diff --git a/egs/librispeech/ASR/transducer_stateless_multi_datasets/train.py b/egs/librispeech/ASR/transducer_stateless_multi_datasets/train.py
index 8db8bc920..8c06eb4f9 100755
--- a/egs/librispeech/ASR/transducer_stateless_multi_datasets/train.py
+++ b/egs/librispeech/ASR/transducer_stateless_multi_datasets/train.py
@@ -516,6 +516,8 @@ def train_one_epoch(
     """
     model.train()
 
+    libri_tot_loss = MetricsTracker()
+    giga_tot_loss = MetricsTracker()
     tot_loss = MetricsTracker()
 
     # index 0: for LibriSpeech
@@ -542,6 +544,8 @@ def train_one_epoch(
         params.batch_idx_train += 1
         batch_size = len(batch["supervisions"]["text"])
 
+        libri = is_libri(batch["supervisions"]["cut"][0])
+
         loss, loss_info = compute_loss(
             params=params,
             model=model,
@@ -551,6 +555,16 @@ def train_one_epoch(
         )
         # summary stats
         tot_loss = (tot_loss * (1 - 1 / params.reset_interval)) + loss_info
+        if libri:
+            libri_tot_loss = (
+                libri_tot_loss * (1 - 1 / params.reset_interval)
+            ) + loss_info
+            prefix = "libri"  # for logging only
+        else:
+            giga_tot_loss = (
+                giga_tot_loss * (1 - 1 / params.reset_interval)
+            ) + loss_info
+            prefix = "giga"
 
         # NOTE: We use reduction==sum and loss is computed over utterances
         # in the batch and there is no normalization to it so far.
@@ -563,19 +577,29 @@ def train_one_epoch(
         if batch_idx % params.log_interval == 0:
             logging.info(
                 f"Epoch {params.cur_epoch}, "
-                f"batch {batch_idx}, loss[{loss_info}], "
-                f"tot_loss[{tot_loss}], batch size: {batch_size}"
+                f"batch {batch_idx}, {prefix}_loss[{loss_info}], "
+                f"tot_loss[{tot_loss}], "
+                f"libri_tot_loss[{libri_tot_loss}], "
+                f"giga_tot_loss[{giga_tot_loss}], "
+                f"batch size: {batch_size}"
             )
 
         if batch_idx % params.log_interval == 0:
-
             if tb_writer is not None:
                 loss_info.write_summary(
-                    tb_writer, "train/current_", params.batch_idx_train
+                    tb_writer,
+                    f"train/current_{prefix}_",
+                    params.batch_idx_train,
                 )
                 tot_loss.write_summary(
                     tb_writer, "train/tot_", params.batch_idx_train
                 )
+                libri_tot_loss.write_summary(
+                    tb_writer, "train/libri_tot_", params.batch_idx_train
+                )
+                giga_tot_loss.write_summary(
+                    tb_writer, "train/giga_tot_", params.batch_idx_train
+                )
 
         if batch_idx > 0 and batch_idx % params.valid_interval == 0:
             logging.info("Computing validation loss")
@@ -738,7 +762,9 @@ def run(rank, world_size, args):
     valid_cuts += librispeech.dev_other_cuts()
     valid_dl = asr_datamodule.valid_dataloaders(valid_cuts)
 
-    for dl in [train_dl, giga_train_dl]:
+    # It's time consuming to include `giga_train_dl` here
+    #  for dl in [train_dl, giga_train_dl]:
+    for dl in [train_dl]:
         scan_pessimistic_batches_for_oom(
             model=model,
             train_dl=dl,