diff --git a/egs/librispeech/ASR/RESULTS.md b/egs/librispeech/ASR/RESULTS.md
index ebf5e89c4b..ee5422abac 100644
--- a/egs/librispeech/ASR/RESULTS.md
+++ b/egs/librispeech/ASR/RESULTS.md
@@ -1526,7 +1526,7 @@ done
 
 You may also decode using LODR + LM shallow fusion. This decoding method is proposed in <https://arxiv.org/pdf/2203.16776.pdf>.
 It subtracts the internal language model score during shallow fusion, which is approximated by a bi-gram model. The bi-gram can be
-generated by `generate-lm.sh`, or you may download it from <https://huggingface.co/marcoyang/librispeech_bigram>.
+generated by `prepare_lm.sh` at stage 4, or you may download it from <https://huggingface.co/marcoyang/librispeech_bigram>.
 
 The decoding command is as follows:
 
diff --git a/egs/librispeech/ASR/generate-lm.sh b/egs/librispeech/ASR/generate-lm.sh
deleted file mode 100755
index dacd276d14..0000000000
--- a/egs/librispeech/ASR/generate-lm.sh
+++ /dev/null
@@ -1,20 +0,0 @@
-#!/usr/bin/env bash
-
-lang_dir=data/lang_bpe_500
-
-for ngram in 2 3 4 5; do
-  if [ ! -f $lang_dir/${ngram}gram.arpa ]; then
-    ./shared/make_kn_lm.py \
-      -ngram-order ${ngram} \
-      -text $lang_dir/transcript_tokens.txt \
-      -lm $lang_dir/${ngram}gram.arpa
-  fi
-
-  if [ ! -f $lang_dir/${ngram}gram.fst.txt ]; then
-    python3 -m kaldilm \
-      --read-symbol-table="$lang_dir/tokens.txt" \
-      --disambig-symbol='#0' \
-      --max-order=${ngram} \
-      $lang_dir/${ngram}gram.arpa > $lang_dir/${ngram}gram.fst.txt
-  fi
-done
diff --git a/egs/librispeech/ASR/local/train_bpe_model.py b/egs/librispeech/ASR/local/train_bpe_model.py
index 43142aee4c..5979d5b986 100755
--- a/egs/librispeech/ASR/local/train_bpe_model.py
+++ b/egs/librispeech/ASR/local/train_bpe_model.py
@@ -28,6 +28,7 @@
 import argparse
 import shutil
 from pathlib import Path
+from typing import Dict
 
 import sentencepiece as spm
 
@@ -57,6 +58,18 @@ def get_args():
     return parser.parse_args()
 
 
+def generate_tokens(lang_dir: Path):
+    """
+    Generate the tokens.txt from a bpe model.
+    """
+    sp = spm.SentencePieceProcessor()
+    sp.load(str(lang_dir / "bpe.model"))
+    token2id: Dict[str, int] = {sp.id_to_piece(i): i for i in range(sp.vocab_size())}
+    with open(lang_dir / "tokens.txt", "w", encoding="utf-8") as f:
+        for sym, i in token2id.items():
+            f.write(f"{sym} {i}\n")
+
+
 def main():
     args = get_args()
     vocab_size = args.vocab_size
@@ -95,6 +108,8 @@ def main():
 
     shutil.copyfile(model_file, f"{lang_dir}/bpe.model")
 
+    generate_tokens(lang_dir)
+
 
 if __name__ == "__main__":
     main()
diff --git a/egs/librispeech/ASR/prepare.sh b/egs/librispeech/ASR/prepare.sh
index 4a5072cc0a..40dc3260d4 100755
--- a/egs/librispeech/ASR/prepare.sh
+++ b/egs/librispeech/ASR/prepare.sh
@@ -6,8 +6,21 @@ export PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION=python
 set -eou pipefail
 
 nj=15
-stage=-1
-stop_stage=100
+# run step 0 to step 5 by default
+stage=0
+stop_stage=5
+
+# Note: This script just prepare the minimal requirements that needed by a
+# transducer training with bpe units.
+#
+# If you want to use ngram or nnlm, please continue running prepare_lm.sh after
+# you succeed running this script.
+#
+# This script also contains the steps to generate phone based units, but they
+# will not run automatically, you can generate the phone based units by
+# bash prepare.sh --stage -1 --stop-stage -1
+# bash prepare.sh --stage 6 --stop-stage 6
+
 
