Update and rename modle_arcitecture.md to deepspeech_architecture.md

doc/src/deepspeech_architecture.md

@@ -1,12 +1,12 @@
-# Deepspeech2 online Model 
-## Arcitecture
+# Deepspeech2
+## Streaming
 
 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. 
 
 To illustrate the model implementation clearly, 3 parts are described in detail.  
 - Data Preparation
-- Backbone
+- Encoder
 - Decoder
 
 
@@ -19,7 +19,7 @@ The arcitecture of the model is shown in Fig.1.
 
 ### 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 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.
 
 #### CMVN
 For CMVN, a subset of traininig set is chosed and be used to caculate the mean and std value of the raw audio. 
@@ -31,7 +31,7 @@ For CMVN, a subset of traininig set is chosed and be used to caculate the mean a
 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
+### Encoder
 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
@@ -51,5 +51,5 @@ By using the command above, the training process can be started. There are 5 sta
 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
+## No Streaming
 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.