Clarify GPU Id use with vocoder training

vocoder/README.md

@@ -16,19 +16,19 @@ You can see here an example (Soon)[Colab Notebook]() training MelGAN with LJSpee
 
 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'''
 
-You need to define other relevant parameters in your ```config.json``` and then start traning with the following command from Mozilla TTS root path.
+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.
 
-```CUDA_VISIBLE_DEVICES='1' python vocoder/train.py --config_path path/to/config.json```
+```CUDA_VISIBLE_DEVICES='0' python vocoder/train.py --config_path path/to/config.json```
 
 Exampled config files can be found under `vocoder/configs/` folder.
 
 You can continue a previous training by the following command.
 
-```CUDA_VISIBLE_DEVICES='1' python vocoder/train.py --continue_path path/to/your/model/folder```
+```CUDA_VISIBLE_DEVICES='0' python vocoder/train.py --continue_path path/to/your/model/folder```
 
 You can fine-tune a pre-trained model by the following command.
 
-```CUDA_VISIBLE_DEVICES='1' python vocoder/train.py --restore_path path/to/your/model.pth.tar```
+```CUDA_VISIBLE_DEVICES='0' python vocoder/train.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.