Update README.md

README.md

@@ -126,7 +126,7 @@ def normalize(batch):
   return batch
 
 def map_wer(batch):
-  model.to(args.device)
+  model.to(device)
   forced_decoder_ids = processor.get_decoder_prompt_ids(language = "es", task = "transcribe")
   inputs = processor(batch["audio"]["array"], sampling_rate=batch["audio"]["sampling_rate"], return_tensors="pt").input_features
   with torch.no_grad():
@@ -138,10 +138,10 @@ def map_wer(batch):
 # process GOLD text
 processed_dataset = dataset.map(normalize)
 # get predictions
-predicted_dataset = processed_dataset.map(map_wer)
+predicted = processed_dataset.map(map_wer)
 
 # word error rate
-wer = wer_metric.compute(references=predicted_dataset['gold_text'], predictions=predicted_dataset['predicted_text'])
+wer = wer_metric.compute(references=predicted['gold_text'], predictions=predicted['predicted_text'])
 wer = round(100 * wer, 2)
 print("WER:", wer)