class_docstr: Constructs a Wav2Vec2 feature extractor.
  
  This feature extractor inherits from [`~feature_extraction_sequence_utils.SequenceFeatureExtractor`] which contains
  most of the main methods. Users should refer to this superclass for more information regarding those methods.
  
  Args:
      feature_size (`int`, defaults to 1):
          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 (Hz).
      padding_value (`float`, defaults to 0.0):
          The value that is used to fill the padding values.
      do_normalize (`bool`, *optional*, defaults to `True`):
          Whether or not to zero-mean unit-variance normalize the input. Normalizing can help to significantly
          improve the performance for some models, *e.g.*,
          [wav2vec2-lv60](https://huggingface.co/models?search=lv60).
      return_attention_mask (`bool`, *optional*, defaults to `False`):
          Whether or not [`~Wav2Vec2FeatureExtractor.__call__`] should return `attention_mask`.
  
          <Tip>
  
          Wav2Vec2 models that have set `config.feat_extract_norm == "group"`, such as
          [wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base-960h), have **not** been trained using
          `attention_mask`. For such models, `input_values` should simply be padded with 0 and no `attention_mask`
          should be passed.
  
          For Wav2Vec2 models that have set `config.feat_extract_norm == "layer"`, such as
          [wav2vec2-lv60](https://huggingface.co/facebook/wav2vec2-large-960h-lv60-self), `attention_mask` should be
          passed for batched inference.
  
          </Tip>
code: def __init__(
      self,
      feature_size=1,
      sampling_rate=16000,
      padding_value=0.0,
      return_attention_mask=False,
      do_normalize=True,
      **kwargs,
  ):
      super().__init__(
          feature_size=feature_size,
          sampling_rate=sampling_rate,
          padding_value=padding_value,
          **kwargs,
      )
      self.return_attention_mask = return_attention_mask
      self.do_normalize = do_normalize
  
file_path: transformers/src/transformers/models/wav2vec2/feature_extraction_wav2vec2.py
imports: from typing import List, Optional, Union
  import numpy
  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 Wav2Vec2FeatureExtractor object from inherited base classes: SequenceFeatureExtractor with example: obj = Wav2Vec2FeatureExtractor(feature_size, sampling_rate, padding_value, return_attention_mask, do_normalize)