class TestGeneration(tf.test.TestCase):
def setUp(self):
self.net = WaveNetModel(batch_size=1,
dilations=[1, 2, 4, 8, 16, 32, 64, 128, 256],
filter_width=2,
residual_channels=16,
dilation_channels=16,
quantization_channels=128,
skip_channels=32)
def testGenerateSimple(self):
'''Generate a few samples using the naive method and
perform sanity checks on the output.'''
waveform = tf.placeholder(tf.int32)
np.random.seed(0)
data = np.random.randint(128, size=1000)
proba = self.net.predict_proba(waveform)
with self.test_session() as sess:
sess.run(tf.initialize_all_variables())
proba = sess.run(proba, feed_dict={waveform: data})
self.assertAllEqual(proba.shape, [128])
self.assertTrue(np.all((proba >= 0) & (proba <= (128 - 1))))
def testGenerateFast(self):
'''Generate a few samples using the fast method and
perform sanity checks on the output.'''
waveform = tf.placeholder(tf.int32)
np.random.seed(0)
data = np.random.randint(128)
proba = self.net.predict_proba_incremental(waveform)
with self.test_session() as sess:
sess.run(tf.initialize_all_variables())
sess.run(self.net.init_ops)
proba = sess.run(proba, feed_dict={waveform: data})
self.assertAllEqual(proba.shape, [128])
self.assertTrue(np.all((proba >= 0) & (proba <= (128 - 1))))
def testCompareSimpleFast(self):
waveform = tf.placeholder(tf.int32)
np.random.seed(0)
data = np.random.randint(128, size=1)
proba = self.net.predict_proba(waveform)
proba_fast = self.net.predict_proba_incremental(waveform)
with self.test_session() as sess:
sess.run(tf.initialize_all_variables())
sess.run(self.net.init_ops)
# Prime the incremental generation with all samples
# except the last one
for x in data[:-1]:
proba_fast_ = sess.run(
[proba_fast, self.net.push_ops],
feed_dict={waveform: x})
# Get the last sample from the incremental generator
proba_fast_ = sess.run(
proba_fast,
feed_dict={waveform: data[-1]})
# Get the sample from the simple generator
proba_ = sess.run(proba, feed_dict={waveform: data})
self.assertAllClose(proba_, proba_fast_)
def main():
args = get_arguments()
started_datestring = "{0:%Y-%m-%dT%H-%M-%S}".format(datetime.now())
logdir = os.path.join(args.logdir, 'generate', started_datestring)
with open(args.wavenet_params, 'r') as config_file:
wavenet_params = json.load(config_file)
sess = tf.Session()
net = WaveNetModel(
batch_size=1,
dilations=wavenet_params['dilations'],
filter_width=wavenet_params['filter_width'],
residual_channels=wavenet_params['residual_channels'],
dilation_channels=wavenet_params['dilation_channels'],
quantization_channels=wavenet_params['quantization_channels'],
skip_channels=wavenet_params['skip_channels'],
use_biases=wavenet_params['use_biases'],
scalar_input=wavenet_params['scalar_input'],
initial_filter_width=wavenet_params['initial_filter_width'])
samples = tf.placeholder(tf.int32) #待初始化的张量占位符
input_ID = tf.placeholder(tf.int32)
startime_fastgeration = time.clock()
if args.fast_generation:
print("#########using_fast_generation")
next_sample = net.predict_proba_incremental(samples, input_ID)
#print next_sample
else:
next_sample = net.predict_proba(samples, input_ID)
if args.fast_generation:
sess.run(tf.initialize_all_variables())
sess.run(net.init_ops)
endtime_fastgernation = time.clock()
#print ('fast_generation time {}'.format(endtime_fastgernation - endtime_fastgernation))
time_of_fast = endtime_fastgernation - startime_fastgeration #1
start_vari_saver = time.clock() #变量save
variables_to_restore = {
var.name[:-2]: var
for var in tf.all_variables()
if not ('state_buffer' in var.name or 'pointer' in var.name)
}
saver = tf.train.Saver(variables_to_restore)
end_vari_saver = time.clock()
print('variables_to_restore{}'.format(end_vari_saver - start_vari_saver))
starttime_restore = time.clock() #恢复从checkpoint
print('Restoring model from {}'.format(args.checkpoint))
saver.restore(sess, args.checkpoint)
endtime_restore = time.clock()
#print ('restore model time{}'.format(endtime_restore - endtime_restore))
time_of_restore = endtime_restore - starttime_restore #2
print('%%%%%%%%%%%%{}'.format(time_of_restore))
#return 0
decode = mu_law_decode(
samples, wavenet_params['quantization_channels']) #namescope(encode)
quantization_channels = wavenet_params['quantization_channels']
time_of_seed = 0
if args.wav_seed:
start_using_create_seed = time.clock()
print('#######using_create_seed')
seed = create_seed(args.wav_seed, wavenet_params['sample_rate'],
quantization_channels)
waveform = sess.run(seed).tolist() #
end_using_create_seed = time.clock()
time_of_seed = end_using_create_seed - start_using_create_seed
#print ('using create_seed time{}'.format(end_using_create_seed - start_using_create_seed))
else:
print('#######not_using_create_seed')
waveform = np.random.randint(quantization_channels,
size=(1, )).tolist()
predict_of_fast_seed = 0
if args.fast_generation and args.wav_seed:
starttime_fast_and_seed = time.clock()
# When using the incremental generation, we need to
# feed in all priming samples one by one before starting the
# actual generation.
# TODO This could be done much more efficiently by passing the waveform
# to the incremental generator as an optional argument, which would be
# used to fill the queues initially.
outputs = [next_sample]
outputs.extend(net.push_ops) #push_ops是一个列表
print('Priming generation...')
for i, x in enumerate(waveform[:-1]):
if i % 1600 == 0:
print('Priming sample {}'.format(i), end='\r')
sys.stdout.flush()
sess.run(outputs,
feed_dict={
samples: x,
input_ID: args.ID_generation
})
print('Done.')
endtime_fast_seed = time.clock()
#print('fast_generation and create_seed time{}'.format(endtime_fast_seed - starttime_fast_and_seed))
predict_of_fast_seed = predict_of_fast_seed + (endtime_fast_seed -
starttime_fast_and_seed)
#return 0
last_sample_timestamp = datetime.now()
predict = 0
index_begin_generate = 0 if (False
== args.fast_generation) else len(waveform)
startime_total_predict_sample = time.clock()
for step in range(args.samples):
if args.fast_generation:
outputs = [next_sample]
outputs.extend(net.push_ops)
window = waveform[-1]
else:
if len(waveform) > args.window: #波形大于窗口？
window = waveform[-args.window:]
else:
window = waveform
outputs = [next_sample]
# Run the WaveNet to predict the next sample.
starttime_run_net_predict = time.clock()
prediction = sess.run(outputs,
feed_dict={
samples: window,
input_ID: args.ID_generation
})[0]
endtime_run_net_predict = time.clock()
print('run net to predict samples per step{}'.format(
endtime_run_net_predict - starttime_run_net_predict))
predict = predict + (endtime_run_net_predict -
starttime_run_net_predict)
# Scale prediction distribution using temperature.尺度化预测!!
np.seterr(divide='ignore') #某种数据规约尺度化
scaled_prediction = np.log(prediction) / args.temperature
scaled_prediction = scaled_prediction - np.logaddexp.reduce(
scaled_prediction)
scaled_prediction = np.exp(scaled_prediction)
np.seterr(divide='warn')
# Prediction distribution at temperature=1.0 should be unchanged after scaling.
if args.temperature == 1.0:
np.testing.assert_allclose(
prediction,
scaled_prediction,
atol=1e-5,
err_msg=
'Prediction scaling at temperature=1.0 is not working as intended.'
)
sample = np.random.choice( #以概率返回某层通道采样
np.arange(quantization_channels),
p=scaled_prediction)
waveform.append(sample)
# Show progress only once per second.
current_sample_timestamp = datetime.now()
time_since_print = current_sample_timestamp - last_sample_timestamp
if time_since_print.total_seconds() > 1.: #以1？？
print('Sample {:3<d}/{:3<d}'.format(step + 1, args.samples),
end='\r')
sys.stdout.flush()
last_sample_timestamp = current_sample_timestamp
# If we have partial writing, save the result so far.
if (args.wav_out_path and args.save_every
and (step + 1) % args.save_every == 0):
print(
'$$$$$$$$$$If we have partial writing, save the result so far')
out = sess.run(decode, feed_dict={samples:
waveform}) #有输入要求的tensor
write_wav(out, wavenet_params['sample_rate'], args.wav_out_path)
endtime_total_predicttime = time.clock()
print('total predic time{}'.format(endtime_total_predicttime -
startime_total_predict_sample))
print('run net predict time{}'.format(predict))
# Introduce a newline to clear the carriage return from the progress.
print()
# Save the result as an audio summary.
'''
datestring = str(datetime.now()).replace(' ', 'T')
writer = tf.train.SummaryWriter(logdir)
tf.audio_summary('generated', decode, wavenet_params['sample_rate'])
summaries = tf.merge_all_summaries()
summary_out = sess.run(summaries,
feed_dict={samples: np.reshape(waveform[index_begin_generate:], [-1, 1]), input_ID: args.ID_generation})
writer.add_summary(summary_out)
'''
# Save the result as a wav file.
if args.wav_out_path:
start_save_wav_time = time.clock()
out = sess.run(decode,
feed_dict={samples: waveform[index_begin_generate:]})
write_wav(out, wavenet_params['sample_rate'], args.wav_out_path)
end_save_wave_time = time.clock()
print('write wave time{}'.format(end_save_wave_time -
start_save_wav_time))
print('time_of_fast_initops{}'.format(time_of_fast))
print('time_of_restore'.format(time_of_restore))
print('time_of_fast_and_seed{}'.format(predict_of_fast_seed))
print('time_of_seed'.format(time_of_seed))
print('Finished generating. The result can be viewed in TensorBoard.')
def main():
args = get_arguments()
started_datestring = "{0:%Y-%m-%dT%H-%M-%S}".format(datetime.now())
logdir = os.path.join(args.logdir, 'generate', started_datestring)
with open(args.wavenet_params, 'r') as config_file:
wavenet_params = json.load(config_file)
labels = tf.placeholder(tf.float32)
data_dir = DATA_DIRECTORY
file_list = FILE_LIST
label_dir = data_dir + 'binary_label_norm/'
audio_dir = data_dir + 'wav/'
label_dim = 425
n_out = 1
iterator = audio_reader.load_generic_audio_label(file_list, audio_dir,
label_dir, label_dim)
audio_test, labels_test, filename = iterator.next()
n_samples_read = audio_test.shape[0]
labels_test = labels_test.reshape(
(1, labels_test.shape[0], labels_test.shape[1]))
sess = tf.Session()
net = WaveNetModel(
batch_size=1,
dilations=wavenet_params['dilations'],
filter_width=wavenet_params['filter_width'],
label_dim=label_dim,
residual_channels=wavenet_params['residual_channels'],
dilation_channels=wavenet_params['dilation_channels'],
skip_channels=wavenet_params['skip_channels'],
quantization_channels=wavenet_params['quantization_channels'],
use_biases=wavenet_params['use_biases'],
scalar_input=wavenet_params['scalar_input'],
initial_filter_width=wavenet_params['initial_filter_width'],
histograms=False)
samples = tf.placeholder(tf.int32)
if args.fast_generation:
next_sample = net.predict_proba_incremental(samples, labels)
else:
next_sample = net.predict_proba(samples, labels)
if args.fast_generation:
sess.run(tf.initialize_all_variables())
sess.run(net.init_ops)
variables_to_restore = {
var.name[:-2]: var
for var in tf.all_variables()
if not ('state_buffer' in var.name or 'pointer' in var.name)
}
saver = tf.train.Saver(variables_to_restore)
print('Restoring model from {}'.format(args.checkpoint))
saver.restore(sess, args.checkpoint)
decode = mu_law_decode(samples, wavenet_params['quantization_channels'])
quantization_channels = wavenet_params['quantization_channels']
if args.wav_seed:
seed = create_seed(args.wav_seed, wavenet_params['sample_rate'],
quantization_channels)
waveform = sess.run(seed).tolist()
else:
waveform = np.random.randint(quantization_channels,
size=(1, )).tolist()
if args.fast_generation and args.wav_seed:
# When using the incremental generation, we need to
# feed in all priming samples one by one before starting the
# actual generation.
# TODO This could be done much more efficiently by passing the waveform
# to the incremental generator as an optional argument, which would be
# used to fill the queues initially.
outputs = [next_sample]
outputs.extend(net.push_ops)
print('Priming generation...')
for i, x in enumerate(waveform[-args.window:-1]):
if i % 100 == 0:
print('Priming sample {}'.format(i))
sess.run(outputs,
feed_dict={
samples: x,
labels: labels_test[:, i:i + 1, :]
})
print('Done.')
last_sample_timestamp = datetime.now()
for step in range(n_samples_read):
if args.fast_generation:
outputs = [next_sample]
outputs.extend(net.push_ops)
window = waveform[-1]
labels_window = labels_test[:, step:step + 1, :]
else:
if len(waveform) > args.window:
window = waveform[-args.window:]
else:
window = waveform
outputs = [next_sample]
labels_window = labels_test[:, step:step + min(
len(window), args.window
), :] # Here there might be a problem with out of index error.
# Run the WaveNet to predict the next sample.
#if (step%100 == 0):
# print('step = ', step, ' , ')
prediction = sess.run(outputs,
feed_dict={
samples: window,
labels: labels_window
})[0]
# Scale prediction distribution using temperature.
np.seterr(divide='ignore')
scaled_prediction = np.log(prediction) / args.temperature
scaled_prediction = scaled_prediction - np.logaddexp.reduce(
scaled_prediction)
scaled_prediction = np.exp(scaled_prediction)
np.seterr(divide='warn')
# Prediction distribution at temperature=1.0 should be unchanged after scaling.
if args.temperature == 1.0:
np.testing.assert_allclose(
prediction,
scaled_prediction,
atol=1e-5,
err_msg=
'Prediction scaling at temperature=1.0 is not working as intended.'
