def train(args):
"""Train with the given args.
Args:
args (namespace): The program arguments.
"""
set_deterministic_pytorch(args)
# check cuda availability
if not torch.cuda.is_available():
logging.warning('cuda is not available')
# get input and output dimension info
with open(args.valid_json, 'rb') as f:
valid_json = json.load(f)['utts']
utts = list(valid_json.keys())
idim = int(valid_json[utts[0]]['input'][0]['shape'][-1])
odim = int(valid_json[utts[0]]['output'][0]['shape'][-1])
logging.info('#input dims : ' + str(idim))
logging.info('#output dims: ' + str(odim))
# specify attention, CTC, hybrid mode
if args.mtlalpha == 1.0:
mtl_mode = 'ctc'
logging.info('Pure CTC mode')
elif args.mtlalpha == 0.0:
mtl_mode = 'att'
logging.info('Pure attention mode')
else:
mtl_mode = 'mtl'
logging.info('Multitask learning mode')
if args.enc_init is not None or args.dec_init is not None:
model = load_trained_modules(idim, odim, args)
elif args.asr_init is not None:
model, _ = load_trained_model(args.asr_init)
else:
model_class = dynamic_import(args.model_module)
model = model_class(idim, odim, args)
assert isinstance(model, ASRInterface)
subsampling_factor = model.subsample[0]
if args.rnnlm is not None:
rnnlm_args = get_model_conf(args.rnnlm, args.rnnlm_conf)
rnnlm = lm_pytorch.ClassifierWithState(
lm_pytorch.RNNLM(len(args.char_list), rnnlm_args.layer,
rnnlm_args.unit))
torch.load(args.rnnlm, rnnlm)
model.rnnlm = rnnlm
# write model config
if not os.path.exists(args.outdir):
os.makedirs(args.outdir)
model_conf = args.outdir + '/model.json'
with open(model_conf, 'wb') as f:
logging.info('writing a model config file to ' + model_conf)
f.write(
json.dumps((idim, odim, vars(args)),
indent=4,
ensure_ascii=False,
sort_keys=True).encode('utf_8'))
for key in sorted(vars(args).keys()):
logging.info('ARGS: ' + key + ': ' + str(vars(args)[key]))
reporter = model.reporter
# check the use of multi-gpu
if args.ngpu > 1:
if args.batch_size != 0:
logging.info('batch size is automatically increased (%d -> %d)' %
(args.batch_size, args.batch_size * args.ngpu))
args.batch_size *= args.ngpu
# set torch device
device = torch.device("cuda" if args.ngpu > 0 else "cpu")
if args.train_dtype in ("float16", "float32", "float64"):
dtype = getattr(torch, args.train_dtype)
else:
dtype = torch.float32
logging.info(device)
logging.info(dtype)
model = model.to(device=device, dtype=dtype)
# Setup an optimizer
if args.opt == 'adadelta':
optimizer = torch.optim.Adadelta(model.parameters(),
rho=0.95,
eps=args.eps,
weight_decay=args.weight_decay)
elif args.opt == 'adam':
optimizer = torch.optim.Adam(model.parameters(),
weight_decay=args.weight_decay)
elif args.opt == 'noam':
from espnet.nets.pytorch_backend.rnn.optimizer import get_std_opt
optimizer = get_std_opt(model, args.adim,
args.transformer_warmup_steps,
args.transformer_lr)
else:
raise NotImplementedError("unknown optimizer: " + args.opt)
# setup apex.amp
if args.train_dtype in ("O0", "O1", "O2", "O3"):
try:
from apex import amp
except ImportError as e:
logging.error(
f"You need to install apex for --train-dtype {args.train_dtype}. "
"See https://github.com/NVIDIA/apex#linux")
raise e
if args.opt == 'noam':
model, optimizer.optimizer = amp.initialize(
model, optimizer.optimizer, opt_level=args.train_dtype)
else:
model, optimizer = amp.initialize(model,
optimizer,
opt_level=args.train_dtype)
use_apex = True
else:
use_apex = False
# FIXME: TOO DIRTY HACK
setattr(optimizer, "target", reporter)
setattr(optimizer, "serialize", lambda s: reporter.serialize(s))
# Setup a converter
converter = CustomConverter(subsampling_factor=subsampling_factor,
dtype=dtype)
# read json data
with open(args.train_json, 'rb') as f:
train_json = json.load(f)['utts']
with open(args.valid_json, 'rb') as f:
valid_json = json.load(f)['utts']
use_sortagrad = args.sortagrad == -1 or args.sortagrad > 0
# make minibatch list (variable length)
train = make_batchset(train_json,
args.batch_size,
args.maxlen_in,
args.maxlen_out,
args.minibatches,
min_batch_size=args.ngpu if args.ngpu > 1 else 1,
shortest_first=use_sortagrad,
count=args.batch_count,
batch_bins=args.batch_bins,
batch_frames_in=args.batch_frames_in,
batch_frames_out=args.batch_frames_out,
batch_frames_inout=args.batch_frames_inout,
iaxis=0,
oaxis=0)
valid = make_batchset(valid_json,
args.batch_size,
args.maxlen_in,
args.maxlen_out,
args.minibatches,
min_batch_size=args.ngpu if args.ngpu > 1 else 1,
count=args.batch_count,
batch_bins=args.batch_bins,
batch_frames_in=args.batch_frames_in,
batch_frames_out=args.batch_frames_out,
batch_frames_inout=args.batch_frames_inout,
iaxis=0,
oaxis=0)
load_tr = LoadInputsAndTargets(
mode='asr',
load_output=True,
preprocess_conf=args.preprocess_conf,
preprocess_args={'train': True} # Switch the mode of preprocessing
)
load_cv = LoadInputsAndTargets(
mode='asr',
load_output=True,
preprocess_conf=args.preprocess_conf,
preprocess_args={'train': False} # Switch the mode of preprocessing
)
# hack to make batchsize argument as 1
# actual bathsize is included in a list
if args.n_iter_processes > 0:
train_iter = ToggleableShufflingMultiprocessIterator(
TransformDataset(train, load_tr),
batch_size=1,
n_processes=args.n_iter_processes,
n_prefetch=8,
maxtasksperchild=20,
shuffle=not use_sortagrad)
valid_iter = ToggleableShufflingMultiprocessIterator(
TransformDataset(valid, load_cv),
batch_size=1,
repeat=False,
shuffle=False,
n_processes=args.n_iter_processes,
n_prefetch=8,
maxtasksperchild=20)
else:
train_iter = ToggleableShufflingSerialIterator(
TransformDataset(train, load_tr),
batch_size=1,
shuffle=not use_sortagrad)
valid_iter = ToggleableShufflingSerialIterator(TransformDataset(
valid, load_cv),
batch_size=1,
repeat=False,
shuffle=False)
# Set up a trainer
updater = CustomUpdater(model,
args.grad_clip,
train_iter,
optimizer,
converter,
device,
args.ngpu,
args.grad_noise,
args.accum_grad,
use_apex=use_apex)
trainer = training.Trainer(updater, (args.epochs, 'epoch'),
out=args.outdir)
if use_sortagrad:
trainer.extend(
ShufflingEnabler([train_iter]),
trigger=(args.sortagrad if args.sortagrad != -1 else args.epochs,
'epoch'))
# Resume from a snapshot
if args.resume:
logging.info('resumed from %s' % args.resume)
torch_resume(args.resume, trainer)
# Evaluate the model with the test dataset for each epoch
trainer.extend(
CustomEvaluator(model, valid_iter, reporter, converter, device,
args.ngpu))
# Save attention weight each epoch
if args.num_save_attention > 0 and args.mtlalpha != 1.0:
data = sorted(list(valid_json.items())[:args.num_save_attention],
key=lambda x: int(x[1]['input'][0]['shape'][1]),
