diff --git a/egs/tedlium3/ASR/README.md b/egs/tedlium3/ASR/README.md
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+
+# Introduction
+
+This recipe includes some different ASR models trained with TedLium3.
+
+# Transducers
+
+There are various folders containing the name `transducer` in this folder.
+The following table lists the differences among them.
+
+|                        | Encoder   | Decoder            |
+|------------------------|-----------|--------------------|
+| `transducer_stateless` | Conformer | Embedding + Conv1d |
+
+
+The decoder in `transducer_stateless` is modified from the paper
+[Rnn-Transducer with Stateless Prediction Network](https://ieeexplore.ieee.org/document/9054419/).
+We place an additional Conv1d layer right after the input embedding layer.
diff --git a/egs/tedlium3/ASR/RESULTS.md b/egs/tedlium3/ASR/RESULTS.md
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+## Results
+
+### TedLium3 BPE training results (Transducer)
+
+#### Conformer encoder + embedding decoder
+
+Using the codes from this PR commit https://github.com/k2-fsa/icefall/pull/183/commits/536ad2252e2d406f24a681743d98bd5f90801b97.
+
+Conformer encoder + non-current decoder. The decoder
+contains only an embedding layer and a Conv1d (with kernel size 2).
+
+The WERs are
+
+|                                    |     dev    |    test    | comment                                  |
+|------------------------------------|------------|------------|------------------------------------------|
+|          greedy search             | 7.19       | 6.57       | --epoch 29, --avg 16, --max-duration 100 |
+|      beam search (beam size 4)     | 7.12       | 6.37       | --epoch 29, --avg 16, --max-duration 100 |
+| modified beam search (beam size 4) | 7.00       | 6.19       | --epoch 29, --avg 16, --max-duration 100 |
+
+The training command for reproducing is given below:
+
+```
+export CUDA_VISIBLE_DEVICES="0,1,2,3"
+
+./transducer_stateless/train.py \
+  --world-size 4 \
+  --num-epochs 30 \
+  --start-epoch 0 \
+  --exp-dir transducer_stateless/exp \
+  --max-duration 200 \
+```
+
+The tensorboard training log can be found at
+https://tensorboard.dev/experiment/DnRwoZF8RRyod4kkfG5q5Q/#scalars
+
+The decoding command is:
+```
+epoch=29
+avg=15
+
+## greedy search
+./transducer_stateless/decode.py \
+  --epoch $epoch \
+  --avg $avg \
+  --exp-dir transducer_stateless/exp \
+  --bpe-model ./data/lang_bpe_500/bpe.model \
+  --max-duration 100
+
+## beam search
+./transducer_stateless/decode.py \
+  --epoch $epoch \
+  --avg $avg \
+  --exp-dir transducer_stateless/exp \
+  --bpe-model ./data/lang_bpe_500/bpe.model \
+  --max-duration 100 \
+  --decoding-method beam_search \
+  --beam-size 4
+
+## modified beam search
+./transducer_stateless/decode.py \
+  --epoch $epoch \
+  --avg $avg \
+  --exp-dir transducer_stateless/exp \
+  --bpe-model ./data/lang_bpe_500/bpe.model \
+  --max-duration 100 \
+  --decoding-method beam_search \
+  --beam-size 4
+```
diff --git a/egs/tedlium3/ASR/prepare.sh b/egs/tedlium3/ASR/prepare.sh
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+#!/usr/bin/env bash
+
+set -eou pipefail
+
+nj=15
+stage=-1
+stop_stage=100
+
+# We assume dl_dir (download dir) contains the following
+# directories and files. If not, they will be downloaded
+# by this script automatically.
+#
+#  - $dl_dir/tedlium3
+#      You can find data, doc, legacy, LM, etc, inside it.
+#      You can download them from https://www.openslr.org/51
+#
+#  - $dl_dir/lm
+#      This directory contains the language model(LM) downloaded from
+#      https://huggingface.co/luomingshuang/tedlium3_lm. About how to get these LM files, you can know it
+#      from https://github.com/luomingshuang/Train_LM_with_kaldilm.
