def train(logdir,args):
# TODO：parse ckpt,arguments,hparams
checkpoint_path = os.path.join(logdir,'model.ckpt')
input_path = args.data_dir
log('Checkpoint path: %s' % checkpoint_path)
log('Loading training data from : %s ' % input_path)
log('Using model : %s' %args.model)
# TODO：set up datafeeder
with tf.variable_scope('datafeeder') as scope:
hp.data_length = None
hp.initial_learning_rate = 0.0001
hp.batch_size = 256
hp.prime = True
hp.stcmd = True
feeder = DataFeeder(args=hp)
log('num_sentences:'+str(len(feeder.wav_lst))) # 283600
hp.input_vocab_size = len(feeder.pny_vocab)
hp.final_output_dim = len(feeder.pny_vocab)
hp.steps_per_epoch = len(feeder.wav_lst)//hp.batch_size
log('steps_per_epoch:' + str(hp.steps_per_epoch)) # 17725
log('pinyin_vocab_size:'+str(hp.input_vocab_size)) # 1292
hp.label_vocab_size = len(feeder.han_vocab)
log('label_vocab_size :' + str(hp.label_vocab_size)) # 6291
# TODO：set up model
global_step = tf.Variable(initial_value=0,name='global_step',trainable=False)
valid_step = 0
# valid_global_step = tf.Variable(initial_value=0,name='valid_global_step',trainable=False)
with tf.variable_scope('model') as scope:
model = create_model(args.model,hp)
model.build_graph()
model.add_loss()
model.add_optimizer(global_step=global_step,loss=model.mean_loss)
# TODO: summary
stats = add_stats(model=model)
valid_stats = add_dev_stats(model)
# TODO：Set up saver and Bookkeeping
time_window = ValueWindow(100)
loss_window = ValueWindow(100)
acc_window = ValueWindow(100)
valid_time_window = ValueWindow(100)
valid_loss_window = ValueWindow(100)
valid_acc_window = ValueWindow(100)
saver = tf.train.Saver(max_to_keep=20)
first_serving = True
# TODO: train
with tf.Session() as sess:
log(hparams_debug_string(hp))
try:
# TODO: Set writer and initializer
summary_writer = tf.summary.FileWriter(logdir + '/train', sess.graph)
summary_writer_dev = tf.summary.FileWriter(logdir + '/dev')
sess.run(tf.global_variables_initializer())
# TODO: Restore
if args.restore_step:
# Restore from a checkpoint if the user requested it.
restore_path = '%s-%d' % (checkpoint_path, args.restore_step)
saver.restore(sess, restore_path)
log('Resuming from checkpoint: %s ' % restore_path)
else:
log('Starting new training run ')
step = 0
# TODO: epochs steps batch
for i in range(args.epochs):
batch_data = feeder.get_lm_batch()
log('Traning epoch '+ str(i)+':')
for j in range(hp.steps_per_epoch):
input_batch, label_batch = next(batch_data)
feed_dict = {
model.x:input_batch,
model.y:label_batch,
}
# TODO: Run one step ~~~
start_time = time.time()
total_step,batch_loss,batch_acc,opt = sess.run([global_step, model.mean_loss,model.acc,model.optimize],feed_dict=feed_dict)
time_window.append(time.time() - start_time)
step = total_step
# TODO: Append loss
loss_window.append(batch_loss)
acc_window.append(batch_acc)
message = 'Step %-7d [%.03f sec/step, loss=%.05f, avg_loss=%.05f,acc=%.05f, avg_acc=%.05f, lr=%.07f]' % (
step, time_window.average, batch_loss, loss_window.average,batch_acc,acc_window.average,K.get_value(model.learning_rate))
log(message)
# TODO: Check loss
if math.isnan(batch_loss):
log('Loss exploded to %.05f at step %d!' % (batch_loss, step))
raise Exception('Loss Exploded')
# TODO: Check sumamry
if step % args.summary_interval == 0:
log('Writing summary at step: %d' % step)
summary_writer.add_summary(sess.run(stats,feed_dict=feed_dict), step)
# TODO: Check checkpoint
if step % args.checkpoint_interval == 0:
log('Saving checkpoint to: %s-%d' % (checkpoint_path, step))
saver.save(sess, checkpoint_path, global_step=step)
log('test acc...')
label,final_pred_label = sess.run([
model.y, model.preds],feed_dict=feed_dict)
log('label.shape :'+str(label.shape)) # (batch_size , label_length)
log('final_pred_label.shape:'+str(np.asarray(final_pred_label).shape)) # (1, batch_size, decode_length<=label_length)
log('label : '+str(label[0]))
log('final_pred_label: '+str( np.asarray(final_pred_label)[0]))
# TODO: serving
if args.serving :#and total_step // hp.steps_per_epoch > 5:
np.save('logdir/lm_pinyin_dict.npy',feeder.pny_vocab)
np.save('logdir/lm_hanzi_dict.npy',feeder.han_vocab)
print(total_step, 'hhhhhhhh')
# TODO: Set up serving builder and signature map
serve_dir = args.serving_dir + '0001'
if os.path.exists(serve_dir):
os.removedirs(serve_dir)
builder = tf.saved_model.builder.SavedModelBuilder(export_dir=serve_dir)
input = tf.saved_model.utils.build_tensor_info(model.x)
output_labels = tf.saved_model.utils.build_tensor_info(model.preds)
prediction_signature = (
tf.saved_model.signature_def_utils.build_signature_def(
inputs={'pinyin': input},
outputs={'hanzi': output_labels},
method_name=tf.saved_model.signature_constants.PREDICT_METHOD_NAME)
)
if first_serving:
first_serving = False
builder.add_meta_graph_and_variables(
sess=sess, tags=[tf.saved_model.tag_constants.SERVING],
signature_def_map={
'predict_Pinyin2Hanzi':
prediction_signature,
},
main_op=tf.tables_initializer(),
strip_default_attrs=True
)
builder.save()
log('Done store serving-model')
raise Exception('Done store serving-model')
# TODO: Validation
# if total_step % hp.steps_per_epoch == 0 and i >= 10:
if total_step % hp.steps_per_epoch == 0:
log('validation...')
valid_start = time.time()
# TODO: validation
valid_hp = copy.deepcopy(hp)
print('feature_type: ',hp.feature_type)
valid_hp.data_type = 'dev'
valid_hp.thchs30 = True
valid_hp.aishell = True
valid_hp.prime = True
valid_hp.stcmd = True
valid_hp.shuffle = True
valid_hp.data_length = None
valid_feeder = DataFeeder(args=valid_hp)
valid_feeder.pny_vocab = feeder.pny_vocab
valid_feeder.han_vocab = feeder.han_vocab
# valid_feeder.am_vocab = feeder.am_vocab
valid_batch_data = valid_feeder.get_lm_batch()
log('valid_num_sentences:' + str(len(valid_feeder.wav_lst))) # 15219
valid_hp.input_vocab_size = len(valid_feeder.pny_vocab)
valid_hp.final_output_dim = len(valid_feeder.pny_vocab)
valid_hp.steps_per_epoch = len(valid_feeder.wav_lst) // valid_hp.batch_size
log('valid_steps_per_epoch:' + str(valid_hp.steps_per_epoch)) # 951
log('valid_pinyin_vocab_size:' + str(valid_hp.input_vocab_size)) # 1124
valid_hp.label_vocab_size = len(valid_feeder.han_vocab)
log('valid_label_vocab_size :' + str(valid_hp.label_vocab_size)) # 3327
# dev 只跑一个epoch就行
with tf.variable_scope('validation') as scope:
for k in range(len(valid_feeder.wav_lst) // valid_hp.batch_size):
valid_input_batch,valid_label_batch = next(valid_batch_data)
valid_feed_dict = {
model.x: valid_input_batch,
model.y: valid_label_batch,
}
# TODO: Run one step
valid_start_time = time.time()
valid_batch_loss,valid_batch_acc = sess.run([model.mean_loss,model.acc], feed_dict=valid_feed_dict)
valid_time_window.append(time.time() - valid_start_time)
valid_loss_window.append(valid_batch_loss)
valid_acc_window.append(valid_batch_acc)
# print('loss',loss,'batch_loss',batch_loss)
message = 'Valid-Step %-7d [%.03f sec/step, valid_loss=%.05f, avg_loss=%.05f, valid_acc=%.05f, avg_acc=%.05f]' % (
valid_step, valid_time_window.average, valid_batch_loss, valid_loss_window.average,valid_batch_acc,valid_acc_window.average)
log(message)
summary_writer_dev.add_summary(sess.run(valid_stats,feed_dict=valid_feed_dict), valid_step)
valid_step += 1
log('Done Validation！Total Time Cost(sec):' + str(time.time()-valid_start))
except Exception as e:
log('Exiting due to exception: %s' % e)
traceback.print_exc()
def test():
parser = argparse.ArgumentParser()
# TODO: add arguments
parser.add_argument('--log_dir',
default=os.path.expanduser('~/my_asr2/logdir/logging'))
parser.add_argument(
'--serving_dir',
default=os.path.expanduser('~/my_asr2/logdir/serving_am/'))
parser.add_argument('--data_dir',
default=os.path.expanduser('~/corpus_zn'))
parser.add_argument('--model', default='ASR_wavnet')
# parser.add_argument('--epochs', type=int, help='Max epochs to run.', default=100)
parser.add_argument('--restore_step',
type=int,
help='Global step to restore from checkpoint.',
default=2100)
parser.add_argument('--serving', type=bool, help='', default=False)
# parser.add_argument('--validation_interval', type=int, help='一个epoch验证5次，每次200步共3200条数据', default=7090) # 35450//5
parser.add_argument('--summary_interval',
type=int,
default=1,
help='Steps between running summary ops.')
# parser.add_argument('--checkpoint_interval', type=int, default=100, help='Steps between writing checkpoints.')
parser.add_argument(
'--hparams',
default='',
help=
'Hyperparameter overrides as a comma-separated list of name=value pairs'
)
args = parser.parse_args()
run_name = args.model
logdir = os.path.join(args.log_dir, 'logs-%s' % run_name)
init(os.path.join(logdir, 'test.log'), run_name)
hp.parse(args.hparams)
# TODO：parse ckpt,arguments,hparams
checkpoint_path = os.path.join(logdir, 'model.ckpt')
input_path = args.data_dir
log('Checkpoint path: %s' % checkpoint_path)
log('Loading training data from : %s ' % input_path)
log('Using model : %s' % args.model)
# TODO：set up datafeeder
with tf.variable_scope('datafeeder') as scope:
hp.data_type = 'test'
hp.feature_type = 'mfcc'
hp.data_length = None
hp.initial_learning_rate = 0.0005
hp.batch_size = 1
hp.aishell = False
hp.prime = False
hp.stcmd = False
hp.AM = True
hp.LM = False
hp.shuffle = False
hp.is_training = False # TODO: 在infer的时候一定要设置为False否则bn会扰乱所有的值！
feeder = DataFeeder_wavnet(args=hp)
log('num_wavs:' + str(len(feeder.wav_lst)))
feeder.am_vocab = np.load('logdir/am_pinyin_dict.npy').tolist()
hp.input_vocab_size = len(feeder.am_vocab)
hp.final_output_dim = len(feeder.am_vocab)
hp.steps_per_epoch = len(feeder.wav_lst) // hp.batch_size
log('steps_per_epoch:' + str(hp.steps_per_epoch))
log('pinyin_vocab_size:' + str(hp.input_vocab_size))
# TODO: set up model
with tf.variable_scope('model') as scope:
model = create_model(args.model, hp)
model.build_graph()
model.add_loss()
model.add_decoder()
# model.add_optimizer(global_step=global_step)
# TODO: summary
stats = add_stats(model)
# TODO：Set up saver and Bookkeeping
time_window = ValueWindow(100)
loss_window = ValueWindow(100)
wer_window = ValueWindow(100)
saver = tf.train.Saver(max_to_keep=20)
# TODO: test
with tf.Session(graph=tf.get_default_graph()) as sess:
log(hparams_debug_string(hp))
try:
# TODO: Set writer and initializer
summary_writer = tf.summary.FileWriter(logdir + '/test',
sess.graph)
sess.run(tf.global_variables_initializer())
# TODO: Restore
if args.restore_step:
# Restore from a checkpoint if the user requested it.
restore_path = '%s-%d' % (checkpoint_path, args.restore_step)
saver.restore(sess, restore_path)
log('Resuming from checkpoint: %s ' % restore_path)
else:
log('Starting new training run ')
# TODO: epochs steps batch
step = 0
batch_data = feeder.get_am_batch()
for j in range(hp.steps_per_epoch):
input_batch = next(batch_data)
feed_dict = {
model.inputs: input_batch['the_inputs'],
model.labels: input_batch['the_labels'],
model.input_lengths: input_batch['input_length'],
model.label_lengths: input_batch['label_length']
}
# TODO: Run one step
start_time = time.time()
array_loss, batch_loss, wer, label, final_pred_label = sess.run(
[
model.ctc_loss, model.batch_loss, model.WER,
model.labels, model.decoded1
],
feed_dict=feed_dict)
time_window.append(time.time() - start_time)
step = step + 1
# TODO: Append loss
loss_window.append(batch_loss)
wer_window.append(wer)
message = 'Step %-7d [%.03f sec/step, loss=%.05f, avg_loss=%.05f, wer=%.05f, avg_wer=%.05f]' % (
step, time_window.average, batch_loss, loss_window.average,
wer, wer_window.average)
log(message)
# TODO: show pred and write summary
log('label.shape :' +
str(label.shape)) # (batch_size , label_length)
log('final_pred_label.shape:' +
str(np.asarray(final_pred_label).shape))
log('label : ' + str(label[0]))
log('final_pred_label: ' +
str(np.asarray(final_pred_label)[0][0]))
log('Writing summary at step: %d' % step)
summary_writer.add_summary(
sess.run(stats, feed_dict=feed_dict), step)
# TODO: Check loss
if math.isnan(batch_loss):
log('Loss exploded to %.05f at step %d!' %
(batch_loss, step))
raise Exception('Loss Exploded')
log('serving step: ' + str(step))
# TODO: Set up serving builder and signature map
serve_dir = args.serving_dir + '0001'
if os.path.exists(serve_dir):
shutil.rmtree(serve_dir)
log('delete exists dirs:' + serve_dir)
builder = tf.saved_model.builder.SavedModelBuilder(
export_dir=serve_dir)
input_spec = tf.saved_model.utils.build_tensor_info(model.inputs)
input_len = tf.saved_model.utils.build_tensor_info(
model.input_lengths)
output_labels = tf.saved_model.utils.build_tensor_info(
model.decoded1)
output_logits = tf.saved_model.utils.build_tensor_info(
model.pred_softmax)
prediction_signature = (
tf.saved_model.signature_def_utils.build_signature_def(
inputs={
'mfcc': input_spec,
'len': input_len
},
outputs={
'label': output_labels,
'logits': output_logits
},
method_name=tf.saved_model.signature_constants.