reverse=True)
if hasattr(model, "module"):
att_vis_fn = model.module.calculate_all_attentions
plot_class = model.module.attention_plot_class
else:
att_vis_fn = model.calculate_all_attentions
plot_class = model.attention_plot_class
att_reporter = plot_class(att_vis_fn,
data,
args.outdir + "/att_ws",
converter=converter,
transform=load_cv,
device=device)
trainer.extend(att_reporter, trigger=(1, 'epoch'))
else:
att_reporter = None
# Make a plot for training and validation values
trainer.extend(
extensions.PlotReport([
'main/loss', 'validation/main/loss', 'main/loss_ctc',
'validation/main/loss_ctc', 'main/loss_att',
'validation/main/loss_att'
],
'epoch',
file_name='loss.png'))
trainer.extend(
extensions.PlotReport(['main/acc', 'validation/main/acc'],
'epoch',
file_name='acc.png'))
trainer.extend(
extensions.PlotReport(['main/cer_ctc', 'validation/main/cer_ctc'],
'epoch',
file_name='cer.png'))
# Save best models
trainer.extend(
snapshot_object(model, 'model.loss.best'),
trigger=training.triggers.MinValueTrigger('validation/main/loss'))
if mtl_mode != 'ctc':
trainer.extend(
snapshot_object(model, 'model.acc.best'),
trigger=training.triggers.MaxValueTrigger('validation/main/acc'))
# save snapshot which contains model and optimizer states
trainer.extend(torch_snapshot(), trigger=(1, 'epoch'))
# epsilon decay in the optimizer
if args.opt == 'adadelta':
if args.criterion == 'acc' and mtl_mode != 'ctc':
trainer.extend(restore_snapshot(model,
args.outdir + '/model.acc.best',
load_fn=torch_load),
trigger=CompareValueTrigger(
'validation/main/acc', lambda best_value,
current_value: best_value > current_value))
trainer.extend(adadelta_eps_decay(args.eps_decay),
trigger=CompareValueTrigger(
'validation/main/acc', lambda best_value,
current_value: best_value > current_value))
elif args.criterion == 'loss':
trainer.extend(restore_snapshot(model,
args.outdir + '/model.loss.best',
load_fn=torch_load),
trigger=CompareValueTrigger(
'validation/main/loss', lambda best_value,
current_value: best_value < current_value))
trainer.extend(adadelta_eps_decay(args.eps_decay),
trigger=CompareValueTrigger(
'validation/main/loss', lambda best_value,
current_value: best_value < current_value))
# Write a log of evaluation statistics for each epoch
trainer.extend(
extensions.LogReport(trigger=(args.report_interval_iters,
'iteration')))
report_keys = [
'epoch', 'iteration', 'main/loss', 'main/loss_ctc', 'main/loss_att',
'validation/main/loss', 'validation/main/loss_ctc',
'validation/main/loss_att', 'main/acc', 'validation/main/acc',
'main/cer_ctc', 'validation/main/cer_ctc', 'elapsed_time'
]
if args.opt == 'adadelta':
trainer.extend(extensions.observe_value(
'eps', lambda trainer: trainer.updater.get_optimizer('main').
param_groups[0]["eps"]),
trigger=(args.report_interval_iters, 'iteration'))
report_keys.append('eps')
if args.report_cer:
report_keys.append('validation/main/cer')
if args.report_wer:
report_keys.append('validation/main/wer')
trainer.extend(extensions.PrintReport(report_keys),
trigger=(args.report_interval_iters, 'iteration'))
trainer.extend(
extensions.ProgressBar(update_interval=args.report_interval_iters))
set_early_stop(trainer, args)
if args.tensorboard_dir is not None and args.tensorboard_dir != "":
trainer.extend(TensorboardLogger(SummaryWriter(args.tensorboard_dir),
att_reporter),
trigger=(args.report_interval_iters, "iteration"))
# Run the training
trainer.run()
check_early_stop(trainer, args.epochs)
def train(args):
"""Train with the given args
:param Namespace args: The program arguments
"""
set_deterministic_pytorch(args)
# check cuda availability
if not torch.cuda.is_available():
logging.warning('cuda is not available')
# get input and output dimension info
with open(args.valid_json, 'rb') as f:
valid_json = json.load(f)['utts']
utts = list(valid_json.keys())
# reverse input and output dimension
idim = int(valid_json[utts[0]]['output'][0]['shape'][1])
odim = int(valid_json[utts[0]]['input'][0]['shape'][1])
if args.use_cbhg:
args.spc_dim = int(valid_json[utts[0]]['input'][1]['shape'][1])
if args.use_speaker_embedding:
args.spk_embed_dim = int(valid_json[utts[0]]['input'][1]['shape'][0])
else:
args.spk_embed_dim = None
logging.info('#input dims : ' + str(idim))
logging.info('#output dims: ' + str(odim))
# write model config
if not os.path.exists(args.outdir):
os.makedirs(args.outdir)
model_conf = args.outdir + '/model.json'
with open(model_conf, 'wb') as f:
logging.info('writing a model config file to' + model_conf)
f.write(
json.dumps((idim, odim, vars(args)), indent=4,
sort_keys=True).encode('utf_8'))
for key in sorted(vars(args).keys()):
logging.info('ARGS: ' + key + ': ' + str(vars(args)[key]))
# specify model architecture
tacotron2 = Tacotron2(idim, odim, args)
logging.info(tacotron2)
# check the use of multi-gpu
if args.ngpu > 1:
tacotron2 = torch.nn.DataParallel(tacotron2,
device_ids=list(range(args.ngpu)))
logging.info('batch size is automatically increased (%d -> %d)' %
(args.batch_size, args.batch_size * args.ngpu))
args.batch_size *= args.ngpu
# set torch device
device = torch.device("cuda" if args.ngpu > 0 else "cpu")
tacotron2 = tacotron2.to(device)
# define loss
model = Tacotron2Loss(tacotron2, args.use_masking, args.bce_pos_weight)
reporter = model.reporter
# Setup an optimizer
optimizer = torch.optim.Adam(model.parameters(),
args.lr,
eps=args.eps,
weight_decay=args.weight_decay)
# FIXME: TOO DIRTY HACK
setattr(optimizer, 'target', reporter)
setattr(optimizer, 'serialize', lambda s: reporter.serialize(s))
# Setup a converter
converter = CustomConverter(
return_targets=True,
use_speaker_embedding=args.use_speaker_embedding,
use_second_target=args.use_cbhg,
preprocess_conf=args.preprocess_conf)
# read json data
with open(args.train_json, 'rb') as f:
train_json = json.load(f)['utts']
with open(args.valid_json, 'rb') as f:
valid_json = json.load(f)['utts']
use_sortagrad = args.sortagrad == -1 or args.sortagrad > 0
if use_sortagrad:
args.batch_sort_key = "input"
# make minibatch list (variable length)
train_batchset = make_batchset(
train_json,
args.batch_size,
args.maxlen_in,
args.maxlen_out,
args.minibatches,
args.batch_sort_key,
min_batch_size=args.ngpu if args.ngpu > 1 else 1,
shortest_first=use_sortagrad)
valid_batchset = make_batchset(
valid_json,
args.batch_size,
args.maxlen_in,
args.maxlen_out,
args.minibatches,
args.batch_sort_key,
min_batch_size=args.ngpu if args.ngpu > 1 else 1,
shortest_first=use_sortagrad)
# hack to make batchsize argument as 1
# actual bathsize is included in a list
if args.n_iter_processes > 0:
