class_docstr: WaveRNN Dataset searchs for all the wav files under root path
  and converts them to acoustic features on the fly.
code: def __init__(
      self,
      ap,
      items,
      seq_len,
      hop_len,
      pad,
      mode,
      mulaw,
      is_training=True,
      verbose=False,
  ):
      self.ap = ap
      self.compute_feat = not isinstance(items[0], (tuple, list))
      self.item_list = items
      self.seq_len = seq_len
      self.hop_len = hop_len
      self.mel_len = seq_len // hop_len
      self.pad = pad
      self.mode = mode
      self.mulaw = mulaw
      self.is_training = is_training
      self.verbose = verbose
  
      assert self.seq_len % self.hop_len == 0
  
file_path: TTS/TTS/vocoder/datasets/wavernn_dataset.py
imports: import torch
  import numpy
  from torch.utils.data import Dataset
label_desc: Use image node_id 1 to create a new WaveRNNDataset object from inherited base classes: Dataset with example: obj = WaveRNNDataset(ap, items, seq_len, hop_len, pad, mode, mulaw, is_training, verbose)