def plot_ctc(self):
"""Plot CTC posteriors during training."""
self.dec_fwd._plot_ctc(mkdir_join(self.save_path, 'ctc'))
if getattr(self, 'dec_fwd_sub1', None) is not None:
self.dec_fwd_sub1._plot_ctc(mkdir_join(self.save_path, 'ctc_sub1'))
if getattr(self, 'dec_fwd_sub2', None) is not None:
self.dec_fwd_sub2._plot_ctc(mkdir_join(self.save_path, 'ctc_sub2'))
def plot_attention(self):
"""Plot attention weights during training."""
# encoder
self.enc._plot_attention(mkdir_join(self.save_path, 'enc_att_weights'))
# decoder
self.dec_fwd._plot_attention(mkdir_join(self.save_path, 'dec_att_weights'))
if getattr(self, 'dec_fwd_sub1', None) is not None:
self.dec_fwd_sub1._plot_attention(mkdir_join(self.save_path, 'dec_att_weights_sub1'))
if getattr(self, 'dec_fwd_sub2', None) is not None:
self.dec_fwd_sub2._plot_attention(mkdir_join(self.save_path, 'dec_att_weights_sub2'))
def _plot_attention(self, save_path, n_cols=2):
"""Plot attention for each head in all encoder layers."""
from matplotlib import pyplot as plt
from matplotlib.ticker import MaxNLocator
_save_path = mkdir_join(save_path, 'enc_att_weights')
# Clean directory
if _save_path is not None and os.path.isdir(_save_path):
shutil.rmtree(_save_path)
os.mkdir(_save_path)
elens = self.data_dict['elens']
for k, aw in self.aws_dict.items():
plt.clf()
n_heads = aw.shape[1]
n_cols_tmp = 1 if n_heads == 1 else n_cols
fig, axes = plt.subplots(max(1, n_heads // n_cols_tmp), n_cols_tmp,
figsize=(20, 8), squeeze=False)
for h in range(n_heads):
ax = axes[h // n_cols_tmp, h % n_cols_tmp]
ax.imshow(aw[-1, h, :elens[-1], :elens[-1]], aspect="auto")
ax.grid(False)
ax.set_xlabel("Input (head%d)" % h)
ax.set_ylabel("Output (head%d)" % h)
ax.xaxis.set_major_locator(MaxNLocator(integer=True))
ax.yaxis.set_major_locator(MaxNLocator(integer=True))
fig.tight_layout()
fig.savefig(os.path.join(_save_path, '%s.png' % k), dvi=500)
plt.close()
def plot_attention(self, n_cols=4):
"""Plot attention for each head in all layers."""
from matplotlib import pyplot as plt
from matplotlib.ticker import MaxNLocator
save_path = mkdir_join(self.save_path, 'att_weights')
# Clean directory
if save_path is not None and os.path.isdir(save_path):
shutil.rmtree(save_path)
os.mkdir(save_path)
for lth in range(self.n_layers):
if not hasattr(self, 'yy_aws_layer%d' % lth):
continue
yy_aws = getattr(self, 'yy_aws_layer%d' % lth)
plt.clf()
fig, axes = plt.subplots(self.n_heads // n_cols, n_cols, figsize=(20, 8))
for h in range(self.n_heads):
if self.n_heads > n_cols:
ax = axes[h // n_cols, h % n_cols]
else:
ax = axes[h]
ax.imshow(yy_aws[-1, h, :, :], aspect="auto")
ax.grid(False)
ax.set_xlabel("Input (head%d)" % h)
ax.set_ylabel("Output (head%d)" % h)
ax.xaxis.set_major_locator(MaxNLocator(integer=True))
ax.yaxis.set_major_locator(MaxNLocator(integer=True))
fig.tight_layout()
fig.savefig(os.path.join(save_path, 'layer%d.png' % (lth)), dvi=500)
plt.close()
def _plot_attention(self, save_path, n_cols=1):
"""Plot attention."""
from matplotlib import pyplot as plt
from matplotlib.ticker import MaxNLocator
_save_path = mkdir_join(save_path, 'dec_att_weights')
# Clean directory
if _save_path is not None and os.path.isdir(_save_path):
shutil.rmtree(_save_path)
os.mkdir(_save_path)
if hasattr(self, 'aws'):
plt.clf()
fig, axes = plt.subplots(max(1, self.score.n_heads // n_cols),
n_cols,
figsize=(20, 8),
squeeze=False)
for h in range(self.score.n_heads):
ax = axes[h // n_cols, h % n_cols]
ax.imshow(self.aws[-1, h, :, :], aspect="auto")
ax.grid(False)
ax.set_xlabel("Input (head%d)" % h)
ax.set_ylabel("Output (head%d)" % h)
ax.xaxis.set_major_locator(MaxNLocator(integer=True))
ax.yaxis.set_major_locator(MaxNLocator(integer=True))
fig.tight_layout()
fig.savefig(os.path.join(_save_path, 'attention.png'), dvi=500)
plt.close()
def _plot_attention(self, save_path, n_cols=2):
"""Plot attention for each head in all layers."""
from matplotlib import pyplot as plt
from matplotlib.ticker import MaxNLocator
for attn in ['yy', 'xy']:
_save_path = mkdir_join(save_path, 'dec_%s_att_weights' % attn)
# Clean directory
if _save_path is not None and os.path.isdir(_save_path):
shutil.rmtree(_save_path)
os.mkdir(_save_path)
for l in range(self.n_layers):
if hasattr(self, '%s_aws_layer%d' % (attn, l)):
aws = getattr(self, '%s_aws_layer%d' % (attn, l))
plt.clf()
fig, axes = plt.subplots(max(1, self.n_heads // n_cols),
n_cols,
figsize=(20, 8),
squeeze=False)
for h in range(self.n_heads):
ax = axes[h // n_cols, h % n_cols]
ax.imshow(aws[-1, h, :, :], aspect="auto")
ax.grid(False)
ax.set_xlabel("Input (head%d)" % h)
ax.set_ylabel("Output (head%d)" % h)
ax.xaxis.set_major_locator(MaxNLocator(integer=True))
ax.yaxis.set_major_locator(MaxNLocator(integer=True))
fig.tight_layout()
fig.savefig(os.path.join(_save_path, 'layer%d.png' % (l)),
dvi=500)
plt.close()
def _plot_attention(self, save_path, n_cols=2):
"""Plot attention for each head in all decoder layers."""
if getattr(self, 'att_weight', 0) == 0:
return
from matplotlib import pyplot as plt
from matplotlib.ticker import MaxNLocator
_save_path = mkdir_join(save_path, 'dec_att_weights')
# Clean directory
if _save_path is not None and os.path.isdir(_save_path):
shutil.rmtree(_save_path)
os.mkdir(_save_path)
elens = self.data_dict['elens']
ylens = self.data_dict['ylens']
# ys = self.data_dict['ys']
for k, aw in self.aws_dict.items():
plt.clf()
n_heads = aw.shape[1]
n_cols_tmp = 1 if n_heads == 1 else n_cols * max(1, n_heads // 4)
fig, axes = plt.subplots(max(1, n_heads // n_cols_tmp),
n_cols_tmp,
figsize=(20 * max(1, n_heads // 4), 8),
squeeze=False)
for h in range(n_heads):
ax = axes[h // n_cols_tmp, h % n_cols_tmp]
if 'yy' in k:
ax.imshow(aw[-1, h, :ylens[-1], :ylens[-1]], aspect="auto")
else:
ax.imshow(aw[-1, h, :ylens[-1], :elens[-1]], aspect="auto")
# NOTE: show the last utterance in a mini-batch
ax.grid(False)
ax.set_xlabel("Input (head%d)" % h)
ax.set_ylabel("Output (head%d)" % h)
ax.xaxis.set_major_locator(MaxNLocator(integer=True))
ax.yaxis.set_major_locator(MaxNLocator(integer=True))
# ax.set_yticks(np.linspace(0, ylens[-1] - 1, ylens[-1]))
# ax.set_yticks(np.linspace(0, ylens[-1] - 1, 1), minor=True)
# ax.set_yticklabels(ys + [''])
fig.tight_layout()
fig.savefig(os.path.join(_save_path, '%s.png' % k), dvi=500)
plt.close()
def _plot_ctc(self, save_path, topk=10):
"""Plot CTC posteriors."""
if self.ctc_weight == 0:
return
from matplotlib import pyplot as plt
_save_path = mkdir_join(save_path, 'ctc')
# Clean directory
if _save_path is not None and os.path.isdir(_save_path):
shutil.rmtree(_save_path)
os.mkdir(_save_path)
elen = self.ctc.data_dict['elens'][-1]
probs = self.ctc.prob_dict['probs'][-1, :elen] # `[T, vocab]`
# NOTE: show the last utterance in a mini-batch
topk_ids = np.argsort(probs, axis=1)
plt.clf()
n_frames = probs.shape[0]
times_probs = np.arange(n_frames)
# NOTE: index 0 is reserved for blank
for idx in set(topk_ids.reshape(-1).tolist()):
if idx == 0:
plt.plot(times_probs,
probs[:, 0],
':',
label='<blank>',
color='grey')
else:
plt.plot(times_probs, probs[:, idx])
plt.xlabel(u'Time [frame]', fontsize=12)
plt.ylabel('Posteriors', fontsize=12)
plt.xticks(list(range(0, int(n_frames) + 1, 10)))
plt.yticks(list(range(0, 2, 1)))
plt.tight_layout()
plt.savefig(os.path.join(_save_path, '%s.png' % 'prob'), dvi=500)
plt.close()
def main():
args = parse()
# Load a conf file
dir_name = os.path.dirname(args.recog_model[0])
conf = load_config(os.path.join(dir_name, 'conf.yml'))
# Overwrite conf
for k, v in conf.items():
if 'recog' not in k:
setattr(args, k, v)
# Setting for logging
if os.path.isfile(os.path.join(args.recog_dir, 'plot.log')):
os.remove(os.path.join(args.recog_dir, 'plot.log'))
set_logger(os.path.join(args.recog_dir, 'plot.log'),
stdout=args.recog_stdout)
for i, s in enumerate(args.recog_sets):
# Load dataset
dataset = Dataset(corpus=args.corpus,
tsv_path=s,
dict_path=os.path.join(dir_name, 'dict.txt'),
wp_model=os.path.join(dir_name, 'wp.model'),
unit=args.unit,
batch_size=args.recog_batch_size,
bptt=args.bptt,
backward=args.backward,
serialize=args.serialize,
is_test=True)
if i == 0:
# Load the LM
model = build_lm(args, dir_name)
topk_list = load_checkpoint(model, args.recog_model[0])
epoch = int(args.recog_model[0].split('-')[-1])
# Model averaging for Transformer
if conf['lm_type'] == 'transformer':
model = average_checkpoints(model,
args.recog_model[0],
n_average=args.recog_n_average,
topk_list=topk_list)
logger.info('epoch: %d' % (epoch - 1))
logger.info('batch size: %d' % args.recog_batch_size)
# logger.info('recog unit: %s' % args.recog_unit)
# logger.info('ensemble: %d' % (len(ensemble_models)))
logger.info('BPTT: %d' % (args.bptt))
logger.info('cache size: %d' % (args.recog_n_caches))
logger.info('cache theta: %.3f' % (args.recog_cache_theta))
logger.info('cache lambda: %.3f' % (args.recog_cache_lambda))
model.cache_theta = args.recog_cache_theta
model.cache_lambda = args.recog_cache_lambda
# GPU setting
model.cuda()
assert args.recog_n_caches > 0
save_path = mkdir_join(args.recog_dir, 'cache')
# Clean directory
if save_path is not None and os.path.isdir(save_path):
shutil.rmtree(save_path)
os.mkdir(save_path)
hidden = None
fig_count = 0
toknen_count = 0
n_tokens = args.recog_n_caches
while True:
ys, is_new_epoch = dataset.next()
for t in range(ys.shape[1] - 1):
loss, hidden = model(ys[:, t:t + 2],
hidden,
is_eval=True,
n_caches=args.recog_n_caches)[:2]
if len(model.cache_attn) > 0:
if toknen_count == n_tokens:
tokens_keys = dataset.idx2token[0](
model.cache_ids[:args.recog_n_caches],
return_list=True)
tokens_query = dataset.idx2token[0](
model.cache_ids[-n_tokens:], return_list=True)
# Slide attention matrix
n_keys = len(tokens_keys)
n_queries = len(tokens_query)
cache_probs = np.zeros(
(n_keys, n_queries)) # `[n_keys, n_queries]`
mask = np.zeros((n_keys, n_queries))
for i, aw in enumerate(model.cache_attn[-n_tokens:]):
cache_probs[:(n_keys - n_queries + i + 1),
i] = aw[0,
-(n_keys - n_queries + i + 1):]
mask[(n_keys - n_queries + i + 1):, i] = 1
plot_cache_weights(cache_probs,
keys=tokens_keys,
queries=tokens_query,
save_path=mkdir_join(
save_path,
str(fig_count) + '.png'),
figsize=(40, 16),
mask=mask)
toknen_count = 0
fig_count += 1
else:
toknen_count += 1
if is_new_epoch:
break
def main(args):
torch.manual_seed(args.seed)
torch.cuda.manual_seed_all(args.seed)
args_init = copy.deepcopy(args)
args_teacher = copy.deepcopy(args)
# Load a conf file
if args.resume:
conf = load_config(
os.path.join(os.path.dirname(args.resume), 'conf.yml'))
for k, v in conf.items():
if k not in ['resume', 'local_rank']:
setattr(args, k, v)