 # We assume dl_dir (download dir) contains the following
 # directories and files. If not, they will be downloaded
@@ -17,6 +30,18 @@ stop_stage=100
 #      You can find BOOKS.TXT, test-clean, train-clean-360, etc, inside it.
 #      You can download them from https://www.openslr.org/12
 #
+#  - $dl_dir/musan
+#      This directory contains the following directories downloaded from
+#       http://www.openslr.org/17/
+#
+#     - music
+#     - noise
+#     - speech
+#
+# lm directory is not necessary for transducer training with bpe units, but it
+# is needed by phone based modeling, you can download it by running
+# bash prepare.sh --stage -1 --stop-stage -1
+# then you can see the following files in the directory.
 #  - $dl_dir/lm
 #      This directory contains the following files downloaded from
 #       http://www.openslr.org/resources/11
@@ -28,14 +53,7 @@ stop_stage=100
 #        - librispeech-vocab.txt
 #        - librispeech-lexicon.txt
 #        - librispeech-lm-norm.txt.gz
-#
-#  - $dl_dir/musan
-#      This directory contains the following directories downloaded from
-#       http://www.openslr.org/17/
-#
-#     - music
-#     - noise
-#     - speech
+
 dl_dir=$PWD/download
 
 . shared/parse_options.sh || exit 1
@@ -60,6 +78,8 @@ log() {
   echo -e "$(date '+%Y-%m-%d %H:%M:%S') (${fname}:${BASH_LINENO[0]}:${FUNCNAME[1]}) $*"
 }
 
+log "Running prepare.sh"
+
 log "dl_dir: $dl_dir"
 
 if [ $stage -le -1 ] && [ $stop_stage -ge -1 ]; then
@@ -159,45 +179,11 @@ if [ $stage -le 4 ] && [ $stop_stage -ge 4 ]; then
 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
-
-  (echo '!SIL SIL'; echo '<SPOKEN_NOISE> SPN'; echo '<UNK> SPN'; ) |
-    cat - $dl_dir/lm/librispeech-lexicon.txt |
-    sort | uniq > $lang_dir/lexicon.txt
-
-  if [ ! -f $lang_dir/L_disambig.pt ]; then
-    ./local/prepare_lang.py --lang-dir $lang_dir
-  fi
-
-  if [ ! -f $lang_dir/L.fst ]; then
-    log "Converting L.pt to L.fst"
-    ./shared/convert-k2-to-openfst.py \
-      --olabels aux_labels \
-      $lang_dir/L.pt \
-      $lang_dir/L.fst
-  fi
-
-  if [ ! -f $lang_dir/L_disambig.fst ]; then
-    log "Converting L_disambig.pt to L_disambig.fst"
-    ./shared/convert-k2-to-openfst.py \
-      --olabels aux_labels \
-      $lang_dir/L_disambig.pt \
-      $lang_dir/L_disambig.fst
-  fi
-fi
-
-
-if [ $stage -le 6 ] && [ $stop_stage -ge 6 ]; then
-  log "Stage 6: Prepare BPE based lang"
+  log "Stage 5: 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"
@@ -217,223 +203,43 @@ if [ $stage -le 6 ] && [ $stop_stage -ge 6 ]; then
         --vocab-size $vocab_size \
         --transcript $lang_dir/transcript_words.txt
     fi
-
-    if [ ! -f $lang_dir/L_disambig.pt ]; then
-      ./local/prepare_lang_bpe.py --lang-dir $lang_dir
-
-      log "Validating $lang_dir/lexicon.txt"
-      ./local/validate_bpe_lexicon.py \
-        --lexicon $lang_dir/lexicon.txt \
-        --bpe-model $lang_dir/bpe.model
-    fi
-
-    if [ ! -f $lang_dir/L.fst ]; then
-      log "Converting L.pt to L.fst"
-      ./shared/convert-k2-to-openfst.py \
-        --olabels aux_labels \
-        $lang_dir/L.pt \
-        $lang_dir/L.fst
-    fi
-
-    if [ ! -f $lang_dir/L_disambig.fst ]; then
-      log "Converting L_disambig.pt to L_disambig.fst"
-      ./shared/convert-k2-to-openfst.py \
-        --olabels aux_labels \
-        $lang_dir/L_disambig.pt \
-        $lang_dir/L_disambig.fst
-    fi
   done
 fi
 
