diff --git a/egs/tal_csasr/ASR/pruned_transducer_stateless5/train.py b/egs/tal_csasr/ASR/pruned_transducer_stateless5/train.py
index d03970265..c0aedd725 100755
--- a/egs/tal_csasr/ASR/pruned_transducer_stateless5/train.py
+++ b/egs/tal_csasr/ASR/pruned_transducer_stateless5/train.py
@@ -602,11 +602,9 @@ def compute_loss(
     feature_lens = supervisions["num_frames"].to(device)
 
     texts = batch["supervisions"]["text"]
-    y = graph_compiler.texts_to_ids_with_bpe(texts)
-    if type(y) == list:
-        y = k2.RaggedTensor(y).to(device)
-    else:
-        y = y.to(device)
+    y = graph_compiler.texts_to_ids(texts, sep="/")
+    y = k2.RaggedTensor(y).to(device)
+
     with torch.set_grad_enabled(is_training):
         simple_loss, pruned_loss = model(
             x=feature,
diff --git a/egs/wenetspeech/ASR/local/prepare_dataset_from_kaldi_dir.py b/egs/wenetspeech/ASR/local/prepare_dataset_from_kaldi_dir.py
new file mode 100644
index 000000000..8412815b1
--- /dev/null
+++ b/egs/wenetspeech/ASR/local/prepare_dataset_from_kaldi_dir.py
@@ -0,0 +1,141 @@
+#!/usr/bin/env python3
+# Copyright    2023  Xiaomi Corp.        (authors: Wei Kang)
+#
+# See ../../../../LICENSE for clarification regarding multiple authors
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import argparse
+import logging
+
+import torch
+import lhotse
+from pathlib import Path
+from lhotse import CutSet, Fbank, FbankConfig, LilcomChunkyWriter, fix_manifests, validate_recordings_and_supervisions
+from icefall.utils import get_executor, str2bool
+
+# Torch's multithreaded behavior needs to be disabled or
+# it wastes a lot of CPU and slow things down.
+# Do this outside of main() in case it needs to take effect
+# even when we are not invoking the main (e.g. when spawning subprocesses).
+torch.set_num_threads(1)
+torch.set_num_interop_threads(1)
+
+def get_args():
+    parser = argparse.ArgumentParser()
+
+    parser.add_argument(
+        "--kaldi-dir",
+        type=str,
+        help="""The directory containing kaldi style manifest, namely wav.scp, text and segments.
+        """,
+    )
+
+    parser.add_argument(
+        "--num-mel-bins",
+        type=int,
+        default=80,
+        help="""The number of mel bank bins.
+        """,
+    )
+
+    parser.add_argument(
+        "--output-dir",
+        type=str,
+        default="data/fbank",
+        help="""The directory where the lhotse manifests and features to write to.
+        """,
+    )
+
+    parser.add_argument(
+        "--dataset",
+        type=str,
+        help="""The name of dataset.
+        """,
+    )
+
+    parser.add_argument(
+        "--partition",
+        type=str,
+        help="""Could be something like train, valid, test and so on.
+        """,
+    )
+
+    parser.add_argument(
+        "--perturb-speed",
+        type=str2bool,
+        default=True,
+        help="""Perturb speed with factor 0.9 and 1.1 on train subset.""",
+    )
+
+    parser.add_argument(
+        "--num-jobs",
+        type=int,
+        default=50,
+        help="The num of jobs to extract feature."
+    )
+
+    return parser.parse_args()
+
+
+def prepare_cuts(args):
+    logging.info(f"Prepare cuts from {args.kaldi_dir}.")
+    recordings, supervisions, _ = lhotse.load_kaldi_data_dir(args.kaldi_dir, 16000)
+    recordings, supervisions = fix_manifests(recordings, supervisions)
+    validate_recordings_and_supervisions(recordings, supervisions)
+    cuts = CutSet.from_manifests(recordings=recordings, supervisions=supervisions)
+    return cuts
+
+
+def compute_feature(args, cuts):
+    extractor = Fbank(FbankConfig(num_mel_bins=args.num_mel_bins))
+    with get_executor() as ex:  # Initialize the executor only once.
+        cuts_filename = f"{args.dataset}_cuts_{args.partition}.jsonl.gz"
+        if (args.output_dir / cuts_filename).is_file():
+            logging.info(f"{cuts_filename} already exists - skipping.")
+            return
+        logging.info(f"Processing {cuts_filename}")
+
+        if "train" in args.partition:
+            if args.perturb_speed:
+                logging.info(f"Doing speed perturb")
+                cuts = (
+                    cuts
+                    + cuts.perturb_speed(0.9)
+                    + cuts.perturb_speed(1.1)
+                )
+        cuts = cuts.compute_and_store_features(
+            extractor=extractor,
+            storage_path=f"{args.output_dir}/{args.dataset}_feats_{args.partition}",
+            # when an executor is specified, make more partitions
+            num_jobs=args.num_jobs if ex is None else 80,
+            executor=ex,
+            storage_type=LilcomChunkyWriter,
+        )
+        cuts.to_file(args.output_dir / cuts_filename)
+
+
+def main(args):
+    args.kaldi_dir = Path(args.kaldi_dir)
+    args.output_dir = Path(args.output_dir)
+    cuts = prepare_cuts(args)
+    compute_feature(args, cuts)
+
+
+if __name__ == '__main__':
+    formatter = "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
+
+    logging.basicConfig(format=formatter, level=logging.INFO)
+    args = get_args()
+    logging.info(vars(args))
+    main(args)
diff --git a/icefall/char_graph_compiler.py b/icefall/char_graph_compiler.py
index 5f9571d42..8c2355c87 100644
--- a/icefall/char_graph_compiler.py
+++ b/icefall/char_graph_compiler.py
@@ -54,7 +54,7 @@ class CharCtcTrainingGraphCompiler(object):
         self.sos_id = self.token_table[sos_token]
         self.eos_id = self.token_table[eos_token]
 