args = compute_subsampling_factor(args)
resume_epoch = int(args.resume.split('-')[-1]) if args.resume else 0
# Load dataset
train_set = build_dataloader(args=args,
tsv_path=args.train_set,
tsv_path_sub1=args.train_set_sub1,
tsv_path_sub2=args.train_set_sub2,
batch_size=args.batch_size,
batch_size_type=args.batch_size_type,
max_n_frames=args.max_n_frames,
resume_epoch=resume_epoch,
sort_by=args.sort_by,
short2long=args.sort_short2long,
sort_stop_epoch=args.sort_stop_epoch,
num_workers=args.workers,
pin_memory=args.pin_memory,
distributed=args.distributed,
word_alignment_dir=args.train_word_alignment,
ctc_alignment_dir=args.train_ctc_alignment)
dev_set = build_dataloader(
args=args,
tsv_path=args.dev_set,
tsv_path_sub1=args.dev_set_sub1,
tsv_path_sub2=args.dev_set_sub2,
batch_size=1 if 'transducer' in args.dec_type else args.batch_size,
batch_size_type='seq'
if 'transducer' in args.dec_type else args.batch_size_type,
max_n_frames=1600,
word_alignment_dir=args.dev_word_alignment,
ctc_alignment_dir=args.dev_ctc_alignment)
eval_sets = [
build_dataloader(args=args, tsv_path=s, batch_size=1, is_test=True)
for s in args.eval_sets
]
args.vocab = train_set.vocab
args.vocab_sub1 = train_set.vocab_sub1
args.vocab_sub2 = train_set.vocab_sub2
args.input_dim = train_set.input_dim
# Set save path
if args.resume:
args.save_path = os.path.dirname(args.resume)
dir_name = os.path.basename(args.save_path)
else:
dir_name = set_asr_model_name(args)
if args.mbr_training:
assert args.asr_init
args.save_path = mkdir_join(os.path.dirname(args.asr_init),
dir_name)
else:
args.save_path = mkdir_join(
args.model_save_dir,
'_'.join(os.path.basename(args.train_set).split('.')[:-1]),
dir_name)
if args.local_rank > 0:
time.sleep(1)
args.save_path = set_save_path(args.save_path) # avoid overwriting
# Set logger
set_logger(os.path.join(args.save_path, 'train.log'), args.stdout,
args.local_rank)
# Load a LM conf file for LM fusion & LM initialization
if not args.resume and args.external_lm:
lm_conf = load_config(
os.path.join(os.path.dirname(args.external_lm), 'conf.yml'))
args.lm_conf = argparse.Namespace()
for k, v in lm_conf.items():
setattr(args.lm_conf, k, v)
assert args.unit == args.lm_conf.unit
assert args.vocab == args.lm_conf.vocab
# Model setting
model = Speech2Text(args, args.save_path, train_set.idx2token[0])
if not args.resume:
# Save nlsyms, dictionary, and wp_model
if args.nlsyms:
shutil.copy(args.nlsyms, os.path.join(args.save_path,
'nlsyms.txt'))
for sub in ['', '_sub1', '_sub2']:
if args.get('dict' + sub):
shutil.copy(
args.get('dict' + sub),
os.path.join(args.save_path, 'dict' + sub + '.txt'))
if args.get('unit' + sub) == 'wp':
shutil.copy(
args.get('wp_model' + sub),
os.path.join(args.save_path, 'wp' + sub + '.model'))
for k, v in sorted(args.items(), key=lambda x: x[0]):
logger.info('%s: %s' % (k, str(v)))
# Count total parameters
for n in sorted(list(model.num_params_dict.keys())):
n_params = model.num_params_dict[n]
logger.info("%s %d" % (n, n_params))
logger.info("Total %.2f M parameters" %
(model.total_parameters / 1000000))
logger.info('torch version: %s' % str(torch.__version__))
logger.info(model)
# Initialize with pre-trained model's parameters
if args.asr_init:
# Load ASR model (full model)
conf_init = load_config(
os.path.join(os.path.dirname(args.asr_init), 'conf.yml'))
for k, v in conf_init.items():
setattr(args_init, k, v)
model_init = Speech2Text(args_init)
load_checkpoint(args.asr_init, model_init)
# Overwrite parameters
param_dict = dict(model_init.named_parameters())
for n, p in model.named_parameters():
if n in param_dict.keys() and p.size() == param_dict[n].size():
if args.asr_init_enc_only and 'enc' not in n:
continue
p.data = param_dict[n].data
logger.info('Overwrite %s' % n)
# Set optimizer
optimizer = set_optimizer(
model,
'sgd' if resume_epoch > args.convert_to_sgd_epoch else args.optimizer,
args.lr, args.weight_decay)
# Wrap optimizer by learning rate scheduler
is_transformer = 'former' in args.enc_type or 'former' in args.dec_type or 'former' in args.dec_type_sub1
scheduler = LRScheduler(
optimizer,
args.lr,
decay_type=args.lr_decay_type,
decay_start_epoch=args.lr_decay_start_epoch,
decay_rate=args.lr_decay_rate,
decay_patient_n_epochs=args.lr_decay_patient_n_epochs,
early_stop_patient_n_epochs=args.early_stop_patient_n_epochs,
lower_better=args.metric not in ['accuracy', 'bleu'],
warmup_start_lr=args.warmup_start_lr,
warmup_n_steps=args.warmup_n_steps,
peak_lr=0.05 / (args.get('transformer_enc_d_model', 0)**0.5)
if 'conformer' in args.enc_type else 1e6,
model_size=args.get('transformer_enc_d_model',
args.get('transformer_dec_d_model', 0)),
factor=args.lr_factor,
noam=args.optimizer == 'noam',
save_checkpoints_topk=10 if is_transformer else 1)
if args.resume:
# Restore the last saved model
load_checkpoint(args.resume, model, scheduler)
# Resume between convert_to_sgd_epoch -1 and convert_to_sgd_epoch
if resume_epoch == args.convert_to_sgd_epoch:
scheduler.convert_to_sgd(model,
args.lr,
args.weight_decay,
decay_type='always',
decay_rate=0.5)
# Load teacher ASR model
teacher = None
if args.teacher:
assert os.path.isfile(args.teacher), 'There is no checkpoint.'
conf_teacher = load_config(
os.path.join(os.path.dirname(args.teacher), 'conf.yml'))
for k, v in conf_teacher.items():
setattr(args_teacher, k, v)
# Setting for knowledge distillation
args_teacher.ss_prob = 0
args.lsm_prob = 0
teacher = Speech2Text(args_teacher)
load_checkpoint(args.teacher, teacher)
# Load teacher LM
teacher_lm = None
if args.teacher_lm:
assert os.path.isfile(args.teacher_lm), 'There is no checkpoint.'
conf_lm = load_config(
os.path.join(os.path.dirname(args.teacher_lm), 'conf.yml'))
args_lm = argparse.Namespace()
for k, v in conf_lm.items():
setattr(args_lm, k, v)
teacher_lm = build_lm(args_lm)
load_checkpoint(args.teacher_lm, teacher_lm)
# GPU setting
args.use_apex = args.train_dtype in ["O0", "O1", "O2", "O3"]
amp, scaler = None, None
if args.n_gpus >= 1:
model.cudnn_setting(
deterministic=((not is_transformer) and (not args.cudnn_benchmark))
or args.cudnn_deterministic,
benchmark=(not is_transformer) and args.cudnn_benchmark)
# Mixed precision training setting
if args.use_apex:
if LooseVersion(torch.__version__) >= LooseVersion("1.6.0"):
scaler = torch.cuda.amp.GradScaler()
else:
from apex import amp
model, scheduler.optimizer = amp.initialize(
model, scheduler.optimizer, opt_level=args.train_dtype)
from neural_sp.models.seq2seq.decoders.ctc import CTC
amp.register_float_function(CTC, "loss_fn")
# NOTE: see https://github.com/espnet/espnet/pull/1779
amp.init()
if args.resume:
load_checkpoint(args.resume, amp=amp)
n = torch.cuda.device_count() // args.local_world_size
device_ids = list(range(args.local_rank * n,
(args.local_rank + 1) * n))
torch.cuda.set_device(device_ids[0])
model.cuda(device_ids[0])
scheduler.cuda(device_ids[0])
if args.distributed:
model = DDP(model, device_ids=device_ids)
else:
model = CustomDataParallel(model,
device_ids=list(range(args.n_gpus)))
if teacher is not None:
teacher.cuda()
if teacher_lm is not None:
teacher_lm.cuda()
else:
model = CPUWrapperASR(model)
# Set process name
logger.info('PID: %s' % os.getpid())
logger.info('USERNAME: %s' % os.uname()[1])
logger.info('#GPU: %d' % torch.cuda.device_count())
setproctitle(args.job_name if args.job_name else dir_name)
# Set reporter
reporter = Reporter(args, model, args.local_rank)
args.wandb_id = reporter.wandb_id
if args.resume:
n_steps = scheduler.n_steps * max(
1, args.accum_grad_n_steps // args.local_world_size)
reporter.resume(n_steps, resume_epoch)
# Save conf file as a yaml file
if args.local_rank == 0:
save_config(args, os.path.join(args.save_path, 'conf.yml'))
if args.external_lm:
save_config(args.lm_conf,
os.path.join(args.save_path, 'conf_lm.yml'))
# NOTE: save after reporter for wandb ID
# Define tasks
if args.mtl_per_batch:
# NOTE: from easier to harder tasks
tasks = []
if args.total_weight - args.bwd_weight - args.ctc_weight - args.sub1_weight - args.sub2_weight > 0:
tasks += ['ys']
if args.bwd_weight > 0:
tasks = ['ys.bwd'] + tasks
if args.ctc_weight > 0:
tasks = ['ys.ctc'] + tasks
if args.mbr_ce_weight > 0:
tasks = ['ys.mbr'] + tasks
for sub in ['sub1', 'sub2']:
if args.get('train_set_' + sub) is not None:
if args.get(sub + '_weight', 0) - args.get(
'ctc_weight_' + sub, 0) > 0:
tasks = ['ys_' + sub] + tasks
if args.get('ctc_weight_' + sub, 0) > 0:
tasks = ['ys_' + sub + '.ctc'] + tasks
else:
tasks = ['all']
if args.get('ss_start_epoch', 0) <= resume_epoch:
model.module.trigger_scheduled_sampling()
if args.get('mocha_quantity_loss_start_epoch', 0) <= resume_epoch:
model.module.trigger_quantity_loss()
start_time_train = time.time()
for ep in range(resume_epoch, args.n_epochs):
train_one_epoch(model, train_set, dev_set, eval_sets, scheduler,
reporter, logger, args, amp, scaler, tasks, teacher,
teacher_lm)
# Save checkpoint and validate model per epoch
if reporter.n_epochs + 1 < args.eval_start_epoch:
scheduler.epoch() # lr decay
reporter.epoch() # plot
# Save model
if args.local_rank == 0:
scheduler.save_checkpoint(model,
args.save_path,
amp=amp,
remove_old=(not is_transformer)
and args.remove_old_checkpoints)
else:
start_time_eval = time.time()
# dev
metric_dev = validate([model.module], dev_set, args,
reporter.n_epochs + 1, logger)
scheduler.epoch(metric_dev) # lr decay
reporter.epoch(metric_dev, name=args.metric) # plot
reporter.add_scalar('dev/' + args.metric, metric_dev)
if scheduler.is_topk or is_transformer:
# Save model
if args.local_rank == 0:
scheduler.save_checkpoint(model,
args.save_path,
amp=amp,
remove_old=(not is_transformer)
and args.remove_old_checkpoints)
# test
if scheduler.is_topk:
for eval_set in eval_sets:
validate([model.module], eval_set, args,
reporter.n_epochs, logger)
logger.info('Evaluation time: %.2f min' %
((time.time() - start_time_eval) / 60))
# Early stopping
if scheduler.is_early_stop:
break
# Convert to fine-tuning stage
if reporter.n_epochs == args.convert_to_sgd_epoch:
scheduler.convert_to_sgd(model,
args.lr,
args.weight_decay,
decay_type='always',
decay_rate=0.5)
if reporter.n_epochs >= args.n_epochs:
break
if args.get('ss_start_epoch', 0) == (ep + 1):
model.module.trigger_scheduled_sampling()
if args.get('mocha_quantity_loss_start_epoch', 0) == (ep + 1):
model.module.trigger_quantity_loss()
logger.info('Total time: %.2f hour' %
((time.time() - start_time_train) / 3600))
reporter.close()
return args.save_path
def eval_word(models,
dataset,
recog_params,
epoch,
recog_dir=None,
streaming=False,
progressbar=False):
"""Evaluate the word-level model by WER.