-if [ $stage -le 7 ] && [ $stop_stage -ge 7 ]; then
-  log "Stage 7: Prepare bigram token-level P for MMI training"
-
-  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 6 ] && [ $stop_stage -ge 6 ]; then
+  log "Stage 6: Prepare phone based lang"
+  lang_dir=data/lang_phone
+  mkdir -p $lang_dir
 
-if [ $stage -le 8 ] && [ $stop_stage -ge 8 ]; then
-  log "Stage 8: Prepare G"
-  # We assume you have installed 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 \
-      $dl_dir/lm/3-gram.pruned.1e-7.arpa > data/lm/G_3_gram.fst.txt
+  if [ ! -f $dl_dir/lm/librispeech-lexicon.txt ]; then
+    log "No lexicon file in $dl_dir/lm, please run :"
+    log "prepare.sh --stage -1 --stop-stage -1"
+    exit -1
   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 \
-      $dl_dir/lm/4-gram.arpa > data/lm/G_4_gram.fst.txt
+  if [ ! -f $lang_dir/lexicon.txt ]; then
+    (echo '!SIL SIL'; echo '<SPOKEN_NOISE> SPN'; echo '<UNK> SPN'; ) |
+      cat - $dl_dir/lm/librispeech-lexicon.txt |
+      sort | uniq > $lang_dir/lexicon.txt
   fi
 
-  for vocab_size in ${vocab_sizes[@]}; do
-    lang_dir=data/lang_bpe_${vocab_size}
-
-    if [ ! -f $lang_dir/HL.fst ]; then
-      ./local/prepare_lang_fst.py  \
-        --lang-dir $lang_dir \
-        --ngram-G ./data/lm/G_3_gram.fst.txt
-    fi
-  done
-fi
-
-if [ $stage -le 9 ] && [ $stop_stage -ge 9 ]; then
-  log "Stage 9: Compile HLG"
-  ./local/compile_hlg.py --lang-dir data/lang_phone
-
-  # Note If ./local/compile_hlg.py throws OOM,
-  # please switch to the following command
-  #
-  # ./local/compile_hlg_using_openfst.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
-
-    # Note If ./local/compile_hlg.py throws OOM,
-    # please switch to the following command
-    #
-    # ./local/compile_hlg_using_openfst.py --lang-dir $lang_dir
-  done
-fi
-
-# Compile LG for RNN-T fast_beam_search decoding
-if [ $stage -le 10 ] && [ $stop_stage -ge 10 ]; then
-  log "Stage 10: Compile LG"
-  ./local/compile_lg.py --lang-dir data/lang_phone
-
-  for vocab_size in ${vocab_sizes[@]}; do
-    lang_dir=data/lang_bpe_${vocab_size}
-    ./local/compile_lg.py --lang-dir $lang_dir
-  done
-fi
-
-if [ $stage -le 11 ] && [ $stop_stage -ge 11 ]; then
-  log "Stage 11: Generate LM training data"
-
-  for vocab_size in ${vocab_sizes[@]}; do
-    log "Processing vocab_size == ${vocab_size}"
-    lang_dir=data/lang_bpe_${vocab_size}
-    out_dir=data/lm_training_bpe_${vocab_size}
-    mkdir -p $out_dir
-
-    ./local/prepare_lm_training_data.py \
-      --bpe-model $lang_dir/bpe.model \
-      --lm-data $dl_dir/lm/librispeech-lm-norm.txt \
-      --lm-archive $out_dir/lm_data.pt
-  done
-fi
-
-if [ $stage -le 12 ] && [ $stop_stage -ge 12 ]; then
-  log "Stage 12: Generate LM validation data"
-
-  for vocab_size in ${vocab_sizes[@]}; do
-    log "Processing vocab_size == ${vocab_size}"
-    out_dir=data/lm_training_bpe_${vocab_size}
-    mkdir -p $out_dir
-
-    if [ ! -f $out_dir/valid.txt ]; then
-      files=$(
-        find "$dl_dir/LibriSpeech/dev-clean" -name "*.trans.txt"
-        find "$dl_dir/LibriSpeech/dev-other" -name "*.trans.txt"
-      )
-      for f in ${files[@]}; do
-        cat $f | cut -d " " -f 2-
-      done > $out_dir/valid.txt
-    fi
-
-    lang_dir=data/lang_bpe_${vocab_size}
-    ./local/prepare_lm_training_data.py \
-      --bpe-model $lang_dir/bpe.model \
-      --lm-data $out_dir/valid.txt \
-      --lm-archive $out_dir/lm_data-valid.pt
-  done
-fi
-
-if [ $stage -le 13 ] && [ $stop_stage -ge 13 ]; then
-  log "Stage 13: Generate LM test data"
-
-  for vocab_size in ${vocab_sizes[@]}; do
-    log "Processing vocab_size == ${vocab_size}"
-    out_dir=data/lm_training_bpe_${vocab_size}
-    mkdir -p $out_dir
-
-    if [ ! -f $out_dir/test.txt ]; then
-      files=$(
-        find "$dl_dir/LibriSpeech/test-clean" -name "*.trans.txt"
-        find "$dl_dir/LibriSpeech/test-other" -name "*.trans.txt"
-      )
-      for f in ${files[@]}; do
-        cat $f | cut -d " " -f 2-
-      done > $out_dir/test.txt
-    fi
-
-    lang_dir=data/lang_bpe_${vocab_size}
-    ./local/prepare_lm_training_data.py \
-      --bpe-model $lang_dir/bpe.model \
-      --lm-data $out_dir/test.txt \
-      --lm-archive $out_dir/lm_data-test.pt
-  done
-fi
+  if [ ! -f $lang_dir/L_disambig.pt ]; then
+    ./local/prepare_lang.py --lang-dir $lang_dir
+  fi
 
