diff --git a/egs/tedlium3/ASR/transducer_stateless/asr_datamodule.py b/egs/tedlium3/ASR/transducer_stateless/asr_datamodule.py
index 7b35166a2..416d2d373 100644
--- a/egs/tedlium3/ASR/transducer_stateless/asr_datamodule.py
+++ b/egs/tedlium3/ASR/transducer_stateless/asr_datamodule.py
@@ -17,6 +17,7 @@
 
 
 import argparse
+import inspect
 import logging
 from functools import lru_cache
 from pathlib import Path
@@ -209,10 +210,22 @@ class TedLiumAsrDataModule:
             logging.info(
                 f"Time warp factor: {self.args.spec_aug_time_warp_factor}"
             )
+            # Judge num_frame_masks for SpecAugment according to Lhotse'version
+            num_frame_masks = (
+                2
+                if (
+                    inspect.signature(SpecAugment.__init__)
+                    .parameters["num_frame_masks"]
+                    .default
+                    == 1
+                )
+                else 10
+            )
+            logging.info(f"Num frame mask: {num_frame_masks}")
             input_transforms.append(
                 SpecAugment(
                     time_warp_factor=self.args.spec_aug_time_warp_factor,
-                    num_frame_masks=10,
+                    num_frame_masks=num_frame_masks,
                     features_mask_size=27,
                     num_feature_masks=2,
                     frames_mask_size=100,
@@ -229,7 +242,6 @@ class TedLiumAsrDataModule:
             input_transforms=input_transforms,
             return_cuts=self.args.return_cuts,
         )
-
         if self.args.on_the_fly_feats:
             # NOTE: the PerturbSpeed transform should be added only if we
             # remove it from data prep stage.
@@ -268,7 +280,7 @@ class TedLiumAsrDataModule:
                 shuffle=self.args.shuffle,
             )
         logging.info("About to create train dataloader")
-
+        # print(train)
         train_dl = DataLoader(
             train,
             sampler=train_sampler,
@@ -341,6 +353,7 @@ class TedLiumAsrDataModule:
     @lru_cache()
     def train_cuts(self) -> CutSet:
         logging.info("About to get train cuts")
+        print(self.args.manifest_dir)
         return load_manifest(self.args.manifest_dir / "cuts_train.json.gz")
 
     @lru_cache()