update evaluation result

README.md

@@ -54,15 +54,15 @@ 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):
-\tspeech_array, sampling_rate = torchaudio.load(batch["path"])
-\tbatch["speech"] = resampler(speech_array).squeeze().numpy()
-\treturn batch
+	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)
 inputs = processor(test_dataset["speech"][:2], sampling_rate=16_000, return_tensors="pt", padding=True)
 
 with torch.no_grad():
-\tlogits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits
+	logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits
 
 predicted_ids = torch.argmax(logits, dim=-1)
 
@@ -90,7 +90,7 @@ import argparse
 lang_id = "zh-HK" 
 model_id = "ctl/wav2vec2-large-xlsr-cantonese"
 
-chars_to_ignore_regex = '[\\,\\?\\.\\!\\-\\;\\:"\\“\\%\\‘\\”\\�\\．\\⋯\\！\\－\\：\\–\\。\\》\\,\\）\\,\\？\\；\\～\\~\\…\\︰\\，\\（\\」\\‧\\《\\﹔\\、\\—\\／\\,\\「\\﹖\\·\\']'
+chars_to_ignore_regex = '[\,\?\.\!\-\;\:"\“\%\‘\”\�\．\⋯\！\－\：\–\。\》\,\）\,\？\；\～\~\…\︰\，\（\」\‧\《\﹔\、\—\／\,\「\﹖\·\']'
 
 test_dataset = load_dataset("common_voice", f"{lang_id}", split="test") 
 cer = load_metric("./cer")
@@ -127,22 +127,6 @@ result = test_dataset.map(evaluate, batched=True, batch_size=16)
 print("CER: {:2f}".format(100 * cer.compute(predictions=result["pred_strings"], references=result["sentence"])))
 ```
 
-### Character Error Rate implementation
-
-Adapting code from [wer](https://github.com/huggingface/datasets/blob/master/metrics/wer/wer.py)
-
-```python
-@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION, _KWARGS_DESCRIPTION)
-class CER(datasets.Metric):
-    def _info(self):
-    \t...
-
-    def _compute(self, predictions, references):
-        preds = [char for seq in predictions for char in list(seq)]
-        refs = [char for seq in references for char in list(seq)]
-        return wer(refs, preds)
-```
-
 
 **Test Result**: 15.51 %