Args:
models (list): models to evaluate
dataset (Dataset): evaluation dataset
recog_params (dict):
epoch (int):
recog_dir (str):
streaming (bool): streaming decoding for the session-level evaluation
progressbar (bool): visualize the progressbar
Returns:
wer (float): Word error rate
cer (float): Character error rate
n_oov_total (int): totol number of OOV
"""
# Reset data counter
dataset.reset(recog_params['recog_batch_size'])
if recog_dir is None:
recog_dir = 'decode_' + dataset.set + '_ep' + str(
epoch) + '_beam' + str(recog_params['recog_beam_width'])
recog_dir += '_lp' + str(recog_params['recog_length_penalty'])
recog_dir += '_cp' + str(recog_params['recog_coverage_penalty'])
recog_dir += '_' + str(
recog_params['recog_min_len_ratio']) + '_' + str(
recog_params['recog_max_len_ratio'])
recog_dir += '_lm' + str(recog_params['recog_lm_weight'])
ref_trn_save_path = mkdir_join(models[0].save_path, recog_dir,
'ref.trn')
hyp_trn_save_path = mkdir_join(models[0].save_path, recog_dir,
'hyp.trn')
else:
ref_trn_save_path = mkdir_join(recog_dir, 'ref.trn')
hyp_trn_save_path = mkdir_join(recog_dir, 'hyp.trn')
wer, cer = 0, 0
n_sub_w, n_ins_w, n_del_w = 0, 0, 0
n_sub_c, n_ins_c, n_del_c = 0, 0, 0
n_word, n_char = 0, 0
n_oov_total = 0
if progressbar:
pbar = tqdm(total=len(dataset))
with open(hyp_trn_save_path, 'w') as f_hyp, open(ref_trn_save_path,
'w') as f_ref:
while True:
batch, is_new_epoch = dataset.next(
recog_params['recog_batch_size'])
if streaming or recog_params['recog_chunk_sync']:
best_hyps_id, _ = models[0].decode_streaming(
batch['xs'],
recog_params,
dataset.idx2token[0],
exclude_eos=True)
else:
best_hyps_id, aws = models[0].decode(
batch['xs'],
recog_params,
idx2token=dataset.idx2token[0] if progressbar else None,
exclude_eos=True,
refs_id=batch['ys'],
utt_ids=batch['utt_ids'],
speakers=batch['sessions' if dataset.corpus ==
'swbd' else 'speakers'],
ensemble_models=models[1:] if len(models) > 1 else [])
for b in range(len(batch['xs'])):
ref = batch['text'][b]
hyp = dataset.idx2token[0](best_hyps_id[b])
n_oov_total += hyp.count('<unk>')
# Resolving UNK
if recog_params['recog_resolving_unk'] and '<unk>' in hyp:
recog_params_char = copy.deepcopy(recog_params)
recog_params_char['recog_lm_weight'] = 0
recog_params_char['recog_beam_width'] = 1
best_hyps_id_char, aw_char = models[0].decode(
batch['xs'][b:b + 1],
recog_params_char,
idx2token=dataset.idx2token[1]
if progressbar else None,
exclude_eos=True,
refs_id=batch['ys_sub1'],
utt_ids=batch['utt_ids'],
speakers=batch['sessions']
if dataset.corpus == 'swbd' else batch['speakers'],
task='ys_sub1')
# TODO(hirofumi): support ys_sub2 and ys_sub3
assert not streaming
hyp = resolve_unk(
hyp,
best_hyps_id_char[0],
aws[b],
aw_char[0],
dataset.idx2token[1],
subsample_factor_word=np.prod(models[0].subsample),
subsample_factor_char=np.prod(
models[0].subsample[:models[0].enc_n_layers_sub1 -
1]))
logger.debug('Hyp (after OOV resolution): %s' % hyp)
hyp = hyp.replace('*', '')
# Compute CER
ref_char = ref
hyp_char = hyp
if dataset.corpus == 'csj':
ref_char = ref.replace(' ', '')
hyp_char = hyp.replace(' ', '')
cer_b, sub_b, ins_b, del_b = compute_wer(
ref=list(ref_char),
hyp=list(hyp_char),
normalize=False)
cer += cer_b
n_sub_c += sub_b
n_ins_c += ins_b
n_del_c += del_b
n_char += len(ref_char)
# Write to trn
speaker = str(batch['speakers'][b]).replace('-', '_')
if streaming:
utt_id = str(batch['utt_ids'][b]) + '_0000000_0000001'
else:
utt_id = str(batch['utt_ids'][b])
f_ref.write(ref + ' (' + speaker + '-' + utt_id + ')\n')
f_hyp.write(hyp + ' (' + speaker + '-' + utt_id + ')\n')
logger.debug('utt-id: %s' % utt_id)
logger.debug('Ref: %s' % ref)
logger.debug('Hyp: %s' % hyp)
logger.debug('-' * 150)
if not streaming:
# Compute WER
wer_b, sub_b, ins_b, del_b = compute_wer(
ref=ref.split(' '),
hyp=hyp.split(' '),
normalize=False)
wer += wer_b
n_sub_w += sub_b
n_ins_w += ins_b
n_del_w += del_b
n_word += len(ref.split(' '))
if progressbar:
pbar.update(1)
if is_new_epoch:
break
if progressbar:
pbar.close()
# Reset data counters
dataset.reset()
if not streaming:
wer /= n_word
n_sub_w /= n_word
n_ins_w /= n_word
n_del_w /= n_word
if n_char > 0:
cer /= n_char
n_sub_c /= n_char
n_ins_c /= n_char
n_del_c /= n_char
logger.debug('WER (%s): %.2f %%' % (dataset.set, wer))
logger.debug('SUB: %.2f / INS: %.2f / DEL: %.2f' %
(n_sub_w, n_ins_w, n_del_w))
logger.debug('CER (%s): %.2f %%' % (dataset.set, cer))
logger.debug('SUB: %.2f / INS: %.2f / DEL: %.2f' %
(n_sub_c, n_ins_c, n_del_c))
logger.debug('OOV (total): %d' % (n_oov_total))
return wer, cer, n_oov_total
def main():
# Load configuration
args, dir_name = parse_args_eval(sys.argv[1:])
# Setting for logging
if os.path.isfile(os.path.join(args.recog_dir, 'align.log')):
os.remove(os.path.join(args.recog_dir, 'align.log'))
set_logger(os.path.join(args.recog_dir, 'align.log'),
stdout=args.recog_stdout)
for i, s in enumerate(args.recog_sets):
# Align all utterances
args.min_n_frames = 0
args.max_n_frames = 1e5
# Load dataloader
dataloader = build_dataloader(args=args,
tsv_path=s,
batch_size=args.recog_batch_size)
if i == 0:
# Load ASR model
model = Speech2Text(args, dir_name)
epoch = int(args.recog_model[0].split('-')[-1])
if args.recog_n_average > 1:
# Model averaging for Transformer
model = average_checkpoints(model,
args.recog_model[0],
n_average=args.recog_n_average)
else:
load_checkpoint(args.recog_model[0], model)
if not args.recog_unit:
args.recog_unit = args.unit
logger.info('recog unit: %s' % args.recog_unit)
logger.info('epoch: %d' % epoch)
logger.info('batch size: %d' % args.recog_batch_size)
# GPU setting
if args.recog_n_gpus >= 1:
model.cudnn_setting(deterministic=True, benchmark=False)
model.cuda()
save_path = mkdir_join(args.recog_dir, 'ctc_forced_alignments')
# Clean directory
if save_path is not None and os.path.isdir(save_path):
shutil.rmtree(save_path)
os.mkdir(save_path)
pbar = tqdm(total=len(dataloader))
while True:
batch, is_new_epoch = dataloader.next()
trigger_points = model.ctc_forced_align(batch['xs'],
batch['ys']) # `[B, L]`
for b in range(len(batch['xs'])):
save_path_spk = mkdir_join(save_path, batch['speakers'][b])
save_path_utt = mkdir_join(save_path_spk,
batch['utt_ids'][b] + '.txt')
tokens = dataloader.idx2token[0](batch['ys'][b],
return_list=True)
with codecs.open(save_path_utt, 'w', encoding="utf-8") as f:
for i, tok in enumerate(tokens):
f.write('%s %d\n' % (tok, trigger_points[b, i]))
f.write('%s %d\n' %
('<eos>', trigger_points[b, len(tokens)]))
pbar.update(len(batch['xs']))
if is_new_epoch:
break
pbar.close()
def eval_word(models,
dataloader,
recog_params,
epoch,
recog_dir=None,
streaming=False,
progressbar=False,
edit_distance=True,
fine_grained=False,
oracle=False,
teacher_force=False):
"""Evaluate a word-level model by WER.
Args:
models (List): models to evaluate
dataloader (torch.utils.data.DataLoader): evaluation dataloader
recog_params (omegaconf.dictconfig.DictConfig): decoding hyperparameters
epoch (int): current epoch
recog_dir (str): directory path to save hypotheses
streaming (bool): streaming decoding for session-level evaluation
progressbar (bool): visualize progressbar
edit_distance (bool): calculate edit-distance (can be skipped for RTF calculation)
fine_grained (bool): calculate fine-grained WER distributions based on input lengths
oracle (bool): calculate oracle WER
teacher_force (bool): conduct decoding in teacher-forcing mode
Returns:
wer (float): Word error rate
cer (float): Character error rate
n_oov_total (int): total number of OOV
"""
if recog_dir is None:
recog_dir = 'decode_' + dataloader.set + '_ep' + \
str(epoch) + '_beam' + str(recog_params.get('recog_beam_width'))
recog_dir += '_lp' + str(recog_params.get('recog_length_penalty'))
recog_dir += '_cp' + str(recog_params.get('recog_coverage_penalty'))
recog_dir += '_' + str(recog_params.get('recog_min_len_ratio')) + '_' + \
str(recog_params.get('recog_max_len_ratio'))
recog_dir += '_lm' + str(recog_params.get('recog_lm_weight'))
ref_trn_path = mkdir_join(models[0].save_path, recog_dir, 'ref.trn')
hyp_trn_path = mkdir_join(models[0].save_path, recog_dir, 'hyp.trn')
else:
ref_trn_path = mkdir_join(recog_dir, 'ref.trn')
hyp_trn_path = mkdir_join(recog_dir, 'hyp.trn')
wer, cer = 0, 0
n_sub_w, n_ins_w, n_del_w = 0, 0, 0
n_sub_c, n_ins_c, n_del_c = 0, 0, 0
n_word, n_char = 0, 0
wer_dist = {} # calculate WER distribution based on input lengths
