diff --git a/egs/tedlium3/ASR/RESULTS.md b/egs/tedlium3/ASR/RESULTS.md
index d5b24a376..407b77fa8 100644
--- a/egs/tedlium3/ASR/RESULTS.md
+++ b/egs/tedlium3/ASR/RESULTS.md
@@ -28,7 +28,7 @@ export CUDA_VISIBLE_DEVICES="0,1,2,3"
   --num-epochs 30 \
   --start-epoch 0 \
   --exp-dir transducer_stateless/exp \
-  --max-duration 200 \
+  --max-duration 200
 ```
 
 The tensorboard training log can be found at
@@ -64,6 +64,8 @@ avg=16
   --exp-dir transducer_stateless/exp \
   --bpe-model ./data/lang_bpe_500/bpe.model \
   --max-duration 100 \
-  --decoding-method beam_search \
+  --decoding-method modified_beam_search \
   --beam-size 4
 ```
+
+A pre-trained model and decoding logs can be found at <https://huggingface.co/luomingshuang/icefall_asr_tedlium3_transducer_stateless>
diff --git a/egs/tedlium3/ASR/local/compute_fbank_tedlium.py b/egs/tedlium3/ASR/local/compute_fbank_tedlium.py
index 3f87fabfb..c1d68a07b 100644
--- a/egs/tedlium3/ASR/local/compute_fbank_tedlium.py
+++ b/egs/tedlium3/ASR/local/compute_fbank_tedlium.py
@@ -1,6 +1,6 @@
 #!/usr/bin/env python3
-# Copyright    2021  Xiaomi Corp.        (authors: Fangjun Kuang
-#                                                  Mingshuang Luo)
+# Copyright    2021  Xiaomi Corp.        (authors: Fangjun Kuang)
+# 	       2022  Xiaomi Crop.        (authors: Mingshuang Luo)
 #
 # See ../../../../LICENSE for clarification regarding multiple authors
 #
@@ -29,7 +29,7 @@ import os
 from pathlib import Path
 
 import torch
-from lhotse import CutSet, Fbank, FbankConfig, LilcomHdf5Writer
+from lhotse import ChunkedLilcomHdf5Writer, CutSet, Fbank, FbankConfig
 from lhotse.recipes.utils import read_manifests_if_cached
 
 from icefall.utils import get_executor
@@ -83,7 +83,7 @@ def compute_fbank_tedlium():
                 # when an executor is specified, make more partitions
                 num_jobs=num_jobs if ex is None else 80,
                 executor=ex,
-                storage_type=LilcomHdf5Writer,
+                storage_type=ChunkedLilcomHdf5Writer,
             )
             cut_set.to_json(output_dir / f"cuts_{partition}.json.gz")
 
diff --git a/egs/tedlium3/ASR/local/convert_transcript_words_to_bpe_ids.py b/egs/tedlium3/ASR/local/convert_transcript_words_to_bpe_ids.py
index e6a3cc783..bbd1b838c 100644
--- a/egs/tedlium3/ASR/local/convert_transcript_words_to_bpe_ids.py
+++ b/egs/tedlium3/ASR/local/convert_transcript_words_to_bpe_ids.py
@@ -1,6 +1,6 @@
 #!/usr/bin/env python3
 
-# Copyright 2021 Xiaomi Corporation (Author: Mingshuang Luo)
+# Copyright    2022 Xiaomi Corporation  (Author: Mingshuang Luo)
 """
 Convert a transcript based on words to a list of BPE ids.
 
@@ -28,12 +28,6 @@ def get_args():
     parser.add_argument(
         "--texts", type=List[str], help="The input transcripts list."
     )
-    parser.add_argument(
-        "--unk-id",
-        type=int,
-        default=2,
-        help="The number id for the token '<unk>'.",
-    )
     parser.add_argument(
         "--bpe-model",
         type=str,
@@ -70,7 +64,7 @@ def convert_texts_into_ids(
                 else:
                     y_ids.extend(id_segments[i])
         else:
-            y_ids = sp.encode([text], out_type=int)[0]
+            y_ids = sp.encode(text, out_type=int)
         y.append(y_ids)
 
     return y
diff --git a/egs/tedlium3/ASR/prepare.sh b/egs/tedlium3/ASR/prepare.sh
index 9a643139f..76fc4ab94 100644
--- a/egs/tedlium3/ASR/prepare.sh
+++ b/egs/tedlium3/ASR/prepare.sh
@@ -59,9 +59,8 @@ if [ $stage -le -1 ] && [ $stop_stage -ge -1 ]; then
   log "Stage -1: Download LM"
   # We assume that you have installed the git-lfs, if not, you could install it
   # using: `sudo apt-get install git-lfs && git-lfs install`
-  [ ! -e $dl_dir/lm ] && mkdir -p $dl_dir/lm
+  mkdir -p $dl_dir/lm
   git clone https://huggingface.co/luomingshuang/tedlium3_lm $dl_dir/lm
-  cd $dl_dir/lm && git lfs pull
 
