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 process_example(
      self, value: tuple[int, np.ndarray] | str | Path | bytes | None
  ) -> str:
      if value is None:
          return ""
      elif isinstance(value, (str, Path)):
          return Path(value).name
      return "(audio)"
  
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 6 for calling the Audio obj's underlying member method code with example usage: obj.process_example(value) and returns: str, str, Path