diff --git a/README.md b/README.md
index 107e713..3a904d0 100644
--- a/README.md
+++ b/README.md
@@ -31,7 +31,7 @@ Distributed and FP16 support relies on work by Christian Sarofeen and NVIDIA's
 2. (OPTIONAL) `tensorboard --logdir=outdir/logdir`
 
 ## Multi-GPU (distributed) and FP16 Training
-1. `python -m multiproc train.py --output_directory=/outdir --log_directory=/logdir --hparams=distributed_run=True --fp16_run=True`
+1. `python -m multiproc train.py --output_directory=outdir --log_directory=logdir --hparams=distributed_run=True,fp16_run=True`
 
 ## Inference
 1. `jupyter notebook --ip=127.0.0.1 --port=31337`
diff --git a/train.py b/train.py
index dd5de2e..ee01b07 100644
--- a/train.py
+++ b/train.py
@@ -78,15 +78,19 @@ def prepare_directories_and_logger(output_directory, log_directory, rank):
 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)
+
+    if hparams.distributed_run:
+        model = DistributedDataParallel(model)
+    elif torch.cuda.device_count() > 1:
+        model = DataParallel(model)
+
     return model
 
 
 def warm_start_model(checkpoint_path, model):
     assert os.path.isfile(checkpoint_path)
     print("Warm starting model from checkpoint '{}'".format(checkpoint_path))
-    checkpoint_dict = torch.load(checkpoint_path)
+    checkpoint_dict = torch.load(checkpoint_path, map_location='cpu')
     model.load_state_dict(checkpoint_dict['state_dict'])
     return model
 
@@ -124,8 +128,13 @@ def validate(model, criterion, valset, iteration, batch_size, n_gpus,
                                 pin_memory=False, collate_fn=collate_fn)
 
         val_loss = 0.0
+        if distributed_run or torch.cuda.device_count() > 1:
+            batch_parser = model.module.parse_batch
+        else:
+            batch_parser = model.parse_batch
+
         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] \
@@ -184,6 +193,10 @@ def train(output_directory, log_directory, checkpoint_path, warm_start, n_gpus,
             epoch_offset = max(0, int(iteration / len(train_loader)))
 
     model.train()
+    if hparams.distributed_run or torch.cuda.device_count() > 1:
+        batch_parser = model.module.parse_batch
+    else:
+        batch_parser = model.parse_batch
     # ================ MAIN TRAINNIG LOOP! ===================
     for epoch in range(epoch_offset, hparams.epochs):
         print("Epoch: {}".format(epoch))
@@ -193,7 +206,7 @@ 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 = batch_parser(batch)
             y_pred = model(x)
             loss = criterion(y_pred, y)
             reduced_loss = reduce_tensor(loss.data, n_gpus)[0] \
@@ -205,7 +218,7 @@ 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)
+                    model.parameters(), hparams.grad_clip_thresh)
 
             optimizer.step()