+#
+#     - lm_3_gram.arpa
+#     - lm_4_gram.arpa
+#
+#  - $dl_dir/musan
+#      This directory contains the following directories downloaded from
+#       http://www.openslr.org/17/
+#
+#     - music
+#     - noise
+#     - speech
+dl_dir=$PWD/download
+
+. shared/parse_options.sh || exit 1
+
+# vocab size for sentence piece models.
+# It will generate data/lang_bpe_xxx,
+# data/lang_bpe_yyy if the array contains xxx, yyy
+vocab_sizes=(
+  5000
+  2000
+  1000
+  500
+)
+
+# All files generated by this script are saved in "data".
+# You can safely remove "data" and rerun this script to regenerate it.
+mkdir -p data
+
+log() {
+  # This function is from espnet
+  local fname=${BASH_SOURCE[1]##*/}
+  echo -e "$(date '+%Y-%m-%d %H:%M:%S') (${fname}:${BASH_LINENO[0]}:${FUNCNAME[1]}) $*"
+}
+
+log "dl_dir: $dl_dir"
+
+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
+  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:
+  #wget --continue http://kaldi-asr.org/models/5/4gram_small.arpa.gz -P $dl_dir/lm/ || exit 1
+  #wget --continue http://kaldi-asr.org/models/5/4gram_big.arpa.gz -P $dl_dir/lm/ || exit 1
+fi
+
+if [ $stage -le 0 ] && [ $stop_stage -ge 0 ]; then
+  log "Stage 0: Download data"
+
+  # If you have pre-downloaded it to /path/to/LibriSpeech,
+  # you can create a symlink
+  #
+  # ln -sfv /path/to/tedlium3 $dl_dir/tedlium3
+  #
+  if [ ! -d $dl_dir/tedlium ]; then
+    lhotse download tedlium $dl_dir
+  fi
+
+  # If you have pre-downloaded it to /path/to/musan,
+  # you can create a symlink
+  #
+  #ln -sfv /path/to/musan $dl_dir/musan
+
+  if [ ! -d $dl_dir/musan ]; then
+    lhotse download musan $dl_dir
+  fi
+fi
+
+if [ $stage -le 1 ] && [ $stop_stage -ge 1 ]; then
+  log "Stage 1: Prepare tedlium3 manifest"
+  # We assume that you have downloaded the tedlium3 corpus
+  # to $dl_dir/tedlium3
+  mkdir -p data/manifests
+  lhotse prepare tedlium $dl_dir/tedlium3 data/manifests
+fi
+
+if [ $stage -le 2 ] && [ $stop_stage -ge 2 ]; then
+  log "Stage 2: Prepare musan manifest"
+  # We assume that you have downloaded the musan corpus
+  # to data/musan
+  mkdir -p data/manifests
+  lhotse prepare musan $dl_dir/musan data/manifests
+fi
+
+if [ $stage -le 3 ] && [ $stop_stage -ge 3 ]; then
+  log "Stage 3: Compute fbank for tedlium3"
+  mkdir -p data/fbank
+  ./local/compute_fbank_tedlium.py
+fi
+
+if [ $stage -le 4 ] && [ $stop_stage -ge 4 ]; then
+  log "Stage 4: Compute fbank for musan"
+  mkdir -p data/fbank
+  ./local/compute_fbank_musan.py
+fi
+
+if [ $stage -le 5 ] && [ $stop_stage -ge 5 ]; then
+  log "Stage 5: Prepare phone based lang"
+  lang_dir=data/lang_phone
+  mkdir -p $lang_dir
+
+  if [ ! -f $lang_dir/train.text ]; then
+    ./local/prepare_transcripts.py \
+      --lang-dir $lang_dir \
+      --manifests-dir data/manifests
+
+  cat download/tedlium3/TEDLIUM.152k.dic |
+  grep -v -w "<s>" |
+  grep -v -w "</s>" |
+  grep -v -w "<unk>" |
+  LANG= LC_ALL= sort |
+  sed 's:([0-9])::g' > $lang_dir/lexicon_words.txt
+
+  (echo '<UNK> <UNK>'; ) |
+    cat - $lang_dir/lexicon_words.txt |
+    sort | uniq > $lang_dir/lexicon.txt
+
+  if [ ! -f $lang_dir/L_disambig.pt ]; then
+    ./local/prepare_lang.py --lang-dir $lang_dir
+  fi
+fi
+
+if [ $stage -le 6 ] && [ $stop_stage -ge 6 ]; then
+  log "Stage 6: Prepare BPE based lang"
+
+  for vocab_size in ${vocab_sizes[@]}; do
+    lang_dir=data/lang_bpe_${vocab_size}
+    mkdir -p $lang_dir
+    # We reuse words.txt from phone based lexicon
+    # so that the two can share G.pt later.
