TypeError: cannot pickle '_io.BufferedReader' object

[akhilsadam]

WaveNet_demo.ipynb - trainer.train(batch_size=16,epochs=10):

Commenting out num_workers in wavenet_training.py did not help, nor did setting num_workers=0.
torch 1.6.0, Python 3.

Thank you for the help!

---

TypeError Traceback (most recent call last)
in
10
11 print('start training...')
---> 12 trainer.train(batch_size=16,epochs=10)

~\Desktop\RUST\AudioSynth\pytorch-wavenet-master\wavenet_training.py in train(self, batch_size, epochs, continue_training_at_step)
62
63 if step % self.snapshot_interval == 0:
---> 64 if self.snapshot_path is None:
65 continue
66 time_string = time.strftime("%Y-%m-%d_%H-%M-%S", time.gmtime())

~\AppData\Roaming\Python\Python38\site-packages\torch\utils\data\dataloader.py in iter(self)
289 return _SingleProcessDataLoaderIter(self)
290 else:
--> 291 return _MultiProcessingDataLoaderIter(self)
292
293 @Property

~\AppData\Roaming\Python\Python38\site-packages\torch\utils\data\dataloader.py in init(self, loader)
735 # before it starts, and del tries to join but will get:
736 # AssertionError: can only join a started process.
--> 737 w.start()
738 self._index_queues.append(index_queue)
739 self._workers.append(w)

C:\Program Files\Python3.8\lib\multiprocessing\process.py in start(self)
119 'daemonic processes are not allowed to have children'
120 _cleanup()
--> 121 self._popen = self._Popen(self)
122 self._sentinel = self._popen.sentinel
123 # Avoid a refcycle if the target function holds an indirect

C:\Program Files\Python3.8\lib\multiprocessing\context.py in _Popen(process_obj)
222 @staticmethod
223 def _Popen(process_obj):
--> 224 return _default_context.get_context().Process._Popen(process_obj)
225
226 class DefaultContext(BaseContext):

C:\Program Files\Python3.8\lib\multiprocessing\context.py in _Popen(process_obj)
324 def _Popen(process_obj):
325 from .popen_spawn_win32 import Popen
--> 326 return Popen(process_obj)
327
328 class SpawnContext(BaseContext):

C:\Program Files\Python3.8\lib\multiprocessing\popen_spawn_win32.py in init(self, process_obj)
91 try:
92 reduction.dump(prep_data, to_child)
---> 93 reduction.dump(process_obj, to_child)
94 finally:
95 set_spawning_popen(None)

C:\Program Files\Python3.8\lib\multiprocessing\reduction.py in dump(obj, file, protocol)
58 def dump(obj, file, protocol=None):
59 '''Replacement for pickle.dump() using ForkingPickler.'''
---> 60 ForkingPickler(file, protocol).dump(obj)
61
62 #

TypeError: cannot pickle '_io.BufferedReader' object

[VanBuren77]

Same problem

[GitHubGeniusOverlord]

Check the solution from #37

[akhilsadam]

This issue is specifically for those who have tried (and failed with) the solution in #37.

commenting out num_workers in wavenet_training.py did not help, nor did setting num_workers=0.
torch 1.6.0, Python 3.

[ZixuWang]

same problem

[LodenRietveld]

On macOs, changing the dataloader init to be this has worked for me:
self.dataloader = torch.utils.data.DataLoader(self.dataset,
batch_size=batch_size,
shuffle=True,
num_workers=8,
pin_memory=False,
multiprocessing_context="fork")