def loss(self, input_batch, name='wavenet'):
'''Creates a WaveNet network and returns the autoencoding loss.
The variables are all scoped to the given name.
'''
with tf.name_scope(name):
input_batch = mu_law_encode(input_batch,
self.quantization_channels)
encoded = self._one_hot(input_batch)
raw_output = self._create_network(encoded)
with tf.name_scope('loss'):
# Shift original input left by one sample, which means that
# each output sample has to predict the next input sample.
shifted = tf.slice(encoded, [0, 1, 0],
[-1, tf.shape(encoded)[1] - 1, -1])
shifted = tf.pad(shifted, [[0, 0], [0, 1], [0, 0]])
prediction = tf.reshape(raw_output,
[-1, self.quantization_channels])
loss = tf.nn.softmax_cross_entropy_with_logits(
prediction,
tf.reshape(shifted, [-1, self.quantization_channels]))
reduced_loss = tf.reduce_mean(loss)
tf.scalar_summary('loss', reduced_loss)
return reduced_loss
def testEncodeDecode(self):
x = np.linspace(-1, 1, 1000).astype(np.float32)
channels = 256
# Test whether decoded signal is roughly equal to
# what was encoded before
with self.test_session() as sess:
encoded = mu_law_encode(x, channels)
x1 = sess.run(mu_law_decode(encoded, channels))
self.assertAllClose(x, x1, rtol=1e-1, atol=0.05)
# Make sure that re-encoding leaves the waveform invariant
with self.test_session() as sess:
encoded = mu_law_encode(x1, channels)
x2 = sess.run(mu_law_decode(encoded, channels))
self.assertAllClose(x1, x2)
def create_seed(filename,
sample_rate,
quantization_channels,
window_size=WINDOW):
audio, _ = librosa.load(filename, sr=sample_rate, mono=True)
quantized = mu_law_encode(audio, quantization_channels)
cut_index = tf.size(quantized) + tf.constant(window_size) - tf.constant(1)
return quantized[:cut_index]
def testEncodeNegativeChannelSize(self):
np.random.seed(1944) # For repeatability of test.
channels = -256
number_of_samples = 1024
x = np.zeros(number_of_samples).astype(np.float32)
manual_encode = manual_mu_law_encode(x, channels)
with self.test_session() as sess:
self.assertRaises(TypeError, sess.run(mu_law_encode(x, channels)))
def testEncodeUniformRandomNoise(self):
np.random.seed(42) # For repeatability of test.
channels = 256
number_of_samples = 2048
x = np.random.uniform(-1, 1, number_of_samples).astype(np.float32)
manual_encode = manual_mu_law_encode(x, channels)
with self.test_session() as sess:
encode = sess.run(mu_law_encode(x, channels))
self.assertAllEqual(manual_encode, encode)
def testEncodeZeros(self):
np.random.seed(1944) # For repeatability of test.
channels = 256
number_of_samples = 1024
x = np.zeros(number_of_samples).astype(np.float32)
manual_encode = manual_mu_law_encode(x, channels)
with self.test_session() as sess:
encode = sess.run(mu_law_encode(x, channels))
self.assertAllEqual(manual_encode, encode)
def testEncodePrecomputed(self):
channels = 256
number_of_samples = 10
x = np.array(
[-1.0, 1.0, 0.6, -0.25, 0.01, 0.33, -0.9999, 0.42, 0.1,
-0.45]).astype(np.float32)
encoded_manual = np.array([0, 255, 243, 32, 157, 230, 0, 235, 203,
18]).astype(np.int32)
with self.test_session() as sess:
encoded = sess.run(mu_law_encode(x, channels))
self.assertAllEqual(encoded_manual, encoded)
def testEncodeRamp(self):
np.random.seed(1944) # For repeatability of test.
channels = 256
number_of_samples = 1024
number_of_steps = 2.0 / number_of_samples
x = np.arange(-1.0, 1.0, number_of_steps).astype(np.float32)
manual_encode = manual_mu_law_encode(x, channels)
with self.test_session() as sess:
encode = sess.run(mu_law_encode(x, channels))
self.assertAllEqual(manual_encode, encode)
def testEncodeIsSurjective(self):
x = np.linspace(-1, 1, 10000).astype(np.float32)
channels = 123
with self.test_session() as sess:
encoded = sess.run(mu_law_encode(x, channels))
self.assertEqual(len(np.unique(encoded)), channels)