diff --git a/train.py b/train.py
index dd5de2e..f914cfc 100644
--- a/train.py
+++ b/train.py
@@ -74,14 +74,17 @@ def prepare_directories_and_logger(output_directory, log_directory, rank):
         logger = None
     return logger
 
-
 def load_model(hparams):
     model = Tacotron2(hparams).cuda()
     model = batchnorm_to_float(model.half()) if hparams.fp16_run else model
-    model = DistributedDataParallel(model) \
-        if hparams.distributed_run else DataParallel(model)
-    return model
 
+    tacotron_model = model
+    if hparams.distributed_run:
+        model = DistributedDataParallel(model)
+    elif torch.cuda.device_count() > 1:
+        model = DataParallel(model)
+
+    return model, tacotron
 
 def warm_start_model(checkpoint_path, model):
     assert os.path.isfile(checkpoint_path)
@@ -114,7 +117,7 @@ def save_checkpoint(model, optimizer, learning_rate, iteration, filepath):
 
 
 def validate(model, criterion, valset, iteration, batch_size, n_gpus,
-             collate_fn, logger, distributed_run, rank):
+             collate_fn, logger, distributed_run, rank, batch_parser):
     """Handles all the validation scoring and printing"""
     model.eval()
     with torch.no_grad():
@@ -125,7 +128,7 @@ def validate(model, criterion, valset, iteration, batch_size, n_gpus,
 
         val_loss = 0.0
         for i, batch in enumerate(val_loader):
-            x, y = model.module.parse_batch(batch)
+            x, y = batch_parser(batch)
             y_pred = model(x)
             loss = criterion(y_pred, y)
             reduced_val_loss = reduce_tensor(loss.data, n_gpus)[0] \
@@ -193,11 +196,11 @@ def train(output_directory, log_directory, checkpoint_path, warm_start, n_gpus,
                 param_group['lr'] = learning_rate
 
             model.zero_grad()
-            x, y = model.module.parse_batch(batch)
+            x, y = tacotron_model.parse_batch(batch)
             y_pred = model(x)
             loss = criterion(y_pred, y)
-            reduced_loss = reduce_tensor(loss.data, n_gpus)[0] \
-                if hparams.distributed_run else loss.data[0]
+            reduced_loss = reduce_tensor(loss.data, n_gpus).item() \
+                if hparams.distributed_run else loss.item()
 
             if hparams.fp16_run:
                 optimizer.backward(loss)
@@ -205,7 +208,7 @@ def train(output_directory, log_directory, checkpoint_path, warm_start, n_gpus,
             else:
                 loss.backward()
                 grad_norm = torch.nn.utils.clip_grad_norm(
-                    model.module.parameters(), hparams.grad_clip_thresh)
+                    tacotron_model.parameters(), hparams.grad_clip_thresh)
 
             optimizer.step()
 
@@ -222,7 +225,7 @@ def train(output_directory, log_directory, checkpoint_path, warm_start, n_gpus,
             if not overflow and (iteration % hparams.iters_per_checkpoint == 0):
                 reduced_val_loss = validate(
                     model, criterion, valset, iteration, hparams.batch_size,
-                    n_gpus, collate_fn, logger, hparams.distributed_run, rank)
+                    n_gpus, collate_fn, logger, hparams.distributed_run, rank, tacotron_model.parse_batch)
 
                 if rank == 0:
                     print("Validation loss {}: {:9f}  ".format(