diff --git a/utils.py b/utils.py
index 633ecff..c843d95 100644
--- a/utils.py
+++ b/utils.py
@@ -4,29 +4,26 @@ import torch
 
 
 def get_mask_from_lengths(lengths):
-    max_len = torch.max(lengths)
-    ids = torch.arange(0, max_len).long().cuda()
+    max_len = torch.max(lengths).item()
+    ids = torch.arange(0, max_len, out=torch.cuda.LongTensor(max_len))
     mask = (ids < lengths.unsqueeze(1)).byte()
     return mask
 
 
-def load_wav_to_torch(full_path, sr):
+def load_wav_to_torch(full_path):
     sampling_rate, data = read(full_path)
-    assert sr == sampling_rate, "{} SR doesn't match {} on path {}".format(
-        sr, sampling_rate, full_path)
-    return torch.FloatTensor(data.astype(np.float32))
+    return torch.FloatTensor(data.astype(np.float32)), sampling_rate
 
 
-def load_filepaths_and_text(filename, sort_by_length, split="|"):
+def load_filepaths_and_text(filename, split="|"):
     with open(filename, encoding='utf-8') as f:
         filepaths_and_text = [line.strip().split(split) for line in f]
-
-    if sort_by_length:
-        filepaths_and_text.sort(key=lambda x: len(x[1]))
-
     return filepaths_and_text
 
 
 def to_gpu(x):
-    x = x.contiguous().cuda(async=True)
+    x = x.contiguous()
+
+    if torch.cuda.is_available():
+        x = x.cuda(non_blocking=True)
     return torch.autograd.Variable(x)