train_iter = ToggleableShufflingMultiprocessIterator(
TransformDataset(train_batchset, converter.transform),
batch_size=1,
n_processes=args.n_iter_processes,
n_prefetch=8,
maxtasksperchild=20,
shuffle=not use_sortagrad)
valid_iter = ToggleableShufflingMultiprocessIterator(
TransformDataset(valid_batchset, converter.transform),
batch_size=1,
repeat=False,
shuffle=False,
n_processes=args.n_iter_processes,
n_prefetch=8,
maxtasksperchild=20)
else:
train_iter = ToggleableShufflingSerialIterator(
TransformDataset(train_batchset, converter.transform),
batch_size=1,
shuffle=not use_sortagrad)
valid_iter = ToggleableShufflingSerialIterator(TransformDataset(
valid_batchset, converter.transform),
batch_size=1,
repeat=False,
shuffle=False)
# Set up a trainer
updater = CustomUpdater(model, args.grad_clip, train_iter, optimizer,
converter, device)
trainer = training.Trainer(updater, (args.epochs, 'epoch'),
out=args.outdir)
# Resume from a snapshot
if args.resume:
logging.info('resumed from %s' % args.resume)
torch_resume(args.resume, trainer)
# Evaluate the model with the test dataset for each epoch
trainer.extend(
CustomEvaluator(model, valid_iter, reporter, converter, device))
# Save snapshot for each epoch
trainer.extend(torch_snapshot(), trigger=(1, 'epoch'))
# Save best models
trainer.extend(
extensions.snapshot_object(tacotron2,
'model.loss.best',
savefun=torch_save),
trigger=training.triggers.MinValueTrigger('validation/main/loss'))
# Save attention figure for each epoch
if args.num_save_attention > 0:
data = sorted(list(valid_json.items())[:args.num_save_attention],
key=lambda x: int(x[1]['input'][0]['shape'][1]),
reverse=True)
if hasattr(tacotron2, "module"):
att_vis_fn = tacotron2.module.calculate_all_attentions
else:
att_vis_fn = tacotron2.calculate_all_attentions
att_reporter = PlotAttentionReport(
att_vis_fn,
data,
args.outdir + '/att_ws',
converter=CustomConverter(
return_targets=False,
use_speaker_embedding=args.use_speaker_embedding,
preprocess_conf=args.preprocess_conf),
device=device,
reverse=True)
trainer.extend(att_reporter, trigger=(1, 'epoch'))
else:
att_reporter = None
# Make a plot for training and validation values
plot_keys = [
'main/loss', 'validation/main/loss', 'main/l1_loss',
'validation/main/l1_loss', 'main/mse_loss', 'validation/main/mse_loss',
'main/bce_loss', 'validation/main/bce_loss'
]
trainer.extend(
extensions.PlotReport(['main/l1_loss', 'validation/main/l1_loss'],
'epoch',
file_name='l1_loss.png'))
trainer.extend(
extensions.PlotReport(['main/mse_loss', 'validation/main/mse_loss'],
'epoch',
file_name='mse_loss.png'))
trainer.extend(
extensions.PlotReport(['main/bce_loss', 'validation/main/bce_loss'],
'epoch',
file_name='bce_loss.png'))
if args.use_cbhg:
plot_keys += [
'main/cbhg_l1_loss', 'validation/main/cbhg_l1_loss',
'main/cbhg_mse_loss', 'validation/main/cbhg_mse_loss'
]
trainer.extend(
extensions.PlotReport(
['main/cbhg_l1_loss', 'validation/main/cbhg_l1_loss'],
'epoch',
file_name='cbhg_l1_loss.png'))
trainer.extend(
extensions.PlotReport(
['main/cbhg_mse_loss', 'validation/main/cbhg_mse_loss'],
'epoch',
file_name='cbhg_mse_loss.png'))
trainer.extend(
extensions.PlotReport(plot_keys, 'epoch', file_name='loss.png'))
# Write a log of evaluation statistics for each epoch
trainer.extend(extensions.LogReport(trigger=(REPORT_INTERVAL,
'iteration')))
report_keys = plot_keys[:]
report_keys[0:0] = ['epoch', 'iteration', 'elapsed_time']
trainer.extend(extensions.PrintReport(report_keys),
trigger=(REPORT_INTERVAL, 'iteration'))
trainer.extend(extensions.ProgressBar(update_interval=REPORT_INTERVAL))
set_early_stop(trainer, args)
if args.tensorboard_dir is not None and args.tensorboard_dir != "":
writer = SummaryWriter(log_dir=args.tensorboard_dir)
trainer.extend(TensorboardLogger(writer, att_reporter))
if use_sortagrad:
trainer.extend(
ShufflingEnabler([train_iter]),
trigger=(args.sortagrad if args.sortagrad != -1 else args.epochs,
'epoch'))
# Run the training
trainer.run()
check_early_stop(trainer, args.epochs)
def train(args):
"""Train E2E-TTS model."""
set_deterministic_pytorch(args)
# check cuda availability
if not torch.cuda.is_available():
logging.warning('cuda is not available')
# get input and output dimension info
with open(args.valid_json, 'rb') as f:
valid_json = json.load(f)['utts']
utts = list(valid_json.keys())
# reverse input and output dimension
idim = int(valid_json[utts[0]]['output'][0]['shape'][1])
odim = int(valid_json[utts[0]]['input'][0]['shape'][1])
logging.info('#input dims : ' + str(idim))
logging.info('#output dims: ' + str(odim))
# get extra input and output dimenstion
if args.use_speaker_embedding:
args.spk_embed_dim = int(valid_json[utts[0]]['input'][1]['shape'][0])
else:
args.spk_embed_dim = None
if args.use_second_target:
args.spc_dim = int(valid_json[utts[0]]['input'][1]['shape'][1])
else:
args.spc_dim = None
# write model config
if not os.path.exists(args.outdir):
os.makedirs(args.outdir)
model_conf = args.outdir + '/model.json'
with open(model_conf, 'wb') as f:
logging.info('writing a model config file to' + model_conf)
f.write(json.dumps((idim, odim, vars(args)),
indent=4, ensure_ascii=False, sort_keys=True).encode('utf_8'))
for key in sorted(vars(args).keys()):
logging.info('ARGS: ' + key + ': ' + str(vars(args)[key]))
# specify model architecture
model_class = dynamic_import(args.model_module)
model = model_class(idim, odim, args)
assert isinstance(model, TTSInterface)
logging.info(model)
reporter = model.reporter
# check the use of multi-gpu
if args.ngpu > 1:
model = torch.nn.DataParallel(model, device_ids=list(range(args.ngpu)))
if args.batch_size != 0:
logging.info('batch size is automatically increased (%d -> %d)' % (
args.batch_size, args.batch_size * args.ngpu))
args.batch_size *= args.ngpu
# set torch device
device = torch.device("cuda" if args.ngpu > 0 else "cpu")
model = model.to(device)
# Setup an optimizer
if args.opt == 'adam':
optimizer = torch.optim.Adam(
model.parameters(), args.lr, eps=args.eps,
weight_decay=args.weight_decay)
elif args.opt == 'noam':
from espnet.nets.pytorch_backend.transformer.optimizer import get_std_opt
optimizer = get_std_opt(model, args.adim, args.transformer_warmup_steps, args.transformer_lr)
else:
raise NotImplementedError("unknown optimizer: " + args.opt)
# FIXME: TOO DIRTY HACK
setattr(optimizer, 'target', reporter)
setattr(optimizer, 'serialize', lambda s: reporter.serialize(s))
# read json data
with open(args.train_json, 'rb') as f:
train_json = json.load(f)['utts']
with open(args.valid_json, 'rb') as f:
valid_json = json.load(f)['utts']
use_sortagrad = args.sortagrad == -1 or args.sortagrad > 0
if use_sortagrad:
args.batch_sort_key = "input"
# make minibatch list (variable length)
train_batchset = make_batchset(train_json, args.batch_size,
args.maxlen_in, args.maxlen_out, args.minibatches,
batch_sort_key=args.batch_sort_key,
min_batch_size=args.ngpu if args.ngpu > 1 else 1,
shortest_first=use_sortagrad,
count=args.batch_count,
batch_bins=args.batch_bins,
batch_frames_in=args.batch_frames_in,
batch_frames_out=args.batch_frames_out,
batch_frames_inout=args.batch_frames_inout,
swap_io=True)
valid_batchset = make_batchset(valid_json, args.batch_size,
args.maxlen_in, args.maxlen_out, args.minibatches,
batch_sort_key=args.batch_sort_key,
min_batch_size=args.ngpu if args.ngpu > 1 else 1,
count=args.batch_count,
batch_bins=args.batch_bins,
batch_frames_in=args.batch_frames_in,
batch_frames_out=args.batch_frames_out,
batch_frames_inout=args.batch_frames_inout,
swap_io=True)
load_tr = LoadInputsAndTargets(
mode='tts',
use_speaker_embedding=args.use_speaker_embedding,
use_second_target=args.use_second_target,
preprocess_conf=args.preprocess_conf,
preprocess_args={'train': True}, # Switch the mode of preprocessing
keep_all_data_on_mem=args.keep_all_data_on_mem,
)
load_cv = LoadInputsAndTargets(
mode='tts',
use_speaker_embedding=args.use_speaker_embedding,
use_second_target=args.use_second_target,
preprocess_conf=args.preprocess_conf,
preprocess_args={'train': False}, # Switch the mode of preprocessing
keep_all_data_on_mem=args.keep_all_data_on_mem,
)
# hack to make batchsize argument as 1
# actual bathsize is included in a list
if args.num_iter_processes > 0:
train_iter = ToggleableShufflingMultiprocessIterator(
TransformDataset(train_batchset, load_tr),
batch_size=1, n_processes=args.num_iter_processes, n_prefetch=8, maxtasksperchild=20,
shuffle=not use_sortagrad)
valid_iter = ToggleableShufflingMultiprocessIterator(
TransformDataset(valid_batchset, load_cv),
batch_size=1, repeat=False, shuffle=False,
n_processes=args.num_iter_processes, n_prefetch=8, maxtasksperchild=20)
else:
train_iter = ToggleableShufflingSerialIterator(
TransformDataset(train_batchset, load_tr),
batch_size=1, shuffle=not use_sortagrad)
valid_iter = ToggleableShufflingSerialIterator(
TransformDataset(valid_batchset, load_cv),
batch_size=1, repeat=False, shuffle=False)
# Set up a trainer
converter = CustomConverter()
updater = CustomUpdater(model, args.grad_clip, train_iter, optimizer, converter, device, args.accum_grad)
trainer = training.Trainer(updater, (args.epochs, 'epoch'), out=args.outdir)
# Resume from a snapshot
if args.resume:
logging.info('resumed from %s' % args.resume)
torch_resume(args.resume, trainer)
# Evaluate the model with the test dataset for each epoch
trainer.extend(CustomEvaluator(model, valid_iter, reporter, converter, device))
# set intervals
save_interval = (args.save_interval_epochs, 'epoch')
report_interval = (args.report_interval_iters, 'iteration')
# Save snapshot for each epoch
trainer.extend(torch_snapshot(), trigger=save_interval)
# Save best models
trainer.extend(snapshot_object(model, 'model.loss.best'),
trigger=training.triggers.MinValueTrigger('validation/main/loss', trigger=save_interval))
# Save attention figure for each epoch
if args.num_save_attention > 0:
data = sorted(list(valid_json.items())[:args.num_save_attention],
key=lambda x: int(x[1]['input'][0]['shape'][1]), reverse=True)
if hasattr(model, "module"):
att_vis_fn = model.module.calculate_all_attentions
plot_class = model.module.attention_plot_class
else:
att_vis_fn = model.calculate_all_attentions
plot_class = model.attention_plot_class
att_reporter = plot_class(
att_vis_fn, data, args.outdir + '/att_ws',
converter=converter,
transform=load_cv,
device=device, reverse=True)
trainer.extend(att_reporter, trigger=save_interval)
else:
att_reporter = None
# Make a plot for training and validation values
if hasattr(model, "module"):
base_plot_keys = model.module.base_plot_keys
else:
base_plot_keys = model.base_plot_keys
plot_keys = []
for key in base_plot_keys:
plot_key = ['main/' + key, 'validation/main/' + key]
trainer.extend(extensions.PlotReport(plot_key, 'epoch', file_name=key + '.png'))
plot_keys += plot_key
trainer.extend(extensions.PlotReport(plot_keys, 'epoch', file_name='all_loss.png'))
# Write a log of evaluation statistics for each epoch
trainer.extend(extensions.LogReport(trigger=report_interval))
report_keys = ['epoch', 'iteration', 'elapsed_time'] + plot_keys
trainer.extend(extensions.PrintReport(report_keys), trigger=report_interval)
trainer.extend(extensions.ProgressBar(), trigger=report_interval)
set_early_stop(trainer, args)
if args.tensorboard_dir is not None and args.tensorboard_dir != "":
writer = SummaryWriter(args.tensorboard_dir)
trainer.extend(TensorboardLogger(writer, att_reporter), trigger=report_interval)
if use_sortagrad:
trainer.extend(ShufflingEnabler([train_iter]),
trigger=(args.sortagrad if args.sortagrad != -1 else args.epochs, 'epoch'))
# Run the training
trainer.run()
check_early_stop(trainer, args.epochs)
def train(args):
"""Train with the given args
:param Namespace args: The program arguments
"""
set_deterministic_pytorch(args)
# check cuda availability
if not torch.cuda.is_available():
logging.warning('cuda is not available')
# get input and output dimension info
with open(args.valid_json, 'rb') as f:
valid_json = json.load(f)['utts']
utts = list(valid_json.keys())
idim = int(valid_json[utts[0]]['input'][0]['shape'][1])
odim = int(valid_json[utts[0]]['output'][0]['shape'][1])
logging.info('#input dims : ' + str(idim))
logging.info('#output dims: ' + str(odim))
# specify attention, CTC, hybrid mode
if args.mtlalpha == 1.0:
mtl_mode = 'ctc'
logging.info('Pure CTC mode')
elif args.mtlalpha == 0.0:
mtl_mode = 'att'
logging.info('Pure attention mode')
else:
mtl_mode = 'mtl'
logging.info('Multitask learning mode')
# specify model architecture
model = E2E(idim, odim, args)
subsampling_factor = model.subsample[0]
if args.rnnlm is not None:
rnnlm_args = get_model_conf(args.rnnlm, args.rnnlm_conf)
rnnlm = lm_pytorch.ClassifierWithState(
lm_pytorch.RNNLM(len(args.char_list), rnnlm_args.layer,
rnnlm_args.unit))
torch.load(args.rnnlm, rnnlm)
model.rnnlm = rnnlm
# write model config
if not os.path.exists(args.outdir):
os.makedirs(args.outdir)
model_conf = args.outdir + '/model.json'
with open(model_conf, 'wb') as f:
logging.info('writing a model config file to ' + model_conf)
f.write(
json.dumps((idim, odim, vars(args)), indent=4,
sort_keys=True).encode('utf_8'))
for key in sorted(vars(args).keys()):
logging.info('ARGS: ' + key + ': ' + str(vars(args)[key]))