n_oov_total = 0
wer_oracle = 0
n_oracle_hit = 0
n_utt = 0
# Reset data counter
dataloader.reset(recog_params.get('recog_batch_size'))
if progressbar:
pbar = tqdm(total=len(dataloader))
with codecs.open(hyp_trn_path, 'w', encoding='utf-8') as f_hyp, \
codecs.open(ref_trn_path, 'w', encoding='utf-8') as f_ref:
for batch in dataloader:
speakers = batch['sessions' if dataloader.corpus ==
'swbd' else 'speakers']
if streaming or recog_params.get('recog_block_sync'):
nbest_hyps_id = models[0].decode_streaming(
batch['xs'],
recog_params,
dataloader.idx2token[0],
exclude_eos=True,
speaker=speakers[0])[0]
else:
nbest_hyps_id, aws = models[0].decode(
batch['xs'],
recog_params,
idx2token=dataloader.idx2token[0],
exclude_eos=True,
refs_id=batch['ys'],
utt_ids=batch['utt_ids'],
speakers=speakers,
ensemble_models=models[1:] if len(models) > 1 else [])
for b in range(len(batch['xs'])):
ref = batch['text'][b]
nbest_hyps = [
dataloader.idx2token[0](hyp_id)
for hyp_id in nbest_hyps_id[b]
]
n_oov_total += nbest_hyps[0].count('<unk>')
# Resolving UNK
if recog_params.get(
'recog_resolving_unk') and '<unk>' in nbest_hyps[0]:
recog_params_char = copy.deepcopy(recog_params)
recog_params_char['recog_lm_weight'] = 0
recog_params_char['recog_beam_width'] = 1
best_hyps_id_char, aw_char = models[0].decode(
batch['xs'][b:b + 1],
recog_params_char,
idx2token=dataloader.idx2token[1],
exclude_eos=True,
refs_id=batch['ys_sub1'],
utt_ids=batch['utt_ids'],
speakers=speakers,
task='ys_sub1')
# TODO(hirofumi): support ys_sub2
assert not streaming
nbest_hyps[0] = resolve_unk(
nbest_hyps[0],
best_hyps_id_char[0],
aws[b],
aw_char[0],
dataloader.idx2token[1],
subsample_factor_word=np.prod(models[0].subsample),
subsample_factor_char=np.prod(
models[0].subsample[:models[0].enc_n_layers_sub1 -
1]))
logger.debug('Hyp (after OOV resolution): %s' %
nbest_hyps[0])
nbest_hyps[0] = nbest_hyps[0].replace('*', '')
# Compute CER
ref_char = ref
hyp_char = nbest_hyps[0]
if dataloader.corpus == 'csj':
ref_char = ref_char.replace(' ', '')
hyp_char = hyp_char.replace(' ', '')
err_b, sub_b, ins_b, del_b = compute_wer(
ref=list(ref_char), hyp=list(hyp_char))
cer += err_b
n_sub_c += sub_b
n_ins_c += ins_b
n_del_c += del_b
n_char += len(ref_char)
# Write to trn
speaker = str(batch['speakers'][b]).replace('-', '_')
if streaming:
utt_id = str(batch['utt_ids'][b]) + '_0000000_0000001'
else:
utt_id = str(batch['utt_ids'][b])
f_ref.write(ref + ' (' + speaker + '-' + utt_id + ')\n')
f_hyp.write(nbest_hyps[0] + ' (' + speaker + '-' + utt_id +
')\n')
logger.debug('utt-id (%d/%d): %s' %
(n_utt + 1, len(dataloader), utt_id))
logger.debug('Ref: %s' % ref)
logger.debug('Hyp: %s' % nbest_hyps[0])
logger.debug('-' * 150)
if edit_distance and not streaming:
# Compute WER
err_b, sub_b, ins_b, del_b = compute_wer(
ref=ref.split(' '), hyp=nbest_hyps[0].split(' '))
wer += err_b
n_sub_w += sub_b
n_ins_w += ins_b
n_del_w += del_b
n_word += len(ref.split(' '))
# Compute oracle WER
if oracle and len(nbest_hyps) > 1:
wers_b = [err_b] + [
compute_wer(ref=ref.split(' '),
hyp=hyp_n.split(' '))[0]
for hyp_n in nbest_hyps[1:]
]
oracle_idx = np.argmin(np.array(wers_b))
if oracle_idx == 0:
n_oracle_hit += len(batch['utt_ids'])
wer_oracle += wers_b[oracle_idx]
# NOTE: OOV resolution is not considered
if fine_grained:
xlen_bin = (batch['xlens'][b] // 200 + 1) * 200
if xlen_bin in wer_dist.keys():
wer_dist[xlen_bin] += [err_b / 100]
else:
wer_dist[xlen_bin] = [err_b / 100]
n_utt += len(batch['utt_ids'])
if progressbar:
pbar.update(len(batch['utt_ids']))
if progressbar:
pbar.close()
# Reset data counters
dataloader.reset(is_new_epoch=True)
if edit_distance and not streaming:
wer /= n_word
n_sub_w /= n_word
n_ins_w /= n_word
n_del_w /= n_word
if n_char > 0:
cer /= n_char
n_sub_c /= n_char
n_ins_c /= n_char
n_del_c /= n_char
if recog_params.get('recog_beam_width') > 1:
logger.info('WER (%s): %.2f %%' % (dataloader.set, wer))
logger.info('SUB: %.2f / INS: %.2f / DEL: %.2f' %
(n_sub_w, n_ins_w, n_del_w))
logger.info('CER (%s): %.2f %%' % (dataloader.set, cer))
logger.info('SUB: %.2f / INS: %.2f / DEL: %.2f' %
(n_sub_c, n_ins_c, n_del_c))
logger.info('OOV (total): %d' % (n_oov_total))
if oracle:
wer_oracle /= n_word
oracle_hit_rate = n_oracle_hit * 100 / n_utt
logger.info('Oracle WER (%s): %.2f %%' %
(dataloader.set, wer_oracle))
logger.info('Oracle hit rate (%s): %.2f %%' %
(dataloader.set, oracle_hit_rate))
if fine_grained:
for len_bin, wers in sorted(wer_dist.items(), key=lambda x: x[0]):
logger.info(' WER (%s): %.2f %% (%d)' %
(dataloader.set, sum(wers) / len(wers), len_bin))
return wer, cer, n_oov_total
def eval_wordpiece(models, dataset, recog_params, epoch,
recog_dir=None, streaming=False, progressbar=False):
"""Evaluate the wordpiece-level model by WER.
Args:
models (list): models to evaluate
dataset (Dataset): evaluation dataset
recog_params (dict):
epoch (int):
recog_dir (str):
streaming (bool): streaming decoding for the session-level evaluation
progressbar (bool): visualize the progressbar
Returns:
wer (float): Word error rate
cer (float): Character error rate
"""
# Reset data counter
dataset.reset()
if recog_dir is None:
recog_dir = 'decode_' + dataset.set + '_ep' + str(epoch) + '_beam' + str(recog_params['recog_beam_width'])
recog_dir += '_lp' + str(recog_params['recog_length_penalty'])
recog_dir += '_cp' + str(recog_params['recog_coverage_penalty'])
recog_dir += '_' + str(recog_params['recog_min_len_ratio']) + '_' + str(recog_params['recog_max_len_ratio'])
recog_dir += '_lm' + str(recog_params['recog_lm_weight'])
ref_trn_save_path = mkdir_join(models[0].save_path, recog_dir, 'ref.trn')
hyp_trn_save_path = mkdir_join(models[0].save_path, recog_dir, 'hyp.trn')
else:
ref_trn_save_path = mkdir_join(recog_dir, 'ref.trn')
hyp_trn_save_path = mkdir_join(recog_dir, 'hyp.trn')
wer, cer = 0, 0
n_sub_w, n_ins_w, n_del_w = 0, 0, 0
n_sub_c, n_ins_c, n_del_c = 0, 0, 0
n_word, n_char = 0, 0
if progressbar:
pbar = tqdm(total=len(dataset))
with open(hyp_trn_save_path, 'w') as f_hyp, open(ref_trn_save_path, 'w') as f_ref:
while True:
batch, is_new_epoch = dataset.next(recog_params['recog_batch_size'])
if streaming or recog_params['recog_chunk_sync']:
best_hyps_id, _ = models[0].decode_streaming(
batch['xs'], recog_params, dataset.idx2token[0],
exclude_eos=True)
else:
best_hyps_id, _ = models[0].decode(
batch['xs'], recog_params, dataset.idx2token[0],
exclude_eos=True,
refs_id=batch['ys'],
utt_ids=batch['utt_ids'],
speakers=batch['sessions' if dataset.corpus == 'swbd' else 'speakers'],
ensemble_models=models[1:] if len(models) > 1 else [])
for b in range(len(batch['xs'])):
ref = batch['text'][b]
if ref[0] == '<':
ref = ref.split('>')[1]
hyp = dataset.idx2token[0](best_hyps_id[b])
# Write to trn
speaker = str(batch['speakers'][b]).replace('-', '_')
if streaming:
utt_id = str(batch['utt_ids'][b]) + '_0000000_0000001'
else:
utt_id = str(batch['utt_ids'][b])
f_ref.write(ref + ' (' + speaker + '-' + utt_id + ')\n')
f_hyp.write(hyp + ' (' + speaker + '-' + utt_id + ')\n')
logger.debug('utt-id: %s' % utt_id)
logger.debug('Ref: %s' % ref)
logger.debug('Hyp: %s' % hyp)
logger.debug('-' * 150)
if not streaming:
# Compute WER
wer_b, sub_b, ins_b, del_b = compute_wer(ref=ref.split(' '),
hyp=hyp.split(' '),
normalize=False)
wer += wer_b
n_sub_w += sub_b
n_ins_w += ins_b
n_del_w += del_b
n_word += len(ref.split(' '))
# Compute CER
if dataset.corpus == 'csj':
ref = ref.replace(' ', '')
hyp = hyp.replace(' ', '')
cer_b, sub_b, ins_b, del_b = compute_wer(ref=list(ref),
hyp=list(hyp),
normalize=False)
cer += cer_b
n_sub_c += sub_b
n_ins_c += ins_b
n_del_c += del_b
n_char += len(ref)
if progressbar:
pbar.update(1)
if is_new_epoch:
break
if progressbar:
pbar.close()
# Reset data counters
dataset.reset()
if not streaming:
wer /= n_word
n_sub_w /= n_word
n_ins_w /= n_word
n_del_w /= n_word
cer /= n_char
n_sub_c /= n_char
n_ins_c /= n_char
n_del_c /= n_char
logger.debug('WER (%s): %.2f %%' % (dataset.set, wer))
logger.debug('SUB: %.2f / INS: %.2f / DEL: %.2f' % (n_sub_w, n_ins_w, n_del_w))
logger.debug('CER (%s): %.2f %%' % (dataset.set, cer))
logger.debug('SUB: %.2f / INS: %.2f / DEL: %.2f' % (n_sub_c, n_ins_c, n_del_c))
return wer, cer
def eval_phone(models,
dataloader,
recog_params,
epoch,
recog_dir=None,
streaming=False,
progressbar=False,
fine_grained=False,
oracle=False,
teacher_force=False):
"""Evaluate a phone-level model by PER.
