(self, audio_data, sample_rate=None)
Caculate power spectrum or log power spectrum of audio data.
    :param audio_data: the audio signal from which to compute spectrum.
                       Should be an (1, N) tensor.
    :param sample_rate: [option]the samplerate of the signal we working with, default is 16kHz.
    :return: A float tensor of size (num_frames, num_frequencies) containing power
            spectrum (output_type=1) or log power spectrum (output_type=2)
            of every frame in speech.
Caculate power spectrum or log power spectrum of audio data.
    :param audio_data: the audio signal from which to compute spectrum.
                       Should be an (1, N) tensor.
    :param sample_rate: [option]the samplerate of the signal we working with, default is 16kHz.
    :return: A float tensor of size (num_frames, num_frequencies) containing power
            spectrum (output_type=1) or log power spectrum (output_type=2)
            of every frame in speech.
Caculate power spectrum or log power spectrum of audio data . : param audio_data : the audio signal from which to compute spectrum . Should be an ( 1 N ) tensor . : param sample_rate : [ option ] the samplerate of the signal we working with default is 16kHz . : return : A float tensor of size ( num_frames num_frequencies ) containing power spectrum ( output_type = 1 ) or log power spectrum ( output_type = 2 ) of every frame in speech .
def call(self, audio_data, sample_rate=None):
    """
    Caculate power spectrum or log power spectrum of audio data.
    :param audio_data: the audio signal from which to compute spectrum.
                       Should be an (1, N) tensor.
    :param sample_rate: [option]the samplerate of the signal we working with, default is 16kHz.
    :return: A float tensor of size (num_frames, num_frequencies) containing power
            spectrum (output_type=1) or log power spectrum (output_type=2)
            of every frame in speech.
    """

    p = self.config
    with tf.name_scope('spectrum'):

      if sample_rate == None:
        sample_rate = tf.constant(p.sample_rate, dtype=tf.int32)

      assert_op = tf.assert_equal(
          tf.constant(p.sample_rate), tf.cast(sample_rate, dtype=tf.int32))
      with tf.control_dependencies([assert_op]):

        sample_rate = tf.cast(sample_rate, dtype=float)
        spectrum = py_x_ops.spectrum(
            audio_data,
            sample_rate,
            window_length=p.window_length,
            frame_length=p.frame_length,
            output_type=p.output_type,
            snip_edges=p.snip_edges,
            raw_energy=p.raw_energy,
            preEph_coeff=p.preeph_coeff,
            window_type=p.window_type,
            remove_dc_offset=p.remove_dc_offset,
            is_fbank=p.is_fbank,
            dither=p.dither)

        return spectrum
def call ( self , audio_data , sample_rate = None ) : p = self . config with tf . name_scope ( 'spectrum' ) : if sample_rate == None : sample_rate = tf . constant ( p . sample_rate , dtype = tf . int32 ) assert_op = tf . assert_equal ( tf . constant ( p . sample_rate ) , tf . cast ( sample_rate , dtype = tf . int32 ) ) with tf . control_dependencies ( [ assert_op ] ) : sample_rate = tf . cast ( sample_rate , dtype = float ) spectrum = py_x_ops . spectrum ( audio_data , sample_rate , window_length = p . window_length , frame_length = p . frame_length , output_type = p . output_type , snip_edges = p . snip_edges , raw_energy = p . raw_energy , preEph_coeff = p . preeph_coeff , window_type = p . window_type , remove_dc_offset = p . remove_dc_offset , is_fbank = p . is_fbank , dither = p . dither ) return spectrum