diff --git a/egs/tedlium3/ASR/2 b/egs/tedlium3/ASR/2
deleted file mode 100644
index eb6f9ea6e..000000000
--- a/egs/tedlium3/ASR/2
+++ /dev/null
@@ -1,231 +0,0 @@
-#!/usr/bin/env bash
-
-# fix segmentation fault reported in https://github.com/k2-fsa/icefall/issues/674
-export PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION=python
-
-set -eou pipefail
-
-stage=0
-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
-#
-#  - $dl_dir/musan
-#      This directory contains the following directories downloaded from
-#       http://www.openslr.org/17/
-#
-#     - music
-#     - noise
-#     - speech
-dl_dir=/DB
-
-. shared/parse_options.sh || exit 1
-
-# vocab size for sentence piece models.
-# It will generate data/lang_bpe_xxx,
-# data/lang_bpe_yyy if the array contains xxx, yyy
-vocab_sizes=(
-  5000
-  2000
-  1000
-  500
-)
-
-# All files generated by this script are saved in "data".
-# You can safely remove "data" and rerun this script to regenerate it.
-mkdir -p data
-
-log() {
-  # This function is from espnet
-  local fname=${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 0 ] && [ $stop_stage -ge 0 ]; then
-  log "Stage 0: Download data"
-
-  # If you have pre-downloaded it to /path/to/tedlium3,
-  # you can create a symlink
-  #
-  # ln -sfv /path/to/tedlium3 $dl_dir/tedlium3
-  #
-  if [ ! -d $dl_dir/tedlium3 ]; then
-    lhotse download tedlium $dl_dir
-    mv $dl_dir/TEDLIUM_release-3 $dl_dir/tedlium3
-  fi
-
-  # Download big and small 4 gram lanuage models
-  if [ ! -d $dl_dir/lm ]; then
-    wget --continue http://kaldi-asr.org/models/5/4gram_small.arpa.gz -P $dl_dir/lm
-    wget --continue http://kaldi-asr.org/models/5/4gram_big.arpa.gz -P $dl_dir/lm
-    gzip -d $dl_dir/lm/4gram_small.arpa.gz $dl_dir/lm/4gram_big.arpa.gz
-  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 manifests"
-  if [ ! -f data/manifests/.tedlium3.done ]; then
-    # 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
-    touch data/manifests/.tedlium3.done
-  fi
-fi
-
-if [ $stage -le 2 ] && [ $stop_stage -ge 2 ]; then
-  log "Stage 2: Prepare musan manifests"
-  # We assume that you have downloaded the musan corpus
-  # to data/musan
-  if [ ! -e data/manifests/.musan.done ]; then
-    mkdir -p data/manifests
-    lhotse prepare musan $dl_dir/musan data/manifests
-    touch data/manifests/.musan.done
-  fi
-fi
-
-if [ $stage -le 3 ] && [ $stop_stage -ge 3 ]; then
-  log "Stage 3: Compute fbank for tedlium3"
-
-  if [ ! -e data/fbank/.tedlium3.done ]; then
-    mkdir -p data/fbank
-
-    python3 ./local/compute_fbank_tedlium.py
-
-    gunzip -c data/fbank/tedlium_cuts_train.jsonl.gz | shuf | \
-    gzip -c > data/fbank/tedlium_cuts_train-shuf.jsonl.gz
-    mv data/fbank/tedlium_cuts_train-shuf.jsonl.gz \
-       data/fbank/tedlium_cuts_train.jsonl.gz
-
-    touch data/fbank/.tedlium3.done
-  fi
-fi
-
-if [ $stage -le 4 ] && [ $stop_stage -ge 4 ]; then
-  log "Stage 4: Compute fbank for musan"
-  if [ ! -e data/fbank/.musan.done ]; then
-    mkdir -p data/fbank
-    python3 ./local/compute_fbank_musan.py
-    touch data/fbank/.musan.done
-  fi
-fi
-
-if [ $stage -le 5 ] && [ $stop_stage -ge 5 ]; then
-  log "Stage 5: Prepare BPE train data and set of words"
-  lang_dir=data/lang
-  mkdir -p $lang_dir
-
-  if [ ! -f $lang_dir/train.txt ]; then
-    gunzip -c $dl_dir/tedlium3/LM/*.en.gz | sed 's: <\/s>::g' > $lang_dir/train_orig.txt
-
-    ./local/prepare_transcripts.py \
-      --input-text-path $lang_dir/train_orig.txt \
-      --output-text-path $lang_dir/train.txt
-  fi
-
-  if [ ! -f $lang_dir/words.txt ]; then
-
-    awk '{print $1}' $dl_dir/tedlium3/TEDLIUM.152k.dic |
-    sed 's:([0-9])::g' | sort | uniq > $lang_dir/words_orig.txt
-
-    ./local/prepare_words.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/words.txt $lang_dir
-
-    ./local/train_bpe_model.py \
-      --lang-dir $lang_dir \
-      --vocab-size $vocab_size \
-      --transcript data/lang/train.txt
-
-    if [ ! -f $lang_dir/L_disambig.pt ]; then
-      ./local/prepare_lang_bpe.py --lang-dir $lang_dir --oov "<unk>"
-    fi
-  done
-fi
-
-if [ $stage -le 7 ] && [ $stop_stage -ge 7 ]; then
-  log "Stage 7: 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_4_gram_small.fst.txt ]; then
-    # It is used in building HLG
-    python3 -m kaldilm \
-      --read-symbol-table="data/lang/words.txt" \
-      --disambig-symbol='#0' \
-      --max-order=4 \
-      --max-arpa-warnings=-1 \
-      $dl_dir/lm/4gram_small.arpa > data/lm/G_4_gram_small.fst.txt
-  fi
-
-  if [ ! -f data/lm/G_4_gram_big.fst.txt ]; then
-    # It is used for LM rescoring
-    python3 -m kaldilm \
-      --read-symbol-table="data/lang/words.txt" \
-      --disambig-symbol='#0' \
-      --max-order=4 \
-      --max-arpa-warnings=-1 \
-      $dl_dir/lm/4gram_big.arpa > data/lm/G_4_gram_big.fst.txt
-  fi
-fi
-
-if [ $stage -le 8 ] && [ $stop_stage -ge 8 ]; then
-  log "Stage 8: Compile HLG"
-
-  for vocab_size in ${vocab_sizes[@]}; do
-    lang_dir=data/lang_bpe_${vocab_size}
-
-    if [ ! -f $lang_dir/HLG.pt ]; then
-      ./local/compile_hlg.py \
-        --lang-dir $lang_dir \
-        --lm G_4_gram_small
-    fi
-  done
-fi
-
-if [ $stage -le 9 ] && [ $stop_stage -ge 9 ]; then
-  log "Stage 9: Split cuts by speaker id"
-  gzip -d data/fbank/tedlium_cuts_test.jsonl.gz
-
-  i=0
-  for spk in $dl_dir/tedlium3/legacy/test/sph/*; do
-	  spk_id=${spk#*sph\/}
-	  spk_id=${spk_id%.sph}
-	  echo $spk_id
-	  cat data/fbank/tedlium_cuts_test.jsonl | grep speaker\":\ \"$spk_id\" > data/fbank/tedlium_cuts_test_$i.jsonl
-	  gzip data/fbank/tedlium_cuts_test_$i.jsonl
-	  i=`expr $i + 1` 
-  done
-
-  gzip data/fbank/tedlium_cuts_test.jsonl
-  #cat data/fbank/tedlium_cuts_test.jsonl.gz | grep 
-
-fi