why it takes too long

[NileZhou]

for 1 second voice , it real need 8 seconds to clean? i make a same generator network, it just takes 0.2 second for 1 second data

[NileZhou]

`import tensorflow as tf
import time

def leakyrelu(x, alpha=0.3, name='lrelu'):
return tf.maximum(x, alpha * x, name=name)

def downconv(x, output_dim, kwidth=5, pool=2, init=None, uniform=False,
bias_init=None, name='downconv'):
""" Downsampled convolution 1d """
x2d = tf.expand_dims(x, 2)
w_init = init
if w_init is None:
w_init = xavier_initializer(uniform=uniform)
with tf.variable_scope(name , reuse=None):
W = tf.get_variable('W', [kwidth, 1, x.get_shape()[-1], output_dim], initializer=w_init)
conv = tf.nn.conv2d(x2d, W, strides=[1, pool, 1, 1], padding='SAME')
if bias_init is not None:
b = tf.get_variable('b', [output_dim], initializer=bias_init)
conv = tf.reshape(tf.nn.bias_add(conv, b), conv.get_shape())
else:
conv = tf.reshape(conv, conv.get_shape())
# reshape back to 1d
conv = tf.reshape(conv, conv.get_shape().as_list()[:2] + [conv.get_shape().as_list()[-1]])
return conv

def deconv(x, output_shape, kwidth=5, dilation=2, init=None, uniform=False,
bias_init=None, name='deconv1d'):
input_shape = x.get_shape()
in_channels = input_shape[-1]
out_channels = output_shape[-1]
assert len(input_shape) >= 3
# reshape the tensor to use 2d operators
x2d = tf.expand_dims(x, 2)
o2d = output_shape[:2] + [1] + [output_shape[-1]]
w_init = init
if w_init is None:
w_init = xavier_initializer(uniform=uniform)
with tf.variable_scope(name , reuse=None):
# filter shape: [kwidth, output_channels, in_channels]
W = tf.get_variable('W', [kwidth, 1, out_channels, in_channels],initializer=w_init)
try:
deconv = tf.nn.conv2d_transpose(x2d, W, output_shape=o2d, strides=[1, dilation, 1, 1])
except AttributeError:
# support for versions of TF before 0.7.0
# based on https://github.com/carpedm20/DCGAN-tensorflow
deconv = tf.nn.conv2d_transpose(x2d, W, output_shape=o2d, strides=[1, dilation, 1, 1])
if bias_init is not None:
b = tf.get_variable('b', [out_channels], initializer=tf.constant_initializer(0.))
deconv = tf.reshape(tf.nn.bias_add(deconv, b), deconv.get_shape())
else:
deconv = tf.reshape(deconv, deconv.get_shape())
# reshape back to 1d
deconv = tf.reshape(deconv, output_shape)
return deconv

batchsize=100

def G(noise):
noise_w = tf.random_normal([100, 16384], mean=0.0, stddev=0.01, dtype=tf.float32)
h_i = tf.expand_dims(noise_w, -1)
skips = []
g_enc_depths = [16, 32, 32, 64, 64, 128, 128, 256, 256, 512, 1024]
for index in range(10):
h_i = downconv(h_i, g_enc_depths[index], 31, init=tf.truncated_normal_initializer(stddev=0.02), name='enc_{}'.format(index))
#9
skips.append(h_i)
h_i = leakyrelu(h_i)
h_i = downconv(h_i, g_enc_depths[10], 31, init=tf.truncated_normal_initializer(stddev=0.02), name='enc_{}'.format(10))
h_i = leakyrelu(h_i)
z = tf.random_normal([batchsize, h_i.get_shape().as_list()[1], g_enc_depths[-1]], mean=0.0, stddev=1, name='z', dtype=tf.float32)
h_i = tf.concat([z, h_i], 2)
g_dec_depths = g_enc_depths[:-1][::-1] + [1]
for index in range(10):
h_i_dim = h_i.get_shape().as_list()
out_shape = [h_i_dim[0], h_i_dim[1] * 2, g_dec_depths[index]]
h_i = deconv(h_i, out_shape, kwidth=31, dilation=2, init=tf.truncated_normal_initializer(stddev=0.02), name='dec_{}'.format(index))
h_i = leakyrelu(h_i)
skip_ = skips[-(index + 1)]
h_i = tf.concat([h_i, skip_], 2)
h_i_dim = h_i.get_shape().as_list()
out_shape = [h_i_dim[0], h_i_dim[1] * 2, g_dec_depths[10]]
h_i = deconv(h_i, out_shape, kwidth=31, dilation=2, init=tf.truncated_normal_initializer(stddev=0.02), name='dec_{}'.format(10))
h_i = tf.tanh(h_i)
return h_i

a=G(1)
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
sess.run([a])
sess.run([a])
print('start timer')
TIME1= time.time()
b, c = sess.run([a, a])
print(time.time() - TIME1)
`

[AoyuQC]

hi, does your version achieve good result? @NileZhou