   # If you want to download Tedlium 4 gram language models
   # using the follow commands:
diff --git a/egs/tedlium3/ASR/transducer_stateless/asr_datamodule.py b/egs/tedlium3/ASR/transducer_stateless/asr_datamodule.py
index d8ebead6b..7b35166a2 100644
--- a/egs/tedlium3/ASR/transducer_stateless/asr_datamodule.py
+++ b/egs/tedlium3/ASR/transducer_stateless/asr_datamodule.py
@@ -175,13 +175,12 @@ class TedLiumAsrDataModule:
 
     def train_dataloaders(self, cuts_train: CutSet) -> DataLoader:
         logging.info("About to get Musan cuts")
-        cuts_musan = load_manifest(
-            self.args.manifest_dir / "cuts_musan.json.gz"
-        )
-
         transforms = []
         if self.args.enable_musan:
             logging.info("Enable MUSAN")
+            cuts_musan = load_manifest(
+                self.args.manifest_dir / "cuts_musan.json.gz"
+            )
             transforms.append(
                 CutMix(
                     cuts=cuts_musan, prob=0.5, snr=(10, 20), preserve_id=True
diff --git a/egs/tedlium3/ASR/transducer_stateless/pretrained.py b/egs/tedlium3/ASR/transducer_stateless/pretrained.py
deleted file mode 120000
index 4143bb116..000000000
--- a/egs/tedlium3/ASR/transducer_stateless/pretrained.py
+++ /dev/null
@@ -1 +0,0 @@
-../../../librispeech/ASR/transducer_stateless/pretrained.py
\ No newline at end of file
diff --git a/egs/tedlium3/ASR/transducer_stateless/pretrained.py b/egs/tedlium3/ASR/transducer_stateless/pretrained.py
new file mode 100644
index 000000000..77bdd0ca6
--- /dev/null
+++ b/egs/tedlium3/ASR/transducer_stateless/pretrained.py
@@ -0,0 +1,343 @@
+#!/usr/bin/env python3
+# Copyright      2021  Xiaomi Corp.        (authors: Fangjun Kuang)
+# 		 2022  Xiaomi Crop.        (authors: Mingshuang Luo)
+#
+# 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.
+"""
+Usage:
+
+(1) greedy search
+./transducer_stateless/pretrained.py \
+        --checkpoint ./transducer_stateless/exp/pretrained.pt \
+        --bpe-model ./data/lang_bpe_500/bpe.model \
+        --method greedy_search \
+        --max-sym-per-frame 1 \
+        /path/to/foo.wav \
+        /path/to/bar.wav \
+
+(2) beam search
+./transducer_stateless/pretrained.py \
+        --checkpoint ./transducer_stateless/exp/pretrained.pt \
+        --bpe-model ./data/lang_bpe_500/bpe.model \
+        --method beam_search \
+        --beam-size 4 \
+        /path/to/foo.wav \
+        /path/to/bar.wav \
+
+(3) modified beam search
+./transducer_stateless/pretrained.py \
+        --checkpoint ./transducer_stateless/exp/pretrained.pt \
+        --bpe-model ./data/lang_bpe_500/bpe.model \
+        --method modified_beam_search \
+        --beam-size 4 \
+        /path/to/foo.wav \
+        /path/to/bar.wav \
+
+You can also use `./transducer_stateless/exp/epoch-xx.pt`.
+
+Note: ./transducer_stateless/exp/pretrained.pt is generated by
+./transducer_stateless/export.py
+"""
+
+
+import argparse
+import logging
+import math
+from typing import List
+
+import kaldifeat
+import sentencepiece as spm
+import torch
+import torch.nn as nn
+import torchaudio
+from beam_search import beam_search, greedy_search, modified_beam_search
+from conformer import Conformer
+from decoder import Decoder
+from joiner import Joiner
+from model import Transducer
+from torch.nn.utils.rnn import pad_sequence
+
+from icefall.env import get_env_info
+from icefall.utils import AttributeDict
+
+
+def get_parser():
+    parser = argparse.ArgumentParser(
+        formatter_class=argparse.ArgumentDefaultsHelpFormatter
+    )
+
+    parser.add_argument(
+        "--checkpoint",
+        type=str,
+        required=True,
+        help="Path to the checkpoint. "
+        "The checkpoint is assumed to be saved by "
+        "icefall.checkpoint.save_checkpoint().",
+    )
+
+    parser.add_argument(
+        "--bpe-model",
+        type=str,
+        help="""Path to bpe.model.
+        Used only when method is ctc-decoding.
+        """,
+    )
+
+    parser.add_argument(
+        "--method",
+        type=str,
+        default="greedy_search",
