Music has always been a way for people to connect with one another. With the advent of machine learning, we can now build intelligent systems that recommend music based on a user’s preferences. In this tutorial, we will learn how to build a music recommender using OpenAI Jukebox and Python.
Table of Contents
- Introduction to OpenAI Jukebox
- Setting up the Environment
- Collecting and Preprocessing Dataset
- Training the Model
- Generating Music Recommendations
- Conclusion
Introduction to OpenAI Jukebox
OpenAI Jukebox is an AI system that can generate high-quality music in various genres and styles. It uses a technique called “conditional generation” to generate music based on user-provided input, such as lyrics, genre, and artist style.
Jukebox is trained on a dataset that consists of several terabytes of high-quality music. During training, it learns to recognize patterns in music and generates new music that is similar to the input provided.
In this tutorial, we will leverage OpenAI Jukebox to build a music recommender system. We will train Jukebox on a dataset of songs and use it to generate music recommendations based on a user’s preferences.
Setting up the Environment
Before we start building our music recommender, let’s set up our Python environment. Follow these steps to get started:
Step 1: Install Python and pip
Make sure you have Python installed on your system. You can download and install Python from the official website: https://www.python.org/downloads/
Once you have Python installed, you will also have pip (a package manager for Python) installed by default.
Step 2: Create a Virtual Environment
We recommend creating a virtual environment for this project to keep your dependencies isolated. Open a terminal and run the following command to create a virtual environment:
$ python -m venv music-recommender-env
This will create a new directory called music-recommender-env which will hold all the dependencies for our project.
Step 3: Activate the Virtual Environment
To activate the virtual environment, run the appropriate command based on your operating system:
Windows:
$ music-recommender-envScriptsactivate
macOS/Linux:
$ source music-recommender-env/bin/activate
Step 4: Install Required Packages
Now that our virtual environment is activated, let’s install the required packages. Run the following command to install the necessary packages:
$ pip install openai
$ pip install tensorflow
We need the openai package to interact with OpenAI Jukebox, and tensorflow package to train our model.
Collecting and Preprocessing Dataset
The first step in building our music recommender is to collect and preprocess a dataset of songs. For this tutorial, we will use a small dataset of songs in MIDI format.
Step 1: Collect the Dataset
Collecting a large, diverse dataset of songs can be a challenging task. There are several online resources such as the Lakh MIDI Dataset and FreeMIDI where you can find MIDI files of songs across different genres.
For this tutorial, we will use a small dataset of 20 songs in MIDI format. You can download the dataset from the following link: https://example.com/songs.zip
Once you have downloaded the dataset, extract the contents to a directory on your system.
Step 2: Preprocess the Dataset
Now that we have our dataset, we need to preprocess it before training our model. We can use the magenta package to convert MIDI files to a more suitable format for training.
Run the following commands to install the magenta package and preprocess the dataset:
$ pip install magenta
$ convert_midi_dir_to_note_sequences --input_dir path/to/dataset --output_file dataset.tfrecord
Replace path/to/dataset with the path to the directory where you extracted the dataset. This command will convert the MIDI files to a TensorFlow-compatible format called TFRecord.
Training the Model
With the preprocessed dataset in hand, we can now train our music recommender model using OpenAI Jukebox.
Step 1: Import Required Libraries
Open a new Python file in your favorite code editor and import the following libraries:
import openai
import tensorflow as tf
Step 2: Configure OpenAI Jukebox
Before we can use OpenAI Jukebox, we need to provide our OpenAI API key. If you don’t have an API key, you can sign up for one at the OpenAI website.
Once you have your API key, set it as an environment variable in your terminal:
Windows:
$ set OPENAI_API_KEY=your-api-key
macOS/Linux:
$ export OPENAI_API_KEY=your-api-key
Step 3: Load the Dataset
Next, let’s load our preprocessed dataset into TensorFlow. Add the following code to load the dataset:
dataset = tf.data.TFRecordDataset(['dataset.tfrecord'])
Make sure to replace dataset.tfrecord with the path to your preprocessed dataset file.
Step 4: Configure the Model
Now, let’s configure the model for training. We’ll start by setting the hyperparameters. These parameters control the training process, such as the learning rate, batch size, and the number of training steps.
learning_rate = 1e-3
batch_size = 16
num_train_steps = 10000
Feel free to adjust these parameters based on your specific requirements.
Next, let’s create the Jukebox model and specify the loss function. Add the following code:
model = openai.models.Jukebox()
model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=learning_rate))
model.fit(dataset.batch(batch_size), steps_per_epoch=num_train_steps)
Step 5: Training the Model
Finally, we are ready to train our music recommender. Run the Python script and wait for the model to train. This process may take several hours, depending on your hardware and the size of your dataset.
Once the training is complete, the model will be saved to disk and ready for generating music recommendations.
Generating Music Recommendations
Now that we have trained our music recommender model, let’s see how we can generate music recommendations based on a user’s preferences.
Step 1: Import Required Libraries
Open a new Python file and import the necessary libraries:
import openai
import midi2audio
import librosa
import soundfile as sf
Step 2: Configure OpenAI Jukebox
Make sure your OpenAI API key is set as an environment variable, as we did previously. This is required to communicate with the OpenAI Jukebox API.
Step 3: Load the Trained Model
To generate music recommendations, we need to load the trained Jukebox model. Add the following code to load the model:
model = openai.models.Jukebox()
Step 4: Generate Music Recommendation
Now, let’s generate a music recommendation based on user input. Add the following code to generate a sample music recommendation:
lyrics = "I'm feeling happy and energetic"
conditioned = model.sample(n=1, lyrics=lyrics, length=240)
audio = conditioned[0].to_audio()
sf.write('recommendation.wav', audio.numpy(), 44100)
Replace the lyrics variable with the user’s input or any other preferences you want to use for generating a music recommendation.
Step 5: Listen to the Recommendation
The generated music recommendation is saved as a WAV file. You can use any media player application to listen to the generated recommendation.
Optionally, you can convert the recommendation to a different audio format using the midi2audio library. Here’s an example:
midi_data = conditioned[0].to_midi()
audio_data = midi2audio.midi_to_audio(midi_data)
sf.write('recommendation.mp3', audio_data, 44100)
Make sure to install the midi2audio library before using this code.
Conclusion
In this tutorial, we learned how to build a music recommender system using OpenAI Jukebox and Python. We explored the steps involved in collecting, preprocessing, and training a dataset of songs. Finally, we saw how to generate music recommendations based on user preferences.
With this knowledge, you can now build your own music recommender system and open up a world of music discovery for your users. Happy recommending!