Fix for #37, #50 and #53 (Windows specific issues) (#54)

* Fixed typeerror (torch.index_select received an invalid combination of arguments)

  File "synthesis.py", line 137, in <module>
    model, text, p=replace_pronunciation_prob, speaker_id=speaker_id, fast=True)
  File "synthesis.py", line 66, in tts
    sequence, text_positions=text_positions, speaker_ids=speaker_ids)
  File "H:\envs\pytorch\lib\site-packages\torch\nn\modules\module.py", line 325, in __call__
    result = self.forward(*input, **kwargs)
  File "H:\Tensorflow_Study\git\deepvoice3_pytorch\deepvoice3_pytorch\__init__.py", line 79, in forward
    text_positions, frame_positions, input_lengths)
  File "H:\envs\pytorch\lib\site-packages\torch\nn\modules\module.py", line 325, in __call__
    result = self.forward(*input, **kwargs)
  File "H:\Tensorflow_Study\git\deepvoice3_pytorch\deepvoice3_pytorch\__init__.py", line 116, in forward
    text_sequences, lengths=input_lengths, speaker_embed=speaker_embed)
  File "H:\envs\pytorch\lib\site-packages\torch\nn\modules\module.py", line 325, in __call__
    result = self.forward(*input, **kwargs)
  File "H:\Tensorflow_Study\git\deepvoice3_pytorch\deepvoice3_pytorch\deepvoice3.py", line 75, in forward
    x = self.embed_tokens(text_sequences) <- change this to long!
  File "H:\envs\pytorch\lib\site-packages\torch\nn\modules\module.py", line 325, in __call__
    result = self.forward(*input, **kwargs)
  File "H:\envs\pytorch\lib\site-packages\torch\nn\modules\sparse.py", line 103, in forward
    self.scale_grad_by_freq, self.sparse
  File "H:\envs\pytorch\lib\site-packages\torch\nn\_functions\thnn\sparse.py", line 59, in forward
    output = torch.index_select(weight, 0, indices.view(-1))
TypeError: torch.index_select received an invalid combination of arguments - got (�[32;1mtorch.cuda.FloatTensor�[0m, �[32;1mint�[0m, �[31;1mtorch.cuda.IntTensor�[0m), but expected (torch.cuda.FloatTensor source, int dim, torch.cuda.LongTensor index)

changed text_sequence to long, as required by torch.index_select.

* Fixed Nonetype error in collect_features

* requirements.txt fix

* Memory Leakage bugfix + hparams change

* Pre-PR modifications

* Pre-PR modifications 2

* Pre-PR modifications 3

* Post-PR modification

* remove requirements.txt

* num_workers to 1 in train.py

deepvoice3_pytorch/deepvoice3.py

@@ -72,7 +72,7 @@ def forward(self, text_sequences, text_positions=None, lengths=None,
         assert self.n_speakers == 1 or speaker_embed is not None
 
         # embed text_sequences
-        x = self.embed_tokens(text_sequences)
+        x = self.embed_tokens(text_sequences.long())
         x = F.dropout(x, p=self.dropout, training=self.training)
 
         # expand speaker embedding for all time steps

hparams.py

@@ -1,5 +1,4 @@
 import tensorflow as tf
-
 # NOTE: If you want full control for model architecture. please take a look
 # at the code and change whatever you want. Some hyper parameters are hardcoded.
 
@@ -28,7 +27,7 @@
     builder="deepvoice3",
 
     # Must be configured depends on the dataset and model you use
-    n_speakers=1,
+    n_speakers=1,	
     speaker_embed_dim=16,
 
     # Audio:
@@ -81,7 +80,7 @@
 
     # Data loader
     pin_memory=True,
-    num_workers=2,
+    num_workers=2, # Set it to 1 when in Windows (MemoryError, THAllocator.c 0x5)
 
     # Loss
     masked_loss_weight=0.5,  # (1-w)*loss + w * masked_loss
@@ -121,9 +120,16 @@
     # 0 tends to prevent word repretetion, but sometime causes skip words
     window_backward=1,
     power=1.4,  # Power to raise magnitudes to prior to phase retrieval
+
+    # GC:
+    # Forced garbage collection probability 
+    # Use only when MemoryError continues in Windows (Disabled by default)
+    #gc_probability = 0.001,
 )
 
 
+
+
 def hparams_debug_string():
     values = hparams.values()
     hp = ['  %s: %s' % (name, values[name]) for name in sorted(values)]

train.py

@@ -21,7 +21,7 @@
 """
 from docopt import docopt
 
-import sys
+import sys, gc, platform
 from os.path import dirname, join
 from tqdm import tqdm, trange
 from datetime import datetime
@@ -130,7 +130,18 @@ def collect_features(self, *args):
             text, speaker_id = args
         else:
             text = args[0]
+        global _frontend
+        if _frontend is None:
+            _frontend = getattr(frontend, hparams.frontend)
         seq = _frontend.text_to_sequence(text, p=hparams.replace_pronunciation_prob)
+
+        if platform.system() == "Windows":
+            if hasattr(hparams, 'gc_probability'):
+                _frontend = None # memory leaking prevention in Windows
+                if np.random.rand() < hparams.gc_probability:
+                    gc.collect() # garbage collection enforced
+                    print("GC done")
+
         if self.multi_speaker:
             return np.asarray(seq, dtype=np.int32), int(speaker_id)
         else:
@@ -712,6 +723,7 @@ def train(model, data_loader, optimizer, writer,
             if global_step > 0 and global_step % hparams.eval_interval == 0:
                 eval_model(global_step, writer, model, checkpoint_dir, ismultispeaker)
 
+
             # Update
             loss.backward()
             if clip_thresh > 0:
@@ -876,6 +888,16 @@ def restore_parts(path, model):
             hparams.parse_json(f.read())
     # Override hyper parameters
     hparams.parse(args["--hparams"])
+
+    # Preventing Windows specific error such as MemoryError 
+    # Also reduces the occurrence of THAllocator.c 0x05 error in Widows build of PyTorch
+    if platform.system() == "Windows":
+        print("Windows Detected - num_workers set to 1")
+        hparams.set_hparam('num_workers',1)
+
+    # Now, print the finalized hparams.
+    print(hparams_debug_string())
+
     assert hparams.name == "deepvoice3"
     print(hparams_debug_string())