MuseGAN is a project on music generation. In essence, we aim to generate polyphonic music of multiple tracks (instruments) with harmonic and rhythmic structure, multi-track interdependency and temporal structure. To our knowledge, our work represents the first approach that deal with these issues altogether.
The models are trained with Lakh Pianoroll Dataset (LPD), a new multi-track piano-roll dataset, in an unsupervised approach. The proposed models are able to generate music either from scratch, or by accompanying a track given by user. Specifically, we use the model to generate pop song phrases consisting of bass, drums, guitar, piano and strings tracks. Sample results are available here.
Hao-Wen Dong*, Wen-Yi Hsiao*, Li-Chia Yang and Yi-Hsuan Yang, "MuseGAN: Multi-track Sequential Generative Adversarial Networks for Symbolic Music Generation and Accompaniment," in AAAI Conference on Artificial Intelligence (AAAI), 2018. [arxiv] [slides]
*These authors contributed equally to this work.
import tensorflow as tf
from musegan.core import MuseGAN
from musegan.components import NowbarHybrid
from config import *
# initialize a tensorflow session
with tf.Session() as sess:
### prerequisites ###
# step 1. initialize the training configuration
t_config = TrainingConfig
# step 2. select the desired model
model = NowbarHybrid(NowBarHybridConfig)
# step 3. initialize the input data object
input_data = InputDataNowBarHybrid(model)
# step 4. load training data
path_train = 'train.npy'
input_data.add_data(path_train, key='train')
# step 5. initialize the museGAN object
musegan = MuseGAN(sess, t_config, model)
### training ###
musegan.train(input_data)
### load and generate samples ###
# load pretrained model
musegan.load(musegan.dir_ckpt)
# add testing data
path_test = 'train.npy'
input_data.add_data(path_test, key='test')
# generate samples
musegan.gen_test(input_data, is_eval=True)-
tra_phr.npy (7.54 G): A numpy array that contains the training data for phrases. Its shape is (50266, 384, 84, 5).
-
tra_bar.npy (4.79 G): A numpy array that contains the training data for bars. Its shape is (127734, 96, 84, 5).