def train(model_config, experiment_id, sup_dataset, load_model=None):
# Determine input and output shapes
disc_input_shape = [model_config["batch_size"], model_config["num_frames"], 0] # Shape of input
if model_config["network"] == "unet":
separator_class = Models.UnetAudioSeparator.UnetAudioSeparator(model_config["num_layers"], model_config["num_initial_filters"],
output_type=model_config["output_type"],
context=model_config["context"],
mono=model_config["mono_downmix"],
upsampling=model_config["upsampling"],
num_sources=model_config["num_sources"],
filter_size=model_config["filter_size"],
merge_filter_size=model_config["merge_filter_size"])
elif model_config["network"] == "unet_spectrogram":
separator_class = Models.UnetSpectrogramSeparator.UnetSpectrogramSeparator(model_config["num_layers"], model_config["num_initial_filters"],
mono=model_config["mono_downmix"],
num_sources=model_config["num_sources"])
else:
raise NotImplementedError
sep_input_shape, sep_output_shape = separator_class.get_padding(np.array(disc_input_shape))
separator_func = separator_class.get_output
# Creating the batch generators
assert((sep_input_shape[1] - sep_output_shape[1]) % 2 == 0)
pad_durations = np.array([float((sep_input_shape[1] - sep_output_shape[1])/2), 0, 0]) / float(model_config["expected_sr"]) # Input context that the input audio has to be padded ON EACH SIDE
sup_batch_gen = batchgen.BatchGen_Paired(
model_config,
sup_dataset,
sep_input_shape,
sep_output_shape,
pad_durations[0]
)
print("Starting worker")
sup_batch_gen.start_workers()
print("Started worker!")
# Placeholders and input normalisation
mix_context, sources = Input.get_multitrack_placeholders(sep_output_shape, model_config["num_sources"], sep_input_shape, "sup")
#tf.summary.audio("mix", mix_context, 22050, collections=["sup"])
mix = Utils.crop(mix_context, sep_output_shape)
print("Training...")
# BUILD MODELS
# Separator
separator_sources = separator_func(mix_context, True, not model_config["raw_audio_loss"], reuse=False) # Sources are output in order [acc, voice] for voice separation, [bass, drums, other, vocals] for multi-instrument separation
# Supervised objective: MSE in log-normalized magnitude space
separator_loss = 0
for (real_source, sep_source) in zip(sources, separator_sources):
if model_config["network"] == "unet_spectrogram" and not model_config["raw_audio_loss"]:
window = functools.partial(window_ops.hann_window, periodic=True)
stfts = tf.contrib.signal.stft(tf.squeeze(real_source, 2), frame_length=1024, frame_step=768,
fft_length=1024, window_fn=window)
real_mag = tf.abs(stfts)
separator_loss += tf.reduce_mean(tf.abs(real_mag - sep_source))
else:
separator_loss += tf.reduce_mean(tf.square(real_source - sep_source))
separator_loss = separator_loss / float(len(sources)) # Normalise by number of sources
# TRAINING CONTROL VARIABLES
global_step = tf.get_variable('global_step', [], initializer=tf.constant_initializer(0), trainable=False, dtype=tf.int64)
increment_global_step = tf.assign(global_step, global_step + 1)
# Set up optimizers
separator_vars = Utils.getTrainableVariables("separator")
print("Sep_Vars: " + str(Utils.getNumParams(separator_vars)))
print("Num of variables" + str(len(tf.global_variables())))
update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
with tf.control_dependencies(update_ops):
with tf.variable_scope("separator_solver"):
separator_solver = tf.train.AdamOptimizer(learning_rate=model_config["init_sup_sep_lr"]).minimize(separator_loss, var_list=separator_vars)
# SUMMARIES
tf.summary.scalar("sep_loss", separator_loss, collections=["sup"])
sup_summaries = tf.summary.merge_all(key='sup')
# Start session and queue input threads
config = tf.ConfigProto()
config.gpu_options.allow_growth=True
sess = tf.Session(config=config)
sess.run(tf.global_variables_initializer())
writer = tf.summary.FileWriter(model_config["log_dir"] + os.path.sep + str(experiment_id),graph=sess.graph)
# CHECKPOINTING
# Load pretrained model to continue training, if we are supposed to
if load_model != None:
restorer = tf.train.Saver(tf.global_variables(), write_version=tf.train.SaverDef.V2)
print("Num of variables" + str(len(tf.global_variables())))
restorer.restore(sess, load_model)
print('Pre-trained model restored from file ' + load_model)
saver = tf.train.Saver(tf.global_variables(), write_version=tf.train.SaverDef.V2)
# Start training loop
run = True
_global_step = sess.run(global_step)
_init_step = _global_step
it = 0
while run:
# TRAIN SEPARATOR
sup_batch = sup_batch_gen.get_batch()
feed = {i:d for i,d in zip(sources, sup_batch[1:])}
feed.update({mix_context : sup_batch[0]})
_, _sup_summaries = sess.run([separator_solver, sup_summaries], feed)
writer.add_summary(_sup_summaries, global_step=_global_step)
# Increment step counter, check if maximum iterations per epoch is achieved and stop in that case
_global_step = sess.run(increment_global_step)
if _global_step - _init_step > model_config["epoch_it"]:
run = False
print("Finished training phase, stopping batch generators")
sup_batch_gen.stop_workers()
# Epoch finished - Save model
print("Finished epoch!")
