diff --git a/README.md b/README.md
index d9b9893426ee3e79758059d2e82a77104db19f36..055bd439e96949110d3f80ec1cdab3c09aae96ad 100644
--- a/README.md
+++ b/README.md
@@ -1,6 +1,6 @@
 # DeepSpeech2 on PaddlePaddle
 
-*DeepSpeech2 on PaddlePaddle* is an open-source implementation of end-to-end Automatic Speech Recognition (ASR) engine, based on [Baidu's Deep Speech 2 paper](http://proceedings.mlr.press/v48/amodei16.pdf), with [PaddlePaddle](https://github.com/PaddlePaddle/Paddle) platform. Our vision is to empower both industrial application and academic research on speech recognition, via an easy-to-use, efficient and scalable implementation, including training, inferencing & testing module, distributed [PaddleCloud](https://github.com/PaddlePaddle/cloud) training, and demo deployment. Besides, several pre-trained models for both English and Mandarin are also released.
+*DeepSpeech2 on PaddlePaddle* is an open-source implementation of end-to-end Automatic Speech Recognition (ASR) engine, based on [Baidu's Deep Speech 2 paper](http://proceedings.mlr.press/v48/amodei16.pdf), with [PaddlePaddle](https://github.com/PaddlePaddle/Paddle) platform. Our vision is to empower both industrial application and academic research on speech recognition, via an easy-to-use, efficient and scalable implementation, including training, inference & testing module, distributed [PaddleCloud](https://github.com/PaddlePaddle/cloud) training, and demo deployment. Besides, several pre-trained models for both English and Mandarin are also released.
 
 ## Table of Contents
 - [Prerequisites](#prerequisites)
@@ -19,12 +19,12 @@
 - [Questions and Help](#questions-and-help)
 
 ## Prerequisites
-- Only support Python 2.7
+- Python 2.7 only supported
 - PaddlePaddle the latest version (please refer to the [Installation Guide](https://github.com/PaddlePaddle/Paddle#installation))
 
