diff --git a/egs/librispeech/ASR/pruned_transducer_stateless7/optim.py b/egs/librispeech/ASR/pruned_transducer_stateless7/optim.py
index 9588a3ef8..8b6f67d3f 100644
--- a/egs/librispeech/ASR/pruned_transducer_stateless7/optim.py
+++ b/egs/librispeech/ASR/pruned_transducer_stateless7/optim.py
@@ -132,7 +132,7 @@ class ScaledAdam(BatchedOptimizer):
 
      Args:
           params:  The parameters or param_groups to optimize (like other Optimizer subclasses)
-                   Unlike common optimizers, which accepts model.parameters() or groups of parameters(),
+                   Unlike common optimizers, which accept model.parameters() or groups of parameters(),
                    this optimizer could accept model.named_parameters() or groups of named_parameters().
                    See comments of function _get_names_of_parameters for its 4 possible cases.
               lr:  The learning rate.  We will typically use a learning rate schedule that starts
@@ -259,7 +259,7 @@ class ScaledAdam(BatchedOptimizer):
         #   p is short for param.
         #   np is short for named_param.
         #   p_or_np is short for param_or_named_param.
-        #   curt is short for current.
+        #   cur is short for current.
         #   group is a dict, e.g. {'params': iterable of parameter, 'lr': 0.05, other fields}.
         #   groups is a List[group]
 
@@ -277,8 +277,8 @@ class ScaledAdam(BatchedOptimizer):
         if not isinstance(iterable_or_groups[0], dict):
             # case 1 or case 3,
             # the input is an iterable of parameter or named parameter.
-            param_iterable_curt_group = []
-            param_names_curt_group = []
+            param_iterable_cur_group = []
+            param_names_cur_group = []
             for p_or_np in iterable_or_groups:
                 if isinstance(p_or_np, tuple):
                     # case 3
@@ -290,17 +290,17 @@ class ScaledAdam(BatchedOptimizer):
                     # Assign a dummy name as a placeholder
                     name = "foo"
                     self.show_dominant_parameters = False
-                param_iterable_curt_group.append(param)
-                param_names_curt_group.append(name)
-            param_groups.append({"params": param_iterable_curt_group})
-            param_groups_names.append(param_names_curt_group)
+                param_iterable_cur_group.append(param)
+                param_names_cur_group.append(name)
+            param_groups.append({"params": param_iterable_cur_group})
+            param_groups_names.append(param_names_cur_group)
         else:
             # case 2 or case 4
             # the input is groups of parameter or named parameter.
-            for p_or_np_curt_group in iterable_or_groups:
-                param_iterable_curt_group = []
-                param_names_curt_group = []
-                p_or_np_iterable = p_or_np_curt_group["params"]
+            for p_or_np_cur_group in iterable_or_groups:
+                param_iterable_cur_group = []
+                param_names_cur_group = []
+                p_or_np_iterable = p_or_np_cur_group["params"]
                 for p_or_np in p_or_np_iterable:
                     if isinstance(p_or_np, tuple):
                         # case 4
@@ -312,8 +312,8 @@ class ScaledAdam(BatchedOptimizer):
                         # Assign a dummy name as a placeholder
                         name = "foo"
                         self.show_dominant_parameters = False
-                    param_iterable_curt_group.append(param)
-                    param_names_curt_group.append(name)
+                    param_iterable_cur_group.append(param)
+                    param_names_cur_group.append(name)
 
                 # The original `params` filed contains named_parameters.
                 # After following assignment,
@@ -321,9 +321,9 @@ class ScaledAdam(BatchedOptimizer):
                 # and other fileds, if exist, are still original values.
                 # So param_groups could be used to initialize
                 # an underlying torch.Optimizer later.
-                p_or_np_curt_group["params"] = param_iterable_curt_group
-                param_groups.append(p_or_np_curt_group)
-                param_groups_names.append(param_names_curt_group)
+                p_or_np_cur_group["params"] = param_iterable_cur_group
+                param_groups.append(p_or_np_cur_group)
+                param_groups_names.append(param_names_cur_group)
         return param_groups, param_groups_names
 
     def __setstate__(self, state):