update en readme

README.md

@@ -11,14 +11,12 @@
 - [Training a Model](#training-a-model)
 - [Inference and Evaluation](#inference-and-evaluation)
 - [Hyper-parameters Tuning](#hyper-parameters-tuning)
-- [Training for Mandarin Language](#training-for-mandarin-language)
 - [Trying Live Demo with Your Own Voice](#trying-live-demo-with-your-own-voice)
-- [Released Models](#released-models)
 - [Experiments and Benchmarks](#experiments-and-benchmarks)
+- [Released Models](#released-models)
 - [Questions and Help](#questions-and-help)
 
 
-
 ## Installation
 
 To avoid the trouble of environment setup, [running in Docker container](#running-in-docker-container) is highly recommended. Otherwise follow the guidelines below to install the dependencies manually.
@@ -238,6 +236,12 @@ For other configuration examples, please refer to `conf/augmenatation.config.exa
 
 Be careful when utilizing the data augmentation technique, as improper augmentation will do harm to the training, due to the enlarged train-test gap.
 
+
+### Training for Mandarin Language
+
+The key steps of training for Mandarin language are same to that of English language and we have also provided an example for Mandarin training with Aishell in ```examples/aishell/local```. As mentioned above, please execute ```sh run_data.sh```, ```sh run_train.sh```, ```sh run_test.sh``` and ```sh run_infer.sh``` to do data preparation, training, testing and inference correspondingly. We have also prepared a pre-trained model (downloaded by ./models/aishell/download_model.sh) for users to try with ```sh run_infer_golden.sh``` and ```sh run_test_golden.sh```. Notice that, different from English LM, the Mandarin LM is character-based and please run ```tools/tune.py``` to find an optimal setting.
+
+
 ## Inference and Evaluation
 
 ### Prepare Language Model
@@ -362,9 +366,6 @@ python3 tune.py --help
 ```
 or refer to `example/librispeech/local/run_tune.sh`.
 
-## Training for Mandarin Language
-
-The key steps of training for Mandarin language are same to that of English language and we have also provided an example for Mandarin training with Aishell in ```examples/aishell/local```. As mentioned above, please execute ```sh run_data.sh```, ```sh run_train.sh```, ```sh run_test.sh``` and ```sh run_infer.sh``` to do data preparation, training, testing and inference correspondingly. We have also prepared a pre-trained model (downloaded by ./models/aishell/download_model.sh) for users to try with ```sh run_infer_golden.sh``` and ```sh run_test_golden.sh```. Notice that, different from English LM, the Mandarin LM is character-based and please run ```tools/tune.py``` to find an optimal setting.
 
 ## Trying Live Demo with Your Own Voice
 
@@ -411,24 +412,6 @@ python3 deploy/demo_server.py --help
 python3 deploy/demo_client.py --help
 ```
 