save_path = saver.save(sess, model_config["model_base_dir"] + os.path.sep + str(experiment_id) + os.path.sep + str(experiment_id), global_step=int(_global_step))
# Close session, clear computational graph
writer.flush()
writer.close()
sess.close()
tf.reset_default_graph()
return save_path
def train(model_config,
sup_dataset,
model_folder,
unsup_dataset=None,
load_model=None):
# Determine input and output shapes
freq_bins = model_config["num_fft"] / 2 + 1 # Make even number of freq bins
disc_input_shape = [
model_config["batch_size"], freq_bins - 1, model_config["num_frames"],
1
] # Shape of discriminator input
separator_class = Models.Unet.Unet(model_config["num_layers"])
sep_input_shape, sep_output_shape = separator_class.getUnetPadding(
np.array(disc_input_shape))
separator_func = separator_class.get_output
# Batch input workers
# Creating the batch generators
padding_durations = [
float(sep_input_shape[2] - sep_output_shape[2]) *
model_config["num_hop"] / model_config["expected_sr"] / 2.0, 0, 0
] # Input context that the input audio has to be padded with while reading audio files
sup_batch_gen = batchgen.BatchGen_Paired(model_config, sup_dataset,
sep_input_shape, sep_output_shape,
padding_durations[0])
# Creating unsupervised batch generator if needed
if unsup_dataset is not None:
unsup_batch_gens = list()
for i in range(3):
shape = (sep_input_shape if i == 0 else sep_output_shape)
unsup_batch_gens.append(
batchgen.BatchGen_Single(model_config, unsup_dataset[i], shape,
padding_durations[i]))
print("Starting worker")
sup_batch_gen.start_workers()
print("Started worker!")
if unsup_dataset is not None:
for gen in unsup_batch_gens:
print("Starting worker")
gen.start_workers()
print("Started worker!")
# Placeholders and input normalisation
mix_context, acc, drums = Input.get_multitrack_placeholders(
sep_output_shape, sep_input_shape, "sup")
mix = Input.crop(mix_context, sep_output_shape)
mix_norm, mix_context_norm, acc_norm, drums_norm = Input.norm(
mix), Input.norm(mix_context), Input.norm(acc), Input.norm(drums)
if unsup_dataset is not None:
mix_context_u, acc_u, drums_u = Input.get_multitrack_placeholders(
sep_output_shape, sep_input_shape, "unsup")
mix_u = Input.crop(mix_context_u, sep_output_shape)
mix_norm_u, mix_context_norm_u, acc_norm_u, drums_norm_u = Input.norm(
mix_u), Input.norm(mix_context_u), Input.norm(acc_u), Input.norm(
drums_u)
print("Training...")
