The full error is below:

Traceback (most recent call last): File "main.py", line 8, in '.'.join(('musegan', CONFIG['exp']['model'], 'models'))) File "C:\Program Files (x86)\Microsoft Visual Studio\Shared\Anaconda3_64\lib\importlib_init_.py", line 126, in import_module return _bootstrap._gcd_import(name[level:], package, level) File "", line 994, in _gcd_import File "", line 971, in _find_and_load File "", line 955, in _find_and_load_unlocked File "", line 665, in _load_unlocked File "", line 678, in exec_module File "", line 219, in _call_with_frames_removed File "C:\Users\PU40005904\Downloads\musegan-master\musegan-master\musegan\musegan\models.py", line 9, in from musegan.utils.metrics import Metrics File "C:\Users\PU40005904\Downloads\musegan-master\musegan-master\musegan\utils\metrics.py", line 7, in import SharedArray as sa ModuleNotFoundError: No module named 'SharedArray'

SharedArray is not being installed when pip install sharedarray is ran, error is:

Failed building wheel for sharedarray Running setup.py clean for sharedarray Failed to build sharedarray Installing collected packages: sharedarray Running setup.py install for sharedarray ... error Complete output from command "c:\program files (x86)\microsoft visual studio\shared\anaconda3_64\python.exe" -u -c "import setuptools, tokenize;file='C:\Users\PU40005904\AppData\Local\Temp\pip-install-6nedy54o\sharedarray\setup.py';f=getattr(tokenize, 'open', open)(file);code=f.read().replace('\r\n', '\n');f.close();exec(compile(code, file, 'exec'))" install --record C:\Users\PU40005904\AppData\Local\Temp\pip-record-n229w795\install-record.txt --single-version-externally-managed --compile: running install running build running build_ext building 'SharedArray' extension creating build creating build\temp.win-amd64-3.6 creating build\temp.win-amd64-3.6\Release creating build\temp.win-amd64-3.6\Release\src C:\Program Files (x86)\Microsoft Visual Studio\2017\Community\VC\Tools\MSVC\14.14.26428\bin\HostX86\x64\cl.exe /c /nologo /Ox /W3 /GL /DNDEBUG /MD "-Ic:\program files (x86)\microsoft visual studio\shared\anaconda3_64\lib\site-packages\numpy\core\include" "-Ic:\program files (x86)\microsoft visual studio\shared\anaconda3_64\include" "-Ic:\program files (x86)\microsoft visual studio\shared\anaconda3_64\include" "-IC:\Program Files (x86)\Microsoft Visual Studio\2017\Community\VC\Tools\MSVC\14.14.26428\ATLMFC\include" "-IC:\Program Files (x86)\Microsoft Visual Studio\2017\Community\VC\Tools\MSVC\14.14.26428\include" "-IC:\Program Files (x86)\Windows Kits\NETFXSDK\4.6.1\include\um" "-IC:\Program Files (x86)\Windows Kits\10\include\10.0.17134.0\ucrt" "-IC:\Program Files (x86)\Windows Kits\10\include\10.0.17134.0\shared" "-IC:\Program Files (x86)\Windows Kits\10\include\10.0.17134.0\um" "-IC:\Program Files (x86)\Windows Kits\10\include\10.0.17134.0\winrt" "-IC:\Program Files (x86)\Windows Kits\10\include\10.0.17134.0\cppwinrt" /Tc.\src\map_owner.c /Fobuild\temp.win-amd64-3.6\Release.\src\map_owner.obj map_owner.c .\src\map_owner.c(19): fatal error C1083: Cannot open include file: 'sys/mman.h': No such file or directory error: command 'C:\Program Files (x86)\Microsoft Visual Studio\2017\Community\VC\Tools\MSVC\14.14.26428\bin\HostX86\x64\cl.exe' failed with exit status 2

