update readme to working code

README.md

@@ -45,18 +45,19 @@ For more information on how the model was trained, please take a look at the [of
 To transcribe audio files the model can be used as a standalone acoustic model as follows:
 ​
 ```python
-​import torch
+import torch
 import torchaudio
+from datasets import load_dataset
 from transformers import MCTCTForCTC, MCTCTProcessor
 
-model = MCTCTForCTC.from_pretrained("speechbrain/mctct-large")
-processor = MCTCTProcessor.from_pretrained("speechbrain/mctct-large")
+model = MCTCTForCTC.from_pretrained("cwkeam/mctct-large")
+processor = MCTCTProcessor.from_pretrained("cwkeam/mctct-large")
 
  # load dummy dataset and read soundfiles
 ds = load_dataset("patrickvonplaten/librispeech_asr_dummy", "clean", split="validation")
  
 # tokenize
-input_features = processor(ds[0]["audio"]["array"], return_tensors="pt", padding="longest").input_features  # Batch size 1
+input_features = processor(ds[0]["audio"]["array"], return_tensors="pt").input_features 
 
 # retrieve logits
 logits = model(input_features).logits