class_docstr: Compute spectrum features of every frame in speech, return a float tensor
  with size (num_frames, num_frequencies).
docstr: Set params.
  :param config: contains nine optional parameters：
        --sample_rate     : Waveform data sample frequency (must match the waveform
                            file, if specified there). (float, default = 16000)
        --window_length               : Window length in seconds. (float, default = 0.025)
        --frame_length                : Hop length in seconds. (float, default = 0.010)
        --snip_edges                  : If True, the last frame (shorter than window_length)
                                will be cutoff. If False, 1 // 2 frame_length data will
                                be padded to data. (bool, default = True)
        ---raw_energy                 : If 1, compute frame energy before preemphasis and windowing.
                                If 2,  compute frame energy after preemphasis and windowing.
                                (int, default = 1)
        --preeph_coeff                : Coefficient for use in frame-signal preemphasis.
                               (float, default = 0.97)
        --window_type                 : Type of window ("hamm"|"hann"|"povey"|"rect"|"blac"|"tria").
                                (string, default = "povey")
        --remove_dc_offset    : Subtract mean from waveform on each frame.
                               (bool, default = true)
        --is_fbank                    : If true, compute power spetrum without frame energy.
                                If false, using the frame energy instead of the square of the
                                constant component of the signal. (bool, default = false)
        --output_type                 : If 1, return power spectrum. If 2, return log-power spectrum.
                                If 3, return magnitude spectrum. (int, default = 2)
        --dither                      : Dithering constant (0.0 means no dither).
                               (float, default = 1) [add robust to training]
  :return: An object of class HParams, which is a set of hyperparameters as name-value pairs.
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code: def params(cls, config=None):
      """
      Set params.
      :param config: contains nine optional parameters：
            --sample_rate     : Waveform data sample frequency (must match the waveform
                                file, if specified there). (float, default = 16000)
            --window_length		: Window length in seconds. (float, default = 0.025)
            --frame_length		: Hop length in seconds. (float, default = 0.010)
            --snip_edges			: If True, the last frame (shorter than window_length)
                                    will be cutoff. If False, 1 // 2 frame_length data will
                                    be padded to data. (bool, default = True)
            ---raw_energy			: If 1, compute frame energy before preemphasis and windowing.
                                    If 2,  compute frame energy after preemphasis and windowing.
                                    (int, default = 1)
            --preeph_coeff		: Coefficient for use in frame-signal preemphasis.
                                   (float, default = 0.97)
            --window_type			: Type of window ("hamm"|"hann"|"povey"|"rect"|"blac"|"tria").
                                    (string, default = "povey")
            --remove_dc_offset	: Subtract mean from waveform on each frame.
                                   (bool, default = true)
            --is_fbank			: If true, compute power spetrum without frame energy.
                                    If false, using the frame energy instead of the square of the
                                    constant component of the signal. (bool, default = false)
            --output_type			: If 1, return power spectrum. If 2, return log-power spectrum.
                                    If 3, return magnitude spectrum. (int, default = 2)
            --dither		        : Dithering constant (0.0 means no dither).
                                   (float, default = 1) [add robust to training]
      :return: An object of class HParams, which is a set of hyperparameters as name-value pairs.
      """
  
      window_length = 0.025
      frame_length = 0.010
      output_type = 2
      sample_rate = 16000
      snip_edges = True
      raw_energy = 1
      preeph_coeff = 0.97
      window_type = "povey"
      remove_dc_offset = True
      is_fbank = False
      dither = 0.0
  
      hparams = HParams(cls=cls)
      hparams.add_hparam("window_length", window_length)
      hparams.add_hparam("frame_length", frame_length)
      hparams.add_hparam("output_type", output_type)
      hparams.add_hparam("sample_rate", sample_rate)
      hparams.add_hparam("snip_edges", snip_edges)
      hparams.add_hparam("raw_energy", raw_energy)
      hparams.add_hparam("preeph_coeff", preeph_coeff)
      hparams.add_hparam("window_type", window_type)
      hparams.add_hparam("remove_dc_offset", remove_dc_offset)
      hparams.add_hparam("is_fbank", is_fbank)
      hparams.add_hparam("dither", dither)
  
      if config is not None:
          hparams.override_from_dict(config)
  
      return hparams
  
file_path: delta/delta/data/frontend/spectrum.py
imports: import tensorflow
  from core.ops import py_x_ops
  from delta.utils.hparam import HParams
  from delta.data.frontend.base_frontend import BaseFrontend
label_desc: Use image node_id 2 for calling the Spectrum obj's underlying member method code with example usage: obj.params(cls, config) and returns: hparams