diff --git a/TTS/vocoder/README.md b/TTS/vocoder/README.md
index e3baf1f9..e0ae8f21 100644
--- a/TTS/vocoder/README.md
+++ b/TTS/vocoder/README.md
@@ -1,36 +1,37 @@
 # Mozilla TTS Vocoders (Experimental)
 
-We provide here different vocoder implementations which can be combined with our TTS models to enable "FASTER THAN REAL-TIME" end-to-end TTS stack.
+Here there are vocoder model implementations which can be combined with the other TTS models.
 
-Currently, there are implementations of the following models.
+Currently, following models are implemented:
 
 - Melgan
 - MultiBand-Melgan
+- ParallelWaveGAN
 - GAN-TTS (Discriminator Only)
 
-It is also very easy to adapt different vocoder models as we provide here a flexible and modular (but not too modular) framework.
+It is also very easy to adapt different vocoder models as we provide a flexible and modular (but not too modular) framework.
 
 ## Training a model
 
 You can see here an example (Soon)[Colab Notebook]() training MelGAN with LJSpeech dataset.
 
-In order to train a new model, you need to collecto all your wav files under a common parent folder and give this path to `data_path` field in '''config.json'''
+In order to train a new model, you need to gather all wav files into a folder and give this folder to `data_path` in '''config.json'''
 
-You need to define other relevant parameters in your ```config.json``` and then start traning with the following command from Mozilla TTS root path, where '0' is the Id of the GPU you wish to use.
+You need to define other relevant parameters in your ```config.json``` and then start traning with the following command.
 
-```CUDA_VISIBLE_DEVICES='0' python vocoder/train.py --config_path path/to/config.json```
+```CUDA_VISIBLE_DEVICES='0' python tts/bin/train_vocoder.py --config_path path/to/config.json```
 
-Exampled config files can be found under `vocoder/configs/` folder.
+Example config files can be found under `tts/vocoder/configs/` folder.
 
-You can continue a previous training by the following command.
+You can continue a previous training run by the following command.
 
-```CUDA_VISIBLE_DEVICES='0' python vocoder/train.py --continue_path path/to/your/model/folder```
+```CUDA_VISIBLE_DEVICES='0' python tts/bin/train_vocoder.py --continue_path path/to/your/model/folder```
 
 You can fine-tune a pre-trained model by the following command.
 
-```CUDA_VISIBLE_DEVICES='0' python vocoder/train.py --restore_path path/to/your/model.pth.tar```
+```CUDA_VISIBLE_DEVICES='0' python tts/bin/train_vocoder.py --restore_path path/to/your/model.pth.tar```
 
-Restoring a model starts a new training in a different output folder. It only restores model weights with the given checkpoint file. However, continuing a training starts from the same conditions the previous training run left off.
+Restoring a model starts a new training in a different folder. It only restores model weights with the given checkpoint file. However, continuing a training starts from the same directory where the previous training run left off.
 
 You can also follow your training runs on Tensorboard as you do with our TTS models.