class_docstr: Constructs a SeamlessM4T feature extractor.
  
  This feature extractor inherits from [`SequenceFeatureExtractor`] 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.
  
  Args:
      feature_size (`int`, *optional*, defaults to 80):
          The feature dimension of the extracted features.
      sampling_rate (`int`, *optional*, defaults to 16000):
          The sampling rate at which the audio files should be digitalized expressed in hertz (Hz).
      num_mel_bins (`int`, *optional*, defaults to 80):
          Number of Mel-frequency bins.
      padding_value (`float`, *optional*, defaults to 0.0):
          The value that is used to fill the padding vectors.
      stride (`int`, *optional*, defaults to 2):
          Stride used to reshape audios from shape (batch_size,num_frames,num_mel_bins) to
          (batch_size,num_frames//stride,num_mel_bins*stride).
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code: def __init__(
      self,
      feature_size=80,
      sampling_rate=16000,
      num_mel_bins=80,
      padding_value=0.0,
      stride=2,
      **kwargs,
  ):
      self.num_mel_bins = num_mel_bins
      self.return_attention_mask = True
      self.stride = stride
  
      mel_filters = mel_filter_bank(
          num_frequency_bins=256,
          num_mel_filters=self.num_mel_bins,
          min_frequency=20,
          max_frequency=sampling_rate // 2,
          sampling_rate=sampling_rate,
          norm=None,
          mel_scale="kaldi",
          triangularize_in_mel_space=True,
      )
  
      self.mel_filters = np.pad(mel_filters, ((0, 1), (0, 0)))
      self.window = window_function(400, "povey", periodic=False)
  
      super().__init__(
          feature_size=feature_size,
          sampling_rate=sampling_rate,
          padding_value=padding_value,
          **kwargs,
      )
  
file_path: transformers/src/transformers/models/seamless_m4t/feature_extraction_seamless_m4t.py
imports: from typing import List, Optional, Union
  import numpy
  from ...utils import is_torch_available
  from ...audio_utils import mel_filter_bank, spectrogram, window_function
  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 SeamlessM4TFeatureExtractor object from inherited base classes: SequenceFeatureExtractor with example: obj = SeamlessM4TFeatureExtractor(feature_size, sampling_rate, num_mel_bins, padding_value, stride)