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 convert_spectrogram_to_audio(
      self,
      spec: torch.Tensor,
      sigma: float = 1.0,
      denoise: bool = True,
      denoiser_strength: float = 0.01,
  ) -> torch.Tensor:
      with self.nemo_infer():
          self.waveglow.remove_weightnorm()
          audio = self.waveglow(
              spec=spec.to(self.waveglow.upsample.weight.dtype),
              sigma=sigma,
          )
          if denoise:
              audio = self.denoise(
                  audio=audio, strength=denoiser_strength
              )
  
      return audio
  
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 4 for calling the WaveGlowModel obj's underlying member method code with example usage: obj.convert_spectrogram_to_audio(spec, sigma, denoise, denoiser_strength) and returns: audio