diff --git a/examples/aishell/asr1/README.md b/examples/aishell/asr1/README.md
index 8c53f95f..da753634 100644
--- a/examples/aishell/asr1/README.md
+++ b/examples/aishell/asr1/README.md
@@ -28,4 +28,4 @@ Need set `decoding.decoding_chunk_size=16` when decoding.
 | 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 |  
\ No newline at end of file
+| transformer | 31.95M  | conf/transformer.yaml | spec_aug | test | attention_rescoring | 3.858648955821991 | 0.053844 |  
diff --git a/examples/aishell3/tts3/README.md b/examples/aishell3/tts3/README.md
index 056f35ba..eb2cca2e 100644
--- a/examples/aishell3/tts3/README.md
+++ b/examples/aishell3/tts3/README.md
@@ -5,7 +5,7 @@ AISHELL-3 is a large-scale and high-fidelity multi-speaker Mandarin speech corpu
 
 We use AISHELL-3 to train a multi-speaker fastspeech2 model here.
 ## Dataset
-### Download and Extract the datasaet
+### Download and Extract
 Download AISHELL-3.
 ```bash
 wget https://www.openslr.org/resources/93/data_aishell3.tgz
@@ -15,7 +15,7 @@ Extract AISHELL-3.
 mkdir data_aishell3
 tar zxvf data_aishell3.tgz -C data_aishell3
 ```
-### Get MFA result of AISHELL-3 and Extract it
+### Get MFA Result and Extract
 We use [MFA2.x](https://github.com/MontrealCorpusTools/Montreal-Forced-Aligner) to get durations for aishell3_fastspeech2.
 You can download from here [aishell3_alignment_tone.tar.gz](https://paddlespeech.bj.bcebos.com/MFA/AISHELL-3/with_tone/aishell3_alignment_tone.tar.gz), or train your own MFA model reference to [use_mfa example](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/examples/other/use_mfa) (use MFA1.x now) of our repo.
 
@@ -32,7 +32,12 @@ Run the command below to
 ```bash
 ./run.sh
 ```
-### Preprocess the dataset
+You can choose a range of stages you want to run, or set `stage` equal to `stop-stage` to use only one stage, for example, run the following command will only preprocess the dataset.
+```bash
+./run.sh --stage 0 --stop-stage 0
+```
+
+### Data Preprocessing
 ```bash
 ./local/preprocess.sh ${conf_path}
 ```
@@ -58,7 +63,7 @@ The dataset is split into 3 parts, namely `train`, `dev` and` test`, each of whi
 
 Also there is a `metadata.jsonl` in each subfolder. It is a table-like file which contains phones, text_lengths, speech_lengths, durations, path of speech features, path of pitch features, path of energy features, speaker and id of each utterance.
 
-### Train the model
+### Model Training
 `./local/train.sh` calls `${BIN_DIR}/train.py`.
 ```bash
 CUDA_VISIBLE_DEVICES=${gpus} ./local/train.sh ${conf_path} ${train_output_path}
@@ -95,7 +100,7 @@ optional arguments:
 5. `--phones-dict` is the path of the phone vocabulary file.
 6. `--speaker-dict`is the path of the  speaker id map file when training a multi-speaker FastSpeech2.
 
-### Synthesize
+### Synthesizing
 We use [parallel wavegan](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/examples/aishell3/voc1) as the neural vocoder.
 Download pretrained parallel wavegan model from [pwg_aishell3_ckpt_0.5.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/pwgan/pwg_aishell3_ckpt_0.5.zip) and unzip it.
 ```bash
diff --git a/examples/aishell3/tts3/run.sh b/examples/aishell3/tts3/run.sh
index 95e4d38f..b375f215 100755
--- a/examples/aishell3/tts3/run.sh
+++ b/examples/aishell3/tts3/run.sh
@@ -11,7 +11,7 @@ conf_path=conf/default.yaml
 train_output_path=exp/default
 ckpt_name=snapshot_iter_482.pdz
 
-# with the following command, you can choice the stage range you want to run
+# with the following command, you can choose the stage range you want to run
 # such as `./run.sh --stage 0 --stop-stage 0`
 # this can not be mixed use with `$1`, `$2` ...
 source ${MAIN_ROOT}/utils/parse_options.sh || exit 1
diff --git a/examples/aishell3/vc0/README.md b/examples/aishell3/vc0/README.md
index 376d4a33..fa5c6694 100644
--- a/examples/aishell3/vc0/README.md
+++ b/examples/aishell3/vc0/README.md
@@ -16,11 +16,15 @@ Run the command below to
 ```bash
 ./run.sh
 ```
-### Preprocess the dataset
+You can choose a range of stages you want to run, or set `stage` equal to `stop-stage` to use only one stage, for example, run the following command will only preprocess the dataset.
+```bash
+./run.sh --stage 0 --stop-stage 0
+```
+### Data Preprocessing
 ```bash
 CUDA_VISIBLE_DEVICES=${gpus} ./local/preprocess.sh ${input} ${preprocess_path} ${alignment} ${ge2e_ckpt_path}
 ```
-#### generate speaker embedding
+#### Generate Speaker Embedding
  Use pretrained GE2E (speaker encoder) to generate speaker embedding for each sentence in AISHELL-3, which has the same file structure with wav files and the format is  `.npy`.
 
 ```bash
@@ -34,7 +38,7 @@ fi
 ```
 
 The computing time of utterance embedding can be x hours.
-#### process wav
+#### Process Wav
 There are silence in the edge of AISHELL-3's wavs, and the audio amplitude is very small, so, we need to remove the silence and normalize the audio. You can the silence remove method based on   volume or energy, but the effect is not very good, We use [MFA](https://github.com/MontrealCorpusTools/Montreal-Forced-Aligner) to get  the alignment of text and  speech, then utilize the alignment results to remove the silence.
 
 We use Montreal Force Aligner 1.0. The label in  aishell3 include pinyin，so the lexicon we provided to MFA is pinyin rather than Chinese characters. And the prosody marks(`$`  and `%`) need to be removed. You shoud preprocess the dataset into the format  which MFA needs, the texts have the same name with wavs and have the suffix `.lab`.
@@ -53,7 +57,7 @@ if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
 fi
 ```
 
-#### preprocess transcription
+#### Preprocess Transcription
 We revert the transcription into `phones` and  `tones`. It is worth noting that our processing here is different from that used for MFA, we separated the tones. This is a processing method, of course, you can only segment initials and vowels.
 
