Update README.md

README.md

@@ -12,27 +12,19 @@ pipeline_tag: automatic-speech-recognition
 
 ### Model Description
 
-This model is fine-tuned on the TIMIT dataset. 
-
-The dataset was preprocessed using Epitran for transliterating text into IPA. 
-
-- **Developed by:** [Eunjung Yeo]
-- **Model type:** [fine-tuned model]
-- **Language(s) (SLP):** [English]
-- **Finetuned from model [optional]:** [XLS-R-300m]
-
-### Model Sources [optional]
-
+- **Developed by:** Eunjung Yeo
+- **Model type:** phone recognizer
+- **Language(s) (SLP):** English
+- **Finetuned from model:** XLS-R-300m
 
 ### Direct Use
-Phone recognition
+- Phone recognition
 
 ### Downstream Use [optional]
 - Analysis of phonetic transcriptions
 - L2 Pronunciation Assessment (Mispronunciation Detection and Diagnosis)
 - Mispronunciation Assessment for pathological speech 
 
-
 ## How to Get Started with the Model
 from transformers import AutoProcessor, AutoModelForCTC
 
@@ -42,11 +34,11 @@ model = AutoModelForCTC.from_pretrained("speech31/XLS-R-english-phoneme")
 ## Training Details
 
 ### Training Data
-TIMIT dataset (Can be downloaded from https://catalog.ldc.upenn.edu/LDC93s1)
+This model is fine-tuned on the TIMIT dataset. 
+(Can be downloaded from https://catalog.ldc.upenn.edu/LDC93s1)
 
 #### Preprocessing 
-
-
+The dataset was preprocessed using Epitran for transliterating text into IPA. 
 
 #### Training Hyperparameters