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@@ -19,10 +19,10 @@ 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: 0.2934
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  - name: Test CER
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  type: cer
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- value: 0.0786
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  - task:
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  name: Automatic Speech Recognition
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  type: automatic-speech-recognition
@@ -33,10 +33,10 @@ 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: 0.5209
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  - name: Test CER
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  type: cer
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- value: 0.1790
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  datasets:
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  - mozilla-foundation/common_voice_15_0
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  language:
@@ -52,8 +52,8 @@ should probably proofread and complete it, then remove this comment. -->
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  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.
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  It achieves the following results on the evaluation set:
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  - Loss: 0.3611
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- - Wer: 0.2992
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- - Cer: 0.0786
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  View the results on Kaggle Notebook: https://www.kaggle.com/code/kingabzpro/wav2vec-2-eval
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@@ -105,13 +105,13 @@ def evaluate(batch):
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  result = test_dataset.map(evaluate, batched=True, batch_size=8)
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- print("WER: {}".format(wer.compute(predictions=result["pred_strings"], references=result["sentence"])))
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- print("CER: {}".format(cer.compute(predictions=result["pred_strings"], references=result["sentence"])))
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  ```
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- **WER: 0.5209850206372026**
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- **CER: 0.17902923538230883**
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  ### Training hyperparameters
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  metrics:
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  - name: Test WER
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  type: wer
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+ value: 29.34
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  - name: Test CER
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  type: cer
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+ value: 7.86
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  - task:
27
  name: Automatic Speech Recognition
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  type: automatic-speech-recognition
 
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  metrics:
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  - name: Test WER
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  type: wer
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+ value: 52.09
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  - name: Test CER
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  type: cer
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+ value: 17.90
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  datasets:
41
  - mozilla-foundation/common_voice_15_0
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  language:
 
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  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.
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  It achieves the following results on the evaluation set:
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  - Loss: 0.3611
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+ - Wer: 29.92%
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+ - Cer: 7.86%
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  View the results on Kaggle Notebook: https://www.kaggle.com/code/kingabzpro/wav2vec-2-eval
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  result = test_dataset.map(evaluate, batched=True, batch_size=8)
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+ print("WER: {}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"])))
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+ print("CER: {}".format(100 * cer.compute(predictions=result["pred_strings"], references=result["sentence"])))
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  ```
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+ **WER: 52.09850206372026**
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+ **CER: 17.902923538230883**
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  ### Training hyperparameters
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