class_docstr: WaveGlow model (https://arxiv.org/abs/1811.00002) that is used to generate audio from mel spectrogram.
class_docstr_tok: ["WaveGlow","model","(","https",":","\/\/arxiv.org\/abs\/1811.00002",")","that","is","used","to","generate","audio","from","mel","spectrogram","."]
code: def __init__(self, cfg: DictConfig, trainer: "Trainer" = None):
      # Convert to Hydra 1.0 compatible DictConfig
      cfg = model_utils.convert_model_config_to_dict_config(cfg)
      cfg = model_utils.maybe_update_config_version(cfg)
  
      super().__init__(cfg=cfg, trainer=trainer)
  
      self.sigma = self._cfg.sigma
      self.audio_to_melspec_precessor = instantiate(
          self._cfg.preprocessor
      )
      self.waveglow = instantiate(self._cfg.waveglow)
      self.loss = WaveGlowLoss()
  
imports: import torch
  from hydra.utils import instantiate
  from omegaconf import DictConfig, open_dict
  from pytorch_lightning.loggers import TensorBoardLogger
  from nemo.collections.tts.losses.waveglowloss import WaveGlowLoss
  from nemo.collections.tts.models.base import GlowVocoder
  from nemo.collections.tts.parts.utils.helpers import OperationMode, waveglow_log_to_tb_func
  from nemo.core.classes import Exportable
  from nemo.core.classes.common import PretrainedModelInfo, typecheck
  from nemo.core.neural_types.elements import AudioSignal, LengthsType, LogDeterminantType, MelSpectrogramType, NormalDistributionSamplesType, VoidType
  from nemo.core.neural_types.neural_type import NeuralType
  from nemo.utils import logging, model_utils
label_desc: Use image node_id 1 to create a new WaveGlowModel object from inherited base classes: GlowVocoder, Exportable with example: obj = WaveGlowModel(cfg, trainer)