RuntimeError: DataLoader worker (pid 2019) is killed by signal

[me2beats]

Using Google colab:
(I used nearly the same code as demo.ipynb)

start training...
epoch 0
ERROR:root:Internal Python error in the inspect module.
Below is the traceback from this internal error.

Traceback (most recent call last):
  File "/usr/local/lib/python3.6/dist-packages/IPython/core/interactiveshell.py", line 2882, in run_code
    exec(code_obj, self.user_global_ns, self.user_ns)
  File "<ipython-input-36-b4e9311c8f81>", line 13, in <module>
    epochs=20)
  File "/content/pytorch-wavenet/wavenet_training.py", line 64, in train
    for (x, target) in iter(self.dataloader):
  File "/usr/local/lib/python3.6/dist-packages/torch/utils/data/dataloader.py", line 336, in __next__
    return self._process_next_batch(batch)
  File "/usr/local/lib/python3.6/dist-packages/torch/utils/data/dataloader.py", line 357, in _process_next_batch
    raise batch.exc_type(batch.exc_msg)
RuntimeError: Traceback (most recent call last):
  File "/usr/local/lib/python3.6/dist-packages/torch/utils/data/dataloader.py", line 106, in _worker_loop
    samples = collate_fn([dataset[i] for i in batch_indices])
  File "/usr/local/lib/python3.6/dist-packages/torch/utils/data/dataloader.py", line 187, in default_collate
    return [default_collate(samples) for samples in transposed]
  File "/usr/local/lib/python3.6/dist-packages/torch/utils/data/dataloader.py", line 187, in <listcomp>
    return [default_collate(samples) for samples in transposed]
  File "/usr/local/lib/python3.6/dist-packages/torch/utils/data/dataloader.py", line 162, in default_collate
    storage = batch[0].storage()._new_shared(numel)
  File "/usr/local/lib/python3.6/dist-packages/torch/storage.py", line 120, in _new_shared
    return cls._new_using_fd(size)
RuntimeError: unable to write to file </torch_2013_2460820068>


During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "/usr/local/lib/python3.6/dist-packages/IPython/core/interactiveshell.py", line 1823, in showtraceback
    stb = value._render_traceback_()
AttributeError: 'RuntimeError' object has no attribute '_render_traceback_'

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "/usr/local/lib/python3.6/dist-packages/IPython/core/ultratb.py", line 1132, in get_records
    return _fixed_getinnerframes(etb, number_of_lines_of_context, tb_offset)
  File "/usr/local/lib/python3.6/dist-packages/IPython/core/ultratb.py", line 313, in wrapped
    return f(*args, **kwargs)
  File "/usr/local/lib/python3.6/dist-packages/IPython/core/ultratb.py", line 358, in _fixed_getinnerframes
    records = fix_frame_records_filenames(inspect.getinnerframes(etb, context))
  File "/usr/lib/python3.6/inspect.py", line 1483, in getinnerframes
    frameinfo = (tb.tb_frame,) + getframeinfo(tb, context)
  File "/usr/lib/python3.6/inspect.py", line 1441, in getframeinfo
    filename = getsourcefile(frame) or getfile(frame)
  File "/usr/lib/python3.6/inspect.py", line 696, in getsourcefile
    if getattr(getmodule(object, filename), '__loader__', None) is not None:
  File "/usr/lib/python3.6/inspect.py", line 733, in getmodule
    if ismodule(module) and hasattr(module, '__file__'):
  File "/usr/local/lib/python3.6/dist-packages/torch/utils/data/dataloader.py", line 227, in handler
    _error_if_any_worker_fails()
RuntimeError: DataLoader worker (pid 2019) is killed by signal: Bus error. Details are lost due to multiprocessing. Rerunning with num_workers=0 may give better error trace.
---------------------------------------------------------------------------

Before that I got

  processed 262 of 265 files
  processed 263 of 265 files
  processed 264 of 265 files
one hot input
the dataset has 129356 items

My data is wav clap oneshots.

What's wrong?

[me2beats]

WaveNet_demo.ipynb doesn't work too (same problem)

The easiest way how to reproduce the error:

Run new Google colab python3 notebook then run this code:

!git clone https://github.com/vincentherrmann/pytorch-wavenet.git

!pip install torch
!pip install librosa

%cd pytorch-wavenet

import torch
from wavenet_model import *
from audio_data import WavenetDataset
from wavenet_training import *
from model_logging import *

# initialize cuda option
dtype = torch.FloatTensor # data type
ltype = torch.LongTensor # label type

use_cuda = torch.cuda.is_available()
if use_cuda:
    print('use gpu')
    dtype = torch.cuda.FloatTensor
    ltype = torch.cuda.LongTensor
    
model = WaveNetModel(layers=10,
                     blocks=3,
                     dilation_channels=32,
                     residual_channels=32,
                     skip_channels=1024,
                     end_channels=512, 
                     output_length=16,
                     dtype=dtype, 
                     bias=True)
# model = load_latest_model_from('snapshots', use_cuda=use_cuda)

print('model: ', model)
print('receptive field: ', model.receptive_field)
print('parameter count: ', model.parameter_count())

data = WavenetDataset(dataset_file='train_samples/bach_chaconne/dataset.npz',
                      item_length=model.receptive_field + model.output_length - 1,
                      target_length=model.output_length,
                      file_location='train_samples/bach_chaconne',
                      test_stride=500)
print('the dataset has ' + str(len(data)) + ' items')



def generate_and_log_samples(step):
    sample_length=32000
    gen_model = load_latest_model_from('snapshots', use_cuda=False)
    print("start generating...")
    samples = generate_audio(gen_model,
                             length=sample_length,
                             temperatures=[0.5])
    tf_samples = tf.convert_to_tensor(samples, dtype=tf.float32)
    logger.audio_summary('temperature_0.5', tf_samples, step, sr=16000)

    samples = generate_audio(gen_model,
                             length=sample_length,
                             temperatures=[1.])
    tf_samples = tf.convert_to_tensor(samples, dtype=tf.float32)
    logger.audio_summary('temperature_1.0', tf_samples, step, sr=16000)
    print("audio clips generated")


logger = TensorboardLogger(log_interval=200,
                           validation_interval=400,
                           generate_interval=1000,
                           generate_function=generate_and_log_samples,
                           log_dir="logs/chaconne_model")

# logger = Logger(log_interval=200,
#                 validation_interval=400,
#                 generate_interval=1000)



trainer = WavenetTrainer(model=model,
                         dataset=data,
                         lr=0.001,
                         snapshot_path='snapshots',
                         snapshot_name='chaconne_model',
                         snapshot_interval=1000,
                         logger=logger,
                         dtype=dtype,
                         ltype=ltype)

print('start training...')
trainer.train(batch_size=16,
              epochs=10)

[taylerpauls]

how did you input several different audio files in your dataset ? did you concatenate them into a npz file or loop through a directory that had all the files in it ?