+        help="""Possible values are:
+          - greedy_search
+          - beam_search
+          - modified_beam_search
+        """,
+    )
+
+    parser.add_argument(
+        "sound_files",
+        type=str,
+        nargs="+",
+        help="The input sound file(s) to transcribe. "
+        "Supported formats are those supported by torchaudio.load(). "
+        "For example, wav and flac are supported. "
+        "The sample rate has to be 16kHz.",
+    )
+
+    parser.add_argument(
+        "--beam-size",
+        type=int,
+        default=4,
+        help="Used only when --method is beam_search and modified_beam_search ",
+    )
+
+    parser.add_argument(
+        "--context-size",
+        type=int,
+        default=2,
+        help="The context size in the decoder. 1 means bigram; "
+        "2 means tri-gram",
+    )
+    parser.add_argument(
+        "--max-sym-per-frame",
+        type=int,
+        default=3,
+        help="""Maximum number of symbols per frame. Used only when
+        --method is greedy_search.
+        """,
+    )
+
+    return parser
+
+
+def get_params() -> AttributeDict:
+    params = AttributeDict(
+        {
+            "sample_rate": 16000,
+            # parameters for conformer
+            "feature_dim": 80,
+            "encoder_out_dim": 512,
+            "subsampling_factor": 4,
+            "attention_dim": 512,
+            "nhead": 8,
+            "dim_feedforward": 2048,
+            "num_encoder_layers": 12,
+            "vgg_frontend": False,
+            "env_info": get_env_info(),
+        }
+    )
+    return params
+
+
+def get_encoder_model(params: AttributeDict) -> nn.Module:
+    encoder = Conformer(
+        num_features=params.feature_dim,
+        output_dim=params.encoder_out_dim,
+        subsampling_factor=params.subsampling_factor,
+        d_model=params.attention_dim,
+        nhead=params.nhead,
+        dim_feedforward=params.dim_feedforward,
+        num_encoder_layers=params.num_encoder_layers,
+        vgg_frontend=params.vgg_frontend,
+    )
+    return encoder
+
+
+def get_decoder_model(params: AttributeDict) -> nn.Module:
+    decoder = Decoder(
+        vocab_size=params.vocab_size,
+        embedding_dim=params.encoder_out_dim,
+        blank_id=params.blank_id,
+        unk_id=params.unk_id,
+        context_size=params.context_size,
+    )
+    return decoder
+
+
+def get_joiner_model(params: AttributeDict) -> nn.Module:
+    joiner = Joiner(
+        input_dim=params.encoder_out_dim,
+        output_dim=params.vocab_size,
+    )
+    return joiner
+
+
+def get_transducer_model(params: AttributeDict) -> nn.Module:
+    encoder = get_encoder_model(params)
+    decoder = get_decoder_model(params)
+    joiner = get_joiner_model(params)
+
+    model = Transducer(
+        encoder=encoder,
+        decoder=decoder,
+        joiner=joiner,
+    )
+    return model
+
+
+def read_sound_files(
+    filenames: List[str], expected_sample_rate: float
+) -> List[torch.Tensor]:
+    """Read a list of sound files into a list 1-D float32 torch tensors.
+    Args:
+      filenames:
+        A list of sound filenames.
+      expected_sample_rate:
+        The expected sample rate of the sound files.
+    Returns:
+      Return a list of 1-D float32 torch tensors.
+    """
+    ans = []
+    for f in filenames:
+        wave, sample_rate = torchaudio.load(f)
+        assert sample_rate == expected_sample_rate, (
+            f"expected sample rate: {expected_sample_rate}. "
+            f"Given: {sample_rate}"
+        )
+        # We use only the first channel
+        ans.append(wave[0])
+    return ans
+
+
+@torch.no_grad()
+def main():
+    parser = get_parser()
+    args = parser.parse_args()
+
+    params = get_params()
+
+    params.update(vars(args))
+
+    sp = spm.SentencePieceProcessor()
+    sp.load(params.bpe_model)
+
+    # <blk> and <unk> are defined in local/train_bpe_model.py
+    params.blank_id = sp.piece_to_id("<blk>")
+    params.unk_id = sp.piece_to_id("<unk>")
+    params.vocab_size = sp.get_piece_size()
+
+    logging.info(f"{params}")