)
sample = np.random.choice(np.arange(quantization_channels),
p=scaled_prediction)
waveform.append(sample)
# Show progress only once per second.
current_sample_timestamp = datetime.now()
time_since_print = current_sample_timestamp - last_sample_timestamp
if time_since_print.total_seconds() > 1.:
print('Sample {:3<d}/{:3<d}'.format(step + 1, args.samples),
end='\r')
last_sample_timestamp = current_sample_timestamp
# If we have partial writing, save the result so far.
if (args.wav_out_path and args.save_every
and (step + 1) % args.save_every == 0):
out = sess.run(decode, feed_dict={samples: waveform})
write_wav(out, wavenet_params['sample_rate'], args.wav_out_path)
# Introduce a newline to clear the carriage return from the progress.
print()
# Save the result as an audio summary.
datestring = str(datetime.now()).replace(' ', 'T')
writer = tf.train.SummaryWriter(logdir)
tf.audio_summary('generated', decode, wavenet_params['sample_rate'])
summaries = tf.merge_all_summaries()
summary_out = sess.run(summaries,
feed_dict={samples: np.reshape(waveform, [-1, 1])})
writer.add_summary(summary_out)
# Save the result as a wav file.
if args.wav_out_path:
out = sess.run(decode, feed_dict={samples: waveform})
write_wav(out, wavenet_params['sample_rate'], args.wav_out_path)
print('Finished generating. The result can be viewed in TensorBoard.')
def main():
args = get_arguments()
started_datestring = "{0:%Y-%m-%dT%H-%M-%S}".format(datetime.now())
#logdir is where logging file is saved. different from where generated mat is saved.
logdir = os.path.join(args.logdir, 'generate', started_datestring)
with open(args.wavenet_params, 'r') as config_file:
wavenet_params = json.load(config_file)
sess = tf.Session()
input_channels = wavenet_params['input_channels']
output_channels = wavenet_params['output_channels']
gt, cut_index = create_seed(args.motion_seed, args.window)
if np.isnan(np.sum(gt)):
print('nan detected')
raise ValueError('NAN detected in seed file')
seed = tf.constant(gt)
net = WaveNetModel(batch_size=1,
dilations=wavenet_params['dilations'],
filter_width=wavenet_params['filter_width'],
residual_channels=wavenet_params['residual_channels'],
dilation_channels=wavenet_params['dilation_channels'],
skip_channels=wavenet_params['skip_channels'],
use_biases=wavenet_params['use_biases'],
input_channels=input_channels,
output_channels=output_channels,
global_condition_channels=args.gc_channels)
samples = tf.placeholder(dtype=tf.float32)
next_sample = net.predict_proba_incremental(samples)
sess.run(tf.initialize_all_variables())
sess.run(net.init_ops)
#TODO: run init_ops only once
variables_to_restore = {
var.name[:-2]: var
for var in tf.all_variables()
if not ('state_buffer' in var.name or 'pointer' in var.name)
}
saver = tf.train.Saver(variables_to_restore)
print('Restoring model from {}'.format(args.checkpoint))
saver.restore(sess, args.checkpoint)
if args.motion_seed:
pass
else:
raise ValueError('motion seed not specified!')
# seed: T x 42 tensor
# tolist() converts a tf tensor to a list
gt_list = sess.run(seed).tolist()
motion = gt_list[:cut_index]
#motion: list of generated data (along with seed)
# When using the incremental generation, we need to
# feed in all priming samples one by one before starting the
# actual generation.
# TODO This could be done much more efficiently by passing the waveform
# to the incremental generator as an optional argument, which would be
# used to fill the queues initially.
outputs = [next_sample]
outputs.extend(net.push_ops)
#TODO: question: everytime runs next_sample <- predict_proba_incremental(samples), will the q be reinitialized? or just use the queue with elements inserted before?
print('Priming generation...')
#for i, x in enumerate(motion[-net.receptive_field: -1]):
for i, x in enumerate(motion[-net.receptive_field:-2]):
if i % 10 == 0:
print('Priming sample {}'.format(i))
sess.run(outputs,
feed_dict={samples: np.reshape(x, (1, input_channels))})
print('Done.')
#TODO: check how next_sample <- net.predict_proba_incremental(samples) works. sample is of size 1 x input_channels.
#TODO: then check if motion[-1] is fed into network twice.
last_sample_timestamp = datetime.now()
for step in range(args.samples):
outputs = [next_sample]
outputs.extend(net.push_ops)
window = motion[-1]
#TODO: why motion[-1] fed into network twice?
# Run the WaveNet to predict the next sample.
prediction = sess.run(
outputs,
feed_dict={samples: np.reshape(window, (1, input_channels))})[0]
#TODO: next_input = np.concatenate((prediction, gt(4:9)), axis=1). motion.append(next_input)
motion.append(
np.concatenate(
(np.reshape(prediction, (1, output_channels)),
np.reshape(gt_list[cut_index + step][output_channels:],
(1, input_channels - output_channels))),
axis=1))
# Show progress only once per second.
current_sample_timestamp = datetime.now()
time_since_print = current_sample_timestamp - last_sample_timestamp
if time_since_print.total_seconds() > 1.:
print('Sample {:3<d}/{:3<d}'.format(step + 1, args.samples),
end='\r')
last_sample_timestamp = current_sample_timestamp
print()
# save result in .mat file
if args.skeleton_out_path:
#TODO: save according to Hanbyul rules
# outdir = os.path.join('logdir','skeleton_generate', os.path.basename(os.path.dirname(args.checkpoint)) + os.path.basename(args.checkpoint)+'window'+str(args.window)+'sample'+str(args.samples))
outdir_base = os.path.join(
args.skeleton_out_path,
os.path.basename(os.path.dirname(args.checkpoint)))
if not os.path.exists(outdir_base):
os.makedirs(outdir_base)
scene_name = os.path.basename(os.path.dirname(args.motion_seed))
scene_dir = os.path.join(outdir_base, scene_name)
if not os.path.exists(scene_dir):
os.makedirs(scene_dir)
filedir = os.path.join(scene_dir, os.path.basename(args.motion_seed))
motion_array = np.array(motion)
np.savetxt(filedir, motion_array[:, :output_channels], delimiter=',')
#sio.savemat(filedir, {'sequence_gt': gt, 'sequence_predict': motion[:, :output_channels], 'global_T': global_T, 'global_Theta': global_Theta,
# 'startFrames': startFrames, 'datatype': foldername, 'testdataPath: '})
print(len(motion))
print('generated filedir:{0}'.format(filedir))
print('Finished generating. The result can be viewed in Matlab.')
def main():
args = get_arguments()
started_datestring = "{0:%Y-%m-%dT%H-%M-%S}".format(datetime.now())
logdir = os.path.join(args.logdir, 'generate', started_datestring)
with open(args.wavenet_params, 'r') as config_file:
wavenet_params = json.load(config_file)
sess = tf.Session()
net = WaveNetModel(
batch_size=1,
dilations=wavenet_params['dilations'],
filter_width=wavenet_params['filter_width'],
residual_channels=wavenet_params['residual_channels'],
dilation_channels=wavenet_params['dilation_channels'],
quantization_channels=wavenet_params['quantization_channels'],
skip_channels=wavenet_params['skip_channels'],
use_biases=wavenet_params['use_biases'],
scalar_input=wavenet_params['scalar_input'],
initial_filter_width=wavenet_params['initial_filter_width'])
samples = tf.placeholder(tf.int32)
if args.fast_generation:
next_sample = net.predict_proba_incremental(samples)
else:
next_sample = net.predict_proba(samples)
if args.fast_generation:
sess.run(tf.initialize_all_variables())
sess.run(net.init_ops)
variables_to_restore = {
var.name[:-2]: var for var in tf.all_variables()
if not ('state_buffer' in var.name or 'pointer' in var.name)}
saver = tf.train.Saver(variables_to_restore)
print('Restoring model from {}'.format(args.checkpoint))
saver.restore(sess, args.checkpoint)
decode = mu_law_decode(samples, wavenet_params['quantization_channels'])
quantization_channels = wavenet_params['quantization_channels']
if args.wav_seed:
seed = create_seed(args.wav_seed,
wavenet_params['sample_rate'],
quantization_channels)
waveform = sess.run(seed).tolist()
else:
waveform = np.random.randint(quantization_channels, size=(1,)).tolist()
if args.fast_generation and args.wav_seed:
# When using the incremental generation, we need to
# feed in all priming samples one by one before starting the
# actual generation.
# TODO This could be done much more efficiently by passing the waveform
# to the incremental generator as an optional argument, which would be
# used to fill the queues initially.
outputs = [next_sample]
outputs.extend(net.push_ops)
print('Priming generation...')
for i, x in enumerate(waveform[:-(args.window + 1)]):
if i % 100 == 0:
print('Priming sample {}'.format(i))
sess.run(outputs, feed_dict={samples: x})
print('Done.')
last_sample_timestamp = datetime.now()
for step in range(args.samples):
if args.fast_generation:
outputs = [next_sample]
outputs.extend(net.push_ops)
window = waveform[-1]
else:
if len(waveform) > args.window:
window = waveform[-args.window:]
else:
window = waveform
outputs = [next_sample]
# Run the WaveNet to predict the next sample.
prediction = sess.run(outputs, feed_dict={samples: window})[0]
sample = np.random.choice(
np.arange(quantization_channels), p=prediction)
waveform.append(sample)
# Show progress only once per second.
current_sample_timestamp = datetime.now()
time_since_print = current_sample_timestamp - last_sample_timestamp
if time_since_print.total_seconds() > 1.:
print('Sample {:3<d}/{:3<d}'.format(step + 1, args.samples),
end='\r')
last_sample_timestamp = current_sample_timestamp
# If we have partial writing, save the result so far.
if (args.wav_out_path and args.save_every and
(step + 1) % args.save_every == 0):
out = sess.run(decode, feed_dict={samples: waveform})
write_wav(out, wavenet_params['sample_rate'], args.wav_out_path)
# Introduce a newline to clear the carriage return from the progress.
print()
# Save the result as an audio summary.
datestring = str(datetime.now()).replace(' ', 'T')
writer = tf.train.SummaryWriter(logdir)
tf.audio_summary('generated', decode, wavenet_params['sample_rate'])
summaries = tf.merge_all_summaries()
summary_out = sess.run(summaries,
feed_dict={samples: np.reshape(waveform, [-1, 1])})
writer.add_summary(summary_out)
# Save the result as a wav file.
if args.wav_out_path:
out = sess.run(decode, feed_dict={samples: waveform})
write_wav(out, wavenet_params['sample_rate'], args.wav_out_path)
print('Finished generating. The result can be viewed in TensorBoard.')
def main():
args = get_arguments()
started_datestring = "{0:%Y-%m-%dT%H-%M-%S}".format(datetime.now())
logdir = os.path.join(args.logdir, 'generate', started_datestring)
with open(args.wavenet_params, 'r') as config_file:
wavenet_params = json.load(config_file)
lc_enabled = args.lc_channels is not None
lc_channels = args.lc_channels
lc_duration = args.lc_duration
sess = tf.Session()
net = WaveNetModel(
batch_size=1,
dilations=wavenet_params['dilations'],
filter_width=wavenet_params['filter_width'],
residual_channels=wavenet_params['residual_channels'],
dilation_channels=wavenet_params['dilation_channels'],
quantization_channels=wavenet_params['quantization_channels'],
skip_channels=wavenet_params['skip_channels'],
use_biases=wavenet_params['use_biases'],
scalar_input=wavenet_params['scalar_input'],
initial_filter_width=wavenet_params['initial_filter_width'],
global_condition_channels=args.gc_channels,
global_condition_cardinality=args.gc_cardinality,
local_condition_channels=args.lc_channels)
samples = tf.placeholder(tf.int32)
lc_piece = tf.placeholder(tf.float32, [1, lc_channels])
local_condition = load_lc(os.path.join(os.getcwd(), args.lc_path))
args.samples = args.lc_duration * len(local_condition)
if args.fast_generation:
next_sample = net.predict_proba_incremental(samples, args.gc_id,
lc_piece)
else:
next_sample = net.predict_proba(samples, args.gc_id, lc_piece)
if args.fast_generation:
sess.run(tf.global_variables_initializer())
sess.run(net.init_ops)
variables_to_restore = {
var.name[:-2]: var
for var in tf.global_variables()
if not ('state_buffer' in var.name or 'pointer' in var.name)
}
saver = tf.train.Saver(variables_to_restore)
print('Restoring model from {}'.format(args.checkpoint))
saver.restore(sess, args.checkpoint)
decode = mu_law_decode(samples, wavenet_params['quantization_channels'])
quantization_channels = wavenet_params['quantization_channels']
if args.wav_seed:
seed = create_seed(args.wav_seed, wavenet_params['sample_rate'],
quantization_channels, net.receptive_field)
waveform = sess.run(seed).tolist()
else:
# Silence with a single random sample at the end.
waveform = [quantization_channels / 2] * (net.receptive_field - 1)
waveform.append(np.random.randint(quantization_channels))
if args.fast_generation and args.wav_seed:
# When using the incremental generation, we need to
# feed in all priming samples one by one before starting the
# actual generation.
# TODO This could be done much more efficiently by passing the waveform
# to the incremental generator as an optional argument, which would be
# used to fill the queues initially.
outputs = [next_sample]
outputs.extend(net.push_ops)
print('Priming generation...')
for i, x in enumerate(waveform[-net.receptive_field:-1]):
if i % 100 == 0:
print('Priming sample {}'.format(i))
sess.run(outputs, feed_dict={samples: x})
print('Done.')
last_sample_timestamp = datetime.now()
for step in range(args.samples):
if args.fast_generation:
outputs = [next_sample]
outputs.extend(net.push_ops)
window = waveform[-1]
else:
if len(waveform) > net.receptive_field:
window = waveform[-net.receptive_field:]
else:
window = waveform
outputs = [next_sample]
if lc_enabled:
if (step % lc_duration == 0):
lc_window = local_condition[:1, :]
local_condition[1:, :]
prediction = sess.run(outputs,
feed_dict={
samples: window,
lc_piece: lc_window
})[0]
else:
# Run the WaveNet to predict the next sample.
prediction = sess.run(outputs, feed_dict={samples: window})[0]
# Scale prediction distribution using temperature.
np.seterr(divide='ignore')
scaled_prediction = np.log(prediction) / args.temperature
scaled_prediction = (scaled_prediction -
np.logaddexp.reduce(scaled_prediction))
scaled_prediction = np.exp(scaled_prediction)
np.seterr(divide='warn')
# Prediction distribution at temperature=1.0 should be unchanged after
# scaling.
if args.temperature == 1.0:
np.testing.assert_allclose(
prediction,
scaled_prediction,
atol=1e-5,
err_msg='Prediction scaling at temperature=1.0 '
'is not working as intended.')
sample = np.random.choice(np.arange(quantization_channels),
p=scaled_prediction)
waveform.append(sample)
# Show progress only once per second.
current_sample_timestamp = datetime.now()
time_since_print = current_sample_timestamp - last_sample_timestamp
if time_since_print.total_seconds() > 1.:
print('Sample {:3<d}/{:3<d}'.format(step + 1, args.samples),
end='\r')
last_sample_timestamp = current_sample_timestamp
# If we have partial writing, save the result so far.
if (args.wav_out_path and args.save_every
and (step + 1) % args.save_every == 0):
out = sess.run(decode, feed_dict={samples: waveform})
write_wav(out, wavenet_params['sample_rate'], args.wav_out_path)
# Introduce a newline to clear the carriage return from the progress.
print()
# Save the result as an audio summary.
datestring = str(datetime.now()).replace(' ', 'T')
writer = tf.summary.FileWriter(logdir)
tf.summary.audio('generated', decode, wavenet_params['sample_rate'])
summaries = tf.summary.merge_all()
summary_out = sess.run(summaries,
feed_dict={samples: np.reshape(waveform, [-1, 1])})
writer.add_summary(summary_out)
# Save the result as a wav file.
if args.wav_out_path:
out = sess.run(decode, feed_dict={samples: waveform})
write_wav(out, wavenet_params['sample_rate'], args.wav_out_path)
print('Finished generating. The result can be viewed in TensorBoard.')