Args:
models (List): models to evaluate
dataloader (torch.utils.data.DataLoader): evaluation dataloader
recog_params (dict):
epoch (int):
recog_dir (str):
streaming (bool): streaming decoding for the session-level evaluation
progressbar (bool): visualize the progressbar
oracle (bool): calculate oracle PER
fine_grained (bool): calculate fine-grained PER distributions based on input lengths
teacher_force (bool): conduct decoding in teacher-forcing mode
Returns:
per (float): Phone error rate
"""
if recog_dir is None:
recog_dir = 'decode_' + dataloader.set + '_ep' + str(
epoch) + '_beam' + str(recog_params['recog_beam_width'])
recog_dir += '_lp' + str(recog_params['recog_length_penalty'])
recog_dir += '_cp' + str(recog_params['recog_coverage_penalty'])
recog_dir += '_' + str(
recog_params['recog_min_len_ratio']) + '_' + str(
recog_params['recog_max_len_ratio'])
ref_trn_path = mkdir_join(models[0].save_path, recog_dir, 'ref.trn')
hyp_trn_path = mkdir_join(models[0].save_path, recog_dir, 'hyp.trn')
else:
ref_trn_path = mkdir_join(recog_dir, 'ref.trn')
hyp_trn_path = mkdir_join(recog_dir, 'hyp.trn')
per = 0
n_sub, n_ins, n_del = 0, 0, 0
n_phone = 0
per_dist = {} # calculate PER distribution based on input lengths
per_oracle = 0
n_oracle_hit = 0
n_utt = 0
# Reset data counter
dataloader.reset(recog_params['recog_batch_size'])
if progressbar:
pbar = tqdm(total=len(dataloader))
with codecs.open(hyp_trn_path, 'w', encoding='utf-8') as f_hyp, \
codecs.open(ref_trn_path, 'w', encoding='utf-8') as f_ref:
while True:
batch, is_new_epoch = dataloader.next(
recog_params['recog_batch_size'])
if streaming or recog_params['recog_block_sync']:
nbest_hyps_id = models[0].decode_streaming(
batch['xs'],
recog_params,
dataloader.idx2token[0],
exclude_eos=True)[0]
else:
nbest_hyps_id = models[0].decode(
batch['xs'],
recog_params,
idx2token=dataloader.idx2token[0] if progressbar else None,
exclude_eos=True,
refs_id=batch['ys'],
utt_ids=batch['utt_ids'],
speakers=batch['sessions' if dataloader.corpus ==
'swbd' else 'speakers'],
ensemble_models=models[1:] if len(models) > 1 else [])[0]
for b in range(len(batch['xs'])):
ref = batch['text'][b]
nbest_hyps = [
dataloader.idx2token[0](hyp_id)
for hyp_id in nbest_hyps_id[b]
]
# Write to trn
speaker = str(batch['speakers'][b]).replace('-', '_')
if streaming:
utt_id = str(batch['utt_ids'][b]) + '_0000000_0000001'
else:
utt_id = str(batch['utt_ids'][b])
f_ref.write(ref + ' (' + speaker + '-' + utt_id + ')\n')
f_hyp.write(nbest_hyps[0] + ' (' + speaker + '-' + utt_id +
')\n')
logger.debug('utt-id: %s' % utt_id)
logger.debug('Ref: %s' % ref)
logger.debug('Hyp: %s' % nbest_hyps[0])
logger.debug('-' * 150)
if not streaming:
# Compute PER
err_b, sub_b, ins_b, del_b = compute_wer(
ref=ref.split(' '), hyp=nbest_hyps[0].split(' '))
per += err_b
n_sub += sub_b
n_ins += ins_b
n_del += del_b
n_phone += len(ref.split(' '))
# Compute oracle PER
if oracle and len(nbest_hyps) > 1:
pers_b = [err_b] + [
compute_wer(ref=ref.split(' '),
hyp=hyp_n.split(' '))[0]
for hyp_n in nbest_hyps[1:]
]
oracle_idx = np.argmin(np.array(pers_b))
if oracle_idx == 0:
n_oracle_hit += 1
per_oracle += pers_b[oracle_idx]
n_utt += 1
if progressbar:
pbar.update(1)
if is_new_epoch:
break
if progressbar:
pbar.close()
# Reset data counters
dataloader.reset()
if not streaming:
per /= n_phone
n_sub /= n_phone
n_ins /= n_phone
n_del /= n_phone
if recog_params['recog_beam_width'] > 1:
logger.info('PER (%s): %.2f %%' % (dataloader.set, per))
logger.info('SUB: %.2f / INS: %.2f / DEL: %.2f' %
(n_sub, n_ins, n_del))
if oracle:
per_oracle /= n_phone
oracle_hit_rate = n_oracle_hit * 100 / n_utt
logger.info('Oracle PER (%s): %.2f %%' %
(dataloader.set, per_oracle))
logger.info('Oracle hit rate (%s): %.2f %%' %
(dataloader.set, oracle_hit_rate))
if fine_grained:
for len_bin, pers in sorted(per_dist.items(), key=lambda x: x[0]):
logger.info(' PER (%s): %.2f %% (%d)' %
(dataloader.set, sum(pers) / len(pers), len_bin))
return per
def main():
args = parse_args_train(sys.argv[1:])
# Load a conf file
if args.resume:
conf = load_config(
os.path.join(os.path.dirname(args.resume), 'conf.yml'))
for k, v in conf.items():
if k != 'resume':
setattr(args, k, v)
# Load dataset
batch_size = args.batch_size * args.n_gpus if args.n_gpus >= 1 else args.batch_size
train_set = Dataset(corpus=args.corpus,
tsv_path=args.train_set,
dict_path=args.dict,
nlsyms=args.nlsyms,
unit=args.unit,
wp_model=args.wp_model,
batch_size=batch_size,
n_epochs=args.n_epochs,
min_n_tokens=args.min_n_tokens,
bptt=args.bptt,
shuffle=args.shuffle,
backward=args.backward,
serialize=args.serialize)
dev_set = Dataset(corpus=args.corpus,
tsv_path=args.dev_set,
dict_path=args.dict,
nlsyms=args.nlsyms,
unit=args.unit,
wp_model=args.wp_model,
batch_size=batch_size,
bptt=args.bptt,
backward=args.backward,
serialize=args.serialize)
eval_sets = [
Dataset(corpus=args.corpus,
tsv_path=s,
dict_path=args.dict,
nlsyms=args.nlsyms,
unit=args.unit,
wp_model=args.wp_model,
batch_size=1,
bptt=args.bptt,
backward=args.backward,
serialize=args.serialize) for s in args.eval_sets
]
args.vocab = train_set.vocab
# Set save path
if args.resume:
save_path = os.path.dirname(args.resume)
dir_name = os.path.basename(save_path)
else:
dir_name = set_lm_name(args)
save_path = mkdir_join(
args.model_save_dir,
'_'.join(os.path.basename(args.train_set).split('.')[:-1]),
dir_name)
save_path = set_save_path(save_path) # avoid overwriting
# Set logger
set_logger(os.path.join(save_path, 'train.log'), stdout=args.stdout)
# Model setting
model = build_lm(args, save_path)
if not args.resume:
# Save the conf file as a yaml file
save_config(vars(args), os.path.join(save_path, 'conf.yml'))
# Save the nlsyms, dictionary, and wp_model
if args.nlsyms:
shutil.copy(args.nlsyms, os.path.join(save_path, 'nlsyms.txt'))
shutil.copy(args.dict, os.path.join(save_path, 'dict.txt'))
if args.unit == 'wp':
shutil.copy(args.wp_model, os.path.join(save_path, 'wp.model'))
for k, v in sorted(vars(args).items(), key=lambda x: x[0]):
logger.info('%s: %s' % (k, str(v)))
# Count total parameters
for n in sorted(list(model.num_params_dict.keys())):
n_params = model.num_params_dict[n]
logger.info("%s %d" % (n, n_params))
logger.info("Total %.2f M parameters" %
(model.total_parameters / 1000000))
logger.info(model)
# Set optimizer
resume_epoch = 0
if args.resume:
epoch = int(args.resume.split('-')[-1])
optimizer = set_optimizer(
model,
'sgd' if epoch > args.convert_to_sgd_epoch else args.optimizer,
args.lr, args.weight_decay)
else:
optimizer = set_optimizer(model, args.optimizer, args.lr,
args.weight_decay)
# Wrap optimizer by learning rate scheduler
is_transformer = args.lm_type in ['transformer', 'transformer_xl']
optimizer = LRScheduler(
optimizer,
args.lr,
decay_type=args.lr_decay_type,
decay_start_epoch=args.lr_decay_start_epoch,
decay_rate=args.lr_decay_rate,
decay_patient_n_epochs=args.lr_decay_patient_n_epochs,
early_stop_patient_n_epochs=args.early_stop_patient_n_epochs,
warmup_start_lr=args.warmup_start_lr,
warmup_n_steps=args.warmup_n_steps,
model_size=getattr(args, 'transformer_d_model', 0),
factor=args.lr_factor,
noam=is_transformer,
save_checkpoints_topk=1)
if args.resume:
# Restore the last saved model
load_checkpoint(args.resume, model, optimizer)
# Resume between convert_to_sgd_epoch -1 and convert_to_sgd_epoch
if resume_epoch == args.convert_to_sgd_epoch:
optimizer.convert_to_sgd(model,
args.lr,
args.weight_decay,
decay_type='always',
decay_rate=0.5)
# GPU setting
use_apex = args.train_dtype in ["O0", "O1", "O2", "O3"]
amp = None
if args.n_gpus >= 1:
model.cudnn_setting(
deterministic=not (is_transformer or args.cudnn_benchmark),
benchmark=args.cudnn_benchmark)
model.cuda()
# Mix precision training setting
if use_apex:
from apex import amp
model, optimizer.optimizer = amp.initialize(
model, optimizer.optimizer, opt_level=args.train_dtype)
amp.init()
if args.resume:
load_checkpoint(args.resume, amp=amp)
model = CustomDataParallel(model,
device_ids=list(range(0, args.n_gpus)))
else:
model = CPUWrapperLM(model)
# Set process name
logger.info('PID: %s' % os.getpid())
logger.info('USERNAME: %s' % os.uname()[1])
logger.info('#GPU: %d' % torch.cuda.device_count())
setproctitle(args.job_name if args.job_name else dir_name)
# Set reporter
reporter = Reporter(save_path)
hidden = None
start_time_train = time.time()
start_time_epoch = time.time()
start_time_step = time.time()
pbar_epoch = tqdm(total=len(train_set))
accum_n_steps = 0
n_steps = optimizer.n_steps * args.accum_grad_n_steps
while True:
# Compute loss in the training set
ys_train, is_new_epoch = train_set.next()
accum_n_steps += 1
loss, hidden, observation = model(ys_train, hidden)
reporter.add(observation)
if use_apex:
with amp.scale_loss(loss, optimizer.optimizer) as scaled_loss:
scaled_loss.backward()
else:
loss.backward()
loss.detach() # Trancate the graph
if args.accum_grad_n_steps == 1 or accum_n_steps >= args.accum_grad_n_steps:
if args.clip_grad_norm > 0:
total_norm = torch.nn.utils.clip_grad_norm_(
model.module.parameters(), args.clip_grad_norm)
reporter.add_tensorboard_scalar('total_norm', total_norm)
optimizer.step()
optimizer.zero_grad()
accum_n_steps = 0
loss_train = loss.item()
del loss
hidden = model.module.repackage_state(hidden)
reporter.add_tensorboard_scalar('learning_rate', optimizer.lr)
# NOTE: loss/acc/ppl are already added in the model
reporter.step()
pbar_epoch.update(ys_train.shape[0] * (ys_train.shape[1] - 1))
n_steps += 1
# NOTE: n_steps is different from the step counter in Noam Optimizer
if n_steps % args.print_step == 0:
# Compute loss in the dev set
ys_dev = dev_set.next(bptt=args.bptt)[0]
loss, _, observation = model(ys_dev, None, is_eval=True)
reporter.add(observation, is_eval=True)
loss_dev = loss.item()
del loss
reporter.step(is_eval=True)
duration_step = time.time() - start_time_step
logger.info(
"step:%d(ep:%.2f) loss:%.3f(%.3f)/lr:%.5f/bs:%d (%.2f min)" %
(n_steps, optimizer.n_epochs + train_set.epoch_detail,
loss_train, loss_dev, optimizer.lr, ys_train.shape[0],
duration_step / 60))
start_time_step = time.time()
# Save fugures of loss and accuracy
if n_steps % (args.print_step * 10) == 0:
reporter.snapshot()
model.module.plot_attention()
# Save checkpoint and evaluate model per epoch
if is_new_epoch:
duration_epoch = time.time() - start_time_epoch
logger.info('========== EPOCH:%d (%.2f min) ==========' %
(optimizer.n_epochs + 1, duration_epoch / 60))
if optimizer.n_epochs + 1 < args.eval_start_epoch:
optimizer.epoch() # lr decay
reporter.epoch() # plot
# Save the model
optimizer.save_checkpoint(model,
save_path,
remove_old=not is_transformer,
amp=amp)
else:
start_time_eval = time.time()
# dev
model.module.reset_length(args.bptt)
ppl_dev, _ = eval_ppl([model.module],
dev_set,
batch_size=1,
bptt=args.bptt)
model.module.reset_length(args.bptt)
optimizer.epoch(ppl_dev) # lr decay
reporter.epoch(ppl_dev, name='perplexity') # plot
logger.info('PPL (%s, ep:%d): %.2f' %
(dev_set.set, optimizer.n_epochs, ppl_dev))
if optimizer.is_topk or is_transformer:
# Save the model
optimizer.save_checkpoint(model,
save_path,
remove_old=not is_transformer,
amp=amp)
# test
ppl_test_avg = 0.
for eval_set in eval_sets:
model.module.reset_length(args.bptt)
ppl_test, _ = eval_ppl([model.module],
eval_set,
batch_size=1,
bptt=args.bptt)
model.module.reset_length(args.bptt)
logger.info(
'PPL (%s, ep:%d): %.2f' %
(eval_set.set, optimizer.n_epochs, ppl_test))
ppl_test_avg += ppl_test
if len(eval_sets) > 0:
logger.info('PPL (avg., ep:%d): %.2f' %
(optimizer.n_epochs,
ppl_test_avg / len(eval_sets)))
duration_eval = time.time() - start_time_eval
logger.info('Evaluation time: %.2f min' % (duration_eval / 60))
# Early stopping
if optimizer.is_early_stop:
break
# Convert to fine-tuning stage
if optimizer.n_epochs == args.convert_to_sgd_epoch:
optimizer.convert_to_sgd(model,
args.lr,
args.weight_decay,
decay_type='always',
decay_rate=0.5)
pbar_epoch = tqdm(total=len(train_set))
if optimizer.n_epochs >= args.n_epochs:
break
start_time_step = time.time()
start_time_epoch = time.time()
duration_train = time.time() - start_time_train
logger.info('Total time: %.2f hour' % (duration_train / 3600))
reporter.tf_writer.close()
pbar_epoch.close()
return save_path
def eval_wordpiece(models, dataset, recog_params, epoch,
recog_dir=None, streaming=False, progressbar=False,
fine_grained=False):
"""Evaluate the wordpiece-level model by WER.
