class_docstr: Creates an audio component that can be used to upload/record audio (as an input) or display audio (as an output).
  Preprocessing: passes the uploaded audio as a {Tuple(int, numpy.array)} corresponding to (sample rate in Hz, audio data as a 16-bit int array whose values range from -32768 to 32767), or as a {str} filepath, depending on `type`.
  Postprocessing: expects a {Tuple(int, numpy.array)} corresponding to (sample rate in Hz, audio data as a float or int numpy array) or as a {str} or {pathlib.Path} filepath or URL to an audio file, or bytes for binary content (recommended for streaming). Note: When converting audio data from float format to WAV, the audio is normalized by its peak value to avoid distortion or clipping in the resulting audio.
  Examples-format: a {str} filepath to a local file that contains audio.
  Demos: main_note, generate_tone, reverse_audio
  Guides: real-time-speech-recognition
code: def preprocess(
      self, payload: FileData | None
  ) -> tuple[int, np.ndarray] | str | None:
      if payload is None:
          return payload
  
      assert payload.path
      # Need a unique name for the file to avoid re-using the same audio file if
      # a user submits the same audio file twice
      temp_file_path = Path(payload.path)
      output_file_name = str(
          temp_file_path.with_name(
              f"{temp_file_path.stem}{temp_file_path.suffix}"
          )
      )
  
      sample_rate, data = processing_utils.audio_from_file(
          temp_file_path
      )
  
      duration = len(data) / sample_rate
      if self.min_length is not None and duration < self.min_length:
          raise Error(
              f"Audio is too short, and must be at least {self.min_length} seconds"
          )
      if self.max_length is not None and duration > self.max_length:
          raise Error(
              f"Audio is too long, and must be at most {self.max_length} seconds"
          )
  
      if self.type == "numpy":
          return sample_rate, data
      elif self.type == "filepath":
          output_file = str(
              Path(output_file_name).with_suffix(f".{self.format}")
          )
          processing_utils.audio_to_file(
              sample_rate, data, output_file, format=self.format
          )
          return output_file
      else:
          raise ValueError(
              "Unknown type: "
              + str(self.type)
              + ". Please choose from: 'numpy', 'filepath'."
          )
  
imports: from __future__ import annotations
  import dataclasses
  from pathlib import Path
  from typing import Any, Callable, Literal
  import httpx
  import numpy
  from gradio_client import utils
  from gradio_client.documentation import document, set_documentation_group
  from gradio import processing_utils, utils
  from gradio.components.base import Component, StreamingInput, StreamingOutput
  from gradio.data_classes import FileData
  from gradio.events import Events
  from gradio.exceptions import Error
label_desc: Use image node_id 3 for calling the Audio obj's underlying member method code with example usage: obj.preprocess(payload) and returns: payload, sample_rate,  data, output_file