 ## Installation
 
-Please install the [prerequisites](#prerequisites) above before moving on.
+Please make sure the above [prerequisites](#prerequisites) have been satisfied before moving on.
 
 ```bash
 git clone https://github.com/PaddlePaddle/models.git
@@ -34,9 +34,9 @@ sh setup.sh
 
 ## Getting Started
 
-Several shell scripts provided in `./examples` will help us to quickly give it a try, for most major modules, including data preparation, model training, case inference and model evaluation, with a few public dataset (e.g. [LibriSpeech](http://www.openslr.org/12/), [Aishell](https://github.com/kaldi-asr/kaldi/tree/master/egs/aishell)). Reading these examples will also help us understand how to make it work with our own data.
+Several shell scripts provided in `./examples` will help us to quickly give it a try, for most major modules, including data preparation, model training, case inference and model evaluation, with a few public dataset (e.g. [LibriSpeech](http://www.openslr.org/12/), [Aishell](http://www.openslr.org/33)). Reading these examples will also help you to understand how to make it work with your own data.
 
-Some of the scripts in `./examples` are configured with 8 GPUs. If you don't have 8 GPUs available, please modify `CUDA_VISIBLE_DEVICE` and `--trainer_count`. If you don't have any GPU available, please set `--use_gpu` to False to use CPUs instead.
+Some of the scripts in `./examples` are configured with 8 GPUs. If you don't have 8 GPUs available, please modify `CUDA_VISIBLE_DEVICES` and `--trainer_count`. If you don't have any GPU available, please set `--use_gpu` to False to use CPUs instead.
 
 Let's take a tiny sampled subset of [LibriSpeech dataset](http://www.openslr.org/12/) for instance.
 
@@ -46,28 +46,28 @@ Let's take a tiny sampled subset of [LibriSpeech dataset](http://www.openslr.org
     cd examples/tiny
     ```
 
-    Notice that this is only a toy example with a tiny sampled subset of LibriSpeech. If we would like to try with the complete dataset (would take several days for training), please go to `examples/librispeech` instead.
+    Notice that this is only a toy example with a tiny sampled subset of LibriSpeech. If you would like to try with the complete dataset (would take several days for training), please go to `examples/librispeech` instead.
 - Prepare the data
 
     ```bash
     sh run_data.sh
     ```
 
-    `run_data.sh` will download dataset, generate manifests, collect normalizer' statistics and build vocabulary. Once the data preparation is done, we will find the data (only part of LibriSpeech) downloaded in `~/.cache/paddle/dataset/speech/libri` and the corresponding manifest files generated in `./data/tiny` as well as a mean stddev file and a vocabulary file. It has to be run for the very first time we run this dataset and is reusable for all further experiments.
+    `run_data.sh` will download dataset, generate manifests, collect normalizer's statistics and build vocabulary. Once the data preparation is done, you will find the data (only part of LibriSpeech) downloaded in `~/.cache/paddle/dataset/speech/libri` and the corresponding manifest files generated in `./data/tiny` as well as a mean stddev file and a vocabulary file. It has to be run for the very first time you run this dataset and is reusable for all further experiments.
 - Train your own ASR model
 
     ```bash
     sh run_train.sh
     ```
 
-    `run_train.sh` will start a training job, with training logs printed to stdout and model checkpoint of every pass/epoch saved to `./checkpoints/tiny`. We can resume the training from these checkpoints, or use them for inference, evaluation and deployment.
+    `run_train.sh` will start a training job, with training logs printed to stdout and model checkpoint of every pass/epoch saved to `./checkpoints/tiny`. These checkpoints could be used for training resuming, inference, evaluation and deployment.
 - Case inference with an existing model
 
     ```bash
     sh run_infer.sh
     ```
 
-    `run_infer.sh` will show us some speech-to-text decoding results for several (default: 10) samples with the trained model. The performance might not be good now as the current model is only trained with a toy subset of LibriSpeech. To see the results with a better model, we can download a well-trained (trained for several days, with the complete LibriSpeech) model and do the inference:
+    `run_infer.sh` will show us some speech-to-text decoding results for several (default: 10) samples with the trained model. The performance might not be good now as the current model is only trained with a toy subset of LibriSpeech. To see the results with a better model, you can download a well-trained (trained for several days, with the complete LibriSpeech) model and do the inference:
 
     ```bash
     sh run_infer_golden.sh
@@ -78,7 +78,7 @@ Let's take a tiny sampled subset of [LibriSpeech dataset](http://www.openslr.org
     sh run_test.sh
     ```
 
-    `run_test.sh` will evaluate the model with Word Error Rate (or Character Error Rate) measurement. Similarly, we can also download a well-trained model and test its performance:
+    `run_test.sh` will evaluate the model with Word Error Rate (or Character Error Rate) measurement. Similarly, you can also download a well-trained model and test its performance:
 
     ```bash
     sh run_test_golden.sh
@@ -100,7 +100,7 @@ More detailed information are provided in the following sections. Wish you a hap
 
 To use your custom data, you only need to generate such manifest files to summarize the dataset. Given such summarized manifests, training, inference and all other modules can be aware of where to access the audio files, as well as their meta data including the transcription labels.
 
-For how to generate such manifest files, please refer to `data/librispeech/librispeech.py`, which download and generate manifests for LibriSpeech dataset.
+For how to generate such manifest files, please refer to `data/librispeech/librispeech.py`, which will download data and generate manifest files for LibriSpeech dataset.
 
 ### Compute Mean & Stddev for Normalizer
 
@@ -142,7 +142,7 @@ python tools/build_vocab.py --help
 
 ## Training a model
 
-`train.py` is the main caller of the training module. We show several examples of usage below.
+`train.py` is the main caller of the training module. Examples of usage are shown below.
 
 - Start training from scratch with 8 GPUs:
 
@@ -172,9 +172,9 @@ or refer to `example/librispeech/run_train.sh`.
 
 ## Data Augmentation Pipeline
 
-Data augmentation has often been a highly effective technique to boost the deep learning performance. We augment our speech data by synthesizing new audios with small random perturbation (label-invariant transformation) added upon raw audios. We don't have to do the syntheses by ourselves, as it is already embedded into the data provider and is done on the fly, randomly for each epoch during training.
+Data augmentation has often been a highly effective technique to boost the deep learning performance. We augment our speech data by synthesizing new audios with small random perturbation (label-invariant transformation) added upon raw audios. You don't have to do the syntheses on your own, as it is already embedded into the data provider and is done on the fly, randomly for each epoch during training.
 
-Six optional augmentation components are provided for us to configured and inserted into the processing pipeline.
+Six optional augmentation components are provided to be selected, configured and inserted into the processing pipeline.
 
   - Volume Perturbation
   - Speed Perturbation
@@ -183,7 +183,7 @@ Six optional augmentation components are provided for us to configured and inser
   - Noise Perturbation (need background noise audio files)
   - Impulse Response (need impulse audio files)
 
-In order to inform the trainer of what augmentation components we need and what their processing orders are, we are required to prepare a *augmentation configuration file* in [JSON](http://www.json.org/) format. For example:
+In order to inform the trainer of what augmentation components are needed and what their processing orders are, it is required to prepare in advance a *augmentation configuration file* in [JSON](http://www.json.org/) format. For example:
 
