Update README.md

examples/wavenet/README.md

-# Wavenet
+# WaveNet
 
-Paddle implementation of wavenet in dynamic graph, a convolutional network based vocoder. Wavenet is proposed in [WaveNet: A Generative Model for Raw Audio](https://arxiv.org/abs/1609.03499), but in thie experiment, the implementation follows the teacher model in [ClariNet: Parallel Wave Generation in End-to-End Text-to-Speech](arxiv.org/abs/1807.07281).
+PaddlePaddle dynamic graph implementation of WaveNet, a convolutional network based vocoder. WaveNet is originally proposed in [WaveNet: A Generative Model for Raw Audio](https://arxiv.org/abs/1609.03499). However, in this experiment, the implementation follows the teacher model in [ClariNet: Parallel Wave Generation in End-to-End Text-to-Speech](arxiv.org/abs/1807.07281).
 
 
 ## Dataset
@@ -24,13 +24,13 @@ tar xjvf LJSpeech-1.1.tar.bz2
 
 ## Train
 
-Train the model using train.py, follow the usage displayed by `python train.py --help`.
+Train the model using train.py. For help on usage, try `python train.py --help`.
 
 ```text
 usage: train.py [-h] [--data DATA] [--config CONFIG] [--output OUTPUT]
                 [--device DEVICE] [--resume RESUME]
 
-Train a wavenet model with LJSpeech.
+Train a WaveNet model with LJSpeech.
 
 optional arguments:
   -h, --help       show this help message and exit
@@ -43,7 +43,7 @@ optional arguments:
 
 1. `--config` is the configuration file to use. The provided configurations 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.
+3. `--resume` is the path of the checkpoint. If it is provided, the model would load the checkpoint before training.
 4. `--output` is the directory to save results, all result are saved in this directory. The structure of the output directory is shown below.
 
 ```text
@@ -53,13 +53,13 @@ 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=./configs/wavenet_single_gaussian.yaml --data=./LJSpeech-1.1/ --output=experiment --device=0
 ```
 
-You can monitor training log via tensorboard, using the script below.
+You can monitor training log via TensorBoard, using the script below.
 
 ```bash
 cd experiment/log
@@ -71,7 +71,7 @@ tensorboard --logdir=.
 usage: synthesis.py [-h] [--data DATA] [--config CONFIG] [--device DEVICE]
                     checkpoint output
 
-Synthesize valid data from LJspeech with a wavenet model.
+Synthesize valid data from LJspeech with a WaveNet model.
 
 positional arguments:
   checkpoint       checkpoint to load.
@@ -85,12 +85,12 @@ optional arguments:
 ```
 
 1. `--config` is the configuration file to use. You should use the same configuration with which you train you model.
-2.  `--data` is the path of the LJspeech dataset. A dataset is not needed for synthesis, but since the input is mel spectrogram, we need to get mel spectrogram from audio files.
+2. `--data` is the path of the LJspeech dataset. A dataset is not needed for synthesis, but since the input is mel spectrogram, we need to get mel spectrogram from audio files.
 3. `checkpoint` is the checkpoint to load.
 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