-## Released Models
-
-#### Speech Model Released
-
-Language  | Model Name | Training Data | Hours of Speech
-:-----------: | :------------: | :----------: |  -------:
-English  | [LibriSpeech Model](https://deepspeech.bj.bcebos.com/eng_models/librispeech_model_fluid.tar.gz) | [LibriSpeech Dataset](http://www.openslr.org/12/) | 960 h
-English  | [BaiduEN8k Model](https://deepspeech.bj.bcebos.com/demo_models/baidu_en8k_model_fluid.tar.gz) | Baidu Internal English Dataset | 8628 h
-Mandarin | [Aishell Model](https://deepspeech.bj.bcebos.com/mandarin_models/aishell_model_fluid.tar.gz) | [Aishell Dataset](http://www.openslr.org/33/) | 151 h
-Mandarin | [BaiduCN1.2k Model](https://deepspeech.bj.bcebos.com/demo_models/baidu_cn1.2k_model_fluid.tar.gz) | Baidu Internal Mandarin Dataset | 1204 h
-
-#### Language Model Released
-
-Language Model | Training Data | Token-based | Size | Descriptions
-:-------------:| :------------:| :-----: | -----: | :-----------------
-[English LM](https://deepspeech.bj.bcebos.com/en_lm/common_crawl_00.prune01111.trie.klm) |  [CommonCrawl(en.00)](http://web-language-models.s3-website-us-east-1.amazonaws.com/ngrams/en/deduped/en.00.deduped.xz) | Word-based | 8.3 GB | Pruned with 0 1 1 1 1; <br/> About 1.85 billion n-grams; <br/> 'trie'  binary with '-a 22 -q 8 -b 8'
-[Mandarin LM Small](https://deepspeech.bj.bcebos.com/zh_lm/zh_giga.no_cna_cmn.prune01244.klm) | Baidu Internal Corpus | Char-based | 2.8 GB | Pruned with 0 1 2 4 4; <br/> About 0.13 billion n-grams; <br/> 'probing' binary with default settings
-[Mandarin LM Large](https://deepspeech.bj.bcebos.com/zh_lm/zhidao_giga.klm) | Baidu Internal Corpus | Char-based | 70.4 GB | No Pruning; <br/> About 3.7 billion n-grams; <br/> 'probing' binary with default settings
 
 ## Experiments and Benchmarks
 
@@ -467,6 +450,27 @@ We compare the training time with 1, 2, 4, 8 Tesla V100 GPUs (with a subset of L
 
 `tools/profile.sh` provides such a profiling tool.
 
+
+## Released Models
+
+#### Speech Model Released
+
+Language  | Model Name | Training Data | Hours of Speech
+:-----------: | :------------: | :----------: |  -------:
+English  | [LibriSpeech Model](https://deepspeech.bj.bcebos.com/eng_models/librispeech_model_fluid.tar.gz) | [LibriSpeech Dataset](http://www.openslr.org/12/) | 960 h
+English  | [BaiduEN8k Model](https://deepspeech.bj.bcebos.com/demo_models/baidu_en8k_model_fluid.tar.gz) | Baidu Internal English Dataset | 8628 h
+Mandarin | [Aishell Model](https://deepspeech.bj.bcebos.com/mandarin_models/aishell_model_fluid.tar.gz) | [Aishell Dataset](http://www.openslr.org/33/) | 151 h
+Mandarin | [BaiduCN1.2k Model](https://deepspeech.bj.bcebos.com/demo_models/baidu_cn1.2k_model_fluid.tar.gz) | Baidu Internal Mandarin Dataset | 1204 h
+
+#### Language Model Released
+
+Language Model | Training Data | Token-based | Size | Descriptions
+:-------------:| :------------:| :-----: | -----: | :-----------------
+[English LM](https://deepspeech.bj.bcebos.com/en_lm/common_crawl_00.prune01111.trie.klm) |  [CommonCrawl(en.00)](http://web-language-models.s3-website-us-east-1.amazonaws.com/ngrams/en/deduped/en.00.deduped.xz) | Word-based | 8.3 GB | Pruned with 0 1 1 1 1; <br/> About 1.85 billion n-grams; <br/> 'trie'  binary with '-a 22 -q 8 -b 8'
+[Mandarin LM Small](https://deepspeech.bj.bcebos.com/zh_lm/zh_giga.no_cna_cmn.prune01244.klm) | Baidu Internal Corpus | Char-based | 2.8 GB | Pruned with 0 1 2 4 4; <br/> About 0.13 billion n-grams; <br/> 'probing' binary with default settings
+[Mandarin LM Large](https://deepspeech.bj.bcebos.com/zh_lm/zhidao_giga.klm) | Baidu Internal Corpus | Char-based | 70.4 GB | No Pruning; <br/> About 3.7 billion n-grams; <br/> 'probing' binary with default settings
+
+
 ## Questions and Help
 
 You are welcome to submit questions and bug reports in [Github Issues](https://github.com/PaddlePaddle/DeepSpeech/issues). You are also welcome to contribute to this project.