reporter = model.reporter
# check the use of multi-gpu
if args.ngpu > 1:
model = torch.nn.DataParallel(model, device_ids=list(range(args.ngpu)))
logging.info('batch size is automatically increased (%d -> %d)' %
(args.batch_size, args.batch_size * args.ngpu))
args.batch_size *= args.ngpu
# set torch device
device = torch.device("cuda" if args.ngpu > 0 else "cpu")
model = model.to(device)
# Setup an optimizer
if args.opt == 'adadelta':
optimizer = torch.optim.Adadelta(model.parameters(),
rho=0.95,
eps=args.eps,
weight_decay=args.weight_decay)
elif args.opt == 'adam':
optimizer = torch.optim.Adam(model.parameters(),
weight_decay=args.weight_decay)
# FIXME: TOO DIRTY HACK
setattr(optimizer, "target", reporter)
setattr(optimizer, "serialize", lambda s: reporter.serialize(s))
# Setup a converter
converter = CustomConverter(subsampling_factor=subsampling_factor,
preprocess_conf=args.preprocess_conf)
# read json data
with open(args.train_json, 'rb') as f:
train_json = json.load(f)['utts']
with open(args.valid_json, 'rb') as f:
valid_json = json.load(f)['utts']
use_sortagrad = args.sortagrad == -1 or args.sortagrad > 0
# make minibatch list (variable length)
train = make_batchset(train_json,
args.batch_size,
args.maxlen_in,
args.maxlen_out,
args.minibatches,
min_batch_size=args.ngpu if args.ngpu > 1 else 1,
shortest_first=use_sortagrad)
valid = make_batchset(valid_json,
args.batch_size,
args.maxlen_in,
args.maxlen_out,
args.minibatches,
min_batch_size=args.ngpu if args.ngpu > 1 else 1)
# hack to make batchsize argument as 1
# actual bathsize is included in a list
if args.n_iter_processes > 0:
train_iter = ToggleableShufflingMultiprocessIterator(
TransformDataset(train, converter.transform),
batch_size=1,
n_processes=args.n_iter_processes,
n_prefetch=8,
maxtasksperchild=20,
shuffle=not use_sortagrad)
valid_iter = ToggleableShufflingMultiprocessIterator(
TransformDataset(valid, converter.transform),
batch_size=1,
repeat=False,
shuffle=False,
n_processes=args.n_iter_processes,
n_prefetch=8,
maxtasksperchild=20)
else:
train_iter = ToggleableShufflingSerialIterator(
TransformDataset(train, converter.transform),
batch_size=1,
shuffle=not use_sortagrad)
valid_iter = ToggleableShufflingSerialIterator(TransformDataset(
valid, converter.transform),
batch_size=1,
repeat=False,
shuffle=False)
# Set up a trainer
updater = CustomUpdater(model, args.grad_clip, train_iter, optimizer,
converter, device, args.ngpu)
trainer = training.Trainer(updater, (args.epochs, 'epoch'),
out=args.outdir)
if use_sortagrad:
trainer.extend(
ShufflingEnabler([train_iter]),
trigger=(args.sortagrad if args.sortagrad != -1 else args.epochs,
'epoch'))
# Resume from a snapshot
if args.resume:
logging.info('resumed from %s' % args.resume)
torch_resume(args.resume, trainer)
# Evaluate the model with the test dataset for each epoch
trainer.extend(
CustomEvaluator(model, valid_iter, reporter, converter, device))
# Save attention weight each epoch
if args.num_save_attention > 0 and args.mtlalpha != 1.0:
data = sorted(list(valid_json.items())[:args.num_save_attention],
key=lambda x: int(x[1]['input'][0]['shape'][1]),
reverse=True)
if hasattr(model, "module"):
att_vis_fn = model.module.calculate_all_attentions
else:
att_vis_fn = model.calculate_all_attentions
att_reporter = PlotAttentionReport(att_vis_fn,
data,
args.outdir + "/att_ws",
converter=converter,
device=device)
trainer.extend(att_reporter, trigger=(1, 'epoch'))
else:
att_reporter = None
# Make a plot for training and validation values
trainer.extend(
extensions.PlotReport([
'main/loss', 'validation/main/loss', 'main/loss_ctc',
'validation/main/loss_ctc', 'main/loss_att',
'validation/main/loss_att'
],
'epoch',
file_name='loss.png'))
trainer.extend(
extensions.PlotReport(['main/acc', 'validation/main/acc'],
'epoch',
file_name='acc.png'))
trainer.extend(
extensions.PlotReport(['main/cer_ctc', 'validation/main/cer_ctc'],
'epoch',
file_name='cer.png'))
# Save best models
trainer.extend(
extensions.snapshot_object(model,
'model.loss.best',
savefun=torch_save),
trigger=training.triggers.MinValueTrigger('validation/main/loss'))
if mtl_mode is not 'ctc':
trainer.extend(
extensions.snapshot_object(model,
'model.acc.best',
savefun=torch_save),
trigger=training.triggers.MaxValueTrigger('validation/main/acc'))
# save snapshot which contains model and optimizer states
trainer.extend(torch_snapshot(), trigger=(1, 'epoch'))
# epsilon decay in the optimizer
if args.opt == 'adadelta':
if args.criterion == 'acc' and mtl_mode is not 'ctc':
trainer.extend(restore_snapshot(model,
args.outdir + '/model.acc.best',
load_fn=torch_load),
trigger=CompareValueTrigger(
'validation/main/acc', lambda best_value,
current_value: best_value > current_value))
trainer.extend(adadelta_eps_decay(args.eps_decay),
trigger=CompareValueTrigger(
'validation/main/acc', lambda best_value,
current_value: best_value > current_value))
elif args.criterion == 'loss':
trainer.extend(restore_snapshot(model,
args.outdir + '/model.loss.best',
load_fn=torch_load),
trigger=CompareValueTrigger(
'validation/main/loss', lambda best_value,
current_value: best_value < current_value))
trainer.extend(adadelta_eps_decay(args.eps_decay),
trigger=CompareValueTrigger(
'validation/main/loss', lambda best_value,
current_value: best_value < current_value))
# Write a log of evaluation statistics for each epoch
trainer.extend(extensions.LogReport(trigger=(REPORT_INTERVAL,
'iteration')))
report_keys = [
'epoch', 'iteration', 'main/loss', 'main/loss_ctc', 'main/loss_att',
'validation/main/loss', 'validation/main/loss_ctc',
'validation/main/loss_att', 'main/acc', 'validation/main/acc',
'main/cer_ctc', 'validation/main/cer_ctc', 'elapsed_time'
]
if args.opt == 'adadelta':
trainer.extend(extensions.observe_value(
'eps', lambda trainer: trainer.updater.get_optimizer('main').
param_groups[0]["eps"]),
trigger=(REPORT_INTERVAL, 'iteration'))
report_keys.append('eps')
if args.report_cer:
report_keys.append('validation/main/cer')
if args.report_wer:
report_keys.append('validation/main/wer')
trainer.extend(extensions.PrintReport(report_keys),
trigger=(REPORT_INTERVAL, 'iteration'))
trainer.extend(extensions.ProgressBar(update_interval=REPORT_INTERVAL))
set_early_stop(trainer, args)
if args.tensorboard_dir is not None and args.tensorboard_dir != "":
writer = SummaryWriter(log_dir=args.tensorboard_dir)
trainer.extend(TensorboardLogger(writer, att_reporter))
# Run the training
trainer.run()
check_early_stop(trainer, args.epochs)
def train(args):
"""Train with the given args.
Args:
args (namespace): The program arguments.