 ```bash
@@ -64,7 +68,7 @@ if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
 fi
 ```
 The default input is  `~/datasets/data_aishell3/train`，which contains `label_train-set.txt`, the processed results are `metadata.yaml` and  `metadata.pickle`. the former is a text format for easy viewing, and the latter is a binary format for direct reading.
-#### extract mel
+#### Extract Mel
 ```python
 if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then
     python3 ${BIN_DIR}/extract_mel.py \
@@ -73,7 +77,7 @@ if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then
 fi
 ```
 
-### Train the model
+### Model Training
 ```bash
 CUDA_VISIBLE_DEVICES=${gpus} ./local/train.sh ${preprocess_path} ${train_output_path}
 ```
diff --git a/examples/aishell3/vc0/run.sh b/examples/aishell3/vc0/run.sh
index 8d3da781..870360c1 100755
--- a/examples/aishell3/vc0/run.sh
+++ b/examples/aishell3/vc0/run.sh
@@ -23,7 +23,7 @@ waveflow_params_path=./waveflow_ljspeech_ckpt_0.3/step-2000000.pdparams
 vc_input=ref_audio
 vc_output=syn_audio
 
-# with the following command, you can choice the stage range you want to run
+# with the following command, you can choose the stage range you want to run
 # such as `./run.sh --stage 0 --stop-stage 0`
 # this can not be mixed use with `$1`, `$2` ...
 source ${MAIN_ROOT}/utils/parse_options.sh || exit 1
diff --git a/examples/aishell3/vc1/README.md b/examples/aishell3/vc1/README.md
index ae53443e..635cde89 100644
--- a/examples/aishell3/vc1/README.md
+++ b/examples/aishell3/vc1/README.md
@@ -5,7 +5,7 @@ This example contains code used to train a [FastSpeech2](https://arxiv.org/abs/2
 3. Vocoder: We use [Parallel Wave GAN](http://arxiv.org/abs/1910.11480) as the neural Vocoder, refer to [voc1](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/examples/aishell3/voc1).
 
 ## Dataset
-### Download and Extract the datasaet
+### Download and Extract
 Download AISHELL-3.
 ```bash
 wget https://www.openslr.org/resources/93/data_aishell3.tgz
@@ -15,11 +15,11 @@ Extract AISHELL-3.
 mkdir data_aishell3
 tar zxvf data_aishell3.tgz -C data_aishell3
 ```
-### Get MFA result of AISHELL-3 and Extract it
+### Get MFA Result and Extract
 We use [MFA2.x](https://github.com/MontrealCorpusTools/Montreal-Forced-Aligner) to get durations for aishell3_fastspeech2.
 You can download from here [aishell3_alignment_tone.tar.gz](https://paddlespeech.bj.bcebos.com/MFA/AISHELL-3/with_tone/aishell3_alignment_tone.tar.gz), or train your own MFA model reference to [use_mfa example](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/examples/other/use_mfa) (use MFA1.x now) of our repo.
 
-## Pretrained GE2E model
+## Pretrained GE2E Model
 We use pretrained GE2E model to generate spwaker embedding for each sentence.
 
 Download pretrained GE2E model from here [ge2e_ckpt_0.3.zip](https://bj.bcebos.com/paddlespeech/Parakeet/released_models/ge2e/ge2e_ckpt_0.3.zip), and `unzip` it.
@@ -38,7 +38,11 @@ Run the command below to
 ```bash
 ./run.sh
 ```
-### Preprocess the dataset
+You can choose a range of stages you want to run, or set `stage` equal to `stop-stage` to use only one stage, for example, run the following command will only preprocess the dataset.
+```bash
+./run.sh --stage 0 --stop-stage 0
+```
+### Data Preprocessing
 ```bash
 CUDA_VISIBLE_DEVICES=${gpus} ./local/preprocess.sh ${conf_path} ${ge2e_ckpt_path}
 ```
@@ -75,14 +79,14 @@ Also there is a `metadata.jsonl` in each subfolder. It is a table-like file whic
 
 The preprocessing step is very similar to that one of [tts3](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/examples/aishell3/tts3), but  there is one more `ge2e/inference` step here.
 
-###  Train the model
+### Model Training
 `./local/train.sh` calls `${BIN_DIR}/train.py`.
 ```bash
 CUDA_VISIBLE_DEVICES=${gpus} ./local/train.sh ${conf_path} ${train_output_path}
 ```
 The training step is very similar to that one of [tts3](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/examples/aishell3/tts3), but  we should set `--voice-cloning=True` when calling `${BIN_DIR}/train.py`.
 
-### Synthesize
+### Synthesizing
 We use [parallel wavegan](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/examples/aishell3/voc1) as the neural vocoder.
 Download pretrained parallel wavegan model from [pwg_aishell3_ckpt_0.5.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/pwgan/pwg_aishell3_ckpt_0.5.zip) and unzip it.
 ```bash
diff --git a/examples/aishell3/vc1/run.sh b/examples/aishell3/vc1/run.sh
index 4eae1bdd..64f4ee3b 100755
--- a/examples/aishell3/vc1/run.sh
+++ b/examples/aishell3/vc1/run.sh
@@ -18,7 +18,7 @@ ge2e_ckpt_path=./ge2e_ckpt_0.3/step-3000000
 # include ".pdparams" here
 ge2e_params_path=${ge2e_ckpt_path}.pdparams
 
-# with the following command, you can choice the stage range you want to run
+# with the following command, you can choose the stage range you want to run
 # such as `./run.sh --stage 0 --stop-stage 0`
 # this can not be mixed use with `$1`, `$2` ...
 source ${MAIN_ROOT}/utils/parse_options.sh || exit 1
diff --git a/examples/aishell3/voc1/README.md b/examples/aishell3/voc1/README.md
index bc28bba1..6ee6d39b 100644
--- a/examples/aishell3/voc1/README.md
+++ b/examples/aishell3/voc1/README.md
@@ -3,7 +3,7 @@ This example contains code used to train a [parallel wavegan](http://arxiv.org/a
 
 AISHELL-3 is a large-scale and high-fidelity multi-speaker Mandarin speech corpus which could be used to train multi-speaker Text-to-Speech (TTS) systems.
 ## Dataset
-### Download and Extract the datasaet
+### Download and Extract
 Download AISHELL-3.
 ```bash
 wget https://www.openslr.org/resources/93/data_aishell3.tgz
@@ -13,7 +13,7 @@ Extract AISHELL-3.
 mkdir data_aishell3
 tar zxvf data_aishell3.tgz -C data_aishell3
 ```
-### Get MFA result of AISHELL-3 and Extract it
+### Get MFA Result and Extract
 We use [MFA2.x](https://github.com/MontrealCorpusTools/Montreal-Forced-Aligner) to get durations for aishell3_fastspeech2.
 You can download from here [aishell3_alignment_tone.tar.gz](https://paddlespeech.bj.bcebos.com/MFA/AISHELL-3/with_tone/aishell3_alignment_tone.tar.gz), or train your own MFA model reference to [use_mfa example](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/examples/other/use_mfa) (use MFA1.x now) of our repo.
 
@@ -29,7 +29,11 @@ Run the command below to
 ```bash
 ./run.sh
 ```
-### Preprocess the dataset
+You can choose a range of stages you want to run, or set `stage` equal to `stop-stage` to use only one stage, for example, run the following command will only preprocess the dataset.
+```bash
+./run.sh --stage 0 --stop-stage 0
+```
+### Data Preprocessing
 ```bash
 ./local/preprocess.sh ${conf_path}
 ```
@@ -53,7 +57,7 @@ The dataset is split into 3 parts, namely `train`, `dev` and `test`, each of whi
 
 Also there is a `metadata.jsonl` in each subfolder. It is a table-like file which contains id and paths to spectrogam of each utterance.
 
-### Train the model
+### Model Training
 ```bash
 CUDA_VISIBLE_DEVICES=${gpus} ./local/train.sh ${conf_path} ${train_output_path}
 ```
@@ -100,7 +104,7 @@ benchmark:
 3. `--output-dir` is the directory to save the results of the experiment. Checkpoints are save in `checkpoints/` inside this directory.
 4. `--ngpu` is the number of gpus to use, if ngpu == 0, use cpu.
 
