Merge branch 'refine_doc' into 'master'

Refine doc

See merge request !28

README.md

 # Parakeet
 
-Parakeet aims to provide a flexible, efficient and state-of-the-art text-to-speech toolkit for the open-source community. It is built on Paddle Fluid dynamic graph, with the support of many influential TTS models proposed by [Baidu Research](http://research.baidu.com) and other academic institutions.  
+Parakeet aims to provide a flexible, efficient and state-of-the-art text-to-speech toolkit for the open-source community. It is built on PaddlePaddle Fluid dynamic graph and includes many influential TTS models proposed by [Baidu Research](http://research.baidu.com) and other research groups.  
 
 <div align="center">
   <img src="images/logo.png" width=450 /> <br>
 </div>
 
+In particular, it features the latest [WaveFlow] (https://arxiv.org/abs/1912.01219) model proposed by Baidu Research. 
+- WaveFlow can synthesize 22.05 kHz high-fidelity speech around 40x faster than real-time on a Nvidia V100 GPU without engineered inference kernels, which is faster than WaveGlow and serveral orders of magnitude faster than WaveNet.
+- WaveFlow is a small-footprint flow-based model for raw audio. It has only 5.9M parameters, which is 15x smalller than WaveGlow (87.9M) and comparable to WaveNet (4.6M).
+- WaveFlow is directly trained with maximum likelihood without probability density distillation and auxiliary losses as used in Parallel WaveNet and ClariNet, which simplifies the training pipeline and reduces the cost of development. 
+
 ### Setup
 
-Make sure the library `libsndfile1` installed, e.g., on Ubuntu
+Make sure the library `libsndfile1` is installed, e.g., on Ubuntu.
 
 ```bash
 sudo apt-get install libsndfile1
@@ -16,7 +21,7 @@ sudo apt-get install libsndfile1
 
 ### Install PaddlePaddle
 
-See [install](https://www.paddlepaddle.org.cn/install/quick) for more details. This repo requires paddlepaddle's version is above 1.7.
+See [install](https://www.paddlepaddle.org.cn/install/quick) for more details. This repo requires paddlepaddle 1.7 or above.
 
 ### Install Parakeet
 

examples/clarinet/README.md

 # Clarinet
 
-Paddle implementation of clarinet in dynamic graph, a convolutional network based vocoder. The implementation is based on the paper [ClariNet: Parallel Wave Generation in End-to-End Text-to-Speech](arxiv.org/abs/1807.07281).
+PaddlePaddle dynamic graph implementation of ClariNet, a convolutional network based vocoder. The implementation is based on the paper [ClariNet: Parallel Wave Generation in End-to-End Text-to-Speech](arxiv.org/abs/1807.07281).
 
 
 ## Dataset
@@ -30,7 +30,7 @@ Train the model using train.py, follow the usage displayed by `python train.py -
 usage: train.py [-h] [--config CONFIG] [--device DEVICE] [--output OUTPUT]
                 [--data DATA] [--resume RESUME] [--wavenet WAVENET]
 
-train a clarinet model with LJspeech and a trained wavenet model.
+train a ClariNet model with LJspeech and a trained WaveNet model.
 
 optional arguments:
   -h, --help         show this help message and exit
@@ -54,12 +54,12 @@ optional arguments:
 ```
 
 5. `--device` is the device (gpu id) to use for training. `-1` means CPU.
-6. `--wavenet` is the path of the wavenet checkpoint to load. if you do not specify `--resume`, then this must be provided.
+6. `--wavenet` is the path of the wavenet checkpoint to load. If you do not specify `--resume`, then this must be provided.
 
 
-Before you start training a clarinet model, you should have trained a wavenet model with single gaussian as output distribution. Make sure the config for teacher matches that for the trained model.
+Before you start training a ClariNet model, you should have trained a WaveNet model with single Gaussian output distribution. Make sure the config of the teacher model matches that of the trained model.
 
