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Without Training, I can run generate.run directly, with dilation_num = 4, generate cost 19s, dilation_num = 8, cost 29s, dilation_num = 14, cost 43s, so the results seems not linearly?
from time import time
from wavenet.utils import make_batch
from wavenet.models import Model, Generator
#from IPython.display import Audio
inputs, targets = make_batch('assets/voice.wav')
num_time_samples = inputs.shape[1]
num_channels = 1
gpu_fraction = 1.0
model = Model(num_time_samples=num_time_samples,
num_channels=num_channels,
gpu_fraction=gpu_fraction)
#Audio(inputs.reshape(inputs.shape[1]), rate=44100)
tic = time()
#model.train(inputs, targets)
toc = time()
print('Training took {} seconds.'.format(toc-tic))
generator = Generator(model)
# Get first sample of input
input_ = inputs[:, 0:1, 0]
tic = time()
predictions = generator.run(input_, 32000)
toc = time()
print('Generating took {} seconds.'.format(toc-tic))
#Audio(predictions, rate=44100)
layers=4
$python test.py
WARNING:tensorflow:From /home/xianning.lu/sing/tf-no-mkl/lib/python2.7/site-packages/tensorflow/python/util/deprecation.py:553: calling conv1d (from tensorflow.python.ops.nn_ops) with data_format=NHWC is deprecated and will be removed in a future version.
Instructions for updating:
`NHWC` for data_format is deprecated, use `NWC` instead
2018-11-09 18:17:04.603404: I tensorflow/core/platform/cpu_feature_guard.cc:141] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA
WARNING:tensorflow:From /home/xianning.lu/sing/tf-no-mkl/lib/python2.7/site-packages/tensorflow/python/util/tf_should_use.py:189: initialize_all_variables (from tensorflow.python.ops.variables) is deprecated and will be removed after 2017-03-02.
Instructions for updating:
Use `tf.global_variables_initializer` instead.
Training took 0.0 seconds.
Make Generator.
Generating took 19.5070331097 seconds.
layers=8
$python test.py
WARNING:tensorflow:From /home/xianning.lu/sing/tf-no-mkl/lib/python2.7/site-packages/tensorflow/python/util/deprecation.py:553: calling conv1d (from tensorflow.python.ops.nn_ops) with data_format=NHWC is deprecated and will be removed in a future version.
Instructions for updating:
`NHWC` for data_format is deprecated, use `NWC` instead
2018-11-09 18:17:51.435269: I tensorflow/core/platform/cpu_feature_guard.cc:141] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA
WARNING:tensorflow:From /home/xianning.lu/sing/tf-no-mkl/lib/python2.7/site-packages/tensorflow/python/util/tf_should_use.py:189: initialize_all_variables (from tensorflow.python.ops.variables) is deprecated and will be removed after 2017-03-02.
Instructions for updating:
Use `tf.global_variables_initializer` instead.
Training took 0.0 seconds.
Make Generator.
Generating took 29.0180389881 seconds.
layers=14
$python test.py
WARNING:tensorflow:From /home/xianning.lu/sing/tf-no-mkl/lib/python2.7/site-packages/tensorflow/python/util/deprecation.py:553: calling conv1d (from tensorflow.python.ops.nn_ops) with data_format=NHWC is deprecated and will be removed in a future version.
Instructions for updating:
`NHWC` for data_format is deprecated, use `NWC` instead
2018-11-09 18:15:56.114761: I tensorflow/core/platform/cpu_feature_guard.cc:141] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA
WARNING:tensorflow:From /home/xianning.lu/sing/tf-no-mkl/lib/python2.7/site-packages/tensorflow/python/util/tf_should_use.py:189: initialize_all_variables (from tensorflow.python.ops.variables) is deprecated and will be removed after 2017-03-02.
Instructions for updating:
Use `tf.global_variables_initializer` instead.
Training took 0.0 seconds.
Make Generator.
Generating took 43.2759749889 seconds.
The text was updated successfully, but these errors were encountered:
Without Training, I can run generate.run directly, with dilation_num = 4, generate cost 19s, dilation_num = 8, cost 29s, dilation_num = 14, cost 43s, so the results seems not linearly?
layers=4
layers=8
layers=14
The text was updated successfully, but these errors were encountered: