diff --git a/examples/aishell/asr1/READEME.md b/examples/aishell/asr1/READEME.md
index fe968870..a873a2dc 100644
--- a/examples/aishell/asr1/READEME.md
+++ b/examples/aishell/asr1/READEME.md
@@ -1,6 +1,6 @@
-# Conformer ASR with Aishell
+# Transformer/Conformer ASR with Aishell
 
-This example contains code used to train a [Conformer](http://arxiv.org/abs/2008.03802) model with [Aishell dataset](http://www.openslr.org/resources/33)
+This example contains code used to train a Transformer or [Conformer](http://arxiv.org/abs/2008.03802) model with [Aishell dataset](http://www.openslr.org/resources/33)
 
 ## Overview
 
@@ -14,16 +14,16 @@ All the scirpts you need are in the ```run.sh```. There are several stages in th
 | 3     | Test the final model performance                             |
 | 4     | Get ctc alignment of test data using the final model         |
 | 5     | Infer the single audio file                                  |
-| 51    | (Not supported at now) Transform the dynamic graph model to static graph model |
-| 101   | (Need further installation) Train language model and Build TLG |
 
-You can choose to run a range of stages by setting the ```stage``` and ```stop_stage ``` . 
+
+You can choose to run a range of  stages by setting the ```stage``` and ```stop_stage ``` . 
 
 For example, if you want to execute the code in stage 2 and stage 3, you can run this script:
 
 ```bash
 bash run.sh --stage 2 --stop_stage 3
 ```
+
 Or you can set ```stage``` equal to ```stop-stage``` to only run one stage.
 For example, if you only want to run ```stage 0```, you can use the script below:
 
@@ -38,10 +38,12 @@ The document below will describe the scripts in the ```run.sh``` in detail.
 ## The environment variables
 
 The path.sh contains the environment variable. 
+
 ```bash
 source path.sh
 ```
-This script needs to be run firstly.
+
+This script needs to be run firstly.  
 
 And another script is also needed:
 
@@ -56,20 +58,20 @@ It will support the way of using```--varibale value``` in the shell scripts.
 ## The local variables
 
 Some local variables are set in the ```run.sh```. 
-```gpus``` denotes the GPU number you want to use. If you set ```gpus=```, it means you only use CPU. 
+```gpus``` denotes the GPU number you want to use. If you set ```gpus=```,  it means you only use CPU. 
 
-```stage``` denotes the number of stage you want to start from in the expriments.
+```stage``` denotes  the number of stage you want to start from in the expriments.
 ```stop stage```denotes the number of stage you want to end at in the expriments. 
 
 ```conf_path``` denotes the config path of the model.
 
 ```avg_num``` denotes the number K of top-K models you want to average to get the final model.
 
-```ckpt``` denotes the checkpoint prefix of the model, e.g. "conformer"
-
 ```audio file``` denotes the file path of the single file you want to infer in stage 6
 
-You can set the local variables when you use the ```run.sh```
+```ckpt``` denotes the checkpoint prefix of the model, e.g. "conformer"
+
+You can set the local variables (except ```ckpt```)  when you use the ```run.sh```
 
 For example, you can set the ```gpus``` and ``avg_num`` when you use the command line.:
 
@@ -81,7 +83,8 @@ bash run.sh --gpus 0,1 --avg_num 20
 
 ## Stage 0: Data processing
 
-To use this example, you need to process data firstly and you can use stage 0 in the ```run.sh``` to do this. The code is shown below:
+To use this example, you need to process data firstly and  you can use stage 0 in the ```run.sh``` to do this. The code is shown below:
+
 ```bash
  if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then
      # prepare data
@@ -101,7 +104,6 @@ You can also just run these scripts in your command line.
 