 ```
 [{
@@ -204,13 +204,13 @@ When the `--augment_conf_file` argument of `trainer.py` is set to the path of th
 
 For other configuration examples, please refer to `conf/augmenatation.config.example`.
 
-Be careful when we are utilizing the data augmentation technique, as improper augmentation will do harm to the training, due to the enlarged train-test gap.
+Be careful when utilizing the data augmentation technique, as improper augmentation will do harm to the training, due to the enlarged train-test gap.
 
 ## Inference and Evaluation
 
 ### Prepare Language Model
 
-A language model is required to improve the decoder's performance. We have prepared two language models (with lossy compression) for users to download and try. One is for English and the other is for Mandarin. We can simply run this to download the preprared language models:
+A language model is required to improve the decoder's performance. We have prepared two language models (with lossy compression) for users to download and try. One is for English and the other is for Mandarin. Users can simply run this to download the preprared language models:
 
 ```bash
 cd models/lm
@@ -223,7 +223,7 @@ TODO: any other requirements or tips to add?
 
 ### Speech-to-text Inference
 
-An inference module caller `infer.py` is provided for us to infer, decode and visualize speech-to-text results for several given audio clips. It might help to have an intuitive and qualitative evaluation of the ASR model's performance.
+An inference module caller `infer.py` is provided to infer, decode and visualize speech-to-text results for several given audio clips. It might help to have an intuitive and qualitative evaluation of the ASR model's performance.
 
 - Inference with GPU:
 
@@ -248,7 +248,7 @@ or refer to `example/librispeech/run_infer.sh`.
 
 ### Evaluate a Model
 
-To evaluate a model's performance quantitatively, we can run:
+To evaluate a model's performance quantitatively, please run:
 
 - Evaluation with GPUs:
 
@@ -275,7 +275,7 @@ or refer to `example/librispeech/run_test.sh`.
 
 The hyper-parameters $\alpha$ (coefficient for language model scorer) and $\beta$ (coefficient for word count scorer) for the [*CTC beam search decoder*](https://arxiv.org/abs/1408.2873) often have a significant impact on the decoder's performance. It would be better to re-tune them on a validation set when the acoustic model is renewed.
 
-`tools/tune.py` performs a 2-D grid search over the hyper-parameter $\alpha$ and $\beta$. We must provide the range of $\alpha$ and $\beta$, as well as the number of their attempts.
+`tools/tune.py` performs a 2-D grid search over the hyper-parameter $\alpha$ and $\beta$. You must provide the range of $\alpha$ and $\beta$, as well as the number of their attempts.
 
 - Tuning with GPU:
 
@@ -297,7 +297,7 @@ The hyper-parameters $\alpha$ (coefficient for language model scorer) and $\beta
     python tools/tune.py --use_gpu False
     ```
 
-After tuning, we can reset $\alpha$ and $\beta$ in the inference and evaluation modules to see if they really help improve the ASR performance.
+After tuning, you can reset $\alpha$ and $\beta$ in the inference and evaluation modules to see if they really help improve the ASR performance.
 
