class_docstr: Transcribes audio files using OpenAI's Whisper's model on your local machine.
  
  For the supported audio formats, languages, and other parameters, see the
  [Whisper API documentation](https://platform.openai.com/docs/guides/speech-to-text) and the official Whisper
  [github repo](https://github.com/openai/whisper).
class_docstr_tok: ["Transcribes","audio","files","using","OpenAI","'s","Whisper","'s","model","on","your","local","machine",".","For","the","supported","audio","formats",",","languages",",","and","other","parameters",",","see","the","[","Whisper","API","documentation","]","(","https",":","\/\/platform.openai.com\/docs\/guides\/speech-to-text",")","and","the","official","Whisper","[","github","repo","]","(","https",":","\/\/github.com\/openai\/whisper",")","."]
docstr: Transcribe the audio files into a list of Documents, one for each input file.
  
  For the supported audio formats, languages, and other parameters, see the
  [Whisper API documentation](https://platform.openai.com/docs/guides/speech-to-text) and the official Whisper
  [github repo](https://github.com/openai/whisper).
  
  :param audio_files: A list of paths or binary streams to transcribe.
  :returns: A list of Documents, one for each file. The content of the document is the transcription text,
      while the document's metadata contains all the other values returned by the Whisper model, such as the
      alignment data. Another key called `audio_file` contains the path to the audio file used for the
      transcription.
docstr_tok: ["Transcribe","the","audio","files","into","a","list","of","Documents",",","one","for","each","input","file",".","For","the","supported","audio","formats",",","languages",",","and","other","parameters",",","see","the","[","Whisper","API","documentation","]","(","https",":","\/\/platform.openai.com\/docs\/guides\/speech-to-text",")","and","the","official","Whisper","[","github","repo","]","(","https",":","\/\/github.com\/openai\/whisper",")",".",":","param","audio_files",":","A","list","of","paths","or","binary","streams","to","transcribe",".",":","returns",":","A","list","of","Documents",",","one","for","each","file",".","The","content","of","the","document","is","the","transcription","text",",","while","the","document","'s","metadata","contains","all","the","other","values","returned","by","the","Whisper","model",",","such","as","the","alignment","data",".","Another","key","called","`","audio_file","`","contains","the","path","to","the","audio","file","used","for","the","transcription","."]
code: def run(
      self,
      sources: List[Union[str, Path, ByteStream]],
      whisper_params: Optional[Dict[str, Any]] = None,
  ):
      """
      Transcribe the audio files into a list of Documents, one for each input file.
  
      For the supported audio formats, languages, and other parameters, see the
      [Whisper API documentation](https://platform.openai.com/docs/guides/speech-to-text) and the official Whisper
      [github repo](https://github.com/openai/whisper).
  
      :param audio_files: A list of paths or binary streams to transcribe.
      :returns: A list of Documents, one for each file. The content of the document is the transcription text,
          while the document's metadata contains all the other values returned by the Whisper model, such as the
          alignment data. Another key called `audio_file` contains the path to the audio file used for the
          transcription.
      """
      if self._model is None:
          raise ComponentError(
              "The component was not warmed up. Run 'warm_up()' before calling 'run()'."
          )
  
      if whisper_params is None:
          whisper_params = self.whisper_params
  
      documents = self.transcribe(sources, **whisper_params)
      return {"documents": documents}
  
file_path: haystack/haystack/components/audio/whisper_local.py
imports: from typing import List, Optional, Dict, Any, Union, Literal, get_args
  import logging
  import tempfile
  from pathlib import Path
  from haystack import component, Document, default_to_dict, ComponentError
  from haystack.dataclasses import ByteStream
  from haystack.lazy_imports import LazyImport
label_desc: Use image node_id 4 for calling the LocalWhisperTranscriber obj's underlying member method code with example usage: obj.run(sources, whisper_params) and returns: dict