# BUILD MODELS
# Separator
separator_acc_norm, separator_drums_norm = separator_func(mix_context_norm,
reuse=False)
separator_acc, separator_drums = Input.denorm(
separator_acc_norm), Input.denorm(separator_drums_norm)
if unsup_dataset is not None:
separator_acc_norm_u, separator_drums_norm_u = separator_func(
mix_context_norm_u, reuse=True)
separator_acc_u, separator_drums_u = Input.denorm(
separator_acc_norm_u), Input.denorm(separator_drums_norm_u)
mask_loss_u = tf.reduce_mean(
tf.square(mix_u - separator_acc_u - separator_drums_u))
mask_loss = tf.reduce_mean(tf.square(mix - separator_acc -
separator_drums))
# SUMMARIES FOR INPUT AND SEPARATOR
tf.summary.scalar("mask_loss", mask_loss, collections=["sup", "unsup"])
if unsup_dataset is not None:
tf.summary.scalar("mask_loss_u", mask_loss_u, collections=["unsup"])
tf.summary.scalar("acc_norm_mean_u",
tf.reduce_mean(acc_norm_u),
collections=["acc_disc"])
tf.summary.scalar("drums_norm_mean_u",
tf.reduce_mean(drums_norm_u),
collections=["drums_disc"])
tf.summary.scalar("acc_sep_norm_mean_u",
tf.reduce_mean(separator_acc_norm_u),
collections=["acc_disc"])
tf.summary.scalar("drums_sep_norm_mean_u",
tf.reduce_mean(separator_drums_norm_u),
collections=["drums_disc"])
tf.summary.scalar("acc_norm_mean",
tf.reduce_mean(acc_norm),
collections=['sup'])
tf.summary.scalar("drums_norm_mean",
tf.reduce_mean(drums_norm),
collections=['sup'])
tf.summary.scalar("acc_sep_norm_mean",
tf.reduce_mean(separator_acc_norm),
collections=['sup'])
tf.summary.scalar("drums_sep_norm_mean",
tf.reduce_mean(separator_drums_norm),
collections=['sup'])
tf.summary.image("sep_acc_norm",
separator_acc_norm,
collections=["sup", "unsup"])
tf.summary.image("sep_drums_norm",
separator_drums_norm,
collections=["sup", "unsup"])
# BUILD DISCRIMINATORS, if unsupervised training
unsup_separator_loss = 0
if unsup_dataset is not None:
disc_func = Models.WGAN_Critic.dcgan
# Define real and fake inputs for both discriminators - if separator output and dsicriminator input shapes do not fit perfectly, we will do a centre crop and only discriminate that part
acc_real_input = Input.crop(acc_norm_u, disc_input_shape)
acc_fake_input = Input.crop(separator_acc_norm_u, disc_input_shape)
drums_real_input = Input.crop(drums_norm_u, disc_input_shape)
drums_fake_input = Input.crop(separator_drums_norm_u, disc_input_shape)
#WGAN
acc_disc_loss, acc_disc_real, acc_disc_fake, acc_grad_pen, acc_wasserstein_dist = \
Models.WGAN_Critic.create_critic(model_config, real_input=acc_real_input, fake_input=acc_fake_input, scope="acc_disc", network_func=disc_func)
drums_disc_loss, drums_disc_real, drums_disc_fake, drums_grad_pen, drums_wasserstein_dist = \
Models.WGAN_Critic.create_critic(model_config, real_input=drums_real_input, fake_input=drums_fake_input, scope="drums_disc", network_func=disc_func)
L_u = -tf.reduce_mean(drums_disc_fake) - tf.reduce_mean(
acc_disc_fake) # WGAN based loss for separator (L_u in paper)
unsup_separator_loss = model_config["alpha"] * L_u + model_config[
"beta"] * mask_loss_u # Unsupervised loss for separator: WGAN-based loss L_u and additive penalty term (mask loss), weighted by alpha and beta (hyperparameters)
# Supervised objective: MSE in log-normalized magnitude space
sup_separator_loss = tf.reduce_mean(tf.square(separator_drums_norm - drums_norm)) + \
tf.reduce_mean(tf.square(separator_acc_norm - acc_norm))
separator_loss = sup_separator_loss + unsup_separator_loss # Total separator loss: Supervised + unsupervised loss
# TRAINING CONTROL VARIABLES
global_step = tf.get_variable('global_step', [],
initializer=tf.constant_initializer(0),
trainable=False,
dtype=tf.int64)
increment_global_step = tf.assign(global_step, global_step + 1)
disc_lr = tf.get_variable('disc_lr', [],
initializer=tf.constant_initializer(
model_config["init_disc_lr"],
dtype=tf.float32),
trainable=False)
unsup_sep_lr = tf.get_variable('unsup_sep_lr', [],
initializer=tf.constant_initializer(
model_config["init_unsup_sep_lr"],
dtype=tf.float32),
trainable=False)
sup_sep_lr = tf.get_variable('sup_sep_lr', [],
initializer=tf.constant_initializer(
model_config["init_sup_sep_lr"],
dtype=tf.float32),
trainable=False)
# Set up optimizers
separator_vars = Utils.getTrainableVariables("separator")
print("Sep_Vars: " + str(Utils.getNumParams(separator_vars)))
acc_disc_vars, drums_disc_vars = Utils.getTrainableVariables(
