diff --git a/egs/aishell/ASR/whisper/train.py b/egs/aishell/ASR/whisper/train.py
index 77035318d..910a4dff8 100644
--- a/egs/aishell/ASR/whisper/train.py
+++ b/egs/aishell/ASR/whisper/train.py
@@ -430,9 +430,9 @@ def get_params() -> AttributeDict:
             "best_train_epoch": -1,
             "best_valid_epoch": -1,
             "batch_idx_train": 0,
-            "log_interval": 1,
+            "log_interval": 50,
             "reset_interval": 200,
-            "valid_interval": 50,  # For the 100h subset, use 800
+            "valid_interval": 99999999999,  # For the 100h subset, use 800
             # parameters for zipformer
             "feature_dim": 80,
             "subsampling_factor": 4,  # not passed in, this is fixed.
@@ -632,8 +632,8 @@ def compute_loss(
     feature = feature.to(device)
     feature = feature.transpose(1, 2)  # (N, C, T)
     # pad feature from B,80,T to B,80,3000
-    feature = torch.nn.functional.pad(feature, (0, 3000 - feature.shape[-1]))
-    print(feature.shape, 23333333)
+    #feature = torch.nn.functional.pad(feature, (0, 3000 - feature.shape[-1]))
+    #print(feature.shape, 23333333)
     supervisions = batch["supervisions"]
     feature_lens = supervisions["num_frames"].to(device)
 
@@ -783,24 +783,24 @@ def train_one_epoch(
     for batch_idx, batch in enumerate(train_dl):
         params.batch_idx_train += 1
         batch_size = len(batch["supervisions"]["text"])
-        # if batch_idx % params.valid_interval == 0 and not params.print_diagnostics:
-        #     logging.info("Computing validation loss")
-        #     valid_info = compute_validation_loss(
-        #         params=params,
-        #         tokenizer=tokenizer,
-        #         model=model,
-        #         valid_dl=valid_dl,
-        #         world_size=world_size,
-        #     )
-        #     model.train()
-        #     logging.info(f"Epoch {params.cur_epoch}, validation: {valid_info}")
-        #     logging.info(
-        #         f"Maximum memory allocated so far is {torch.cuda.max_memory_allocated()//1000000}MB"
-        #     )
-        #     if tb_writer is not None:
-        #         valid_info.write_summary(
-        #             tb_writer, "train/valid_", params.batch_idx_train
-        #         )
+        if batch_idx % params.valid_interval == 0 and not params.print_diagnostics:
+            logging.info("Computing validation loss")
+            valid_info = compute_validation_loss(
+                params=params,
+                tokenizer=tokenizer,
+                model=model,
+                valid_dl=valid_dl,
+                world_size=world_size,
+            )
+            model.train()
+            logging.info(f"Epoch {params.cur_epoch}, validation: {valid_info}")
+            logging.info(
+                f"Maximum memory allocated so far is {torch.cuda.max_memory_allocated()//1000000}MB"
+            )
+            if tb_writer is not None:
+                valid_info.write_summary(
+                    tb_writer, "train/valid_", params.batch_idx_train
+                )
         try:
             with torch.cuda.amp.autocast(enabled=params.use_fp16):
                 loss, loss_info = compute_loss(
@@ -967,8 +967,10 @@ def run(rank, world_size, args):
 
 
     logging.info("About to create model")
-    model = whisper.load_model("medium")
-    #model = load_model("medium")
+    #model = whisper.load_model("medium")
+    # TODO download model only on rank 0
+    # TODO may change compute validation loss using multiple cards
+    model = load_model("medium")
     del model.alignment_heads
     tokenizer = whisper.tokenizer.get_tokenizer(
         model.is_multilingual, language="zh", task="transcribe"