PREDICT_METHOD_NAME))
builder.add_meta_graph_and_variables(
sess=sess,
tags=[tf.saved_model.tag_constants.SERVING],
signature_def_map={
'predict_AudioSpec2Pinyin': prediction_signature,
},
main_op=tf.tables_initializer(),
strip_default_attrs=False)
builder.save()
log('Done store serving-model')
except Exception as e:
log('Exiting due to exception: %s' % e)
traceback.print_exc()
def train(self, dataset_train, dataset_test, dataset_train_lengths,
dataset_test_lengths):
# Setup data loaders
with tf.variable_scope('train_iterator'):
self.iterator_data_train = dataset_train.make_initializable_iterator(
)
self.iterator_length_train = dataset_train_lengths.make_initializable_iterator(
)
next_train_data = self.iterator_data_train.get_next()
next_train_length = self.iterator_length_train.get_next()
with tf.variable_scope('test_iterator'):
self.iterator_data_test = dataset_test.make_initializable_iterator(
)
self.iterator_length_test = dataset_test_lengths.make_initializable_iterator(
)
next_test_data = self.iterator_data_test.get_next()
next_test_length = self.iterator_length_test.get_next()
# Set up model
self.initializers = [
i.initializer for i in [
self.iterator_data_test, self.iterator_data_train,
self.iterator_length_test, self.iterator_length_train
]
]
self.model, self.stats = self.model_train_mode(next_train_data,
next_train_length,
self.global_step)
self.eval_model = self.model_eval_mode(next_test_data,
next_test_length)
if self.all_params.use_ema:
self.saver = create_shadow_saver(self.model, self.global_step)
else:
self.saver = tf.train.Saver(max_to_keep=100)
# Book keeping
step = 0
time_window = ValueWindow(100)
loss_window = ValueWindow(100)
print('Training set to a maximum of {} steps'.format(self.all_params.train_steps)) \
# Memory allocation on the GPU as needed
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
# Train
print('Starting training')
with tf.Session(config=config) as sess:
summary_writer = tf.summary.FileWriter(self.tensorboard_dir,
sess.graph)
# Allow the full trace to be stored at run time.
options = tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE)
# Create a fresh metadata object:
run_metadata = tf.RunMetadata()
sess.run(tf.global_variables_initializer())
for init in self.initializers:
sess.run(init)
# saved model restoring
if self.all_params.restore:
# Restore saved model if the user requested it, default = True
try:
checkpoint_state = tf.train.get_checkpoint_state(
self.save_dir)
if (checkpoint_state
and checkpoint_state.model_checkpoint_path):
print('Loading checkpoint {}'.format(
checkpoint_state.model_checkpoint_path))
self.saver.restore(
sess, checkpoint_state.model_checkpoint_path)
else:
print('No model to load at {}'.format(self.save_dir))
self.saver.save(sess,
self.checkpoint_path,
global_step=self.global_step)
except tf.errors.OutOfRangeError as e:
print('Cannot restore checkpoint: {}'.format(e))
else:
print('Starting new training!')
self.saver.save(sess,
self.checkpoint_path,
global_step=self.global_step)
# Training loop
while not self.coord.should_stop(
) and step < self.all_params.train_steps:
start_time = time.time()
step, loss, opt = sess.run(
[self.global_step, self.model.loss, self.model.optimize],
options=options,
run_metadata=run_metadata)
time_window.append(time.time() - start_time)
loss_window.append(loss)
message = 'Step {:7d} [{:.3f} sec/step, loss={:.5f}, avg_loss={:.5f}]'.format(
step, time_window.average, loss, loss_window.average)
print(message)
if np.isnan(loss) or loss > 100.:
print('Loss exploded to {:.5f} at step {}'.format(
loss, step))
raise Exception('Loss exploded')
if step % self.all_params.summary_interval == 0:
print('\nWriting summary at step {}'.format(step))
summary_writer.add_summary(sess.run(self.stats), step)
if step % self.all_params.checkpoint_interval == 0 or step == self.all_params.train_steps:
print('Saving model!')
# Save model and current global step
self.saver.save(sess,
self.checkpoint_path,
global_step=self.global_step)
if step % self.all_params.eval_interval == 0:
# Run eval and save eval stats
print('\nRunning evaluation at step {}'.format(step))
all_logits = []
all_outputs = []
all_targets = []
all_lengths = []
val_losses = []
for i in tqdm(range(4)):
val_loss, logits, outputs, targets, lengths = sess.run(
[
self.eval_model.loss, self.eval_model.logits,
self.eval_model.outputs,
self.eval_model.targets,
self.eval_model.input_lengths
])
all_logits.append(logits)
all_outputs.append(outputs)
all_targets.append(targets)
all_lengths.append(lengths)
val_losses.append(val_loss)
logits = [l for logits in all_logits for l in logits]
outputs = [o for output in all_outputs for o in output]
targets = [t for target in all_targets for t in target]
lengths = [l for length in all_lengths for l in length]
logits = np.array([
e for o, l in zip(logits, lengths) for e in o[:l]
]).reshape(-1)
outputs = np.array([
e for o, l in zip(outputs, lengths) for e in o[:l]
]).reshape(-1)
targets = np.array([
e for t, l in zip(targets, lengths) for e in t[:l]
]).reshape(-1)
val_loss = sum(val_losses) / len(val_losses)
assert len(targets) == len(outputs)
capture_rate, fig_path = evaluate_and_plot(
outputs,
targets,
index=np.arange(0, len(targets)),
model_name=self.all_params.name
or self.all_params.self.model,
weight=self.all_params.capture_weight,
out_dir=self.eval_dir,
use_tf=False,
sess=sess,
step=step)
add_eval_stats(summary_writer, step, val_loss,
capture_rate)
tensorboard_file = os.path.join(
self.tensorboard_dir,
os.listdir(self.tensorboard_dir)[0])
###### Replace these lines ###########################
print(f'train_loss: {float(loss)}')
print(f'validation_loss{float(val_loss)}')
print(f'validation_capture_rate{float(capture_rate)}')
######################################################
print('Training complete after {} global steps!'.format(
self.all_params.train_steps))
def train(train_loader, model, device, mels_criterion, stop_criterion,
optimizer, scheduler, writer, train_dir):
batch_time = ValueWindow()
data_time = ValueWindow()
losses = ValueWindow()
# switch to train mode
model.train()
end = time.time()
global global_epoch
global global_step
for i, (txts, mels, stop_tokens, txt_lengths,
mels_lengths) in enumerate(train_loader):
scheduler.adjust_learning_rate(optimizer, global_step)
# measure data loading time
data_time.update(time.time() - end)
if device > -1:
txts = txts.cuda(device)
mels = mels.cuda(device)
stop_tokens = stop_tokens.cuda(device)
txt_lengths = txt_lengths.cuda(device)
mels_lengths = mels_lengths.cuda(device)
# compute output
frames, decoder_frames, stop_tokens_predict, alignment = model(
txts, txt_lengths, mels)
decoder_frames_loss = mels_criterion(decoder_frames,
mels,
lengths=mels_lengths)
frames_loss = mels_criterion(frames, mels, lengths=mels_lengths)
stop_token_loss = stop_criterion(stop_tokens_predict,
stop_tokens,
lengths=mels_lengths)
loss = decoder_frames_loss + frames_loss + stop_token_loss
#print(frames_loss, decoder_frames_loss)
losses.update(loss.item())
# compute gradient and do SGD step
optimizer.zero_grad()
loss.backward()
if hparams.clip_thresh > 0:
grad_norm = torch.nn.utils.clip_grad_norm_(
model.get_trainable_parameters(), hparams.clip_thresh)
optimizer.step()
# measure elapsed time
batch_time.update(time.time() - end)
end = time.time()
if i % hparams.print_freq == 0:
log('Epoch: [{0}][{1}/{2}]\t'
'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t'
'Data {data_time.val:.3f} ({data_time.avg:.3f})\t'
'Loss {loss.val:.4f} ({loss.avg:.4f})'.format(
global_epoch,
i,
len(train_loader),
batch_time=batch_time,
data_time=data_time,
loss=losses))
# Logs
writer.add_scalar("loss", float(loss.item()), global_step)
writer.add_scalar(
"avg_loss in {} window".format(losses.get_dinwow_size),
float(losses.avg), global_step)
writer.add_scalar("stop_token_loss", float(stop_token_loss.item()),
global_step)
writer.add_scalar("decoder_frames_loss",
float(decoder_frames_loss.item()), global_step)
writer.add_scalar("output_frames_loss", float(frames_loss.item()),
global_step)
if hparams.clip_thresh > 0:
writer.add_scalar("gradient norm", grad_norm, global_step)
writer.add_scalar("learning rate", optimizer.param_groups[0]['lr'],
global_step)
global_step += 1
dst_alignment_path = join(train_dir,
"{}_alignment.png".format(global_step))
alignment = alignment.cpu().detach().numpy()
plot_alignment(alignment[0, :txt_lengths[0], :mels_lengths[0]],
dst_alignment_path,
info="{}, {}".format(hparams.builder, global_step))
def train(output_directory, log_directory, checkpoint_path, warm_start, n_gpus,
rank, group_name, hparams, refine_from):
"""Training and validation logging results to tensorboard and stdout
Params
------
output_directory (string): directory to save checkpoints
log_directory (string) directory to save tensorboard logs
checkpoint_path(string): checkpoint path
n_gpus (int): number of gpus
rank (int): rank of current gpu
hparams (object): comma separated list of "name=value" pairs.
"""
if hparams.distributed_run:
init_distributed(hparams, n_gpus, rank, group_name)
torch.manual_seed(hparams.seed)
torch.cuda.manual_seed(hparams.seed)
model = load_model(hparams)
learning_rate = hparams.initial_learning_rate
optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate,
weight_decay=hparams.weight_decay)
if hparams.use_GAN and hparams.GAN_type=='lsgan':
from discriminator import Lsgan_Loss, Calculate_Discrim
model_D = Calculate_Discrim(hparams).cuda() if torch.cuda.is_available() else Calculate_Discrim(hparams)
lsgan_loss = Lsgan_Loss(hparams)
optimizer_D = torch.optim.Adam(model_D.parameters(), lr=learning_rate,
weight_decay=hparams.weight_decay)
if hparams.use_GAN and hparams.GAN_type=='wgan-gp':
from discriminator import Wgan_GP, GP
model_D = Wgan_GP(hparams).cuda() if torch.cuda.is_available() else Wgan_GP(hparams)
calc_gradient_penalty = GP(hparams).cuda() if torch.cuda.is_available() else GP(hparams)
optimizer_D = torch.optim.Adam(model_D.parameters(), lr=learning_rate,
weight_decay=hparams.weight_decay)
if hparams.is_partial_refine:
refine_list=['speaker_embedding.weight',
'spkemb_projection.weight',
'spkemb_projection.bias',
'projection.weight',
'projection.bias',
'encoder.encoders.0.norm1.w.weight',
'encoder.encoders.0.norm1.w.bias',
'encoder.encoders.0.norm1.b.weight',
'encoder.encoders.0.norm1.b.bias',
'encoder.encoders.0.norm2.w.weight',
'encoder.encoders.0.norm2.w.bias',
'encoder.encoders.0.norm2.b.weight',
'encoder.encoders.0.norm2.b.bias',
'encoder.encoders.1.norm1.w.weight',
'encoder.encoders.1.norm1.w.bias',
'encoder.encoders.1.norm1.b.weight',
'encoder.encoders.1.norm1.b.bias',
'encoder.encoders.1.norm2.w.weight',
'encoder.encoders.1.norm2.w.bias',
'encoder.encoders.1.norm2.b.weight',
'encoder.encoders.1.norm2.b.bias',
'encoder.encoders.2.norm1.w.weight',
'encoder.encoders.2.norm1.w.bias',
'encoder.encoders.2.norm1.b.weight',
'encoder.encoders.2.norm1.b.bias',
'encoder.encoders.2.norm2.w.weight',
'encoder.encoders.2.norm2.w.bias',
'encoder.encoders.2.norm2.b.weight',
'encoder.encoders.2.norm2.b.bias',
'encoder.encoders.3.norm1.w.weight',
'encoder.encoders.3.norm1.w.bias',
'encoder.encoders.3.norm1.b.weight',
'encoder.encoders.3.norm1.b.bias',
'encoder.encoders.3.norm2.w.weight',
'encoder.encoders.3.norm2.w.bias',
'encoder.encoders.3.norm2.b.weight',
'encoder.encoders.3.norm2.b.bias',
'encoder.encoders.4.norm1.w.weight',
'encoder.encoders.4.norm1.w.bias',
'encoder.encoders.4.norm1.b.weight',
'encoder.encoders.4.norm1.b.bias',
'encoder.encoders.4.norm2.w.weight',
'encoder.encoders.4.norm2.w.bias',
'encoder.encoders.4.norm2.b.weight',
'encoder.encoders.4.norm2.b.bias',
'encoder.encoders.5.norm1.w.weight',
'encoder.encoders.5.norm1.w.bias',
'encoder.encoders.5.norm1.b.weight',
'encoder.encoders.5.norm1.b.bias',
'encoder.encoders.5.norm2.w.weight',
'encoder.encoders.5.norm2.w.bias',
'encoder.encoders.5.norm2.b.weight',
'encoder.encoders.5.norm2.b.bias',
'encoder.after_norm.w.weight',
'encoder.after_norm.w.bias',
'encoder.after_norm.b.weight',
'encoder.after_norm.b.bias',
'duration_predictor.norm.0.w.weight',
'duration_predictor.norm.0.w.bias',
'duration_predictor.norm.0.b.weight',
'duration_predictor.norm.0.b.bias',
'duration_predictor.norm.1.w.weight',
'duration_predictor.norm.1.w.bias',
'duration_predictor.norm.1.b.weight',
'duration_predictor.norm.1.b.bias',
'decoder.encoders.0.norm1.w.weight',
'decoder.encoders.0.norm1.w.bias',
'decoder.encoders.0.norm1.b.weight',
'decoder.encoders.0.norm1.b.bias',
'decoder.encoders.0.norm2.w.weight',
'decoder.encoders.0.norm2.w.bias',
'decoder.encoders.0.norm2.b.weight',
'decoder.encoders.0.norm2.b.bias',
'decoder.encoders.1.norm1.w.weight',
'decoder.encoders.1.norm1.w.bias',
'decoder.encoders.1.norm1.b.weight',
'decoder.encoders.1.norm1.b.bias',
'decoder.encoders.1.norm2.w.weight',
'decoder.encoders.1.norm2.w.bias',
'decoder.encoders.1.norm2.b.weight',
'decoder.encoders.1.norm2.b.bias',
'decoder.encoders.2.norm1.w.weight',
'decoder.encoders.2.norm1.w.bias',
'decoder.encoders.2.norm1.b.weight',
'decoder.encoders.2.norm1.b.bias',
'decoder.encoders.2.norm2.w.weight',
'decoder.encoders.2.norm2.w.bias',
'decoder.encoders.2.norm2.b.weight',
'decoder.encoders.2.norm2.b.bias',
'decoder.encoders.3.norm1.w.weight',
'decoder.encoders.3.norm1.w.bias',
'decoder.encoders.3.norm1.b.weight',
'decoder.encoders.3.norm1.b.bias',
'decoder.encoders.3.norm2.w.weight',
'decoder.encoders.3.norm2.w.bias',
'decoder.encoders.3.norm2.b.weight',
'decoder.encoders.3.norm2.b.bias',
'decoder.encoders.4.norm1.w.weight',
'decoder.encoders.4.norm1.w.bias',
'decoder.encoders.4.norm1.b.weight',
'decoder.encoders.4.norm1.b.bias',
'decoder.encoders.4.norm2.w.weight',
'decoder.encoders.4.norm2.w.bias',
'decoder.encoders.4.norm2.b.weight',
'decoder.encoders.4.norm2.b.bias',
'decoder.encoders.5.norm1.w.weight',
'decoder.encoders.5.norm1.w.bias',
'decoder.encoders.5.norm1.b.weight',
'decoder.encoders.5.norm1.b.bias',
'decoder.encoders.5.norm2.w.weight',
'decoder.encoders.5.norm2.w.bias',
'decoder.encoders.5.norm2.b.weight',
'decoder.encoders.5.norm2.b.bias',
'decoder.after_norm.w.weight',
'decoder.after_norm.w.bias',
'decoder.after_norm.b.weight',
