A few cleanup of #54

Author: r9y9

hparams.py

@@ -27,7 +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:
@@ -80,7 +80,7 @@
 
     # Data loader
     pin_memory=True,
-    num_workers=2, # Set it to 1 when in Windows (MemoryError, THAllocator.c 0x5)
+    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
@@ -120,16 +120,14 @@
     # 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 
+    # 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 +21,9 @@
 """
 from docopt import docopt
 
-import sys, gc, platform
+import sys
+import gc
+import platform
 from os.path import dirname, join
 from tqdm import tqdm, trange
 from datetime import datetime
@@ -134,14 +136,14 @@ def collect_features(self, *args):
         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
+                _frontend = None  # memory leaking prevention in Windows
                 if np.random.rand() < hparams.gc_probability:
-                    gc.collect() # garbage collection enforced
+                    gc.collect()  # garbage collection enforced
                     print("GC done")
-        
+
         if self.multi_speaker:
             return np.asarray(seq, dtype=np.int32), int(speaker_id)
         else:
@@ -723,7 +725,6 @@ 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:
@@ -888,15 +889,12 @@ def restore_parts(path, model):
             hparams.parse_json(f.read())
     # Override hyper parameters
     hparams.parse(args["--hparams"])
-    
-    # Preventing Windows specific error such as MemoryError 
+
+    # 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())
+        hparams.set_hparam('num_workers', 1)
 
     assert hparams.name == "deepvoice3"
     print(hparams_debug_string())