def main():
args = get_arguments()
try:
directories = validate_directories(args)
except ValueError as e:
print("Some arguments are wrong:")
print(str(e))
return
logdir = directories['logdir']
restore_from = directories['restore_from']
# Even if we restored the model, we will treat it as new training
# if the trained model is written into an arbitrary location.
is_overwritten_training = logdir != restore_from
with open(args.wavenet_params, 'r') as f:
wavenet_params = json.load(f)
# Create coordinator.
coord = tf.train.Coordinator()
# Load raw waveform from VCTK corpus.
with tf.name_scope('create_inputs'):
# Allow silence trimming to be skipped by specifying a threshold near
# zero.
silence_threshold = args.silence_threshold if args.silence_threshold > EPSILON else None
gc_enabled = args.gc_channels is not None
reader = AudioReader(
args.data_dir,
coord,
sample_rate=wavenet_params['sample_rate'],
gc_enabled=gc_enabled,
receptive_field=WaveNetModel.calculate_receptive_field(
wavenet_params['filter_width'], wavenet_params['dilations'],
wavenet_params['scalar_input'],
wavenet_params['initial_filter_width']),
sample_size=args.sample_size,
silence_threshold=silence_threshold)
audio_batch = reader.dequeue(args.batch_size)
if gc_enabled:
gc_id_batch = reader.dequeue_gc(args.batch_size)
else:
gc_id_batch = None
def main():
args = get_arguments()
try:
directories = validate_directories(args)
except ValueError as e:
print("Some arguments are wrong:")
print(str(e))
return
logdir = directories['logdir']
logdir_root = directories['logdir_root']
restore_from = directories['restore_from']
# Even if we restored the model, we will treat it as new training
# if the trained model is written into an arbitrary location.
is_overwritten_training = logdir != restore_from
with open(args.wavenet_params, 'r') as f:
wavenet_params = json.load(f)
# Create coordinator.
coord = tf.train.Coordinator()
# Load raw waveform from VCTK corpus.
with tf.name_scope('create_inputs'):
reader = AudioReader(args.data_dir,
coord,
sample_rate=wavenet_params['sample_rate'],
sample_size=args.sample_size)
audio_batch = reader.dequeue(args.batch_size)
# Create network.
net = WaveNet(
batch_size=args.batch_size,
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"])
loss = net.loss(audio_batch)
optimizer = tf.train.AdamOptimizer(learning_rate=args.learning_rate)
trainable = tf.trainable_variables()
optim = optimizer.minimize(loss, var_list=trainable)
# Set up logging for TensorBoard.
writer = tf.train.SummaryWriter(logdir)
writer.add_graph(tf.get_default_graph())
run_metadata = tf.RunMetadata()
summaries = tf.merge_all_summaries()
# Set up session
sess = tf.Session(config=tf.ConfigProto(log_device_placement=False))
init = tf.initialize_all_variables()
sess.run(init)
# Saver for storing checkpoints of the model.
saver = tf.train.Saver()
try:
saved_global_step = load(saver, sess, restore_from)
if is_overwritten_training or 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
except:
print("Something went wrong while restoring checkpoint. "
"We will terminate training to avoid accidentally overwriting "
"the previous model.")
raise
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
reader.start_threads(sess)
try:
last_saved_step = saved_global_step
for step in range(saved_global_step + 1, args.num_steps):
start_time = time.time()
if args.store_metadata and step % 50 == 0:
# Slow run that stores extra information for debugging.
print('Storing metadata')
run_options = tf.RunOptions(
trace_level=tf.RunOptions.FULL_TRACE)
summary, loss_value, _ = sess.run([summaries, loss, optim],
options=run_options,
run_metadata=run_metadata)
writer.add_summary(summary, step)
writer.add_run_metadata(run_metadata,
'step_{:04d}'.format(step))
tl = timeline.Timeline(run_metadata.step_stats)
timeline_path = os.path.join(logdir, 'timeline.trace')
with open(timeline_path, 'w') as f:
f.write(tl.generate_chrome_trace_format(show_memory=True))
else:
summary, loss_value, _ = sess.run([summaries, loss, optim])
writer.add_summary(summary, step)
duration = time.time() - start_time
print('step {:d} - loss = {:.3f}, ({:.3f} sec/step)'.format(
step, loss_value, duration))
if step % 50 == 0:
save(saver, sess, logdir, step)
last_saved_step = step
except KeyboardInterrupt:
# Introduce a line break after ^C is displayed so save message
# is on its own line.
print()
finally:
if step > last_saved_step:
save(saver, sess, logdir, step)
coord.request_stop()
coord.join(threads)
#AUDIO_FILE_PATH = '/Users/andrewszot/Downloads/VCTK-Corpus'
gc_enabled = False
reader = AudioReader(
AUDIO_FILE_PATH,
coord,
sample_rate=wavenet_params['sample_rate'],
gc_enabled=gc_enabled,
receptive_field=calculate_receptive_field(wavenet_params["filter_width"],
wavenet_params["dilations"],
wavenet_params["scalar_input"],
wavenet_params["initial_filter_width"]),
sample_size=39939,
silence_threshold=silence_threshold)
audio_batch = reader.dequeue(1)
if gc_enabled:
gc_id_batch = reader.dequeue_gc(1)
else:
gc_id_batch = None
global_step = tf.Variable(0, trainable=False)
sess = tf.Session(config=tf.ConfigProto(log_device_placement=False))
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
reader.start_threads(sess)
saver = tf.train.Saver()
try:
# 100K