code: def slice(self, waveform):
      samples = (
          librosa.to_mono(waveform)
          if len(waveform.shape) > 1
          else waveform
      )
      if samples.shape[0] <= self.min_length:
          return {
              "0": {"slice": False, "split_time": f"0,{len(waveform)}"}
          }
      rms_list = librosa.feature.rms(
          y=samples,
          frame_length=self.win_size,
          hop_length=self.hop_size,
      ).squeeze(0)
      sil_tags, silence_start, clip_start = [], None, 0
      for i, rms in enumerate(rms_list):
          if (
              rms < self.threshold
          ):  # Keep looping while frame is silent.
              if (
                  silence_start is None
              ):  # Record start of silent frames.
                  silence_start = i
              continue
          if silence_start is None:
              continue  # Keep looping while frame is not silent and silence start has not been recorded.
          # Clear recorded silence start if interval is not enough or clip is too short
          is_leading_silence = (
              silence_start == 0 and i > self.max_sil_kept
          )
          need_slice_middle = (
              i - silence_start >= self.min_interval
              and i - clip_start >= self.min_length
          )
          if not is_leading_silence and not need_slice_middle:
              silence_start = None
              continue
          if (
              i - silence_start <= self.max_sil_kept
          ):  # Need slicing. Record the range of silent frames to be removed.
              pos = (
                  rms_list[silence_start : i + 1].argmin()
                  + silence_start
              )
              sil_tags.append(
                  (0, pos) if silence_start == 0 else (pos, pos)
              )
              clip_start = pos
          elif i - silence_start <= self.max_sil_kept * 2:
              pos = rms_list[
                  i
                  - self.max_sil_kept : silence_start
                  + self.max_sil_kept
                  + 1
              ].argmin()
              pos += i - self.max_sil_kept
              pos_l = (
                  rms_list[
                      silence_start : silence_start
                      + self.max_sil_kept
                      + 1
                  ].argmin()
                  + silence_start
              )
              pos_r = (
                  rms_list[i - self.max_sil_kept : i + 1].argmin()
                  + i
                  - self.max_sil_kept
              )
              if silence_start == 0:
                  sil_tags.append((0, pos_r))
                  clip_start = pos_r
              else:
                  sil_tags.append((min(pos_l, pos), max(pos_r, pos)))
                  clip_start = max(pos_r, pos)
          else:
              pos_l = (
                  rms_list[
                      silence_start : silence_start
                      + self.max_sil_kept
                      + 1
                  ].argmin()
                  + silence_start
              )
              pos_r = (
                  rms_list[i - self.max_sil_kept : i + 1].argmin()
                  + i
                  - self.max_sil_kept
              )
              sil_tags.append(
                  (0, pos_r) if silence_start == 0 else (pos_l, pos_r)
              )
              clip_start = pos_r
          silence_start = None
      total_frames = rms_list.shape[0]
      if (
          silence_start is not None
          and total_frames - silence_start >= self.min_interval
      ):  # Deal with trailing silence.
          silence_end = min(
              total_frames, silence_start + self.max_sil_kept
          )
          pos = (
              rms_list[silence_start : silence_end + 1].argmin()
              + silence_start
          )
          sil_tags.append((pos, total_frames + 1))
      if len(sil_tags) == 0:
          return {
              "0": {"slice": False, "split_time": f"0,{len(waveform)}"}
          }  # Apply and return slices.
      chunks = []
      if sil_tags[0][0]:
          chunks.append(
              {
                  "slice": False,
                  "split_time": f"0,{min(waveform.shape[0], sil_tags[0][0] * self.hop_size)}",
              }
          )
      for i in range(0, len(sil_tags)):
          if i:
              chunks.append(
                  {
                      "slice": False,
                      "split_time": f"{sil_tags[i - 1][1] * self.hop_size},{min(waveform.shape[0], sil_tags[i][0] * self.hop_size)}",
                  }
              )
          chunks.append(
              {
                  "slice": True,
                  "split_time": f"{sil_tags[i][0] * self.hop_size},{min(waveform.shape[0], sil_tags[i][1] * self.hop_size)}",
              }
          )
      if sil_tags[-1][1] * self.hop_size < len(waveform):
          chunks.append(
              {
                  "slice": False,
                  "split_time": f"{sil_tags[-1][1] * self.hop_size},{len(waveform)}",
              }
          )
      chunk_dict = {}
      for i in range(len(chunks)):
          chunk_dict[str(i)] = chunks[i]
      return chunk_dict
  
imports: import math
  from typing import Optional, Tuple
  from tinygrad import Tensor, dtypes
  import librosa
  import soundfile
  import numpy
  import parselmouth
label_desc: Use image node_id 3 for calling the Slicer obj's underlying member method code with example usage: obj.slice(waveform) and returns: chunk_dict, dict, dict