MuseGAN is an innovative project that brings creativity and technology together to generate polyphonic music with multiple tracks, essentially creating a harmonious ‘symphony’ of sounds. Whether you’re looking to generate music from scratch or complement an existing track, MuseGAN has got you covered. In this guide, we’ll walk you through the steps to get started with MuseGAN and troubleshoot common issues along the way.

What You Need Before You Start

Before diving into MuseGAN, ensure you have the following prerequisites:

• Python installed on your machine.
• Pipenv or pip to manage your Python packages.

Installing Dependencies

Using pipenv (recommended approach):

• First, ensure pipenv is installed. If not, run the following command:

pip install pipenv

• Now, install the dependencies with:

pipenv install

• Activate the virtual environment:

pipenv shell

Or using pip:

• Install dependencies using:

pip install -r requirements.txt

Preparing Training Data

MuseGAN utilizes the Lakh Pianoroll Dataset for training, which includes various multitrack pianorolls. Here’s how to prepare it:

• Download the training data by running:

sh scripts/download_data.sh

• Store the training data to shared memory:

sh scripts/process_data.sh

Alternatively, you can manually download training data using this link: train_x_lpd_5_phr.npz.

Running Your First MuseGAN Experiment

Setting up your first experiment is an exciting step! Follow these guidelines:

• Run this command to set up a new experiment:

sh scripts/setup_exp.sh .exp/my_experiment "Some notes on my experiment"

• Modify your configuration and model parameters as needed.
• To train the model:

sh scripts/run_train.sh .exp/my_experiment 0

• To run a complete experiment (training + inference + interpolation):

sh scripts/run_exp.sh .exp/my_experiment 0

Using Pretrained Models

To save time, you can download pretrained models:

• Download using the script:

sh scripts/download_models.sh

• Or manually download from this link: pretrained_models.tar.gz.
• Run inference with a pretrained model:

sh scripts/run_inference.sh .exp/default 0

Outputs and Formats

MuseGAN generates results in various formats. By default, you’ll get:

• .npy: raw numpy arrays
• .png: image files
• .npz: multitrack pianoroll files

To convert the generated pianorolls into MIDI files, use the following:

python
from pypianoroll import Multitrack
m = Multitrack('.test.npz')
m.write('.test.mid')

Troubleshooting Common Issues

As you embark on your music generation journey, you may encounter a few hiccups. Here are some common troubleshooting suggestions:

• If you face issues with installing dependencies, double-check your Python and pip versions.
• For unexpected errors during training, inspect your data formats and file configurations.
• If you need more assistance or collaboration on AI development projects, reach out to our community. For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

Conclusion

Using MuseGAN to generate polyphonic music can be a rewarding experience. With a blend of creativity and technical skills, the potential to produce unique musical pieces is limitless. At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.