def main():
midi_dims = 88
args = get_arguments()
started_datestring = "{0:%Y-%m-%dT%H-%M-%S}".format(datetime.now())
logdir = os.path.join(args.logdir, 'generate', started_datestring)
checkpoint = tf.train.latest_checkpoint(args.resdir)
print('checkpoint: ', checkpoint)
wavenet_params_fname = args.resdir + 'wavenet_params.json'
print('wavenet params fname', wavenet_params_fname)
with open(wavenet_params_fname, 'r') as config_file:
wavenet_params = json.load(config_file)
wavenet_params['midi_dims'] = midi_dims
sess = tf.Session()
net = WaveNetModel(
batch_size=1,
dilations=wavenet_params['dilations'],
filter_width=wavenet_params['filter_width'],
residual_channels=wavenet_params['residual_channels'],
dilation_channels=wavenet_params['dilation_channels'],
skip_channels=wavenet_params['skip_channels'],
use_biases=wavenet_params['use_biases'],
scalar_input=wavenet_params['scalar_input'],
midi_dims=wavenet_params['midi_dims'],
initial_filter_width=wavenet_params['initial_filter_width'],
global_condition_channels=args.gc_channels,
global_condition_cardinality=args.gc_cardinality)
samples = tf.placeholder(tf.float32, shape=(None, midi_dims))
if args.fast_generation:
next_sample = net.predict_proba_incremental(samples, args.gc_id)
else:
next_sample = net.predict_proba(samples, args.gc_id)
if args.fast_generation:
sess.run(tf.global_variables_initializer())
sess.run(net.init_ops)
variables_to_restore = {
var.name[:-2]: var
for var in tf.global_variables()
if not ('state_buffer' in var.name or 'pointer' in var.name)
}
saver = tf.train.Saver(variables_to_restore)
print('vars to restore')
for key in variables_to_restore.keys():
print(key, variables_to_restore[key])
print('Restoring model from {}'.format(checkpoint))
saver.restore(sess, checkpoint)
if args.wav_seed:
seed = create_seed(args.wav_seed, wavenet_params['sample_rate'],
midi_dims, net.receptive_field)
waveform = sess.run(seed).tolist()
else:
# Silence with a single random sample at the end.
#waveform = [quantization_channels / 2] * (net.receptive_field - 1)
#waveform.append(np.random.randint(quantization_channels))
random_note = np.zeros((1, midi_dims))
random_note[0, np.random.randint(0, midi_dims - 1)] = 1.0
waveform = np.concatenate((np.zeros(
(net.receptive_field - 1, midi_dims)), random_note),
axis=0)
if args.fast_generation and args.wav_seed:
print('fast gen')
# When using the incremental generation, we need to
# feed in all priming samples one by one before starting the
# actual generation.
# TODO This could be done much more efficiently by passing the waveform
# to the incremental generator as an optional argument, which would be
# used to fill the queues initially.
outputs = [next_sample]
outputs.extend(net.push_ops)
print('Priming generation...')
for i, x in enumerate(waveform[-net.receptive_field:-1]):
if i % 100 == 0:
print('Priming sample {}'.format(i))
sess.run(outputs, feed_dict={samples: x})
print('Done.')
print('receptive field is %d' % net.receptive_field)
last_sample_timestamp = datetime.now()
for step in range(args.samples):
if args.fast_generation:
outputs = [next_sample]
outputs.extend(net.push_ops)
window = np.expand_dims(waveform[-1, :], 0)
else:
if len(waveform) > net.receptive_field:
window = waveform[-net.receptive_field:]
else:
window = waveform
outputs = [next_sample]
print(step, 'wave shape', waveform.shape, 'window shape', window.shape)
# Run the WaveNet to predict the next sample.
prediction = sess.run(outputs, feed_dict={samples: window})[0]
# Scale prediction distribution using temperature.
#np.seterr(divide='ignore')
#scaled_prediction = np.log(prediction) / args.temperature
#scaled_prediction = (scaled_prediction -
# np.logaddexp.reduce(scaled_prediction))
#scaled_prediction = np.exp(scaled_prediction)
#np.seterr(divide='warn')
# Prediction distribution at temperature=1.0 should be unchanged after
# scaling.
#if args.temperature == 1.0:
# np.testing.assert_allclose(
# prediction, scaled_prediction, atol=1e-5,
# err_msg='Prediction scaling at temperature=1.0 '
# 'is not working as intended.')
sample = 1 * (prediction > 0.5)
print('num notes', np.count_nonzero(sample))
#sample = np.random.choice(
# np.arange(quantization_channels), p=scaled_prediction)
waveform = np.concatenate((waveform, np.expand_dims(sample, 0)),
axis=0)
# Show progress only once per second.
current_sample_timestamp = datetime.now()
time_since_print = current_sample_timestamp - last_sample_timestamp
if time_since_print.total_seconds() > 1.:
print('Sample {:3<d}/{:3<d}'.format(step + 1, args.samples),
end='\r')
last_sample_timestamp = current_sample_timestamp
# Introduce a newline to clear the carriage return from the progress.
print()
# Save the result as an audio summary.
datestring = str(datetime.now()).replace(' ', 'T')
#writer = tf.summary.FileWriter(logdir)
#tf.summary.audio('generated', decode, wavenet_params['sample_rate'])
#summaries = tf.summary.merge_all()
#print('waveform', waveform);
#summary_out = sess.run(summaries, feed_dict={samples: waveform})
#writer.add_summary(summary_out)
# Save the result as a wav file.
if args.wav_out_path is None:
args.wav_out_path = args.resdir
#out = sess.run(decode, feed_dict={samples: waveform})
print(args.wav_out_path)
filename = args.wav_out_path + ('sample_%d.mid' % int(args.gen_num))
midiwrite(filename, waveform)
def main():
args = get_arguments()
started_datestring = "{0:%Y-%m-%dT%H-%M-%S}".format(datetime.now())
logdir = os.path.join(args.logdir, 'generate', started_datestring)
with open(args.wavenet_params, 'r') as config_file:
wavenet_params = json.load(config_file)
sess = tf.Session()
net = WaveNetModel(
batch_size=1,
dilations=wavenet_params["dilations"],
filter_width=wavenet_params["filter_width"],
residual_channels=wavenet_params["residual_channels"],
dilation_channels=wavenet_params["dilation_channels"],
skip_channels=wavenet_params["skip_channels"],
use_biases=wavenet_params["use_biases"],
scalar_input=wavenet_params["scalar_input"],
initial_filter_width=wavenet_params["initial_filter_width"],
histograms=False,
global_condition_channels=args.gc_channels,
global_condition_cardinality=args.gc_cardinality,
MFSC_channels=wavenet_params["MFSC_channels"],
AP_channels=wavenet_params["AP_channels"],
F0_channels=wavenet_params["F0_channels"],
phone_channels=wavenet_params["phones_channels"],
phone_pos_channels=wavenet_params["phone_pos_channels"])
samples = tf.placeholder(tf.float32)
lc = tf.placeholder(tf.float32)
AP_channels = wavenet_params["AP_channels"]
MFSC_channels = wavenet_params["MFSC_channels"]
if args.fast_generation:
next_sample = net.predict_proba_incremental(
samples, args.gc_id) ########### understand shape of next_sample
else:
outputs = net.predict_proba(samples, AP_channels, lc, args.gc_id)
outputs = tf.reshape(outputs, [1, AP_channels])
if args.fast_generation:
sess.run(tf.global_variables_initializer())
sess.run(net.init_ops)
variables_to_restore = {
var.name[:-2]: var
for var in tf.global_variables()
if not ('state_buffer' in var.name or 'pointer' in var.name)
}
saver = tf.train.Saver(variables_to_restore)
print('Restoring model from {}'.format(args.checkpoint))
saver.restore(sess, args.checkpoint)
if args.wav_seed:
# seed = create_seed(args.wav_seed,
# wavenet_params['sample_rate'],
# quantization_channels,
# net.receptive_field)
# waveform = sess.run(seed).tolist()
pass
else:
# Silence with a single random sample at the end.
waveform = np.zeros(
(net.receptive_field - 1, AP_channels + MFSC_channels))
waveform = np.append(waveform,
np.random.randn(1, AP_channels + MFSC_channels),
axis=0)
lc_array, mfsc_array = load_lc(
scramble=True) # clip_id:[003, 004, 007, 010, 012 ...]
lc_array = np.pad(lc_array, ((net.receptive_field, 0), (0, 0)),
'constant',
constant_values=((0, 0), (0, 0)))
mfsc_array = np.pad(mfsc_array, ((net.receptive_field, 0), (0, 0)),
'constant',
constant_values=((0, 0), (0, 0)))
if args.fast_generation and args.wav_seed:
# When using the incremental generation, we need to
# feed in all priming samples one by one before starting the
# actual generation.
# TODO This could be done much more efficiently by passing the waveform
# to the incremental generator as an optional argument, which would be
# used to fill the queues initially.
outputs = [next_sample]
outputs.extend(
net.push_ops
) ################# understand net.push_ops, understand shape of outputs
print('Priming generation...')
for i, x in enumerate(waveform[-net.receptive_field:-1]):
if i % 100 == 0:
print('Priming sample {}'.format(i))
sess.run(outputs, feed_dict={samples: x})
print('Done.')
last_sample_timestamp = datetime.now()
for step in range(args.samples):
if args.fast_generation:
outputs = [next_sample]
outputs.extend(net.push_ops)
window = waveform[-1]
else:
if len(waveform) > net.receptive_field:
window = waveform[-net.receptive_field:, :]
else:
window = waveform
# Run the WaveNet to predict the next sample.
window = window.reshape(1, window.shape[-2], window.shape[-1])
prediction = sess.run(outputs,
feed_dict={
samples:
window,
lc:
lc_array[step + 1:step + 1 +
net.receptive_field, :].reshape(
1, net.receptive_field, -1)
})
prediction = np.concatenate(
(prediction, mfsc_array[step + net.receptive_field].reshape(1,
-1)),
axis=-1)
waveform = np.append(waveform, prediction, axis=0)
# Show progress only once per second.
current_sample_timestamp = datetime.now()
time_since_print = current_sample_timestamp - last_sample_timestamp
if time_since_print.total_seconds() > 1.:
print('Frame {:3<d}/{:3<d}'.format(step + 1, args.samples),
end='\r')
last_sample_timestamp = current_sample_timestamp
# If we have partial writing, save the result so far.
if (args.wav_out_path and args.save_every
and (step + 1) % args.save_every == 0):
np.save(args.wav_out_path, waveform)
# Introduce a newline to clear the carriage return from the progress.
print()
if args.wav_out_path:
np.save(
args.wav_out_path,
waveform[:, :4]) # only take the first four columns, which are aps
print('Finished generating. The result was saved as .npy file.')
def main():
args = get_arguments()
started_datestring = "{0:%Y-%m-%dT%H-%M-%S}".format(datetime.now())
#logdir is where logging file is saved. different from where generated mat is saved.
logdir = os.path.join(args.logdir, 'generate', started_datestring)
with open(args.wavenet_params, 'r') as config_file:
wavenet_params = json.load(config_file)
sess = tf.Session()
#skeleton_channels = wavenet_params['skeleton_channels']
input_channels = wavenet_params['input_channels']
output_channels = wavenet_params['output_channels']
#gt, cut_index, gc_id = create_seed(os.path.join(args.motion_seed, os.path.basename(args.motion_seed)), args.window)
gt, cut_index = create_seed(
os.path.join(args.motion_seed, os.path.basename(args.motion_seed)),
args.window)
if np.isnan(np.sum(gt)):
print('nan detected')
raise ValueError('NAN detected in seed file')
#if skeleton_channels == 45 or skeleton_channels == 42:
# seed = tf.constant(gt[:cut_index, 45 - skeleton_channels:])
#else:
# seed = tf.constant(gt[:cut_index, :])
seed = tf.constant(gt)
net = WaveNetModel(batch_size=1,
dilations=wavenet_params['dilations'],
filter_width=wavenet_params['filter_width'],
residual_channels=wavenet_params['residual_channels'],
dilation_channels=wavenet_params['dilation_channels'],
skip_channels=wavenet_params['skip_channels'],
use_biases=wavenet_params['use_biases'],
input_channels=input_channels,
output_channels=output_channels,
global_condition_channels=args.gc_channels)
samples = tf.placeholder(dtype=tf.float32)
#todo: Q: how does samples represent T x 42 data? does predict_proba_incremental memorize? A: samples can store multiple frames. T x 42 dim
if args.fast_generation:
#next_sample = net.predict_proba_incremental(samples, args.gc_id)
#next_sample = net.predict_proba_incremental(samples, gc_id)
next_sample = net.predict_proba_incremental(samples)
else:
#next_sample = net.predict_proba(samples, args.gc_id)
#next_sample = net.predict_proba(samples, gc_id)
next_sample = net.predict_proba_incremental(samples)
if args.fast_generation:
sess.run(tf.initialize_all_variables())
sess.run(net.init_ops)
variables_to_restore = {
var.name[:-2]: var
for var in tf.all_variables()
if not ('state_buffer' in var.name or 'pointer' in var.name)
}
saver = tf.train.Saver(variables_to_restore)
print('Restoring model from {}'.format(args.checkpoint))
saver.restore(sess, args.checkpoint)
if args.motion_seed:
pass
else:
raise ValueError('motion seed not specified!')
# seed: T x 42 tensor
# tolist() converts a tf tensor to a list
gt_list = sess.run(seed).tolist()
motion = gt_list[:cut_index]
#motion[i]: ith frame, list of 42 features
if args.fast_generation and args.motion_seed:
# When using the incremental generation, we need to
# feed in all priming samples one by one before starting the
# actual generation.
# TODO This could be done much more efficiently by passing the waveform
# to the incremental generator as an optional argument, which would be
# used to fill the queues initially.
outputs = [next_sample]
outputs.extend(net.push_ops)
print('Priming generation...')
for i, x in enumerate(motion[-net.receptive_field:-1]):
if i % 10 == 0:
print('Priming sample {}'.format(i))
sess.run(outputs,
feed_dict={samples: np.reshape(x, (1, input_channels))})
print('Done.')
last_sample_timestamp = datetime.now()
for step in range(args.samples):
if args.fast_generation:
outputs = [next_sample]
outputs.extend(net.push_ops)
window = motion[-1]
else:
if len(motion) > net.receptive_field:
window = motion[-net.receptive_field:]
else:
window = motion
outputs = [next_sample]
#TODO: why motion[-1] fed into network twice?