-### Synthesize
+### Synthesizing
 `./local/synthesize.sh` calls `${BIN_DIR}/synthesize.py`, which can synthesize waveform from `metadata.jsonl`.
 ```bash
 CUDA_VISIBLE_DEVICES=${gpus} ./local/synthesize.sh ${conf_path} ${train_output_path} ${ckpt_name}
diff --git a/examples/aishell3/voc1/run.sh b/examples/aishell3/voc1/run.sh
index 7d0fdb21..4f426ea0 100755
--- a/examples/aishell3/voc1/run.sh
+++ b/examples/aishell3/voc1/run.sh
@@ -11,7 +11,7 @@ conf_path=conf/default.yaml
 train_output_path=exp/default
 ckpt_name=snapshot_iter_5000.pdz
 
-# with the following command, you can choice the stage range you want to run
+# with the following command, you can choose the stage range you want to run
 # such as `./run.sh --stage 0 --stop-stage 0`
 # this can not be mixed use with `$1`, `$2` ...
 source ${MAIN_ROOT}/utils/parse_options.sh || exit 1
diff --git a/examples/csmsc/tts2/README.md b/examples/csmsc/tts2/README.md
index 5ebf3cf4..80e50fe9 100644
--- a/examples/csmsc/tts2/README.md
+++ b/examples/csmsc/tts2/README.md
@@ -2,10 +2,10 @@
 This example contains code used to train a [SpeedySpeech](http://arxiv.org/abs/2008.03802) model with [Chinese Standard Mandarin Speech Copus](https://www.data-baker.com/open_source.html). NOTE that we only implement the student part of the Speedyspeech model. The ground truth alignment used to train the model is extracted from the dataset using [MFA](https://github.com/MontrealCorpusTools/Montreal-Forced-Aligner).
 
 ## Dataset
-### Download and Extract the datasaet
+### Download and Extract
 Download CSMSC from it's [Official Website](https://test.data-baker.com/data/index/source).
 
-### Get MFA result of CSMSC and Extract it
+### Get MFA Result and Extract
 We use [MFA](https://github.com/MontrealCorpusTools/Montreal-Forced-Aligner) to get durations for SPEEDYSPEECH.
 You can download from here [baker_alignment_tone.tar.gz](https://paddlespeech.bj.bcebos.com/MFA/BZNSYP/with_tone/baker_alignment_tone.tar.gz), or train your own MFA model reference to  [use_mfa example](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/examples/other/use_mfa) of our repo.
 
@@ -23,7 +23,11 @@ Run the command below to
 ```bash
 ./run.sh
 ```
-### Preprocess the dataset
+You can choose a range of stages you want to run, or set `stage` equal to `stop-stage` to use only one stage, for example, run the following command will only preprocess the dataset.
+```bash
+./run.sh --stage 0 --stop-stage 0
+```
+### Data Preprocessing
 ```bash
 ./local/preprocess.sh ${conf_path}
 ```
@@ -47,7 +51,7 @@ The dataset is split into 3 parts, namely `train`, `dev` and `test`, each of whi
 
 Also there is a `metadata.jsonl` in each subfolder. It is a table-like file which contains phones, tones, durations, path of spectrogram, and id of each utterance.
 
-### Train the model
+### Model Training
 `./local/train.sh` calls `${BIN_DIR}/train.py`.
 ```bash
 CUDA_VISIBLE_DEVICES=${gpus} ./local/train.sh ${conf_path} ${train_output_path} || exit -1
@@ -88,7 +92,7 @@ optional arguments:
 5. `--phones-dict` is the path of the phone vocabulary file.
 6. `--tones-dict` is the path of the tone vocabulary file.
 
-### Synthesize
+### Synthesizing
 We use [parallel wavegan](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/examples/csmsc/voc1) as the neural vocoder.
 Download pretrained parallel wavegan model from [pwg_baker_ckpt_0.4.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/pwgan/pwg_baker_ckpt_0.4.zip) and unzip it.
 ```bash
@@ -200,7 +204,7 @@ optional arguments:
 7. `--phones-dict` is the path of the phone vocabulary file.
 8. `--tones-dict` is the path of the tone vocabulary file.
 
-### Inference
+### Inferencing
 After Synthesize, we will get static models of speedyspeech and pwgan in `${train_output_path}/inference`.
 `./local/inference.sh` calls `${BIN_DIR}/inference.py`, which provides a paddle static model inference example for speedyspeech + pwgan synthesize.
 ```bash
diff --git a/examples/csmsc/tts2/run.sh b/examples/csmsc/tts2/run.sh
index 200e8192..8b8f53bd 100755
--- a/examples/csmsc/tts2/run.sh
+++ b/examples/csmsc/tts2/run.sh
@@ -11,7 +11,7 @@ conf_path=conf/default.yaml
 train_output_path=exp/default
 ckpt_name=snapshot_iter_76.pdz
 
-# with the following command, you can choice the stage range you want to run
+# with the following command, you can choose the stage range you want to run
 # such as `./run.sh --stage 0 --stop-stage 0`
 # this can not be mixed use with `$1`, `$2` ...
 source ${MAIN_ROOT}/utils/parse_options.sh || exit 1
diff --git a/examples/csmsc/tts3/README.md b/examples/csmsc/tts3/README.md
index 104964c8..c99690e1 100644
--- a/examples/csmsc/tts3/README.md
+++ b/examples/csmsc/tts3/README.md
@@ -2,10 +2,10 @@
 This example contains code used to train a [Fastspeech2](https://arxiv.org/abs/2006.04558) model with [Chinese Standard Mandarin Speech Copus](https://www.data-baker.com/open_source.html).
 
 ## Dataset
-### Download and Extract the datasaet
+### Download and Extract
 Download CSMSC from it's [Official Website](https://test.data-baker.com/data/index/source).
 
-### Get MFA result of CSMSC and Extract it
+### Get MFA Result and Extract
 We use [MFA](https://github.com/MontrealCorpusTools/Montreal-Forced-Aligner) to get durations for fastspeech2.
 You can download from here [baker_alignment_tone.tar.gz](https://paddlespeech.bj.bcebos.com/MFA/BZNSYP/with_tone/baker_alignment_tone.tar.gz), or train your own MFA model reference to  [use_mfa example](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/examples/other/use_mfa) of our repo.
 
@@ -23,7 +23,11 @@ Run the command below to
 ```bash
 ./run.sh
 ```
-### Preprocess the dataset
+You can choose a range of stages you want to run, or set `stage` equal to `stop-stage` to use only one stage, for example, run the following command will only preprocess the dataset.
+```bash
+./run.sh --stage 0 --stop-stage 0
+```
+### Data Preprocessing
 ```bash
 ./local/preprocess.sh ${conf_path}
 ```
@@ -50,7 +54,7 @@ The dataset is split into 3 parts, namely `train`, `dev` and` test`, each of whi
 
 Also there is a `metadata.jsonl` in each subfolder. It is a table-like file which contains phones, text_lengths, speech_lengths, durations, path of speech features, path of pitch features, path of energy features, speaker and id of each utterance.
 
-### Train the model
+### Model Training
 ```bash
 CUDA_VISIBLE_DEVICES=${gpus} ./local/train.sh ${conf_path} ${train_output_path}
 ```
@@ -86,7 +90,7 @@ optional arguments:
 4. `--ngpu` is the number of gpus to use, if ngpu == 0, use cpu.
 5. `--phones-dict` is the path of the phone vocabulary file.
 
