class_docstr: Data class that bundles associated information to use pretrained :py:class:`~torchaudio.models.Wav2Vec2Model` for forced alignment.
  
  This class provides interfaces for instantiating the pretrained model along with
  the information necessary to retrieve pretrained weights and additional data
  to be used with the model.
  
  Torchaudio library instantiates objects of this class, each of which represents
  a different pretrained model. Client code should access pretrained models via these
  instances.
  
  Please see below for the usage and the available values.
  
  Example - Feature Extraction
      >>> import torchaudio
      >>>
      >>> bundle = torchaudio.pipelines.MMS_FA
      >>>
      >>> # Build the model and load pretrained weight.
      >>> model = bundle.get_model()
      Downloading:
      100%|███████████████████████████████| 1.18G/1.18G [00:05<00:00, 216MB/s]
      >>>
      >>> # Resample audio to the expected sampling rate
      >>> waveform = torchaudio.functional.resample(waveform, sample_rate, bundle.sample_rate)
      >>>
      >>> # Estimate the probability of token distribution
      >>> emission, _ = model(waveform)
      >>>
      >>> # Generate frame-wise alignment
      >>> alignment, scores = torchaudio.functional.forced_align(
      >>>     emission, targets, input_lengths, target_lengths, blank=0)
      >>>
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docstr: Get the labels corresponding to the feature dimension of emission.
  
  The first is blank token, and it is customizable.
  
  Args:
      star (str or None, optional): Change or disable star token. (default: ``"*"``)
      blank (str, optional): Change the blank token. (default: ``'-'``)
  
  Returns:
      Tuple[str, ...]:
      For models fine-tuned on ASR, returns the tuple of strings representing
      the output class labels.
  
  Example
      >>> from torchaudio.pipelines import MMS_FA as bundle
      >>> bundle.get_labels()
      ('-', 'a', 'i', 'e', 'n', 'o', 'u', 't', 's', 'r', 'm', 'k', 'l', 'd', 'g', 'h', 'y', 'b', 'p', 'w', 'c', 'v', 'j', 'z', 'f', "'", 'q', 'x', '*')
      >>> bundle.get_labels(star=None)
      ('-', 'a', 'i', 'e', 'n', 'o', 'u', 't', 's', 'r', 'm', 'k', 'l', 'd', 'g', 'h', 'y', 'b', 'p', 'w', 'c', 'v', 'j', 'z', 'f', "'", 'q', 'x')
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code: def get_labels(
      self, star: Optional[str] = "*", blank: str = "-"
  ) -> Tuple[str, ...]:
      """Get the labels corresponding to the feature dimension of emission.
  
      The first is blank token, and it is customizable.
  
      Args:
          star (str or None, optional): Change or disable star token. (default: ``"*"``)
          blank (str, optional): Change the blank token. (default: ``'-'``)
  
      Returns:
          Tuple[str, ...]:
          For models fine-tuned on ASR, returns the tuple of strings representing
          the output class labels.
  
      Example
          >>> from torchaudio.pipelines import MMS_FA as bundle
          >>> bundle.get_labels()
          ('-', 'a', 'i', 'e', 'n', 'o', 'u', 't', 's', 'r', 'm', 'k', 'l', 'd', 'g', 'h', 'y', 'b', 'p', 'w', 'c', 'v', 'j', 'z', 'f', "'", 'q', 'x', '*')
          >>> bundle.get_labels(star=None)
          ('-', 'a', 'i', 'e', 'n', 'o', 'u', 't', 's', 'r', 'm', 'k', 'l', 'd', 'g', 'h', 'y', 'b', 'p', 'w', 'c', 'v', 'j', 'z', 'f', "'", 'q', 'x')
      """  # noqa: E501
      labels = super().get_labels(blank=blank)
      return labels if star is None else (*labels, star)
  
file_path: audio/src/torchaudio/pipelines/_wav2vec2/impl.py
imports: from dataclasses import dataclass
  from typing import Any, Dict, Optional, Tuple
  from torch.nn import Module
  from .None import aligner, utils
label_desc: Use image node_id 1 for calling the Wav2Vec2FABundle obj's underlying member method code with example usage: obj.get_labels(star, blank) and returns: unknown