-    def texts_to_ids(self, texts: List[str]) -> List[List[int]]:
+    def texts_to_ids(self, texts: List[str], sep: str = "") -> List[List[int]]:
         """Convert a list of texts to a list-of-list of token IDs.
 
         Args:
@@ -63,36 +63,21 @@ class CharCtcTrainingGraphCompiler(object):
             An example containing two strings is given below:
 
                 ['你好中国', '北京欢迎您']
+          sep:
+            The separator of the items in one sequence, mainly no separator for
+            Chinese (one character a token), "/" for Chinese characters plus BPE
+            token and pinyin tokens.
         Returns:
           Return a list-of-list of token IDs.
         """
+        assert sep in ("", "/"), sep
         ids: List[List[int]] = []
         whitespace = re.compile(r"([ \t])")
         for text in texts:
-            text = re.sub(whitespace, "", text)
-            sub_ids = [
-                self.token_table[txt] if txt in self.token_table else self.oov_id
-                for txt in text
-            ]
-            ids.append(sub_ids)
-        return ids
-
-    def texts_to_ids_with_bpe(self, texts: List[str]) -> List[List[int]]:
-        """Convert a list of texts (which include chars and bpes)
-           to a list-of-list of token IDs.
-
-        Args:
-          texts:
-            It is a list of strings.
-            An example containing two strings is given below:
-
-                [['你', '好', '▁C', 'hina'], ['北','京', '▁', 'welcome', '您']
-        Returns:
-          Return a list-of-list of token IDs.
-        """
-        ids: List[List[int]] = []
-        for text in texts:
-            text = text.split("/")
+            if sep == "":
+                text = re.sub(whitespace, "", text)
+            else:
+                text = text.split(sep)
             sub_ids = [
                 self.token_table[txt] if txt in self.token_table else self.oov_id
                 for txt in text