diff --git a/egs/librispeech/ASR/conformer_ctc/asr_datamodule.py b/egs/librispeech/ASR/conformer_ctc/asr_datamodule.py
index e5fcc5893..7570faccc 120000
--- a/egs/librispeech/ASR/conformer_ctc/asr_datamodule.py
+++ b/egs/librispeech/ASR/conformer_ctc/asr_datamodule.py
@@ -218,11 +218,9 @@ class LibriSpeechAsrDataModule:
             input_transforms.append(
                 SpecAugment(
                     time_warp_factor=self.args.spec_aug_time_warp_factor,
-                    num_frame_masks=10,
                     features_mask_size=27,
                     num_feature_masks=2,
                     frames_mask_size=100,
-                    max_frames_mask_fraction=0.4,
                 )
             )
         else:
@@ -411,9 +409,9 @@ class SpecAugment(torch.nn.Module):
         time_warp_factor: Optional[int] = 80,
         num_feature_masks: int = 1,
         features_mask_size: int = 13,
-        num_frame_masks: int = 1,
+        num_frame_masks: int = 10,
         frames_mask_size: int = 70,
-        max_frames_mask_fraction: float = 0.2,
+        max_frames_mask_fraction: float = 0.4,
         p=0.5,
     ):
         """
@@ -425,10 +423,11 @@ class SpecAugment(torch.nn.Module):
         :param features_mask_size: the width of the feature mask (expressed in the number of masked feature bins).
             This is the ``F`` parameter from the SpecAugment paper.
         :param num_frame_masks: how many frame (temporal) masks should be applied. Set to ``0`` to disable.
+            This is the maximum (it's also constrained by max_frames_mask_fraction).
         :param frames_mask_size: the width of the frame (temporal) masks (expressed in the number of masked frames).
             This is the ``T`` parameter from the SpecAugment paper.
         :param max_frames_mask_fraction: limits the size of the frame (temporal) mask to this value times the length
-            of the utterance (or supervision segment).
+            of the utterance (or supervision segment). It is the total masked fraction including all masked regions.
             This is the parameter denoted by ``p`` in the SpecAugment paper.
         :param p: the probability of applying this transform.
             It is different from ``p`` in the SpecAugment paper!
@@ -517,11 +516,12 @@ class SpecAugment(torch.nn.Module):
                     mask_value=mean,
                     axis=2,
                 ).squeeze(0)
-            for _ in range(self.num_frame_masks):
-                _max_tot_mask_frames = self.max_frames_mask_fraction * features.size(0)
-                num_frame_masks = min(self.num_frame_masks, math.ceil(_max_tot_mask_frames / self.frames_mask_size))
-                max_mask_frames = min(self.frames_mask_size, _max_tot_mask_frames // num_frame_masks)
-                
+            
+            _max_tot_mask_frames = self.max_frames_mask_fraction * features.size(0)
+            num_frame_masks = min(self.num_frame_masks, math.ceil(_max_tot_mask_frames / self.frames_mask_size))
+            max_mask_frames = min(self.frames_mask_size, _max_tot_mask_frames // num_frame_masks)
+
+            for _ in range(num_frame_masks):      
                 features = mask_along_axis(
                     features.unsqueeze(0),
                     mask_param=max_mask_frames,