-if [ $stage -le 14 ] && [ $stop_stage -ge 14 ]; then
-  log "Stage 14: Sort LM training data"
-  # Sort LM training data by sentence length in descending order
-  # for ease of training.
-  #
-  # Sentence length equals to the number of BPE tokens
-  # in a sentence.
+  if [ ! -f $lang_dir/L.fst ]; then
+    log "Converting L.pt to L.fst"
+    ./shared/convert-k2-to-openfst.py \
+      --olabels aux_labels \
+      $lang_dir/L.pt \
+      $lang_dir/L.fst
+  fi
 
-  for vocab_size in ${vocab_sizes[@]}; do
-    out_dir=data/lm_training_bpe_${vocab_size}
-    mkdir -p $out_dir
-    ./local/sort_lm_training_data.py \
-      --in-lm-data $out_dir/lm_data.pt \
-      --out-lm-data $out_dir/sorted_lm_data.pt \
-      --out-statistics $out_dir/statistics.txt
-
-    ./local/sort_lm_training_data.py \
-      --in-lm-data $out_dir/lm_data-valid.pt \
-      --out-lm-data $out_dir/sorted_lm_data-valid.pt \
-      --out-statistics $out_dir/statistics-valid.txt
-
-    ./local/sort_lm_training_data.py \
-      --in-lm-data $out_dir/lm_data-test.pt \
-      --out-lm-data $out_dir/sorted_lm_data-test.pt \
-      --out-statistics $out_dir/statistics-test.txt
-  done
+  if [ ! -f $lang_dir/L_disambig.fst ]; then
+    log "Converting L_disambig.pt to L_disambig.fst"
+    ./shared/convert-k2-to-openfst.py \
+      --olabels aux_labels \
+      $lang_dir/L_disambig.pt \
+      $lang_dir/L_disambig.fst
+  fi
 fi
diff --git a/egs/librispeech/ASR/prepare_lm.sh b/egs/librispeech/ASR/prepare_lm.sh
new file mode 100755
index 0000000000..a8eb5ca784
--- /dev/null
+++ b/egs/librispeech/ASR/prepare_lm.sh
@@ -0,0 +1,262 @@
+#!/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
+
+# This script generate Ngram LM / NNLM and related files that needed by decoding.
+
+# We assume dl_dir (download dir) contains the following
+# directories and files. If not, they will be downloaded
+# by this script automatically.
+#
+#  - $dl_dir/lm
+#      This directory contains the following files downloaded from
+#       http://www.openslr.org/resources/11
+#
+#        - 3-gram.pruned.1e-7.arpa.gz
+#        - 3-gram.pruned.1e-7.arpa
+#        - 4-gram.arpa.gz
+#        - 4-gram.arpa
+#        - librispeech-vocab.txt
+#        - librispeech-lexicon.txt
+#        - librispeech-lm-norm.txt.gz
+#
+
+. prepare.sh --stage -1 --stop-stage 6 || exit 1
+
+log "Running prepare_lm.sh"
+
+stage=0
+stop_stage=100
+
+if [ $stage -le 0 ] && [ $stop_stage -ge 0 ]; then
+  log "Stage 0: Prepare BPE based lexicon."
+
+  for vocab_size in ${vocab_sizes[@]}; do
+    lang_dir=data/lang_bpe_${vocab_size}
+    # 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/L_disambig.pt ]; then
+      ./local/prepare_lang_bpe.py --lang-dir $lang_dir
+
+      log "Validating $lang_dir/lexicon.txt"
+      ./local/validate_bpe_lexicon.py \
+        --lexicon $lang_dir/lexicon.txt \
+        --bpe-model $lang_dir/bpe.model
+    fi
+
+    if [ ! -f $lang_dir/L.fst ]; then
+      log "Converting L.pt to L.fst"
+      ./shared/convert-k2-to-openfst.py \
+        --olabels aux_labels \