Args:
models (list): models to evaluate
dataset (Dataset): evaluation dataset
recog_params (dict):
epoch (int):
recog_dir (str):
streaming (bool): streaming decoding for the session-level evaluation
progressbar (bool): visualize the progressbar
fine_grained (bool): calculate fine-grained WER distributions based on input lengths
Returns:
wer (float): Word error rate
cer (float): Character error rate
"""
# Reset data counter
dataset.reset(recog_params['recog_batch_size'])
if recog_dir is None:
recog_dir = 'decode_' + dataset.set + '_ep' + str(epoch) + '_beam' + str(recog_params['recog_beam_width'])
recog_dir += '_lp' + str(recog_params['recog_length_penalty'])
recog_dir += '_cp' + str(recog_params['recog_coverage_penalty'])
recog_dir += '_' + str(recog_params['recog_min_len_ratio']) + '_' + str(recog_params['recog_max_len_ratio'])
recog_dir += '_lm' + str(recog_params['recog_lm_weight'])
ref_trn_save_path = mkdir_join(models[0].save_path, recog_dir, 'ref.trn')
hyp_trn_save_path = mkdir_join(models[0].save_path, recog_dir, 'hyp.trn')
else:
ref_trn_save_path = mkdir_join(recog_dir, 'ref.trn')
hyp_trn_save_path = mkdir_join(recog_dir, 'hyp.trn')
wer, cer = 0, 0
n_sub_w, n_ins_w, n_del_w = 0, 0, 0
n_sub_c, n_ins_c, n_del_c = 0, 0, 0
n_word, n_char = 0, 0
n_streamable, quantity_rate, n_utt = 0, 0, 0
last_success_frame_ratio = 0
if progressbar:
pbar = tqdm(total=len(dataset))
# calculate WER distribution based on input lengths
wer_dist = {}
with open(hyp_trn_save_path, 'w') as f_hyp, open(ref_trn_save_path, 'w') as f_ref:
while True:
batch, is_new_epoch = dataset.next(recog_params['recog_batch_size'])
if streaming or recog_params['recog_chunk_sync']:
best_hyps_id, _ = models[0].decode_streaming(
batch['xs'], recog_params, dataset.idx2token[0],
exclude_eos=True)
else:
best_hyps_id, _ = models[0].decode(
batch['xs'], recog_params,
idx2token=dataset.idx2token[0] if progressbar else None,
exclude_eos=True,
refs_id=batch['ys'],
utt_ids=batch['utt_ids'],
speakers=batch['sessions' if dataset.corpus == 'swbd' else 'speakers'],
ensemble_models=models[1:] if len(models) > 1 else [])
for b in range(len(batch['xs'])):
ref = batch['text'][b]
if ref[0] == '<':
ref = ref.split('>')[1]
hyp = dataset.idx2token[0](best_hyps_id[b])
# Write to trn
speaker = str(batch['speakers'][b]).replace('-', '_')
if streaming:
utt_id = str(batch['utt_ids'][b]) + '_0000000_0000001'
else:
utt_id = str(batch['utt_ids'][b])
f_ref.write(ref + ' (' + speaker + '-' + utt_id + ')\n')
f_hyp.write(hyp + ' (' + speaker + '-' + utt_id + ')\n')
logger.debug('utt-id: %s' % utt_id)
logger.debug('Ref: %s' % ref)
logger.debug('Hyp: %s' % hyp)
logger.debug('-' * 150)
if not streaming:
# Compute WER
wer_b, sub_b, ins_b, del_b = compute_wer(ref=ref.split(' '),
hyp=hyp.split(' '),
normalize=False)
wer += wer_b
n_sub_w += sub_b
n_ins_w += ins_b
n_del_w += del_b
n_word += len(ref.split(' '))
if fine_grained:
xlen_bin = (batch['xlens'][b] // 200 + 1) * 200
if xlen_bin in wer_dist.keys():
wer_dist[xlen_bin] += [wer_b / 100]
else:
wer_dist[xlen_bin] = [wer_b / 100]
# Compute CER
if dataset.corpus == 'csj':
ref = ref.replace(' ', '')
hyp = hyp.replace(' ', '')
cer_b, sub_b, ins_b, del_b = compute_wer(ref=list(ref),
hyp=list(hyp),
normalize=False)
cer += cer_b
n_sub_c += sub_b
n_ins_c += ins_b
n_del_c += del_b
n_char += len(ref)
if models[0].streamable():
n_streamable += 1
else:
last_success_frame_ratio += models[0].last_success_frame_ratio()
quantity_rate += models[0].quantity_rate()
n_utt += 1
if progressbar:
pbar.update(1)
if is_new_epoch:
break
if progressbar:
pbar.close()
# Reset data counters
dataset.reset()
if not streaming:
wer /= n_word
n_sub_w /= n_word
n_ins_w /= n_word
n_del_w /= n_word
cer /= n_char
n_sub_c /= n_char
n_ins_c /= n_char
n_del_c /= n_char
if n_utt - n_streamable > 0:
last_success_frame_ratio /= (n_utt - n_streamable)
n_streamable /= n_utt
quantity_rate /= n_utt
if fine_grained:
for len_bin, wers in sorted(wer_dist.items(), key=lambda x: x[0]):
logger.info(' WER (%s): %.2f %% (%d)' % (dataset.set, sum(wers) / len(wers), len_bin))
logger.debug('WER (%s): %.2f %%' % (dataset.set, wer))
logger.debug('SUB: %.2f / INS: %.2f / DEL: %.2f' % (n_sub_w, n_ins_w, n_del_w))
logger.debug('CER (%s): %.2f %%' % (dataset.set, cer))
logger.debug('SUB: %.2f / INS: %.2f / DEL: %.2f' % (n_sub_c, n_ins_c, n_del_c))
logger.info('Streamablility (%s): %.2f %%' % (dataset.set, n_streamable * 100))
logger.info('Quantity rate (%s): %.2f %%' % (dataset.set, quantity_rate * 100))
logger.info('Last success frame ratio (%s): %.2f %%' % (dataset.set, last_success_frame_ratio))
return wer, cer
def eval_wordpiece_bleu(models,
dataloader,
params,
epoch=-1,
rank=0,
save_dir=None,
streaming=False,
progressbar=False,
edit_distance=True,
fine_grained=False,
oracle=False,
teacher_force=False):
"""Evaluate a wordpiece-level model by corpus-level BLEU.
Args:
models (List): models to evaluate
dataloader (torch.utils.data.DataLoader): evaluation dataloader
params (omegaconf.dictconfig.DictConfig): decoding hyperparameters
epoch (int): current epoch
rank (int): rank of current process group
save_dir (str): directory path to save hypotheses
streaming (bool): streaming decoding for session-level evaluation
progressbar (bool): visualize progressbar
edit_distance (bool): calculate edit-distance (can be skipped for RTF calculation)
fine_grained (bool): calculate fine-grained corpus-level BLEU distributions based on input lengths
oracle (bool): calculate oracle corpsu-level BLEU
teacher_force (bool): conduct decoding in teacher-forcing mode
Returns:
c_bleu (float): corpus-level 4-gram BLEU
"""
if save_dir is None:
save_dir = 'decode_' + dataloader.set + '_ep' + \
str(epoch) + '_beam' + str(params.get('recog_beam_width'))
save_dir += '_lp' + str(params.get('recog_length_penalty'))
save_dir += '_cp' + str(params.get('recog_coverage_penalty'))
save_dir += '_' + str(params.get('recog_min_len_ratio')) + '_' + \
str(params.get('recog_max_len_ratio'))
save_dir += '_lm' + str(params.get('recog_lm_weight'))
ref_trn_path = mkdir_join(models[0].save_path,
save_dir,
'ref.trn',
rank=rank)
hyp_trn_path = mkdir_join(models[0].save_path,
save_dir,
'hyp.trn',
rank=rank)
else:
ref_trn_path = mkdir_join(save_dir, 'ref.trn', rank=rank)
hyp_trn_path = mkdir_join(save_dir, 'hyp.trn', rank=rank)
list_of_references_dist = {
} # calculate corpus-level BLEU distribution bucketed by input lengths
hypotheses_dist = {}
hypotheses_oracle = []
n_oracle_hit = 0
n_utt = 0
# Reset data counter
dataloader.reset(params.get('recog_batch_size'), 'seq')
if progressbar:
pbar = tqdm(total=len(dataloader))
list_of_references = []
hypotheses = []
if rank == 0:
f_hyp = codecs.open(hyp_trn_path, 'w', encoding='utf-8')
f_ref = codecs.open(ref_trn_path, 'w', encoding='utf-8')
for batch in dataloader:
if streaming or params.get('recog_block_sync'):
nbest_hyps_id = models[0].decode_streaming(
batch['xs'],
params,
dataloader.idx2token[0],
exclude_eos=True,
speaker=batch['speakers'][0])[0]
else:
nbest_hyps_id = models[0].decode(
batch['xs'],
params,
idx2token=dataloader.idx2token[0],
exclude_eos=True,
refs_id=batch['ys'],
utt_ids=batch['utt_ids'],
speakers=batch['speakers'],
ensemble_models=models[1:] if len(models) > 1 else [],
teacher_force=teacher_force)[0]
for b in range(len(batch['xs'])):
ref = batch['text'][b]
if ref[0] == '<':
ref = ref.split('>')[1]
nbest_hyps = [
dataloader.idx2token[0](hyp_id) for hyp_id in nbest_hyps_id[b]
]
# Write to trn
speaker = str(batch['speakers'][b]).replace('-', '_')
if streaming:
utt_id = str(batch['utt_ids'][b]) + '_0000000_0000001'
else:
utt_id = str(batch['utt_ids'][b])
if rank == 0:
f_ref.write(ref + ' (' + speaker + '-' + utt_id + ')\n')
f_hyp.write(nbest_hyps[0] + ' (' + speaker + '-' + utt_id +
')\n')
logger.debug('utt-id (%d/%d): %s' %
(n_utt + 1, len(dataloader), utt_id))
logger.debug('Ref: %s' % ref)
logger.debug('Hyp: %s' % nbest_hyps[0])
logger.debug('-' * 150)
if edit_distance and not streaming:
list_of_references += [[ref.split(' ')]]
hypotheses += [nbest_hyps[0].split(' ')]
if fine_grained:
xlen_bin = (batch['xlens'][b] // 200 + 1) * 200
if xlen_bin in hypotheses_dist.keys():
list_of_references_dist[xlen_bin] += [[ref.split(' ')]]
hypotheses_dist[xlen_bin] += [hypotheses[-1]]
else:
list_of_references_dist[xlen_bin] = [[ref.split(' ')]]
hypotheses_dist[xlen_bin] = [hypotheses[-1]]
# Compute oracle corpus-level BLEU (selected by sentence-level BLEU)
if oracle and len(nbest_hyps) > 1:
s_blues_b = [
sentence_bleu(ref.split(' '), hyp_n.split(' '))
for hyp_n in nbest_hyps
]
oracle_idx = np.argmax(np.array(s_blues_b))
if oracle_idx == 0:
n_oracle_hit += len(batch['utt_ids'])
hypotheses_oracle += [nbest_hyps[oracle_idx].split(' ')]
n_utt += len(batch['utt_ids'])
if progressbar:
pbar.update(len(batch['utt_ids']))
if rank == 0:
f_hyp.close()
f_ref.close()
if progressbar:
pbar.close()
# Reset data counters
dataloader.reset(is_new_epoch=True)
c_bleu = corpus_bleu(list_of_references, hypotheses) * 100
if edit_distance and not streaming:
if oracle:
c_bleu_oracle = corpus_bleu(list_of_references,
hypotheses_oracle) * 100
oracle_hit_rate = n_oracle_hit * 100 / n_utt
logger.info('Oracle corpus-level BLEU (%s): %.2f %%' %
(dataloader.set, c_bleu_oracle))
logger.info('Oracle hit rate (%s): %.2f %%' %
(dataloader.set, oracle_hit_rate))
if fine_grained:
for len_bin, hypotheses_bin in sorted(hypotheses_dist.items(),
key=lambda x: x[0]):
c_bleu_bin = corpus_bleu(list_of_references_dist[len_bin],
hypotheses_bin) * 100
logger.info(' corpus-level BLEU (%s): %.2f %% (%d)' %
(dataloader.set, c_bleu_bin, len_bin))
logger.info('Corpus-level BLEU (%s): %.2f %%' % (dataloader.set, c_bleu))
return c_bleu
def main():
args = parse()
# Load a conf file
if args.resume:
conf = load_config(
os.path.join(os.path.dirname(args.resume), 'conf.yml'))
for k, v in conf.items():
if k != 'resume':
setattr(args, k, v)
# Load dataset
train_set = Dataset(corpus=args.corpus,
tsv_path=args.train_set,
dict_path=args.dict,
nlsyms=args.nlsyms,
unit=args.unit,
wp_model=args.wp_model,
batch_size=args.batch_size * args.n_gpus,
n_epochs=args.n_epochs,
min_n_tokens=args.min_n_tokens,
bptt=args.bptt,