'decoder.after_norm.b.bias']
if hparams.is_refine_style:
style_list= ['gst.ref_enc.convs.0.weight',
'gst.ref_enc.convs.1.weight',
'gst.ref_enc.convs.1.bias',
'gst.ref_enc.convs.3.weight',
'gst.ref_enc.convs.4.weight',
'gst.ref_enc.convs.4.bias',
'gst.ref_enc.convs.6.weight',
'gst.ref_enc.convs.7.weight',
'gst.ref_enc.convs.7.bias',
'gst.ref_enc.convs.9.weight',
'gst.ref_enc.convs.10.weight',
'gst.ref_enc.convs.10.bias',
'gst.ref_enc.convs.12.weight',
'gst.ref_enc.convs.13.weight',
'gst.ref_enc.convs.13.bias',
'gst.ref_enc.convs.15.weight',
'gst.ref_enc.convs.16.weight',
'gst.ref_enc.convs.16.bias',
'gst.ref_enc.gru.weight_ih_l0,'
'gst.ref_enc.gru.weight_hh_l0',
'gst.ref_enc.gru.bias_ih_l0',
'gst.ref_enc.gru.bias_hh_l0',
'gst.stl.gst_embs',
'gst.stl.mha.linear_q.weight',
'gst.stl.mha.linear_q.bias',
'gst.stl.mha.linear_k.weight',
'gst.stl.mha.linear_k.bias',
'gst.stl.mha.linear_v.weight',
'gst.stl.mha.linear_v.bias',
'gst.stl.mha.linear_out.weight',
'gst.stl.mha.linear_out.bias',
'gst.choosestl.choose_mha.linear_q.weight',
'gst.choosestl.choose_mha.linear_q.bias',
'gst.choosestl.choose_mha.linear_k.weight',
'gst.choosestl.choose_mha.linear_k.bias',
'gst.choosestl.choose_mha.linear_v.weight',
'gst.choosestl.choose_mha.linear_v.bias',
'gst.choosestl.choose_mha.linear_out.weight',
'gst.choosestl.choose_mha.linear_out.bias',
'gst_projection.weight',
'gst_projection.bias'
]
refine_list += style_list
for name, param in model.named_parameters():
if hparams.is_partial_refine:
if name in refine_list:
param.requires_grad = True
else:
param.requires_grad = False
print(name, param.requires_grad, param.shape)
if hparams.distributed_run:
model = apply_gradient_allreduce(model)
if hparams.use_GAN:
model_D = apply_gradient_allreduce(model_D)
logger = prepare_directories_and_logger(output_directory, log_directory, rank)
train_loader, valset, collate_fn, trainset = prepare_dataloaders(hparams)
# Load checkpoint if one exists
iteration = 0
epoch_offset = 0
if not checkpoint_path:
checkpoint_path = get_checkpoint_path(output_directory) if not hparams.is_partial_refine else get_checkpoint_path(refine_from)
if checkpoint_path is not None:
if warm_start:
model = warm_start_model(
checkpoint_path, model, hparams.ignore_layers)
else:
model, optimizer, _learning_rate, iteration = load_checkpoint(
checkpoint_path, model, optimizer, hparams, style_list=style_list if hparams.is_refine_style else None)
if hparams.use_saved_learning_rate:
learning_rate = _learning_rate
iteration = (iteration + 1) if not hparams.is_partial_refine else 0# next iteration is iteration + 1
epoch_offset = max(0, int(iteration / len(train_loader))) if not hparams.is_partial_refine else 0
model.train()
if hparams.use_GAN:
model_D.train()
else:
hparams.use_GAN = True
hparams.Generator_pretrain_step = hparams.iters
is_overflow = False
epoch = epoch_offset
time_window = ValueWindow(100)
loss_window = ValueWindow(100)
# ================ MAIN TRAINNIG LOOP! ===================
while iteration <= hparams.iters:
# print("Epoch: {}".format(epoch))
if hparams.distributed_run and hparams.batch_criterion == 'utterance':
train_loader.sampler.set_epoch(epoch)
for i, batch in enumerate(train_loader):
start = time.perf_counter()
learning_rate = learning_rate_decay(iteration, hparams)
if hparams.use_GAN:
# Discriminator turn
if iteration > hparams.Generator_pretrain_step:
for param_group in optimizer_D.param_groups:
param_group['lr'] = learning_rate
optimizer.zero_grad()
optimizer_D.zero_grad()
for name, param in model.named_parameters():
param.requires_grad = False
for name, param in model_D.named_parameters():
param.requires_grad = True
loss, loss_dict, weight, pred_outs, ys, olens = model(*model._parse_batch(batch,hparams,utt_mels=trainset.utt_mels if hparams.is_refine_style else None))
if hparams.GAN_type=='lsgan':
discrim_gen_output, discrim_target_output = model_D(pred_outs + (torch.randn(pred_outs.size()).cuda() if hparams.add_noise else 0), ys + (torch.randn(pred_outs.size()).cuda() if hparams.add_noise else 0), olens)
loss_D = lsgan_loss(discrim_gen_output, discrim_target_output, train_object='D')
loss_G = lsgan_loss(discrim_gen_output, discrim_target_output, train_object='G')
loss_D.backward(retain_graph=True)
if hparams.GAN_type=='wgan-gp':
D_real = model_D(ys, olens)
D_real = -D_real.mean()
D_real.backward(retain_graph=True)
D_fake = model_D(pred_outs, olens)
D_fake = D_fake.mean()
D_fake.backward()
gradient_penalty = calc_gradient_penalty(model_D, ys.data, pred_outs.data, olens.data)
gradient_penalty.backward()
D_cost = D_real + D_fake + gradient_penalty
Wasserstein_D = -D_real - D_fake
grad_norm_D = torch.nn.utils.clip_grad_norm_(model_D.parameters(), hparams.grad_clip_thresh)
optimizer_D.step()
print('\n')
if hparams.GAN_type=='lsgan':
print("Epoch:{} step:{} loss_D: {:>9.6f}, loss_G: {:>9.6f}, Grad Norm: {:>9.6f}".format(epoch, iteration, loss_D, loss_G, grad_norm_D))
if hparams.GAN_type=='wgan-gp':
print("Epoch:{} step:{} D_cost: {:>9.6f}, Wasserstein_D: {:>9.6f}, GP: {:>9.6f}, Grad Norm: {:>9.6f}".format(epoch, iteration, D_cost, Wasserstein_D, gradient_penalty, grad_norm_D))
# Generator turn
for param_group in optimizer.param_groups:
param_group['lr'] = learning_rate
optimizer.zero_grad()
if iteration > hparams.Generator_pretrain_step:
for name, param in model.named_parameters():
if hparams.is_partial_refine:
if name in refine_list:
param.requires_grad = True
else:
param.requires_grad = True
for name, param in model_D.named_parameters():
param.requires_grad = False
optimizer_D.zero_grad()
loss, loss_dict, weight, pred_outs, ys, olens = model(*model._parse_batch(batch,hparams,utt_mels=trainset.utt_mels if hparams.is_refine_style else None))
if iteration > hparams.Generator_pretrain_step:
if hparams.GAN_type=='lsgan':
discrim_gen_output, discrim_target_output = model_D(pred_outs, ys, olens)
loss_D = lsgan_loss(discrim_gen_output, discrim_target_output, train_object='D')
loss_G = lsgan_loss(discrim_gen_output, discrim_target_output, train_object='G')
if hparams.GAN_type=='wgan-gp':
loss_G = model_D(pred_outs, olens)
loss_G = -loss_G.mean()
loss = loss + loss_G*hparams.GAN_alpha*abs(loss.item()/loss_G.item())
if hparams.distributed_run:
reduced_loss = reduce_tensor(loss.data, n_gpus).item()
if loss_dict:
for key in loss_dict:
loss_dict[key] = reduce_tensor(loss_dict[key].data, n_gpus).item()
else:
reduced_loss = loss.item()
if loss_dict:
for key in loss_dict:
loss_dict[key] = loss_dict[key].item()
loss.backward()
grad_norm = torch.nn.utils.clip_grad_norm_(model.parameters(), hparams.grad_clip_thresh)
optimizer.step()
duration = time.perf_counter() - start
time_window.append(duration)
loss_window.append(reduced_loss)
if not is_overflow and (rank == 0):
if iteration % hparams.log_per_checkpoint == 0:
if hparams.GAN_type=='lsgan':
print("Epoch:{} step:{} Train loss: {:>9.6f}, avg loss: {:>9.6f}, Grad Norm: {:>9.6f}, {:>5.2f}s/it, {:s} loss: {:>9.6f}, D_loss: {:>9.6f}, G_loss: {:>9.6f}, duration loss: {:>9.6f}, ssim loss: {:>9.6f}, lr: {:>4}".format(
epoch, iteration, reduced_loss, loss_window.average, grad_norm, time_window.average, hparams.loss_type, loss_dict[hparams.loss_type], loss_D.item() if iteration > hparams.Generator_pretrain_step else 0, loss_G.item() if iteration > hparams.Generator_pretrain_step else 0, loss_dict["duration_loss"], loss_dict["ssim_loss"], learning_rate))
if hparams.GAN_type=='wgan-gp':
print("Epoch:{} step:{} Train loss: {:>9.6f}, avg loss: {:>9.6f}, Grad Norm: {:>9.6f}, {:>5.2f}s/it, {:s} loss: {:>9.6f}, G_loss: {:>9.6f}, duration loss: {:>9.6f}, ssim loss: {:>9.6f}, lr: {:>4}".format(
epoch, iteration, reduced_loss, loss_window.average, grad_norm, time_window.average, hparams.loss_type, loss_dict[hparams.loss_type], loss_G.item() if iteration > hparams.Generator_pretrain_step else 0, loss_dict["duration_loss"], loss_dict["ssim_loss"], learning_rate))
logger.log_training(
reduced_loss, grad_norm, learning_rate, duration, iteration, loss_dict)
if not is_overflow and (iteration % hparams.iters_per_checkpoint == 0):
if valset is not None:
validate(model, valset, iteration, hparams.batch_criterion,
hparams.batch_size, n_gpus, collate_fn, logger,
hparams.distributed_run, rank)
if rank == 0:
checkpoint_path = os.path.join(
output_directory, "checkpoint_{}_refine_{}".format(iteration, hparams.training_files.split('/')[-2].split('_')[-1]) if hparams.is_partial_refine else "checkpoint_{}".format(iteration))
save_checkpoint(model, optimizer, learning_rate, iteration,
checkpoint_path)
iteration += 1
torch.cuda.empty_cache()
epoch += 1
sess.run(dev_iterator.initializer, {num_frames: max_frames})
try:
checkpoint_state = tf.train.get_checkpoint_state(train_hparams.checkpoint_path)
if (checkpoint_state and checkpoint_state.model_checkpoint_path):
log('Loading checkpoint {}'.format(checkpoint_state.model_checkpoint_path), slack=True)
saver.restore(sess, checkpoint_state.model_checkpoint_path)
else:
log('No model to load at {}'.format(train_hparams.checkpoint_path), slack=True)
saver.save(sess, checkpoint_path, global_step=train_resnet.global_step)
except OutOfRangeError as e:
log('Cannot restore checkpoint: {}'.format(e), slack=True)
step = 0
time_window = ValueWindow(100)
loss_window = ValueWindow(100)
acc_window = ValueWindow(100)
while step < train_hparams.train_steps:
start_time = time.time()
fetches = [train_resnet.global_step, train_resnet.train_op, train_resnet.cost, train_resnet.accuracy]
feed_dict = {handle: train_handle}
try:
step, _, loss, acc = sess.run(fetches=fetches, feed_dict=feed_dict)
except OutOfRangeError as e:
sess.run(train_iterator.initializer, {num_frames: max_frames})
continue
time_window.append(time.time() - start_time)
def train_epoch(model, train_loader, loss_fn, optimizer, scheduler, batch_size,
epoch, start_stpe):
model.train()
count = 0
total_loss = 0
n = batch_size
step = start_stpe
examples = []
total_loss_window = ValueWindow(100)
post_loss_window = ValueWindow(100)
post_acc_window = ValueWindow(100)
for x, y in train_loader:
count += 1
examples.append([x[0], y[0]])
if count % 8 == 0:
examples.sort(key=lambda x: len(x[-1]))
examples = (np.vstack([ex[0] for ex in examples]),
np.vstack([ex[1] for ex in examples]))
batches = [(examples[0][i:i + n], examples[1][i:i + n])
for i in range(0,
len(examples[-1]) + 1 - n, n)]
if len(examples[-1]) % n != 0:
batches.append(
(np.vstack((examples[0][-(len(examples[-1]) % n):],
examples[0][:n - (len(examples[0]) % n)])),
np.vstack((examples[1][-(len(examples[-1]) % n):],
examples[1][:n - (len(examples[-1]) % n)]))))
for batch in batches: # mini batch
# train_data(?, 7, 80), train_label(?, 7)
step += 1
train_data = torch.as_tensor(batch[0],
dtype=torch.float32).to(DEVICE)
train_label = torch.as_tensor(batch[1],
dtype=torch.float32).to(DEVICE)
optimizer.zero_grad(True)
midnet_output, postnet_output, alpha = model(train_data)
postnet_accuracy, pipenet_accuracy = prediction(
train_label, midnet_output, postnet_output)
loss, postnet_loss, pipenet_loss, attention_loss = loss_fn(
model, train_label, postnet_output, midnet_output, alpha)
total_loss += loss.detach().item()
loss.backward()
nn.utils.clip_grad_norm_(model.parameters(), 5, norm_type=2)
optimizer.step()
scheduler.step()
lr = scheduler._rate
total_loss_window.append(loss.detach().item())
post_loss_window.append(postnet_loss.detach().item())
post_acc_window.append(postnet_accuracy)
if step % 10 == 0:
print(
'{} Epoch: {}, Step: {}, overall loss: {:.5f}, postnet loss: {:.5f}, '
'postnet acc: {:.4f}, lr :{:.5f}'.format(
datetime.now().strftime(_format)[:-3], epoch, step,
total_loss_window.average,
post_loss_window.average, post_acc_window.average,
lr))
if step % 50_000 == 0:
print('{} save checkpoint.'.format(
datetime.now().strftime(_format)[:-3]))
checkpoint = {
"model": model.state_dict(),
'optimizer': optimizer.state_dict(),
"epoch": epoch,
'step': step,
'scheduler_lr': scheduler._rate,
'scheduler_step': scheduler._step
}
if not os.path.isdir("./checkpoint"):
os.mkdir("./checkpoint")
torch.save(
checkpoint, './checkpoint/STAM_weights_%s_%s.pth' %
(str(epoch), str(step / 1_000_000)))
gc.collect()
torch.cuda.empty_cache()
del batches, examples
examples = []
def train(logdir,args):
# TODO：parse ckpt,arguments,hparams
checkpoint_path = os.path.join(logdir,'model.ckpt')
input_path = args.data_dir
log('Checkpoint path: %s' % checkpoint_path)
log('Loading training data from : %s ' % input_path)
log('Using model : %s' %args.model)
# TODO：set up datafeeder
with tf.variable_scope('datafeeder') as scope:
hp.aishell=True
hp.prime=False
hp.stcmd=False
hp.data_path = 'D:/pycharm_proj/corpus_zn/'
hp.initial_learning_rate = 0.001
hp.decay_learning_rate=False
hp.data_length = 512
hp.batch_size = 64
feeder = DataFeeder(args=hp)
log('num_wavs:'+str(len(feeder.wav_lst))) # 283600
hp.input_vocab_size = len(feeder.pny_vocab)
hp.final_output_dim = len(feeder.pny_vocab)
hp.steps_per_epoch = len(feeder.wav_lst)//hp.batch_size
log('steps_per_epoch:' + str(hp.steps_per_epoch)) # 17725
log('pinyin_vocab_size:'+str(hp.input_vocab_size)) # 1292
hp.label_vocab_size = len(feeder.han_vocab)
log('label_vocab_size :' + str(hp.label_vocab_size)) # 6291
# TODO：set up model
global_step = tf.Variable(initial_value=0,name='global_step',trainable=False)
valid_step = 0
# valid_global_step = tf.Variable(initial_value=0,name='valid_global_step',trainable=False)
with tf.variable_scope('model') as scope:
model = create_model(args.model,hp)
model.build_graph()
model.add_loss()
model.add_decoder()
model.add_optimizer(global_step=global_step)
# TODO: summary
stats = add_stats(model=model)
valid_stats = add_dev_stats(model)
# TODO：Set up saver and Bookkeeping
time_window = ValueWindow(100)
loss_window = ValueWindow(100)
wer_window = ValueWindow(100)
valid_time_window = ValueWindow(100)
valid_loss_window = ValueWindow(100)
valid_wer_window = ValueWindow(100)
saver = tf.train.Saver(max_to_keep=20)
first_serving = True
# TODO: train
with tf.Session() as sess:
try:
# TODO: Set writer and initializer
summary_writer = tf.summary.FileWriter(logdir, sess.graph)
sess.run(tf.global_variables_initializer())
# TODO: Restore
if args.restore_step:
