diff --git a/egs/aishell/ASR/RESULTS.md b/egs/aishell/ASR/RESULTS.md
index 2da77ce3f..465f8ce85 100644
--- a/egs/aishell/ASR/RESULTS.md
+++ b/egs/aishell/ASR/RESULTS.md
@@ -1,22 +1,22 @@
 ## Results
 
 ### Aishell training results (Conformer-MMI)
-#### 2021-12-01
-(Pingfeng Luo): Result of <https://github.com/k2-fsa/icefall/pull/137>
+#### 2021-12-04
+(Pingfeng Luo): Result of <https://github.com/k2-fsa/icefall/pull/140>
 
-The tensorboard log for training is available at <https://tensorboard.dev/experiment/dyp3vWE9RE6SkqBAgLJjUw/>
+The tensorboard log for training is available at <https://tensorboard.dev/experiment/PSRYVbptRGynqpPRSykp1g>
 
-And pretrained model is available at <https://huggingface.co/pfluo/icefall_aishell_model>
+And pretrained model is available at <https://huggingface.co/pfluo/icefall_aishell_mmi_model>
 
-The best decoding results (CER) are listed below, we got this results by averaging models from epoch 20 to 49, and using `attention-decoder` decoder with num_paths equals to 100.
+The best decoding results (CER) are listed below, we got this results by averaging models from epoch 61 to 85, and using `attention-decoder` decoder with num_paths equals to 100.
 
 ||test|
 |--|--|
-|CER| 5.12% |
+|CER| 4.94% |
 
 ||lm_scale|attention_scale|
 |--|--|--|
-|test|1.5|0.5|
+|test|1.1|0.3|
 
 You can use the following commands to reproduce our results:
 
@@ -31,12 +31,12 @@ export CUDA_VISIBLE_DEVICES="0,1,2,3,4,5,6,7,8"
 python conformer_mmi/train.py --bucketing-sampler True \
                               --max-duration 200 \
                               --start-epoch 0 \
-                              --num-epochs 50 \
+                              --num-epochs 90 \
                               --world-size 8
 
 python conformer_mmi/decode.py --nbest-scale 0.5 \
-                               --epoch 49 \
-                               --avg 20 \
+                               --epoch 85 \
+                               --avg 25 \
                                --method attention-decoder \
                                --max-duration 20 \
                                --num-paths 100
diff --git a/egs/aishell/ASR/conformer_mmi/decode.py b/egs/aishell/ASR/conformer_mmi/decode.py
index 164d86c4c..1d0b3daad 100755
--- a/egs/aishell/ASR/conformer_mmi/decode.py
+++ b/egs/aishell/ASR/conformer_mmi/decode.py
@@ -1,5 +1,6 @@
 #!/usr/bin/env python3
 # Copyright 2021 Xiaomi Corporation (Author: Liyong Guo, Fangjun Kuang)
+# Copyright 2021                    Pingfeng Luo
 #
 # See ../../../../LICENSE for clarification regarding multiple authors
 #
diff --git a/egs/aishell/ASR/conformer_mmi/train.py b/egs/aishell/ASR/conformer_mmi/train.py
index 1ff3e2c3b..14ddaf5fd 100755
--- a/egs/aishell/ASR/conformer_mmi/train.py
+++ b/egs/aishell/ASR/conformer_mmi/train.py
@@ -1,6 +1,7 @@
 #!/usr/bin/env python3
 # Copyright    2021  Xiaomi Corp.        (authors: Fangjun Kuang,
 #                                                  Wei Kang)
+# Copyright    2021                       Pingfeng Luo
 #
 # See ../../../../LICENSE for clarification regarding multiple authors
 #
@@ -80,7 +81,7 @@ def get_parser():
     parser.add_argument(
         "--num-epochs",
         type=int,
-        default=50,
+        default=90,
         help="Number of epochs to train.",
     )
 
diff --git a/egs/aishell/ASR/prepare.sh b/egs/aishell/ASR/prepare.sh
index 27d4c0dd8..1e78d79d9 100755
--- a/egs/aishell/ASR/prepare.sh
+++ b/egs/aishell/ASR/prepare.sh
@@ -111,63 +111,66 @@ if [ $stage -le 4 ] && [ $stop_stage -ge 4 ]; then
   ./local/compute_fbank_musan.py
 fi
 
+lang_phone_dir=data/lang_phone
+lang_char_dir=data/lang_char
 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
+  mkdir -p $lang_phone_dir
 
   (echo '!SIL SIL'; echo '<SPOKEN_NOISE> SPN'; echo '<UNK> SPN'; ) |
     cat - $dl_dir/aishell/resource_aishell/lexicon.txt |
-    sort | uniq > $lang_dir/lexicon.txt
+    sort | uniq > $lang_phone_dir/lexicon.txt
 
-  ./local/generate_unique_lexicon.py --lang-dir $lang_dir
+  ./local/generate_unique_lexicon.py --lang-dir $lang_phone_dir
 
