Update README.md

README.md

@@ -132,14 +132,14 @@ test_dataset = test_dataset.map(speech_file_to_array_fn)
 # Preprocessing the datasets.
 # We need to read the audio files as arrays
 def evaluate(batch):
-\\tinputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)
+\\\\tinputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)
 
-\\twith torch.no_grad():
-\\t\\tlogits = model(inputs.input_values.to(DEVICE), attention_mask=inputs.attention_mask.to(DEVICE)).logits
+\\\\twith torch.no_grad():
+\\\\t\\\\tlogits = model(inputs.input_values.to(DEVICE), attention_mask=inputs.attention_mask.to(DEVICE)).logits
 
-\\tpred_ids = torch.argmax(logits, dim=-1)
-\\tbatch["pred_strings"] = processor.batch_decode(pred_ids)
-\\treturn batch
+\\\\tpred_ids = torch.argmax(logits, dim=-1)
+\\\\tbatch["pred_strings"] = processor.batch_decode(pred_ids)
+\\\\treturn batch
 
 result = test_dataset.map(evaluate, batched=True, batch_size=8)
 
@@ -152,7 +152,7 @@ print(f"CER: {cer.compute(predictions=predictions, references=references, chunk_
 
 **Test Result**:
 
-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.
+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.
 
 | Model | WER | CER |
 | ------------- | ------------- | ------------- |