessary modules:

from jukebox.sample import sample_model

Next, let’s define a function to generate lyrics:

def generate_lyrics():
    model = sample_model(
        model_name='5b_lyrics',  # Use the same model configuration as during training
        ckpt_path='checkpoint/lyrics/step_0',  # Path to the trained model checkpoint
        prompt_length=10  # Length of the prompt to start generating lyrics
    )

    lyrics = model[0]['prompt']  # Initial prompt for lyrics generation
    generated = model.generate_lyrics(lyrics, temperature=0.9)  # Generate lyrics with given temperature
    print(generated)

In the generate_lyrics function, we load the trained model using the sample_model function. We provide the same model name and the path to the trained model checkpoint file.

We specify the initial prompt for generating lyrics and use the generate_lyrics method to generate lyrics with a given temperature. Higher temperature values (e.g., 1.0) result in more random output, while lower values (e.g., 0.5) produce more focused and deterministic output.

To generate lyrics, call the function:

generate_lyrics()

This will print the generated lyrics to the console.

Congratulations! You have successfully built a lyrics generator using OpenAI Jukebox and Python. Experiment with different artist datasets, training configurations, and temperature values to generate unique and creative lyrics for your songs.

Remember to always respect copyright laws and ensure that you have the necessary rights and permissions to use the lyrics generated by the model.

Conclusion

In this tutorial, we learned how to use OpenAI Jukebox and Python to build a lyrics generator. We covered the steps for setting up the environment, installing necessary libraries, collecting a lyrics dataset, preprocessing the dataset, training a lyrics generator model, and generating lyrics with the trained model.

OpenAI Jukebox provides a powerful framework for generating creative content like lyrics, music, and poetry. With further experimentation and fine-tuning, you can improve the quality and coherence of the generated lyrics.

Feel free to explore other use cases and applications of OpenAI Jukebox to enhance your creativity and artistic endeavors. Happy generating!

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