 ```bash
 source path.sh
-source ${MAIN_ROOT}/utils/parse_options.sh
 bash ./local/data.sh
 ```
 
@@ -128,47 +130,52 @@ data/
 ## Stage 1: Model training
 
 If you want to train the model. you can use stage 1 in the ```run.sh```. The code is shown below. 
+
 ```bash
 if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
      # train model, all `ckpt` under `exp` dir
-     CUDA_VISIBLE_DEVICES=${gpus} ./local/train.sh ${conf_path} ${ckpt}
+     CUDA_VISIBLE_DEVICES=${gpus} ./local/train.sh ${conf_path}  ${ckpt}
  fi
 ```
 
 If you want to train the model, you can use the script below to execute stage 0 and stage 1:
+
 ```bash
 bash run.sh --stage 0 --stop_stage 1
 ```
+
 or you can run these scripts in the command line (only use CPU).
+
 ```bash
 source path.sh
-source ${MAIN_ROOT}/utils/parse_options.sh
 bash ./local/data.sh
-CUDA_VISIBLE_DEVICES= ./local/train.sh conf/conformer.yaml conformer
+CUDA_VISIBLE_DEVICES= ./local/train.sh conf/conformer.yaml  conformer
 ```
 
 
 
-## Stage 2: Top-k models averaging
+## Stage 2:  Top-k Models Averaging
+
+After training the model,  we need to get the final model for testing and inference. In every epoch, the model checkpoint is saved, so we can choose the best model from them based on the validation loss or we can sort them and average the parameters of the top-k models  to get the final model.  We can use stage 2 to do this, and the code is shown below:
 
-After training the model, we need to get the final model for testing and inference. In every epoch, the model checkpoint is saved, so we can choose the best model from them based on the validation loss or we can sort them and average the parameters of the top-k models to get the final model. We can use stage 2 to do this, and the code is shown below:
 ```bash
  if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
      # avg n best model
      avg.sh best exp/${ckpt}/checkpoints ${avg_num}
  fi
 ```
+
 The ```avg.sh``` is in the ```../../../utils/``` which is define in the ```path.sh```.
-If you want to get the final model, you can use the script below to execute stage 0, stage 1, and stage 2:
+If you want to get the final model,  you can use the script below to execute stage 0, stage 1, and stage 2:
 
 ```bash
 bash run.sh --stage 0 --stop_stage 2
 ```
 
 or you can run these scripts in the command line (only use CPU).
+
 ```bash
 source path.sh
-source ${MAIN_ROOT}/utils/parse_options.sh
 bash ./local/data.sh
 CUDA_VISIBLE_DEVICES= ./local/train.sh conf/conformer.yaml conformer
 avg.sh best exp/conformer/checkpoints 20
@@ -187,7 +194,7 @@ The test stage is to evaluate the model performance.. The code of test stage is
  fi
 ```
 
-If you want to train a model and test it, you can use the script below to execute stage 0, stage 1, stage 2, and stage 3:
+If you want to train a model and test it,  you can use the script below to execute stage 0, stage 1,  stage 2, and stage 3 :
 
