Update modle_arcitecture.md

doc/src/modle_arcitecture.md

@@ -1,4 +1,5 @@
-# Model Arcitecture
+# Deepspeech2 online Model 
+## Arcitecture
 
 The implemented arcitecure of Deepspeech2 online model is based on [Deepspeech2 model](https://arxiv.org/pdf/1512.02595.pdf) with some changes. 
 The model is mainly composed of 2D convolution subsampling layer and stacked single direction rnn layers. 
@@ -16,35 +17,39 @@ The arcitecture of the model is shown in Fig.1.
 <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 \<blank\> and \<eos\>. The \<blank\> represents the blank label in CTC, the <eos> represents the start and the end of the label. For mandarin, the vocabulary dictionary is composed of chinese characters statisticed from the training set and three additional characters, which are \<blank\>, \<unk\> and \<eos\>. The \<unk\> represents the unknown characters. 
+### 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\>.  
 
-### CMVN
+#### CMVN
 For CMVN, a subset of traininig set is chosed and be used to caculate the mean and std value of the raw audio. 
  
-### Feature Extraction
+#### Feature Extraction
  For feature extraction, three methods are implemented, which are linear (FFT without using filter bank), fbank and mfcc.
  Currently, the released deepspeech2 online model use the linear feature extraction method.
  <!--
 For a single utterance $x^i$ sampled from the training set $S$,
  $ S= {(x^1,y^1),(x^2,y^2),...,(x^m,y^m)}$, where $y^i$ is the label correspodding to the ${x^i}
 -->
-## Backbone
+### Backbone
 The Backbone is composed of two 2D convolution subsampling layers and a number of stacked single direction rnn layers. The 2D convolution subsampling layers extract feature represention from the raw audio feature and reduce the length of audio feature at the same time. After passing through the convolution subsampling layers, then the feature represention are input into the stacked rnn layers. For rnn layers, LSTM cell and GRU cell are provided.
 
-## Decoder
-To got the possibilities of characters of each frame, the feature represention of each frame output from the stacked rnn layers are then input into the dense layer to do projection. The output dim of dense layer is same with the vocabulary size. After projection, the softmax function is used to make frame-level feature representation be the possibilities of characters. While making model inference, the character possibilities of each frame are input into the CTC decoder to get the final speech recognition results.
+### Decoder
+To got the character possibilities of each frame, the feature represention of each frame output from the backbone are input into a projection layer which is implemented as a dense layer to do projection. The output dim of the projection layer is same with the vocabulary size. After projection layer, the softmax function is used to make frame-level feature representation be the possibilities of characters. While making model inference, the character possibilities of each frame are input into the CTC decoder to get the final speech recognition results.
 
-# Training Process
+## Training Process
 ```
  cd example/aishell/s0
  bash run.sh --stage 0 --stop_stage 2
 ```
-By using the command above, the training process can be started. There are 5 stages in run.sh, and the first 3 stages are used for training process. The stage 0 is used for data preparation, in which the dataset will be downloaded, and the manifest files of dataset, vocabulary dictionary and CMVN file will be generated in "./data/". The stage 1 is used for training the model, the log file and model checkpoint is saved in "exp/deepspeech2_online/". The stage 2 is used to generated final model for predicting by averaging the top k model parameters.  
+By using the command above, the training process can be started. There are 5 stages in run.sh, and the first 3 stages are used for training process. The stage 0 is used for data preparation, in which the dataset will be downloaded, and the manifest files of the datasets, vocabulary dictionary and CMVN file will be generated in "./data/". The stage 1 is used for training the model, the log files and model checkpoint is saved in "exp/deepspeech2_online/". The stage 2 is used to generated final model for predicting by averaging the top-k model parameters.  
  
-# Testing Process
+## Testing Process
  ```
  bash run.sh --stage 3 --stop_stage 5
 ```
-After the training process, we use stage 3,4,5 for testing process. The stage 3 test the model generated by the stage 2 and provided the CER index of the test set. The stage 4 transform the model from dynamic graph to static graph by using "jit". The stage 5 test the model in static graph.
+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.
+
+ 
+# Deepspeech2 offline Model
+The deepspeech2 offline model is similarity to the deepspeech2 online model. The main difference between them is the offline model use the bi-directional rnn layers while the online model use the single direction rnn layers.