----------------------------------------

Command ""c:\program files (x86)\microsoft visual studio\shared\anaconda3_64\python.exe" -u -c "import setuptools, tokenize;file='C:\Users\PU40005904\AppData\Local\Temp\pip-install-6nedy54o\sharedarray\setup.py';f=getattr(tokenize, 'open', open)(file);code=f.read().replace('\r\n', '\n');f.close();exec(compile(code, file, 'exec'))" install --record C:\Users\PU40005904\AppData\Local\Temp\pip-record-n229w795\install-record.txt --single-version-externally-managed --compile" failed with error code 1 in C:\Users\PU40005904\AppData\Local\Temp\pip-install-6nedy54o\sharedarray\

If someone has a way to make this work, please help. thanks.

Hi, I am trying to run training process using this line ./scripts/run_train.sh "./exp/my_experiment/" "0" in google colab with gpu and have this error:

musegan.train INFO Using parameters: {'beat_resolution': 12, 'condition_track_idx': None, 'data_shape': [4, 48, 84, 5], 'is_accompaniment': False, 'is_conditional': False, 'latent_dim': 128, 'nets': {'discriminator': 'default', 'generator': 'default'}, 'use_binary_neurons': False} musegan.train INFO Using configurations: {'adam': {'beta1': 0.5, 'beta2': 0.9}, 'batch_size': 64, 'colormap': [[1.0, 0.0, 0.0], [1.0, 0.5, 0.0], [0.0, 1.0, 0.0], [0.0, 0.0, 1.0], [0.0, 0.5, 1.0]], 'config': './exp/my_experiment//config.yaml', 'data_filename': 'train_x_lpd_5_phr', 'data_source': 'sa', 'eval_dir': '/content/musegan/exp/my_experiment/eval', 'evaluate_steps': 100, 'exp_dir': '/content/musegan/exp/my_experiment', 'gan_loss_type': 'wasserstein', 'gpu': '0', 'initial_learning_rate': 0.001, 'learning_rate_schedule': {'end': 50000, 'end_value': 0.0, 'start': 45000}, 'log_dir': '/content/musegan/exp/my_experiment/logs/train', 'log_loss_steps': 100, 'midi': {'is_drums': [1, 0, 0, 0, 0], 'lowest_pitch': 24, 'programs': [0, 0, 25, 33, 48], 'tempo': 100}, 'model_dir': '/content/musegan/exp/my_experiment/model', 'n_dis_updates_per_gen_update': 5, 'n_jobs': 20, 'params': './exp/my_experiment//params.yaml', 'sample_dir': '/content/musegan/exp/my_experiment/samples', 'sample_grid': [8, 8], 'save_array_samples': True, 'save_checkpoint_steps': 10000, 'save_image_samples': True, 'save_pianoroll_samples': True, 'save_samples_steps': 100, 'save_summaries_steps': 0, 'slope_schedule': {'end': 50000, 'end_value': 5.0, 'start': 10000}, 'src_dir': '/content/musegan/exp/my_experiment/src', 'steps': 50000, 'use_gradient_penalties': True, 'use_learning_rate_decay': True, 'use_random_transpose': False, 'use_slope_annealing': False, 'use_train_test_split': False} musegan.train INFO Loading training data. musegan.train INFO Training data size: 102378 musegan.train INFO Building dataset. tensorflow WARNING From /usr/local/lib/python3.6/dist-packages/tensorflow/python/data/ops/dataset_ops.py:429: py_func (from tensorflow.python.ops.script_ops) is deprecated and will be removed in a future version. Instructions for updating: tf.py_func is deprecated in TF V2. Instead, use tf.py_function, which takes a python function which manipulates tf eager tensors instead of numpy arrays. It's easy to convert a tf eager tensor to an ndarray (just call tensor.numpy()) but having access to eager tensors means tf.py_functions can use accelerators such as GPUs as well as being differentiable using a gradient tape.

