old_contents: 
  import numpy as np
  
  from .vad import vad
  
  def trim(data, fs, fs_vad=16000, hoplength=30, vad_mode=0, thr = 0.015):
      """ Voice activity detection.
      Trim leading and trailing silence from an audio signal by using vad.
      Parameters
      ----------
      data : ndarray
          numpy array of mono (1 ch) speech data.
          1-d or 2-d, if 2-d, shape must be (1, time_length) or (time_length, 1).
          if data type is int, -32768 < data < 32767.
          if data type is float, -1 < data < 1.
      fs : int
          Sampling frequency of data.
      fs_vad : int, optional
          Sampling frequency for webrtcvad.
          fs_vad must be 8000, 16000, 32000 or 48000.
          Default is 16000.
      hoplength : int, optional
          Step size[milli second].
          hoplength must be 10, 20, or 30.
          Default is 0.1.
      vad_mode : int, optional
          set vad aggressiveness.
          As vad_mode increases, it becomes more aggressive.
          vad_mode must be 0, 1, 2 or 3.
          Default is 0.
  
      Returns
      -------
      trimed_data : ndarray
          trimed_data. trimed input data.
          If voice activity can't be detected, return None.
      """
      vact = vad(data, fs, fs_vad, hoplength, vad_mode)
      vact_diff = np.diff(vact).astype('int')
      start_i = np.where(vact_diff == 1)[0]
      end_i =   np.where(vact_diff == -1)[0]
      if len(start_i) == 0 and  len(end_i) == 0:
          return None
      if len(start_i) < 1:
          start_i = np.hstack((end_i, 0))
      if len(end_i) < 1:
          end_i = np.hstack((end_i, len(vact)-1))
      if end_i[0] <= start_i[0]:
          start_i = np.hstack((0,start_i))
      if len(start_i) > len(end_i):
          end_i = np.hstack((end_i, len(vact)-1))
      thr_ind=[]
  
      for i, (s, e) in enumerate(zip(start_i, end_i)):
          power = np.mean(data[s:e]**2)**0.5
          if power > thr:
              thr_ind.append(i)
      sec = (start_i[thr_ind[0]], end_i[thr_ind[-1]])
  
      return data[sec[0]:sec[1]]
new_contents: 
  import numpy as np
  
  from .vad import vad
  
  def trim(data, fs, fs_vad=16000, hoplength=30, vad_mode=0, thr = 0.015):
      """ Voice activity detection.
      Trim leading and trailing silence from an audio signal by using vad.
      Parameters
      ----------
      data : ndarray
          numpy array of mono (1 ch) speech data.
          1-d or 2-d, if 2-d, shape must be (1, time_length) or (time_length, 1).
          if data type is int, -32768 < data < 32767.
          if data type is float, -1 < data < 1.
      fs : int
          Sampling frequency of data.
      fs_vad : int, optional
          Sampling frequency for webrtcvad.
          fs_vad must be 8000, 16000, 32000 or 48000.
          Default is 16000.
      hoplength : int, optional
          Step size[milli second].
          hoplength must be 10, 20, or 30.
          Default is 0.1.
      vad_mode : int, optional
          set vad aggressiveness.
          As vad_mode increases, it becomes more aggressive.
          vad_mode must be 0, 1, 2 or 3.
          Default is 0.
  
      Returns
      -------
      trimed_data : ndarray
          trimed_data. trimed input data.
          If voice activity can't be detected, return None.
      """
      vact = vad(data, fs, fs_vad, hoplength, vad_mode)
      vact_diff = np.diff(vact).astype('int')
      start_i = np.where(vact_diff == 1)[0]
      end_i =   np.where(vact_diff == -1)[0]
      if len(start_i) == 0 and  len(end_i) == 0:
          return None
      if len(start_i) < 1:
          start_i = np.hstack((end_i, 0))
      if len(end_i) < 1:
          end_i = np.hstack((end_i, len(vact)-1))
      if end_i[0] <= start_i[0]:
          start_i = np.hstack((0,start_i))
      if len(start_i) > len(end_i):
          end_i = np.hstack((end_i, len(vact)-1))
      
      thr_ind=[]
      for i, (s, e) in enumerate(zip(start_i, end_i)):
          power = np.mean(data[s:e]**2)**0.5
          if power > thr:
              thr_ind.append(i)
  
      if len(thr_ind) == 0:
          return None
  
      sec = (start_i[thr_ind[0]], end_i[thr_ind[-1]])
  
      return data[sec[0]:sec[1]]
subject: fix all sections are below the threshold case