"""
# display chainer version
logging.info('chainer version = ' + chainer.__version__)
set_deterministic_chainer(args)
# check cuda and cudnn availability
if not chainer.cuda.available:
logging.warning('cuda is not available')
if not chainer.cuda.cudnn_enabled:
logging.warning('cudnn is not available')
# get input and output dimension info
with open(args.valid_json, 'rb') as f:
valid_json = json.load(f)['utts']
utts = list(valid_json.keys())
idim = int(valid_json[utts[0]]['input'][0]['shape'][1])
odim = int(valid_json[utts[0]]['output'][0]['shape'][1])
logging.info('#input dims : ' + str(idim))
logging.info('#output dims: ' + str(odim))
# specify attention, CTC, hybrid mode
if args.mtlalpha == 1.0:
mtl_mode = 'ctc'
logging.info('Pure CTC mode')
elif args.mtlalpha == 0.0:
mtl_mode = 'att'
logging.info('Pure attention mode')
else:
mtl_mode = 'mtl'
logging.info('Multitask learning mode')
# specify model architecture
logging.info('import model module: ' + args.model_module)
model_class = dynamic_import(args.model_module)
model = model_class(idim, odim, args, flag_return=False)
assert isinstance(model, ASRInterface)
# write model config
if not os.path.exists(args.outdir):
os.makedirs(args.outdir)
model_conf = args.outdir + '/model.json'
with open(model_conf, 'wb') as f:
logging.info('writing a model config file to ' + model_conf)
f.write(
json.dumps((idim, odim, vars(args)),
indent=4,
ensure_ascii=False,
sort_keys=True).encode('utf_8'))
for key in sorted(vars(args).keys()):
logging.info('ARGS: ' + key + ': ' + str(vars(args)[key]))
# Set gpu
ngpu = args.ngpu
if ngpu == 1:
gpu_id = 0
# Make a specified GPU current
chainer.cuda.get_device_from_id(gpu_id).use()
model.to_gpu() # Copy the model to the GPU
logging.info('single gpu calculation.')
elif ngpu > 1:
gpu_id = 0
devices = {'main': gpu_id}
for gid in six.moves.xrange(1, ngpu):
devices['sub_%d' % gid] = gid
logging.info('multi gpu calculation (#gpus = %d).' % ngpu)
logging.info('batch size is automatically increased (%d -> %d)' %
(args.batch_size, args.batch_size * args.ngpu))
else:
gpu_id = -1
logging.info('cpu calculation')
# Setup an optimizer
if args.opt == 'adadelta':
optimizer = chainer.optimizers.AdaDelta(eps=args.eps)
elif args.opt == 'adam':
optimizer = chainer.optimizers.Adam()
elif args.opt == 'noam':
optimizer = chainer.optimizers.Adam(alpha=0,
beta1=0.9,
beta2=0.98,
eps=1e-9)
else:
raise NotImplementedError('args.opt={}'.format(args.opt))
optimizer.setup(model)
optimizer.add_hook(chainer.optimizer.GradientClipping(args.grad_clip))
# Setup Training Extensions
if 'transformer' in args.model_module:
from espnet.nets.chainer_backend.transformer.training import CustomConverter
from espnet.nets.chainer_backend.transformer.training import CustomParallelUpdater
from espnet.nets.chainer_backend.transformer.training import CustomUpdater
else:
from espnet.nets.chainer_backend.rnn.training import CustomConverter
from espnet.nets.chainer_backend.rnn.training import CustomParallelUpdater
from espnet.nets.chainer_backend.rnn.training import CustomUpdater
# Setup a converter
converter = CustomConverter(subsampling_factor=model.subsample[0])
# read json data
with open(args.train_json, 'rb') as f:
train_json = json.load(f)['utts']
with open(args.valid_json, 'rb') as f:
valid_json = json.load(f)['utts']
# set up training iterator and updater
load_tr = LoadInputsAndTargets(
mode='asr',
load_output=True,
preprocess_conf=args.preprocess_conf,
preprocess_args={'train': True} # Switch the mode of preprocessing
)
load_cv = LoadInputsAndTargets(
mode='asr',
load_output=True,
preprocess_conf=args.preprocess_conf,
preprocess_args={'train': False} # Switch the mode of preprocessing
)
use_sortagrad = args.sortagrad == -1 or args.sortagrad > 0
accum_grad = args.accum_grad
if ngpu <= 1:
# make minibatch list (variable length)
train = make_batchset(train_json,
args.batch_size,
args.maxlen_in,
args.maxlen_out,
args.minibatches,
min_batch_size=args.ngpu if args.ngpu > 1 else 1,
shortest_first=use_sortagrad,
count=args.batch_count,
batch_bins=args.batch_bins,
batch_frames_in=args.batch_frames_in,
batch_frames_out=args.batch_frames_out,
batch_frames_inout=args.batch_frames_inout,
iaxis=0,
oaxis=0)
# hack to make batchsize argument as 1
# actual batchsize is included in a list
if args.n_iter_processes > 0:
train_iters = [
ToggleableShufflingMultiprocessIterator(
TransformDataset(train, load_tr),
batch_size=1,
n_processes=args.n_iter_processes,
n_prefetch=8,
maxtasksperchild=20,
shuffle=not use_sortagrad)
]
else:
train_iters = [
ToggleableShufflingSerialIterator(TransformDataset(
train, load_tr),
batch_size=1,
shuffle=not use_sortagrad)
]
# set up updater
updater = CustomUpdater(train_iters[0],
optimizer,
converter=converter,
device=gpu_id,
accum_grad=accum_grad)
else:
if args.batch_count not in ("auto", "seq") and args.batch_size == 0:
raise NotImplementedError(
"--batch-count 'bin' and 'frame' are not implemented in chainer multi gpu"
)
# set up minibatches
train_subsets = []
for gid in six.moves.xrange(ngpu):
# make subset
train_json_subset = {
k: v
for i, (k, v) in enumerate(train_json.items())
if i % ngpu == gid
}
# make minibatch list (variable length)
train_subsets += [
make_batchset(train_json_subset, args.batch_size,
args.maxlen_in, args.maxlen_out,
args.minibatches)
]
# each subset must have same length for MultiprocessParallelUpdater
maxlen = max([len(train_subset) for train_subset in train_subsets])
for train_subset in train_subsets:
if maxlen != len(train_subset):
for i in six.moves.xrange(maxlen - len(train_subset)):
train_subset += [train_subset[i]]
# hack to make batchsize argument as 1
# actual batchsize is included in a list
if args.n_iter_processes > 0:
train_iters = [
ToggleableShufflingMultiprocessIterator(
TransformDataset(train_subsets[gid], load_tr),
batch_size=1,
n_processes=args.n_iter_processes,
n_prefetch=8,
maxtasksperchild=20,
shuffle=not use_sortagrad)
for gid in six.moves.xrange(ngpu)
]
else:
train_iters = [
ToggleableShufflingSerialIterator(TransformDataset(
train_subsets[gid], load_tr),
batch_size=1,
shuffle=not use_sortagrad)
for gid in six.moves.xrange(ngpu)
]
# set up updater
updater = CustomParallelUpdater(train_iters,
optimizer,
converter=converter,
devices=devices)
# Set up a trainer
trainer = training.Trainer(updater, (args.epochs, 'epoch'),
out=args.outdir)
if use_sortagrad:
trainer.extend(
ShufflingEnabler(train_iters),
trigger=(args.sortagrad if args.sortagrad != -1 else args.epochs,
'epoch'))
if args.opt == 'noam':
from espnet.nets.chainer_backend.transformer.training import VaswaniRule
trainer.extend(VaswaniRule('alpha',
d=args.adim,
warmup_steps=args.transformer_warmup_steps,
scale=args.transformer_lr),
trigger=(1, 'iteration'))
# Resume from a snapshot
if args.resume:
chainer.serializers.load_npz(args.resume, trainer)
# set up validation iterator
valid = make_batchset(valid_json,
args.batch_size,
args.maxlen_in,
args.maxlen_out,
args.minibatches,
min_batch_size=args.ngpu if args.ngpu > 1 else 1,
count=args.batch_count,
batch_bins=args.batch_bins,
batch_frames_in=args.batch_frames_in,
batch_frames_out=args.batch_frames_out,
batch_frames_inout=args.batch_frames_inout,
iaxis=0,
oaxis=0)
if args.n_iter_processes > 0:
