class_docstr: Constructs a Speech2Text feature extractor.
  
  This feature extractor inherits from [`Speech2TextFeatureExtractor`] which contains most of the main methods. Users
  should refer to this superclass for more information regarding those methods.
  
  This class extracts mel-filter bank features from raw speech using TorchAudio and applies utterance-level cepstral
  mean and variance normalization to the extracted features.
  
  Args:
      feature_size (`int`, defaults to 80):
          The feature dimension of the extracted features.
      sampling_rate (`int`, defaults to 16000):
          The sampling rate at which the audio files should be digitalized expressed in Hertz per second (Hz).
      num_mel_bins (`int`, defaults to 80):
          Number of Mel-frequency bins.
      padding_value (`float`, defaults to 0.0):
          The value that is used to fill the padding vectors.
      do_ceptral_normalize (`bool`, *optional*, defaults to `True`):
          Whether or not to apply utterance-level cepstral mean and variance normalization to extracted features.
      normalize_means (`bool`, *optional*, defaults to `True`):
          Whether or not to zero-mean normalize the extracted features.
      normalize_vars (`bool`, *optional*, defaults to `True`):
          Whether or not to unit-variance normalize the extracted features.
code: def normalize(
      self,
      input_features: List[np.ndarray],
      attention_mask: Optional[np.ndarray] = None,
  ) -> List[np.ndarray]:
      lengths = (
          attention_mask.sum(-1)
          if attention_mask is not None
          else [x.shape[0] for x in input_features]
      )
      return [
          self.utterance_cmvn(
              x,
              n,
              self.normalize_means,
              self.normalize_vars,
              self.padding_value,
          )
          for x, n in zip(input_features, lengths)
      ]
  
file_path: H2O/h2o_flexgen/benchmark/third_party/transformers/src/transformers/models/speech_to_text/feature_extraction_speech_to_text.py
imports: from typing import List, Optional, Union
  import numpy
  import torch
  import torchaudio.compliance.kaldi
  from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
  from ...feature_extraction_utils import BatchFeature
  from ...utils import PaddingStrategy, TensorType, logging
label_desc: Use image node_id 4 for calling the Speech2TextFeatureExtractor obj's underlying member method code with example usage: obj.normalize(input_features, attention_mask) and returns: unknown