# Restore from a checkpoint if the user requested it.
restore_path = '%s-%d' % (checkpoint_path, args.restore_step)
saver.restore(sess, restore_path)
log('Resuming from checkpoint: %s ' % restore_path)
else:
log('Starting new training run ')
step = 0
# TODO: epochs steps batch
for i in range(args.epochs):
batch_data = feeder.get_am_batch()
log('Traning epoch '+ str(i)+':')
for j in range(hp.steps_per_epoch):
input_batch = next(batch_data)
feed_dict = {model.inputs:input_batch['the_inputs'],
model.labels:input_batch['the_labels'],
model.input_lengths:input_batch['input_length'],
model.label_lengths:input_batch['label_length']}
# TODO: Run one step
start_time = time.time()
total_step, array_loss, batch_loss,opt = sess.run([global_step, model.ctc_loss,
model.batch_loss,model.optimize],feed_dict=feed_dict)
time_window.append(time.time() - start_time)
step = total_step
# TODO: Append loss
# loss = np.sum(array_loss).item()/hp.batch_size
loss_window.append(batch_loss)
# print('loss',loss,'batch_loss',batch_loss)
message = 'Step %-7d [%.03f sec/step, loss=%.05f, avg_loss=%.05f, lr=%.07f]' % (
step, time_window.average, batch_loss, loss_window.average,K.get_value(model.learning_rate))
log(message)
# ctcloss返回值是[batch_size,1]形式的所以刚开始没sum报错only size-1 arrays can be converted to Python scalars
# TODO: Check loss
if math.isnan(batch_loss):
log('Loss exploded to %.05f at step %d!' % (batch_loss, step))
raise Exception('Loss Exploded')
# TODO: Check sumamry
if step % args.summary_interval == 0:
log('Writing summary at step: %d' % step)
summary_writer.add_summary(sess.run(stats,feed_dict=feed_dict), step)
# TODO: Check checkpoint
if step % args.checkpoint_interval == 0:
log('Saving checkpoint to: %s-%d' % (checkpoint_path, step))
saver.save(sess, checkpoint_path, global_step=step)
log('test acc...')
# eval_start_time = time.time()
# with tf.name_scope('eval') as scope:
# with open(os.path.expanduser('~/my_asr2/datasets/resource/preprocessedData/dev-meta.txt'), encoding='utf-8') as f:
# metadata = [line.strip().split('|') for line in f]
# random.shuffle(metadata)
# eval_loss = []
# batch_size = args.hp.batch_size
# batchs = len(metadata)//batch_size
# for i in range(batchs):
# batch = metadata[i*batch_size : i*batch_size+batch_size]
# batch = list(map(eval_get_example,batch))
# batch = eval_prepare_batch(batch)
# feed_dict = {'labels':batch[0],}
# label,final_pred_label ,log_probabilities = sess.run([
# model.labels[0], model.decoded[0], model.log_probabilities[0]])
# # 刚开始没有加[]会报错 https://github.com/tensorflow/tensorflow/issues/11840
# print('label: ' ,label)
# print('final_pred_label: ', final_pred_label[0])
# log('eval time: %.03f, avg_eval_loss: %.05f' % (time.time()-eval_start_time,np.mean(eval_loss)))
label,final_pred_label ,log_probabilities,y_pred2 = sess.run([
model.labels, model.decoded, model.log_probabilities,model.y_pred2],feed_dict=feed_dict)
# 刚开始没有加[]会报错 https://github.com/tensorflow/tensorflow/issues/11840
print('label.shape :',label.shape) # (batch_size , label_length)
print('final_pred_label.shape:',np.asarray(final_pred_label).shape) # (1, batch_size, decode_length<=label_length)
print('y_pred2.shape : ', y_pred2.shape)
print('label: ' ,label[0])
print('y_pred2 : ', y_pred2[0])
print('final_pred_label: ', np.asarray(final_pred_label)[0][0])
# 刚开始打不出来，因为使用的tf.nn.ctc_beam_decoder，这个返回的是sparse tensor所以打不出来
# 后来用keras封装的decoder自动将sparse转成dense tensor才能打出来
# waveform = audio.inv_spectrogram(spectrogram.T)
# audio.save_wav(waveform, os.path.join(logdir, 'step-%d-audio.wav' % step))
# plot.plot_alignment(alignment, os.path.join(logdir, 'step-%d-align.png' % step),
# info='%s, %s, %s, step=%d, loss=%.5f' % (
# args.model, commit, time_string(), step, loss))
# log('Input: %s' % sequence_to_text(input_seq))
# TODO: Check stop
if step % hp.steps_per_epoch ==0:
# TODO: Set up serving builder and signature map
serve_dir = args.serving_dir + '_' + str(total_step//hp.steps_per_epoch -1)
if os.path.exists(serve_dir):
os.removedirs(serve_dir)
builder = tf.saved_model.builder.SavedModelBuilder(export_dir=serve_dir)
input_spec = tf.saved_model.utils.build_tensor_info(model.inputs)
input_len = tf.saved_model.utils.build_tensor_info(model.input_lengths)
output_labels = tf.saved_model.utils.build_tensor_info(model.decoded2)
output_logits = tf.saved_model.utils.build_tensor_info(model.pred_logits)
prediction_signature = (
tf.saved_model.signature_def_utils.build_signature_def(
inputs={'spec': input_spec, 'len': input_len},
outputs={'label': output_labels, 'logits': output_logits},
method_name=tf.saved_model.signature_constants.PREDICT_METHOD_NAME)
)
if first_serving:
first_serving = False
builder.add_meta_graph_and_variables(
sess=sess, tags=[tf.saved_model.tag_constants.SERVING, 'ASR'],
signature_def_map={
'predict_AudioSpec2Pinyin':
prediction_signature,
},
main_op=tf.tables_initializer(),
strip_default_attrs=True
)
else:
builder.add_meta_graph_and_variables(
sess=sess, tags=[tf.saved_model.tag_constants.SERVING, 'ASR'],
signature_def_map={
'predict_AudioSpec2Pinyin':
prediction_signature,
},
strip_default_attrs=True
)
builder.save()
log('Done store serving-model')
# TODO: Validation
if step % hp.steps_per_epoch ==0 and i >= 10:
log('validation...')
valid_start = time.time()
# TODO: validation
valid_hp = hp
valid_hp.data_type = 'dev'
valid_hp.thchs30 = True
valid_hp.aishell = False
valid_hp.prime = False
valid_hp.stcmd = False
valid_hp.shuffle = True
valid_hp.data_length = None
valid_feeder = DataFeeder(args=valid_hp)
valid_batch_data = valid_feeder.get_am_batch()
log('valid_num_wavs:' + str(len(valid_feeder.wav_lst))) # 15219
valid_hp.input_vocab_size = len(valid_feeder.pny_vocab)
valid_hp.final_output_dim = len(valid_feeder.pny_vocab)
valid_hp.steps_per_epoch = len(valid_feeder.wav_lst) // valid_hp.batch_size
log('valid_steps_per_epoch:' + str(valid_hp.steps_per_epoch)) # 951
log('valid_pinyin_vocab_size:' + str(valid_hp.input_vocab_size)) # 1124
valid_hp.label_vocab_size = len(valid_feeder.han_vocab)
log('valid_label_vocab_size :' + str(valid_hp.label_vocab_size)) # 3327
words_num = 0
word_error_num = 0
# dev 只跑一个epoch就行
with tf.variable_scope('validation') as scope:
for k in range(len(valid_feeder.wav_lst) // valid_hp.batch_size):
valid_input_batch = next(valid_batch_data)
valid_feed_dict = {model.inputs: valid_input_batch['the_inputs'],
model.labels: valid_input_batch['the_labels'],
model.input_lengths: valid_input_batch['input_length'],
model.label_lengths: valid_input_batch['label_length']}
# TODO: Run one step
valid_start_time = time.time()
valid_batch_loss,valid_WER = sess.run([model.batch_loss,model.WER], feed_dict=valid_feed_dict)
valid_time_window.append(time.time() - valid_start_time)
valid_loss_window.append(valid_batch_loss)
valid_wer_window.append(valid_WER)
# print('loss',loss,'batch_loss',batch_loss)
message = 'Valid-Step %-7d [%.03f sec/step, valid_loss=%.05f, avg_loss=%.05f, WER=%.05f, avg_WER=%.05f, lr=%.07f]' % (
valid_step, valid_time_window.average, valid_batch_loss, valid_loss_window.average,valid_WER,valid_wer_window.average,K.get_value(model.learning_rate))
log(message)
summary_writer.add_summary(sess.run(valid_stats,feed_dict=valid_feed_dict), valid_step)
valid_step += 1
log('Done Validation！Total Time Cost(sec):' + str(time.time()-valid_start))
except Exception as e:
log('Exiting due to exception: %s' % e)
traceback.print_exc()
def train(log_dir, args, hparams, use_hvd=False):
if use_hvd:
import horovod.tensorflow as hvd
# Initialize Horovod.
hvd.init()
else:
hvd = None
eval_dir, eval_plot_dir, eval_wav_dir, meta_folder, plot_dir, save_dir, tensorboard_dir, wav_dir = init_dir(
log_dir)
checkpoint_path = os.path.join(save_dir, 'centaur_model.ckpt')
input_path = os.path.join(args.base_dir, args.input_dir)
log('Checkpoint path: {}'.format(checkpoint_path))
log('Loading training data from: {}'.format(input_path))
log('Using model: {}'.format(args.model))
log(hparams_debug_string())
# Start by setting a seed for repeatability
tf.set_random_seed(hparams.random_seed)
# Set up data feeder
coord = tf.train.Coordinator()
with tf.variable_scope('datafeeder'):
feeder = Feeder(coord, input_path, hparams)
# Set up model:
global_step = tf.Variable(0, name='global_step', trainable=False)
model, stats = model_train_mode(feeder, hparams, global_step, hvd=hvd)
eval_model = model_test_mode(feeder, hparams)
# Embeddings metadata
char_embedding_meta = os.path.join(meta_folder, 'CharacterEmbeddings.tsv')
if not os.path.isfile(char_embedding_meta):
with open(char_embedding_meta, 'w', encoding='utf-8') as f:
for symbol in symbols:
if symbol == ' ':
symbol = '\\s' # For visual purposes, swap space with \s
f.write('{}\n'.format(symbol))
char_embedding_meta = char_embedding_meta.replace(log_dir, '..')