backward=args.backward,
serialize=args.serialize)
dev_set = Dataset(corpus=args.corpus,
tsv_path=args.dev_set,
dict_path=args.dict,
nlsyms=args.nlsyms,
unit=args.unit,
wp_model=args.wp_model,
batch_size=args.batch_size * args.n_gpus,
bptt=args.bptt,
backward=args.backward,
serialize=args.serialize)
eval_sets = []
for s in args.eval_sets:
eval_sets += [
Dataset(corpus=args.corpus,
tsv_path=s,
dict_path=args.dict,
nlsyms=args.nlsyms,
unit=args.unit,
wp_model=args.wp_model,
batch_size=1,
bptt=args.bptt,
backward=args.backward,
serialize=args.serialize)
]
args.vocab = train_set.vocab
# Set save path
if args.resume:
save_path = os.path.dirname(args.resume)
dir_name = os.path.basename(save_path)
else:
dir_name = make_model_name(args)
save_path = mkdir_join(
args.model,
'_'.join(os.path.basename(args.train_set).split('.')[:-1]),
dir_name)
save_path = set_save_path(save_path) # avoid overwriting
# Set logger
logger = set_logger(os.path.join(save_path, 'train.log'), key='training')
# Model setting
if 'gated_conv' in args.lm_type:
model = GatedConvLM(args)
else:
model = RNNLM(args)
model.save_path = save_path
if args.resume:
# Set optimizer
epoch = int(args.resume.split('-')[-1])
model.set_optimizer(
optimizer='sgd'
if epoch > conf['convert_to_sgd_epoch'] + 1 else conf['optimizer'],
learning_rate=float(conf['learning_rate']), # on-the-fly
weight_decay=float(conf['weight_decay']))
# Restore the last saved model
model, checkpoint = load_checkpoint(model, args.resume, resume=True)
lr_controller = checkpoint['lr_controller']
epoch = checkpoint['epoch']
step = checkpoint['step']
ppl_dev_best = checkpoint['metric_dev_best']
# Resume between convert_to_sgd_epoch and convert_to_sgd_epoch + 1
if epoch == conf['convert_to_sgd_epoch'] + 1:
model.set_optimizer(optimizer='sgd',
learning_rate=args.learning_rate,
weight_decay=float(conf['weight_decay']))
logger.info('========== Convert to SGD ==========')
else:
# Save the conf file as a yaml file
save_config(vars(args), os.path.join(model.save_path, 'conf.yml'))
# Save the nlsyms, dictionar, and wp_model
if args.nlsyms:
shutil.copy(args.nlsyms, os.path.join(model.save_path,
'nlsyms.txt'))
shutil.copy(args.dict, os.path.join(model.save_path, 'dict.txt'))
if args.unit == 'wp':
shutil.copy(args.wp_model, os.path.join(model.save_path,
'wp.model'))
for k, v in sorted(vars(args).items(), key=lambda x: x[0]):
logger.info('%s: %s' % (k, str(v)))
# Count total parameters
for n in sorted(list(model.num_params_dict.keys())):
nparams = model.num_params_dict[n]
logger.info("%s %d" % (n, nparams))
logger.info("Total %.2f M parameters" %
(model.total_parameters / 1000000))
logger.info(model)
# Set optimizer
model.set_optimizer(optimizer=args.optimizer,
learning_rate=float(args.learning_rate),
weight_decay=float(args.weight_decay))
epoch, step = 1, 1
ppl_dev_best = 10000
# Set learning rate controller
lr_controller = Controller(
learning_rate=float(args.learning_rate),
decay_type=args.decay_type,
decay_start_epoch=args.decay_start_epoch,
decay_rate=args.decay_rate,
decay_patient_n_epochs=args.decay_patient_n_epochs,
lower_better=True,
best_value=ppl_dev_best)
train_set.epoch = epoch - 1 # start from index:0
# GPU setting
if args.n_gpus >= 1:
model = CustomDataParallel(model,
device_ids=list(range(0, args.n_gpus, 1)),
deterministic=False,
benchmark=True)
model.cuda()
logger.info('PID: %s' % os.getpid())
logger.info('USERNAME: %s' % os.uname()[1])
# Set process name
if args.job_name:
setproctitle(args.job_name)
else:
setproctitle(dir_name)
# Set reporter
reporter = Reporter(model.module.save_path, tensorboard=True)
hidden = None
start_time_train = time.time()
start_time_epoch = time.time()
start_time_step = time.time()
not_improved_epoch = 0
pbar_epoch = tqdm(total=len(train_set))
while True:
# Compute loss in the training set
ys_train, is_new_epoch = train_set.next()
model.module.optimizer.zero_grad()
loss, hidden, reporter = model(ys_train, hidden, reporter)
if len(model.device_ids) > 1:
loss.backward(torch.ones(len(model.device_ids)))
else:
loss.backward()
loss.detach() # Trancate the graph
if args.clip_grad_norm > 0:
torch.nn.utils.clip_grad_norm_(model.module.parameters(),
args.clip_grad_norm)
model.module.optimizer.step()
loss_train = loss.item()
del loss
if 'gated_conv' not in args.lm_type:
hidden = model.module.repackage_hidden(hidden)
reporter.step(is_eval=False)
if step % args.print_step == 0:
# Compute loss in the dev set
ys_dev = dev_set.next()[0]
loss, _, reporter = model(ys_dev, None, reporter, is_eval=True)
loss_dev = loss.item()
del loss
reporter.step(is_eval=True)
duration_step = time.time() - start_time_step
logger.info(
"step:%d(ep:%.2f) loss:%.3f(%.3f)/ppl:%.3f(%.3f)/lr:%.5f/bs:%d (%.2f min)"
% (step, train_set.epoch_detail, loss_train, loss_dev,
np.exp(loss_train), np.exp(loss_dev), lr_controller.lr,
ys_train.shape[0], duration_step / 60))
start_time_step = time.time()
step += args.n_gpus
pbar_epoch.update(ys_train.shape[0] * (ys_train.shape[1] - 1))
# Save fugures of loss and accuracy
if step % (args.print_step * 10) == 0:
reporter.snapshot()
# Save checkpoint and evaluate model per epoch
if is_new_epoch:
duration_epoch = time.time() - start_time_epoch
logger.info('========== EPOCH:%d (%.2f min) ==========' %
(epoch, duration_epoch / 60))
if epoch < args.eval_start_epoch:
# Save the model
save_checkpoint(model.module,
model.module.save_path,
lr_controller,
epoch,
step - 1,
ppl_dev_best,
remove_old_checkpoints=True)
else:
start_time_eval = time.time()
# dev
ppl_dev, _ = eval_ppl([model.module],
dev_set,
batch_size=1,
bptt=args.bptt)
logger.info('PPL (%s): %.2f' % (dev_set.set, ppl_dev))
# Update learning rate
model.module.optimizer = lr_controller.decay(
model.module.optimizer, epoch=epoch, value=ppl_dev)
if ppl_dev < ppl_dev_best:
ppl_dev_best = ppl_dev
not_improved_epoch = 0
logger.info('||||| Best Score |||||')
# Save the model
save_checkpoint(model.module,
model.module.save_path,
lr_controller,
epoch,
step - 1,
ppl_dev_best,
remove_old_checkpoints=True)
# test
ppl_test_avg = 0.
for eval_set in eval_sets:
ppl_test, _ = eval_ppl([model.module],
eval_set,
batch_size=1,
bptt=args.bptt)
logger.info('PPL (%s): %.2f' %
(eval_set.set, ppl_test))
ppl_test_avg += ppl_test
if len(eval_sets) > 0:
logger.info('PPL (avg.): %.2f' %
(ppl_test_avg / len(eval_sets)))
else:
not_improved_epoch += 1
duration_eval = time.time() - start_time_eval
logger.info('Evaluation time: %.2f min' % (duration_eval / 60))
# Early stopping
if not_improved_epoch == args.not_improved_patient_n_epochs:
break
# Convert to fine-tuning stage
if epoch == args.convert_to_sgd_epoch:
model.module.set_optimizer(
'sgd',
learning_rate=args.learning_rate,
weight_decay=float(args.weight_decay))
lr_controller = Controller(
learning_rate=args.learning_rate,
decay_type='epoch',
decay_start_epoch=epoch,
decay_rate=0.5,
lower_better=True)
logger.info('========== Convert to SGD ==========')
pbar_epoch = tqdm(total=len(train_set))
if epoch == args.n_epochs:
break
start_time_step = time.time()
start_time_epoch = time.time()
epoch += 1
duration_train = time.time() - start_time_train
logger.info('Total time: %.2f hour' % (duration_train / 3600))
if reporter.tensorboard:
reporter.tf_writer.close()
pbar_epoch.close()
return model.module.save_path
def main():
args = parse()
# Load a conf file
dir_name = os.path.dirname(args.recog_model[0])
conf = load_config(os.path.join(dir_name, 'conf.yml'))
# Overwrite conf
for k, v in conf.items():
if 'recog' not in k:
setattr(args, k, v)
recog_params = vars(args)
# Setting for logging
if os.path.isfile(os.path.join(args.recog_dir, 'plot.log')):
os.remove(os.path.join(args.recog_dir, 'plot.log'))
logger = set_logger(os.path.join(args.recog_dir, 'plot.log'),
key='decoding', stdout=args.recog_stdout)
for i, s in enumerate(args.recog_sets):
subsample_factor = 1
subsample = [int(s) for s in args.subsample.split('_')]
if args.conv_poolings:
for p in args.conv_poolings.split('_'):
p = int(p.split(',')[0].replace('(', ''))
if p > 1:
subsample_factor *= p
subsample_factor *= np.prod(subsample)
# Load dataset
dataset = Dataset(corpus=args.corpus,
tsv_path=s,
dict_path=os.path.join(dir_name, 'dict.txt'),
dict_path_sub1=os.path.join(dir_name, 'dict_sub1.txt') if os.path.isfile(
os.path.join(dir_name, 'dict_sub1.txt')) else False,
nlsyms=args.nlsyms,
wp_model=os.path.join(dir_name, 'wp.model'),
unit=args.unit,
unit_sub1=args.unit_sub1,
batch_size=args.recog_batch_size,
is_test=True)
if i == 0:
# Load the ASR model
model = Speech2Text(args, dir_name)
model = load_checkpoint(model, args.recog_model[0])[0]
epoch = int(args.recog_model[0].split('-')[-1])
if not args.recog_unit:
args.recog_unit = args.unit
logger.info('recog unit: %s' % args.recog_unit)
logger.info('epoch: %d' % (epoch - 1))
logger.info('batch size: %d' % args.recog_batch_size)
# GPU setting
model.cuda()
save_path = mkdir_join(args.recog_dir, 'ctc_probs')
# Clean directory
if save_path is not None and os.path.isdir(save_path):
shutil.rmtree(save_path)
os.mkdir(save_path)
while True:
batch, is_new_epoch = dataset.next(recog_params['recog_batch_size'])
best_hyps_id, _, _ = model.decode(batch['xs'], recog_params,
exclude_eos=False)
# Get CTC probs
ctc_probs, indices_topk, xlens = model.get_ctc_probs(
batch['xs'], temperature=1, topk=min(100, model.vocab))
# NOTE: ctc_probs: '[B, T, topk]'
for b in range(len(batch['xs'])):
tokens = dataset.idx2token[0](best_hyps_id[b], return_list=True)
spk = batch['speakers'][b]
plot_ctc_probs(
ctc_probs[b, :xlens[b]],
indices_topk[b],
n_frames=xlens[b],
subsample_factor=subsample_factor,
spectrogram=batch['xs'][b][:, :dataset.input_dim],
save_path=mkdir_join(save_path, spk, batch['utt_ids'][b] + '.png'),
figsize=(20, 8))
hyp = ' '.join(tokens)
logger.info('utt-id: %s' % batch['utt_ids'][b])
logger.info('Ref: %s' % batch['text'][b].lower())
logger.info('Hyp: %s' % hyp)
logger.info('-' * 50)
if is_new_epoch:
break
def eval_char(models,
dataloader,
params,
epoch=-1,
rank=0,
save_dir=None,
streaming=False,
progressbar=False,
task_idx=0,
edit_distance=True,
fine_grained=False,
oracle=False,
teacher_force=False):
"""Evaluate a character-level model by WER & CER.
