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Update README.md

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@@ -132,14 +132,14 @@ test_dataset = test_dataset.map(speech_file_to_array_fn)
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  # Preprocessing the datasets.
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  # We need to read the audio files as arrays
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  def evaluate(batch):
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- \\tinputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)
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- \\twith torch.no_grad():
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- \\t\\tlogits = model(inputs.input_values.to(DEVICE), attention_mask=inputs.attention_mask.to(DEVICE)).logits
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- \\tpred_ids = torch.argmax(logits, dim=-1)
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- \\tbatch["pred_strings"] = processor.batch_decode(pred_ids)
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- \\treturn batch
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  result = test_dataset.map(evaluate, batched=True, batch_size=8)
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@@ -152,7 +152,7 @@ print(f"CER: {cer.compute(predictions=predictions, references=references, chunk_
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  **Test Result**:
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- My model may report better scores than others because of some specificity of my evaluation script, so I ran the same evaluation script on other models (on 2021-04-22) to make a fairer comparison.
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  | Model | WER | CER |
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  | ------------- | ------------- | ------------- |
 
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  # Preprocessing the datasets.
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  # We need to read the audio files as arrays
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  def evaluate(batch):
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+ \\\\tinputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)
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+ \\\\twith torch.no_grad():
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+ \\\\t\\\\tlogits = model(inputs.input_values.to(DEVICE), attention_mask=inputs.attention_mask.to(DEVICE)).logits
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+ \\\\tpred_ids = torch.argmax(logits, dim=-1)
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+ \\\\tbatch["pred_strings"] = processor.batch_decode(pred_ids)
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+ \\\\treturn batch
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  result = test_dataset.map(evaluate, batched=True, batch_size=8)
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  **Test Result**:
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+ In the table below I report the Word Error Rate (WER) and the Character Error Rate (CER) of the model. I ran the evaluation script described above on other models as well (on 2021-04-22). Note that the table below may show different results from those already reported, this may have been caused due to some specificity of the other evaluation scripts used.
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  | Model | WER | CER |
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  | ------------- | ------------- | ------------- |