class_docstr: UnivNet model (https://arxiv.org/abs/2106.07889) that is used to generate audio from mel spectrogram.
class_docstr_tok: ["UnivNet","model","(","https",":","\/\/arxiv.org\/abs\/2106.07889",")","that","is","used","to","generate","audio","from","mel","spectrogram","."]
code: def list_available_models(cls) -> "Optional[Dict[str, str]]":
      list_of_models = []
      model = PretrainedModelInfo(
          pretrained_model_name="tts_en_lj_univnet",
          location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/tts_en_lj_univnet/versions/1.7.0/files/tts_en_lj_univnet.nemo",
          description="This model is trained on LJSpeech sampled at 22050Hz, and has been tested on generating female English voices with an American accent.",
          class_=cls,
      )
      list_of_models.append(model)
  
      model = PretrainedModelInfo(
          pretrained_model_name="tts_en_libritts_univnet",
          location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/tts_en_libritts_univnet/versions/1.7.0/files/tts_en_libritts_multispeaker_univnet.nemo",
          description="This model is trained on all LibriTTS training data (train-clean-100, train-clean-360, and train-other-500) sampled at 22050Hz, and has been tested on generating English voices.",
          class_=cls,
      )
      list_of_models.append(model)
  
      return list_of_models
  
imports: import itertools
  from typing import Dict
  import torch
  import torch.nn.functional
  from hydra.utils import instantiate
  from omegaconf import DictConfig, OmegaConf, open_dict
  from pytorch_lightning.loggers.wandb import WandbLogger
  from nemo.collections.tts.losses.hifigan_losses import DiscriminatorLoss, GeneratorLoss
  from nemo.collections.tts.losses.stftlosses import MultiResolutionSTFTLoss
  from nemo.collections.tts.models.base import Vocoder
  from nemo.collections.tts.modules.univnet_modules import MultiPeriodDiscriminator, MultiResolutionDiscriminator
  from nemo.collections.tts.parts.utils.helpers import get_batch_size, get_num_workers, plot_spectrogram_to_numpy
  from nemo.core import Exportable
  from nemo.core.classes.common import PretrainedModelInfo, typecheck
  from nemo.core.neural_types.elements import AudioSignal, MelSpectrogramType
  from nemo.core.neural_types.neural_type import NeuralType
  from nemo.core.optim.lr_scheduler import compute_max_steps, prepare_lr_scheduler
  from nemo.utils import logging, model_utils
label_desc: Use image node_id 14 for calling the UnivNetModel obj's underlying member method code with example usage: obj.list_available_models(cls) and returns: list_of_models