+
+    device = torch.device("cpu")
+    if torch.cuda.is_available():
+        device = torch.device("cuda", 0)
+
+    logging.info(f"device: {device}")
+
+    logging.info("Creating model")
+    model = get_transducer_model(params)
+
+    checkpoint = torch.load(args.checkpoint, map_location="cpu")
+    model.load_state_dict(checkpoint["model"], strict=False)
+    model.to(device)
+    model.eval()
+    model.device = device
+
+    logging.info("Constructing Fbank computer")
+    opts = kaldifeat.FbankOptions()
+    opts.device = device
+    opts.frame_opts.dither = 0
+    opts.frame_opts.snip_edges = False
+    opts.frame_opts.samp_freq = params.sample_rate
+    opts.mel_opts.num_bins = params.feature_dim
+
+    fbank = kaldifeat.Fbank(opts)
+
+    logging.info(f"Reading sound files: {params.sound_files}")
+    waves = read_sound_files(
+        filenames=params.sound_files, expected_sample_rate=params.sample_rate
+    )
+    waves = [w.to(device) for w in waves]
+
+    logging.info("Decoding started")
+    features = fbank(waves)
+    feature_lengths = [f.size(0) for f in features]
+
+    features = pad_sequence(
+        features, batch_first=True, padding_value=math.log(1e-10)
+    )
+
+    feature_lengths = torch.tensor(feature_lengths, device=device)
+
+    with torch.no_grad():
+        encoder_out, encoder_out_lens = model.encoder(
+            x=features, x_lens=feature_lengths
+        )
+
+    num_waves = encoder_out.size(0)
+    hyps = []
+    msg = f"Using {params.method}"
+    if params.method == "beam_search":
+        msg += f" with beam size {params.beam_size}"
+    logging.info(msg)
+    for i in range(num_waves):
+        # fmt: off
+        encoder_out_i = encoder_out[i:i+1, :encoder_out_lens[i]]
+        # fmt: on
+        if params.method == "greedy_search":
+            hyp = greedy_search(
+                model=model,
+                encoder_out=encoder_out_i,
+                max_sym_per_frame=params.max_sym_per_frame,
+            )
+        elif params.method == "beam_search":
+            hyp = beam_search(
+                model=model, encoder_out=encoder_out_i, beam=params.beam_size
+            )
+        elif params.method == "modified_beam_search":
+            hyp = modified_beam_search(
+                model=model, encoder_out=encoder_out_i, beam=params.beam_size
+            )
+        else:
+            raise ValueError(f"Unsupported method: {params.method}")
+
+        hyps.append(sp.decode(hyp).split())
+
+    s = "\n"
+    for filename, hyp in zip(params.sound_files, hyps):
+        words = " ".join(hyp)
+        s += f"{filename}:\n{words}\n\n"
+    logging.info(s)
+
+    logging.info("Decoding Done")
+
+
+if __name__ == "__main__":
+    formatter = (
+        "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
+    )
+
+    logging.basicConfig(format=formatter, level=logging.INFO)
+    main()
diff --git a/egs/tedlium3/ASR/transducer_stateless/train.py b/egs/tedlium3/ASR/transducer_stateless/train.py
index 7d1fa3eda..b3dba4409 100755
--- a/egs/tedlium3/ASR/transducer_stateless/train.py
+++ b/egs/tedlium3/ASR/transducer_stateless/train.py
@@ -26,7 +26,7 @@ export CUDA_VISIBLE_DEVICES="0,1,2,3"
   --num-epochs 30 \
   --start-epoch 0 \
   --exp-dir transducer_stateless/exp \
-  --max-duration 200 \
+  --max-duration 200
 """
 
 
@@ -394,9 +394,9 @@ def compute_loss(
     feature = feature.to(device)
 
     supervisions = batch["supervisions"]
-    feature_lens = supervisions["num_frames"].to(device)[: feature.size(0)]
+    feature_lens = supervisions["num_frames"].to(device)
 
-    texts = batch["supervisions"]["text"][: feature.size(0)]
+    texts = batch["supervisions"]["text"]
 
     unk_id = params.unk_id
     y = convert_texts_into_ids(texts, unk_id, sp=sp)
@@ -625,7 +625,9 @@ def run(rank, world_size, 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 max seconds
+        # Here, we set max as 20.0.
+        # If you want to use a big max-duration, you can set it as 17.0.
         return 1.0 <= c.duration <= 20.0
 
     num_in_total = len(train_cuts)