# Run the WaveNet to predict the next sample.
prediction = sess.run(
outputs,
feed_dict={samples: np.reshape(window, (1, input_channels))})[0]
# prediction = sess.run(outputs, feed_dict={samples: window})[0]
#TODO: next_input = np.concatenate((prediction, gt(4:9)), axis=1). motion.append(next_input)
motion.append(
np.concatenate(
(prediction, gt_list[cut_index + step][input_channels:]),
axis=1))
# Show progress only once per second.
current_sample_timestamp = datetime.now()
time_since_print = current_sample_timestamp - last_sample_timestamp
if time_since_print.total_seconds() > 1.:
print('Sample {:3<d}/{:3<d}'.format(step + 1, args.samples),
end='\r')
last_sample_timestamp = current_sample_timestamp
print()
# save result in .mat file
if args.skeleton_out_path:
#TODO: save according to Hanbyul rules
# outdir = os.path.join('logdir','skeleton_generate', os.path.basename(os.path.dirname(args.checkpoint)) + os.path.basename(args.checkpoint)+'window'+str(args.window)+'sample'+str(args.samples))
outdir = os.path.join(
args.skeleton_out_path,
os.path.basename(os.path.dirname(args.checkpoint)))
if not os.path.exists(outdir):
os.makedirs(outdir)
filedir = os.path.join(
outdir,
str(os.path.basename(args.motion_seed)) + '.mat')
# filedir = os.path.join(outdir, (sub+args.skeleton_out_path))
sio.savemat(filedir, {'wavenet_predict': motion, 'gt': gt})
# out = sess.run(decode, feed_dict={samples: motion})
# todo: write skeleton writer
# write_skeleton(motion, args.wav_out_path)
print(len(motion))
print('generated filedir:{0}'.format(filedir))
print('Finished generating. The result can be viewed in Matlab.')
def main():
args = get_arguments()
started_datestring = "{0:%Y-%m-%dT%H-%M-%S}".format(datetime.now())
logdir = os.path.join(args.logdir, 'generate', started_datestring)
if args.wavenet_params.startswith('wavenet_params/'):
with open(args.wavenet_params, 'r') as config_file:
wavenet_params = json.load(config_file)
elif args.wavenet_params.startswith('wavenet_params'):
with open('wavenet_params/'+args.wavenet_params, 'r') as config_file:
wavenet_params = json.load(config_file)
else:
with open('wavenet_params/wavenet_params_'+args.wavenet_params, 'r') as config_file:
wavenet_params = json.load(config_file)
sess = tf.Session()
net = WaveNetModel(
batch_size=1,
dilations=wavenet_params['dilations'],
filter_width=wavenet_params['filter_width'],
residual_channels=wavenet_params['residual_channels'],
dilation_channels=wavenet_params['dilation_channels'],
quantization_channels=wavenet_params['quantization_channels'],
skip_channels=wavenet_params['skip_channels'],
use_biases=wavenet_params['use_biases'],
scalar_input=wavenet_params['scalar_input'],
initial_filter_width=wavenet_params['initial_filter_width'],
global_condition_channels=args.gc_channels,
global_condition_cardinality=args.gc_cardinality)
samples = tf.placeholder(tf.int32)
if args.fast_generation:
next_sample = net.predict_proba_incremental(samples, args.gc_id)
else:
next_sample = net.predict_proba(samples, args.gc_id)
if args.fast_generation:
sess.run(tf.global_variables_initializer())
sess.run(net.init_ops)
variables_to_restore = {
var.name[:-2]: var for var in tf.global_variables()
if not ('state_buffer' in var.name or 'pointer' in var.name)}
saver = tf.train.Saver(variables_to_restore)
print('Restoring model from {}'.format(args.checkpoint))
saver.restore(sess, args.checkpoint)
decode = mu_law_decode(samples, wavenet_params['quantization_channels'])
quantization_channels = wavenet_params['quantization_channels']
if args.wav_seed:
seed = create_seed(args.wav_seed,
wavenet_params['sample_rate'],
quantization_channels,
net.receptive_field,
args.silence_threshold)
waveform = sess.run(seed).tolist()
else:
# Silence with a single random sample at the end.
waveform = [quantization_channels / 2] * (net.receptive_field - 1)
waveform.append(np.random.randint(quantization_channels))
if args.fast_generation and args.wav_seed:
# When using the incremental generation, we need to
# feed in all priming samples one by one before starting the
# actual generation.
# TODO This could be done much more efficiently by passing the waveform
# to the incremental generator as an optional argument, which would be
# used to fill the queues initially.
outputs = [next_sample]
outputs.extend(net.push_ops)
print('Priming generation...')
for i, x in enumerate(waveform[-net.receptive_field: -1]):
if i % 100 == 0:
print('Priming sample {}'.format(i))
sess.run(outputs, feed_dict={samples: x})
print('Done.')
last_sample_timestamp = datetime.now()
#frequency to period change
if args.tfrequency is not None:
if args.tperiod is not None:
raise ValueError("Frequency and Period both assigned. Assign only one of them.")
else:
PERIOD = dynamic.frequency_to_period(args.tfrequency)
else:
if args.tperiod is not None:
PERIOD = args.tperiod
else:
PERIOD = 1
# generate an array of temperature for each step when called "dynamic" in tempurature-change variable
if args.temperature_change == "dynamic" and args.tform is not None:
temp_array = dynamic.generate_value(0, wavenet_params['sample_rate'], args.tform, args.tmin, args.tmax, PERIOD, args.samples, args.tphaseshift)
for step in range(args.samples):
if args.fast_generation:
outputs = [next_sample]
outputs.extend(net.push_ops)
window = waveform[-1]
else:
if len(waveform) > net.receptive_field:
window = waveform[-net.receptive_field:]
else:
window = waveform
outputs = [next_sample]
# Run the WaveNet to predict the next sample.
prediction = sess.run(outputs, feed_dict={samples: window})[0]
# Scale prediction distribution using temperature.
np.seterr(divide='ignore')
# temperature change
if args.temperature_change == None: #static
_temp_temperature = args.temperature
elif args.temperature_change == "dynamic":
if args.tform == None: #random
if step % int(args.samples/5) == 0:
_temp_temperature = args.temperature * np.random.rand()
else:
_temp_temperature = temp_array[1][step]
else:
raise Exception("wrong temperature_change value")
scaled_prediction = np.log(prediction) / _temp_temperature
scaled_prediction = (scaled_prediction -
np.logaddexp.reduce(scaled_prediction))
scaled_prediction = np.exp(scaled_prediction)
np.seterr(divide='warn')
# Prediction distribution at temperature=1.0 should be unchanged after
# scaling.
if args.temperature == 1.0 and args.temperature_change == None:
np.testing.assert_allclose(
prediction, scaled_prediction, atol=1e-5,
err_msg = 'Prediction scaling at temperature=1.0 is not working as intended.')
sample = np.random.choice(
np.arange(quantization_channels), p=scaled_prediction)
# Show progress only once per second.
current_sample_timestamp = datetime.now()
time_since_print = current_sample_timestamp - last_sample_timestamp
if time_since_print.total_seconds() > 1.:
print('Sample {:3<d}/{:3<d}, temperature {:3<f}'.format(step + 1, args.samples, _temp_temperature),
end='\r')
last_sample_timestamp = current_sample_timestamp
# If we have partial writing, save the result so far.
if (args.wav_out_path and args.save_every and
(step + 1) % args.save_every == 0):
out = sess.run(decode, feed_dict={samples: waveform})
write_wav(out, wavenet_params['sample_rate'], args.wav_out_path)
# Introduce a newline to clear the carriage return from the progress.
print()
# Save the result as an audio summary.
datestring = str(datetime.now()).replace(' ', 'T')
writer = tf.summary.FileWriter(logdir)
tf.summary.audio('generated', decode, wavenet_params['sample_rate'])
summaries = tf.summary.merge_all()
summary_out = sess.run(summaries,
feed_dict={samples: np.reshape(waveform, [-1, 1])})
writer.add_summary(summary_out)
# Save the result as a wav file.
if args.wav_out_path:
out = sess.run(decode, feed_dict={samples: waveform})
write_wav(out, wavenet_params['sample_rate'], args.wav_out_path)
print('Finished generating. The result can be viewed in TensorBoard.')
def main():
args = get_arguments()
started_datestring = "{0:%Y-%m-%dT%H-%M-%S}".format(datetime.now())
logdir = os.path.join(args.logdir, 'generate', started_datestring)
with open(args.wavenet_params, 'r') as config_file:
wavenet_params = json.load(config_file)
sess = tf.Session()
net = WaveNetModel(
batch_size=1,
dilations=wavenet_params['dilations'],
filter_width=wavenet_params['filter_width'],
residual_channels=wavenet_params['residual_channels'],
dilation_channels=wavenet_params['dilation_channels'],
quantization_channels=wavenet_params['quantization_channels'],
skip_channels=wavenet_params['skip_channels'],
use_biases=wavenet_params['use_biases'],
scalar_input=wavenet_params['scalar_input'],
initial_filter_width=wavenet_params['initial_filter_width'],
global_condition_channels=args.gc_channels,
global_condition_cardinality=args.gc_cardinality)
gi_sampler = get_generator_input_sampler()
# White noise generator params
white_mean = 0
white_sigma = 1
white_length = 20234
white_noise = gi_sampler(white_mean, white_sigma, white_length)
loss = net.loss(input_batch=tf.convert_to_tensor(white_noise,
dtype=np.float32),
name='generator')
samples = tf.placeholder(tf.int32)
if args.fast_generation:
next_sample = net.predict_proba_incremental(samples, args.gc_id)
else:
next_sample = net.predict_proba(samples, args.gc_id)
if args.fast_generation:
sess.run(tf.global_variables_initializer())
sess.run(net.init_ops)
decode = mu_law_decode(samples, wavenet_params['quantization_channels'])
quantization_channels = wavenet_params['quantization_channels']
'''
# Silence with a single random sample at the end.
waveform = [quantization_channels / 2] * (net.receptive_field - 1)
waveform.append(np.random.randint(quantization_channels))
'''
waveform = [0]
last_sample_timestamp = datetime.now()
for step in range(args.samples):
if args.fast_generation:
outputs = [next_sample]
outputs.extend(net.push_ops)
window = waveform[-1]
else:
if len(waveform) > net.receptive_field:
window = waveform[-net.receptive_field:]
else:
window = waveform
outputs = [next_sample]
# Run the WaveNet to predict the next sample.
prediction = sess.run(outputs, feed_dict={samples: window})[0]
# Scale prediction distribution using temperature.
np.seterr(divide='ignore')
scaled_prediction = np.log(prediction) / args.temperature
scaled_prediction = (scaled_prediction -
np.logaddexp.reduce(scaled_prediction))
scaled_prediction = np.exp(scaled_prediction)
np.seterr(divide='warn')
# Prediction distribution at temperature=1.0 should be unchanged after
# scaling.
if args.temperature == 1.0:
np.testing.assert_allclose(
prediction,
scaled_prediction,
atol=1e-5,
err_msg='Prediction scaling at temperature=1.0 '
'is not working as intended.')
sample = np.random.choice(np.arange(quantization_channels),
p=scaled_prediction)
waveform.append(sample)
# Introduce a newline to clear the carriage return from the progress.
print()
del waveform[0]
print(waveform)
print()
print('Finished generating. The result can be viewed in TensorBoard.')
def main():
args = get_arguments()
started_datestring = "{0:%Y-%m-%dT%H-%M-%S}".format(datetime.now())
logdir = os.path.join(args.logdir, 'generate', started_datestring)
with open(args.wavenet_params, 'r') as config_file:
wavenet_params = json.load(config_file)
if (args.using_magna):
styles = wavenet_params['styles']
header = get_data(N_CLASSES)[0]
conditions = np.zeros(N_CLASSES)
for style in styles:
conditions[header.index(style)] = 1
cardinality = N_CLASSES
else:
conditions = args.gc_id
cardinality = args.gc_cardinality
sess = tf.Session()
net = WaveNetModel(
batch_size=1,
dilations=wavenet_params['dilations'],
filter_width=wavenet_params['filter_width'],
residual_channels=wavenet_params['residual_channels'],
dilation_channels=wavenet_params['dilation_channels'],
quantization_channels=wavenet_params['quantization_channels'],
skip_channels=wavenet_params['skip_channels'],
use_biases=wavenet_params['use_biases'],
scalar_input=wavenet_params['scalar_input'],
initial_filter_width=wavenet_params['initial_filter_width'],
global_condition_channels=args.gc_channels,
global_condition_cardinality=cardinality,
glove_channels=args.glove_channels,
residual_postproc=wavenet_params["residual_postproc"],
use_magna=args.using_magna)
samples = tf.placeholder(tf.int32)
if args.text is not None:
nlp = spacy.load('en', vectors='en_glove_cc_300_1m_vectors')
word_vec = nlp(u'%s' % args.text).vector
else:
word_vec = None
if args.fast_generation:
next_sample = net.predict_proba_incremental(
samples, conditions, word_vec)
else:
next_sample = net.predict_proba(samples, conditions, word_vec)
if args.fast_generation:
sess.run(tf.initialize_all_variables())
sess.run(net.init_ops)
variables_to_restore = {
var.name[:-2]: var for var in tf.all_variables()
if not ('state_buffer' in var.name or 'pointer' in var.name)}
saver = tf.train.Saver(variables_to_restore)
print('Restoring model from {}'.format(args.checkpoint))
saver.restore(sess, args.checkpoint)
decode = mu_law_decode(samples, wavenet_params['quantization_channels'])
quantization_channels = wavenet_params['quantization_channels']
if args.wav_seed:
seed = create_seed(args.wav_seed,
wavenet_params['sample_rate'],
quantization_channels)
waveform = sess.run(seed).tolist()
else:
waveform = np.random.randint(quantization_channels, size=(1,)).tolist()
if args.fast_generation and args.wav_seed:
# When using the incremental generation, we need to
# feed in all priming samples one by one before starting the
# actual generation.
# TODO This could be done much more efficiently by passing the waveform
# to the incremental generator as an optional argument, which would be
# used to fill the queues initially.
outputs = [next_sample]
outputs.extend(net.push_ops)
print('Priming generation...')
for i, x in enumerate(waveform[:-(args.window + 1)]):
if i % 100 == 0:
print('Priming sample {}'.format(i))
sess.run(outputs, feed_dict={samples: x})
print('Done.')
last_sample_timestamp = datetime.now()
for step in range(args.samples):
if args.fast_generation:
outputs = [next_sample]
outputs.extend(net.push_ops)
window = waveform[-1]
else:
if len(waveform) > args.window:
window = waveform[-args.window:]
else:
window = waveform
outputs = [next_sample]
# Run the WaveNet to predict the next sample.
prediction = sess.run(outputs, feed_dict={samples: window})[0]
# Scale prediction distribution using temperature.
np.seterr(divide='ignore')
scaled_prediction = np.log(prediction) / args.temperature
scaled_prediction = scaled_prediction - \
np.logaddexp.reduce(scaled_prediction)
scaled_prediction = np.exp(scaled_prediction)
np.seterr(divide='warn')
# Prediction distribution at temperature=1.0 should be unchanged after
# scaling.
if args.temperature == 1.0:
np.testing.assert_allclose(
prediction, scaled_prediction, atol=1e-5,
err_msg='Prediction scaling at temperature=1.0 is not working as intended.')