 ```bash
 bash run.sh --stage 0 --stop_stage 3
@@ -197,7 +204,6 @@ or you can run these scripts in the command line (only use CPU).
 
 ```bash
 source path.sh
-source ${MAIN_ROOT}/utils/parse_options.sh
 bash ./local/data.sh
 CUDA_VISIBLE_DEVICES= ./local/train.sh conf/conformer.yaml conformer
 avg.sh best exp/conformer/checkpoints 20
@@ -206,7 +212,75 @@ CUDA_VISIBLE_DEVICES= ./local/test.sh conf/conformer.yaml exp/conformer/checkpoi
 
 
 
-## Stage 4: CTC alignment 
+## Pretrained Model
+
+You can get the pretrained transfomer or conformer using the scripts below:
+
+```bash
+Conformer:
+wget https://deepspeech.bj.bcebos.com/release2.1/aishell/s1/aishell.release.tar.gz
+
+Chunk Conformer:
+wget https://deepspeech.bj.bcebos.com/release2.1/aishell/s1/aishell.chunk.release.tar.gz
+
+Transfomer:
+wget https://paddlespeech.bj.bcebos.com/s2t/aishell/asr1/transformer.model.tar.gz
+
+```
+
+using the ```tar``` scripts to unpack the model  and then you can use the script to test the modle.
+
+For example:
+
+```
+wget https://paddlespeech.bj.bcebos.com/s2t/aishell/asr1/transformer.model.tar.gz
+tar xzvf transformer.model.tar.gz
+source path.sh
+# If you have process the data and get the manifest file， you can skip the following 2 steps
+bash local/data.sh --stage -1 --stop_stage -1
+bash local/data.sh --stage 2  --stop_stage 2
+
+CUDA_VISIBLE_DEVICES= ./local/test.sh conf/transformer.yaml exp/transformer/checkpoints/avg_20
+```
+
+
+
+The performance of the released models are shown below:
+
+### Conformer
+
+| Model     | Params | Config              | Augmentation     | Test set | Decode method          | Loss | CER      |
+| --------- | ------ | ------------------- | ---------------- | -------- | ---------------------- | ---- | -------- |
+| conformer | 47.07M | conf/conformer.yaml | spec_aug + shift | test     | attention              | -    | 0.059858 |
+| conformer | 47.07M | conf/conformer.yaml | spec_aug + shift | test     | ctc_greedy_search      | -    | 0.062311 |
+| conformer | 47.07M | conf/conformer.yaml | spec_aug + shift | test     | ctc_prefix_beam_search | -    | 0.062196 |
+| conformer | 47.07M | conf/conformer.yaml | spec_aug + shift | test     | attention_rescoring    | -    | 0.054694 |
+
+
+### Chunk Conformer
+
+Need set `decoding.decoding_chunk_size=16` when decoding.
+
+| Model     | Params | Config                    | Augmentation     | Test set | Decode method          | Chunk Size & Left Chunks | Loss | CER      |
+| --------- | ------ | ------------------------- | ---------------- | -------- | ---------------------- | ------------------------ | ---- | -------- |
+| conformer | 47.06M | conf/chunk_conformer.yaml | spec_aug + shift | test     | attention              | 16, -1                   | -    | 0.061939 |
+| conformer | 47.06M | conf/chunk_conformer.yaml | spec_aug + shift | test     | ctc_greedy_search      | 16, -1                   | -    | 0.070806 |
+| conformer | 47.06M | conf/chunk_conformer.yaml | spec_aug + shift | test     | ctc_prefix_beam_search | 16, -1                   | -    | 0.070739 |
+| conformer | 47.06M | conf/chunk_conformer.yaml | spec_aug + shift | test     | attention_rescoring    | 16, -1                   | -    | 0.059400 |
+
+
+### Transformer 
+
+| Model       | Params | Config                | Augmentation | Test set | Decode method          | Loss              | CER      |
+| ----------- | ------ | --------------------- | ------------ | -------- | ---------------------- | ----------------- | -------- |
+| transformer | 31.95M | conf/transformer.yaml | spec_aug     | test     | attention              | 3.858648955821991 | 0.057293 |
+| transformer | 31.95M | conf/transformer.yaml | spec_aug     | test     | ctc_greedy_search      | 3.858648955821991 | 0.061837 |
+| transformer | 31.95M | conf/transformer.yaml | spec_aug     | test     | ctc_prefix_beam_search | 3.858648955821991 | 0.061685 |
+| transformer | 31.95M | conf/transformer.yaml | spec_aug     | test     | attention_rescoring    | 3.858648955821991 | 0.053844 |
+
+
+
+## Stage 4: CTC Alignment 
 
 If you want to get the alignment between the audio and the text, you can use the ctc alignment. The code of this stage is shown below:
 
@@ -217,7 +291,7 @@ If you want to get the alignment between the audio and the text, you can use the
  fi
 ```
 
-If you want to train the model, test it and do the alignment, you can use the script below to execute stage 0, stage 1, stage 2, and stage 3 :
+If you want to train the model, test it and do the alignment,  you can use the script below to execute stage 0, stage 1, stage 2, and stage 3 :
 
 ```bash
 bash run.sh --stage 0 --stop_stage 4
@@ -234,7 +308,6 @@ or you can also use these scripts in the command line (only use CPU).
 
 ```bash
 source path.sh
-source ${MAIN_ROOT}/utils/parse_options.sh
 bash ./local/data.sh
 CUDA_VISIBLE_DEVICES= ./local/train.sh conf/conformer.yaml conformer
 avg.sh best exp/conformer/checkpoints 20
@@ -245,61 +318,29 @@ CUDA_VISIBLE_DEVICES= ./local/align.sh conf/conformer.yaml exp/conformer/checkpo
 
 
 
-## Stage 5: Single audio file inference
+## Stage 5: Single Audio File Inference
 
-In some situations, you want to use the trained model to do the inference for the single audio file. You can use stage 5. The code is shown below
+In some situations, you want to use the trained model to do the inference for the single audio file. You can use stage  5. The code is shown below
 
 ```bash
  if [ ${stage} -le 5 ] && [ ${stop_stage} -ge 5 ]; then
      # test a single .wav file
-     CUDA_VISIBLE_DEVICES=0 ./local/test_hub.sh ${conf_path} exp/${ckpt}/checkpoints/${avg_ckpt} ${audio_file} || exit -1
+     CUDA_VISIBLE_DEVICES=0 ./local/test_wav.sh ${conf_path} exp/${ckpt}/checkpoints/${avg_ckpt} ${audio_file} || exit -1
  fi
 ```
 
 you can train the model by yourself using ```bash run.sh --stage 0 --stop_stage 3```, or you can download the pretrained model by the script below:
 
