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 __init__(
      self,
      feature_size=80,
      sampling_rate=16000,
      num_mel_bins=80,
      padding_value=0.0,
      do_ceptral_normalize=True,
      normalize_means=True,
      normalize_vars=True,
      **kwargs,
  ):
      super().__init__(
          feature_size=feature_size,
          sampling_rate=sampling_rate,
          padding_value=padding_value,
          **kwargs,
      )
      self.num_mel_bins = num_mel_bins
      self.do_ceptral_normalize = do_ceptral_normalize
      self.normalize_means = normalize_means
      self.normalize_vars = normalize_vars
      self.return_attention_mask = True
  
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 1 to create a new Speech2TextFeatureExtractor object from inherited base classes: SequenceFeatureExtractor with example: obj = Speech2TextFeatureExtractor(feature_size, sampling_rate, num_mel_bins, padding_value, do_ceptral_normalize, normalize_means, normalize_vars)