-### Synthesize
+### Synthesizing
 We use [parallel wavegan](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/examples/csmsc/voc1) as the neural vocoder.
 Download pretrained parallel wavegan model from [pwg_baker_ckpt_0.4.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/pwgan/pwg_baker_ckpt_0.4.zip) and unzip it.
 ```bash
@@ -191,7 +195,7 @@ optional arguments:
 5. `--output-dir` is the directory to save synthesized audio files.
 6. `--ngpu` is the number of gpus to use, if ngpu == 0, use cpu.
 
-### Inference
+### Inferencing
 After Synthesize, we will get static models of fastspeech2 and pwgan in `${train_output_path}/inference`.
 `./local/inference.sh` calls `${BIN_DIR}/inference.py`, which provides a paddle static model inference example for fastspeech2 + pwgan synthesize.
 ```bash
diff --git a/examples/csmsc/tts3/run.sh b/examples/csmsc/tts3/run.sh
index 718d6076..c1ddd3b9 100755
--- a/examples/csmsc/tts3/run.sh
+++ b/examples/csmsc/tts3/run.sh
@@ -11,7 +11,7 @@ conf_path=conf/default.yaml
 train_output_path=exp/default
 ckpt_name=snapshot_iter_153.pdz
 
-# with the following command, you can choice the stage range you want to run
+# with the following command, you can choose the stage range you want to run
 # such as `./run.sh --stage 0 --stop-stage 0`
 # this can not be mixed use with `$1`, `$2` ...
 source ${MAIN_ROOT}/utils/parse_options.sh || exit 1
diff --git a/examples/csmsc/voc1/README.md b/examples/csmsc/voc1/README.md
index 86114a42..9d516be4 100644
--- a/examples/csmsc/voc1/README.md
+++ b/examples/csmsc/voc1/README.md
@@ -1,11 +1,11 @@
 # Parallel WaveGAN with CSMSC
 This example contains code used to train a [parallel wavegan](http://arxiv.org/abs/1910.11480) model with [Chinese Standard Mandarin Speech Copus](https://www.data-baker.com/open_source.html).
 ## Dataset
-### Download and Extract the datasaet
+### Download and Extract
 Download CSMSC from the [official website](https://www.data-baker.com/data/index/source) and extract it to `~/datasets`. Then the dataset is in directory `~/datasets/BZNSYP`.
 
-### Get MFA results for silence trim
-We use [MFA](https://github.com/MontrealCorpusTools/Montreal-Forced-Aligner) results to  cut silence in the edge of audio.
+### Get MFA Result and Extract
+We use [MFA](https://github.com/MontrealCorpusTools/Montreal-Forced-Aligner) results to cut silence in the edge of audio.
 You can download from here [baker_alignment_tone.tar.gz](https://paddlespeech.bj.bcebos.com/MFA/BZNSYP/with_tone/baker_alignment_tone.tar.gz), or train your own MFA model reference to  [use_mfa example](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/examples/other/use_mfa) of our repo.
 
 ## Get Started
@@ -20,7 +20,11 @@ Run the command below to
 ```bash
 ./run.sh
 ```
-### Preprocess the dataset
+You can choose a range of stages you want to run, or set `stage` equal to `stop-stage` to use only one stage, for example, run the following command will only preprocess the dataset.
+```bash
+./run.sh --stage 0 --stop-stage 0
+```
+### Data Preprocessing
 ```bash
 ./local/preprocess.sh ${conf_path}
 ```
@@ -43,7 +47,7 @@ The dataset is split into 3 parts, namely `train`, `dev` and `test`, each of whi
 
 Also there is a `metadata.jsonl` in each subfolder. It is a table-like file which contains id and paths to spectrogam of each utterance.
 
-### Train the model
+### Model Training
 ```bash
 CUDA_VISIBLE_DEVICES=${gpus} ./local/train.sh ${conf_path} ${train_output_path}
 ```
@@ -90,7 +94,7 @@ benchmark:
 3. `--output-dir` is the directory to save the results of the experiment. Checkpoints are save in `checkpoints/` inside this directory.
 4. `--ngpu` is the number of gpus to use, if ngpu == 0, use cpu.
 
-### Synthesize
+### Synthesizing
 `./local/synthesize.sh` calls `${BIN_DIR}/synthesize.py`, which can synthesize waveform from `metadata.jsonl`.
 ```bash
 CUDA_VISIBLE_DEVICES=${gpus} ./local/synthesize.sh ${conf_path} ${train_output_path} ${ckpt_name}
diff --git a/examples/csmsc/voc1/run.sh b/examples/csmsc/voc1/run.sh
index 16309543..cab1ac38 100755
--- a/examples/csmsc/voc1/run.sh
+++ b/examples/csmsc/voc1/run.sh
@@ -11,7 +11,7 @@ conf_path=conf/default.yaml
 train_output_path=exp/default
 ckpt_name=snapshot_iter_5000.pdz
 
-# with the following command, you can choice the stage range you want to run
+# with the following command, you can choose the stage range you want to run
 # such as `./run.sh --stage 0 --stop-stage 0`
 # this can not be mixed use with `$1`, `$2` ...
 source ${MAIN_ROOT}/utils/parse_options.sh || exit 1
diff --git a/examples/csmsc/voc3/README.md b/examples/csmsc/voc3/README.md
index 4925b649..0a64d1a1 100644
--- a/examples/csmsc/voc3/README.md
+++ b/examples/csmsc/voc3/README.md
@@ -1,11 +1,11 @@
 # Multi Band MelGAN with CSMSC
 This example contains code used to train a [Multi Band MelGAN](https://arxiv.org/abs/2005.05106) model with [Chinese Standard Mandarin Speech Copus](https://www.data-baker.com/open_source.html).
 ## Dataset
-### Download and Extract the datasaet
+### Download and Extract
 Download CSMSC from the [official website](https://www.data-baker.com/data/index/source) and extract it to `~/datasets`. Then the dataset is in directory `~/datasets/BZNSYP`.
 
-### Get MFA results for silence trim
-We use [MFA](https://github.com/MontrealCorpusTools/Montreal-Forced-Aligner) results to  cut silence in the edge of audio.
+### Get MFA Result and Extract
+We use [MFA](https://github.com/MontrealCorpusTools/Montreal-Forced-Aligner) results to cut silence in the edge of audio.
 You can download from here [baker_alignment_tone.tar.gz](https://paddlespeech.bj.bcebos.com/MFA/BZNSYP/with_tone/baker_alignment_tone.tar.gz), or train your own MFA model reference to [use_mfa example](https://github.com/PaddlePaddle/Parakeet/tree/develop/examples/use_mfa) of our repo.
 
 ## Get Started
@@ -20,7 +20,11 @@ Run the command below to
 ```bash
 ./run.sh
 ```
-### Preprocess the dataset
+You can choose a range of stages you want to run, or set `stage` equal to `stop-stage` to use only one stage, for example, run the following command will only preprocess the dataset.
+```bash
+./run.sh --stage 0 --stop-stage 0
+```
+### Data Preprocessing
 ```bash
 ./local/preprocess.sh ${conf_path}
 ```
@@ -43,7 +47,7 @@ The dataset is split into 3 parts, namely `train`, `dev` and `test`, each of whi
 
 Also there is a `metadata.jsonl` in each subfolder. It is a table-like file which contains id and paths to spectrogam of each utterance.
 
-### Train the model
+### Model Training
 ```bash
 CUDA_VISIBLE_DEVICES=${gpus} ./local/train.sh ${conf_path} ${train_output_path}
 ```
@@ -75,7 +79,7 @@ optional arguments:
 3. `--output-dir` is the directory to save the results of the experiment. Checkpoints are save in `checkpoints/` inside this directory.
 4. `--ngpu` is the number of gpus to use, if ngpu == 0, use cpu.
 
-### Synthesize
+### Synthesizing
 `./local/synthesize.sh` calls `${BIN_DIR}/synthesize.py`, which can synthesize waveform from `metadata.jsonl`.
 ```bash
 CUDA_VISIBLE_DEVICES=${gpus} ./local/synthesize.sh ${conf_path} ${train_output_path} ${ckpt_name}
@@ -106,7 +110,7 @@ optional arguments:
 4. `--output-dir` is the directory to save the synthesized audio files.
 5. `--ngpu` is the number of gpus to use, if ngpu == 0, use cpu.
 
