class_docstr: This class is taken from the original WHAMR! scripts.
  The original script can be found in
  http://wham.whisper.ai/
  
  This class is used to simulate the room-impulse-responses (RIRs) in the WHAMR dataset.
class_docstr_tok: ["This","class","is","taken","from","the","original","WHAMR","!","scripts",".","The","original","script","can","be","found","in","http",":","\/\/wham.whisper.ai\/","This","class","is","used","to","simulate","the","room-impulse-responses","(","RIRs",")","in","the","WHAMR","dataset","."]
code: def __init__(
      self, p, mics, s1, s2, T60, fs=16000, t0=0.0, sigma2_awgn=None
  ):
      self.T60 = T60
      self.max_rir_len = np.ceil(T60 * fs).astype(int)
  
      volume = p[0] * p[1] * p[2]
      surface_area = 2 * (p[0] * p[1] + p[0] * p[2] + p[1] * p[2])
      absorption = (
          24
          * volume
          * np.log(10.0)
          / (constants.get("c") * surface_area * T60)
      )
  
      # minimum max order to guarantee complete filter of length T60
      max_order = np.ceil(T60 * constants.get("c") / min(p)).astype(int)
  
      super().__init__(
          p,
          fs=fs,
          t0=t0,
          absorption=absorption,
          max_order=max_order,
          sigma2_awgn=sigma2_awgn,
          sources=None,
          mics=None,
      )
  
      self.add_source(s1)
      self.add_source(s2)
  
      self.add_microphone_array(
          pra.MicrophoneArray(np.array(mics).T, fs)
      )
  
file_path: speechbrain/recipes/WHAMandWHAMR/enhancement/wham_room.py
imports: import numpy
  import pyroomacoustics
  from pyroomacoustics.parameters import constants
  from scipy.signal import resample_poly
label_desc: Use image node_id 1 to create a new WhamRoom object from inherited base classes: pra.room with example: obj = WhamRoom(p, mics, s1, s2, T60, fs, t0, sigma2_awgn)