+        $lang_dir/L.pt \
+        $lang_dir/L.fst
+    fi
+
+    if [ ! -f $lang_dir/L_disambig.fst ]; then
+      log "Converting L_disambig.pt to L_disambig.fst"
+      ./shared/convert-k2-to-openfst.py \
+        --olabels aux_labels \
+        $lang_dir/L_disambig.pt \
+        $lang_dir/L_disambig.fst
+    fi
+  done
+fi
+
+if [ $stage -le 1 ] && [ $stop_stage -ge 1 ]; then
+  log "Stage 1: Prepare word level G"
+  # We assume you have installed 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 \
+      $dl_dir/lm/3-gram.pruned.1e-7.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 \
+      $dl_dir/lm/4-gram.arpa > data/lm/G_4_gram.fst.txt
+  fi
+
+  for vocab_size in ${vocab_sizes[@]}; do
+    lang_dir=data/lang_bpe_${vocab_size}
+
+    if [ ! -f $lang_dir/HL.fst ]; then
+      ./local/prepare_lang_fst.py  \
+        --lang-dir $lang_dir \
+        --ngram-G ./data/lm/G_3_gram.fst.txt
+    fi
+  done
+fi
+
+if [ $stage -le 2 ] && [ $stop_stage -ge 2 ]; then
+  log "Stage 2: Compile HLG"
+  ./local/compile_hlg.py --lang-dir data/lang_phone
+
+  # Note If ./local/compile_hlg.py throws OOM,
+  # please switch to the following command
+  #
+  # ./local/compile_hlg_using_openfst.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
+
+    # Note If ./local/compile_hlg.py throws OOM,
+    # please switch to the following command
+    #
+    # ./local/compile_hlg_using_openfst.py --lang-dir $lang_dir
+  done
+fi
+
+# Compile LG for RNN-T fast_beam_search decoding
+if [ $stage -le 3 ] && [ $stop_stage -ge 3 ]; then
+  log "Stage 3: Compile LG"
+  ./local/compile_lg.py --lang-dir data/lang_phone
+
+  for vocab_size in ${vocab_sizes[@]}; do
+    lang_dir=data/lang_bpe_${vocab_size}
+    ./local/compile_lg.py --lang-dir $lang_dir
+  done
+fi
+
+if [ $stage -le 4 ] && [ $stop_stage -ge 4 ]; then
+  log "Stage 4: Prepare token level ngram G"
+  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
+
+    for ngram in 2 3 4 5; do
+      if [ ! -f $lang_dir/${ngram}gram.arpa ]; then
+        ./shared/make_kn_lm.py \
+          -ngram-order ${ngram} \
+          -text $lang_dir/transcript_tokens.txt \
+          -lm $lang_dir/${ngram}gram.arpa
+      fi
+    
+      if [ ! -f $lang_dir/${ngram}gram.fst.txt ]; then
+        python3 -m kaldilm \
+          --read-symbol-table="$lang_dir/tokens.txt" \
+          --disambig-symbol='#0' \
+          --max-order=${ngram} \
+          $lang_dir/${ngram}gram.arpa > $lang_dir/${ngram}gram.fst.txt
+      fi
+    done
+  done
+fi
+
+if [ $stage -le 5 ] && [ $stop_stage -ge 5 ]; then
+  log "Stage 5: Generate NNLM training data"
+
+  for vocab_size in ${vocab_sizes[@]}; do
+    log "Processing vocab_size == ${vocab_size}"
+    lang_dir=data/lang_bpe_${vocab_size}
+    out_dir=data/lm_training_bpe_${vocab_size}
+    mkdir -p $out_dir
+
+    ./local/prepare_lm_training_data.py \
+      --bpe-model $lang_dir/bpe.model \
+      --lm-data $dl_dir/lm/librispeech-lm-norm.txt \
+      --lm-archive $out_dir/lm_data.pt
+  done
+fi
+
+if [ $stage -le 6 ] && [ $stop_stage -ge 6 ]; then