-## Finetune
+## Fine-tuning
 Since there are no `noise` in the input of Multi Band MelGAN, the  audio quality is not so good (see [espnet issue](https://github.com/espnet/espnet/issues/3536#issuecomment-916035415)), we refer to the method proposed in [HiFiGAN](https://arxiv.org/abs/2010.05646),  finetune Multi Band MelGAN with the predicted mel-spectrogram from `FastSpeech2`.
 
 The length of mel-spectrograms should align with the length of wavs, so we should generate mels using ground truth alignment.
@@ -144,7 +148,7 @@ Run the command below
 By default, `finetune.sh` will use `conf/finetune.yaml` as config, the dump-dir is `dump_finetune`, the experiment dir is `exp/finetune`.
 
 TODO: 
-The hyperparameter of  `finetune.yaml` is not good enough, a smaller `learning_rate` should be used (more `milestones` should be set).
+The hyperparameter of `finetune.yaml` is not good enough, a smaller `learning_rate` should be used (more `milestones` should be set).
 
 ## Pretrained Models
 Pretrained model can be downloaded here [mb_melgan_baker_ckpt_0.5.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/mb_melgan/mb_melgan_baker_ckpt_0.5.zip).
diff --git a/examples/csmsc/voc3/run.sh b/examples/csmsc/voc3/run.sh
index 360f6ec2..3e7d7e2a 100755
--- a/examples/csmsc/voc3/run.sh
+++ b/examples/csmsc/voc3/run.sh
@@ -11,7 +11,7 @@ conf_path=conf/default.yaml
 train_output_path=exp/default
 ckpt_name=snapshot_iter_50000.pdz
 
-# with the following command, you can choice the stage range you want to run
+# with the following command, you can choose the stage range you want to run
 # such as `./run.sh --stage 0 --stop-stage 0`
 # this can not be mixed use with `$1`, `$2` ...
 source ${MAIN_ROOT}/utils/parse_options.sh || exit 1
diff --git a/examples/ljspeech/tts0/README.md b/examples/ljspeech/tts0/README.md
index 305add20..d49d6d6b 100644
--- a/examples/ljspeech/tts0/README.md
+++ b/examples/ljspeech/tts0/README.md
@@ -1,4 +1,4 @@
-# Tacotron2  with LJSpeech
+# Tacotron2 with LJSpeech
 PaddlePaddle dynamic graph implementation of Tacotron2, a neural network architecture for speech synthesis directly from text. The implementation is based on [Natural TTS Synthesis by Conditioning WaveNet on Mel Spectrogram Predictions](https://arxiv.org/abs/1712.05884).
 
 ## Dataset
@@ -18,11 +18,15 @@ Run the command below to
 ```bash
 ./run.sh
 ```
-### Preprocess the dataset
+You can choose a range of stages you want to run, or set `stage` equal to `stop-stage` to use only one stage, for example, run the following command will only preprocess the dataset.
+```bash
+./run.sh --stage 0 --stop-stage 0
+```
+### Data Preprocessing
 ```bash
 ./local/preprocess.sh ${conf_path}
 ```
-### Train the model
+### Model Training
 `./local/train.sh` calls `${BIN_DIR}/train.py`.
 ```bash
 CUDA_VISIBLE_DEVICES=${gpus} ./local/train.sh ${conf_path} ${train_output_path}
@@ -51,7 +55,7 @@ By default, training will be resumed from the latest checkpoint in `--output`, i
 And if you want to resume from an other existing model, you should set `checkpoint_path` to be the checkpoint path you want to load.
 **Note: The checkpoint path cannot contain the file extension.**
 
-### Synthesize
+### Synthesizing
 `./local/synthesize.sh` calls `${BIN_DIR}/synthesize.py`,  which synthesize **mels**  from text_list here.
 ```bash
 CUDA_VISIBLE_DEVICES=${gpus} ./local/synthesize.sh ${train_output_path} ${ckpt_name}
diff --git a/examples/ljspeech/tts0/run.sh b/examples/ljspeech/tts0/run.sh
index 1da80c96..47c76c3d 100755
--- a/examples/ljspeech/tts0/run.sh
+++ b/examples/ljspeech/tts0/run.sh
@@ -11,7 +11,7 @@ preprocess_path=preprocessed_ljspeech
 train_output_path=output
 ckpt_name=step-35000
 
-# with the following command, you can choice the stage range you want to run
+# with the following command, you can choose the stage range you want to run
 # such as `./run.sh --stage 0 --stop-stage 0`
 # this can not be mixed use with `$1`, `$2` ...
 source ${MAIN_ROOT}/utils/parse_options.sh || exit 1
diff --git a/examples/ljspeech/tts1/README.md b/examples/ljspeech/tts1/README.md
index 8a43ecd9..c2d0c59e 100644
--- a/examples/ljspeech/tts1/README.md
+++ b/examples/ljspeech/tts1/README.md
@@ -1,11 +1,9 @@
 # TransformerTTS with LJSpeech
 ## Dataset
-### Download the datasaet
+We experiment with the LJSpeech dataset. Download and unzip [LJSpeech](https://keithito.com/LJ-Speech-Dataset/).
+
 ```bash
 wget https://data.keithito.com/data/speech/LJSpeech-1.1.tar.bz2
-```
-### Extract the dataset
-```bash
 tar xjvf LJSpeech-1.1.tar.bz2
 ```
 ## Get Started
@@ -20,7 +18,11 @@ Run the command below to
 ```bash
 ./run.sh
 ```
-### Preprocess the dataset
+You can choose a range of stages you want to run, or set `stage` equal to `stop-stage` to use only one stage, for example, run the following command will only preprocess the dataset.
+```bash
+./run.sh --stage 0 --stop-stage 0
+```
+### Data Preprocessing
 ```bash
 ./local/preprocess.sh ${conf_path}
 ```
@@ -44,7 +46,7 @@ The dataset is split into 3 parts, namely `train`, `dev` and` test`, each of whi
 
 Also there is a `metadata.jsonl` in each subfolder. It is a table-like file which contains phones, text_lengths, speech_lengths, path of speech features, speaker and id of each utterance.
 
