From 4ff2b2f6a6fc6c5bd7a3f35007d8f3c58a576fb4 Mon Sep 17 00:00:00 2001
From: Anand <40825655+anand372@users.noreply.github.com>
Date: Sun, 22 Sep 2019 19:36:52 +0530
Subject: [PATCH] Update README.md

some typo errors which were identified:
integrade->integrate
listenning->listening to
---
 README.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/README.md b/README.md
index 50b62059..dc29a569 100644
--- a/README.md
+++ b/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