fix img link; rsl format;

docs/source/asr/deepspeech_architecture.md

@@ -14,10 +14,11 @@ In addition, the training process and the testing process are also introduced.
 The arcitecture of the model is shown in Fig.1.
 
 <p align="center">
-<img src="../images/ds2onlineModel.png" width=800>
+<img src="../../images/ds2onlineModel.png" width=800>
 <br/>Fig.1 The Arcitecture of deepspeech2 online model
 </p>
 
+
 ### Data Preparation
 #### Vocabulary
 For English data, the vocabulary dictionary is composed of 26 English characters with " ' ", space, \<blank\> and \<eos\>. The \<blank\> represents the blank label in CTC, the \<unk\> represents the unknown character and the \<eos\> represents the start and the end characters. For mandarin, the vocabulary dictionary is composed of chinese characters statisticed from the training set and three additional characters are added. The added characters are \<blank\>, \<unk\> and \<eos\>.  For both English and mandarin data, we set the default indexs that \<blank\>=0, \<unk\>=1 and \<eos\>= last index.
@@ -130,7 +131,7 @@ By using the command above, the training process can be started. There are 5 sta
 Using the command below, you can test the deepspeech2 online model.
  ```
  bash run.sh --stage 3 --stop_stage 5 --model_type online --conf_path conf/deepspeech2_online.yaml
-```
+ ```
 The detail commands are:
 ```
 conf_path=conf/deepspeech2_online.yaml
@@ -152,7 +153,7 @@ if [ ${stage} -le 5 ] && [ ${stop_stage} -ge 5 ]; then
     # test export ckpt avg_n
     CUDA_VISIBLE_DEVICES=0 ./local/test_export.sh ${conf_path} exp/${ckpt}/checkpoints/${avg_ckpt}.jit ${model_type}|| exit -1
 fi
- ```
+```
 After the training process, we use stage 3,4,5 for testing process. The stage 3 is for testing the model generated in the stage 2 and provided the CER index of the test set. The stage 4 is for transforming the model from dynamic graph to static graph by using "paddle.jit" library. The stage 5 is for testing the model in static graph.
 
 
@@ -161,12 +162,13 @@ The deepspeech2 offline model is similarity to the deepspeech2 online model. The
 
 The arcitecture of the model is shown in Fig.2.
 <p align="center">
-<img src="../images/ds2offlineModel.png" width=800>
+<img src="../../images/ds2offlineModel.png" width=800>
 <br/>Fig.2 The Arcitecture of deepspeech2 offline model
 </p>
 
 
 
+
 For data preparation and decoder, the deepspeech2 offline model is same with the deepspeech2 online model.
 
 The code of encoder and decoder for deepspeech2 offline model is in:
@@ -182,7 +184,7 @@ For training and testing, the "model_type" and the "conf_path" must be set.
 # Training offline
 cd examples/aishell/s0
 bash run.sh --stage 0 --stop_stage 2 --model_type offline --conf_path conf/deepspeech2.yaml
-```
+ ```
 ```
 # Testing offline
 cd examples/aishell/s0

docs/source/tts/README.md

@@ -2,10 +2,11 @@
 Parakeet aims to provide a flexible, efficient and state-of-the-art text-to-speech toolkit for the open-source community. It is built on PaddlePaddle dynamic graph and includes many influential TTS models.  
 
 <div align="center">
-  <img src="docs/images/logo.png" width=300 /> <br>
+  <img src="../../images/logo.png" width=300 /> <br>
 </div>
 
-## News  <img src="./docs/images/news_icon.png" width="40"/>
+
+## News  <img src="../../images/news_icon.png" width="40"/>
 - Oct-12-2021, Refector examples code.
 - Oct-12-2021, Parallel WaveGAN with LJSpeech. Check [examples/GANVocoder/parallelwave_gan/ljspeech](./examples/GANVocoder/parallelwave_gan/ljspeech).
 - Oct-12-2021, FastSpeech2/FastPitch with LJSpeech. Check [examples/fastspeech2/ljspeech](./examples/fastspeech2/ljspeech).

examples/aishell/README.md

@@ -5,7 +5,8 @@
 
 ## Data
 
-| Data Subset | Duration in Seconds |
-| data/manifest.train |  1.23 ~ 14.53125 |
-| data/manifest.dev  | 1.645 ~ 12.533 |  
-| data/manifest.test | 1.859125 ~ 14.6999375 |
+| Data Subset         | Duration in Seconds   |
+| ------------------- | --------------------- |
+| data/manifest.train | 1.23 ~ 14.53125       |
+| data/manifest.dev   | 1.645 ~ 12.533        |
+| data/manifest.test  | 1.859125 ~ 14.6999375 |

examples/librispeech/README.md

 # ASR
 
-* s0 is for deepspeech2 offline
-* s1 is for transformer/conformer/U2
-* s2 is for transformer/conformer/U2 w/ kaldi feat
-need install Kaldi
+* s0 is for deepspeech2 offline 
+* s1 is for transformer/conformer/U2 
+* s2 is for transformer/conformer/U2 w/ kaldi feat, need install Kaldi 
 
 ## Data
 | Data Subset | Duration in Seconds |

examples/librispeech/s0/README.md

 # LibriSpeech
 
-## Data
-| Data Subset | Duration in Seconds |
-| --- | --- |
-| data/manifest.train |  0.83s ~ 29.735s |
-| data/manifest.dev | 1.065 ~ 35.155s |  
-| data/manifest.test-clean | 1.285s ~ 34.955s |
-
 ## Deepspeech2
-
 | Model | Params | release |  Config | Test set | Loss | WER |  
 | --- | --- | --- | --- | --- | --- | --- |  
 | DeepSpeech2 | 42.96M | 2.2.0 | conf/deepspeech2.yaml + spec_aug | test-clean | 14.49190807 | 0.067283 |  

examples/other/punctuation_restoration/README.md

 # Punctation Restoration
 
-Please using [PaddleSpeechTask](https://github.com/745165806/PaddleSpeechTask] to do this task.
+Please using [PaddleSpeechTask](https://github.com/745165806/PaddleSpeechTask) to do this task.
+