musegan.model INFO Building model. musegan.model INFO Building training nodes. tensorflow WARNING From /usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version. Instructions for updating: Colocations handled automatically by placer. tensorflow WARNING From /content/musegan/src/musegan/presets/ops.py:16: conv3d_transpose (from tensorflow.python.layers.convolutional) is deprecated and will be removed in a future version. Instructions for updating: Use keras.layers.conv3d_transpose instead. tensorflow WARNING From /content/musegan/src/musegan/presets/ops.py:21: batch_normalization (from tensorflow.python.layers.normalization) is deprecated and will be removed in a future version. Instructions for updating: Use keras.layers.batch_normalization instead. tensorflow WARNING From /content/musegan/src/musegan/presets/ops.py:12: conv3d (from tensorflow.python.layers.convolutional) is deprecated and will be removed in a future version. Instructions for updating: Use keras.layers.conv3d instead. tensorflow WARNING From /content/musegan/src/musegan/presets/ops.py:8: dense (from tensorflow.python.layers.core) is deprecated and will be removed in a future version. Instructions for updating: Use keras.layers.dense instead. musegan.model INFO Building losses. tensorflow WARNING From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/math_ops.py:3066: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version. Instructions for updating: Use tf.cast instead. musegan.model INFO Building training ops. tensorflow WARNING From /usr/local/lib/python3.6/dist-packages/tensorflow/python/training/learning_rate_decay_v2.py:321: div (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version. Instructions for updating: Deprecated in favor of operator or tf.math.divide. musegan.model INFO Building summaries. musegan.train INFO Number of trainable parameters in Model: 3,943,968 musegan.train INFO Number of trainable parameters in Generator: 2,578,127 musegan.train INFO Number of trainable parameters in Discriminator: 1,365,841 musegan.train INFO Loading sample_z. musegan.model INFO Building prediction nodes. musegan.train INFO Training start. tensorflow INFO Create CheckpointSaverHook. tensorflow INFO Graph was finalized. 2019-06-09 19:03:56.671015: I tensorflow/core/platform/cpu_feature_guard.cc:141] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA 2019-06-09 19:03:56.852445: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:998] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero 2019-06-09 19:03:56.853229: I tensorflow/compiler/xla/service/service.cc:150] XLA service 0x10943340 executing computations on platform CUDA. Devices: 2019-06-09 19:03:56.853277: I tensorflow/compiler/xla/service/service.cc:158] StreamExecutor device (0): Tesla T4, Compute Capability 7.5 2019-06-09 19:03:56.855500: I tensorflow/core/platform/profile_utils/cpu_utils.cc:94] CPU Frequency: 2300000000 Hz 2019-06-09 19:03:56.855683: I tensorflow/compiler/xla/service/service.cc:150] XLA service 0x109431e0 executing computations on platform Host. Devices: 2019-06-09 19:03:56.855721: I tensorflow/compiler/xla/service/service.cc:158] StreamExecutor device (0): , 2019-06-09 19:03:56.856041: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1433] Found device 0 with properties: name: Tesla T4 major: 7 minor: 5 memoryClockRate(GHz): 1.59 pciBusID: 0000:00:04.0 totalMemory: 14.73GiB freeMemory: 14.60GiB 2019-06-09 19:03:56.856089: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1512] Adding visible gpu devices: 0 2019-06-09 19:03:56.856787: I tensorflow/core/common_runtime/gpu/gpu_device.cc:984] Device interconnect StreamExecutor with strength 1 edge matrix: 2019-06-09 19:03:56.856809: I tensorflow/core/common_runtime/gpu/gpu_device.cc:990] 0 2019-06-09 19:03:56.856819: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1003] 0: N 2019-06-09 19:03:56.857079: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1115] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 14202 MB memory) -> physical GPU (device: 0, name: Tesla T4, pci bus id: 0000:00:04.0, compute capability: 7.5) tensorflow WARNING From /usr/local/lib/python3.6/dist-packages/tensorflow/python/training/saver.py:1266: checkpoint_exists (from tensorflow.python.training.checkpoint_management) is deprecated and will be removed in a future version. Instructions for updating: Use standard file APIs to check for files with this prefix. tensorflow INFO Restoring parameters from /content/musegan/exp/my_experiment/model/model.ckpt-0 tensorflow WARNING From /usr/local/lib/python3.6/dist-packages/tensorflow/python/training/saver.py:1070: get_checkpoint_mtimes (from tensorflow.python.training.checkpoint_management) is deprecated and will be removed in a future version. Instructions for updating: Use standard file utilities to get mtimes. tensorflow INFO Running local_init_op. tensorflow INFO Done running local_init_op. tensorflow INFO Saving checkpoints for 0 into /content/musegan/exp/my_experiment/model/model.ckpt. 2019-06-09 19:04:11.607825: I tensorflow/stream_executor/dso_loader.cc:152] successfully opened CUDA library libcublas.so.10.0 locally musegan.train INFO step=100, gen_loss=-1.2372E+02, dis_loss=-1.9598E+02 musegan.train INFO Running sampler musegan.train INFO Running evaluation ./scripts/run_train.sh: line 19: 879 Bus error (core dumped) python3 "$DIR/../src/train.py" --exp_dir "$1" --params "$1/params.yaml" --config "$1/config.yaml" --gpu "$gpu"

