Update README.md

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