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):
"""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
