update readme

README.md

@@ -87,20 +87,20 @@ resampler = lambda sr, y: librosa.resample(y.numpy().squeeze(), sr, 16_000)
 
 # Preprocessing the datasets.
 def speech_file_to_array_fn(batch):
-     batch["sentence"] = wakati.parse(batch["sentence"]).strip()
-     batch["sentence"] = re.sub(chars_to_ignore_regex,'', batch["sentence"]).strip()
-     speech_array, sampling_rate = torchaudio.load(batch["path"])
-     batch["speech"] = resampler(sampling_rate, speech_array).squeeze()
-     return batch
+    batch["sentence"] = wakati.parse(batch["sentence"]).strip()
+    batch["sentence"] = re.sub(chars_to_ignore_regex,'', batch["sentence"]).strip()
+    speech_array, sampling_rate = torchaudio.load(batch["path"])
+    batch["speech"] = resampler(sampling_rate, speech_array).squeeze()
+    return batch
 test_dataset = test_dataset.map(speech_file_to_array_fn)
 
 # evaluate function
 def evaluate(batch):
     inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)
     with torch.no_grad():
-    logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits
-    pred_ids = torch.argmax(logits, dim=-1)
-    batch["pred_strings"] = processor.batch_decode(pred_ids)
+      logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits
+      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)
 print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"])))