# Book keeping
step = 0
time_window = ValueWindow(100)
loss_window = ValueWindow(100)
saver = tf.train.Saver(max_to_keep=2)
log('Centaur training set to a maximum of {} steps'.format(
args.train_steps))
# Memory allocation on the GPU as needed
config = tf.ConfigProto()
config.allow_soft_placement = True
config.gpu_options.allow_growth = True
if use_hvd:
config.gpu_options.visible_device_list = str(hvd.local_rank())
# Train
with tf.Session(config=config) as sess:
try:
# Init model and load weights
sess.run(tf.global_variables_initializer())
sess.run(tf.local_variables_initializer())
sess.run(tf.tables_initializer())
# saved model restoring
if args.restore:
# Restore saved model if the user requested it, default = True
restore_model(saver, sess, global_step, save_dir,
checkpoint_path, args.reset_global_step)
else:
log('Starting new training!', slack=True)
saver.save(sess, checkpoint_path, global_step=global_step)
# initializing feeder
start_step = sess.run(global_step)
feeder.start_threads(sess, start_step=start_step)
# Horovod bcast vars across workers
if use_hvd:
# Horovod: broadcast initial variable states from rank 0 to all other processes.
# This is necessary to ensure consistent initialization of all workers when
# training is started with random weights or restored from a checkpoint.
bcast = hvd.broadcast_global_variables(0)
bcast.run()
log('Worker{}: Initialized'.format(hvd.rank()))
# Training loop
summary_writer = tf.summary.FileWriter(tensorboard_dir, sess.graph)
while not coord.should_stop() and step < args.train_steps:
start_time = time.time()
step, loss, opt = sess.run(
[global_step, model.loss, model.train_op])
if use_hvd:
main_process = hvd.rank() == 0
if main_process:
time_window.append(time.time() - start_time)
loss_window.append(loss)
message = 'Step {:7d} [{:.3f} sec/step, loss={:.5f}, avg_loss={:.5f}]'.format(
step, time_window.average, loss, loss_window.average)
log(message,
end='\r',
slack=(step % args.checkpoint_interval == 0))
if np.isnan(loss) or loss > 100.:
log('Loss exploded to {:.5f} at step {}'.format(
loss, step))
raise Exception('Loss exploded')
if step % args.summary_interval == 0:
log('\nWriting summary at step {}'.format(step))
summary_writer.add_summary(sess.run(stats), step)
if step % args.eval_interval == 0:
run_eval(args, eval_dir, eval_model, eval_plot_dir,
eval_wav_dir, feeder, hparams, sess, step,
summary_writer)
if step % args.checkpoint_interval == 0 or step == args.train_steps or step == 300:
save_current_model(args, checkpoint_path, global_step,
hparams, loss, model, plot_dir,
saver, sess, step, wav_dir)
if step % args.embedding_interval == 0 or step == args.train_steps or step == 1:
update_character_embedding(char_embedding_meta,
save_dir, summary_writer)
log('Centaur training complete after {} global steps!'.format(
args.train_steps),
slack=True)
return save_dir
except Exception as e:
log('Exiting due to exception: {}'.format(e), slack=True)
traceback.print_exc()
coord.request_stop(e)
def train(log_dir, args):
checkpoint_path = os.path.join(hdfs_ckpts, log_dir, 'model.ckpt')
log(hp.to_string(), is_print=False)
log('Loading training data from: %s' % args.tfr_dir)
log('Checkpoint path: %s' % checkpoint_path)
log('Using model: sygst tacotron2')
tf_dset = TFDataSet(hp, args.tfr_dir)
feats = tf_dset.get_train_next()
# Set up model:
global_step = tf.Variable(0, name='global_step', trainable=False)
training = tf.placeholder_with_default(True, shape=(), name='training')
with tf.name_scope('model'):
model = Tacotron2SYGST(hp)
model(feats['inputs'],
mel_inputs=feats['mel_targets'],
spec_inputs=feats['linear_targets'],
spec_lengths=feats['spec_lengths'],
ref_inputs=feats['mel_targets'],
ref_lengths=feats['spec_lengths'],
arousal_labels=feats['soft_arousal_labels'],
valence_labels=feats['soft_valance_labels'],
training=training)
"""
text_x, mel_x, spec_x, spec_len, aro, val = debug_data(2, 5, 10)
model(text_x, mel_x, spec_x, spec_len, mel_x, spec_len, aro, val, training=training)
"""
model.add_loss()
model.add_optimizer(global_step)
stats = model.add_stats()
# Bookkeeping:
step = 0
time_window = ValueWindow(100)
loss_window = ValueWindow(100)
saver = tf.train.Saver(max_to_keep=50, keep_checkpoint_every_n_hours=2)
# Train!
config = tf.ConfigProto(allow_soft_placement=True,
gpu_options=tf.GPUOptions(allow_growth=True))
with tf.Session(config=config) as sess:
try:
summary_writer = tf.summary.FileWriter(log_dir, sess.graph)
sess.run(tf.global_variables_initializer())
if args.restore_step:
# Restore from a checkpoint if the user requested it.
restore_path = '%s-%s' % (checkpoint_path, args.restore_step)
saver.restore(sess, restore_path)
log('Resuming from checkpoint: %s' % restore_path, slack=True)
else:
log('Starting a new training run ...', slack=True)
"""
fetches = [global_step, model.optimize, model.loss, model.mel_loss, model.spec_loss,
model.stop_loss, model.arousal_loss, model.valence_loss, model.mel_grad_norms_max,
model.spec_grad_norms_max, model.stop_grad_norms_max, model.aro_grad_norms_max, model.val_grad_norms_max]
"""
fetches = [
global_step, model.optimize, model.loss, model.mel_loss,
model.spec_loss, model.stop_loss, model.arousal_loss,
model.valence_loss
]
for _ in range(_max_step):
start_time = time.time()
sess.run(debug.get_ops())
# step, _, loss, mel_loss, spec_loss, stop_loss, aro_loss, val_loss, mel_g, spec_g, stop_g, aro_g, val_g = sess.run(fetches)
step, _, loss, mel_loss, spec_loss, stop_loss, aro_loss, val_loss = sess.run(
fetches)
time_window.append(time.time() - start_time)
loss_window.append(loss)
"""
message = 'Step %-7d [%.3f sec/step,ml=%.3f,spl=%.3f,sl=%.3f,al=%.3f,vl=%.3f,mg=%.4f,spg=%.4f,sg=%.4f,ag=%.4f,vg=%.4f]' % (
step, time_window.average, mel_loss, spec_loss, stop_loss, aro_loss, val_loss, mel_g, spec_g, stop_g, aro_g, val_g)
"""
message = 'Step %-7d [%.3f sec/step,ml=%.3f,spl=%.3f,sl=%.3f,al=%.3f,vl=%.3f]' % (
step, time_window.average, mel_loss, spec_loss, stop_loss,
aro_loss, val_loss)
log(message, slack=(step % args.checkpoint_interval == 0))
if loss > 100 or math.isnan(loss):
log('Loss exploded to %.5f at step %d!' % (loss, step),
slack=True)
raise Exception('Loss Exploded')
if step % args.summary_interval == 0:
log('Writing summary at step: %d' % step)
try:
summary_writer.add_summary(sess.run(stats), step)
except Exception as e:
log(f'summary failed and ignored: {str(e)}')
if step % args.checkpoint_interval == 0:
log('Saving checkpoint to: %s-%d' %
(checkpoint_path, step))
saver.save(sess, checkpoint_path, global_step=step)
log('Saving audio and alignment...')
gt_mel, gt_spec, seq, mel, spec, align = sess.run([
model.mel_targets[0], model.spec_targets[0],
model.text_targets[0], model.mel_outputs[0],
model.spec_outputs[0], model.alignment_outputs[0]
])
text = sequence_to_text(seq)
wav = audio.inv_spectrogram(hp, spec.T)
wav_path = os.path.join(log_dir,
'step-%d-audio.wav' % step)
mel_path = os.path.join(log_dir, 'step-%d-mel.png' % step)
spec_path = os.path.join(log_dir,
'step-%d-spec.png' % step)
align_path = os.path.join(log_dir,
'step-%d-align.png' % step)
info = '%s, %s, step=%d, loss=%.5f\n %s' % (
args.model, time_string(), step, loss, text)
plot.plot_alignment(align, align_path, info=info)
plot.plot_mel(mel, mel_path, info=info, gt_mel=gt_mel)
plot.plot_mel(spec, spec_path, info=info, gt_mel=gt_spec)
audio.save_wav(hp, wav, wav_path)
log('Input: %s' % text)
except Exception as e:
log('Exiting due to exception: %s' % e, slack=True)
traceback.print_exc()
def train(args):
if args.model_path is None:
msg = 'Prepare for new run ...'
output_dir = os.path.join(
args.log_dir, args.run_name + '_' +
datetime.datetime.now().strftime('%m%d_%H%M'))
if not os.path.exists(output_dir):
os.makedirs(output_dir)
ckpt_dir = os.path.join(
args.ckpt_dir, args.run_name + '_' +
datetime.datetime.now().strftime('%m%d_%H%M'))
if not os.path.exists(ckpt_dir):
os.makedirs(ckpt_dir)
else:
msg = 'Restart previous run ...\nlogs to save to %s, ckpt to save to %s, model to load from %s' % \
(args.log_dir, args.ckpt_dir, args.model_path)
output_dir = args.log_dir
ckpt_dir = args.ckpt_dir
if not os.path.isdir(output_dir):
print('Invalid log dir: %s' % output_dir)
return
if not os.path.isdir(ckpt_dir):
print('Invalid ckpt dir: %s' % ckpt_dir)
return
set_logger(os.path.join(output_dir, 'outputs.log'))
logging.info(msg)
global device
if args.device is not None:
logging.info('Setting device to ' + args.device)
device = torch.device(args.device)
else:
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
logging.info('Setting up...')
hparams.parse(args.hparams)
logging.info(hparams_debug_string())
model = EdgeClassification
if hparams.use_roberta:
logging.info('Using Roberta...')
model = RobertaEdgeClassification
global_step = 0
if args.model_path is None:
if hparams.load_pretrained:
logging.info('Load online pretrained model...' + (
('cached at ' +
args.cache_path) if args.cache_path is not None else ''))
if hparams.use_roberta:
model = model.from_pretrained('roberta-base',
cache_dir=args.cache_path,
hparams=hparams)
else:
model = model.from_pretrained('bert-base-uncased',
cache_dir=args.cache_path,
hparams=hparams)
else:
logging.info('Build model from scratch...')
if hparams.use_roberta:
config = BertConfig.from_pretrained('bert-base-uncased')
else:
config = RobertaConfig.from_pretrained('roberta-base')
model = model(config=config, hparams=hparams)
else:
if not os.path.isdir(args.model_path):
raise OSError(str(args.model_path) + ' not found')
logging.info('Load saved model from %s ...' % (args.model_path))
model = model.from_pretrained(args.model_path, hparams=hparams)
step = args.model_path.split('_')[-1]
if step.isnumeric():
global_step = int(step)
logging.info('Initial step=%d' % global_step)
if hparams.use_roberta:
tokenizer = RobertaTokenizer.from_pretrained('roberta-base')
else:
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
hparams.parse(args.hparams)
logging.info(hparams_debug_string())
if hparams.text_sample_eval:
if args.eval_text_path is None:
raise ValueError('eval_text_path not given')
if ':' not in args.eval_text_path:
eval_data_paths = [args.eval_text_path]
else:
eval_data_paths = args.eval_text_path.split(':')
eval_feeder = []
for p in eval_data_paths:
name = os.path.split(p)[-1]
if name.endswith('.tsv'):
name = name[:-4]
eval_feeder.append(
(name, ExternalTextFeeder(p, hparams, tokenizer, 'dev')))
else:
eval_feeder = [('', DataFeeder(args.data_dir, hparams, tokenizer,
'dev'))]
tb_writer = SummaryWriter(output_dir)
no_decay = ['bias', 'LayerNorm.weight']
optimizer_grouped_parameters = [{
'params': [
p for n, p in model.named_parameters()
if not any(nd in n for nd in no_decay)
],
'weight_decay':
hparams.weight_decay
}, {
'params': [
p for n, p in model.named_parameters()
if any(nd in n for nd in no_decay)
],
'weight_decay':
0.0
}]
optimizer = AdamW(optimizer_grouped_parameters,
lr=hparams.learning_rate,
eps=hparams.adam_epsilon)
scheduler = get_linear_schedule_with_warmup(
optimizer,
num_warmup_steps=hparams.warmup_steps,
lr_decay_step=hparams.lr_decay_step,
max_lr_decay_rate=hparams.max_lr_decay_rate)
acc_step = global_step * hparams.gradient_accumulation_steps
time_window = ValueWindow()
loss_window = ValueWindow()
acc_window = ValueWindow()
model.to(device)
model.zero_grad()
tr_loss = tr_acc = 0.0
start_time = time.time()
if args.model_path is not None:
logging.info('Load saved model from %s ...' % (args.model_path))
if os.path.exists(os.path.join(args.model_path, 'optimizer.pt')) \
and os.path.exists(os.path.join(args.model_path, 'scheduler.pt')):
optimizer.load_state_dict(
torch.load(os.path.join(args.model_path, 'optimizer.pt')))
optimizer.load_state_dict(optimizer.state_dict())
scheduler.load_state_dict(
torch.load(os.path.join(args.model_path, 'scheduler.pt')))
scheduler.load_state_dict(scheduler.state_dict())
else:
logging.warning('Could not find saved optimizer/scheduler')
if global_step > 0:
logs = run_eval(args, model, eval_feeder)
for key, value in logs.items():
tb_writer.add_scalar(key, value, global_step)
logging.info('Start training...')