-example script:
+Example script:
 
 ```bash
 python train.py --config=./configs/clarinet_ljspeech.yaml --data=./LJSpeech-1.1/ --output=experiment --device=0 --conditioner=wavenet_checkpoint/conditioner --conditioner=wavenet_checkpoint/teacher
@@ -77,7 +77,7 @@ tensorboard --logdir=.
 usage: synthesis.py [-h] [--config CONFIG] [--device DEVICE] [--data DATA]
                     checkpoint output
 
-train a clarinet model with LJspeech and a trained wavenet model.
+train a ClariNet model with LJspeech and a trained WaveNet model.
 
 positional arguments:
   checkpoint       checkpoint to load from.
@@ -96,7 +96,7 @@ optional arguments:
 4. `output_path` is the directory to save results. The output path contains the generated audio files (`*.wav`).
 5. `--device` is the device (gpu id) to use for training. `-1` means CPU.
 
-example script:
+Example script:
 
 ```bash
 python synthesis.py --config=./configs/wavenet_single_gaussian.yaml --data=./LJSpeech-1.1/ --device=0 experiment/checkpoints/step_500000 generated

examples/deepvoice3/README.md

-# Deepvoice 3
+# Deep Voice 3
 
-Paddle implementation of deepvoice 3 in dynamic graph, a convolutional network based text-to-speech synthesis model. The implementation is based on [Deep Voice 3: Scaling Text-to-Speech with Convolutional Sequence Learning](https://arxiv.org/abs/1710.07654).
+PaddlePaddle dynamic graph implementation of Deep Voice 3, a convolutional network based text-to-speech generative model. The implementation is based on [Deep Voice 3: Scaling Text-to-Speech with Convolutional Sequence Learning](https://arxiv.org/abs/1710.07654).
 
-We implement Deepvoice 3 in paddle fluid with dynamic graph, which is convenient for flexible network architectures.
+We implement Deep Voice 3 using Paddle Fluid with dynamic graph, which is convenient for building flexible network architectures.
 
 ## Dataset
 
@@ -15,9 +15,9 @@ tar xjvf LJSpeech-1.1.tar.bz2
 
 ## Model Architecture
 
-![DeepVoice3 model architecture](./images/model_architecture.png)
+![Deep Voice 3 model architecture](./images/model_architecture.png)
 
-The model consists of an encoder, a decoder and a converter (and a speaker embedding for multispeaker models). The encoder, together with the decoder forms the seq2seq part of the model, and the converter forms the postnet part.
+The model consists of an encoder, a decoder and a converter (and a speaker embedding for multispeaker models). The encoder and the decoder together form the seq2seq part of the model, and the converter forms the postnet part.
 
 ## Project Structure
 
@@ -37,7 +37,7 @@ Train the model using train.py, follow the usage displayed by `python train.py -
 ```text
 usage: train.py [-h] [-c CONFIG] [-s DATA] [-r RESUME] [-o OUTPUT] [-g DEVICE]
 
-Train a deepvoice 3 model with LJSpeech dataset.
+Train a Deep Voice 3 model with LJSpeech dataset.
 
 optional arguments:
   -h, --help            show this help message and exit
@@ -55,7 +55,7 @@ optional arguments:
 1. `--config` is the configuration file to use. The provided `ljspeech.yaml` can be used directly. And you can change some values in the configuration file and train the model with a different config.
 2. `--data` is the path of the LJSpeech dataset, the extracted folder from the downloaded archive (the folder which contains metadata.txt).
 3. `--resume` is the path of the checkpoint. If it is provided, the model would load the checkpoint before trainig.
-4. `--output` is the directory to save results, all result are saved in this directory. The structure of the output directory is shown below.
+4. `--output` is the directory to save results, all results are saved in this directory. The structure of the output directory is shown below.
 
 ```text
 ├── checkpoints      # checkpoint
@@ -69,7 +69,7 @@ optional arguments:
 
 5. `--device` is the device (gpu id) to use for training. `-1` means CPU.
 
-example script:
+Example script:
 
 ```bash
 python train.py --config=./ljspeech.yaml --data=./LJSpeech-1.1/ --output=experiment --device=0
@@ -86,7 +86,7 @@ tensorboard --logdir=.
 ```text
 usage: synthesis.py [-h] [-c CONFIG] [-g DEVICE] checkpoint text output_path
 
-Synthsize waveform with a checkpoint.
+Synthsize waveform from a checkpoint.
 
 positional arguments:
   checkpoint            checkpoint to load.
@@ -107,7 +107,7 @@ optional arguments:
 4. `output_path` is the directory to save results. The output path contains the generated audio files (`*.wav`) and attention plots (*.png) for each sentence.
 5. `--device` is the device (gpu id) to use for training. `-1` means CPU.
 
-example script:
+Example script:
 
 ```bash
 python synthesis.py --config=./ljspeech.yaml --device=0 experiment/checkpoints/model_step_005000000 sentences.txt generated