 ```bash
 python tune.py --help
@@ -308,9 +308,9 @@ TODO: add figure.
 
 ## Distributed Cloud Training
 
-We provide a cloud training module for users to do the distributed cluster training on [PaddleCloud](https://github.com/PaddlePaddle/cloud), to achieve a much faster training speed with multiple machines. To start with this, please first install PaddleCloud client and register a PaddleCloud account, as described in [PaddleCloud Usage](https://github.com/PaddlePaddle/cloud/blob/develop/doc/usage_cn.md#%E4%B8%8B%E8%BD%BD%E5%B9%B6%E9%85%8D%E7%BD%AEpaddlecloud).
+We also provide a cloud training module for users to do the distributed cluster training on [PaddleCloud](https://github.com/PaddlePaddle/cloud), to achieve a much faster training speed with multiple machines. To start with this, please first install PaddleCloud client and register a PaddleCloud account, as described in [PaddleCloud Usage](https://github.com/PaddlePaddle/cloud/blob/develop/doc/usage_cn.md#%E4%B8%8B%E8%BD%BD%E5%B9%B6%E9%85%8D%E7%BD%AEpaddlecloud).
 
-Then, we take the following steps to submit a training job:
+Please take the following steps to submit a training job:
 
 - Go to directory:
 
@@ -332,7 +332,7 @@ Then, we take the following steps to submit a training job:
     - Upload these tar files to PaddleCloud filesystem.
     - Create cloud manifests by replacing local filesystem paths with PaddleCloud filesystem paths. New manifests will be used to inform the cloud jobs of audio files' location and their meta information.
 
-    It should be done only once for the very first time we do the cloud training. Later, the data is kept persisitent on the cloud filesystem and reusable for further job submissions.
+    It should be done only once for the very first time to do the cloud training. Later, the data is kept persisitent on the cloud filesystem and reusable for further job submissions.
 
     For argument details please refer to [Train DeepSpeech2 on PaddleCloud](https://github.com/PaddlePaddle/models/tree/develop/deep_speech_2/cloud).
 
@@ -349,7 +349,7 @@ Then, we take the following steps to submit a training job:
     ```bash
     sh pcloud_submit.sh
     ```
-    we submit a training job to PaddleCloud. And the job name will be printed when the submission is finished. Now our training job is running well on the PaddleCloud.
+    a training job has been submitted to PaddleCloud, with the job name printed to the console.
 
   - Get training logs
 
@@ -375,9 +375,9 @@ TODO: to be added
 
 ## Trying Live Demo with Your Own Voice
 
-Until now, we have trained and tested our ASR model qualitatively (`infer.py`) and quantitatively (`test.py`) with existing audio files. But we have not yet try the model with our own speech. `deploy/demo_server.py` and `deploy/demo_client.py` helps quickly build up a real-time demo ASR engine with the trained model, enabling us to test and play around with the demo, with our own voice.
+Until now, an ASR model is trained and tested qualitatively (`infer.py`) and quantitatively (`test.py`) with existing audio files. But it is not yet tested with your own speech. `deploy/demo_server.py` and `deploy/demo_client.py` helps quickly build up a real-time demo ASR engine with the trained model, enabling you to test and play around with the demo, with your own voice.
 
