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v1/README.md

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+# MuseGAN
+
+<font color=red><strong><i>Warning: this version is no longer maintained</i></strong></font>
+
+[MuseGAN](https://salu133445.github.io/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](https://salu133445.github.io/lakh-pianoroll-dataset/)
+(LPD), a new [multi-track piano-roll](https://salu133445.github.io/musegan/data)
+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](https://salu133445.github.io/musegan/results).
+
+## Papers
+
+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](http://arxiv.org/abs/1709.06298)]
+[[slides](https://salu133445.github.io/musegan/pdf/musegan-aaai2018-slides.pdf)]
+
+Hao-Wen Dong\*, Wen-Yi Hsiao\*, Li-Chia Yang and Yi-Hsuan Yang,
+"**MuseGAN: Demonstration of a Convolutional GAN Based Model for Generating
+Multi-track Piano-rolls**,"
+in *ISMIR Late-Breaking and Demo Session*, 2017.
+(non-peer reviewed two-page extended abstract)
+[[paper](https://salu133445.github.io/musegan/pdf/musegan-ismir2017-lbd-paper.pdf)]
+[[poster](https://salu133445.github.io/musegan/pdf/musegan-ismir2017-lbd-poster.pdf)]
+
+\* *These authors contributed equally to this work.*
+
+## Usage
+
+```python
+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 a museGAN object
+    musegan = MuseGAN(sess, t_config, model)
+
+    # === Training ===
+    musegan.train(input_data)
+
+    # === Load a Pretrained Model ===
+    musegan.load(musegan.dir_ckpt)
+
+    # === Generate Samples ===
+    path_test = 'train.npy'
+    input_data.add_data(path_test, key='test')
+    musegan.gen_test(input_data, is_eval=True)
+```
+
+## Training Data
+
+- [tra_phr.npy](https://drive.google.com/uc?id=1-bQCO6ZxpIgdMM7zXhNJViovHjtBKXde&export=download)
+  (7.54 GB) contains 50,266 four-bar phrases. The shape is (50266, 384, 84, 5).
+- [tra_bar.npy](https://drive.google.com/uc?id=1Xxj6WU82fcgY9UtBpXJGOspoUkMu58xC&export=download)
+  (4.79 GB) contains 127,734 bars. The shape is (127734, 96, 84, 5).

