accuracy changes

README.md

@@ -24,10 +24,10 @@ model-index:
     metrics:
        - name: Test WER
          type: wer
-         value: 26.55
+         value: 36.69
        - name: Validation WER
          type: wer
-         value: 36.53
+         value: 36.69
 ---
 # Wav2Vec2-Large-XLSR-53-Arabic
 
@@ -197,9 +197,9 @@ result = test_dataset.map(evaluate, batched=True, batch_size=8)
 print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"])))
 ```
 
-**Test Result**: 36.53%
+**Test Result**: 36.69%
 
 
 ## Future Work
 
-One can use *data augmentation*, *transliteration*, or *attention_mask* to increase the accuracy.  
+One can use *data augmentation*, *transliteration*, or *attention_mask* to increase the accuracy.