sample = np.random.choice(
np.arange(quantization_channels), p=scaled_prediction)
waveform.append(sample)
# Show progress only once per second.
current_sample_timestamp = datetime.now()
time_since_print = current_sample_timestamp - last_sample_timestamp
if time_since_print.total_seconds() > 1.:
print('Sample {:3<d}/{:3<d}'.format(step + 1, args.samples),
end='\r')
last_sample_timestamp = current_sample_timestamp
# If we have partial writing, save the result so far.
if (args.wav_out_path and args.save_every and
(step + 1) % args.save_every == 0):
out = sess.run(decode, feed_dict={samples: waveform})
write_wav(out, wavenet_params['sample_rate'], args.wav_out_path)
# Introduce a newline to clear the carriage return from the progress.
print()
# Save the result as an audio summary.
datestring = str(datetime.now()).replace(' ', 'T')
writer = tf.train.SummaryWriter(logdir)
tf.audio_summary('generated', decode, wavenet_params['sample_rate'])
summaries = tf.merge_all_summaries()
summary_out = sess.run(summaries,
feed_dict={samples: np.reshape(waveform, [-1, 1])})
writer.add_summary(summary_out)
# Save the result as a wav file.
if args.wav_out_path:
out = sess.run(decode, feed_dict={samples: waveform})
write_wav(out, wavenet_params['sample_rate'], args.wav_out_path)
print('Finished generating. The result can be viewed in TensorBoard.')
def main():
with tf.Graph().as_default():
coord = tf.train.Coordinator()
sess = tf.Session()
batch_size = 1
hidden1_units = 5202
hidden2_units = 2601
hidden3_units = 1300
hidden4_units = 650
hidden5_units = 325
max_training_steps = 1
global_step = tf.Variable(0, name='global_step', trainable=False)
initial_training_learning_rate = 3e-2
training_learning_rate = tf.train.exponential_decay(
initial_training_learning_rate,
global_step,
100,
0.9,
staircase=True)
inputs_placeholder, labels_placeholder = placeholder_inputs(batch_size)
logits = ffnn.inference(inputs_placeholder, hidden1_units,
hidden2_units, hidden3_units, hidden4_units,
hidden5_units)
loss = ffnn.loss(logits, labels_placeholder)
train_op = ffnn.training(loss, training_learning_rate, global_step)
eval_correct = ffnn.evaluation(logits, labels_placeholder)
summary = tf.summary.merge_all()
init = tf.global_variables_initializer()
saver = tf.train.Saver()
summary_writer = tf.summary.FileWriter('./logdir', sess.graph)
sess.run(init)
args = get_arguments()
# Load parameters from wavenet params json file
with open(args.wavenet_params, 'r') as f:
wavenet_params = json.load(f)
quantization_channels = wavenet_params['quantization_channels']
if args.restore_from != None:
restore_from = args.restore_from
print("Restoring from: ")
print(restore_from)
else:
restore_from = ""
try:
saved_global_step = load(saver, sess, restore_from)
if saved_global_step is None:
# The first training step will be saved_global_step + 1,
# therefore we put -1 here for new or overwritten trainings.
saved_global_step = -1
else:
counter = saved_global_step % label_batch_size
except:
print(
"Something went wrong while restoring checkpoint. "
"We will terminate training to avoid accidentally overwriting "
"the previous model.")
raise
# TODO: Find a more robust way to find different data sets
# Training data
directory = './sampleTrue'
reader = AudioReader(directory,
coord,
sample_rate=16000,
gc_enabled=False,
receptive_field=5117,
sample_size=15117,
silence_threshold=0.05)
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
reader.start_threads(sess)
directory = './sampleFalse'
reader2 = AudioReader(directory,
coord,
sample_rate=16000,
gc_enabled=False,
receptive_field=5117,
sample_size=15117,
silence_threshold=0.05)
threads2 = tf.train.start_queue_runners(sess=sess, coord=coord)
reader2.start_threads(sess)
total_loss = 0
for step in range(saved_global_step + 1, max_training_steps):
start_time = time.time()
batch_data = []
label_data = []
if (step % 100 == 0):
print('Current learning rate: %6f' %
(sess.run(training_learning_rate)))
for b in range(batch_size):
label = randint(0, 1)
if label == 1:
data = sess.run(reader.dequeue(1))
while (len(data[0]) < ffnn.INPUT_SIZE):
data = sess.run(reader.dequeue(1))
else:
data = sess.run(reader2.dequeue(1))
while (len(data[0]) < ffnn.INPUT_SIZE):
data = sess.run(reader2.dequeue(1))
data = np.array(data[0])
cut = []
for i in range(ffnn.INPUT_SIZE):
cut.append(data[i])
data = cut
# processing
samples = process(data, quantization_channels, 1)
batch_data.append(samples)
label_data.append(label)
feed_dict = fill_feed_dict(batch_data, label_data,
inputs_placeholder, labels_placeholder)
_, loss_value = sess.run([train_op, loss], feed_dict=feed_dict)
duration = time.time() - start_time
total_loss = total_loss + loss_value
print('Step %d: loss = %.7f (%.3f sec)' %
(step, loss_value, duration))
'''
if step % 100 == 0 or (step + 1) == max_training_steps:
average = total_loss / (step + 1)
print('Cumulative average loss: %6f' % (average))
# TODO: Update train script to add data to new directory
checkpoint_file = os.path.join('./logdir/init-train/', 'model.ckpt')
print("Generating checkpoint file...")
saver.save(sess, checkpoint_file, global_step=step)
'''
# Lambda for white noise sampler
gi_sampler = get_generator_input_sampler()
# Intialize generator WaveNet
G = WaveNetModel(
batch_size=1,
dilations=wavenet_params["dilations"],
filter_width=wavenet_params["filter_width"],
residual_channels=wavenet_params["residual_channels"],
dilation_channels=wavenet_params["dilation_channels"],
skip_channels=wavenet_params["skip_channels"],
quantization_channels=wavenet_params["quantization_channels"],
use_biases=wavenet_params["use_biases"],
initial_filter_width=wavenet_params["initial_filter_width"])
# White noise generator params
white_mean = 0
white_sigma = 1
white_length = ffnn.INPUT_SIZE
white_noise = gi_sampler(white_mean, white_sigma, white_length)
white_noise = process(white_noise, quantization_channels, 1)
white_noise_t = tf.convert_to_tensor(white_noise)
# initialize generator
w_loss, w_prediction = G.loss(input_batch=white_noise_t,
name='generator')
G_variables = tf.trainable_variables(scope='wavenet')
optimizer = optimizer_factory[args.optimizer](learning_rate=3e-2,
momentum=args.momentum)
optim = optimizer.minimize(w_loss, var_list=G_variables)
init = tf.global_variables_initializer()
sess.run(init)
print(sess.run(tf.shape(w_prediction)))
# main GAN training loop
for step in range(NUM_EPOCHS):
batch_data = []
label_data = []
# train D on real
for d_index in range(batch_size):
data = sess.run(reader.dequeue(1))
data = data[0]
d_real_data = process(data, quantization_channels, 1)
batch_data.append(d_real_data)
label_data.append(1)
feed_dict = fill_feed_dict(batch_data, label_data,
inputs_placeholder, labels_placeholder)
_, d_real_loss = sess.run([train_op, loss], feed_dict=feed_dict)
print("Real loss")
print(d_real_loss)
batch_data = []
label_data = []
# train D on fake
for d_index in range(batch_size):
samples = tf.placeholder(tf.int32)
if args.fast_generation:
next_sample = G.predict_proba_incremental(
samples, args.gc_id)
else:
next_sample = G.predict_proba(samples, args.gc_id)
if args.fast_generation:
sess.run(tf.global_variables_initializer())
sess.run(G.init_ops)
waveform = [0]
for step in range(ffnn.INPUT_SIZE):
if args.fast_generation:
outputs = [next_sample]
outputs.extend(G.push_ops)
window = waveform[-1]
else:
if len(waveform) > G.receptive_field:
window = waveform[-G.receptive_field:]
else:
window = waveform
outputs = [next_sample]
# Run the WaveNet to predict the next sample.
prediction = sess.run(outputs, feed_dict={samples:
window})[0]
# Scale prediction distribution using temperature.
np.seterr(divide='ignore')
scaled_prediction = np.log(prediction) / 1
scaled_prediction = (
scaled_prediction -
np.logaddexp.reduce(scaled_prediction))
scaled_prediction = np.exp(scaled_prediction)
np.seterr(divide='warn')
sample = np.random.choice(np.arange(quantization_channels),
p=scaled_prediction)
waveform.append(sample)
del waveform[0]
d_fake_data = process(waveform, quantization_channels, 0)
batch_data.append(d_fake_data)
label_data.append(0)
feed_dict = fill_feed_dict(batch_data, label_data,
inputs_placeholder, labels_placeholder)
_, d_fake_loss = sess.run([train_op, loss], feed_dict=feed_dict)
print("Fake loss")
print(d_fake_loss)
batch_data = []
label_data = []
# train G, but don't train D
for g_index in range(batch_size):
samples = tf.placeholder(tf.int32)
if args.fast_generation:
next_sample = G.predict_proba_incremental(
samples, args.gc_id)
else:
next_sample = G.predict_proba(samples, args.gc_id)
if args.fast_generation:
sess.run(tf.global_variables_initializer())
sess.run(G.init_ops)
waveform = [0]
for step in range(ffnn.INPUT_SIZE):
if args.fast_generation:
outputs = [next_sample]
outputs.extend(G.push_ops)
window = waveform[-1]
else:
if len(waveform) > G.receptive_field:
window = waveform[-G.receptive_field:]
else:
window = waveform
outputs = [next_sample]
# Run the WaveNet to predict the next sample.
prediction = sess.run(outputs, feed_dict={samples:
window})[0]
# Scale prediction distribution using temperature.
np.seterr(divide='ignore')
scaled_prediction = np.log(prediction) / 1
scaled_prediction = (
scaled_prediction -
np.logaddexp.reduce(scaled_prediction))
scaled_prediction = np.exp(scaled_prediction)
np.seterr(divide='warn')
sample = np.random.choice(np.arange(quantization_channels),
p=scaled_prediction)
waveform.append(sample)
del waveform[0]
g_data = process(waveform, quantization_channels, 0)
batch_data.append(g_data)
label_data.append(1)
feed_dict = fill_feed_dict(batch_data, label_data,
inputs_placeholder, labels_placeholder)
_, g_loss = sess.run([optim, loss], feed_dict=feed_dict)
print("Generator loss")
print(g_loss)
'''
def main():
with tf.device(
'/cpu:0'): # cpu가 더 빠르다. gpu로 설정하면 Error. tf.device 없이 하면 더 느려진다.
config = get_arguments()
started_datestring = "{0:%Y-%m-%dT%H-%M-%S}".format(datetime.now())
logdir = os.path.join(config.logdir, 'generate', started_datestring)
print('logdir0-------------' + logdir)
if not os.path.exists(logdir):
os.makedirs(logdir)
load_hparams(hparams, config.checkpoint_dir)
sess = tf.Session()
scalar_input = hparams.scalar_input
net = WaveNetModel(
batch_size=config.batch_size,
dilations=hparams.dilations,
filter_width=hparams.filter_width,
residual_channels=hparams.residual_channels,
dilation_channels=hparams.dilation_channels,
quantization_channels=hparams.quantization_channels,
out_channels=hparams.out_channels,
skip_channels=hparams.skip_channels,
use_biases=hparams.use_biases,
scalar_input=hparams.scalar_input,
global_condition_channels=hparams.gc_channels,
global_condition_cardinality=config.gc_cardinality,
local_condition_channels=hparams.num_mels,
upsample_factor=hparams.upsample_factor,
legacy=hparams.legacy,
residual_legacy=hparams.residual_legacy,
train_mode=False
) # train 단계에서는 global_condition_cardinality를 AudioReader에서 파악했지만, 여기서는 넣어주어야 함
if scalar_input:
samples = tf.placeholder(tf.float32, shape=[net.batch_size, None])
else:
samples = tf.placeholder(
tf.int32, shape=[net.batch_size, None]
) # samples: mu_law_encode로 변환된 것. one-hot으로 변환되기 전. (batch_size, 길이)
# local condition이 (N,T,num_mels) 여야 하지만, 길이 1까지로 들어가야하기 때무넹, (N,1,num_mels) --> squeeze하면 (N,num_mels)
upsampled_local_condition = tf.placeholder(
tf.float32, shape=[net.batch_size, hparams.num_mels])
next_sample = net.predict_proba_incremental(
samples, upsampled_local_condition, [config.gc_id] * net.batch_size
) # Fast Wavenet Generation Algorithm-1611.09482 algorithm 적용
# making local condition data. placeholder - upsampled_local_condition 넣어줄 upsampled local condition data를 만들어 보자.
print('logdir0-------------' + logdir)
mel_input = np.load(config.mel)
sample_size = mel_input.shape[0] * hparams.hop_size
mel_input = np.tile(mel_input, (config.batch_size, 1, 1))
with tf.variable_scope('wavenet', reuse=tf.AUTO_REUSE):
upsampled_local_condition_data = net.create_upsample(
mel_input, upsample_type=hparams.upsample_type)
var_list = [
var for var in tf.global_variables() if 'queue' not in var.name
]
saver = tf.train.Saver(var_list)
print('Restoring model from {}'.format(config.checkpoint_dir))
load(saver, sess, config.checkpoint_dir)
init_op = tf.group(tf.initialize_all_variables(),
net.queue_initializer)
sess.run(init_op) # 이 부분이 없으면, checkpoint에서 복원된 값들이 들어 있다.
quantization_channels = hparams.quantization_channels
if config.wav_seed:
# wav_seed의 길이가 receptive_field보다 작으면, padding이라도 해야 되는 거 아닌가? 그냥 짧으면 짧은 대로 return함 --> 그래서 너무 짧으면 error
seed = create_seed(config.wav_seed, hparams.sample_rate,
quantization_channels, net.receptive_field,
scalar_input) # --> mu_law encode 된 것.
if scalar_input:
waveform = seed.tolist()
else:
waveform = sess.run(
seed).tolist() # [116, 114, 120, 121, 127, ...]
print('Priming generation...')
for i, x in enumerate(waveform[-net.receptive_field:-1]
): # 제일 마지막 1개는 아래의 for loop의 첫 loop에서 넣어준다.
if i % 100 == 0:
print('Priming sample {}/{}'.format(
i, net.receptive_field),
end='\r')
sess.run(next_sample,
feed_dict={
samples:
np.array([x] * net.batch_size).reshape(
net.batch_size, 1),
upsampled_local_condition:
np.zeros([net.batch_size, hparams.num_mels])
})
print('Done.')
waveform = np.array([waveform[-net.receptive_field:]] *
net.batch_size)
else:
# Silence with a single random sample at the end.
if scalar_input:
waveform = [0.0] * (net.receptive_field - 1)
waveform = np.array(waveform * net.batch_size).reshape(
net.batch_size, -1)
waveform = np.concatenate(
[
waveform, 2 * np.random.rand(net.batch_size).reshape(
net.batch_size, -1) - 1
],
axis=-1) # -1~1사이의 random number를 만들어 끝에 붙힌다.
