Conditional- MuseGAN

An investigation of using various conditional parameters for musical generation, based on: MuseGAN

We train the model with training data collected from Lakh Pianoroll Dataset to generate pop song phrases consisting of bass, drums, guitar, piano and strings tracks.

We then use a variety of song-data features as conditional generators for the music.

Prerequisites

Below we assume the working directory is the repository root.

Install dependencies

Using pipenv (recommended)

Make sure pipenv is installed. (If not, simply run pip install pipenv.)

# Install the dependencies
pipenv install
# Activate the virtual environment
pipenv shell

Using pip

# Install the dependencies
pip install -r requirements.txt

Prepare training data

The training data is collected from Lakh Pianoroll Dataset (LPD), a new multitrack pianoroll dataset.

# Download the training data
./scripts/download_data.sh
# Store the training data to shared memory
./scripts/process_data.sh

You can also download the training data manually (train_x_lpd_5_phr.npz).

Scripts

We provide several shell scripts for easy managing the experiments. (See here for a detailed documentation.)

Below we assume the working directory is the repository root.

Train a new model

Run the following command to set up a new experiment with default settings.

# Set up a new experiment
./scripts/setup_exp.sh "./exp/my_experiment/" "Some notes on my experiment"

Modify the configuration and model parameter files for experimental settings.

You can either train the model:

# Train the model
./scripts/run_train.sh "./exp/my_experiment/" "0"

or run the experiment (training + inference + interpolation):

# Run the experiment
./scripts/run_exp.sh "./exp/my_experiment/" "0"

Use pretrained models

Download pretrained models
```
# Download the pretrained models
./scripts/download_pretrained_models.sh
```
You can also download the pretrained models manually (pretrained_models.tar.gz).

You can either perform inference from a trained model:

# Run inference from a pretrained model
./scripts/run_inference.sh "./exp/default/" "0"

or perform interpolation from a trained model:

# Run interpolation from a pretrained model
./scripts/run_interpolation.sh "./exp/default/" "0"

Sample Results

Some sample results can be found in ./exp/ directory. More samples can be downloaded from the following links.

sample_results.tar.gz (54.7 MB): sample inference and interpolation results
training_samples.tar.gz (18.7 MB): sample generated results at different steps

Papers

Convolutional Generative Adversarial Networks with Binary Neurons for Polyphonic Music Generation
Hao-Wen Dong and Yi-Hsuan Yang
in Proceedings of the 19th International Society for Music Information Retrieval Conference (ISMIR), 2018.
[website] [arxiv] [paper] [slides(long)] [slides(short)] [poster] [code]

MuseGAN: Multi-track Sequential Generative Adversarial Networks for Symbolic Music Generation and Accompaniment
Hao-Wen Dong*, Wen-Yi Hsiao*, Li-Chia Yang and Yi-Hsuan Yang, (*equal contribution)
in Proceedings of the 32nd AAAI Conference on Artificial Intelligence (AAAI), 2018.
[website] [arxiv] [paper] [slides] [code]

MuseGAN: Demonstration of a Convolutional GAN Based Model for Generating Multi-track Piano-rolls
Hao-Wen Dong*, Wen-Yi Hsiao*, Li-Chia Yang and Yi-Hsuan Yang (*equal contribution)
in ISMIR Late-Breaking Demos Session, 2017. (non-refereed two-page extended abstract)
[paper] [poster]

NoahGallant/conditional-musegan