I found the project very interesting. I want to run to learn. I tried to run main.py but I came across the error: FileNotFoundError: [Errno 2] No such file or directory: 'lastfm_alternative_8b_phrase'.

I downloaded this lastfm_alternative_8b_phrase.npy file. I put it in the training_data folder and also inside the musegan folder. Then I tried to enter the full path of the file. I also removed the .npy extension but it still did not work. what am I doing wrong?

Thank you!

hello, how can i achieve same results on your official website? The current training results are not continuous in time. Can you help me? what should I do？ Looking forward to your reply. thx

Hi ,I don't know how to get 'tra_X_phrase_all' in main.py, I run the preprocess code and I only get many npz files. What I should do to get the 'tra_X_phrase_all' file? Thank you very much!

I've been trying to convert wav file into midi file using the software WaoN, waves to notes transcriber. But the problem is, irrespective of what instruments are in the .wav file, the .mid always have a single instrumental piano and hence one track. I need to get multitrack output, by which I mean, tracks of piano, drums, and like these.

Using a python module pypianoroll -

For a song that is already in midi, here, let it be - "Bohemian Rhapsody", I get output something like this:

>>> pypianoroll.Multitrack('Bohemian_Rhapsody.mid')
Multitrack(tracks=['', '', '', '', '', '', '', '', '', '', '', '', ''], tempo=array([78.000078, 78.000078, 78.000078, ..., 78.000078, 78.000078,
   78.000078]), downbeat=array([ True, False, False, ..., False, False, False]), beat_resolution=24, name=unknown)

It shows, it has 13 different track in it.

But when I convert a wav of this file, into midi using WaoN, there it shows me

>>> pypianoroll.Multitrack('Bohemian_Rhapsody.mid')
Multitrack(tracks=[''], tempo=array([120., 120., 120., ..., 120., 120., 120.]), downbeat=None, beat_resolution=24, name=unknown)

With a single track.

This happens every time, with every file; when I get a result in multitrack.

I can use some help. Can anyone point out what's wrong? Or give a suggestion on how to proceed?

We accidentally deleted the original training data (tra_phr.npy and tra_bar.npy). If you have a copy, please let us know so that we can put them back on the website. Thanks!

Couldn't successfully run 'pip install -r requirements.txt' because 1.10.1 is too old. I change tensorflow version to 1.15.2 in the requirements.txt and successfully install it.

Hi! Thanks for sharing the MuseGan source code!!

But, how can I consist of dataset using my midi data? If you don't mind, could you share the source code or reference(documents) which how did you consist 'train_x_lpd_5_phr.npz' with midi data? I already looked the 'https://github.com/wayne391/symbolic-musical-datasets/tree/master/5-track-pianoroll' and 'https://salu133445.github.io/pypianoroll/' but I'm still not sure about it.

