updated WER score

README.md

@@ -1,3 +1,4 @@
+---
 language: nl
 datasets:
 - common_voicemetrics:
@@ -11,7 +12,7 @@ license: apache-2.0
 model-index:
 - name: `simonsr XLSR Wav2Vec2 Large 53`
   results:
-  - task: 
+  - task:
       name: Speech Recognition
       type: automatic-speech-recognition
     dataset:
@@ -21,7 +22,7 @@ model-index:
     metrics:
        - name: Test WER
          type: wer
-         value: {wer_result_on_test} #TODO (IMPORTANT): replace {wer_result_on_test} with the WER error rate you achieved on the common_voice test set. It should be in the format XX.XX (don't add the % sign here). **Please** remember to fill out this value after you evaluated your model, so that your model appears on the leaderboard. If you fill out this model card before evaluating your model, please remember to edit the model card afterward to fill in your value
+         value: 38.74
 ---
 
 # Wav2Vec2-Large-XLSR-53-Dutch
@@ -79,7 +80,7 @@ from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
 import unidecode
 import re
 
-test_dataset = load_dataset("common_voice", "nl", split="test") 
+test_dataset = load_dataset("common_voice", "nl", split="test")
 wer = load_metric("wer")
 
 processor = Wav2Vec2Processor.from_pretrained("{model_id}") #TODO: replace {model_id} with your model id. The model id consists of {your_username}/{your_modelname}, *e.g.* `elgeish/wav2vec2-large-xlsr-53-arabic`
@@ -91,7 +92,7 @@ resampler = torchaudio.transforms.Resample(48_000, 16_000)
 
 # Preprocessing the datasets.
 # We need to read the aduio files as arrays
-def speech_file_to_array_fn(batch):  
+def speech_file_to_array_fn(batch):
   batch["sentence"] = unidecode.unidecode(batch["sentence"])
 	batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower()
 	speech_array, sampling_rate = torchaudio.load(batch["path"])
@@ -117,8 +118,7 @@ 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**: XX.XX %  # TODO: write output of print here. IMPORTANT: Please remember to also replace {wer_result_on_test} at the top of with this value here. tags.
-
+**Test Result**: 38.74 %
 
 ## Training