(self, hparams)
return batch
Get the Tensor expressions from the reader.

    Args:
      hparams: Hyperparameters object with specgram parameters.

    Returns:
      A dict of key:tensor pairs. This includes "pitch", "wav", and "key".
Get the Tensor expressions from the reader.
Get the Tensor expressions from the reader .
def get_baseline_batch(self, hparams):
    """Get the Tensor expressions from the reader.

    Args:
      hparams: Hyperparameters object with specgram parameters.

    Returns:
      A dict of key:tensor pairs. This includes "pitch", "wav", and "key".
    """
    example = self.get_example(hparams.batch_size)
    audio = tf.slice(example["audio"], [0], [64000])
    audio = tf.reshape(audio, [1, 64000])
    pitch = tf.slice(example["pitch"], [0], [1])
    velocity = tf.slice(example["velocity"], [0], [1])
    instrument_source = tf.slice(example["instrument_source"], [0], [1])
    instrument_family = tf.slice(example["instrument_family"], [0], [1])
    qualities = tf.slice(example["qualities"], [0], [10])
    qualities = tf.reshape(qualities, [1, 10])

    # Get Specgrams
    hop_length = hparams.hop_length
    n_fft = hparams.n_fft
    if hop_length and n_fft:
      specgram = utils.tf_specgram(
          audio,
          n_fft=n_fft,
          hop_length=hop_length,
          mask=hparams.mask,
          log_mag=hparams.log_mag,
          re_im=hparams.re_im,
          dphase=hparams.dphase,
          mag_only=hparams.mag_only)
      shape = [1] + SPECGRAM_REGISTRY[(n_fft, hop_length)]
      if hparams.mag_only:
        shape[-1] = 1
      specgram = tf.reshape(specgram, shape)
      tf.logging.info("SPECGRAM BEFORE PADDING", specgram)

      if hparams.pad:
        # Pad and crop specgram to 256x256
        num_padding = 2**int(np.ceil(np.log(shape[2]) / np.log(2))) - shape[2]
        tf.logging.info("num_pading: %d" % num_padding)
        specgram = tf.reshape(specgram, shape)
        specgram = tf.pad(specgram, [[0, 0], [0, 0], [0, num_padding], [0, 0]])
        specgram = tf.slice(specgram, [0, 0, 0, 0], [-1, shape[1] - 1, -1, -1])
        tf.logging.info("SPECGRAM AFTER PADDING", specgram)

    # Form a Batch
    if self.is_training:
      (audio, velocity, pitch, specgram,
       instrument_source, instrument_family,
       qualities) = tf.train.shuffle_batch(
           [
               audio, velocity, pitch, specgram,
               instrument_source, instrument_family, qualities
           ],
           batch_size=hparams.batch_size,
           capacity=20 * hparams.batch_size,
           min_after_dequeue=10 * hparams.batch_size,
           enqueue_many=True)
    elif hparams.batch_size > 1:
      (audio, velocity, pitch, specgram,
       instrument_source, instrument_family, qualities) = tf.train.batch(
           [
               audio, velocity, pitch, specgram,
               instrument_source, instrument_family, qualities
           ],
           batch_size=hparams.batch_size,
           capacity=10 * hparams.batch_size,
           enqueue_many=True)

    audio.set_shape([hparams.batch_size, 64000])

    batch = dict(
        pitch=pitch,
        velocity=velocity,
        audio=audio,
        instrument_source=instrument_source,
        instrument_family=instrument_family,
        qualities=qualities,
        spectrogram=specgram)

    return batch
def get_baseline_batch ( self , hparams ) : example = self . get_example ( hparams . batch_size ) audio = tf . slice ( example [ "audio" ] , [ 0 ] , [ 64000 ] ) audio = tf . reshape ( audio , [ 1 , 64000 ] ) pitch = tf . slice ( example [ "pitch" ] , [ 0 ] , [ 1 ] ) velocity = tf . slice ( example [ "velocity" ] , [ 0 ] , [ 1 ] ) instrument_source = tf . slice ( example [ "instrument_source" ] , [ 0 ] , [ 1 ] ) instrument_family = tf . slice ( example [ "instrument_family" ] , [ 0 ] , [ 1 ] ) qualities = tf . slice ( example [ "qualities" ] , [ 0 ] , [ 10 ] ) qualities = tf . reshape ( qualities , [ 1 , 10 ] ) # Get Specgrams hop_length = hparams . hop_length n_fft = hparams . n_fft if hop_length and n_fft : specgram = utils . tf_specgram ( audio , n_fft = n_fft , hop_length = hop_length , mask = hparams . mask , log_mag = hparams . log_mag , re_im = hparams . re_im , dphase = hparams . dphase , mag_only = hparams . mag_only ) shape = [ 1 ] + SPECGRAM_REGISTRY [ ( n_fft , hop_length ) ] if hparams . mag_only : shape [ - 1 ] = 1 specgram = tf . reshape ( specgram , shape ) tf . logging . info ( "SPECGRAM BEFORE PADDING" , specgram ) if hparams . pad : # Pad and crop specgram to 256x256 num_padding = 2 ** int ( np . ceil ( np . log ( shape [ 2 ] ) / np . log ( 2 ) ) ) - shape [ 2 ] tf . logging . info ( "num_pading: %d" % num_padding ) specgram = tf . reshape ( specgram , shape ) specgram = tf . pad ( specgram , [ [ 0 , 0 ] , [ 0 , 0 ] , [ 0 , num_padding ] , [ 0 , 0 ] ] ) specgram = tf . slice ( specgram , [ 0 , 0 , 0 , 0 ] , [ - 1 , shape [ 1 ] - 1 , - 1 , - 1 ] ) tf . logging . info ( "SPECGRAM AFTER PADDING" , specgram ) # Form a Batch if self . is_training : ( audio , velocity , pitch , specgram , instrument_source , instrument_family , qualities ) = tf . train . shuffle_batch ( [ audio , velocity , pitch , specgram , instrument_source , instrument_family , qualities ] , batch_size = hparams . batch_size , capacity = 20 * hparams . batch_size , min_after_dequeue = 10 * hparams . batch_size , enqueue_many = True ) elif hparams . batch_size > 1 : ( audio , velocity , pitch , specgram , instrument_source , instrument_family , qualities ) = tf . train . batch ( [ audio , velocity , pitch , specgram , instrument_source , instrument_family , qualities ] , batch_size = hparams . batch_size , capacity = 10 * hparams . batch_size , enqueue_many = True ) audio . set_shape ( [ hparams . batch_size , 64000 ] ) batch = dict ( pitch = pitch , velocity = velocity , audio = audio , instrument_source = instrument_source , instrument_family = instrument_family , qualities = qualities , spectrogram = specgram ) return batch