-We start the demo's server in one console by:
+To start the demo's server, please run this in one console:
 
 ```bash
 CUDA_VISIBLE_DEVICES=0 \
@@ -387,7 +387,7 @@ python deploy/demo_server.py \
 --host_port 8086
 ```
 
-For the machine (might not be the same machine) to run the demo's client, we have to do the following installation before moving on.
+For the machine (might not be the same machine) to run the demo's client, please do the following installation before moving on.
 
 For example, on MAC OS X:
 
@@ -397,7 +397,7 @@ pip install pyaudio
 pip install pynput
 ```
 
-Then we can start the client in another console by:
+Then to start the client, please run this in another console:
 
 ```bash
 CUDA_VISIBLE_DEVICES=0 \
@@ -406,11 +406,11 @@ python -u deploy/demo_client.py \
 --host_port 8086
 ```
 
-Now, in the client console, press the `whitespace` key, hold, and start speaking. Until we finish our utterance, we release the key to let the speech-to-text results shown in the console. To quit the client, just press `ESC` key.
+Now, in the client console, press the `whitespace` key, hold, and start speaking. Until finishing your utterance, release the key to let the speech-to-text results shown in the console. To quit the client, just press `ESC` key.
 
-Notice that `deploy/demo_client.py` must be run in a machine with a microphone device, while `deploy/demo_server.py` could be run in one without any audio recording hardware, e.g. any remote server machine. Just be careful to set the `host_ip` and `host_port` argument with the actual accessible IP address and port, if the server and client are running with two separate machines. Nothing should be done if they are running in one single machine.
+Notice that `deploy/demo_client.py` must be run on a machine with a microphone device, while `deploy/demo_server.py` could be run on one without any audio recording hardware, e.g. any remote server machine. Just be careful to set the `host_ip` and `host_port` argument with the actual accessible IP address and port, if the server and client are running with two separate machines. Nothing should be done if they are running on one single machine.
 
-We can also refer to `examples/mandarin/run_demo_server.sh` for example, which will first download a pre-trained Mandarin model (trained with 3000 hours of internal speech data) and then start the demo server with the model. With running `examples/mandarin/run_demo_client.sh`, we can speak Mandarin to test it. If we would like to try some other models, just update `--model_path` argument in the script.  
+Please also refer to `examples/mandarin/run_demo_server.sh`, which will first download a pre-trained Mandarin model (trained with 3000 hours of internal speech data) and then start the demo server with the model. With running `examples/mandarin/run_demo_client.sh`, you can speak Mandarin to test it. If you would like to try some other models, just update `--model_path` argument in the script.  
 
 For more help on arguments:
 
diff --git a/deploy/demo_server.py b/deploy/demo_server.py
index 2d3931f74bb44261c7fe2e342921a0b8c8d2ec40..a7157001cf8ecf766329910350a51bea0f1c5275 100644
--- a/deploy/demo_server.py
+++ b/deploy/demo_server.py
@@ -46,7 +46,7 @@ add_arg('vocab_path',       str,
         'data/librispeech/eng_vocab.txt',
         "Filepath of vocabulary.")
 add_arg('model_path',       str,
-        './checkpoints/params.latest.tar.gz',
+        './checkpoints/libri/params.latest.tar.gz',
         "If None, the training starts from scratch, "
         "otherwise, it resumes from the pre-trained model.")
 add_arg('lang_model_path',  str,
diff --git a/examples/librispeech/run_train.sh b/examples/librispeech/run_train.sh