Thanks :)

Hello @salu133445 and the rest of the crew :)

First off, I would like to thank you for sharing this project as an open source one! I think you've done an amazing job with this project.

However, I'm a bit confused as to what exactly I should focus on if I want to start off from the most recent or the most well optimized base? Would you actually recommend going for bmusegan?

musegan/V2 seems like a great start that's much more simplified and seems just as recent (and I think I'm having success porting it to Google Colab), but then it says there: "Warning: this version is no longer maintained", so I'm confused. Does that statement refer to musegan in general (as being replaced by bmusegan) or just to the musegan in the V2 folder?

I apologize if this was not the right place to ask questions like these, but I assumed it is ok, since I've read other posts here in the issues. Please bear with my newbiness about how to use Github, I'm both forking and posting an issue for the first time ever :)

./scripts/process_data.sh Loading data from '/mnt/c/Users/kiomer/musegan/scripts/../data/train_x_lpd_5_phr.npz'. Traceback (most recent call last): File "/mnt/c/Users/kiomer/musegan/scripts/../src/process_data.py", line 76, in main() File "/mnt/c/Users/kiomer/musegan/scripts/../src/process_data.py", line 63, in main with np.load(filepath) as loaded: File "/home/kiomer/.local/lib/python3.8/site-packages/numpy/lib/npyio.py", line 444, in load raise ValueError("Cannot load file containing pickled data " ValueError: Cannot load file containing pickled data when allow_pickle=False

Problem as above

Bumps setuptools from 39.1.0 to 65.5.1.

Dependabot will resolve any conflicts with this PR as long as you don't alter it yourself. You can also trigger a rebase manually by commenting @dependabot rebase.

Hi, In musegan, inappropriate dependency versioning constraints can cause risks.

Below are the dependencies and version constraints that the project is using

setuptools==39.1.0
absl-py==0.4.1
astor==0.7.1
gast==0.2.0
grpcio==1.14.2
imageio==2.3.0
Markdown==2.6.11
mido==1.2.8
numpy==1.14.5
Pillow==5.2.0
pretty-midi==0.2.8
protobuf==3.6.1
pypianoroll==0.4.6
PyYAML==3.13
scipy==1.1.0
SharedArray==3.0.0
six==1.11.0
tensorboard==1.10.0
tensorflow-gpu==1.10.1
termcolor==1.1.0
Werkzeug==0.14.1

The version constraint == will introduce the risk of dependency conflicts because the scope of dependencies is too strict. The version constraint No Upper Bound and * will introduce the risk of the missing API Error because the latest version of the dependencies may remove some APIs.

After further analysis, in this project, The version constraint of dependency imageio can be changed to >=1.1-linux32,<=2.19.3. The version constraint of dependency numpy can be changed to >=1.8.0,<=1.23.0rc3.

The above modification suggestions can reduce the dependency conflicts as much as possible, and introduce the latest version as much as possible without calling Error in the projects.

The invocation of the current project includes all the following methods.