# wavefor: shape(batch_size,net.receptive_field )
else:
waveform = [quantization_channels / 2] * (
net.receptive_field - 1
) # 필요한 receptive_field 크기보다 1개 작게 만든 후, 아래에서 random하게 1개를 덧붙힌다.
waveform = np.array(waveform * net.batch_size).reshape(
net.batch_size, -1)
waveform = np.concatenate(
[
waveform,
np.random.randint(quantization_channels,
size=net.batch_size).reshape(
net.batch_size, -1)
],
axis=-1) # one hot 변환 전. (batch_size, 5117)
start_time = time.time()
upsampled_local_condition_data = sess.run(
upsampled_local_condition_data)
last_sample_timestamp = datetime.now()
for step in range(sample_size): # 원하는 길이를 구하기 위해 loop sample_size
window = waveform[:,
-1:] # 제일 끝에 있는 1개만 samples에 넣어 준다. window: shape(N,1)
# Run the WaveNet to predict the next sample.
# fast가 아닌경우. window: [128.0, 128.0, ..., 128.0, 178, 185]
# fast인 경우, window는 숫자 1개.
prediction = sess.run(
next_sample,
feed_dict={
samples:
window,
upsampled_local_condition:
upsampled_local_condition_data[:, step, :]
}
) # samples는 mu law encoding된 것. 계산 과정에서 one hot으로 변환된다. --> (batch_size,256)
if scalar_input:
sample = prediction # logistic distribution으로부터 sampling 되었기 때문에, randomness가 있다.
else:
# Scale prediction distribution using temperature.
# 다음 과정은 config.temperature==1이면 각 원소를 합으로 나누어주는 것에 불과. 이미 softmax를 적용한 겂이므로, 합이 1이된다. 그래서 값의 변화가 없다.
# config.temperature가 1이 아니며, 각 원소의 log취한 값을 나눈 후, 합이 1이 되도록 rescaling하는 것이 된다.
np.seterr(divide='ignore')
scaled_prediction = np.log(
prediction
) / config.temperature # config.temperature인 경우는 값의 변화가 없다.
scaled_prediction = (
scaled_prediction - np.logaddexp.reduce(
scaled_prediction, axis=-1, keepdims=True)
) # np.log(np.sum(np.exp(scaled_prediction)))
scaled_prediction = np.exp(scaled_prediction)
np.seterr(divide='warn')
# Prediction distribution at temperature=1.0 should be unchanged after
# scaling.
if config.temperature == 1.0:
np.testing.assert_allclose(
prediction,
scaled_prediction,
atol=1e-5,
err_msg=
'Prediction scaling at temperature=1.0 is not working as intended.'
)
# argmax로 선택하지 않기 때문에, 같은 입력이 들어가도 달라질 수 있다.
sample = [[
np.random.choice(np.arange(quantization_channels), p=p)
] for p in scaled_prediction] # choose one sample per batch
waveform = np.concatenate([waveform, sample],
axis=-1) #window.shape: (N,1)
# Show progress only once per second.
current_sample_timestamp = datetime.now()
time_since_print = current_sample_timestamp - last_sample_timestamp
if time_since_print.total_seconds() > 1.:
duration = time.time() - start_time
print('Sample {:3<d}/{:3<d}, ({:.3f} sec/step)'.format(
step + 1, sample_size, duration),
end='\r')
last_sample_timestamp = current_sample_timestamp
# Introduce a newline to clear the carriage return from the progress.
print()
# Save the result as a wav file.
if hparams.input_type == 'raw':
out = waveform[:, net.receptive_field:]
elif hparams.input_type == 'mulaw':
decode = mu_law_decode(samples,
quantization_channels,
quantization=False)
out = sess.run(
decode, feed_dict={samples: waveform[:, net.receptive_field:]})
else: # 'mulaw-quantize'
decode = mu_law_decode(samples,
quantization_channels,
quantization=True)
out = sess.run(
decode, feed_dict={samples: waveform[:, net.receptive_field:]})
# save wav
for i in range(net.batch_size):
config.wav_out_path = logdir + '/test-{}.wav'.format(i)
mel_path = config.wav_out_path.replace(".wav", ".png")
gen_mel_spectrogram = audio.melspectrogram(out[i], hparams).astype(
np.float32).T
audio.save_wav(out[i], config.wav_out_path,
hparams.sample_rate) # save_wav 내에서 out[i]의 값이 바뀐다.
plot.plot_spectrogram(gen_mel_spectrogram,
mel_path,
title='generated mel spectrogram',
target_spectrogram=mel_input[i])
print('Finished generating.')
def main():
args = get_arguments()
started_datestring = "{0:%Y-%m-%dT%H-%M-%S}".format(datetime.now())
logdir = os.path.join(args.logdir, 'generate', started_datestring)
with open(args.wavenet_params, 'r') as config_file:
wavenet_params = json.load(config_file)
sess = tf.Session()
net = WaveNetModel(
batch_size=1,
dilations=wavenet_params['dilations'],
filter_width=wavenet_params['filter_width'],
residual_channels=wavenet_params['residual_channels'],
dilation_channels=wavenet_params['dilation_channels'],
quantization_channels=wavenet_params['quantization_channels'],
skip_channels=wavenet_params['skip_channels'],
use_biases=wavenet_params['use_biases'])
samples = tf.placeholder(tf.int32)
if args.fast_generation:
next_sample = net.predict_proba_incremental(samples)
else:
next_sample = net.predict_proba(samples)
if args.fast_generation:
sess.run(tf.initialize_all_variables())
sess.run(net.init_ops)
variables_to_restore = {
var.name[:-2]: var
for var in tf.all_variables()
if not ('state_buffer' in var.name or 'pointer' in var.name)
}
saver = tf.train.Saver(variables_to_restore)
print('Restoring model from {}'.format(args.checkpoint))
saver.restore(sess, args.checkpoint)
# decode = samples
# Creating a copy of samples
decode = tf.identity(samples)
quantization_channels = wavenet_params['quantization_channels']
with open(fname) as f:
content = f.readlines()
waveform = [float(x.replace('\r', '').strip()) for x in content]
dataframe = pd.read_csv(fname, engine='python')
dataset = dataframe.values
# split into train and test sets
train_size = int(len(dataset) * SPLIT)
test_size = len(dataset) - train_size
testX, testY = create_test_dataset(dataset, train_size)
print(str(len(testX)) + " steps to go...")
testP = []
# train, test = waveform[0:train_size,:], waveform[train_size:len(dataset),:]
# waveform = [144.048970239,143.889691754,143.68922135,143.644718903,143.698498762,143.710396703,143.756327831,143.843187531,143.975287002,143.811912129]
last_sample_timestamp = datetime.now()
for step in range(len(testX)):
# for step in range(2):
# if len(waveform) > args.window:
# window = waveform[-args.window:]
# else:
window = testX[step]
outputs = [next_sample]
# Run the WaveNet to predict the next sample.
prediction = sess.run(outputs, feed_dict={samples: window})[0]
# sample = np.random.choice(np.arange(quantization_channels), p=prediction)
sample = np.arange(quantization_channels)[np.argmax(prediction)]
# waveform.append(sample)
testP.append(sample)
print(step, sample)
# Introduce a newline to clear the carriage return from the progress.
testPredict = np.array(testP).reshape((-1, 1))
# Save the result as a wav file.
# if args.text_out_path:
# out = sess.run(decode, feed_dict={samples: waveform})
# write_text(out, args.text_out_path)
testScore = math.sqrt(mean_squared_error(testY, testPredict[:, 0]))
print('Test Score: %.2f RMSE' % (testScore))
testPredictPlot = np.empty_like(dataset, dtype=float)
testPredictPlot[:, :] = np.nan
testPredictPlot[train_size + 1:len(dataset) - 1, :] = testPredict
# plot baseline and predictions
plt.plot(dataset)
plt.plot(testPredictPlot)
plt.show()
print('Finished generating.')
def main():
args = get_arguments()
started_datestring = "{0:%Y-%m-%dT%H-%M-%S}".format(datetime.now())
logdir = os.path.join(args.logdir, 'generate', started_datestring)
with open(args.wavenet_params, 'r') as config_file:
wavenet_params = json.load(config_file)
sess = tf.Session()
net = WaveNetModel(
batch_size=1,
dilations=wavenet_params['dilations'],
filter_width=wavenet_params['filter_width'],
residual_channels=wavenet_params['residual_channels'],
dilation_channels=wavenet_params['dilation_channels'],
quantization_channels=wavenet_params['quantization_channels'],
skip_channels=wavenet_params['skip_channels'],
use_biases=wavenet_params['use_biases'])
samples = tf.placeholder(tf.int32)
if args.fast_generation:
next_sample = net.predict_proba_incremental(samples)
else:
next_sample = net.predict_proba(samples)
if args.fast_generation:
sess.run(tf.initialize_all_variables())
sess.run(net.init_ops)
variables_to_restore = {
var.name[:-2]: var for var in tf.all_variables()
if not ('state_buffer' in var.name or 'pointer' in var.name)}
saver = tf.train.Saver(variables_to_restore)
print('Restoring model from {}'.format(args.checkpoint))
saver.restore(sess, args.checkpoint)
decode = mu_law_decode(samples, wavenet_params['quantization_channels'])
quantization_channels = wavenet_params['quantization_channels']
if args.wav_seed:
seed = create_seed(args.wav_seed,
wavenet_params['sample_rate'],
quantization_channels)
waveform = sess.run(seed).tolist()
else:
waveform = np.random.randint(quantization_channels, size=(1,)).tolist()
if args.fast_generation and args.wav_seed:
# When using the incremental generation, we need to
# feed in all priming samples one by one before starting the
# actual generation.
# TODO This could be done much more efficiently by passing the waveform
# to the incremental generator as an optional argument, which would be
# used to fill the queues initially.
outputs = [next_sample]
outputs.extend(net.push_ops)
print('Priming generation...')
for i, x in enumerate(waveform[:-(args.window + 1)]):
if i % 100 == 0:
print('Priming sample {}'.format(i))
sess.run(outputs, feed_dict={samples: x})
print('Done.')
last_sample_timestamp = datetime.now()
for step in range(args.samples):
if args.fast_generation:
outputs = [next_sample]
outputs.extend(net.push_ops)
window = waveform[-1]
else:
if len(waveform) > args.window:
window = waveform[-args.window:]
else:
window = waveform
outputs = [next_sample]
# Run the WaveNet to predict the next sample.
prediction = sess.run(outputs, feed_dict={samples: window})[0]
sample = np.random.choice(
np.arange(quantization_channels), p=prediction)
waveform.append(sample)
# Show progress only once per second.
current_sample_timestamp = datetime.now()
time_since_print = current_sample_timestamp - last_sample_timestamp
if time_since_print.total_seconds() > 1.:
print('Sample {:3<d}/{:3<d}'.format(step + 1, args.samples),
end='\r')
last_sample_timestamp = current_sample_timestamp
# If we have partial writing, save the result so far.
if (args.wav_out_path and args.save_every and
(step + 1) % args.save_every == 0):
out = sess.run(decode, feed_dict={samples: waveform})
write_wav(out, wavenet_params['sample_rate'], args.wav_out_path)
# Introduce a newline to clear the carriage return from the progress.
print()
# Save the result as an audio summary.
datestring = str(datetime.now()).replace(' ', 'T')
writer = tf.train.SummaryWriter(logdir)
tf.audio_summary('generated', decode, wavenet_params['sample_rate'])
summaries = tf.merge_all_summaries()
summary_out = sess.run(summaries,
feed_dict={samples: np.reshape(waveform, [-1, 1])})
writer.add_summary(summary_out)
# Save the result as a wav file.
if args.wav_out_path:
out = sess.run(decode, feed_dict={samples: waveform})
write_wav(out, wavenet_params['sample_rate'], args.wav_out_path)
print('Finished generating. The result can be viewed in TensorBoard.')
def main():
args = get_arguments()
started_datestring = "{0:%Y-%m-%dT%H-%M-%S}".format(datetime.now())
logdir = os.path.join(args.logdir, 'generate', started_datestring)
with open(args.wavenet_params, 'r') as config_file:
wavenet_params = json.load(config_file)
sess = tf.Session()
net = WaveNetModel(
batch_size=1,
dilations=wavenet_params["dilations"],
filter_width=wavenet_params["filter_width"],
residual_channels=wavenet_params["residual_channels"],
dilation_channels=wavenet_params["dilation_channels"],
skip_channels=wavenet_params["skip_channels"],
use_biases=wavenet_params["use_biases"],
scalar_input=wavenet_params["scalar_input"],
initial_filter_width=wavenet_params["initial_filter_width"],
histograms=False,
global_condition_channels=args.gc_channels,
global_condition_cardinality=args.gc_cardinality,
MFSC_channels=wavenet_params["MFSC_channels"],
AP_channels=wavenet_params["AP_channels"],
F0_channels=wavenet_params["F0_channels"],
phone_channels=wavenet_params["phones_channels"],
phone_pos_channels=wavenet_params["phone_pos_channels"])
samples = tf.placeholder(tf.float32)
lc = tf.placeholder(tf.float32)
AP_channels = wavenet_params["AP_channels"]
MFSC_channels = wavenet_params["MFSC_channels"]
if args.fast_generation:
next_sample = net.predict_proba_incremental(samples, args.gc_id) ########### understand shape of next_sample
else:
# samples = tf.reshape(samples, [1, samples.shape[-2], samples.shape[-1]])
# lc = tf.reshape(lc, [1, lc.shape[-2], lc.shape[-1]])
# mvn1, mvn2 , mvn3 ,mvn4, w1, w2, w3, w4 = net.predict_proba(samples, lc, args.gc_id)
outputs = net.predict_proba(samples, AP_channels, lc, args.gc_id)
outputs = tf.reshape(outputs, [1, AP_channels])
if args.fast_generation:
sess.run(tf.global_variables_initializer())
sess.run(net.init_ops)
variables_to_restore = {
var.name[:-2]: var for var in tf.global_variables()
if not ('state_buffer' in var.name or 'pointer' in var.name)}
saver = tf.train.Saver(variables_to_restore)
print('Restoring model from {}'.format(args.checkpoint))
saver.restore(sess, args.checkpoint)
# decode = mu_law_decode(samples, wavenet_params['quantization_channels'])
# quantization_channels = wavenet_params['quantization_channels']
# MFSC_channels = wavenet_params["MFSC_channels"]
if args.wav_seed:
# seed = create_seed(args.wav_seed,
# wavenet_params['sample_rate'],
# quantization_channels,
# net.receptive_field)
# waveform = sess.run(seed).tolist()
pass
else:
# Silence with a single random sample at the end.
# waveform = [quantization_channels / 2] * (net.receptive_field - 1)
# waveform.append(np.random.randint(quantization_channels))
waveform = np.zeros((net.receptive_field - 1, AP_channels + MFSC_channels))
waveform = np.append(waveform, np.random.randn(1, AP_channels + MFSC_channels), axis=0)
lc_array, mfsc_array = load_lc(scramble=True) # clip_id:[003, 004, 007, 010, 012 ...]
