Update README.md

README.md

@@ -77,6 +77,11 @@ Please visit - http://web.eecs.umich.edu/~mihalcea/downloads/MELD.Raw.tar.gz to
 - In any folder, each video clip in the raw data corresponds to one utterance in the corresponding .csv file. The video clips are named in the format: diaX1\_uttX2.mp4, where X1 is the Dialogue\_ID and X2 is the Utterance_ID as provided in the corresponding .csv file, denoting the particular utterance.
 - For example, consider the video clip **dia6_utt1.mp4** in **train.tar.gz**. The corresponding utterance for this video clip will be in the file **train_sent_emp.csv** with **Dialogue_ID=6** and **Utterance_ID=1**, which is *'You liked it? You really liked it?'*
 
+# Reading the Data
+There are 2 python scripts provided in './utils/':
+- read_meld.py \- displays the path of the video file corresponding to an utterance in the .csv file from MELD.
+- read_emorynlp \- displays the path of the video file corresponding to an utterance in the .csv file from Multimodal EmoryNLP Emotion Detection dataset.
+
 ## Labelling
 For experimentation, all the labels are represented as one-hot encodings, the indices for which are as follows:
 - **Emotion** - {'neutral': 0, 'surprise': 1, 'fear': 2, 'sadness': 3, 'joy': 4, 'disgust': 5, 'anger': 6}. Therefore, the label corresponding to the emotion *'joy'* would be [0., 0., 0., 0., 1., 0., 0.]