if hparams.text_sample_train:
train_feeder = PrebuiltTrainFeeder(args.train_text_path, hparams,
tokenizer, 'train')
else:
train_feeder = DataFeeder(args.data_dir, hparams, tokenizer, 'train')
while True:
batch = train_feeder.next_batch()
model.train()
outputs = model(input_ids=batch.input_ids.to(device),
attention_mask=batch.input_mask.to(device),
token_type_ids=None if batch.token_type_ids is None
else batch.token_type_ids.to(device),
labels=batch.labels.to(device))
loss = outputs['loss']
preds = outputs['preds']
acc = torch.mean((preds.cpu() == batch.labels).float())
preds = preds.cpu().detach().numpy()
labels = batch.labels.detach().numpy()
t_acc = np.sum(np.logical_and(preds == 1, labels
== 1)) / np.sum(labels == 1)
f_acc = np.sum(np.logical_and(preds == 0, labels
== 0)) / np.sum(labels == 0)
if hparams.gradient_accumulation_steps > 1:
loss = loss / hparams.gradient_accumulation_steps
acc = acc / hparams.gradient_accumulation_steps
tr_loss += loss.item()
tr_acc += acc.item()
loss.backward()
acc_step += 1
if acc_step % hparams.gradient_accumulation_steps != 0:
continue
torch.nn.utils.clip_grad_norm_(model.parameters(),
hparams.max_grad_norm)
optimizer.step()
scheduler.step(None)
model.zero_grad()
global_step += 1
step_time = time.time() - start_time
time_window.append(step_time)
loss_window.append(tr_loss)
acc_window.append(tr_acc)
if global_step % args.save_steps == 0:
# Save model checkpoint
model_to_save = model.module if hasattr(model, 'module') else model
cur_ckpt_dir = os.path.join(ckpt_dir,
'checkpoint_%d' % (global_step))
if not os.path.exists(cur_ckpt_dir):
os.makedirs(cur_ckpt_dir)
model_to_save.save_pretrained(cur_ckpt_dir)
torch.save(args, os.path.join(cur_ckpt_dir, 'training_args.bin'))
torch.save(optimizer.state_dict(),
os.path.join(cur_ckpt_dir, 'optimizer.pt'))
torch.save(scheduler.state_dict(),
os.path.join(cur_ckpt_dir, 'scheduler.pt'))
logging.info("Saving model checkpoint to %s", cur_ckpt_dir)
if global_step % args.logging_steps == 0:
logs = run_eval(args, model, eval_feeder)
learning_rate_scalar = scheduler.get_lr()[0]
logs['learning_rate'] = learning_rate_scalar
logs['loss'] = loss_window.average
logs['acc'] = acc_window.average
for key, value in logs.items():
tb_writer.add_scalar(key, value, global_step)
message = 'Step %-7d [%.03f sec/step, loss=%.05f, avg_loss=%.05f, acc=%.05f, avg_acc=%.05f, t_acc=%.05f, f_acc=%.05f]' % (
global_step, step_time, tr_loss, loss_window.average, tr_acc,
acc_window.average, t_acc, f_acc)
logging.info(message)
tr_loss = tr_acc = 0.0
start_time = time.time()
def train(log_dir, args):
save_dir = os.path.join(log_dir, 'pretrained/')
checkpoint_path = os.path.join(save_dir, 'model.ckpt')
input_path = os.path.join(args.base_dir, args.input)
log('Checkpoint path: {}'.format(checkpoint_path))
log('Loading training data from: {}'.format(input_path))
log('Using model: {}'.format(args.model))
log(hparams_debug_string())
#Set up feeder
coord = tf.train.Coordinator()
with tf.variable_scope('datafeeder') as scope:
feeder = Feeder(coord, input_path, hparams)
#Set up model
step_count = 0
try:
#simple text file to keep count of global step
with open(os.path.join(log_dir, 'step_counter.txt'), 'r') as file:
step_count = int(file.read())
except:
print(
'no step_counter file found, assuming there is no saved checkpoint'
)
global_step = tf.Variable(step_count, name='global_step', trainable=False)
with tf.variable_scope('model') as scope:
model = create_model(args.model, hparams)
model.initialize(feeder.inputs, feeder.input_lengths,
feeder.mel_targets)
model.add_loss()
model.add_optimizer(global_step)
stats = add_stats(model)
#Book keeping
step = 0
time_window = ValueWindow(100)
loss_window = ValueWindow(100)
saver = tf.train.Saver(max_to_keep=5, keep_checkpoint_every_n_hours=2)
#Memory allocation on the GPU as needed
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
#Train
with tf.Session(config=config) as sess:
try:
summary_writer = tf.summary.FileWriter(log_dir, sess.graph)
sess.run(tf.global_variables_initializer())
#saved model restoring
if args.restore:
#Restore saved model if the user requested it, Default = True.
try:
checkpoint_state = tf.train.get_checkpoint_state(log_dir)
except tf.errors.OutOfRangeError as e:
log('Cannot restore checkpoint: {}'.format(e))
if (checkpoint_state and checkpoint_state.model_checkpoint_path):
log('Loading checkpoint {}'.format(
checkpoint_state.model_checkpoint_path))
saver.restore(sess, checkpoint_state.model_checkpoint_path)
else:
if not args.restore:
log('Starting new training!')
else:
log('No model to load at {}'.format(save_dir))
#initiating feeder
feeder.start_in_session(sess)
#Training loop
while not coord.should_stop():
start_time = time.time()
step, loss, opt = sess.run(
[global_step, model.loss, model.optimize])
time_window.append(time.time() - start_time)
loss_window.append(loss)
message = 'Step {:7d} [{:.3f} sec/step, loss={:.5f}, avg_loss={:.5f}]'.format(
step, time_window.average, loss, loss_window.average)
log(message, end='\r')
if loss > 100 or np.isnan(loss):
log('Loss exploded to {:.5f} at step {}'.format(
loss, step))
raise Exception('Loss exploded')
if step % args.summary_interval == 0:
log('\nWriting summary at step: {}'.format(step))
summary_writer.add_summary(sess.run(stats), step)
if step % args.checkpoint_interval == 0:
with open(os.path.join(log_dir, 'step_counter.txt'),
'w') as file:
file.write(str(step))
log('Saving checkpoint to: {}-{}'.format(
checkpoint_path, step))
saver.save(sess, checkpoint_path, global_step=step)
input_seq, prediction = sess.run(
[model.inputs[0], model.output[0]])
#Save an example prediction at this step
log('Input at step {}: {}'.format(
step, sequence_to_text(input_seq)))
log('Model prediction: {}'.format(
class_to_str(prediction)))
except Exception as e:
log('Exiting due to exception: {}'.format(e), slack=True)
traceback.print_exc()
coord.request_stop(e)
def train():
checkpoint_path = os.path.join(Config.LogDir, 'model.ckpt')
save_dir = os.path.join(Config.LogDir, 'pretrained/')
input_path = Config.DataDir
#Set up data feeder
coord = tf.train.Coordinator()
with tf.variable_scope('datafeeder') as scope:
feeder = Feeder(coord, input_path)
#Set up model:
step_count = 0
try:
#simple text file to keep count of global step
with open('step_counter.txt', 'r') as file:
step_count = int(file.read())
except:
print('no step_counter file found, assuming there is no saved checkpoint')
global_step = tf.Variable(step_count, name='global_step', trainable=False)
model = Tacotron2.Tacotron2(global_step, feeder.inputs, feeder.input_lengths, feeder.mel_targets, feeder.target_lengths)
model.buildTacotron2()
model.addLoss(feeder.masks)
#Book keeping
step = 0
time_window = ValueWindow(100)
loss_window = ValueWindow(100)
saver = tf.train.Saver(max_to_keep=5, keep_checkpoint_every_n_hours=2)
#Train
with tf.Session() as sess:
try:
sess.run(tf.global_variables_initializer())
#saver.restore(sess, checkpoint_state.model_checkpoint_path)
#initiating feeder
feeder.start_in_session(sess)
#Training loop
while not coord.should_stop():
start_time = time.time()
step, loss, _ = sess.run([model.global_step, model.loss, model.optim])
time_window.append(time.time() - start_time)
loss_window.append(loss)
if step % 1 == 0:
message = 'Step {:7d} [{:.3f} sec/step, loss={:.5f}, avg_loss={:.5f}]'.format(
step, time_window.average, loss, loss_window.average)
print (message)
'''
if loss > 100 or np.isnan(loss):
log('Loss exploded to {:.5f} at step {}'.format(loss, step))
raise Exception('Loss exploded')
if step % Config.CheckpointInterval == 0:
with open('step_counter.txt', 'w') as file:
file.write(str(step))
log('Saving checkpoint to: {}-{}'.format(checkpoint_path, step))
saver.save(sess, checkpoint_path, global_step=step)
# Unlike the original tacotron, we won't save audio
# because we yet have to use wavenet as vocoder
log('Saving alignement..')
input_seq, prediction, alignment = sess.run([model.inputs[0],
model.mel_outputs[0],
model.alignments[0],
])
#save predicted spectrogram to disk (for plot and manual evaluation purposes)
mel_filename = 'ljspeech-mel-prediction-step-{}.npy'.format(step)
np.save(os.path.join(log_dir, mel_filename), prediction.T, allow_pickle=False)
#save alignment plot to disk (evaluation purposes)
plot.plot_alignment(alignment, os.path.join(log_dir, 'step-{}-align.png'.format(step)),
info='{}, {}, step={}, loss={:.5f}'.format(args.model, time_string(), step, loss))
log('Input at step {}: {}'.format(step, sequence_to_text(input_seq)))
'''
except Exception as e:
#log('Exiting due to exception: {}'.format(e), slack=True)
traceback.print_exc()
coord.request_stop(e)
def train(log_dir, args):
checkpoint_path = os.path.join(log_dir, 'model.ckpt')
input_path = os.path.join(args.base_dir, 'training/train.txt')
logger.log('Checkpoint path: %s' % checkpoint_path)
logger.log('Loading training data from: %s' % input_path)
# set up DataFeeder
coordi = tf.train.Coordinator()
with tf.compat.v1.variable_scope('data_feeder'):
feeder = DataFeeder(coordi, input_path)
# set up Model
global_step = tf.Variable(0, name='global_step', trainable=False)
with tf.compat.v1.variable_scope('model'):
model = Tacotron()
model.init(feeder.inputs,
feeder.input_lengths,
mel_targets=feeder.mel_targets,
linear_targets=feeder.linear_targets)
model.add_loss()
model.add_optimizer(global_step)
stats = add_stats(model)
# book keeping
step = 0
loss_window = ValueWindow(100)
time_window = ValueWindow(100)
saver = tf.compat.v1.train.Saver(max_to_keep=5,
keep_checkpoint_every_n_hours=2)
# start training already!
with tf.compat.v1.Session() as sess:
try:
summary_writer = tf.summary.FileWriter(log_dir, sess.graph)
# initialize parameters
sess.run(tf.compat.v1.global_variables_initializer())
# if requested, restore from step
if (args.restore_step):
restore_path = '%s-%d' % (checkpoint_path, args.restore_step)
saver.restore(sess, restore_path)
logger.log('Resuming from checkpoint: %s' % restore_path)
else:
logger.log('Starting a new training!')
feeder.start_in_session(sess)
while not coordi.should_stop():
start_time = time.time()
step, loss, opt = sess.run(
[global_step, model.loss, model.optimize])
time_window.append(time.time() - start_time)
loss_window.append(loss)
msg = 'Step %-7d [%.03f sec/step, loss=%.05f, avg_loss=%.05f]' % (
step, time_window.average, loss, loss_window.average)
logger.log(msg)
if loss > 100 or math.isnan(loss):
# bad situation
logger.log('Loss exploded to %.05f at step %d!' %
(loss, step))
raise Exception('Loss Exploded')
if step % args.summary_interval == 0:
# it's time to write summary
logger.log('Writing summary at step: %d' % step)
summary_writer.add_summary(sess.run(stats), step)
if step % args.checkpoint_interval == 0:
# it's time to save a checkpoint
logger.log('Saving checkpoint to: %s-%d' %
(checkpoint_path, step))
saver.save(sess, checkpoint_path, global_step=step)
logger.log('Saving audio and alignment...')
input_seq, spectrogram, alignment = sess.run([
model.inputs[0], model.linear_outputs[0],
model.alignments[0]
])
# convert spectrogram to waveform
waveform = audio.spectrogram_to_wav(spectrogram.T)
# save it
audio.save_audio(
waveform,
os.path.join(log_dir, 'step-%d-audio.wav' % step))
plotter.plot_alignment(
alignment,
os.path.join(log_dir, 'step-%d-align.png' % step),
info='%s, %s, step=%d, loss=%.5f' %
('tacotron', time_string(), step, loss))
logger.log('Input: %s' % sequence_to_text(input_seq))
except Exception as e:
logger.log('Exiting due to exception %s' % e)
traceback.print_exc()
coordi.request_stop(e)
def train(log_dir, config):
config.data_paths = config.data_paths # ['datasets/moon']
data_dirs = config.data_paths # ['datasets/moon\\data']
num_speakers = len(data_dirs)
config.num_test = config.num_test_per_speaker * num_speakers # 2*1
if num_speakers > 1 and hparams.model_type not in [
"multi-speaker", "simple"
]:
raise Exception("[!] Unkown model_type for multi-speaker: {}".format(
config.model_type))
commit = get_git_commit() if config.git else 'None'
checkpoint_path = os.path.join(
log_dir, 'model.ckpt'
) # 'logdir-tacotron\\moon_2018-08-28_13-06-42\\model.ckpt'
#log(' [*] git recv-parse HEAD:\n%s' % get_git_revision_hash()) # hccho: 주석 처리
log('=' * 50)
#log(' [*] dit diff:\n%s' % get_git_diff())
log('=' * 50)
log(' [*] Checkpoint path: %s' % checkpoint_path)
log(' [*] Loading training data from: %s' % data_dirs)
log(' [*] Using model: %s' %
config.model_dir) # 'logdir-tacotron\\moon_2018-08-28_13-06-42'
log(hparams_debug_string())
# Set up DataFeeder:
coord = tf.train.Coordinator()
with tf.variable_scope('datafeeder') as scope:
# DataFeeder의 6개 placeholder: train_feeder.inputs, train_feeder.input_lengths, train_feeder.loss_coeff, train_feeder.mel_targets, train_feeder.linear_targets, train_feeder.speaker_id
train_feeder = DataFeederTacotron2(coord,
data_dirs,
hparams,
config,
32,
data_type='train',
batch_size=config.batch_size)
test_feeder = DataFeederTacotron2(coord,
data_dirs,
hparams,
config,
8,
data_type='test',
batch_size=config.num_test)
# Set up model:
global_step = tf.Variable(0, name='global_step', trainable=False)
with tf.variable_scope('model') as scope:
model = create_model(hparams)
model.initialize(inputs=train_feeder.inputs,
input_lengths=train_feeder.input_lengths,
num_speakers=num_speakers,
speaker_id=train_feeder.speaker_id,
mel_targets=train_feeder.mel_targets,
linear_targets=train_feeder.linear_targets,
is_training=True,
loss_coeff=train_feeder.loss_coeff,
stop_token_targets=train_feeder.stop_token_targets)
model.add_loss()
model.add_optimizer(global_step)
train_stats = add_stats(model, scope_name='train') # legacy
with tf.variable_scope('model', reuse=True) as scope:
test_model = create_model(hparams)
test_model.initialize(
inputs=test_feeder.inputs,
input_lengths=test_feeder.input_lengths,
num_speakers=num_speakers,
speaker_id=test_feeder.speaker_id,
mel_targets=test_feeder.mel_targets,
linear_targets=test_feeder.linear_targets,
is_training=False,
loss_coeff=test_feeder.loss_coeff,
stop_token_targets=test_feeder.stop_token_targets)
test_model.add_loss()
# Bookkeeping:
step = 0
time_window = ValueWindow(100)
loss_window = ValueWindow(100)
saver = tf.train.Saver(max_to_keep=None, keep_checkpoint_every_n_hours=2)
sess_config = tf.ConfigProto(log_device_placement=False,
allow_soft_placement=True)
sess_config.gpu_options.allow_growth = True
# Train!
#with tf.Session(config=sess_config) as sess:
with tf.Session(config=sess_config) as sess:
try:
summary_writer = tf.summary.FileWriter(log_dir, sess.graph)
sess.run(tf.global_variables_initializer())
if config.load_path:
# Restore from a checkpoint if the user requested it.
restore_path = get_most_recent_checkpoint(config.model_dir)
saver.restore(sess, restore_path)
log('Resuming from checkpoint: %s at commit: %s' %
(restore_path, commit),
slack=True)
elif config.initialize_path:
restore_path = get_most_recent_checkpoint(
config.initialize_path)
saver.restore(sess, restore_path)
log('Initialized from checkpoint: %s at commit: %s' %
(restore_path, commit),
slack=True)
zero_step_assign = tf.assign(global_step, 0)
sess.run(zero_step_assign)
start_step = sess.run(global_step)
log('=' * 50)
log(' [*] Global step is reset to {}'.format(start_step))
log('=' * 50)
else:
log('Starting new training run at commit: %s' % commit,
slack=True)
start_step = sess.run(global_step)
train_feeder.start_in_session(sess, start_step)
test_feeder.start_in_session(sess, start_step)
while not coord.should_stop():
start_time = time.time()
step, loss, opt = sess.run(
[global_step, model.loss_without_coeff, model.optimize])
time_window.append(time.time() - start_time)
loss_window.append(loss)
message = 'Step %-7d [%.03f sec/step, loss=%.05f, avg_loss=%.05f]' % (
step, time_window.average, loss, loss_window.average)
log(message, slack=(step % config.checkpoint_interval == 0))
if loss > 100 or math.isnan(loss):
log('Loss exploded to %.05f at step %d!' % (loss, step),
slack=True)
raise Exception('Loss Exploded')
if step % config.summary_interval == 0:
log('Writing summary at step: %d' % step)
summary_writer.add_summary(sess.run(train_stats), step)
if step % config.checkpoint_interval == 0:
log('Saving checkpoint to: %s-%d' %
(checkpoint_path, step))
saver.save(sess, checkpoint_path, global_step=step)
if step % config.test_interval == 0:
log('Saving audio and alignment...')