# print ("before pading:", lc_array.shape)
# lc_array = lc_array.reshape(1, lc_array.shape[-2], lc_array.shape[-1])
lc_array = np.pad(lc_array, ((net.receptive_field, 0), (0, 0)), 'constant', constant_values=((0, 0),(0,0)))
mfsc_array = np.pad(mfsc_array, ((net.receptive_field, 0), (0, 0)), 'constant', constant_values=((0, 0),(0,0)))
# print ("after pading:", lc_array.shape)
if args.fast_generation and args.wav_seed:
# When using the incremental generation, we need to
# feed in all priming samples one by one before starting the
# actual generation.
# TODO This could be done much more efficiently by passing the waveform
# to the incremental generator as an optional argument, which would be
# used to fill the queues initially.
outputs = [next_sample]
outputs.extend(net.push_ops) ################# understand net.push_ops, understand shape of outputs
print('Priming generation...')
for i, x in enumerate(waveform[-net.receptive_field: -1]):
if i % 100 == 0:
print('Priming sample {}'.format(i))
sess.run(outputs, feed_dict={samples: x})
print('Done.')
last_sample_timestamp = datetime.now()
for step in range(args.samples):
if args.fast_generation:
outputs = [next_sample]
outputs.extend(net.push_ops)
window = waveform[-1]
else:
if len(waveform) > net.receptive_field:
window = waveform[-net.receptive_field:, :]
else:
window = waveform
# outputs = [next_sample]
############################################## TODO ############################################
# Modified code to get one output of the GMM
# outputs = estimate_output(mvn1, mvn2, mvn3, mvn4, w1, w2, w3, w4, MFSC_channels)
# outputs = tf.reshape(outputs, [1,60])
# outputs = w1 * mvn1.sample([1]) + w2 * mvn2.sample([1]) + w3 * mvn3.sample([1]) + w4 * mvn4.sample([1])
# Run the WaveNet to predict the next sample.
window = window.reshape(1, window.shape[-2], window.shape[-1])
# print ("lc_batch_shape:", lc_array[step+1: step+1+net.receptive_field, :].reshape(1, net.receptive_field, -1).shape)
prediction = sess.run(outputs, feed_dict={samples: window,
lc: lc_array[step+1: step+1+net.receptive_field, :].reshape(1, net.receptive_field, -1)})
# print(prediction.shape)
prediction = np.concatenate((prediction, mfsc_array[step + net.receptive_field].reshape(1,-1)), axis=-1)
waveform = np.append(waveform, prediction, axis=0)
########### temp control is in model.py currently ############
# # Scale prediction distribution using temperature.
# np.seterr(divide='ignore')
# scaled_prediction = np.log(prediction) / args.temperature ############# understand this
# scaled_prediction = (scaled_prediction -
# np.logaddexp.reduce(scaled_prediction))
# scaled_prediction = np.exp(scaled_prediction)
# np.seterr(divide='warn')
# # Prediction distribution at temperature=1.0 should be unchanged after
# # scaling.
# if args.temperature == 1.0:
# np.testing.assert_allclose(
# prediction, scaled_prediction, atol=1e-5,
# err_msg='Prediction scaling at temperature=1.0 '
# 'is not working as intended.')
# sample = np.random.choice( ############ replace with sampling multivariate gaussian
# np.arange(quantization_channels), p=scaled_prediction)
# waveform.append(sample)
# Show progress only once per second.
current_sample_timestamp = datetime.now()
time_since_print = current_sample_timestamp - last_sample_timestamp
if time_since_print.total_seconds() > 1.:
print('Frame {:3<d}/{:3<d}'.format(step + 1, args.samples),
end='\r')
last_sample_timestamp = current_sample_timestamp
# If we have partial writing, save the result so far.
if (args.wav_out_path and args.save_every and
(step + 1) % args.save_every == 0):
# out = sess.run(decode, feed_dict={samples: waveform})
# write_wav(out, wavenet_params['sample_rate'], args.wav_out_path)
np.save(args.wav_out_path, waveform)
# Introduce a newline to clear the carriage return from the progress.
print()
# Save the result as an audio summary.
# datestring = str(datetime.now()).replace(' ', 'T')
# writer = tf.summary.FileWriter(logdir)
# tf.summary.audio('generated', decode, wavenet_params['sample_rate'])
# summaries = tf.summary.merge_all()
# summary_out = sess.run(summaries,
# feed_dict={samples: np.reshape(waveform, [-1, 1])})
# writer.add_summary(summary_out)
# Save the result as a numpy file.
if args.wav_out_path:
# out = sess.run(decode, feed_dict={samples: waveform})
# write_wav(out, wavenet_params['sample_rate'], args.wav_out_path)
np.save(args.wav_out_path, waveform[:,:4]) # only take the first four columns, which are aps
print('Finished generating. The result can be viewed in TensorBoard.')
class TestGeneration(tf.test.TestCase):
def setUp(self):
self.net = WaveNetModel(batch_size=1,
dilations=[1, 2, 4, 8, 16, 32, 64, 128, 256],
filter_width=2,
residual_channels=16,
dilation_channels=16,
quantization_channels=128,
skip_channels=32)
def testGenerateSimple(self):
'''Generate a few samples using the naive method and
perform sanity checks on the output.'''
waveform = tf.placeholder(tf.int32)
np.random.seed(0)
data = np.random.randint(128, size=1000)
proba = self.net.predict_proba(waveform)
with self.test_session() as sess:
sess.run(tf.global_variables_initializer())
proba = sess.run(proba, feed_dict={waveform: data})
self.assertAllEqual(proba.shape, [128])
self.assertTrue(np.all((proba >= 0) & (proba <= (128 - 1))))
def testGenerateFast(self):
'''Generate a few samples using the fast method and
perform sanity checks on the output.'''
waveform = tf.placeholder(tf.int32)
np.random.seed(0)
data = np.random.randint(128)
proba = self.net.predict_proba_incremental(waveform)
with self.test_session() as sess:
sess.run(tf.global_variables_initializer())
sess.run(self.net.init_ops)
proba = sess.run(proba, feed_dict={waveform: data})
self.assertAllEqual(proba.shape, [128])
self.assertTrue(np.all((proba >= 0) & (proba <= (128 - 1))))
def testCompareSimpleFast(self):
waveform = tf.placeholder(tf.int32)
np.random.seed(0)
data = np.random.randint(128, size=1000)
proba = self.net.predict_proba(waveform)
proba_fast = self.net.predict_proba_incremental(waveform)
with self.test_session() as sess:
sess.run(tf.global_variables_initializer())
sess.run(self.net.init_ops)
# Prime the incremental generation with all samples
# except the last one
for x in data[:-1]:
proba_fast_ = sess.run([proba_fast, self.net.push_ops],
feed_dict={waveform: x})
# Get the last sample from the incremental generator
proba_fast_ = sess.run(proba_fast, feed_dict={waveform: data[-1]})
# Get the sample from the simple generator
proba_ = sess.run(proba, feed_dict={waveform: data})
self.assertAllClose(proba_, proba_fast_)
def main(checkpoint=None):
#print("\n\nGenerating.\nPlease wait.\n\n")
title_BOOL = True
#title=""
args = get_arguments()
# create folder in which to put txt files of generated poems
directory = "GENERATED/" + args.start_time
if not os.path.exists(directory):
os.makedirs(directory)
started_datestring = "{0:%Y-%m-%dT%H-%M-%S}".format(datetime.now())
logdir = os.path.join(args.logdir, 'generate', started_datestring)
with open(args.wavenet_params, 'r') as config_file:
wavenet_params = json.load(config_file)
sess = tf.Session()
net = WaveNetModel(
batch_size=1,
dilations=wavenet_params['dilations'],
filter_width=wavenet_params['filter_width'],
residual_channels=wavenet_params['residual_channels'],
dilation_channels=wavenet_params['dilation_channels'],
quantization_channels=wavenet_params['quantization_channels'],
skip_channels=wavenet_params['skip_channels'],
use_biases=wavenet_params['use_biases'])
samples = tf.placeholder(tf.int32)
if args.fast_generation:
next_sample = net.predict_proba_incremental(samples)
else:
next_sample = net.predict_proba(samples)
if args.fast_generation:
sess.run(tf.initialize_all_variables())
sess.run(net.init_ops)
variables_to_restore = {
var.name[:-2]: var
for var in tf.all_variables()
if not ('state_buffer' in var.name or 'pointer' in var.name)
}
saver = tf.train.Saver(variables_to_restore)
powr = int((len(wavenet_params['dilations']) / 2) - 1)
md = ''.join(
args.checkpoint.split("-")[-1:]
) #map(str.lstrip("[").rstrip("]").strip(",") , args.checkpoint.split("-")[-1:])
#STORAGE
words = "\n\n"
if checkpoint == None:
intro = """DIR: {}\tMODEL: {}\t\tLOSS: {}\ndilations: {}\t\t\t\tfilter_width: {}\t\tresidual_channels: {}\ndilation_channels: {}\t\t\tskip_channels: {}\tquantization_channels: {}\n_______________________________________________________________________________________________""".format(
args.checkpoint.split("/")[-2], md, args.loss, "2^" + str(powr),
wavenet_params['filter_width'],
wavenet_params['residual_channels'],
wavenet_params['dilation_channels'],
wavenet_params['skip_channels'],
wavenet_params['quantization_channels'])
saver.restore(sess, args.checkpoint)
else:
print('Restoring model from PARAMETER {}'.format(checkpoint))
saver.restore(sess, args.checkpoint)
decode = samples
quantization_channels = wavenet_params['quantization_channels']
waveform = [32.]
last_sample_timestamp = datetime.now()
limit = args.samples - 1
print("")
for step in range(args.samples):
# COUNTDOWN
#print(step,args.samples,int(args.samples)-int(step), end="\r")
#print("")
print("Generating:", step, "/", args.samples, end="\r")
if args.fast_generation:
outputs = [next_sample]
outputs.extend(net.push_ops)
window = waveform[-1]
else:
if len(waveform) > args.window:
window = waveform[-args.window:]
else:
window = waveform
outputs = [next_sample]
# Run the WaveNet to predict the next sample.
prediction = sess.run(outputs, feed_dict={samples: window})[0]
sample = np.random.choice(np.arange(quantization_channels),
p=prediction)
waveform.append(sample)
# CAPITALIZE TITLE
if title_BOOL:
# STORAGE
words += chr(sample).title()
#check for newline
if sample == 10:
#print("GOT IT___________")
title_BOOL = False
words += "\n\n"
else:
# STORAGE
words += chr(sample)
#TYPEWRITER
#sys.stdout.write(words[-1])
if args.text_out_path == None:
args.text_out_path = "GENERATED/{}/{}_DIR-{}_Model-{}_Loss-{}_Chars-{}.txt".format(
args.start_time,
datetime.strftime(datetime.now(), '%Y-%m-%d_%H-%M'),
args.checkpoint.split("/")[-2],
args.checkpoint.split("-")[-1], args.loss, args.samples)
# If we have partial writing, save the result so far.
if (args.text_out_path and args.save_every
and (step + 1) % args.save_every == 0):
out = sess.run(decode, feed_dict={samples: waveform})
#write_text(out, args.text_out_path,intro,words)
#print (step, end="\r")
# Introduce a newline to clear the carriage return from the progress.
#print()
ml = "Model: {} | Loss: {} | {}".format(
args.checkpoint.split("-")[-1], args.loss,
args.checkpoint.split("/")[-2])
# Save the result as a wav file.
if args.text_out_path:
out = sess.run(decode, feed_dict={samples: waveform})
print(" ", end="\r")
write_text(out, args.text_out_path, intro, words, ml)
def main(checkpoint=None):
title_BOOL = True
title = ""
args = get_arguments()
started_datestring = "{0:%Y-%m-%dT%H-%M-%S}".format(datetime.now())
logdir = os.path.join(args.logdir, 'generate', started_datestring)
with open(args.wavenet_params, 'r') as config_file:
wavenet_params = json.load(config_file)
sess = tf.Session()
net = WaveNetModel(
batch_size=1,
dilations=wavenet_params['dilations'],
filter_width=wavenet_params['filter_width'],
residual_channels=wavenet_params['residual_channels'],
dilation_channels=wavenet_params['dilation_channels'],
quantization_channels=wavenet_params['quantization_channels'],
skip_channels=wavenet_params['skip_channels'],
use_biases=wavenet_params['use_biases'])
samples = tf.placeholder(tf.int32)
if args.fast_generation:
next_sample = net.predict_proba_incremental(samples)
else:
next_sample = net.predict_proba(samples)
if args.fast_generation:
sess.run(tf.initialize_all_variables())
sess.run(net.init_ops)
variables_to_restore = {
var.name[:-2]: var
for var in tf.all_variables()
if not ('state_buffer' in var.name or 'pointer' in var.name)
}
saver = tf.train.Saver(variables_to_restore)
powr = int((len(wavenet_params['dilations']) / 2) - 1)
md = args.checkpoint.split(
"-"
)[-1:] #map(str.lstrip("[").rstrip("]").strip(",") , args.checkpoint.split("-")[-1:])
if checkpoint == None:
intro = """\n________________________________________________________________________________________________\n\n\t\t~~~~~~******~~~~~~\n________________________________________________________________________________________________\n\n\tDIR: {}\n\tMODEL: {}\n\tLOSS: {}\n\n
dilations={}\t filter_width={}\t residual_channels={}
dilation_channels={}\tquantization_channels={}\tskip_channels={}\n________________________________________________________________________________________________\n\n""".format(
args.checkpoint.split("/")[-2], md, args.loss, "2^" + str(powr),
wavenet_params['filter_width'],
wavenet_params['residual_channels'],
wavenet_params['dilation_channels'],
wavenet_params['quantization_channels'],
wavenet_params['skip_channels'])
print(intro)
saver.restore(sess, args.checkpoint)
else:
print('Restoring model from PARAMETER {}'.format(checkpoint))
saver.restore(sess, args.checkpoint)
decode = samples
quantization_channels = wavenet_params['quantization_channels']
waveform = [32.]
