Update README.md

Updated new model WER score

README.md

@@ -23,7 +23,7 @@ model-index:
     metrics:
        - name: Test WER
          type: wer
-         value: 35.815197
+         value: 33.333333
 ---
 
 # Wav2Vec2-Large-XLSR-53-Finnish
@@ -87,7 +87,7 @@ processor = Wav2Vec2Processor.from_pretrained("aapot/wav2vec2-large-xlsr-53-finn
 model = Wav2Vec2ForCTC.from_pretrained("aapot/wav2vec2-large-xlsr-53-finnish")
 model.to("cuda")
 
-chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"\“\%\‘\”\�\'\...\…\–\é]'
+chars_to_ignore_regex = '[\\,\\?\\.\\!\\-\\;\\:\\"\\“\\%\\‘\\”\\�\\'\\...\\…\\–\\é]'
 resampler = torchaudio.transforms.Resample(48_000, 16_000)
 
 # Preprocessing the datasets.
@@ -117,11 +117,11 @@ 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**: 35.815197 %
+**Test Result**: 33.333333 %
 
 
 ## Training
 
-The Common Voice `train` and `validation` datasets were used for training as well as CSS10 Finnish and Finnish parliament session 2 datasets.
+The Common Voice `train`, `validation` and `other` datasets were used for training as well as CSS10 Finnish and Finnish parliament session 2 datasets.
 
 The script used for training COMING SOON