valid_iter = chainer.iterators.MultiprocessIterator(
TransformDataset(valid, load_cv),
batch_size=1,
repeat=False,
shuffle=False,
n_processes=args.n_iter_processes,
n_prefetch=8,
maxtasksperchild=20)
else:
valid_iter = chainer.iterators.SerialIterator(TransformDataset(
valid, load_cv),
batch_size=1,
repeat=False,
shuffle=False)
# Evaluate the model with the test dataset for each epoch
trainer.extend(
BaseEvaluator(valid_iter, model, converter=converter, device=gpu_id))
# Save attention weight each epoch
if args.num_save_attention > 0 and args.mtlalpha != 1.0:
data = sorted(list(valid_json.items())[:args.num_save_attention],
key=lambda x: int(x[1]['input'][0]['shape'][1]),
reverse=True)
if hasattr(model, "module"):
att_vis_fn = model.module.calculate_all_attentions
plot_class = model.module.attention_plot_class
else:
att_vis_fn = model.calculate_all_attentions
plot_class = model.attention_plot_class
logging.info('Using custom PlotAttentionReport')
att_reporter = plot_class(att_vis_fn,
data,
args.outdir + "/att_ws",
converter=converter,
transform=load_cv,
device=gpu_id)
trainer.extend(att_reporter, trigger=(1, 'epoch'))
else:
att_reporter = None
# Take a snapshot for each specified epoch
trainer.extend(
extensions.snapshot(filename='snapshot.ep.{.updater.epoch}'),
trigger=(1, 'epoch'))
# Make a plot for training and validation values
trainer.extend(
extensions.PlotReport([
'main/loss', 'validation/main/loss', 'main/loss_ctc',
'validation/main/loss_ctc', 'main/loss_att',
'validation/main/loss_att'
],
'epoch',
file_name='loss.png'))
trainer.extend(
extensions.PlotReport(['main/acc', 'validation/main/acc'],
'epoch',
file_name='acc.png'))
# Save best models
trainer.extend(
extensions.snapshot_object(model, 'model.loss.best'),
trigger=training.triggers.MinValueTrigger('validation/main/loss'))
if mtl_mode != 'ctc':
trainer.extend(
extensions.snapshot_object(model, 'model.acc.best'),
trigger=training.triggers.MaxValueTrigger('validation/main/acc'))
# epsilon decay in the optimizer
if args.opt == 'adadelta':
if args.criterion == 'acc' and mtl_mode != 'ctc':
trainer.extend(restore_snapshot(model,
args.outdir + '/model.acc.best'),
trigger=CompareValueTrigger(
'validation/main/acc', lambda best_value,
current_value: best_value > current_value))
trainer.extend(adadelta_eps_decay(args.eps_decay),
trigger=CompareValueTrigger(
'validation/main/acc', lambda best_value,
current_value: best_value > current_value))
elif args.criterion == 'loss':
trainer.extend(restore_snapshot(model,
args.outdir + '/model.loss.best'),
trigger=CompareValueTrigger(
'validation/main/loss', lambda best_value,
current_value: best_value < current_value))
trainer.extend(adadelta_eps_decay(args.eps_decay),
trigger=CompareValueTrigger(
'validation/main/loss', lambda best_value,
current_value: best_value < current_value))
# Write a log of evaluation statistics for each epoch
trainer.extend(
extensions.LogReport(trigger=(args.report_interval_iters,
'iteration')))
report_keys = [
'epoch', 'iteration', 'main/loss', 'main/loss_ctc', 'main/loss_att',
'validation/main/loss', 'validation/main/loss_ctc',
'validation/main/loss_att', 'main/acc', 'validation/main/acc',
'elapsed_time'
]
if args.opt == 'adadelta':
trainer.extend(extensions.observe_value(
'eps', lambda trainer: trainer.updater.get_optimizer('main').eps),
trigger=(args.report_interval_iters, 'iteration'))
report_keys.append('eps')
trainer.extend(extensions.PrintReport(report_keys),
trigger=(args.report_interval_iters, 'iteration'))
trainer.extend(
extensions.ProgressBar(update_interval=args.report_interval_iters))
set_early_stop(trainer, args)
if args.tensorboard_dir is not None and args.tensorboard_dir != "":
writer = SummaryWriter(args.tensorboard_dir)
trainer.extend(TensorboardLogger(writer, att_reporter),
trigger=(args.report_interval_iters, 'iteration'))
# Run the training
trainer.run()
check_early_stop(trainer, args.epochs)
def train(args):
"""Train with the given args.
Args:
args (namespace): The program arguments.
"""
set_deterministic_pytorch(args)
# check cuda availability
if not torch.cuda.is_available():
logging.warning('cuda is not available')
# get input and output dimension info
with open(args.valid_json, 'rb') as f:
valid_json = json.load(f)['utts']
utts = list(valid_json.keys())
idim = int(valid_json[utts[0]]['input'][0]['shape'][-1])
odim = int(valid_json[utts[0]]['output'][0]['shape'][-1])
logging.info('#input dims : ' + str(idim))
logging.info('#output dims: ' + str(odim))
# specify attention, CTC, hybrid mode
if args.mtlalpha == 1.0:
mtl_mode = 'ctc'
logging.info('Pure CTC mode')
elif args.mtlalpha == 0.0:
mtl_mode = 'att'
logging.info('Pure attention mode')
else:
mtl_mode = 'mtl'
logging.info('Multitask learning mode')
asr_model, mt_model = None, None
# Initialize encoder with pre-trained ASR encoder
if args.asr_model:
asr_model, _ = load_trained_model(args.asr_model)
assert isinstance(asr_model, ASRInterface)
# Initialize decoder with pre-trained MT decoder
if args.mt_model:
mt_model, _ = load_trained_model(args.mt_model)
assert isinstance(mt_model, MTInterface)
# specify model architecture
model_class = dynamic_import(args.model_module)
# TODO(hirofumi0810) better to simplify the E2E model interface by only allowing idim, odim, and args
# the pre-trained ASR and MT model arguments should be removed here and we should implement an additional method
# to attach these models
if asr_model is None and mt_model is None:
model = model_class(idim, odim, args)
elif mt_model is None:
model = asr_model
else:
model = model_class(idim,
odim,
args,
asr_model=asr_model,
mt_model=mt_model)
assert isinstance(model, ASRInterface)
subsampling_factor = model.subsample[0]
# delete pre-trained models
if args.asr_model:
del asr_model
if args.mt_model:
del mt_model
if args.slu_model and args.slu_loss:
model.add_slu(args.slu_model, args.slu_loss, args.slu_tune_weights,
args.slu_pooling)
if args.rnnlm is not None:
rnnlm_args = get_model_conf(args.rnnlm, args.rnnlm_conf)
rnnlm = lm_pytorch.ClassifierWithState(
lm_pytorch.RNNLM(len(args.char_list), rnnlm_args.layer,
rnnlm_args.unit))
torch.load(args.rnnlm, rnnlm)
model.rnnlm = rnnlm
# write model config
if not os.path.exists(args.outdir):
os.makedirs(args.outdir)
model_conf = args.outdir + '/model.json'
with open(model_conf, 'wb') as f:
logging.info('writing a model config file to ' + model_conf)
f.write(
json.dumps((idim, odim, vars(args)),
indent=4,
ensure_ascii=False,
sort_keys=True).encode('utf_8'))
for key in sorted(vars(args).keys()):
logging.info('ARGS: ' + key + ': ' + str(vars(args)[key]))
reporter = model.reporter
# check the use of multi-gpu
if args.ngpu > 1:
model = torch.nn.DataParallel(model, device_ids=list(range(args.ngpu)))
if args.batch_size != 0:
logging.info('batch size is automatically increased (%d -> %d)' %
(args.batch_size, args.batch_size * args.ngpu))
args.batch_size *= args.ngpu
# set torch device
device = torch.device("cuda" if args.ngpu > 0 else "cpu")
model = model.to(device)
scheduler = None
# Setup an optimizer
if args.opt == 'adadelta':
optimizer = torch.optim.Adadelta(model.parameters(),
rho=0.95,
eps=args.eps,
weight_decay=args.weight_decay)
elif args.opt == 'adam':