-  if [ ! -f data/lang_phone/L_disambig.pt ]; then
+  if [ ! -f $lang_phone_dir/L_disambig.pt ]; then
     ./local/prepare_lang.py
   fi
 
   # Train a bigram P for MMI training
-  if [ ! -f $lang_dir/transcript_words.txt ]; then
+  if [ ! -f $lang_phone_dir/transcript_words.txt ]; then
     log "Generate data to train phone based bigram P"
-    aishell_text=aishell/data_aishell/transcript/aishell_transcript_v0.8.txt
-    cat ${dl_dir}/${aishell_text} | cut -d " " -f 2- > $lang_dir/transcript_words.txt
+    aishell_text=$dl_dir/aishell/data_aishell/transcript/aishell_transcript_v0.8.txt
+    aishell_train_uid=$dl_dir/aishell/data_aishell/transcript/aishell_train_uid
+    find data/aishell/data_aishell/wav/train -name "*.wav" | sed 's/\.wav//g' | awk -F '/' '{print $NF}' > $aishell_train_uid
+    awk 'NR==FNR{uid[$1]=$1} NR!=FNR{if($1 in uid) print $0}' $aishell_train_uid $aishell_text | cut -d " " -f 2- > $lang_phone_dir/transcript_words.txt
   fi
 
-  if [ ! -f $lang_dir/transcript_tokens.txt ]; then
+  if [ ! -f $lang_phone_dir/transcript_tokens.txt ]; then
     ./local/convert_transcript_words_to_tokens.py \
-      --lexicon $lang_dir/uniq_lexicon.txt \
-      --transcript $lang_dir/transcript_words.txt \
+      --lexicon $lang_phone_dir/uniq_lexicon.txt \
+      --transcript $lang_phone_dir/transcript_words.txt \
       --oov "<UNK>" \
-      > $lang_dir/transcript_tokens.txt
+      > $lang_phone_dir/transcript_tokens.txt
   fi
 
-  if [ ! -f $lang_dir/P.arpa ]; then
+  if [ ! -f $lang_phone_dir/P.arpa ]; then
     ./shared/make_kn_lm.py \
       -ngram-order 2 \
-      -text $lang_dir/transcript_tokens.txt \
-      -lm $lang_dir/P.arpa
+      -text $lang_phone_dir/transcript_tokens.txt \
+      -lm $lang_phone_dir/P.arpa
   fi
 
-  if [ ! -f $lang_dir/P.fst.txt ]; then
+  if [ ! -f $lang_phone_dir/P.fst.txt ]; then
     python3 -m kaldilm \
-      --read-symbol-table="$lang_dir/tokens.txt" \
+      --read-symbol-table="$lang_phone_dir/tokens.txt" \
       --disambig-symbol='#0' \
       --max-order=2 \
-      $lang_dir/P.arpa > $lang_dir/P.fst.txt
+      $lang_phone_dir/P.arpa > $lang_phone_dir/P.fst.txt
   fi
 fi
 
 if [ $stage -le 6 ] && [ $stop_stage -ge 6 ]; then
   log "Stage 6: Prepare char based lang"
-  mkdir -p data/lang_char
+  mkdir -p $lang_char_dir
   # We reuse words.txt from phone based lexicon
   # so that the two can share G.pt later.
-  cp data/lang_phone/words.txt data/lang_char
+  cp $lang_phone_dir/words.txt $lang_char_dir
 
   cat $dl_dir/aishell/data_aishell/transcript/aishell_transcript_v0.8.txt |
-  cut -d " " -f 2- | sed -e 's/[ \t\r\n]*//g' > data/lang_char/text
+  cut -d " " -f 2- | sed -e 's/[ \t\r\n]*//g' > $lang_char_dir/text
 
-  if [ ! -f data/lang_char/L_disambig.pt ]; then
+  if [ ! -f $lang_char_dir/L_disambig.pt ]; then
     ./local/prepare_char.py
   fi
 fi
@@ -181,7 +184,7 @@ if [ $stage -le 7 ] && [ $stop_stage -ge 7 ]; then
   if [ ! -f data/lm/G_3_gram.fst.txt ]; then
     # It is used in building HLG
     python3 -m kaldilm \
-      --read-symbol-table="data/lang_phone/words.txt" \
+      --read-symbol-table="$lang_phone_dir/words.txt" \
       --disambig-symbol='#0' \
       --max-order=3 \
       $dl_dir/lm/3-gram.unpruned.arpa > data/lm/G_3_gram.fst.txt
@@ -190,6 +193,6 @@ fi
 
 if [ $stage -le 8 ] && [ $stop_stage -ge 8 ]; then
   log "Stage 8: Compile HLG"
-  ./local/compile_hlg.py --lang-dir data/lang_phone
-  ./local/compile_hlg.py --lang-dir data/lang_char
+  ./local/compile_hlg.py --lang-dir $lang_phone_dir
+  ./local/compile_hlg.py --lang-dir $lang_char_dir
 fi