Recalculate metrics on the whole test set

README.md

@@ -23,10 +23,10 @@ model-index:
     metrics:
        - name: Test WER
          type: wer
-         value: 42.00
+         value: 42.26
 ---
 
-# Wav2Vec2-Large-XLSR-53-Sakha
+# Wav2Vec2-Large-XLSR-53-Hungarian
 
 Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Hungarian using the [Common Voice](https://huggingface.co/datasets/common_voice) dataset.
 When using this model, make sure that your speech input is sampled at 16kHz.
@@ -94,7 +94,7 @@ processor = Wav2Vec2Processor.from_pretrained("anton-l/wav2vec2-large-xlsr-53-hu
 model = Wav2Vec2ForCTC.from_pretrained("anton-l/wav2vec2-large-xlsr-53-hungarian")
 model.to("cuda")
 
-cv_test = pd.read_csv("cv-corpus-6.1-2020-12-11/hu/test.tsv", sep='\t')
+cv_test = pd.read_csv("cv-corpus-6.1-2020-12-11/hu/test.tsv", sep='\\t')
 clips_path = "cv-corpus-6.1-2020-12-11/hu/clips/"
 
 def clean_sentence(sent):
@@ -127,7 +127,7 @@ for i, row in tqdm(cv_test.iterrows(), total=cv_test.shape[0]):
 print("WER: {:2f}".format(100 * wer.compute(predictions=preds, references=targets)))
 ```
 
-**Test Result**: 42.00 %  
+**Test Result**: 42.26 %  
 
 
 ## Training