optimizer = torch.optim.Adam(model.parameters(),
weight_decay=args.weight_decay)
elif args.opt == 'noam':
from espnet.nets.pytorch_backend.transformer.optimizer import get_std_opt
optimizer = get_std_opt(model, args.adim,
args.transformer_warmup_steps,
args.transformer_lr)
elif args.opt == 'adamw':
from transformers import AdamW, WarmupLinearSchedule
# Prepare optimizer and schedule (linear warmup and decay)
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':
0.0
}, {
'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=5e-5, eps=1e-8)
else:
raise NotImplementedError("unknown optimizer: " + args.opt)
# FIXME: TOO DIRTY HACK
setattr(optimizer, "target", reporter)
setattr(optimizer, "serialize", lambda s: reporter.serialize(s))
# Setup a converter
converter = CustomConverter(subsampling_factor=subsampling_factor)
# read json data
with open(args.train_json, 'rb') as f:
train_json = json.load(f)['utts']
with open(args.valid_json, 'rb') as f:
valid_json = json.load(f)['utts']
use_sortagrad = args.sortagrad == -1 or args.sortagrad > 0
# make minibatch list (variable length)
train = make_batchset(train_json,
args.batch_size,
args.maxlen_in,
args.maxlen_out,
args.minibatches,
min_batch_size=args.ngpu if args.ngpu > 1 else 1,
shortest_first=use_sortagrad,
count=args.batch_count,
batch_bins=args.batch_bins,
batch_frames_in=args.batch_frames_in,
batch_frames_out=args.batch_frames_out,
batch_frames_inout=args.batch_frames_inout)
valid = make_batchset(valid_json,
args.batch_size,
args.maxlen_in,
args.maxlen_out,
args.minibatches,
min_batch_size=args.ngpu if args.ngpu > 1 else 1,
count=args.batch_count,
batch_bins=args.batch_bins,
batch_frames_in=args.batch_frames_in,
batch_frames_out=args.batch_frames_out,
batch_frames_inout=args.batch_frames_inout)
load_tr = LoadInputsAndTargets(
mode='asr',
load_output=True,
preprocess_conf=args.preprocess_conf,
preprocess_args={'train': True} # Switch the mode of preprocessing
)
load_cv = LoadInputsAndTargets(
mode='asr',
load_output=True,
preprocess_conf=args.preprocess_conf,
preprocess_args={'train': False} # Switch the mode of preprocessing
)
# hack to make batchsize argument as 1
# actual bathsize is included in a list
if args.n_iter_processes > 0:
train_iter = ToggleableShufflingMultiprocessIterator(
TransformDataset(train, load_tr),
batch_size=1,
n_processes=args.n_iter_processes,
n_prefetch=8,
maxtasksperchild=20,
shuffle=not use_sortagrad)
valid_iter = ToggleableShufflingMultiprocessIterator(
TransformDataset(valid, load_cv),
batch_size=1,
repeat=False,
shuffle=False,
n_processes=args.n_iter_processes,
n_prefetch=8,
maxtasksperchild=20)
else:
train_iter = ToggleableShufflingSerialIterator(
TransformDataset(train, load_tr),
batch_size=1,
shuffle=not use_sortagrad)
valid_iter = ToggleableShufflingSerialIterator(TransformDataset(
valid, load_cv),
batch_size=1,
repeat=False,
shuffle=False)
# Set up a trainer
updater = CustomUpdater(model, args.grad_clip, train_iter, optimizer,
converter, device, args.ngpu, args.grad_noise,
args.accum_grad)
trainer = training.Trainer(updater, (args.epochs, 'epoch'),
out=args.outdir)
if use_sortagrad:
trainer.extend(
ShufflingEnabler([train_iter]),
trigger=(args.sortagrad if args.sortagrad != -1 else args.epochs,
'epoch'))
if scheduler:
trainer.extend(scheduler.step(), name='transformer_warmup')
# Resume from a snapshot
if args.resume:
logging.info('resumed from %s' % args.resume)
torch_resume(args.resume, trainer)
# Evaluate the model with the test dataset for each epoch
trainer.extend(
CustomEvaluator(model, valid_iter, reporter, converter, device))
# Save attention weight each epoch
if args.num_save_attention > 0 and args.mtlalpha != 1.0:
data = sorted(list(valid_json.items())[:args.num_save_attention],
key=lambda x: int(x[1]['input'][0]['shape'][1]),
reverse=True)
if hasattr(model, "module"):
att_vis_fn = model.module.calculate_all_attentions
plot_class = model.module.attention_plot_class
else:
att_vis_fn = model.calculate_all_attentions
plot_class = model.attention_plot_class
att_reporter = plot_class(att_vis_fn,
data,
args.outdir + "/att_ws",
converter=converter,
transform=load_cv,
device=device)
trainer.extend(att_reporter, trigger=(1, 'epoch'))
else:
att_reporter = None
# Make a plot for training and validation values
trainer.extend(
extensions.PlotReport([
'main/loss', 'validation/main/loss', 'main/loss_ctc',
'validation/main/loss_ctc', 'main/loss_att',
'validation/main/loss_att'
],
'epoch',
file_name='loss.png'))
trainer.extend(
extensions.PlotReport(['main/acc', 'validation/main/acc'],
'epoch',
file_name='acc.png'))
trainer.extend(
extensions.PlotReport(['main/cer_ctc', 'validation/main/cer_ctc'],
'epoch',
file_name='cer.png'))
# Save best models
trainer.extend(
snapshot_object(model, 'model.loss.best'),
trigger=training.triggers.MinValueTrigger('validation/main/loss'))
if mtl_mode != 'ctc':
trainer.extend(
snapshot_object(model, 'model.acc.best'),
trigger=training.triggers.MaxValueTrigger('validation/main/acc'))
# save snapshot which contains model and optimizer states
trainer.extend(torch_snapshot())
# epsilon decay in the optimizer
if args.opt == 'adadelta':
if args.criterion == 'acc' and mtl_mode != 'ctc':
trainer.extend(restore_snapshot(model,
args.outdir + '/model.acc.best',
load_fn=torch_load),
trigger=CompareValueTrigger(
'validation/main/acc', lambda best_value,
current_value: best_value > current_value))
trainer.extend(adadelta_eps_decay(args.eps_decay),
trigger=CompareValueTrigger(
'validation/main/acc', lambda best_value,
current_value: best_value > current_value))
elif args.criterion == 'loss':
trainer.extend(restore_snapshot(model,
args.outdir + '/model.loss.best',
load_fn=torch_load),
trigger=CompareValueTrigger(
'validation/main/loss', lambda best_value,
current_value: best_value < current_value))
trainer.extend(adadelta_eps_decay(args.eps_decay),
trigger=CompareValueTrigger(
'validation/main/loss', lambda best_value,
current_value: best_value < current_value))
# Write a log of evaluation statistics for each epoch
trainer.extend(
extensions.LogReport(trigger=(args.report_interval_iters,
'iteration')))
report_keys = [
'epoch', 'iteration', 'main/loss', 'main/loss_ctc', 'main/loss_att',
'validation/main/loss', 'validation/main/loss_ctc',
'validation/main/loss_att', 'main/acc', 'validation/main/acc',
'main/cer_ctc', 'validation/main/cer_ctc', 'elapsed_time'
]
if args.opt == 'adadelta':
trainer.extend(extensions.observe_value(
'eps', lambda trainer: trainer.updater.get_optimizer('main').
param_groups[0]["eps"]),
trigger=(args.report_interval_iters, 'iteration'))
report_keys.append('eps')
if args.report_cer:
report_keys.append('validation/main/cer')
if args.report_wer:
report_keys.append('validation/main/wer')
trainer.extend(extensions.PrintReport(report_keys),
trigger=(args.report_interval_iters, 'iteration'))
trainer.extend(
extensions.ProgressBar(update_interval=args.report_interval_iters))
set_early_stop(trainer, args)
if args.tensorboard_dir is not None and args.tensorboard_dir != "":
trainer.extend(TensorboardLogger(SummaryWriter(args.tensorboard_dir),
att_reporter),
trigger=(args.report_interval_iters, "iteration"))
# Run the training
trainer.run()
check_early_stop(trainer, args.epochs)