Update README.md

README.md

@@ -50,20 +50,13 @@ The model can be used for automatic-speech-recognition as follows:
 import torch
 from transformers import Wav2Vec2Processor, HubertForCTC
 from datasets import load_dataset
-import soundfile as sf
 
 processor = Wav2Vec2Processor.from_pretrained("facebook/hubert-xlarge-ls960-ft")
 model = HubertForCTC.from_pretrained("facebook/hubert-xlarge-ls960-ft")
-
-def map_to_array(batch):
-    speech, _ = sf.read(batch["file"])
-    batch["speech"] = speech
-    return batch
     
 ds = load_dataset("patrickvonplaten/librispeech_asr_dummy", "clean", split="validation")
-ds = ds.map(map_to_array)
 
-input_values = processor(ds["speech"][0], return_tensors="pt").input_values  # Batch size 1
+input_values = processor(ds[0]["audio"]["array"],, return_tensors="pt").input_values  # Batch size 1
 logits = model(input_values).logits
 predicted_ids = torch.argmax(logits, dim=-1)
 transcription = processor.decode(predicted_ids[0])