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Update README.md

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  1. README.md +3 -3
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@@ -23,7 +23,7 @@ model-index:
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  metrics:
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  - name: Test WER
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  type: wer
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- value: 50.92
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  ---
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  # Wav2Vec2-Large-XLSR-53-Irish
@@ -87,7 +87,7 @@ processor = Wav2Vec2Processor.from_pretrained("cpierse/wav2vec2-large-xlsr-53-ir
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  model = Wav2Vec2ForCTC.from_pretrained("cpierse/wav2vec2-large-xlsr-53-irish")
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  model.to("cuda")
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- chars_to_ignore_regex = '[\\,\\?\\.\\!\\-\\;\\:\\"\\“]'
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  resampler = torchaudio.transforms.Resample(48_000, 16_000)
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  # Preprocessing the datasets.
@@ -117,5 +117,5 @@ result = test_dataset.map(evaluate, batched=True, batch_size=8)
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  print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"])))
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  ```
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- **Test Result**: 50.92 %
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  metrics:
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  - name: Test WER
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  type: wer
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+ value: 43.06
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  ---
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  # Wav2Vec2-Large-XLSR-53-Irish
 
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  model = Wav2Vec2ForCTC.from_pretrained("cpierse/wav2vec2-large-xlsr-53-irish")
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  model.to("cuda")
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+ chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"\“\%\‘\”\�\„\«\(\»\)\’\']'
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  resampler = torchaudio.transforms.Resample(48_000, 16_000)
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  # Preprocessing the datasets.
 
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  print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"])))
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  ```
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+ **Test Result**: 43.06 %
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