v1/config.py

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+'''
+Model Configuration
+'''
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import numpy as np
+from shutil import copyfile
+import os
+import SharedArray as sa
+import tensorflow as tf
+import glob
+
+print('[*] config...')
+
+# class Dataset:
+TRACK_NAMES = ['bass', 'drums', 'guitar', 'piano', 'strings']
+
+def get_colormap():
+    colormap = np.array([[1., 0., 0.],
+                         [0., 1., 0.],
+                         [0., 0., 1.],
+                         [1., .5, 0.],
+                         [0., .5, 1.]])
+    return tf.constant(colormap, dtype=tf.float32, name='colormap')
+
+###########################################################################
+# Training
+###########################################################################
+
+class TrainingConfig:
+    is_eval = True
+    batch_size = 64
+    epoch = 20
+    iter_to_save = 100
+    sample_size = 64
+    print_batch = True
+    drum_filter = np.tile([1,0.3,0,0,0,0.3], 16)
+    scale_mask = [1., 0., 1., 0., 1., 1., 0., 1., 0., 1., 0., 1.]
+    inter_pair = [(0,2), (0,3), (0,4), (2,3), (2,4), (3,4)]
+    track_names = TRACK_NAMES
+    track_dim = len(track_names)
+    eval_map = np.array([
+                    [1, 1, 1, 1, 1],  # metric_is_empty_bar
+                    [1, 1, 1, 1, 1],  # metric_num_pitch_used
+                    [1, 0, 1, 1, 1],  # metric_too_short_note_ratio
+                    [1, 0, 1, 1, 1],  # metric_polyphonic_ratio
+                    [1, 0, 1, 1, 1],  # metric_in_scale
+                    [0, 1, 0, 0, 0],  # metric_drum_pattern
+                    [1, 0, 1, 1, 1]   # metric_num_chroma_used
+                ])
+
+    exp_name = 'exp'
+    gpu_num = '1'
+
+
+###########################################################################
+# Model Config
+###########################################################################
+
+class ModelConfig:
+    output_w = 96
+    output_h = 84
+    lamda = 10
+    batch_size = 64
+    beta1 = 0.5
+    beta2 = 0.9
+    lr = 2e-4
+    is_bn = True
+    colormap = get_colormap()
+
+# image
+class MNISTConfig(ModelConfig):
+    output_w = 28
+    output_h = 28
+    z_dim = 74
+    output_dim = 1
+
+# RNN
+class RNNConfig(ModelConfig):
+    track_names = ['All']
+    track_dim = 1
+    output_bar = 4
+    z_inter_dim = 128
+    output_dim = 5
+    acc_idx = None
+    state_size = 128
+
+# onebar
+class OneBarHybridConfig(ModelConfig):
+    track_names = TRACK_NAMES
+    track_dim = 5
+    acc_idx = None
+    z_inter_dim = 64
+    z_intra_dim = 64
+    output_dim = 1
+
+class OneBarJammingConfig(ModelConfig):
+    track_names = TRACK_NAMES
+    track_dim = 5
+    acc_idx = None
+    z_intra_dim = 128
+    output_dim = 1
+
+class OneBarComposerConfig(ModelConfig):
+    track_names = ['All']
+    track_dim = 1
+    acc_idx = None
+    z_inter_dim = 128
+    output_dim = 5
+
+# nowbar
+
+class NowBarHybridConfig(ModelConfig):
+    track_names = TRACK_NAMES
+    track_dim = 5
+    acc_idx = 4
+    z_inter_dim = 64
+    z_intra_dim = 64
+    output_dim = 1
+
+class NowBarJammingConfig(ModelConfig):
+    track_names = TRACK_NAMES
+    track_dim = 5
+    acc_idx = 4
+    z_intra_dim = 128
+    output_dim = 1
+
+class NowBarComposerConfig(ModelConfig):
+    track_names = ['All']
+    track_dim = 1
+    acc_idx = 4
+    z_inter_dim = 128
+    output_dim = 5
+
+# Temporal
+class TemporalHybridConfig(ModelConfig):
+    track_names = TRACK_NAMES
+    track_dim = 5
+    output_bar = 4
+    z_inter_dim = 32
+    z_intra_dim = 32
+    acc_idx = None
+    output_dim = 1
+
+class TemporalJammingConfig(ModelConfig):
+    track_names = TRACK_NAMES
+    track_dim = 5
+    output_bar = 4
+    z_intra_dim = 64
+    output_dim = 1
+
+class TemporalComposerConfig(ModelConfig):
+    track_names = ['All']
+    track_dim = 1
+    output_bar = 4
+    z_inter_dim = 64
+    acc_idx = None
+    output_dim = 5
+
+class NowBarTemporalHybridConfig(ModelConfig):
+    track_names = TRACK_NAMES
+    acc_idx = 4
+    track_dim = 5
+    output_bar = 4
+    z_inter_dim = 32
+    z_intra_dim = 32
+    acc_idx = 4
+    output_dim = 1

v1/main.py

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+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import os
+import scipy.misc
+import numpy as np
+import tensorflow as tf
+from pprint import pprint
+import SharedArray as sa
+
+from musegan.core import *
+from musegan.components import *
+from input_data import *
+from config import *
+
+#assign GPU
+
+
+if __name__ == '__main__':
+
+    """ Create TensorFlow Session """
+
+    t_config = TrainingConfig
+
+    os.environ['CUDA_VISIBLE_DEVICES'] = t_config.gpu_num
+    config = tf.ConfigProto()
+    config.gpu_options.allow_growth = True
+
+    with tf.Session(config=config) as sess:
+
+        path_x_train_phr =  'tra_X_phrase_all' # (50266, 384, 84, 5)
+
+        # Temporal
+            # hybrid
+        t_config.exp_name = 'exps/temporal_hybrid'
+        model = TemporalHybrid(TemporalHybridConfig)
+        input_data = InputDataTemporalHybrid(model)
+        input_data.add_data_sa(path_x_train_phr, 'train')
+
+        musegan = MuseGAN(sess, t_config, model)
+        musegan.train(input_data)
+
+        musegan.load(musegan.dir_ckpt)
+        musegan.gen_test(input_data, is_eval=True)
+
+