def testGenerateAudioSample(self, batch_size, channels=16):
audio = np.random.randn(batch_size, 1)
encoding = np.random.randn(batch_size, channels)
with tf.Graph().as_default(), self.test_session() as sess:
net = fastgen.load_fastgen_nsynth(batch_size=batch_size)
sess.run(tf.global_variables_initializer())
audio_gen = fastgen.generate_audio_sample(sess, net, audio, encoding)
self.assertEqual(audio_gen.shape, audio.shape)
def testGenerateAudioSample(self, batch_size, channels=16):
audio = np.random.randn(batch_size, 1)
encoding = np.random.randn(batch_size, channels)
with tf.Graph().as_default(), self.test_session() as sess:
net = fastgen.load_fastgen_nsynth(batch_size=batch_size)
sess.run(tf.global_variables_initializer())
audio_gen = fastgen.generate_audio_sample(sess, net, audio, encoding)
self.assertEqual(audio_gen.shape, audio.shape)
def synth_samples(encodings, checkpoint_path="model.ckpt-200000"):
"""Synthesize audio from an array of encodings.
Args:
encodings: Numpy array with shape [batch_size, time, dim].
save_paths: Iterable of output file names.
checkpoint_path: Location of the pretrained model. [model.ckpt-200000]
samples_per_save: Save files after every amount of generated samples.
"""
session_config = tf.ConfigProto(allow_soft_placement=True)
session_config.gpu_options.allow_growth = True
with tf.Graph().as_default(), tf.Session(config=session_config) as sess:
net = load_fastgen_nsynth(batch_size=encodings.shape[0])
saver = tf.train.Saver()
saver.restore(sess, checkpoint_path)
# Get lengths
batch_size, encoding_length, _ = encodings.shape
hop_length = Config().ae_hop_length
total_length = encoding_length * hop_length
# initialize queues w/ 0s
sess.run(net["init_ops"])
# Regenerate the audio file sample by sample
audio_batch = np.zeros((batch_size, total_length), dtype=np.float32)
audio = np.zeros([batch_size, 1])
for sample_i in range(total_length):
encoding_i = sample_i // hop_length
audio = generate_audio_sample(sess, net, audio,
encodings[:, encoding_i, :])
audio_batch[:, sample_i] = audio[:, 0]
if sample_i % 100 == 0:
tf.logging.info("Sample: %d" % sample_i)
return audio_batch
def testLoadFastgenNsynth(self, batch_size):
with tf.Graph().as_default():
net = fastgen.load_fastgen_nsynth(batch_size=batch_size)
self.assertEqual(net['X'].shape, (batch_size, 1))
self.assertEqual(net['encoding'].shape, (batch_size, 16))
self.assertEqual(net['predictions'].shape, (batch_size, 256))
def testLoadFastgenNsynth(self, batch_size):
with tf.Graph().as_default():
net = fastgen.load_fastgen_nsynth(batch_size=batch_size)
self.assertEqual(net['X'].shape, (batch_size, 1))
self.assertEqual(net['encoding'].shape, (batch_size, 16))
self.assertEqual(net['predictions'].shape, (batch_size, 256))
