class_docstr: A ready-to-use Encoder Decoder for speech-to-unit translation model
  
  The class can be used  to  run the entire encoder-decoder S2UT model
  (translate_file()) to translate speech. The given YAML must contains the fields
  specified in the *_NEEDED[] lists.
  
  Example
  -------
  >>> from speechbrain.inference.ST import EncoderDecoderS2UT
  >>> tmpdir = getfixture("tmpdir")
  >>> s2ut_model = EncoderDecoderS2UT.from_hparams(source="speechbrain/s2st-transformer-fr-en-hubert-l6-k100-cvss", savedir=tmpdir) # doctest: +SKIP
  >>> s2ut_model.translate_file("speechbrain/s2st-transformer-fr-en-hubert-l6-k100-cvss/example-fr.wav") # doctest: +SKIP
code: def __init__(self, *args, **kwargs):
      super().__init__(*args, **kwargs)
      self.sample_rate = self.hparams.sample_rate
  
file_path: speechbrain/speechbrain/inference/ST.py
imports: import torch
  from speechbrain.inference.interfaces import Pretrained
label_desc: Use image node_id 1 to create a new EncoderDecoderS2UT object from inherited base classes: Pretrained with example: obj = EncoderDecoderS2UT()