-### Train the model
+### Model Training
 `./local/train.sh` calls `${BIN_DIR}/train.py`.
 ```bash
 CUDA_VISIBLE_DEVICES=${gpus} ./local/train.sh ${conf_path} ${train_output_path}
@@ -77,7 +79,7 @@ optional arguments:
 4. `--ngpu` is the number of gpus to use, if ngpu == 0, use cpu.
 5. `--phones-dict` is the path of the phone vocabulary file.
 
-## Synthesize
+## Synthesizing
 We use [waveflow](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/examples/ljspeech/voc0) as the neural vocoder.
 Download Pretrained WaveFlow Model with residual channel equals 128 from [waveflow_ljspeech_ckpt_0.3.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/waveflow/waveflow_ljspeech_ckpt_0.3.zip) and unzip it.
 ```bash
diff --git a/examples/ljspeech/tts1/run.sh b/examples/ljspeech/tts1/run.sh
index 6e7a6060..48c4c915 100755
--- a/examples/ljspeech/tts1/run.sh
+++ b/examples/ljspeech/tts1/run.sh
@@ -11,7 +11,7 @@ conf_path=conf/default.yaml
 train_output_path=exp/default
 ckpt_name=snapshot_iter_403.pdz
 
-# with the following command, you can choice the stage range you want to run
+# with the following command, you can choose the stage range you want to run
 # such as `./run.sh --stage 0 --stop-stage 0`
 # this can not be mixed use with `$1`, `$2` ...
 source ${MAIN_ROOT}/utils/parse_options.sh || exit 1
diff --git a/examples/ljspeech/tts3/README.md b/examples/ljspeech/tts3/README.md
index 5bdaf4b8..bb5c7a69 100644
--- a/examples/ljspeech/tts3/README.md
+++ b/examples/ljspeech/tts3/README.md
@@ -2,10 +2,10 @@
 This example contains code used to train a [Fastspeech2](https://arxiv.org/abs/2006.04558) model with [LJSpeech-1.1](https://keithito.com/LJ-Speech-Dataset/).
 
 ## Dataset
-### Download and Extract the datasaet
+### Download and Extract
 Download LJSpeech-1.1 from the [official website](https://keithito.com/LJ-Speech-Dataset/).
 
-### Get MFA result of LJSpeech-1.1 and Extract it
+### Get MFA Result and Extract
 We use [MFA](https://github.com/MontrealCorpusTools/Montreal-Forced-Aligner) to get durations for fastspeech2.
 You can download from here [ljspeech_alignment.tar.gz](https://paddlespeech.bj.bcebos.com/MFA/LJSpeech-1.1/ljspeech_alignment.tar.gz), or train your own MFA model reference to [use_mfa example](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/examples/other/use_mfa) of our repo.
 
@@ -22,7 +22,11 @@ Run the command below to
 ```bash
 ./run.sh
 ```
-### Preprocess the dataset
+You can choose a range of stages you want to run, or set `stage` equal to `stop-stage` to use only one stage, for example, run the following command will only preprocess the dataset.
+```bash
+./run.sh --stage 0 --stop-stage 0
+```
+### Data Preprocessing
 ```bash
 ./local/preprocess.sh ${conf_path}
 ```
@@ -49,7 +53,7 @@ The dataset is split into 3 parts, namely `train`, `dev` and` test`, each of whi
 
 Also there is a `metadata.jsonl` in each subfolder. It is a table-like file which contains phones, text_lengths, speech_lengths, durations, path of speech features, path of pitch features, path of energy features, speaker and id of each utterance.
 
-### Train the model
+### Model Training
 `./local/train.sh` calls `${BIN_DIR}/train.py`.
 ```bash
 CUDA_VISIBLE_DEVICES=${gpus} ./local/train.sh ${conf_path} ${train_output_path}
@@ -85,7 +89,7 @@ optional arguments:
 4. `--ngpu` is the number of gpus to use, if ngpu == 0, use cpu.
 5. `--phones-dict` is the path of the phone vocabulary file.
 
-### Synthesize
+### Synthesizing
 We use [parallel wavegan](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/examples/ljspeech/voc1) as the neural vocoder.
 Download pretrained parallel wavegan model from [pwg_ljspeech_ckpt_0.5.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/pwgan/pwg_ljspeech_ckpt_0.5.zip) and unzip it.
 ```bash
diff --git a/examples/ljspeech/tts3/run.sh b/examples/ljspeech/tts3/run.sh
index 143debd2..c64fa888 100755
--- a/examples/ljspeech/tts3/run.sh
+++ b/examples/ljspeech/tts3/run.sh
@@ -11,7 +11,7 @@ conf_path=conf/default.yaml
 train_output_path=exp/default
 ckpt_name=snapshot_iter_201.pdz
 
-# with the following command, you can choice the stage range you want to run
+# with the following command, you can choose the stage range you want to run
 # such as `./run.sh --stage 0 --stop-stage 0`
 # this can not be mixed use with `$1`, `$2` ...
 source ${MAIN_ROOT}/utils/parse_options.sh || exit 1
diff --git a/examples/ljspeech/voc0/README.md b/examples/ljspeech/voc0/README.md
index 0d4e6c51..725eb617 100644
--- a/examples/ljspeech/voc0/README.md
+++ b/examples/ljspeech/voc0/README.md
@@ -1,11 +1,9 @@
 # WaveFlow with LJSpeech
 ## Dataset
-### Download the datasaet.
+We experiment with the LJSpeech dataset. Download and unzip [LJSpeech](https://keithito.com/LJ-Speech-Dataset/).
+
 ```bash
 wget https://data.keithito.com/data/speech/LJSpeech-1.1.tar.bz2
-```
-### Extract the dataset.
-```bash
 tar xjvf LJSpeech-1.1.tar.bz2
 ```
 ## Get Started
@@ -19,11 +17,15 @@ Run the command below to
 ```bash
 ./run.sh
 ```
-### Preprocess the dataset.
+You can choose a range of stages you want to run, or set `stage` equal to `stop-stage` to use only one stage, for example, run the following command will only preprocess the dataset.
+```bash
+./run.sh --stage 0 --stop-stage 0
+```
+### Data Preprocessing
 ```bash
 ./local/preprocess.sh ${preprocess_path}
 ```
-### Train the model
+### Model Training
 `./local/train.sh` calls `${BIN_DIR}/train.py`.
 ```bash
 CUDA_VISIBLE_DEVICES=${gpus} ./local/train.sh ${preprocess_path} ${train_output_path}
@@ -35,7 +37,7 @@ The training script requires 4 command line arguments.
 
 If you want distributed training, set a larger `--ngpu` (e.g. 4). Note that distributed training with cpu is not supported yet.
 
-### Synthesize
+### Synthesizing
 `./local/synthesize.sh` calls `${BIN_DIR}/synthesize.py`, which can synthesize waveform from mels.
 ```bash
 CUDA_VISIBLE_DEVICES=${gpus} ./local/synthesize.sh ${input_mel_path} ${train_output_path} ${ckpt_name}
diff --git a/examples/ljspeech/voc0/run.sh b/examples/ljspeech/voc0/run.sh
index a4f1ac38..ddd82cb4 100755
--- a/examples/ljspeech/voc0/run.sh
+++ b/examples/ljspeech/voc0/run.sh
@@ -13,7 +13,7 @@ train_output_path=output
 input_mel_path=../tts0/output/test
 ckpt_name=step-10000
 
