class_docstr: HiFi-GAN model (https://arxiv.org/abs/2010.05646) that is used to generate audio from mel spectrogram.
class_docstr_tok: ["HiFi-GAN","model","(","https",":","\/\/arxiv.org\/abs\/2010.05646",")","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_hifigan",
          location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/tts_hifigan/versions/1.0.0rc1/files/tts_hifigan.nemo",
          description="This model is trained on LJSpeech audio sampled at 22050Hz and mel spectrograms generated from"
          " Tacotron2, TalkNet, and FastPitch. This model 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_lj_hifigan_ft_mixertts",
          location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/tts_en_lj_hifigan/versions/1.6.0/files/tts_en_lj_hifigan_ft_mixertts.nemo",
          description="This model is trained on LJSpeech audio sampled at 22050Hz and mel spectrograms generated from"
          " Mixer-TTS. This model 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_lj_hifigan_ft_mixerttsx",
          location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/tts_en_lj_hifigan/versions/1.6.0/files/tts_en_lj_hifigan_ft_mixerttsx.nemo",
          description="This model is trained on LJSpeech audio sampled at 22050Hz and mel spectrograms generated from"
          " Mixer-TTS-X. This model 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_hifitts_hifigan_ft_fastpitch",
          location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/tts_en_multispeaker_fastpitchhifigan/versions/1.10.0/files/tts_en_hifitts_hifigan_ft_fastpitch.nemo",
          description="This model is trained on HiFiTTS audio sampled at 44100Hz and mel spectrograms generated from"
          " FastPitch. This model has been tested on generating male and female English voices with an American accent.",
          class_=cls,
      )
      list_of_models.append(model)
  
      # de-DE, single male speaker, 22050 Hz, Thorsten Müller’s German Neutral-TTS Dataset, 21.02
      model = PretrainedModelInfo(
          pretrained_model_name="tts_de_hifigan_singleSpeaker_thorstenNeutral_2102",
          location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/tts_de_fastpitchhifigan/versions/1.15.0/files/tts_de_hifigan_thorstens2102.nemo",
          description="This model is finetuned from the HiFiGAN pretrained checkpoint `tts_en_lj_hifigan_ft_mixerttsx`"
          " by the mel-spectrograms generated from the FastPitch checkpoint `tts_de_fastpitch_singleSpeaker_thorstenNeutral_2102`."
          " This model has been tested on generating male German neutral voices.",
          class_=cls,
      )
      list_of_models.append(model)
  
      # de-DE, single male speaker, 22050 Hz, Thorsten Müller’s German Neutral-TTS Dataset, 22.10
      model = PretrainedModelInfo(
          pretrained_model_name="tts_de_hifigan_singleSpeaker_thorstenNeutral_2210",
          location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/tts_de_fastpitchhifigan/versions/1.15.0/files/tts_de_hifigan_thorstens2210.nemo",
          description="This model is finetuned from the HiFiGAN pretrained checkpoint `tts_en_lj_hifigan_ft_mixerttsx`"
          " by the mel-spectrograms generated from the FastPitch checkpoint `tts_de_fastpitch_singleSpeaker_thorstenNeutral_2210`."
          " This model has been tested on generating male German neutral voices.",
          class_=cls,
      )
      list_of_models.append(model)
  
      # de-DE, multi-speaker, 5 speakers, 44100 Hz, HUI-Audio-Corpus-German Clean.
      model = PretrainedModelInfo(
          pretrained_model_name="tts_de_hui_hifigan_ft_fastpitch_multispeaker_5",
          location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/tts_de_fastpitch_multispeaker_5/versions/1.11.0/files/tts_de_hui_hifigan_ft_fastpitch_multispeaker_5.nemo",
          description="This model is finetuned from the HiFiGAN pretrained checkpoint `tts_en_hifitts_hifigan_ft_fastpitch` "
          "by the mel-spectrograms generated from the FastPitch checkpoint `tts_de_fastpitch_multispeaker_5`. This model "
          "has been tested on generating male and female German voices.",
          class_=cls,
      )
      list_of_models.append(model)
  
      # Spanish, multi-speaker, 44100 Hz, Latin American Spanish OpenSLR
      model = PretrainedModelInfo(
          pretrained_model_name="tts_es_hifigan_ft_fastpitch_multispeaker",
          location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/tts_es_multispeaker_fastpitchhifigan/versions/1.15.0/files/tts_es_hifigan_ft_fastpitch_multispeaker.nemo",
          description="This model is trained on the audio from 6 crowdsourced Latin American Spanish OpenSLR "
          "datasets and finetuned on the mel-spectrograms generated from the FastPitch checkpoint "
          "`tts_es_fastpitch_multispeaker`. This model has been tested on generating male and female "
          "Spanish voices with Latin American accents.",
          class_=cls,
      )
      list_of_models.append(model)
  
      # zh, single female speaker, 22050Hz, SFSpeech Bilingual Chinese/English dataset, improved model using richer
      # dict and jieba word segmenter for polyphone disambiguation.
      model = PretrainedModelInfo(
          pretrained_model_name="tts_zh_hifigan_sfspeech",
          location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/tts_zh_fastpitch_hifigan_sfspeech/versions/1.15.0/files/tts_zh_hifigan_sfspeech.nemo",
          description="This model is finetuned from the HiFiGAN pretrained checkpoint `tts_en_lj_hifigan_ft_mixerttsx`"
          " by the mel-spectrograms generated from the FastPitch checkpoint `tts_zh_fastpitch_sfspeech`."
          " This model has been tested on generating female Mandarin Chinese voices.",
          class_=cls,
      )
      list_of_models.append(model)
  
      return list_of_models
  
imports: import itertools
  from pathlib import Path
  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, FeatureMatchingLoss, GeneratorLoss
  from nemo.collections.tts.models.base import Vocoder
  from nemo.collections.tts.modules.hifigan_modules import MultiPeriodDiscriminator, MultiScaleDiscriminator
  from nemo.collections.tts.parts.utils.callbacks import LoggingCallback
  from nemo.collections.tts.parts.utils.helpers import get_batch_size, get_num_workers, plot_spectrogram_to_numpy
  from nemo.core.classes 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 20 for calling the HifiGanModel obj's underlying member method code with example usage: obj.list_available_models(cls) and returns: list_of_models