class_docstr: A ready-to-use Whisper ASR model
  
  The class can be used  to  run the entire encoder-decoder whisper model
  (transcribe()) to transcribe speech. The given YAML must contains the fields
  specified in the *_NEEDED[] lists.
  
  Example
  -------
  >>> from speechbrain.inference.ASR import WhisperASR
  >>> tmpdir = getfixture("tmpdir")
  >>> asr_model = WhisperASR.from_hparams(source="speechbrain/asr-whisper-large-v2-commonvoice-fr", savedir=tmpdir,) # doctest: +SKIP
  >>> asr_model.transcribe_file("speechbrain/asr-whisper-large-v2-commonvoice-fr/example-fr.mp3") # doctest: +SKIP
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code: def __init__(self, *args, **kwargs):
      super().__init__(*args, **kwargs)
      self.tokenizer = self.hparams.whisper.tokenizer
      self.tokenizer.set_prefix_tokens(
          self.hparams.language, "transcribe", False
      )
      self.hparams.decoder.set_decoder_input_tokens(
          self.tokenizer.prefix_tokens
      )
  
imports: import torch
  import sentencepiece
  import speechbrain
  from speechbrain.inference.interfaces import Pretrained
  from speechbrain.utils.fetching import fetch
  from speechbrain.utils.data_utils import split_path
label_desc: Use image node_id 1 to create a new WhisperASR object from inherited base classes: Pretrained with example: obj = WhisperASR()