remove comment.

Author: vincentqb

examples/pipeline_wav2letter/datasets.py

@@ -38,18 +38,15 @@ def __len__(self):
         return len(self.dataset)
 
     def process_datapoint(self, item):
-        transformed = item[0]  # .to(device)
+        transformed = item[0]
         target = item[2].lower()
 
         transformed = self.transforms(transformed)
         transformed = transformed[0, ...].transpose(0, -1)
 
-        # target = " " + target + " "
         target = self.encode(target)
         target = torch.tensor(target, dtype=torch.long, device=transformed.device)
 
-        # transformed = transformed.to("cpu")
-        # target = target.to("cpu")
         return transformed, target
 
 
@@ -78,7 +75,6 @@ def create(tags, cache=True):
             ]
         )
 
-        # data = diskcache_iterator(data)
         data = MapMemoryCache(data)
         return data
 

examples/pipeline_wav2letter/main.py

@@ -443,7 +443,6 @@ def main(rank, args):
 
     training, validation = split_process_librispeech(
         [args.dataset_train, args.dataset_valid],
-        # [transforms_train, transforms_valid],
         [transforms, transforms],
         language_model,
         root=args.dataset_root,
@@ -473,7 +472,6 @@ def main(rank, args):
         devices = list(range(rank * n, (rank + 1) * n))
         model = model.to(devices[0])
         model = torch.nn.parallel.DistributedDataParallel(model, device_ids=devices)
-        # model = torch.nn.parallel.DistributedDataParallel(model, find_unused_parameters=True)
     else:
         devices = ["cuda" if torch.cuda.is_available() else "cpu"]
         model = model.to(devices[0], non_blocking=True)
@@ -526,8 +524,6 @@ def main(rank, args):
     criterion = torch.nn.CTCLoss(
         blank=language_model.mapping[char_blank], zero_infinity=False
     )
-    # criterion = torch.nn.MSELoss()
-    # criterion = torch.nn.NLLLoss()
 
     # Data Loader