-# with the following command, you can choice the stage range you want to run
+# with the following command, you can choose the stage range you want to run
 # such as `./run.sh --stage 0 --stop-stage 0`
 # this can not be mixed use with `$1`, `$2` ...
 source ${MAIN_ROOT}/utils/parse_options.sh || exit 1
diff --git a/examples/ljspeech/voc1/README.md b/examples/ljspeech/voc1/README.md
index 24f6dbca..7cb69b15 100644
--- a/examples/ljspeech/voc1/README.md
+++ b/examples/ljspeech/voc1/README.md
@@ -1,10 +1,10 @@
 # Parallel WaveGAN with the LJSpeech-1.1
 This example contains code used to train a [parallel wavegan](http://arxiv.org/abs/1910.11480) model with [LJSpeech-1.1](https://keithito.com/LJ-Speech-Dataset/).
 ## Dataset
-### Download and Extract the datasaet
+### Download and Extract
 Download LJSpeech-1.1 from the [official website](https://keithito.com/LJ-Speech-Dataset/).
-### Get MFA results for silence trim
-We use [MFA](https://github.com/MontrealCorpusTools/Montreal-Forced-Aligner) results to  cut silence in the edge of audio.
+### Get MFA Result and Extract
+We use [MFA](https://github.com/MontrealCorpusTools/Montreal-Forced-Aligner) results to cut silence in the edge of audio.
 You can download from here [ljspeech_alignment.tar.gz](https://paddlespeech.bj.bcebos.com/MFA/LJSpeech-1.1/ljspeech_alignment.tar.gz), or train your own MFA model reference to [use_mfa example](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/examples/other/use_mfa) of our repo.
 
 ## Get Started
@@ -19,8 +19,11 @@ Run the command below to
 ```bash
 ./run.sh
 ```
-
-### Preprocess the dataset
+You can choose a range of stages you want to run, or set `stage` equal to `stop-stage` to use only one stage, for example, run the following command will only preprocess the dataset.
+```bash
+./run.sh --stage 0 --stop-stage 0
+```
+### Data Preprocessing
 ```bash
 ./local/preprocess.sh ${conf_path}
 ```
@@ -44,7 +47,7 @@ The dataset is split into 3 parts, namely `train`, `dev` and `test`, each of whi
 
 Also there is a `metadata.jsonl` in each subfolder. It is a table-like file which contains id and paths to spectrogam of each utterance.
 
-### Train the model
+### Model Training
 `./local/train.sh` calls `${BIN_DIR}/train.py`.
 ```bash
 CUDA_VISIBLE_DEVICES=${gpus} ./local/train.sh ${conf_path} ${train_output_path}
@@ -91,7 +94,7 @@ benchmark:
 3. `--output-dir` is the directory to save the results of the experiment. Checkpoints are save in `checkpoints/` inside this directory.
 4. `--ngpu` is the number of gpus to use, if ngpu == 0, use cpu.
 
-### Synthesize
+### Synthesizing
 `./local/synthesize.sh` calls `${BIN_DIR}/synthesize.py`, which can synthesize waveform from `metadata.jsonl`.
 ```bash
 CUDA_VISIBLE_DEVICES=${gpus} ./local/synthesize.sh ${conf_path} ${train_output_path} ${ckpt_name}
@@ -122,7 +125,7 @@ optional arguments:
 4. `--output-dir` is the directory to save the synthesized audio files.
 5. `--ngpu` is the number of gpus to use, if ngpu == 0, use cpu.
 
-## Pretrained Models
+## Pretrained Model
 Pretrained models can be downloaded here. [pwg_ljspeech_ckpt_0.5.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/pwgan/pwg_ljspeech_ckpt_0.5.zip)
 
 Parallel WaveGAN checkpoint contains files listed below.
diff --git a/examples/ljspeech/voc1/run.sh b/examples/ljspeech/voc1/run.sh
index 16309543..cab1ac38 100755
--- a/examples/ljspeech/voc1/run.sh
+++ b/examples/ljspeech/voc1/run.sh
@@ -11,7 +11,7 @@ conf_path=conf/default.yaml
 train_output_path=exp/default
 ckpt_name=snapshot_iter_5000.pdz
 
-# with the following command, you can choice the stage range you want to run
+# with the following command, you can choose the stage range you want to run
 # such as `./run.sh --stage 0 --stop-stage 0`
 # this can not be mixed use with `$1`, `$2` ...
 source ${MAIN_ROOT}/utils/parse_options.sh || exit 1
diff --git a/examples/other/ge2e/README.md b/examples/other/ge2e/README.md
index d58ca513..2b3f91b5 100644
--- a/examples/other/ge2e/README.md
+++ b/examples/other/ge2e/README.md
@@ -24,8 +24,11 @@ If you want to use other datasets, you can also download and preprocess it as lo
 ```bash
 ./run.sh
 ```
-
-### Preprocess Datasets
+You can choose a range of stages you want to run, or set `stage` equal to `stop-stage` to use only one stage, for example, run the following command will only preprocess the dataset.
+```bash
+./run.sh --stage 0 --stop-stage 0
+```
+### Data Preprocessing
 `./local/preprocess.sh` calls `${BIN_DIR}/preprocess.py`.
 ```bash
 ./local/preprocess.sh ${datasets_root} ${preprocess_path} ${dataset_names}
@@ -62,7 +65,7 @@ In `${BIN_DIR}/preprocess.py`:
 2.  `--output_dir` is the directory to save the preprocessed dataset
 3.  `--dataset_names` is the dataset to preprocess. If there are multiple datasets in `--datasets_root` to preprocess, the names can be joined with comma. Currently supported dataset names are  librispeech_other, voxceleb1, voxceleb2, aidatatang_200zh and magicdata.
 
-### Train the model
+### Model Training
 `./local/train.sh` calls `${BIN_DIR}/train.py`.
 ```bash
 CUDA_VISIBLE_DEVICES=${gpus} ./local/train.sh ${preprocess_path} ${train_output_path}
@@ -79,7 +82,7 @@ Other options are described below.
 - `--opts` is command line options to further override config files. It should be the last comman line options passed with multiple key-value pairs separated by spaces.
 - `--checkpoint_path` specifies the checkpoiont to load before training, extension is not included. A parameter file ( `.pdparams`) and an optimizer state file ( `.pdopt`) with the same name is used. This option has a higher priority than auto-resuming from the `--output` directory.
 
