(self, audio_data, sample_rate=None)
Caculate fbank features 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, num_channels) containing
               fbank features of every frame in speech.
Caculate fbank features 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, num_channels) containing
               fbank features of every frame in speech.
Caculate fbank features 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 num_channels ) containing fbank features of every frame in speech .
def call(self, audio_data, sample_rate=None):
    """
       Caculate fbank features 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, num_channels) containing
               fbank features of every frame in speech.
    """
    p = self.config
    with tf.name_scope('fbank'):

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

      if p.upper_frequency_limit <= 0:
        p.upper_frequency_limit = p.sample_rate / 2.0 + p.upper_frequency_limit
      elif (p.upper_frequency_limit <= p.lower_frequency_limit) or (
          p.upper_frequency_limit > p.sample_rate / 2.0):
        p.upper_frequency_limit = p.sample_rate / 2.0

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

        spectrum = self.spect(audio_data, sample_rate)
        spectrum = tf.expand_dims(spectrum, 0)

        fbank = py_x_ops.fbank(
            spectrum,
            sample_rate,
            upper_frequency_limit=p.upper_frequency_limit,
            lower_frequency_limit=p.lower_frequency_limit,
            filterbank_channel_count=p.filterbank_channel_count,
            is_log10=p.is_log10)

        fbank = tf.squeeze(fbank, axis=0)
        shape = tf.shape(fbank)
        nframe = shape[0]
        nfbank = shape[1]
        fbank = tf.reshape(fbank, (nframe, nfbank, 1))

        return fbank
def call ( self , audio_data , sample_rate = None ) : p = self . config with tf . name_scope ( 'fbank' ) : if sample_rate == None : sample_rate = tf . constant ( p . sample_rate , dtype = tf . int32 ) if p . upper_frequency_limit <= 0 : p . upper_frequency_limit = p . sample_rate / 2.0 + p . upper_frequency_limit elif ( p . upper_frequency_limit <= p . lower_frequency_limit ) or ( p . upper_frequency_limit > p . sample_rate / 2.0 ) : p . upper_frequency_limit = p . sample_rate / 2.0 assert_op = tf . assert_equal ( tf . constant ( p . sample_rate ) , tf . cast ( sample_rate , dtype = tf . int32 ) ) with tf . control_dependencies ( [ assert_op ] ) : spectrum = self . spect ( audio_data , sample_rate ) spectrum = tf . expand_dims ( spectrum , 0 ) fbank = py_x_ops . fbank ( spectrum , sample_rate , upper_frequency_limit = p . upper_frequency_limit , lower_frequency_limit = p . lower_frequency_limit , filterbank_channel_count = p . filterbank_channel_count , is_log10 = p . is_log10 ) fbank = tf . squeeze ( fbank , axis = 0 ) shape = tf . shape ( fbank ) nframe = shape [ 0 ] nfbank = shape [ 1 ] fbank = tf . reshape ( fbank , ( nframe , nfbank , 1 ) ) return fbank