index 5485475e937e291393d434402452a1a5212172a8..07575dde1240c9491061941bbd9388bb2ab03432 100644
--- a/examples/librispeech/run_train.sh
+++ b/examples/librispeech/run_train.sh
@@ -3,6 +3,7 @@
 pushd ../.. > /dev/null
 
 # train model
+# if you wish to resume from an exists model, uncomment --init_model_path
 CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 \
 python -u train.py \
 --batch_size=512 \
diff --git a/examples/tiny/run_train.sh b/examples/tiny/run_train.sh
index c66ec4e56133254b5f8fdf47deeecb39c7ead0db..74d82712e6e2e812da0b22552654d1ef7eadffbb 100644
--- a/examples/tiny/run_train.sh
+++ b/examples/tiny/run_train.sh
@@ -3,6 +3,7 @@
 pushd ../.. > /dev/null
 
 # train model
+# if you wish to resume from an exists model, uncomment --init_model_path
 CUDA_VISIBLE_DEVICES=0,1,2,3 \
 python -u train.py \
 --batch_size=16 \
diff --git a/infer.py b/infer.py
index 73e200b496585c86c475b8820c0300a02db12861..d9c4c67763a43da8f5ec61fa2b2ae08234c419b6 100644
--- a/infer.py
+++ b/infer.py
@@ -38,10 +38,10 @@ add_arg('vocab_path',       str,
         'data/librispeech/vocab.txt',
         "Filepath of vocabulary.")
 add_arg('lang_model_path',  str,
-        'model_zoo/lm/common_crawl_00.prune01111.trie.klm',
+        'models/lm/common_crawl_00.prune01111.trie.klm',
         "Filepath for language model.")
 add_arg('model_path',       str,
-        './checkpoints/params.latest.tar.gz',
+        './checkpoints/libri/params.latest.tar.gz',
         "If None, the training starts from scratch, "
         "otherwise, it resumes from the pre-trained model.")
 add_arg('decoding_method',  str,
diff --git a/test.py b/test.py
index 791bfd58503894f8f83227409bc427f9bd065148..18089f33254e2503caddc36c48f142b5de7455ad 100644
--- a/test.py
+++ b/test.py
@@ -39,11 +39,11 @@ add_arg('vocab_path',       str,
         'data/librispeech/vocab.txt',
         "Filepath of vocabulary.")
 add_arg('model_path',       str,
-        './checkpoints/params.latest.tar.gz',
+        './checkpoints/libri/params.latest.tar.gz',
         "If None, the training starts from scratch, "
         "otherwise, it resumes from the pre-trained model.")
 add_arg('lang_model_path',  str,
-        'model_zoo/lm/common_crawl_00.prune01111.trie.klm',
+        'models/lm/common_crawl_00.prune01111.trie.klm',
         "Filepath for language model.")
 add_arg('decoding_method',  str,
         'ctc_beam_search',
diff --git a/tools/tune.py b/tools/tune.py
index 25e495f19e391db7977e31c01b694cfd957d9d04..96c25a3ebcc2534264a9e66f336d4c32b50d728a 100644
--- a/tools/tune.py
+++ b/tools/tune.py
@@ -44,10 +44,10 @@ add_arg('vocab_path',       str,
         'data/librispeech/vocab.txt',
         "Filepath of vocabulary.")
 add_arg('lang_model_path',  str,
-        'model_zoo/lm/common_crawl_00.prune01111.trie.klm',
+        'models/lm/common_crawl_00.prune01111.trie.klm',
         "Filepath for language model.")
 add_arg('model_path',       str,
-        './checkpoints/params.latest.tar.gz',
+        './checkpoints/libri/params.latest.tar.gz',
         "If None, the training starts from scratch, "
         "otherwise, it resumes from the pre-trained model.")
 add_arg('error_rate_type',  str,
diff --git a/train.py b/train.py
index bbf1cd7290b56a5ca1e045f1878e42560f5289ea..406484a183f4244356b3d6cee3d3fc758837eaf9 100644
--- a/train.py
+++ b/train.py
@@ -48,7 +48,7 @@ add_arg('init_model_path',  str,
         "If None, the training starts from scratch, "
         "otherwise, it resumes from the pre-trained model.")
 add_arg('output_model_dir', str,
-        "./checkpoints",
+        "./checkpoints/libri",
         "Directory for saving checkpoints.")
 add_arg('augment_conf_path',str,
         'conf/augmentation.config',