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).
class_docstr_tok: ["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",")","."]
docstr: Get mel-filter bank features using TorchAudio. Note that TorchAudio requires 16-bit signed integers as inputs
  and hence the waveform should not be normalized before feature extraction.
docstr_tok: ["Get","mel-filter","bank","features","using","TorchAudio",".","Note","that","TorchAudio","requires","16-bit","signed","integers","as","inputs","and","hence","the","waveform","should","not","be","normalized","before","feature","extraction","."]
code: def _extract_fbank_features(
      self,
      waveform: np.ndarray,
  ) -> np.ndarray:
      """
      Get mel-filter bank features using TorchAudio. Note that TorchAudio requires 16-bit signed integers as inputs
      and hence the waveform should not be normalized before feature extraction.
      """
      # by default, it extracts the left channel if stereo
      if len(waveform.shape) == 2:
          waveform = waveform[0]
  
      waveform = np.squeeze(waveform) * (
          2**15
      )  # Kaldi compliance: 16-bit signed integers
      features = spectrogram(
          waveform,
          self.window,
          frame_length=400,
          hop_length=160,
          fft_length=512,
          power=2.0,
          center=False,
          preemphasis=0.97,
          mel_filters=self.mel_filters,
          log_mel="log",
          mel_floor=1.192092955078125e-07,
          remove_dc_offset=True,
      ).T
      return features
  
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 3 for calling the SeamlessM4TFeatureExtractor obj's underlying member method code with example usage: obj._extract_fbank_features(waveform) and returns: features