Updating description

app.py

@@ -332,12 +332,12 @@ interface = gr.Interface(
     To efficiently compute text difficulty, a Distil-Bert pre-trained model is fine-tuned for regression using The CommonLit Ease of Readability (CLEAR) 
     Corpus. https://educationaldatamining.org/EDM2021/virtual/static/pdf/EDM21_paper_35.pdf This dataset contains over 110,000 pairwise comparisons of 
     ~1100 teachers responded to the question, "Which text is easier for students to understand?". This model is trained end-end (regression layer down to 
-    the first attention layer to ensure the best performance- Merchant et al. 2020
+    the first attention layer) to ensure the best performance- Merchant et al. 2020
     
     Speech Pronunciaion Scoring: The Wave2Vec 2.0 model is utilized to convert audio into text in real-time. The model predicts words or phonemes (smallest 
-    unit of speech distinguishing one word (or word element) from another) from the input audio from the user. Due to the nature of the model, users with poor 
-    pronunciation get inaccurate results. This project attempts to score pronunciation by asking a user to read a target excerpt into the microphone. We then
-    pass this audio through Wave2Vec to get the inferred intended words. We measure the loss as the Levenshtein distance between the target and actual transcripts- 
+    unit of speech distinguishing one word (or word element) from another) from the user input audio. Due to the nature of the model, users with poor 
+    pronunciation receive inaccurate translations. This project attempts to score pronunciation by asking a user to read a target excerpt into the microphone. We then
+    pass this audio through Wave2Vec 2.0 to get the inferred intended words. We measure the loss as the Levenshtein distance between the target and actual transcripts- 
     the Levenshtein distance between two words is the minimum number of single-character edits required to change one word into the other.
 
     Lexical Diversity Score: The lexical diversity score is computed by taking the ratio of unique similar words to total similar words squared. The similarity is computed