+    cp data/lang_phone/words.txt $lang_dir
+
+    if [ ! -f $lang_dir/transcript_words.txt ]; then
+      log "Generate data for BPE training"
+      cat data/lang_phone/train.text | cut -d " " -f 2-
+      > $lang_dir/transcript_words.txt
+      # remove the <unk> for transcript_words.txt
+      sed -i 's/ <unk>//g' $lang_dir/transcript_words.txt
+      sed -i 's/<unk> //g' $lang_dir/transcript_words.txt
+      sed -i 's/<unk>//g' $lang_dir/transcript_words.txt
+    fi
+
+    ./local/train_bpe_model.py \
+      --lang-dir $lang_dir \
+      --vocab-size $vocab_size \
+      --transcript $lang_dir/transcript_words.txt
+
+    if [ ! -f $lang_dir/L_disambig.pt ]; then
+      ./local/prepare_lang_bpe.py --lang-dir $lang_dir
+    fi
+  done
+fi
+
+if [ $stage -le 7 ] && [ $stop_stage -ge 7 ]; then
+  log "Stage 7: Prepare bigram P"
+
+  for vocab_size in ${vocab_sizes[@]}; do
+    lang_dir=data/lang_bpe_${vocab_size}
+
+    if [ ! -f $lang_dir/transcript_tokens.txt ]; then
+      ./local/convert_transcript_words_to_tokens.py \
+        --lexicon $lang_dir/lexicon.txt \
+        --transcript $lang_dir/transcript_words.txt \
+        --oov "<UNK>" \
+        > $lang_dir/transcript_tokens.txt
+    fi
+
+    if [ ! -f $lang_dir/P.arpa ]; then
+      ./shared/make_kn_lm.py \
+        -ngram-order 2 \
+        -text $lang_dir/transcript_tokens.txt \
+        -lm $lang_dir/P.arpa
+    fi
+
+    if [ ! -f $lang_dir/P.fst.txt ]; then
+      python3 -m kaldilm \
+        --read-symbol-table="$lang_dir/tokens.txt" \
+        --disambig-symbol='#0' \
+        --max-order=2 \
+        $lang_dir/P.arpa > $lang_dir/P.fst.txt
+    fi
+  done
+fi
+
+if [ $stage -le 8 ] && [ $stop_stage -ge 8 ]; then
+  log "Stage 8: Prepare G"
+  # We assume you have install kaldilm, if not, please install
+  # it using: pip install kaldilm
+
+  mkdir -p data/lm
+  if [ ! -f data/lm/G_3_gram.fst.txt ]; then
+    # It is used in building HLG
+    python3 -m kaldilm \
+      --read-symbol-table="data/lang_phone/words.txt" \
+      --disambig-symbol='#0' \
+      --max-order=3 \
+      data/lm/lm_3_gram.arpa > data/lm/G_3_gram.fst.txt
+  fi
+
+  if [ ! -f data/lm/G_4_gram.fst.txt ]; then
+    # It is used for LM rescoring
+    python3 -m kaldilm \
+      --read-symbol-table="data/lang_phone/words.txt" \
+      --disambig-symbol='#0' \
+      --max-order=4 \
+      data/lm/lm_4_gram.arpa > data/lm/G_4_gram.fst.txt
+  fi
+fi
+echo 'completing the G building....'
+if [ $stage -le 9 ] && [ $stop_stage -ge 9 ]; then
+  log "Stage 9: Compile HLG"
+  ./local/compile_hlg.py --lang-dir data/lang_phone
+
+  for vocab_size in ${vocab_sizes[@]}; do
+    lang_dir=data/lang_bpe_${vocab_size}
+    ./local/compile_hlg.py --lang-dir $lang_dir
+  done
+fi