diff --git a/egs/libriheavy/ASR/zipformer_prompt_asr/train_baseline.py b/egs/libriheavy/ASR/zipformer_prompt_asr/train_baseline.py
index 7075c9154..32302602c 100644
--- a/egs/libriheavy/ASR/zipformer_prompt_asr/train_baseline.py
+++ b/egs/libriheavy/ASR/zipformer_prompt_asr/train_baseline.py
@@ -1,8 +1,9 @@
 #!/usr/bin/env python3
 # Copyright    2021-2022  Xiaomi Corp.        (authors: Fangjun Kuang,
 #                                                       Wei Kang,
-#                                                       Mingshuang Luo,)
-#                                                       Zengwei Yao)
+#                                                       Mingshuang Luo
+#                                                       Zengwei Yao,
+#                                                       Xiaoyu Yang)
 #
 # See ../../../../LICENSE for clarification regarding multiple authors
 #
@@ -21,21 +22,35 @@
 Usage:
 
 
-# For mix precision training:
+# For mix precision training, using MCP style transcript:
 
 export CUDA_VISIBLE_DEVICES="0,1,2,3"
 
-./zipformer/train.py \
+./zipformer_prompt_asr/train_baseline.py \
   --world-size 4 \
   --num-epochs 30 \
   --start-epoch 1 \
   --use-fp16 1 \
-  --exp-dir zipformer/exp \
+  --exp-dir zipformer_prompt_asr/exp \
+  --transcript-style MCP \
+  --max-duration 1000
+
+# For mix precision training, using UC style transcript:
+
+export CUDA_VISIBLE_DEVICES="0,1,2,3"
+
+./zipformer_prompt_asr/train_baseline.py \
+  --world-size 4 \
+  --num-epochs 30 \
+  --start-epoch 1 \
+  --use-fp16 1 \
+  --exp-dir zipformer_prompt_asr/exp \
+  --transcript-style UC \
   --max-duration 1000
 
 # To train a streaming model
 
-./zipformer/train.py \
+./zipformer_prompt_asr/train_baseline.py \
   --world-size 4 \
   --num-epochs 30 \
   --start-epoch 1 \
@@ -100,7 +115,7 @@ from icefall.utils import (
 LRSchedulerType = Union[torch.optim.lr_scheduler._LRScheduler, optim.LRScheduler]
 
 
-def get_first(
+def get_mixed_cased_with_punc(
     texts: List[str],
     pre_texts: List[str],
     context_list: Optional[str] = None,
@@ -479,6 +494,16 @@ def get_parser():
         help="Whether to use half precision training.",
     )
 
+    parser.add_argument(
+        "--transcript-style",
+        type=str,
+        default="UC",
+        choices=["UC", "MCP"],
+        help="""The transcript style used for training. UC stands for upper-cased text w/o punctuations,
+        MCP stands for mix-cased text with punctuation.
+        """,
+    )
+
     add_model_arguments(parser)
 
     return parser
@@ -1223,7 +1248,11 @@ def run(rank, world_size, args):
     else:
         sampler_state_dict = None
 
-    text_sampling_func = get_upper_only_alpha
+    if params.transcript_style == "UC":
+        text_sampling_func = get_upper_only_alpha
+    else:
+        text_sampling_func = get_mixed_cased_with_punc
+    logging.info(f"Using {params.transcript_style} style for training.")
     logging.info(f"Text sampling func: {text_sampling_func}")
     train_dl = libriheavy.train_dataloaders(
         train_cuts,