num_test = config.num_test
fetches = [
model.inputs[:num_test],
model.linear_outputs[:num_test],
model.alignments[:num_test],
test_model.inputs[:num_test],
test_model.linear_outputs[:num_test],
test_model.alignments[:num_test],
]
sequences, spectrograms, alignments, test_sequences, test_spectrograms, test_alignments = sess.run(
fetches)
#librosa는 ffmpeg가 있어야 한다.
save_and_plot(
sequences[:1], spectrograms[:1], alignments[:1],
log_dir, step, loss, "train"
) # spectrograms: (num_test,200,1025), alignments: (num_test,encoder_length,decoder_length)
save_and_plot(test_sequences, test_spectrograms,
test_alignments, log_dir, step, loss, "test")
if step == 50:
log("Stop at 300000's step (last loss= %.05f)" %
loss_window.average)
coord.request_stop(e)
except Exception as e:
log('Exiting due to exception: %s' % e, slack=True)
traceback.print_exc()
coord.request_stop(e)
def train(log_dir, config):
config.data_paths = config.data_paths # 파싱된 명령행 인자값 중 데이터 경로 : default='datasets/kr_example'
data_dirs = [os.path.join(data_path, "data") \
for data_path in config.data_paths]
num_speakers = len(data_dirs) # 학습하는 화자 수 측정 : 단일화자 모델-1, 다중화자 모델-2
config.num_test = config.num_test_per_speaker * num_speakers
if num_speakers > 1 and hparams.model_type not in ["deepvoice", "simple"]: # 다중화자 모델 학습일 때 모델 타입이 "deepvoice"나 "simple"이 아니라면
raise Exception("[!] Unkown model_type for multi-speaker: {}".format(config.model_type)) # hparams.modle_type을 config.model_type으로 오타남.
commit = get_git_commit() if config.git else 'None' # git 관련된거여서 무시
checkpoint_path = os.path.join(log_dir, 'model.ckpt') # checkpoint_path 경로 지정-model.skpt 파일 경로
log(' [*] git recv-parse HEAD:\n%s' % get_git_revision_hash()) # git log
log('='*50) # 줄 구분용 =====
#log(' [*] dit diff:\n%s' % get_git_diff())
log('='*50) # 줄 구분용 =====
log(' [*] Checkpoint path: %s' % checkpoint_path) # check_point 경로 출력
log(' [*] Loading training data from: %s' % data_dirs)
log(' [*] Using model: %s' % config.model_dir)
log(hparams_debug_string())
# Set up DataFeeder:
coord = tf.train.Coordinator() # 쓰레드 사용 선언
with tf.variable_scope('datafeeder') as scope:
train_feeder = DataFeeder(
coord, data_dirs, hparams, config, 32,
data_type='train', batch_size=hparams.batch_size)
# def __init__(self, coordinator, data_dirs, hparams, config, batches_per_group, data_type, batch_size):
test_feeder = DataFeeder(
coord, data_dirs, hparams, config, 8,
data_type='test', batch_size=config.num_test)
# Set up model:
is_randomly_initialized = config.initialize_path is None
global_step = tf.Variable(0, name='global_step', trainable=False)
with tf.variable_scope('model') as scope:
model = create_model(hparams) # Tacotron 모델 생성
model.initialize(
train_feeder.inputs, train_feeder.input_lengths,
num_speakers, train_feeder.speaker_id,
train_feeder.mel_targets, train_feeder.linear_targets,
train_feeder.loss_coeff,
is_randomly_initialized=is_randomly_initialized)
model.add_loss()
model.add_optimizer(global_step)
train_stats = add_stats(model, scope_name='stats') # legacy
with tf.variable_scope('model', reuse=True) as scope:
test_model = create_model(hparams) # Tacotron test모델 생성
test_model.initialize(
test_feeder.inputs, test_feeder.input_lengths,
num_speakers, test_feeder.speaker_id,
test_feeder.mel_targets, test_feeder.linear_targets,
test_feeder.loss_coeff, rnn_decoder_test_mode=True,
is_randomly_initialized=is_randomly_initialized)
test_model.add_loss()
test_stats = add_stats(test_model, model, scope_name='test') # model의 loss값같은것들을 tensorboard에 기록 / model에 test_model, model2에 model
test_stats = tf.summary.merge([test_stats, train_stats])
# Bookkeeping:
step = 0
time_window = ValueWindow(100) # ValueWindow 클래스 window_size = 100
loss_window = ValueWindow(100)
saver = tf.train.Saver(max_to_keep=None, keep_checkpoint_every_n_hours=2) # 2시간에 한번씩 자동저장, checkpoint 삭제 안됨
sess_config = tf.ConfigProto(
log_device_placement=False, # log_device_placement 작성하는동안 할당장치 알려줌.
allow_soft_placement=True) # allow_soft_placement False면 GPU없을때 오류남
sess_config.gpu_options.allow_growth=True # 탄력적으로 GPU메모리 사용
# Train!
#with tf.Session(config=sess_config) as sess:
with tf.Session() as sess: # with문 내의 모든 명령들은 CPU 혹은 GPU 사용 선언
try:
summary_writer = tf.summary.FileWriter(log_dir, sess.graph) # summary 오퍼레이션이 평가된 결과 및 텐서보드 그래프를 파라미터 형식으로 log_dir 에 저장
sess.run(tf.global_variables_initializer()) # 데이터셋이 로드되고 그래프가 모두 정의되면 변수를 초기화하여 훈련 시작
if config.load_path: # log의 설정 값들 경로를 지정하였다면
# Restore from a checkpoint if the user requested it.
restore_path = get_most_recent_checkpoint(config.model_dir) # 가장 마지막에 저장된 파일경로 저장
saver.restore(sess, restore_path) # restore_path 값 가져오기
log('Resuming from checkpoint: %s at commit: %s' % (restore_path, commit), slack=True) # git과 slack을 이용한 log 출력
elif config.initialize_path: # log의 설정 값들로 초기화하여 사용하기로 지정하였다면
restore_path = get_most_recent_checkpoint(config.initialize_path) # 지정된 경로에서 가장 마지막에 저장된 파일경로 저장
saver.restore(sess, restore_path) # restore_path 값 가져오기
log('Initialized from checkpoint: %s at commit: %s' % (restore_path, commit), slack=True) # git과 slack을 이용한 log 출력
zero_step_assign = tf.assign(global_step, 0) # global_step의 텐서 객체 참조 변수 값을 0으로 바꿔주는 명령어 지정
sess.run(zero_step_assign) # 변수들을 모두 0으로 바꾸는 명령어 실행
start_step = sess.run(global_step) # global_step 값 부분을 시작지점으로 하여 연산 시작
log('='*50)
log(' [*] Global step is reset to {}'. \
format(start_step)) # 즉, 연산 시작 부분이 0으로 초기화 되었다고 알려줌.
log('='*50)
else:
log('Starting new training run at commit: %s' % commit, slack=True) # 과거의 데이터를 사용하지 않을 경우 새로운 학습이라고 log 출력
start_step = sess.run(global_step) # 연산 시작지점 가져오기
train_feeder.start_in_session(sess, start_step)
test_feeder.start_in_session(sess, start_step)
while not coord.should_stop(): # 쓰레드가 멈춰야하는 상황이 아니라면
start_time = time.time() # 시작시간 지정(1970년 1월 1일 이후 경과된 시간을 UTC 기준으로 초로 반환)
step, loss, opt = sess.run(
[global_step, model.loss_without_coeff, model.optimize],
feed_dict=model.get_dummy_feed_dict()) # step 값은 global_step 값으로 지정, loss 값은
time_window.append(time.time() - start_time)
loss_window.append(loss)
message = 'Step %-7d [%.03f sec/step, loss=%.05f, avg_loss=%.05f]' % (
step, time_window.average, loss, loss_window.average)
log(message, slack=(step % config.checkpoint_interval == 0))
if loss > 100 or math.isnan(loss):
log('Loss exploded to %.05f at step %d!' % (loss, step), slack=True)
raise Exception('Loss Exploded')
if step % config.summary_interval == 0:
log('Writing summary at step: %d' % step)
feed_dict = {
**model.get_dummy_feed_dict(),
**test_model.get_dummy_feed_dict()
}
summary_writer.add_summary(sess.run(
test_stats, feed_dict=feed_dict), step)
if step % config.checkpoint_interval == 0:
log('Saving checkpoint to: %s-%d' % (checkpoint_path, step))
saver.save(sess, checkpoint_path, global_step=step)
if step % config.test_interval == 0:
log('Saving audio and alignment...')
num_test = config.num_test
fetches = [
model.inputs[:num_test],
model.linear_outputs[:num_test],
model.alignments[:num_test],
test_model.inputs[:num_test],
test_model.linear_outputs[:num_test],
test_model.alignments[:num_test],
]
feed_dict = {
**model.get_dummy_feed_dict(),
**test_model.get_dummy_feed_dict()
}
sequences, spectrograms, alignments, \
test_sequences, test_spectrograms, test_alignments = \
sess.run(fetches, feed_dict=feed_dict)
save_and_plot(sequences[:1], spectrograms[:1], alignments[:1],
log_dir, step, loss, "train")
save_and_plot(test_sequences, test_spectrograms, test_alignments,
log_dir, step, loss, "test")
except Exception as e:
log('Exiting due to exception: %s' % e, slack=True)
traceback.print_exc()
coord.request_stop(e)
def train(log_dir, config):
config.data_paths = config.data_paths
data_dirs = [os.path.join(data_path, "data") \
for data_path in config.data_paths]
num_speakers = len(data_dirs)
config.num_test = config.num_test_per_speaker * num_speakers
if num_speakers > 1 and hparams.model_type not in ["deepvoice", "simple"]:
raise Exception("[!] Unkown model_type for multi-speaker: {}".format(
config.model_type))
commit = get_git_commit() if config.git else 'None'
checkpoint_path = os.path.join(log_dir, 'model.ckpt')
#log(' [*] git recv-parse HEAD:\n%s' % get_git_revision_hash())
log('=' * 50)
#log(' [*] dit diff:\n%s' % get_git_diff())
log('=' * 50)
log(' [*] Checkpoint path: %s' % checkpoint_path)
log(' [*] Loading training data from: %s' % data_dirs)
log(' [*] Using model: %s' % config.model_dir)
log(hparams_debug_string())
# Set up DataFeeder:
coord = tf.train.Coordinator()
with tf.variable_scope('datafeeder') as scope:
train_feeder = DataFeeder(coord,
data_dirs,
hparams,
config,
32,
data_type='train',
batch_size=hparams.batch_size)
test_feeder = DataFeeder(coord,
data_dirs,
hparams,
config,
8,
data_type='test',
batch_size=config.num_test)
# Set up model:
is_randomly_initialized = config.initialize_path is None
global_step = tf.Variable(0, name='global_step', trainable=False)
with tf.variable_scope('model') as scope:
model = create_model(hparams)
model.initialize(train_feeder.inputs,
train_feeder.input_lengths,
num_speakers,
train_feeder.speaker_id,
train_feeder.mel_targets,
train_feeder.linear_targets,
train_feeder.loss_coeff,
is_randomly_initialized=is_randomly_initialized)
model.add_loss()
model.add_optimizer(global_step)
train_stats = add_stats(model, scope_name='stats') # legacy
with tf.variable_scope('model', reuse=True) as scope:
test_model = create_model(hparams)
test_model.initialize(test_feeder.inputs,
test_feeder.input_lengths,
num_speakers,
test_feeder.speaker_id,
test_feeder.mel_targets,
test_feeder.linear_targets,
test_feeder.loss_coeff,
rnn_decoder_test_mode=True,
is_randomly_initialized=is_randomly_initialized)
test_model.add_loss()
test_stats = add_stats(test_model, model, scope_name='test')
test_stats = tf.summary.merge([test_stats, train_stats])
# Bookkeeping:
step = 0
time_window = ValueWindow(100)
loss_window = ValueWindow(100)
saver = tf.train.Saver(max_to_keep=None, keep_checkpoint_every_n_hours=2)
sess_config = tf.ConfigProto(log_device_placement=False,
allow_soft_placement=True)
sess_config.gpu_options.allow_growth = True
# Train!
#with tf.Session(config=sess_config) as sess:
with tf.Session() as sess:
try:
summary_writer = tf.summary.FileWriter(log_dir, sess.graph)
sess.run(tf.global_variables_initializer())
if config.load_path:
# Restore from a checkpoint if the user requested it.
restore_path = get_most_recent_checkpoint(config.model_dir)
saver.restore(sess, restore_path)
log('Resuming from checkpoint: %s at commit: %s' %
(restore_path, commit),
slack=True)
elif config.initialize_path:
restore_path = get_most_recent_checkpoint(
config.initialize_path)
saver.restore(sess, restore_path)
log('Initialized from checkpoint: %s at commit: %s' %
(restore_path, commit),
slack=True)
zero_step_assign = tf.assign(global_step, 0)
sess.run(zero_step_assign)
start_step = sess.run(global_step)
log('=' * 50)
log(' [*] Global step is reset to {}'. \
format(start_step))
log('=' * 50)
else:
log('Starting new training run at commit: %s' % commit,
slack=True)
start_step = sess.run(global_step)
train_feeder.start_in_session(sess, start_step)
test_feeder.start_in_session(sess, start_step)
while not coord.should_stop():
start_time = time.time()
step, loss, opt = sess.run(
[global_step, model.loss_without_coeff, model.optimize],
feed_dict=model.get_dummy_feed_dict())
time_window.append(time.time() - start_time)
loss_window.append(loss)
message = 'Step %-7d [%.03f sec/step, loss=%.05f, avg_loss=%.05f]' % (
step, time_window.average, loss, loss_window.average)
log(message, slack=(step % config.checkpoint_interval == 0))
if loss > 100 or math.isnan(loss):
log('Loss exploded to %.05f at step %d!' % (loss, step),
slack=True)
raise Exception('Loss Exploded')
if step % config.summary_interval == 0:
log('Writing summary at step: %d' % step)
feed_dict = {
**model.get_dummy_feed_dict(),
**test_model.get_dummy_feed_dict()
}
summary_writer.add_summary(
sess.run(test_stats, feed_dict=feed_dict), step)
if step % config.checkpoint_interval == 0:
log('Saving checkpoint to: %s-%d' %
(checkpoint_path, step))
saver.save(sess, checkpoint_path, global_step=step)
if step % config.test_interval == 0:
log('Saving audio and alignment...')
