Merge pull request #827 from Jackwaterveg/doc

Doc 修正了一些小问题

docs/src/deepspeech_architecture.md

@@ -20,7 +20,7 @@ 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 both English and mandarin data, we set the default indexs that \<blank\>=0, \<unk\>=1 and \<eos\>= last index.
+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.
 ```
  # The code to build vocabulary
  cd examples/aishell/s0
@@ -65,17 +65,21 @@ python3 ../../../utils/compute_mean_std.py \
  ```
 
 ### 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. Adding one fully connected (fc) layer after rnn layer is optional, if the number of rnn layers is less than 5, adding one fc layer after rnn layers is recommand.
+The encoder 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 the stacked rnn layers, LSTM cell and GRU cell are provided to use. Adding one fully connected (fc) layer after the stacked rnn layers is optional. If the number of stacked rnn layers is less than 5, adding one fc layer after stacked rnn layers is recommand.
+
 The code of Encoder is in:
 ```
 vi deepspeech/models/ds2_online/deepspeech2.py
 ```
 
 ### 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.
-The code of Encoder is in:
+To got the character possibilities of each frame, the feature represention of each frame output from the encoder are input into a projection layer which is implemented as a dense layer to do feature projection. The output dim of the projection layer is same with the vocabulary size. After projection layer, the softmax function is used to transform the 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.
+
+The code of the decoder is in:
 ```
+# The code of constructing the decoder in model
 vi deepspeech/models/ds2_online/deepspeech2.py
+# The code of CTC Decoder
 vi deepspeech/modules/ctc.py
 ```
 
@@ -119,7 +123,8 @@ if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
     avg.sh exp/${ckpt}/checkpoints ${avg_num}
 fi
 ```
-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 based on validation loss.  
+
+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 based on validation loss.  
 
 ## Testing Process
 Using the command below, you can test the deepspeech2 online model.
@@ -152,7 +157,7 @@ After the training process, we use stage 3,4,5 for testing process. The stage 3
 
 
 ## Non-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 and the fc layer is not used.
+The deepspeech2 offline model is similarity to the deepspeech2 online model. The main difference between them is the offline model use the stacked bi-directional rnn layers while the online model use the single direction rnn layers and the fc layer is not used. For the stacked bi-directional rnn layers in the offline model, the rnn cell and gru cell are provided to use.
 
 The arcitecture of the model is shown in Fig.2.
 <p align="center">
@@ -162,9 +167,9 @@ The arcitecture of the model is shown in Fig.2.
 
 
 
-For data preparation, decoder, the deepspeech2 offline model is same with the deepspeech2 online model.
+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:
+The code of encoder and decoder for deepspeech2 offline model is in:
 ```
 vi deepspeech/models/ds2/deepspeech2.py
 ```

docs/src/install.md

@@ -6,13 +6,14 @@ To avoid the trouble of environment setup, [running in Docker container](#runnin
 - Python >= 3.7
 - PaddlePaddle 2.0.0 or later (please refer to the [Installation Guide](https://www.paddlepaddle.org.cn/documentation/docs/en/beginners_guide/index_en.html))
 
-## Setup
+## Setup (Important)
 
 - Make sure these libraries or tools installed: `pkg-config`, `flac`, `ogg`, `vorbis`, `boost`, `sox, and `swig`, e.g. installing them via `apt-get`:
 
 ```bash
 sudo apt-get install -y sox pkg-config libflac-dev libogg-dev libvorbis-dev libboost-dev swig python3-dev
 ```
+The version of `swig` should >= 3.0
 
 or, installing them via `yum`: