Fix readme again

README.md

@@ -87,49 +87,49 @@ processor = Wav2Vec2Processor.from_pretrained("PereLluis13/Wav2Vec2-Large-XLSR-5
 model = Wav2Vec2ForCTC.from_pretrained("PereLluis13/Wav2Vec2-Large-XLSR-53-catalan") 
 model.to("cuda")
 
-chars_to_ignore_regex = '[\\,\\?\\.\\!\\;\\:\\"\\“]'
+chars_to_ignore_regex = '[\,\?\.\!\;\:\"\“]'
 resampler = torchaudio.transforms.Resample(48_000, 16_000)
 
 # Preprocessing the datasets.
 # We need to read the aduio files as arrays
 def speech_file_to_array_fn(batch):
-\tbatch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower()
-\tspeech_array, sampling_rate = torchaudio.load(batch["path"])
-\tbatch["speech"] = resampler(speech_array).squeeze().numpy()
-\treturn batch
+    batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower()
+    speech_array, sampling_rate = torchaudio.load(batch["path"])
+    batch["speech"] = resampler(speech_array).squeeze().numpy()
+    return batch
 
 test_dataset = test_dataset.map(speech_file_to_array_fn)
 
 # Preprocessing the datasets.
 # We need to read the aduio files as arrays
 def evaluate(batch):
-\tinputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)
+    inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)
 
-\twith torch.no_grad():
-\t\tlogits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits
+    with torch.no_grad():
+        logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits
 
-\tpred_ids = torch.argmax(logits, dim=-1)
-\tbatch["pred_strings"] = processor.batch_decode(pred_ids)
-\treturn batch
+    pred_ids = torch.argmax(logits, dim=-1)
+    batch["pred_strings"] = processor.batch_decode(pred_ids)
+    return batch
 
 result = test_dataset.map(evaluate, batched=True, batch_size=8)
 import jiwer
 
 # Chunk WER computation due to memory issues, taken from https://huggingface.co/pcuenq/wav2vec2-large-xlsr-53-es
 def chunked_wer(targets, predictions, chunk_size=None):                                          
-\tif chunk_size is None: return jiwer.wer(targets, predictions)                                
-\tstart = 0                                                                                    
-\tend = chunk_size                                                                             
-\tH, S, D, I = 0, 0, 0, 0                                                                      
-\twhile start < len(targets):                                                                  
-\t\tchunk_metrics = jiwer.compute_measures(targets[start:end], predictions[start:end])       
-\t\tH = H + chunk_metrics["hits"]                                                            
-\t\tS = S + chunk_metrics["substitutions"]                                                   
-\t\tD = D + chunk_metrics["deletions"]                                                       
-\t\tI = I + chunk_metrics["insertions"]                                                      
-\t\tstart += chunk_size                                                                      
-\t\tend += chunk_size                                                                        
-\treturn float(S + D + I) / float(H + S + D)
+    if chunk_size is None: return jiwer.wer(targets, predictions)                                
+    start = 0                                                                                    
+    end = chunk_size                                                                             
+    H, S, D, I = 0, 0, 0, 0                                                                      
+    while start < len(targets):                                                                  
+        chunk_metrics = jiwer.compute_measures(targets[start:end], predictions[start:end])       
+        H = H + chunk_metrics["hits"]                                                            
+        S = S + chunk_metrics["substitutions"]                                                   
+        D = D + chunk_metrics["deletions"]                                                       
+        I = I + chunk_metrics["insertions"]                                                      
+        start += chunk_size                                                                      
+        end += chunk_size                                                                        
+    return float(S + D + I) / float(H + S + D)
 
 print("WER: {:2f}".format(100 * chunked_wer(result["sentence"], result["pred_strings"], chunk_size=4000)))
 ```