imageio.imwrite

numpy.linalg.norm

is_piano
segment_quality
args.input_dir.glob
ops.dense.get_shape
args.outfile.endswith
pprint.pformat
tensorflow.sqrt
tensorflow.reduce_mean.get_shape
get_metric_ops
musegan.utils.backup_src
tracks.append
tensorflow.assign_add
os.path.dirname
tensorflow.train.AdamOptimizer
tensorflow.variable_scope
tensorflow.maximum
pianoroll.set_shape
tensorflow.tile
pypianoroll.Track
tensorflow.truncated_normal_initializer
tensorflow.summary.scalar
tensorflow.norm
op.outputs.consumers.consumers
argparse.ArgumentParser.add_argument
tensorflow.layers.conv2d_transpose
musegan.config.SHUFFLE_BUFFER_SIZE.dataset.shuffle.repeat.batch.prefetch
is_string
tconv_layer
len
musegan.data.get_dataset.make_one_shot_iterator
update_not_none
tensorflow.zeros
setup
input
sys.exit
join
binary_ops.binary_stochastic_ST
numpy.pad
numpy.random.choice
shutil.copytree
diff.transpose.reshape
f.readline.split.strip
self.dis
musegan.config.SHUFFLE_BUFFER_SIZE.dataset.shuffle.repeat
numpy.errstate
pass_through_sigmoid
tensorflow.get_default_graph.gradient_override_map
numpy.arccos
musegan.utils.update_not_none
tensorflow.trainable_variables
tensorflow.get_collection
_array_to_image.items
numpy.roll
main
op.graph.get_operation_by_name
pianorolls.items
ok_segment_list.extend
binary_round
tensorflow.train.polynomial_decay
musegan.utils.load_component
_gen_data
f.readline.split
numpy.zeros
logging.FileHandler.setFormatter
_save_pianoroll
numpy.arange
musegan.io_utils.image_grid
numpy.prod
numpy.matmul
self.get_train_nodes
load_data_from_npy
tensorflow.pad.get_shape
polyphonic_rate
xv.flatten.flatten
song_ok_segments.append
tensorflow.transpose
_drum_pattern_mask
numpy.concatenate.nonzero
shutil.rmtree
scipy.stats.truncnorm.rvs
array.astype
tensorflow.expand_dims
op.outputs.consumers
tensorflow.python.framework.ops.name_scope
os.path.splitext
slerp
load_or_create_samples
argparse.ArgumentParser
tensorflow.nn.relu
tensorflow.reduce_mean
musegan.data.get_dataset
qualified_note_rate
ops.get_normalization
numpy.linspace
tensorflow.py_func
check_which_family
logging.FileHandler
numpy.diff.transpose
self.gen
binary_stochastic_REINFORCE
tensorflow.truncated_normal
musegan.model.Model
vars
numpy.sum
tensorflow.Variable
musegan.io_utils.vector_to_image
numpy.random.uniform
tensorflow.cast
tensorflow.reduce_sum
max
create_shared_array
yaml.safe_load
sum
tensorflow.get_default_graph.get_collection
conv_layer
os.path.basename
numpy.save
f.readline
tensorflow.train.Saver
numpy.concatenate
input.lower
is_guitar
tensorflow.group
numpy.load
numpy.product
SharedArray.attach
musegan.metrics.get_save_metric_ops
tensorflow.round
musegan.data.load_data
numpy.reshape
tensorflow.matmul
logging.getLogger.info
data.astype.astype
dict2.items
_save_array
SharedArray.delete
empty_bar_rate
numpy.dot
_scale_mask
params.load_component
_to_tonal_space
musegan.config.SHUFFLE_BUFFER_SIZE.dataset.shuffle.repeat.batch
range
tensorflow.constant.reshape
numpy.expand_dims
random_transpose
get_n_params
numpy.cos
tensorflow.shape
ops.conv3d
musegan.io_utils.pianoroll_to_image
save_array_ops.append
int
pianoroll.squeeze.clip.reshape
is_bass
numpy.reshape.astype
tensor_logger_values.items
list
_save_colored_images
tensorflow.assign
numpy.tile
tensorflow.layers.dense
musegan.config.SHUFFLE_BUFFER_SIZE.dataset.shuffle.repeat.batch.shuffle
logging.basicConfig
to_chroma
tensorflow.count_nonzero
tensorflow.control_dependencies
arrays.items
tensorflow.data.Dataset.from_generator
_save_images
tensorflow.losses.sigmoid_cross_entropy
