Update README.md

README.md

@@ -23,16 +23,17 @@ We build a CTC-based phoneme recognition model using wav2vec 2.0 (W2V2) for chil
 We show W2V2-MyST-Pro is helpful for improving children's vocalization classification task on two corpus, including [Rapid-ABC](https://openaccess.thecvf.com/content_cvpr_2013/html/Rehg_Decoding_Childrens_Social_2013_CVPR_paper.html) and [BabbleCor](https://osf.io/rz4tx/). 
 
 ## Model Sources
-For more information regarding this model, please checkout our paper: (TO-DO)
+For more information regarding this model, please checkout our paper:
 - **Paper:** https://arxiv.org/pdf/2309.07287.pdf
   
 ## Model Description
 
 <!-- Provide a longer summary of what this model is. -->
 Folder contains the best checkpoint of the following setting
-- **W2V2-MyST by fine-tuning on Librispeech 960h**: save_960h/wav2vec2.ckpt
-- **W2V2-Pro trained on phone sequence**: save_MyST_Providence_ep45_filtered/wav2vec2.ckpt
-- **W2V2-Pro trained on consonant/vowel sequence**: save_MyST_Providence_ep45_filtered_cv_only/wav2vec2.ckpt
+- **W2V2-Libri100h**: save_100h/wav2vec2.ckpt
+- **W2V2-MyST**: save_100h_MyST/wav2vec2.ckpt
+- **W2V2-Libri100h-Pro**: save_100h_Providence/wav2vec2.ckpt
+- **W2V2-MyST-Pro**: save_100h_MyST_Providence/wav2vec2.ckpt
 
 ## Uses
 **We develop our complete fine-tuning recipe using SpeechBrain toolkit available at**