Update README.md

README.md

@@ -44,19 +44,12 @@ To transcribe audio files the model can be used as a standalone acoustic model a
  # load model and tokenizer
  processor = Wav2Vec2Processor.from_pretrained("facebook/wav2vec2-base-960h")
  model = Wav2Vec2ForCTC.from_pretrained("facebook/wav2vec2-base-960h")
- 
- # define function to read in sound file
- def map_to_array(batch):
-     speech, _ = sf.read(batch["file"])
-     batch["speech"] = speech
-     return batch
      
  # load dummy dataset and read soundfiles
  ds = load_dataset("patrickvonplaten/librispeech_asr_dummy", "clean", split="validation")
- ds = ds.map(map_to_array)
  
  # tokenize
- input_values = processor(ds["speech"][:2], return_tensors="pt", padding="longest").input_values  # Batch size 1
+ input_values = processor(ds[0]["audio"]["array"], return_tensors="pt", padding="longest").input_values  # Batch size 1
  
  # retrieve logits
  logits = model(input_values).logits