num_test = config.num_test
fetches = [
model.inputs[:num_test],
model.linear_outputs[:num_test],
model.alignments[:num_test],
test_model.inputs[:num_test],
test_model.linear_outputs[:num_test],
test_model.alignments[:num_test],
]
feed_dict = {
**model.get_dummy_feed_dict(),
**test_model.get_dummy_feed_dict()
}
sequences, spectrograms, alignments, \
test_sequences, test_spectrograms, test_alignments = \
sess.run(fetches, feed_dict=feed_dict)
save_and_plot(sequences[:1], spectrograms[:1],
alignments[:1], log_dir, step, loss, "train")
save_and_plot(test_sequences, test_spectrograms,
test_alignments, log_dir, step, loss, "test")
except Exception as e:
log('Exiting due to exception: %s' % e, slack=True)
traceback.print_exc()
coord.request_stop(e)
def validate(val_loader, model, device, mels_criterion, stop_criterion, writer,
val_dir):
batch_time = ValueWindow()
losses = ValueWindow()
# switch to evaluate mode
model.eval()
global global_epoch
global global_step
with torch.no_grad():
end = time.time()
for i, (txts, mels, stop_tokens, txt_lengths,
mels_lengths) in enumerate(val_loader):
# measure data loading time
batch_time.update(time.time() - end)
if device > -1:
txts = txts.cuda(device)
mels = mels.cuda(device)
stop_tokens = stop_tokens.cuda(device)
txt_lengths = txt_lengths.cuda(device)
mels_lengths = mels_lengths.cuda(device)
# compute output
frames, decoder_frames, stop_tokens_predict, alignment = model(
txts, txt_lengths, mels)
decoder_frames_loss = mels_criterion(decoder_frames,
mels,
lengths=mels_lengths)
frames_loss = mels_criterion(frames, mels, lengths=mels_lengths)
stop_token_loss = stop_criterion(stop_tokens_predict,
stop_tokens,
lengths=mels_lengths)
loss = decoder_frames_loss + frames_loss + stop_token_loss
losses.update(loss.item())
# measure elapsed time
batch_time.update(time.time() - end)
end = time.time()
if i % hparams.print_freq == 0:
log('Epoch: [{0}]\t'
'Test: [{1}/{2}]\t'
'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t'
'Loss {loss.val:.4f} ({loss.avg:.4f})'.format(
global_epoch,
i,
len(val_loader),
batch_time=batch_time,
loss=losses))
# Logs
writer.add_scalar("loss", float(loss.item()), global_step)
writer.add_scalar(
"avg_loss in {} window".format(losses.get_dinwow_size),
float(losses.avg), global_step)
writer.add_scalar("stop_token_loss", float(stop_token_loss.item()),
global_step)
writer.add_scalar("decoder_frames_loss",
float(decoder_frames_loss.item()), global_step)
writer.add_scalar("output_frames_loss", float(frames_loss.item()),
global_step)
dst_alignment_path = join(val_dir,
"{}_alignment.png".format(global_step))
alignment = alignment.cpu().detach().numpy()
plot_alignment(alignment[0, :txt_lengths[0], :mels_lengths[0]],
dst_alignment_path,
info="{}, {}".format(hparams.builder, global_step))
return losses.avg
def train(log_dir, args):
save_dir = os.path.join(log_dir, 'pretrained/')
checkpoint_path = os.path.join(save_dir, 'model.ckpt')
input_path = os.path.join(args.base_dir, args.input)
plot_dir = os.path.join(log_dir, 'plots')
os.makedirs(plot_dir, exist_ok=True)
log('Checkpoint path: {}'.format(checkpoint_path))
log('Loading training data from: {}'.format(input_path))
log('Using model: {}'.format(args.model))
log(hparams_debug_string())
#Set up data feeder
coord = tf.train.Coordinator()
with tf.variable_scope('datafeeder') as scope:
feeder = Feeder(coord, input_path, hparams)
#Set up model:
step_count = 0
try:
#simple text file to keep count of global step
with open(os.path.join(log_dir, 'step_counter.txt'), 'r') as file:
step_count = int(file.read())
except:
print('no step_counter file found, assuming there is no saved checkpoint')
global_step = tf.Variable(step_count, name='global_step', trainable=False)
with tf.variable_scope('model') as scope:
model = create_model(args.model, hparams)
model.initialize(feeder.inputs, feeder.input_lengths, feeder.mel_targets, feeder.token_targets)
model.add_loss()
model.add_optimizer(global_step)
stats = add_stats(model)
#Book keeping
step = 0
time_window = ValueWindow(100)
loss_window = ValueWindow(100)
saver = tf.train.Saver(max_to_keep=5)
#Memory allocation on the GPU as needed
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
#Train
with tf.Session(config=config) as sess:
try:
summary_writer = tf.summary.FileWriter(log_dir, sess.graph)
sess.run(tf.global_variables_initializer())
#saved model restoring
if args.restore:
#Restore saved model if the user requested it, Default = True.
try:
checkpoint_state = tf.train.get_checkpoint_state(save_dir)
except tf.errors.OutOfRangeError as e:
log('Cannot restore checkpoint: {}'.format(e))
if (checkpoint_state and checkpoint_state.model_checkpoint_path):
log('Loading checkpoint {}'.format(checkpoint_state.model_checkpoint_path))
saver.restore(sess, checkpoint_state.model_checkpoint_path)
else:
if not args.restore:
log('Starting new training!')
else:
log('No model to load at {}'.format(save_dir))
#initiating feeder
feeder.start_in_session(sess)
#Training loop
while not coord.should_stop():
start_time = time.time()
step, loss, opt = sess.run([global_step, model.loss, model.optimize])
time_window.append(time.time() - start_time)
loss_window.append(loss)
message = 'Step {:7d} [{:.3f} sec/step, loss={:.5f}, avg_loss={:.5f}]'.format(
step, time_window.average, loss, loss_window.average)
log(message, end='\r')
if loss > 100 or np.isnan(loss):
log('Loss exploded to {:.5f} at step {}'.format(loss, step))
raise Exception('Loss exploded')
if step % args.summary_interval == 0:
log('\nWriting summary at step: {}'.format(step))
summary_writer.add_summary(sess.run(stats), step)
if step % args.checkpoint_interval == 0:
with open(os.path.join(log_dir,'step_counter.txt'), 'w') as file:
file.write(str(step))
log('Saving checkpoint to: {}-{}'.format(checkpoint_path, step))
saver.save(sess, checkpoint_path, global_step=step)
# Unlike the original tacotron, we won't save audio
# because we yet have to use wavenet as vocoder
log('Saving alignement and Mel-Spectrograms..')
input_seq, prediction, alignment, target = sess.run([model.inputs[0],
model.mel_outputs[0],
model.alignments[0],
model.mel_targets[0],
])
#save predicted spectrogram to disk (for plot and manual evaluation purposes)
mel_filename = 'ljspeech-mel-prediction-step-{}.npy'.format(step)
np.save(os.path.join(log_dir, mel_filename), prediction, allow_pickle=False)
#save alignment plot to disk (control purposes)
plot.plot_alignment(alignment, os.path.join(plot_dir, 'step-{}-align.png'.format(step)),
info='{}, {}, step={}, loss={:.5f}'.format(args.model, time_string(), step, loss))
#save real mel-spectrogram plot to disk (control purposes)
plot.plot_spectrogram(target, os.path.join(plot_dir, 'step-{}-real-mel-spectrogram.png'.format(step)),
info='{}, {}, step={}, Real'.format(args.model, time_string(), step, loss))
#save predicted mel-spectrogram plot to disk (control purposes)
plot.plot_spectrogram(prediction, os.path.join(plot_dir, 'step-{}-pred-mel-spectrogram.png'.format(step)),
info='{}, {}, step={}, loss={:.5}'.format(args.model, time_string(), step, loss))
log('Input at step {}: {}'.format(step, sequence_to_text(input_seq)))
except Exception as e:
log('Exiting due to exception: {}'.format(e), slack=True)
traceback.print_exc()
coord.request_stop(e)
def main():
parser = argparse.ArgumentParser()
parser.add_argument('-config', type=str, default='config/hparams.yaml')
parser.add_argument('-load_model', type=str, default=None)
parser.add_argument('-model_name', type=str, default='P_S_Transformer_debug',
help='model name')
# parser.add_argument('-batches_per_allreduce', type=int, default=1,
# help='number of batches processed locally before '
# 'executing allreduce across workers; it multiplies '
# 'total batch size.')
parser.add_argument('-num_wokers', type=int, default=0,
help='how many subprocesses to use for data loading. '
'0 means that the data will be loaded in the main process')
parser.add_argument('-log', type=str, default='train.log')
opt = parser.parse_args()
configfile = open(opt.config)
config = AttrDict(yaml.load(configfile,Loader=yaml.FullLoader))
log_name = opt.model_name or config.model.name
log_folder = os.path.join(os.getcwd(),'logdir/logging',log_name)
if not os.path.isdir(log_folder):
os.mkdir(log_folder)
logger = init_logger(log_folder+'/'+opt.log)
# TODO: build dataloader
train_datafeeder = DataFeeder(config,'debug')
# TODO: build model or load pre-trained model
global global_step
global_step = 0
learning_rate = CustomSchedule(config.model.d_model)
# learning_rate = 0.00002
optimizer = tf.keras.optimizers.Adam(learning_rate, beta_1=config.optimizer.beta1, beta_2=config.optimizer.beta2,
epsilon=config.optimizer.epsilon)
logger.info('config.optimizer.beta1:' + str(config.optimizer.beta1))
logger.info('config.optimizer.beta2:' + str(config.optimizer.beta2))
logger.info('config.optimizer.epsilon:' + str(config.optimizer.epsilon))
# print(str(config))
model = Speech_transformer(config=config,logger=logger)
#Create the checkpoint path and the checkpoint manager. This will be used to save checkpoints every n epochs.
checkpoint_path = log_folder
ckpt = tf.train.Checkpoint(transformer=model, optimizer=optimizer)
ckpt_manager = tf.train.CheckpointManager(ckpt, checkpoint_path, max_to_keep=5)
# if a checkpoint exists, restore the latest checkpoint.
if ckpt_manager.latest_checkpoint:
ckpt.restore(ckpt_manager.latest_checkpoint)
logger.info('Latest checkpoint restored!!')
else:
logger.info('Start new run')
# define metrics and summary writer
train_loss = tf.keras.metrics.Mean(name='train_loss')
train_accuracy = tf.keras.metrics.SparseCategoricalAccuracy(name='train_accuracy')
# summary_writer = tf.keras.callbacks.TensorBoard(log_dir=log_folder)
summary_writer = summary_ops_v2.create_file_writer_v2(log_folder+'/train')
# @tf.function
def train_step(batch_data):
inp = batch_data['the_inputs'] # batch*time*feature
tar = batch_data['the_labels'] # batch*time
# inp_len = batch_data['input_length']
# tar_len = batch_data['label_length']
gtruth = batch_data['ground_truth']
tar_inp = tar
tar_real = gtruth
# enc_padding_mask, combined_mask, dec_padding_mask = create_masks(inp[:,:,0], tar_inp)
combined_mask = create_combined_mask(tar=tar_inp)
with tf.GradientTape() as tape:
predictions, _ = model(inp, tar_inp, True, None,
combined_mask, None)
# logger.info('config.train.label_smoothing_epsilon:' + str(config.train.label_smoothing_epsilon))
loss = LableSmoothingLoss(tar_real, predictions,config.model.vocab_size,config.train.label_smoothing_epsilon)
gradients = tape.gradient(loss, model.trainable_variables)
clipped_gradients, _ = tf.clip_by_global_norm(gradients, 1.0)
optimizer.apply_gradients(zip(gradients, model.trainable_variables))
train_loss(loss)
train_accuracy(tar_real, predictions)
time_window = ValueWindow(100)
loss_window = ValueWindow(100)
acc_window = ValueWindow(100)
logger.info('config.train.epoches:' + str(config.train.epoches))
first_time = True
for epoch in range(config.train.epoches):
logger.info('start epoch '+ str(epoch))
logger.info('total wavs: '+ str(len(train_datafeeder)))
logger.info('batch size: ' + str(train_datafeeder.batch_size))
logger.info('batch per epoch: ' + str(len(train_datafeeder)//train_datafeeder.batch_size))
train_data = train_datafeeder.get_batch()
start_time = time.time()
train_loss.reset_states()
train_accuracy.reset_states()
for step in range(len(train_datafeeder)//train_datafeeder.batch_size):
batch_data = next(train_data)
step_time = time.time()
train_step(batch_data)
if first_time:
model.summary()
first_time=False
time_window.append(time.time()-step_time)
loss_window.append(train_loss.result())
acc_window.append(train_accuracy.result())
message = 'Step %-7d [%.03f sec/step, loss=%.05f, avg_loss=%.05f, acc=%.05f, avg_acc=%.05f]' % (
global_step, time_window.average, train_loss.result(), loss_window.average, train_accuracy.result(),acc_window.average)
logger.info(message)
if global_step % 10 == 0:
with summary_ops_v2.always_record_summaries():
with summary_writer.as_default():
summary_ops_v2.scalar('train_loss', train_loss.result(), step=global_step)
summary_ops_v2.scalar('train_acc', train_accuracy.result(), step=global_step)
global_step += 1
ckpt_save_path = ckpt_manager.save()
logger.info('Saving checkpoint for epoch {} at {}'.format(epoch+1, ckpt_save_path))
logger.info('Time taken for 1 epoch: {} secs\n'.format(time.time() - start_time))
