diff --git a/egs/tedlium3/ASR/.lora.sh.swp b/egs/tedlium3/ASR/.lora.sh.swp
index debe70d26..688dbd961 100644
Binary files a/egs/tedlium3/ASR/.lora.sh.swp and b/egs/tedlium3/ASR/.lora.sh.swp differ
diff --git a/egs/tedlium3/ASR/lora.sh b/egs/tedlium3/ASR/lora.sh
index f27b74630..216ad1905 100755
--- a/egs/tedlium3/ASR/lora.sh
+++ b/egs/tedlium3/ASR/lora.sh
@@ -63,7 +63,6 @@ else
 			--additional-block True \
 			--prune-range 10 \
 			--spk-id $2 \
-			--prefix vox
 		touch ./pruned_transducer_stateless_d2v_v2/$1/.train.done
 	fi
 fi
diff --git a/egs/tedlium3/ASR/pruned_transducer_stateless/.train.py.swp b/egs/tedlium3/ASR/pruned_transducer_stateless/.train.py.swp
index ed5985376..59b44523e 100644
Binary files a/egs/tedlium3/ASR/pruned_transducer_stateless/.train.py.swp and b/egs/tedlium3/ASR/pruned_transducer_stateless/.train.py.swp differ
diff --git a/egs/tedlium3/ASR/pruned_transducer_stateless_d2v_v2/.train_tta.py.swp b/egs/tedlium3/ASR/pruned_transducer_stateless_d2v_v2/.train_tta.py.swp
index f3d14bf04..1fa2e8fbc 100644
Binary files a/egs/tedlium3/ASR/pruned_transducer_stateless_d2v_v2/.train_tta.py.swp and b/egs/tedlium3/ASR/pruned_transducer_stateless_d2v_v2/.train_tta.py.swp differ
diff --git a/egs/tedlium3/ASR/pruned_transducer_stateless_d2v_v2/train_tta.py b/egs/tedlium3/ASR/pruned_transducer_stateless_d2v_v2/train_tta.py
index 143a23f09..0468e016e 100755
--- a/egs/tedlium3/ASR/pruned_transducer_stateless_d2v_v2/train_tta.py
+++ b/egs/tedlium3/ASR/pruned_transducer_stateless_d2v_v2/train_tta.py
@@ -1345,23 +1345,19 @@ def run(rank, world_size, args, wb=None):
         register_inf_check_hooks(model)
 
     #librispeech = LibriSpeechAsrDataModule(args)
-    ted = TedLiumAsrDataModule(args)
-
-    train_cuts = librispeech.train_clean_100_cuts()
-    if params.full_libri:
-        train_cuts += librispeech.train_clean_360_cuts()
-        train_cuts += librispeech.train_other_500_cuts()
+    tedlium = TedLiumAsrDataModule(args)
+    train_cuts = tedlium.train_cuts()
 
     def remove_short_and_long_utt(c: Cut):
-        # Keep only utterances with duration between 1 second and 20 seconds
+        # Keep only utterances with duration between 1 second and 17 seconds
         #
-        # Caution: There is a reason to select 20.0 here. Please see
+        # Caution: There is a reason to select 17.0 here. Please see
         # ../local/display_manifest_statistics.py
         #
         # You should use ../local/display_manifest_statistics.py to get
         # an utterance duration distribution for your dataset to select
         # the threshold
-        return 1.0 <= c.duration <= 20.0
+        return 1.0 <= c.duration <= 17.0
 
     train_cuts = train_cuts.filter(remove_short_and_long_utt)
 
@@ -1372,15 +1368,12 @@ def run(rank, world_size, args, wb=None):
     else:
         sampler_state_dict = None
 
-    train_dl = librispeech.train_dataloaders(
-        train_cuts, sampler_state_dict=sampler_state_dict
-    )
+    train_dl = tedlium.train_dataloaders(train_cuts)
+    valid_cuts = tedlium.dev_cuts()
+    valid_dl = tedlium.valid_dataloaders(valid_cuts)
 
-    valid_cuts = librispeech.dev_clean_cuts()
-    valid_cuts += librispeech.dev_other_cuts()
-    valid_dl = librispeech.valid_dataloaders(valid_cuts)
-    
     scaler = GradScaler(enabled=params.use_fp16, init_scale=1.0)
+    
     if checkpoints and "grad_scaler" in checkpoints:
         logging.info("Loading grad scaler state dict")
         scaler.load_state_dict(checkpoints["grad_scaler"])
@@ -1537,24 +1530,18 @@ def run_pea(rank, world_size, args, wb=None):
     scheduler_pea = Eden(optimizer_pea, 10000, 7) 
     optimizer, scheduler = optimizer_pea, scheduler_pea
     
-    librispeech = LibriSpeechAsrDataModule(args)
-    train_cuts = librispeech.vox_cuts(option=params.spk_id)
+    tedlium = TedLiumAsrDataModule(args)
+    train_cuts = tedlium.user_test_cuts(spk_id=params.spk_id)
     
     def remove_short_and_long_utt(c: Cut):
         return 1.0 <= c.duration <= 20.0
 
     train_cuts = train_cuts.filter(remove_short_and_long_utt)
-    
     sampler_state_dict = None
+    
+    train_dl = tedlium.train_dataloaders(train_cuts)
+    valid_dl = None
 
-    train_dl = librispeech.train_dataloaders(
-        train_cuts, sampler_state_dict=sampler_state_dict
-    )
-    
-    valid_cuts = librispeech.dev_clean_cuts(option=params.gender)
-    valid_cuts += librispeech.dev_other_cuts(option=params.gender)
-    valid_dl = librispeech.valid_dataloaders(valid_cuts)
-    
     scaler = GradScaler(enabled=params.use_fp16, init_scale=1.0)
 
     for epoch in range(params.start_epoch, params.num_epochs + 1):