"acc_disc"), Utils.getTrainableVariables("drums_disc")
print("Drums_Disc_Vars: " + str(Utils.getNumParams(drums_disc_vars)))
print("Acc_Disc_Vars: " + str(Utils.getNumParams(acc_disc_vars)))
if unsup_dataset is not None:
with tf.variable_scope("drums_disc_solver"):
drums_disc_solver = tf.train.AdamOptimizer(
learning_rate=disc_lr).minimize(
drums_disc_loss,
var_list=drums_disc_vars,
colocate_gradients_with_ops=True)
with tf.variable_scope("acc_disc_solver"):
acc_disc_solver = tf.train.AdamOptimizer(
learning_rate=disc_lr).minimize(
acc_disc_loss,
var_list=acc_disc_vars,
colocate_gradients_with_ops=True)
with tf.variable_scope("unsup_separator_solver"):
unsup_separator_solver = tf.train.AdamOptimizer(
learning_rate=unsup_sep_lr).minimize(
separator_loss,
var_list=separator_vars,
colocate_gradients_with_ops=True)
else:
with tf.variable_scope("separator_solver"):
sup_separator_solver = (tf.train.AdamOptimizer(
learning_rate=sup_sep_lr).minimize(
sup_separator_loss,
var_list=separator_vars,
colocate_gradients_with_ops=True))
# SUMMARIES FOR DISCRIMINATORS AND LOSSES
acc_disc_summaries = tf.summary.merge_all(key="acc_disc")
drums_disc_summaries = tf.summary.merge_all(key="drums_disc")
tf.summary.scalar("sup_sep_loss",
sup_separator_loss,
collections=['sup', "unsup"])
tf.summary.scalar("unsup_sep_loss",
unsup_separator_loss,
collections=['unsup'])
tf.summary.scalar("sep_loss", separator_loss, collections=["sup", "unsup"])
sup_summaries = tf.summary.merge_all(key='sup')
unsup_summaries = tf.summary.merge_all(key='unsup')
# Start session
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
sess = tf.Session(config=config)
sess.run(tf.global_variables_initializer())
writer = tf.summary.FileWriter(model_config["log_dir"] + os.path.sep +
model_folder,
graph=sess.graph)
# CHECKPOINTING
# Load pretrained model to continue training, if we are supposed to
if load_model is not None:
restorer = tf.train.Saver(tf.global_variables(),
write_version=tf.train.SaverDef.V2)
print("Num of variables: " + str(len(tf.global_variables())))
restorer.restore(sess, load_model)
print('Pre-trained model restored from file ' + load_model)
saver = tf.train.Saver(tf.global_variables(),
write_version=tf.train.SaverDef.V2)
# Start training loop
run = True
_global_step = sess.run(global_step)
_init_step = _global_step
it = 0
while run:
if unsup_dataset is not None:
# TRAIN DISCRIMINATORS
for disc_it in range(model_config["num_disc"]):
batches = list()
for gen in unsup_batch_gens:
batches.append(gen.get_batch())
_, _acc_disc_summaries = sess.run(
[acc_disc_solver, acc_disc_summaries],
feed_dict={
mix_context_u: batches[0],
acc_u: batches[1]
})
_, _drums_disc_summaries = sess.run(
[drums_disc_solver, drums_disc_summaries],
feed_dict={
mix_context_u: batches[0],
drums_u: batches[2]
})
writer.add_summary(_acc_disc_summaries, global_step=it)
writer.add_summary(_drums_disc_summaries, global_step=it)
it += 1
# TRAIN SEPARATOR
sup_batch = sup_batch_gen.get_batch()
if unsup_dataset is not None:
# SUP + UNSUPERVISED TRAINING
unsup_batches = list()
for gen in unsup_batch_gens:
unsup_batches.append(gen.get_batch())
_, _unsup_summaries, _sup_summaries = sess.run(
[unsup_separator_solver, unsup_summaries, sup_summaries],
feed_dict={
mix_context: sup_batch[0],
acc: sup_batch[1],
drums: sup_batch[2],
mix_context_u: unsup_batches[0],
acc_u: unsup_batches[1],
drums_u: unsup_batches[2]
})
writer.add_summary(_unsup_summaries, global_step=_global_step)
else:
# PURELY SUPERVISED TRAINING
_, _sup_summaries = sess.run([sup_separator_solver, sup_summaries],
feed_dict={
mix_context: sup_batch[0],
acc: sup_batch[1],
drums: sup_batch[2]
})
writer.add_summary(_sup_summaries, global_step=_global_step)
# Increment step counter, check if maximum iterations per epoch is achieved and stop in that case
_global_step = sess.run(increment_global_step)
if _global_step - _init_step > model_config["epoch_it"]:
run = False
print("Finished training phase, stopping batch generators")
sup_batch_gen.stop_workers()
if unsup_dataset is not None:
for gen in unsup_batch_gens:
gen.stop_workers()
# Epoch finished - Save model
print("Finished epoch!")
save_path = saver.save(sess,
model_config["model_base_dir"] + os.path.sep +
model_folder + os.path.sep + model_folder,
global_step=int(_global_step))
# Close session, clear computational graph
writer.flush()
writer.close()
sess.close()
tf.reset_default_graph()
return save_path