-```
-wget https://deepspeech.bj.bcebos.com/release2.1/aishell/s1/aishell.release.tar.gz
-tar aishell.release.tar.gz
-```
-
-You need to prepare an audio file, please confirm the sample rate of the audio is 16K. Assume the path of the audio file is ```data/test_audio.wav```, you can get the result by running the script below.
-
 ```bash
-CUDA_VISIBLE_DEVICES= ./local/test_hub.sh conf/conformer.yaml exp/conformer/checkpoints/avg_20 data/test_audio.wav
+wget https://paddlespeech.bj.bcebos.com/s2t/aishell/asr1/transformer.model.tar.gz
+tar xzvf transformer.model.tar.gz
 ```
 
-
-
-## Stage 51: Static model transforming（not supported at now）
-
-To transform the dynamic model to static model，stage 51 can be used. The code of this stage is shown below:
-
-```bash
- if [ ${stage} -le 5 ] && [ ${stop_stage} -ge 5 ]; then
-     # export ckpt avg_n
-     CUDA_VISIBLE_DEVICES=0 ./local/export.sh ${conf_path} exp/${ckpt}/checkpoints/${avg_ckpt} exp/${ckpt}/checkpoints/${avg_ckpt}.jit
- fi
-```
-
-It is not supported at now, so we set a large stage number for this stage.
-
-
-
-## Stage: 101 Language model training and TLG building (Need further installation! )
-
-You need to install the kaldi and srilm to use stage 101, it is used for training language model and building TLG. To do further installation, you need to do these:
+You need to prepare an audio file, please confirm the sample rate of the audio is 16K. Assume the path of the audio file is ```data/test_audio.wav```,  you can get the result by running the script below.
 
 ```bash
-# go to the root of the repo
-cd ../../../ 
-# Do the further installation
-pip install -e .
-cd tools
-bash extras/install_openblas.sh
-bash extras/install_kaldi.sh
+CUDA_VISIBLE_DEVICES= ./local/test_wav.sh conf/transformer.yaml exp/transformer/checkpoints/avg_20 data/test_audio.wav
 ```
 
-You need to be patient, since installing the kaldi takes some time.
 
 
diff --git a/examples/aishell/asr1/run.sh b/examples/aishell/asr1/run.sh
index c687e966..41af2445 100644
--- a/examples/aishell/asr1/run.sh
+++ b/examples/aishell/asr1/run.sh
@@ -14,7 +14,7 @@ avg_ckpt=avg_${avg_num}
 ckpt=$(basename ${conf_path} | awk -F'.' '{print $1}')
 echo "checkpoint name ${ckpt}"
 
-audio_file="data/tmp.wav"
+audio_file="data/test_single_audio.wav"
 
 if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then
     # prepare data
@@ -44,14 +44,16 @@ fi
 # Optionally, you can add LM and test it with runtime.
 if [ ${stage} -le 5 ] && [ ${stop_stage} -ge 5 ]; then
     # test a single .wav file
-    CUDA_VISIBLE_DEVICES=0 ./local/test_hub.sh ${conf_path} exp/${ckpt}/checkpoints/${avg_ckpt} ${audio_file} || exit -1
+    CUDA_VISIBLE_DEVICES=0 ./local/test_wav.sh ${conf_path} exp/${ckpt}/checkpoints/${avg_ckpt} ${audio_file} || exit -1
 fi
 
-# if [ ${stage} -le 51 ] && [ ${stop_stage} -ge 51 ]; then
-#     # export ckpt avg_n
-#     CUDA_VISIBLE_DEVICES=0 ./local/export.sh ${conf_path} exp/${ckpt}/checkpoints/${avg_ckpt} exp/${ckpt}/checkpoints/${avg_ckpt}.jit
-# fi
+# Not supported at now!!!
+if [ ${stage} -le 51 ] && [ ${stop_stage} -ge 51 ]; then
+     # export ckpt avg_n
+     CUDA_VISIBLE_DEVICES=0 ./local/export.sh ${conf_path} exp/${ckpt}/checkpoints/${avg_ckpt} exp/${ckpt}/checkpoints/${avg_ckpt}.jit
+fi
 
+# Need further installation! Read the install.md to complete further installation
 if [ ${stage} -le 101 ] && [ ${stop_stage} -ge 101 ]; then
     echo "warning: deps on kaldi and srilm, please make sure installed."
     # train lm and build TLG