docstr: Args:
      ap (TTS.tts.utils.AudioProcessor): audio processor object.
      meta_data (list): list of dataset instances.
      seq_len (int): voice segment length in seconds.
      verbose (bool): print diagnostic information.
docstr_tok: ["Args",":","ap","(","TTS.tts.utils.AudioProcessor",")",":","audio","processor","object",".","meta_data","(","list",")",":","list","of","dataset","instances",".","seq_len","(","int",")",":","voice","segment","length","in","seconds",".","verbose","(","bool",")",":","print","diagnostic","information","."]
code: def __init__(
      self,
      config,
      ap,
      meta_data,
      voice_len=1.6,
      num_classes_in_batch=64,
      num_utter_per_class=10,
      verbose=False,
      augmentation_config=None,
      use_torch_spec=None,
  ):
      """
      Args:
          ap (TTS.tts.utils.AudioProcessor): audio processor object.
          meta_data (list): list of dataset instances.
          seq_len (int): voice segment length in seconds.
          verbose (bool): print diagnostic information.
      """
      super().__init__()
      self.config = config
      self.items = meta_data
      self.sample_rate = ap.sample_rate
      self.seq_len = int(voice_len * self.sample_rate)
      self.num_utter_per_class = num_utter_per_class
      self.ap = ap
      self.verbose = verbose
      self.use_torch_spec = use_torch_spec
      self.classes, self.items = self.__parse_items()
  
      self.classname_to_classid = {
          key: i for i, key in enumerate(self.classes)
      }
  
      # Data Augmentation
      self.augmentator = None
      self.gaussian_augmentation_config = None
      if augmentation_config:
          self.data_augmentation_p = augmentation_config["p"]
          if self.data_augmentation_p and (
              "additive" in augmentation_config
              or "rir" in augmentation_config
          ):
              self.augmentator = AugmentWAV(ap, augmentation_config)
  
          if "gaussian" in augmentation_config.keys():
              self.gaussian_augmentation_config = augmentation_config[
                  "gaussian"
              ]
  
      if self.verbose:
          print("\n > DataLoader initialization")
          print(f" | > Classes per Batch: {num_classes_in_batch}")
          print(f" | > Number of instances : {len(self.items)}")
          print(f" | > Sequence length: {self.seq_len}")
          print(f" | > Num Classes: {len(self.classes)}")
          print(f" | > Classes: {self.classes}")
  
file_path: TTS/TTS/encoder/dataset.py
imports: import random
  import torch
  from torch.utils.data import Dataset
  from TTS.encoder.utils.generic_utils import AugmentWAV
label_desc: Use image node_id 1 to create a new EncoderDataset object from inherited base classes: Dataset with example: obj = EncoderDataset(config, ap, meta_data, voice_len, num_classes_in_batch, num_utter_per_class, verbose, augmentation_config, use_torch_spec)