Update README.md

some typo errors which were identified:
integrade->integrate 
listenning->listening to

README.md

@@ -69,7 +69,7 @@ Audio length is approximately 6 secs.
 
 
 ## Datasets and Data-Loading
-TTS provides a generic dataloder easy to use for new datasets. You need to write an preprocessor function to integrade your own dataset.Check ```datasets/preprocess.py``` to see some examples. After the function, you need to set ```dataset``` field in ```config.json```. Do not forget other data related fields too.  
+TTS provides a generic dataloder easy to use for new datasets. You need to write an preprocessor function to integrate your own dataset.Check ```datasets/preprocess.py``` to see some examples. After the function, you need to set ```dataset``` field in ```config.json```. Do not forget other data related fields too.  
 
 Some of the open-sourced datasets that we successfully applied TTS, are linked below.
 
@@ -82,7 +82,7 @@ Some of the open-sourced datasets that we successfully applied TTS, are linked b
 ## Training and Fine-tuning LJ-Speech
 Here you can find a [CoLab](https://gist.github.com/erogol/97516ad65b44dbddb8cd694953187c5b) notebook for a hands-on example, training LJSpeech. Or you can manually follow the guideline below. 
 
-To start with, split ```metadata.csv``` into train and validation subsets respectively ```metadata_train.csv``` and ```metadata_val.csv```. Note that for text-to-speech, validation performance might be misleading since the loss value does not directly measure the voice quality to the human ear and it also does not measure the attention module performance. Therefore, running the model with new sentences and listenning the results is the best way to go. 
+To start with, split ```metadata.csv``` into train and validation subsets respectively ```metadata_train.csv``` and ```metadata_val.csv```. Note that for text-to-speech, validation performance might be misleading since the loss value does not directly measure the voice quality to the human ear and it also does not measure the attention module performance. Therefore, running the model with new sentences and listening to the results is the best way to go. 
 
 ```
 shuf metadata.csv > metadata_shuf.csv