Update README.md (#2)

- Update README.md (0bb7df249f92aa7919cd954970640c5c1824ed90)


Co-authored-by: Abid Ali Awan <kingabzpro@users.noreply.huggingface.co>

README.md

@@ -19,10 +19,10 @@ model-index:
     metrics:
       - name: Test WER
         type: wer
-        value: 0.2934
+        value: 29.34
       - name: Test CER
         type: cer
-        value: 0.0786
+        value: 7.86
   - task:
       name: Automatic Speech Recognition
       type: automatic-speech-recognition
@@ -33,10 +33,10 @@ model-index:
     metrics:
       - name: Test WER
         type: wer
-        value: 0.5209
+        value: 52.09
       - name: Test CER
         type: cer
-        value: 0.1790
+        value: 17.90
 datasets:
 - mozilla-foundation/common_voice_15_0
 language:
@@ -52,8 +52,8 @@ should probably proofread and complete it, then remove this comment. -->
 This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the None dataset.
 It achieves the following results on the evaluation set:
 - Loss: 0.3611
-- Wer: 0.2992
-- Cer: 0.0786
+- Wer: 29.92%
+- Cer: 7.86%
 
 View the results on Kaggle Notebook: https://www.kaggle.com/code/kingabzpro/wav2vec-2-eval
 
@@ -105,13 +105,13 @@ def evaluate(batch):
 
 result = test_dataset.map(evaluate, batched=True, batch_size=8)
 
-print("WER: {}".format(wer.compute(predictions=result["pred_strings"], references=result["sentence"])))
-print("CER: {}".format(cer.compute(predictions=result["pred_strings"], references=result["sentence"])))
+print("WER: {}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"])))
+print("CER: {}".format(100 * cer.compute(predictions=result["pred_strings"], references=result["sentence"])))
 
 ```
-**WER: 0.5209850206372026**
+**WER: 52.09850206372026**
 
-**CER: 0.17902923538230883**
+**CER: 17.902923538230883**
 
 ### Training hyperparameters