Updated Readme.md

Provided vital information for the Model.

README.md

@@ -31,20 +31,31 @@ It achieves the following results on the evaluation set:
 - Loss: 0.2967
 - Wer: 0.1740
 
-## Model description
+## Usage
 
-More information needed
+In order to evaluate this model on an entire dataset, the evaluation codes available in the whisper-finetune repository can be used.
 
-## Intended uses & limitations
+The same repository also provides the scripts for faster inference using whisper-jax.
 
-More information needed
 
 ## Training and evaluation data
 
-More information needed
+Training Data: 
+  - [Snow Mountain Dataset for Kangri Language](https://huggingface.co/datasets/bridgeconn/snow-mountain)
+  
+Evaluation Data: 
+  - [Snow Mountain Dataset for Kangri Language](https://huggingface.co/datasets/bridgeconn/snow-mountain)
+  - [Kangri Translators Dataset ](https://drive.google.com/drive/folders/16BdOieekGRAo2bFOQDd4YhE2LpgiRnqQ?usp=share_link)
 
 ## Training procedure
 
+We implemented Cross-Lingual Phoneme Recognition - a process that leverages patterns in resource-rich languages such as Hindi to recognize utterances in resource-poor languages
+such as Kangri. By fine-tuning a pre-trained model of the Whisper-Hindi-Large-V2 on a customised dataset - we have achieved SoTa accuracy.
+A customised dataset - consisting of the brigdeconn/snow-mountain and sentences collected from Kangri translators was created. This was then split using the 80/20 
+split rule. The results were evaluated with 5000 steps. The model decreases the word error rate by 0.6% after the initial 1000 steps. The Validation Loss increases due to
+more data being introduced.
+
+
 ### Training hyperparameters
 
 The following hyperparameters were used during training: