class_docstr: Constructs a Speech2Text processor which wraps a Speech2Text feature extractor and a Speech2Text tokenizer into a
  single processor.
  
  [`Speech2TextProcessor`] offers all the functionalities of [`Speech2TextFeatureExtractor`] and
  [`Speech2TextTokenizer`]. See the [`~Speech2TextProcessor.__call__`] and [`~Speech2TextProcessor.decode`] for more
  information.
  
  Args:
      feature_extractor (`Speech2TextFeatureExtractor`):
          An instance of [`Speech2TextFeatureExtractor`]. The feature extractor is a required input.
      tokenizer (`Speech2TextTokenizer`):
          An instance of [`Speech2TextTokenizer`]. The tokenizer is a required input.
docstr: Temporarily sets the tokenizer for processing the input. Useful for encoding the labels when fine-tuning
  Speech2Text.
code: def as_target_processor(self):
      """
      Temporarily sets the tokenizer for processing the input. Useful for encoding the labels when fine-tuning
      Speech2Text.
      """
      warnings.warn(
          "`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your "
          "labels by using the argument `text` of the regular `__call__` method (either in the same call as "
          "your audio inputs, or in a separate call."
      )
      self._in_target_context_manager = True
      self.current_processor = self.tokenizer
      yield
      self.current_processor = self.feature_extractor
      self._in_target_context_manager = False
  
file_path: transformers/src/transformers/models/speech_to_text/processing_speech_to_text.py
imports: import warnings
  from contextlib import contextmanager
  from ...processing_utils import ProcessorMixin
label_desc: Use image node_id 5 for calling the Speech2TextProcessor obj's underlying member method code with example usage: obj.as_target_processor() without return types