class_docstr: Speech-to-text alignment model (https://arxiv.org/pdf/2108.10447.pdf) that is used to learn alignments between mel spectrogram and text.
class_docstr_tok: ["Speech-to-text","alignment","model","(","https",":","\/\/arxiv.org\/pdf\/2108.10447.pdf",")","that","is","used","to","learn","alignments","between","mel","spectrogram","and","text","."]
docstr: This method returns a list of pre-trained model which can be instantiated directly from NVIDIA's NGC cloud.
  Returns:
      List of available pre-trained models.
docstr_tok: ["This","method","returns","a","list","of","pre-trained","model","which","can","be","instantiated","directly","from","NVIDIA","'s","NGC","cloud",".","Returns",":","List","of","available","pre-trained","models","."]
code: def list_available_models(cls) -> List[PretrainedModelInfo]:
      """
      This method returns a list of pre-trained model which can be instantiated directly from NVIDIA's NGC cloud.
      Returns:
          List of available pre-trained models.
      """
      list_of_models = []
  
      # en-US, ARPABET-based
      model = PretrainedModelInfo(
          pretrained_model_name="tts_en_radtts_aligner",
          location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/tts_en_radtts_aligner/versions/ARPABET_1.11.0/files/Aligner.nemo",
          description="This model is trained on LJSpeech sampled at 22050Hz with and can be used to align text and audio.",
          class_=cls,
      )
      list_of_models.append(model)
  
      # en-US, IPA-based
      model = PretrainedModelInfo(
          pretrained_model_name="tts_en_radtts_aligner_ipa",
          location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/tts_en_radtts_aligner/versions/IPA_1.13.0/files/Aligner.nemo",
          description="This model is trained on LJSpeech sampled at 22050Hz with and can be used to align text and audio.",
          class_=cls,
      )
      list_of_models.append(model)
  
      return list_of_models
  
imports: from typing import List
  import numpy
  import omegaconf
  import torch
  from hydra.utils import instantiate
  from omegaconf import DictConfig
  from pytorch_lightning import Trainer
  from pytorch_lightning.loggers import WandbLogger
  from torch import nn
  from nemo.collections.tts.losses.aligner_loss import BinLoss, ForwardSumLoss
  from nemo.collections.tts.parts.utils.helpers import binarize_attention, g2p_backward_compatible_support, get_mask_from_lengths, plot_alignment_to_numpy
  from nemo.core.classes import ModelPT
  from nemo.core.classes.common import PretrainedModelInfo
  from nemo.utils import logging, model_utils
label_desc: Use image node_id 13 for calling the AlignerModel obj's underlying member method code with example usage: obj.list_available_models(cls) and returns: list_of_models