text: from transformers import AutoProcessor
  from pyctcdecode import build_ctcdecoder
  from huggingface_hub import Repository
  
  
  with open("5gram.arpa", "r") as read_file, open("5gram_correct.arpa", "w") as write_file:
      has_added_eos = False
      for line in read_file:
      if not has_added_eos and "ngram 1=" in line:
          count=line.strip().split("=")[-1]
          write_file.write(line.replace(f"{count}", f"{int(count)+1}"))
      elif not has_added_eos and "<s>" in line:
          write_file.write(line)
          write_file.write(line.replace("<s>", "</s>"))
          has_added_eos = True
      else:
          write_file.write(line)
  
  
  processor = AutoProcessor.from_pretrained("mfleck/wav2vec2-large-xls-r-300m-german-with-lm")
  
  vocab_dict = processor.tokenizer.get_vocab()
  sorted_vocab_dict = {k.lower(): v for k, v in sorted(vocab_dict.items(), key=lambda item: item[1])}
  
  
  decoder = build_ctcdecoder(
      labels=list(sorted_vocab_dict.keys()),
      kenlm_model_path="/home/debian/boost/5gram_correct.arpa",
  )
  
  from transformers import Wav2Vec2ProcessorWithLM
  
  processor_with_lm = Wav2Vec2ProcessorWithLM(
      feature_extractor=processor.feature_extractor,
      tokenizer=processor.tokenizer,
      decoder=decoder
  )
  
  
  
  repo = Repository(local_dir="wav2vec2-large-xls-r-300m-german-with-lm", clone_from="mfleck/wav2vec2-large-xls-r-300m-german-with-lm")
  
  processor_with_lm.save_pretrained("wav2vec2-large-xls-r-300m-german-with-lm")