save_image_ops.append
musegan.utils.load_yaml
numpy.random.randint
imageio.imwrite
tensorflow.square
SharedArray.create
os.path.realpath
tensorflow.train.NanTensorHook
tensorflow.get_default_graph.add_to_collection
musegan.data.get_samples
tensorflow.ones
numpy.pad.reshape
numpy.array
os.path.join
numpy.empty
l_bar.get_shape
os.makedirs
_array_to_image
l_bar.get_shape.as_list
tensorflow.python.framework.ops.RegisterGradient
_save_image_grid
musegan.utils.make_sure_path_exists
tensorflow.constant_initializer
loss_fn
logging.getLogger
numpy.transpose
format
array.reshape
enumerate
norm
ops.dense
sess.should_stop
numpy.diff
ops.tconv3d
importlib.import_module
numpy.random.permutation
numpy.count_nonzero
os.path.exists
pypianoroll.Multitrack
zip
set_pianoroll_shape
tensorflow.random_uniform
tensorflow.placeholder
logger.addHandler
name.startswith
tensorflow.reduce_any
numpy.savez_compressed
open
numpy.flip
tensorflow.ceil
filepath.endswith
diff.transpose.reshape.nonzero
ACTIVATION
add_file_handler
tensorflow.gradients
_qualified_note_rate
params.get
get_input_z
tensorflow.reshape
sess.run
numpy.sin
n_pitches_used
in_scale_rate
open.close
dataset.make_one_shot_iterator.get_next
tensorflow.add_to_collection
str
getattr
tensorflow.train.global_step
numpy.copyto
tensorflow.as_string
tensorflow.train.MonitoredTrainingSession
numpy.issubdtype
bernoulli_sample
self.get_predict_nodes
tensorflow.constant
tensorflow.Session
tensorflow.ConfigProto
tensorflow.layers.conv3d
_get_filepath
TypeError
is_instr_act
musegan.io_utils.save_pianoroll
musegan.losses.get_adv_losses
tensorflow.ones_like
setup_dirs
numpy.linalg.norm
logging.FileHandler.setLevel
binary_stochastic_ST
tensorflow.concat
yv.flatten.flatten
tensorflow.sigmoid
tensorflow.reshape.astype
tensorflow.cond
ValueError
numpy.repeat
var.get_shape
numpy.where
_tonal_matrix
tensorflow.train.get_or_create_global_step
harmonicity
tensorflow.train.Saver.restore
musegan.utils.load_yaml.get
shutil.ignore_patterns
op.graph.get_collection.get
tensorflow.train.get_global_step
pianoroll.squeeze.clip
logging.Logger.manager.loggerDict.keys
print
tensorflow.layers.conv3d_transpose
load_yaml
open.write
argparse.ArgumentParser.parse_args
drum_in_pattern_rate
numpy.append
set_label_shape
load_training_data
tensorflow.get_variable
parse_arguments
compiled_list.append
tensorflow.layers.batch_normalization
tensorflow.zeros_like
musegan.utils.setup_loggers
pianorolls.append
args.input_dir.rglob
logging.Formatter
pianoroll.squeeze.clip.astype
op.graph.get_collection
array.astype.astype
label.set_shape
pypianoroll.Multitrack.save
tensorflow.gradients.get_shape
tensorflow.pad
lerp
tensorflow.train.AdamOptimizer.minimize
musegan.config.SHUFFLE_BUFFER_SIZE.dataset.shuffle.repeat.batch.map
tensorflow.layers.conv2d
pianoroll.squeeze.clip.squeeze
load_data_from_npz
get_scheduled_variable
tensorflow.get_default_graph
tensorflow.name_scope
save_pianoroll_ops.append
tensor.get_shape
model
numpy.meshgrid
tensorflow.nn.tanh
map
get_metric_ops.items

@developer Could please help me check this issue? May I pull a request to fix it? Thank you very much.

When I run collect_data.sh, I am trying to train using a single MIDI file, That error occur.

So I Add that code.

And run again, but another error again...

I can't find a way to fix that error. How to fix this error?

I want to train and run inference on three separate models (Jamming, hybrid and composer). I want to know if I can change something in the config/params to achieve this?