+  log "Stage 6: Generate NNLM validation data"
+
+  for vocab_size in ${vocab_sizes[@]}; do
+    log "Processing vocab_size == ${vocab_size}"
+    out_dir=data/lm_training_bpe_${vocab_size}
+    mkdir -p $out_dir
+
+    if [ ! -f $out_dir/valid.txt ]; then
+      files=$(
+        find "$dl_dir/LibriSpeech/dev-clean" -name "*.trans.txt"
+        find "$dl_dir/LibriSpeech/dev-other" -name "*.trans.txt"
+      )
+      for f in ${files[@]}; do
+        cat $f | cut -d " " -f 2-
+      done > $out_dir/valid.txt
+    fi
+
+    lang_dir=data/lang_bpe_${vocab_size}
+    ./local/prepare_lm_training_data.py \
+      --bpe-model $lang_dir/bpe.model \
+      --lm-data $out_dir/valid.txt \
+      --lm-archive $out_dir/lm_data-valid.pt
+  done
+fi
+
+if [ $stage -le 7 ] && [ $stop_stage -ge 7 ]; then
+  log "Stage 7: Generate NNLM test data"
+
+  for vocab_size in ${vocab_sizes[@]}; do
+    log "Processing vocab_size == ${vocab_size}"
+    out_dir=data/lm_training_bpe_${vocab_size}
+    mkdir -p $out_dir
+
+    if [ ! -f $out_dir/test.txt ]; then
+      files=$(
+        find "$dl_dir/LibriSpeech/test-clean" -name "*.trans.txt"
+        find "$dl_dir/LibriSpeech/test-other" -name "*.trans.txt"
+      )
+      for f in ${files[@]}; do
+        cat $f | cut -d " " -f 2-
+      done > $out_dir/test.txt
+    fi
+
+    lang_dir=data/lang_bpe_${vocab_size}
+    ./local/prepare_lm_training_data.py \
+      --bpe-model $lang_dir/bpe.model \
+      --lm-data $out_dir/test.txt \
+      --lm-archive $out_dir/lm_data-test.pt
+  done
+fi
+
+if [ $stage -le 8 ] && [ $stop_stage -ge 8 ]; then
+  log "Stage 8: Sort NNLM training data"
+  # Sort LM training data by sentence length in descending order
+  # for ease of training.
+  #
+  # Sentence length equals to the number of BPE tokens
+  # in a sentence.
+
+  for vocab_size in ${vocab_sizes[@]}; do
+    out_dir=data/lm_training_bpe_${vocab_size}
+    mkdir -p $out_dir
+    ./local/sort_lm_training_data.py \
+      --in-lm-data $out_dir/lm_data.pt \
+      --out-lm-data $out_dir/sorted_lm_data.pt \
+      --out-statistics $out_dir/statistics.txt
+
+    ./local/sort_lm_training_data.py \
+      --in-lm-data $out_dir/lm_data-valid.pt \
+      --out-lm-data $out_dir/sorted_lm_data-valid.pt \
+      --out-statistics $out_dir/statistics-valid.txt
+
+    ./local/sort_lm_training_data.py \
+      --in-lm-data $out_dir/lm_data-test.pt \
+      --out-lm-data $out_dir/sorted_lm_data-test.pt \
+      --out-statistics $out_dir/statistics-test.txt
+  done
+fi
diff --git a/egs/librispeech/ASR/prepare_mmi.sh b/egs/librispeech/ASR/prepare_mmi.sh
new file mode 100755
index 0000000000..d8a6e0caf5
--- /dev/null
+++ b/egs/librispeech/ASR/prepare_mmi.sh
@@ -0,0 +1,45 @@
+#!/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
+
+
+. prepare.sh --stage -1 --stop-stage 6 || exit 1
+
+log "Running prepare_mmi.sh"
+
+stage=0
+stop_stage=100
+
+if [ $stage -le 0 ] && [ $stop_stage -ge 0 ]; then
+  log "Stage 0: Prepare bigram token-level P for MMI training"
+
+  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