last_sample_timestamp = datetime.now()
for step in range(args.samples):
if args.fast_generation:
outputs = [next_sample]
outputs.extend(net.push_ops)
window = waveform[-1]
else:
if len(waveform) > args.window:
window = waveform[-args.window:]
else:
window = waveform
outputs = [next_sample]
# Run the WaveNet to predict the next sample.
prediction = sess.run(outputs, feed_dict={samples: window})[0]
sample = np.random.choice(np.arange(quantization_channels),
p=prediction)
waveform.append(sample)
# CAPITALIZE TITLE
if title_BOOL:
# SHOW character by character in terminal
sys.stdout.write(chr(sample).capitalize())
title += chr(sample).capitalize()
#check for newline
if sample == 10:
title_BOOL = False
sys.stdout.write("\n\n")
else:
# SHOW character by character in terminal
sys.stdout.write(chr(sample))
if args.text_out_path == None:
args.text_out_path = "GENERATED/{}_DIR-{}_Model-{}_Loss-{}_Chars-{}.txt".format(
datetime.strftime(datetime.now(), '%Y-%m-%d_%H:%M'),
args.checkpoint.split("/")[-2],
args.checkpoint.split("-")[-1], args.loss, args.samples)
# If we have partial writing, save the result so far.
if (args.text_out_path and args.save_every
and (step + 1) % args.save_every == 0):
out = sess.run(decode, feed_dict={samples: waveform})
write_text(out, args.text_out_path, intro)
# Introduce a newline to clear the carriage return from the progress.
print()
# Save the result as a wav file.
if args.text_out_path:
out = sess.run(decode, feed_dict={samples: waveform})
write_text(out, args.text_out_path, intro)
def main():
config = get_arguments()
started_datestring = "{0:%Y-%m-%dT%H-%M-%S}".format(datetime.now())
logdir = os.path.join(config.logdir, 'generate', started_datestring)
if not os.path.exists(logdir):
os.makedirs(logdir)
load_hparams(hparams, config.checkpoint_dir)
with tf.device('/cpu:0'):
sess = tf.Session()
scalar_input = hparams.scalar_input
net = WaveNetModel(
batch_size=BATCH_SIZE,
dilations=hparams.dilations,
filter_width=hparams.filter_width,
residual_channels=hparams.residual_channels,
dilation_channels=hparams.dilation_channels,
quantization_channels=hparams.quantization_channels,
out_channels=hparams.out_channels,
skip_channels=hparams.skip_channels,
use_biases=hparams.use_biases,
scalar_input=scalar_input,
initial_filter_width=hparams.initial_filter_width,
global_condition_channels=hparams.gc_channels,
global_condition_cardinality=config.gc_cardinality,
train_mode=False
) # train 단계에서는 global_condition_cardinality를 AudioReader에서 파악했지만, 여기서는 넣어주어야 함
if scalar_input:
samples = tf.placeholder(tf.float32, shape=[net.batch_size, None])
else:
samples = tf.placeholder(
tf.int32, shape=[net.batch_size, None]
) # samples: mu_law_encode로 변환된 것. one-hot으로 변환되기 전. (batch_size, 길이)
next_sample = net.predict_proba_incremental(
samples, [config.gc_id] * net.batch_size
) # Fast Wavenet Generation Algorithm-1611.09482 algorithm 적용
var_list = [
var for var in tf.global_variables() if 'queue' not in var.name
]
saver = tf.train.Saver(var_list)
print('Restoring model from {}'.format(config.checkpoint_dir))
load(saver, sess, config.checkpoint_dir)
sess.run(
net.queue_initializer) # 이 부분이 없으면, checkpoint에서 복원된 값들이 들어 있다.
quantization_channels = hparams.quantization_channels
if config.wav_seed:
# wav_seed의 길이가 receptive_field보다 작으면, padding이라도 해야 되는 거 아닌가? 그냥 짧으면 짧은 대로 return함 --> 그래서 너무 짧으면 error
seed = create_seed(config.wav_seed, hparams.sample_rate,
quantization_channels, net.receptive_field,
scalar_input) # --> mu_law encode 된 것.
if scalar_input:
waveform = seed.tolist()
else:
waveform = sess.run(
seed).tolist() # [116, 114, 120, 121, 127, ...]
print('Priming generation...')
for i, x in enumerate(waveform[-net.receptive_field:-1]
): # 제일 마지막 1개는 아래의 for loop의 첫 loop에서 넣어준다.
if i % 100 == 0:
print('Priming sample {}'.format(i))
sess.run(next_sample,
feed_dict={
samples:
np.array([x] * net.batch_size).reshape(
net.batch_size, 1)
})
print('Done.')
waveform = np.array([waveform[-net.receptive_field:]] *
net.batch_size)
else:
# Silence with a single random sample at the end.
if scalar_input:
waveform = [0.0] * (net.receptive_field - 1)
waveform = np.array(waveform * net.batch_size).reshape(
net.batch_size, -1)
waveform = np.concatenate(
[
waveform, 2 * np.random.rand(net.batch_size).reshape(
net.batch_size, -1) - 1
],
axis=-1) # -1~1사이의 random number를 만들어 끝에 붙힌다.
else:
waveform = [quantization_channels / 2] * (
net.receptive_field - 1
) # 필요한 receptive_field 크기보다 1개 작게 만든 후, 아래에서 random하게 1개를 덧붙힌다.
waveform = np.array(waveform * net.batch_size).reshape(
net.batch_size, -1)
waveform = np.concatenate(
[
waveform,
np.random.randint(quantization_channels,
size=net.batch_size).reshape(
net.batch_size, -1)
],
axis=-1) # one hot 변환 전. (batch_size, 5117)
last_sample_timestamp = datetime.now()
for step in range(config.samples): # 원하는 길이를 구하기 위해 loop
window = waveform[:, -1:] # 제일 끝에 있는 1개만 samples에 넣어 준다.
# Run the WaveNet to predict the next sample.
# fast가 아닌경우. window: [128.0, 128.0, ..., 128.0, 178, 185]
# fast인 경우, window는 숫자 1개.
prediction = sess.run(
next_sample, feed_dict={samples: window}
) # samples는 mu law encoding된 것. 계산 과정에서 one hot으로 변환된다. --> (batch_size,256)
if scalar_input:
sample = prediction
else:
# Scale prediction distribution using temperature.
# 다음 과정은 config.temperature==1이면 각 원소를 합으로 나누어주는 것에 불과. 이미 softmax를 적용한 겂이므로, 합이 1이된다. 그래서 값의 변화가 없다.
# config.temperature가 1이 아니며, 각 원소의 log취한 값을 나눈 후, 합이 1이 되도록 rescaling하는 것이 된다.
np.seterr(divide='ignore')
scaled_prediction = np.log(
prediction
) / config.temperature # config.temperature인 경우는 값의 변화가 없다.
scaled_prediction = (
scaled_prediction - np.logaddexp.reduce(
scaled_prediction, axis=-1, keepdims=True)
) # np.log(np.sum(np.exp(scaled_prediction)))
scaled_prediction = np.exp(scaled_prediction)
np.seterr(divide='warn')
# Prediction distribution at temperature=1.0 should be unchanged after
# scaling.
if config.temperature == 1.0:
np.testing.assert_allclose(
prediction,
scaled_prediction,
atol=1e-5,
err_msg=
'Prediction scaling at temperature=1.0 is not working as intended.'
)
sample = [[
np.random.choice(np.arange(quantization_channels), p=p)
] for p in scaled_prediction] # choose one sample per batch
waveform = np.concatenate([waveform, sample], axis=-1)
# Show progress only once per second.
current_sample_timestamp = datetime.now()
time_since_print = current_sample_timestamp - last_sample_timestamp
if time_since_print.total_seconds() > 1.:
print('Sample {:3<d}/{:3<d}'.format(step + 1, config.samples),
end='\r')
last_sample_timestamp = current_sample_timestamp
# Introduce a newline to clear the carriage return from the progress.
print()
# Save the result as a wav file.
if scalar_input:
out = waveform
else:
decode = mu_law_decode(samples, quantization_channels)
out = sess.run(decode, feed_dict={samples: waveform})
for i in range(net.batch_size):
config.wav_out_path = logdir + '/test-{}.wav'.format(i)
write_wav(out[i], hparams.sample_rate, config.wav_out_path)
print('Finished generating.')
def main():
args = get_arguments()
started_datestring = "{0:%Y-%m-%dT%H-%M-%S}".format(datetime.now())
logdir = os.path.join(args.logdir, 'generate', started_datestring)
with open(args.wavenet_params, 'r') as config_file:
wavenet_params = json.load(config_file)
sess = tf.Session()
net = WaveNetModel(
batch_size=1,
dilations=wavenet_params['dilations'],
filter_width=wavenet_params['filter_width'],
residual_channels=wavenet_params['residual_channels'],
dilation_channels=wavenet_params['dilation_channels'],
quantization_channels=wavenet_params['quantization_channels'],
skip_channels=wavenet_params['skip_channels'],
use_biases=wavenet_params['use_biases'])
samples = tf.placeholder(tf.int32)
if args.fast_generation:
next_sample = net.predict_proba_incremental(samples)
else:
next_sample = net.predict_proba(samples)
if args.fast_generation:
sess.run(tf.initialize_all_variables())
sess.run(net.init_ops)
variables_to_restore = {
var.name[:-2]: var
for var in tf.all_variables()
if not ('state_buffer' in var.name or 'pointer' in var.name)
}
saver = tf.train.Saver(variables_to_restore)
print('Restoring model from {}'.format(args.checkpoint))
saver.restore(sess, args.checkpoint)
decode = samples
quantization_channels = wavenet_params['quantization_channels']
waveform = [32.]
last_sample_timestamp = datetime.now()
for step in range(args.samples):
if args.fast_generation:
outputs = [next_sample]
outputs.extend(net.push_ops)
window = waveform[-1]
else:
if len(waveform) > args.window:
window = waveform[-args.window:]
else:
window = waveform
outputs = [next_sample]
# Run the WaveNet to predict the next sample.
prediction = sess.run(outputs, feed_dict={samples: window})[0]
sample = np.random.choice(np.arange(quantization_channels),
p=prediction)
waveform.append(sample)
# Show progress only once per second.
current_sample_timestamp = datetime.now()
time_since_print = current_sample_timestamp - last_sample_timestamp
if time_since_print.total_seconds() > 1.:
print('Sample {:3<d}/{:3<d}'.format(step + 1, args.samples),
end='\r')
last_sample_timestamp = current_sample_timestamp
# If we have partial writing, save the result so far.
if (args.text_out_path and args.save_every
and (step + 1) % args.save_every == 0):
out = sess.run(decode, feed_dict={samples: waveform})
write_text(out, args.text_out_path)
# Introduce a newline to clear the carriage return from the progress.
print()
# Save the result as a wav file.
if args.text_out_path:
out = sess.run(decode, feed_dict={samples: waveform})
write_text(out, args.text_out_path)
print('Finished generating.')
def main():
args = get_arguments()
started_datestring = "{0:%Y-%m-%dT%H-%M-%S}".format(datetime.now())
logdir = os.path.join(args.logdir, 'generate', started_datestring)
with open(args.wavenet_params, 'r') as config_file:
wavenet_params = json.load(config_file)
sess = tf.Session(
# config=tf.ConfigProto(device_count={'GPU': 0})
)
# Build the WaveNet model
net = WaveNetModel(
batch_size=1,
dilations=wavenet_params['dilations'],
filter_width=wavenet_params['filter_width'],
residual_channels=wavenet_params['residual_channels'],
quantization_channels=wavenet_params['quantization_channels'],
skip_channels=wavenet_params['skip_channels'],
gc_channels=args.gc_channels,
gc_cardinality=args.gc_cardinality,
lc_channels=args.lc_channels)
# Create placeholders
# Default to fast generation
samples = tf.placeholder(tf.int32)
lc = tf.placeholder(tf.float32) if args.lc_embedding else None
gc = args.gc_id or None
# TODO: right now we pre-calculated lc embeddings of the same length
# as the audio we'd like to generate so they're naturally algined.
# Add function to load a length of `args.n_samples` of embeddings
# from pre-calculated (full-length) embeddings.
if args.lc_embedding is not None:
lc_embedding = load_lc_embedding(args.lc_embedding)
lc_embedding = tf.convert_to_tensor(lc_embedding)
lc_embedding = tf.reshape(lc_embedding, [1, -1, args.lc_channels])
lc_embedding = net._enc_upsampling_conv(lc_embedding, args.n_samples)
lc_embedding = tf.reshape(lc_embedding, [-1, args.lc_channels])
next_sample = net.predict_proba_incremental(samples, gc, lc)
sess.run(tf.global_variables_initializer())
sess.run(net.init_ops)
# Group the ops we need to run
output_ops = [next_sample]
output_ops.extend(net.push_ops)
# Convert mu-law encoded samples back to (-1, 1) of R
QUANTIZATION_CHANNELS = wavenet_params['quantization_channels']
decode = mu_law_decode(samples, QUANTIZATION_CHANNELS)
variables_to_restore = {
var.name[:-2]: var
for var in tf.global_variables()
if not ('state_buffer' in var.name or 'pointer' in var.name)
}
saver = tf.train.Saver(variables_to_restore)
ckpt = tf.train.get_checkpoint_state(args.checkpoint)
if ckpt and ckpt.model_checkpoint_path:
saver.restore(sess, ckpt.model_checkpoint_path)
print('Restoring model from {}'.format(ckpt.model_checkpoint_path))
if args.wav_seed:
seed = create_seed(args.wav_seed, wavenet_params['sample_rate'],
QUANTIZATION_CHANNELS, net.receptive_field)
waveform = sess.run(seed).tolist()
else:
# Silence with a single random sample at the end.
waveform = [0] * (net.receptive_field - 1)
waveform.append(
np.random.randint(-QUANTIZATION_CHANNELS // 2,
QUANTIZATION_CHANNELS // 2))
if args.lc_embedding is not None:
lc_embedding = sess.run(lc_embedding)
if args.wav_seed:
# When using the incremental generation, we need to
# feed in all priming samples one by one before starting the
# actual generation.
# TODO This could be done much more efficiently by passing the waveform
# to the incremental generator as an optional argument, which would be
# used to fill the queues initially.
print('Priming generation...')
for i, x in enumerate(waveform[-net.receptive_field:-1]):
if i % 100 == 0:
print('Priming sample {}'.format(i))
lc_ = lc_embedding[i, :]
sess.run(output_ops, feed_dict={samples: x, lc: lc_})
print('Done.')
last_sample_timestamp = datetime.now()
lc_ = None
import sys
for step in range(args.n_samples):
if step % 1000 == 0:
print("Generating {} of {}.".format(step, args.n_samples))
sys.stdout.flush()
window = waveform[-1]
if args.lc_embedding is not None:
lc_ = lc_embedding[step, :]
# Run the WaveNet to predict the next sample.
feed_dict = {samples: window}
if lc_ is not None:
feed_dict[lc] = lc_
results = sess.run(output_ops, feed_dict=feed_dict)
pred = results[0]
# Scale prediction distribution using temperature.
np.seterr(divide='ignore')
scaled_prediction = np.log(pred) / args.temperature
scaled_prediction = (scaled_prediction -
np.logaddexp.reduce(scaled_prediction))
scaled_prediction = np.exp(scaled_prediction)
np.seterr(divide='warn')
# Prediction distribution at temperature=1.0 should be unchanged
# after scaling.
if args.temperature == 1.0:
np.testing.assert_allclose(
pred,
scaled_prediction,
atol=1e-5,
err_msg='Prediction scaling at temperature=1.0 '
'is not working as intended.')
sample = np.random.choice(np.arange(-QUANTIZATION_CHANNELS // 2,
QUANTIZATION_CHANNELS // 2),
p=scaled_prediction)
waveform.append(sample)
# If we have partial writing, save the result so far.
if (args.wav_out_path and args.save_every
and (step + 1) % args.save_every == 0):
out = sess.run(decode, feed_dict={samples: waveform})
write_wav(out, wavenet_params['sample_rate'], args.wav_out_path)
# Introduce a newline to clear the carriage return from the progress.
print()
# Save the result as an audio summary.
datestring = str(datetime.now()).replace(' ', 'T')
writer = tf.summary.FileWriter(logdir)
tf.summary.audio('generated', decode, wavenet_params['sample_rate'])
summaries = tf.summary.merge_all()
summary_out = sess.run(summaries,
feed_dict={samples: np.reshape(waveform, [-1, 1])})
writer.add_summary(summary_out)
# Save the result as a wav file.
if args.wav_out_path:
out = sess.run(decode, feed_dict={samples: waveform})
write_wav(out, wavenet_params['sample_rate'], args.wav_out_path)
print('Finished generating. The result can be viewed in TensorBoard.')