-###  Inference
+###  Inferencing
 When training is done, run the command below to generate utterance embedding for each utterance in a dataset.
 `./local/inference.sh` calls `${BIN_DIR}/inference.py`.
 ```bash
diff --git a/examples/other/ge2e/run.sh b/examples/other/ge2e/run.sh
index d7954bd2..e69b34a7 100755
--- a/examples/other/ge2e/run.sh
+++ b/examples/other/ge2e/run.sh
@@ -15,7 +15,7 @@ infer_input=infer_input
 infer_output=infer_output
 ckpt_name=step-10000
 
-# with the following command, you can choice the stage range you want to run
+# with the following command, you can choose the stage range you want to run
 # such as `./run.sh --stage 0 --stop-stage 0`
 # this can not be mixed use with `$1`, `$2` ...
 source ${MAIN_ROOT}/utils/parse_options.sh || exit 1
diff --git a/examples/vctk/tts3/README.md b/examples/vctk/tts3/README.md
index 894d6b14..aab00573 100644
--- a/examples/vctk/tts3/README.md
+++ b/examples/vctk/tts3/README.md
@@ -5,7 +5,7 @@ This example contains code used to train a [Fastspeech2](https://arxiv.org/abs/2
 ### Download and Extract the datasaet
 Download VCTK-0.92 from the [official website](https://datashare.ed.ac.uk/handle/10283/3443).
 
-### Get MFA result of VCTK and Extract it
+### Get MFA Result and Extract
 We use [MFA](https://github.com/MontrealCorpusTools/Montreal-Forced-Aligner) to get durations for fastspeech2.
 You can download from here [vctk_alignment.tar.gz](https://paddlespeech.bj.bcebos.com/MFA/VCTK-Corpus-0.92/vctk_alignment.tar.gz), or train your own MFA model reference to [use_mfa example](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/examples/other/use_mfa) of our repo.
 ps: we remove three speakers in VCTK-0.92 (see [reorganize_vctk.py](https://github.com/PaddlePaddle/PaddleSpeech/blob/develop/examples/other/use_mfa/local/reorganize_vctk.py)):
@@ -25,7 +25,11 @@ Run the command below to
 ```bash
 ./run.sh
 ```
-### Preprocess the dataset
+You can choose a range of stages you want to run, or set `stage` equal to `stop-stage` to use only one stage, for example, run the following command will only preprocess the dataset.
+```bash
+./run.sh --stage 0 --stop-stage 0
+```
+### Data Preprocessing
 ```bash
 ./local/preprocess.sh ${conf_path}
 ```
@@ -52,7 +56,7 @@ The dataset is split into 3 parts, namely `train`, `dev` and` test`, each of whi
 
 Also there is a `metadata.jsonl` in each subfolder. It is a table-like file which contains phones, text_lengths, speech_lengths, durations, path of speech features, path of pitch features, path of energy features, speaker and id of each utterance.
 
-### Train the model
+### Model Training
 ```bash
 CUDA_VISIBLE_DEVICES=${gpus} ./local/train.sh ${conf_path} ${train_output_path}
 ```
@@ -87,7 +91,7 @@ optional arguments:
 3. `--output-dir` is the directory to save the results of the experiment. Checkpoints are save in `checkpoints/` inside this directory.
 4. `--phones-dict` is the path of the phone vocabulary file.
 
-### Synthesize
+### Synthesizing
 We use [parallel wavegan](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/examples/vctk/voc1) as the neural vocoder.
 
 Download pretrained parallel wavegan model from [pwg_vctk_ckpt_0.5.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/pwgan/pwg_vctk_ckpt_0.5.zip)and unzip it.
diff --git a/examples/vctk/tts3/run.sh b/examples/vctk/tts3/run.sh
index 0562ef3f..a2b849bc 100755
--- a/examples/vctk/tts3/run.sh
+++ b/examples/vctk/tts3/run.sh
@@ -11,7 +11,7 @@ conf_path=conf/default.yaml
 train_output_path=exp/default
 ckpt_name=snapshot_iter_331.pdz
 
-# with the following command, you can choice the stage range you want to run
+# with the following command, you can choose the stage range you want to run
 # such as `./run.sh --stage 0 --stop-stage 0`
 # this can not be mixed use with `$1`, `$2` ...
 source ${MAIN_ROOT}/utils/parse_options.sh || exit 1
diff --git a/examples/vctk/voc1/README.md b/examples/vctk/voc1/README.md
index 8692f010..154fd7cd 100644
--- a/examples/vctk/voc1/README.md
+++ b/examples/vctk/voc1/README.md
@@ -2,11 +2,11 @@
 This example contains code used to train a [parallel wavegan](http://arxiv.org/abs/1910.11480) model with [VCTK](https://datashare.ed.ac.uk/handle/10283/3443).
 
 ## Dataset
-### Download and Extract the datasaet
+### Download and Extract
 Download VCTK-0.92  from the [official website](https://datashare.ed.ac.uk/handle/10283/3443) and extract it to `~/datasets`. Then the dataset is in directory `~/datasets/VCTK-Corpus-0.92`.
 
-### Get MFA results for silence trim
-We use [MFA](https://github.com/MontrealCorpusTools/Montreal-Forced-Aligner) results to  cut silence in the edge of audio.
+### Get MFA Result and Extract
+We use [MFA](https://github.com/MontrealCorpusTools/Montreal-Forced-Aligner) results to cut silence in the edge of audio.
 You can download from here [vctk_alignment.tar.gz](https://paddlespeech.bj.bcebos.com/MFA/VCTK-Corpus-0.92/vctk_alignment.tar.gz), or train your own MFA model reference to [use_mfa example](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/examples/other/use_mfa) of our repo.
 ps: we remove three speakers in VCTK-0.92 (see [reorganize_vctk.py](https://github.com/PaddlePaddle/PaddleSpeech/blob/develop/examples/other/use_mfa/local/reorganize_vctk.py)):
 1. `p315`, because no txt for it.
@@ -24,7 +24,11 @@ Run the command below to
 ```bash
 ./run.sh
 ```
-### Preprocess the dataset
+You can choose a range of stages you want to run, or set `stage` equal to `stop-stage` to use only one stage, for example, run the following command will only preprocess the dataset.
+```bash
+./run.sh --stage 0 --stop-stage 0
+```
+### Data Preprocessing
 ```bash
 ./local/preprocess.sh ${conf_path}
 ```
@@ -48,7 +52,7 @@ The dataset is split into 3 parts, namely `train`, `dev` and `test`, each of whi
 
 Also there is a `metadata.jsonl` in each subfolder. It is a table-like file which contains id and paths to spectrogam of each utterance.
 
-### Train the model
+### Model Training
 ```bash
 CUDA_VISIBLE_DEVICES=${gpus} ./local/train.sh ${conf_path} ${train_output_path}
 ```
@@ -95,7 +99,7 @@ benchmark:
 3. `--output-dir` is the directory to save the results of the experiment. Checkpoints are save in `checkpoints/` inside this directory.
 4. `--ngpu` is the number of gpus to use, if ngpu == 0, use cpu.
 
-### Synthesize
+### Synthesizing
 `./local/synthesize.sh` calls `${BIN_DIR}/synthesize.py`, which can synthesize waveform from `metadata.jsonl`.
 ```bash
 CUDA_VISIBLE_DEVICES=${gpus} ./local/synthesize.sh ${conf_path} ${train_output_path} ${ckpt_name}
@@ -126,7 +130,7 @@ optional arguments:
 4. `--output-dir` is the directory to save the synthesized audio files.
 5. `--ngpu` is the number of gpus to use, if ngpu == 0, use cpu.
 
-## Pretrained Models
+## Pretrained Model
 Pretrained models can be downloaded here [pwg_vctk_ckpt_0.5.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/pwgan/pwg_vctk_ckpt_0.5.zip).
 
 Parallel WaveGAN checkpoint contains files listed below.
diff --git a/examples/vctk/voc1/run.sh b/examples/vctk/voc1/run.sh
index 7d0fdb21..4f426ea0 100755
--- a/examples/vctk/voc1/run.sh
+++ b/examples/vctk/voc1/run.sh
@@ -11,7 +11,7 @@ conf_path=conf/default.yaml
 train_output_path=exp/default
 ckpt_name=snapshot_iter_5000.pdz
 
-# with the following command, you can choice the stage range you want to run
+# with the following command, you can choose the stage range you want to run
 # such as `./run.sh --stage 0 --stop-stage 0`
 # this can not be mixed use with `$1`, `$2` ...
 source ${MAIN_ROOT}/utils/parse_options.sh || exit 1