Args:
models (List): models to evaluate
dataloader (torch.utils.data.DataLoader): evaluation dataloader
params (omegaconf.dictconfig.DictConfig): decoding hyperparameters
epoch (int): current epoch
rank (int): rank of current process group
save_dir (str): directory path to save hypotheses
streaming (bool): streaming decoding for session-level evaluation
progressbar (bool): visualize progressbar
edit_distance (bool): calculate edit-distance (can be skipped for RTF calculation)
task_idx (int): index of target task in interest
0: main task
1: sub task
2: sub sub task
fine_grained (bool): calculate fine-grained WER distributions based on input lengths
oracle (bool): calculate oracle WER
teacher_force (bool): conduct decoding in teacher-forcing mode
Returns:
wer (float): Word error rate
cer (float): Character error rate
"""
if save_dir is None:
save_dir = 'decode_' + dataloader.set + '_ep' + \
str(epoch) + '_beam' + str(params.get('recog_beam_width'))
save_dir += '_lp' + str(params.get('recog_length_penalty'))
save_dir += '_cp' + str(params.get('recog_coverage_penalty'))
save_dir += '_' + str(params.get('recog_min_len_ratio')) + '_' + \
str(params.get('recog_max_len_ratio'))
save_dir += '_lm' + str(params.get('recog_lm_weight'))
ref_trn_path = mkdir_join(models[0].save_path,
save_dir,
'ref.trn',
rank=rank)
hyp_trn_path = mkdir_join(models[0].save_path,
save_dir,
'hyp.trn',
rank=rank)
else:
ref_trn_path = mkdir_join(save_dir, 'ref.trn', rank=rank)
hyp_trn_path = mkdir_join(save_dir, 'hyp.trn', rank=rank)
wer, cer = 0, 0
n_sub_w, n_ins_w, n_del_w = 0, 0, 0
n_sub_c, n_ins_c, n_del_c = 0, 0, 0
n_word, n_char = 0, 0
cer_dist = {} # calculate CER distribution based on input lengths
cer_oracle = 0
n_oracle_hit = 0
n_streamable, quantity_rate, n_utt = 0, 0, 0
last_success_frame_ratio = 0
# Reset data counter
dataloader.reset(params.get('recog_batch_size'), 'seq')
if progressbar:
pbar = tqdm(total=len(dataloader))
if rank == 0:
f_hyp = codecs.open(hyp_trn_path, 'w', encoding='utf-8')
f_ref = codecs.open(ref_trn_path, 'w', encoding='utf-8')
if task_idx == 0:
task = 'ys'
elif task_idx == 1:
task = 'ys_sub1'
elif task_idx == 2:
task = 'ys_sub2'
elif task_idx == 3:
task = 'ys_sub3'
for batch in dataloader:
speakers = batch['sessions' if dataloader.corpus ==
'swbd' else 'speakers']
if streaming or params.get('recog_block_sync'):
nbest_hyps_id = models[0].decode_streaming(batch['xs'],
params,
dataloader.idx2token[0],
exclude_eos=True,
speaker=speakers[0])[0]
else:
nbest_hyps_id = models[0].decode(
batch['xs'],
params,
idx2token=dataloader.idx2token[0],
exclude_eos=True,
refs_id=batch['ys'] if task_idx == 0 else batch['ys_sub' +
str(task_idx)],
utt_ids=batch['utt_ids'],
speakers=speakers,
task=task,
ensemble_models=models[1:] if len(models) > 1 else [],
teacher_force=teacher_force)[0]
for b in range(len(batch['xs'])):
# assert len(batch['xs']) == 1, 'batch is 1'
ref = batch['text'][b]
nbest_hyps_tmp = [
dataloader.idx2token[0](hyp_id) for hyp_id in nbest_hyps_id[b]
]
# print(nbest_hyps_id)
# print(nbest_hyps_tmp)
# assert False, 'vv'
# Truncate the first and last spaces for the char_space unit
nbest_hyps = []
for hyp in nbest_hyps_tmp:
if len(hyp) > 0 and hyp[0] == ' ':
hyp = hyp[1:]
if len(hyp) > 0 and hyp[-1] == ' ':
hyp = hyp[:-1]
nbest_hyps.append(hyp)
# Write to trn
speaker = str(batch['speakers'][b]).replace('-', '_')
if streaming:
utt_id = str(batch['utt_ids'][b]) + '_0000000_0000001'
else:
utt_id = str(batch['utt_ids'][b])
if rank == 0:
f_ref.write(ref + ' (' + speaker + '-' + utt_id + ')\n')
f_hyp.write(nbest_hyps[0] + ' (' + speaker + '-' + utt_id +
')\n')
logger.debug('utt-id (%d/%d): %s' %
(n_utt + 1, len(dataloader), utt_id))
logger.debug('Ref: %s' % ref)
logger.debug('Hyp: %s' % nbest_hyps[0])
logger.debug('-' * 150)
if edit_distance and not streaming:
if ('char' in dataloader.unit and 'nowb' not in dataloader.unit
) or (task_idx > 0 and dataloader.unit_sub1 == 'char'):
# Compute WER
err_b, sub_b, ins_b, del_b = compute_wer(
ref=ref.split(' '), hyp=nbest_hyps[0].split(' '))
wer += err_b
n_sub_w += sub_b
n_ins_w += ins_b
n_del_w += del_b
n_word += len(ref.split(' '))
# NOTE: sentence error rate for Chinese
# Compute CER
if dataloader.corpus == 'csj':
ref = ref.replace(' ', '')
nbest_hyps[0] = nbest_hyps[0].replace(' ', '')
err_b, sub_b, ins_b, del_b = compute_wer(ref=list(ref),
hyp=list(
nbest_hyps[0]))
cer += err_b
n_sub_c += sub_b
n_ins_c += ins_b
n_del_c += del_b
n_char += len(ref)
# Compute oracle CER
if oracle and len(nbest_hyps) > 1:
cers_b = [err_b] + [
compute_wer(ref=list(ref), hyp=list(hyp_n))[0]
for hyp_n in nbest_hyps[1:]
]
oracle_idx = np.argmin(np.array(cers_b))
if oracle_idx == 0:
n_oracle_hit += len(batch['utt_ids'])
cer_oracle += cers_b[oracle_idx]
if fine_grained:
xlen_bin = (batch['xlens'][b] // 200 + 1) * 200
if xlen_bin in cer_dist.keys():
cer_dist[xlen_bin] += [err_b / 100]
else:
cer_dist[xlen_bin] = [err_b / 100]
if models[0].streamable():
n_streamable += len(batch['utt_ids'])
else:
last_success_frame_ratio += models[
0].last_success_frame_ratio()
quantity_rate += models[0].quantity_rate()
n_utt += len(batch['utt_ids'])
if progressbar:
pbar.update(len(batch['utt_ids']))
if rank == 0:
f_hyp.close()
f_ref.close()
if progressbar:
pbar.close()
# Reset data counters
dataloader.reset(is_new_epoch=True)
if edit_distance and not streaming:
if ('char' in dataloader.unit and 'nowb' not in dataloader.unit) or (
task_idx > 0 and dataloader.unit_sub1 == 'char'):
wer /= n_word
n_sub_w /= n_word
n_ins_w /= n_word
n_del_w /= n_word
else:
wer = n_sub_w = n_ins_w = n_del_w = 0
cer /= n_char
n_sub_c /= n_char
n_ins_c /= n_char
n_del_c /= n_char
if n_utt - n_streamable > 0:
last_success_frame_ratio /= (n_utt - n_streamable)
n_streamable /= n_utt
quantity_rate /= n_utt
if params.get('recog_beam_width') > 1:
logger.info('WER (%s): %.2f %%' % (dataloader.set, wer))
logger.info('SUB: %.2f / INS: %.2f / DEL: %.2f' %
(n_sub_w, n_ins_w, n_del_w))
logger.info('CER (%s): %.2f %%' % (dataloader.set, cer))
logger.info('SUB: %.2f / INS: %.2f / DEL: %.2f' %
(n_sub_c, n_ins_c, n_del_c))
if oracle:
cer_oracle /= n_char
oracle_hit_rate = n_oracle_hit * 100 / n_utt
logger.info('Oracle CER (%s): %.2f %%' %
(dataloader.set, cer_oracle))
logger.info('Oracle hit rate (%s): %.2f %%' %
(dataloader.set, oracle_hit_rate))
if fine_grained:
for len_bin, cers in sorted(cer_dist.items(), key=lambda x: x[0]):
logger.info(' CER (%s): %.2f %% (%d)' %
(dataloader.set, sum(cers) / len(cers), len_bin))
logger.info('Streamability (%s): %.2f %%' %
(dataloader.set, n_streamable * 100))
logger.info('Quantity rate (%s): %.2f %%' %
(dataloader.set, quantity_rate * 100))
logger.info('Last success frame ratio (%s): %.2f %%' %
(dataloader.set, last_success_frame_ratio))
return wer, cer
def eval_phone(models,
dataset,
recog_params,
epoch,
recog_dir=None,
progressbar=False):
"""Evaluate a phone-level model by PER.
Args:
models (list): models to evaluate
dataset (Dataset): evaluation dataset
recog_params (dict):
epoch (int):
recog_dir (str):
progressbar (bool): visualize the progressbar
Returns:
per (float): Phone error rate
"""
# Reset data counter
dataset.reset()
if recog_dir is None:
recog_dir = 'decode_' + dataset.set + '_ep' + str(
epoch) + '_beam' + str(recog_params['recog_beam_width'])
recog_dir += '_lp' + str(recog_params['recog_length_penalty'])
recog_dir += '_cp' + str(recog_params['recog_coverage_penalty'])
recog_dir += '_' + str(
recog_params['recog_min_len_ratio']) + '_' + str(
recog_params['recog_max_len_ratio'])
ref_trn_save_path = mkdir_join(models[0].save_path, recog_dir,
'ref.trn')
hyp_trn_save_path = mkdir_join(models[0].save_path, recog_dir,
'hyp.trn')
else:
ref_trn_save_path = mkdir_join(recog_dir, 'ref.trn')
hyp_trn_save_path = mkdir_join(recog_dir, 'hyp.trn')
per = 0
n_sub, n_ins, n_del = 0, 0, 0
n_phone = 0
if progressbar:
pbar = tqdm(total=len(dataset))
with open(hyp_trn_save_path, 'w') as f_hyp, open(ref_trn_save_path,
'w') as f_ref:
while True:
batch, is_new_epoch = dataset.getitem(
recog_params['recog_batch_size'])
best_hyps_id, _, _ = models[0].decode(
batch['xs'],
recog_params,
dataset.idx2token[0],
exclude_eos=True,
refs_id=batch['ys'],
utt_ids=batch['utt_ids'],
speakers=batch['sessions']
if dataset.corpus == 'swbd' else batch['speakers'],
ensemble_models=models[1:] if len(models) > 1 else [])
for b in range(len(batch['xs'])):
ref = batch['text'][b]
hyp = dataset.idx2token[0](best_hyps_id[b])
# Write to trn
utt_id = str(batch['utt_ids'][b])
speaker = str(batch['speakers'][b]).replace('-', '_')
f_ref.write(ref + ' (' + speaker + '-' + utt_id + ')\n')
f_hyp.write(hyp + ' (' + speaker + '-' + utt_id + ')\n')
logger.info('utt-id: %s' % batch['utt_ids'][b])
logger.info('Ref: %s' % ref)
logger.info('Hyp: %s' % hyp)
logger.info('-' * 150)
# Compute PER
per_b, sub_b, ins_b, del_b = compute_wer(ref=ref.split(' '),
hyp=hyp.split(' '),
normalize=False)
per += per_b
n_sub += sub_b
n_ins += ins_b
n_del += del_b
n_phone += len(ref.split(' '))
if progressbar:
pbar.update(1)
if is_new_epoch:
break
if progressbar:
pbar.close()
# Reset data counters
dataset.reset()
per /= n_phone
n_sub /= n_phone
n_ins /= n_phone
n_del /= n_phone
logger.info('PER (%s): %.2f %%' % (dataset.set, per))
logger.info('SUB: %.2f / INS: %.2f / DEL: %.2f' % (n_sub, n_ins, n_del))
return per
def eval_wordpiece_bleu(models,
dataset,
recog_params,
epoch,
recog_dir=None,
streaming=False,
progressbar=False,
fine_grained=False):
"""Evaluate the wordpiece-level model by BLEU.
Args:
models (list): models to evaluate
dataset (Dataset): evaluation dataset
recog_params (dict):
epoch (int):
recog_dir (str):
streaming (bool): streaming decoding for the session-level evaluation
progressbar (bool): visualize the progressbar
fine_grained (bool): calculate fine-grained BLEU distributions based on input lengths
Returns:
bleu (float): 4-gram BLEU
"""
if recog_dir is None:
recog_dir = 'decode_' + dataset.set + '_ep' + str(
epoch) + '_beam' + str(recog_params['recog_beam_width'])
recog_dir += '_lp' + str(recog_params['recog_length_penalty'])
recog_dir += '_cp' + str(recog_params['recog_coverage_penalty'])
recog_dir += '_' + str(
recog_params['recog_min_len_ratio']) + '_' + str(
recog_params['recog_max_len_ratio'])
recog_dir += '_lm' + str(recog_params['recog_lm_weight'])
ref_trn_path = mkdir_join(models[0].save_path, recog_dir, 'ref.trn')
hyp_trn_path = mkdir_join(models[0].save_path, recog_dir, 'hyp.trn')
else:
ref_trn_path = mkdir_join(recog_dir, 'ref.trn')
hyp_trn_path = mkdir_join(recog_dir, 'hyp.trn')
s_bleu = 0
n_sentence = 0
s_bleu_dist = {
} # calculate sentence-level BLEU distribution based on input lengths
# Reset data counter
dataset.reset(recog_params['recog_batch_size'])
if progressbar:
pbar = tqdm(total=len(dataset))
list_of_references = []
hypotheses = []
with codecs.open(hyp_trn_path, 'w', encoding='utf-8') as f_hyp, \
codecs.open(ref_trn_path, 'w', encoding='utf-8') as f_ref:
while True:
batch, is_new_epoch = dataset.next(
recog_params['recog_batch_size'])
if streaming or recog_params['recog_chunk_sync']:
best_hyps_id, _ = models[0].decode_streaming(
batch['xs'],
recog_params,
dataset.idx2token[0],
exclude_eos=True)
else:
best_hyps_id, _ = models[0].decode(
batch['xs'],
recog_params,
idx2token=dataset.idx2token[0] if progressbar else None,
exclude_eos=True,
refs_id=batch['ys'],
utt_ids=batch['utt_ids'],
speakers=batch['sessions' if dataset.corpus ==
'swbd' else 'speakers'],
ensemble_models=models[1:] if len(models) > 1 else [])
for b in range(len(batch['xs'])):
ref = batch['text'][b]
if ref[0] == '<':
ref = ref.split('>')[1]
hyp = dataset.idx2token[0](best_hyps_id[b])
# Write to trn
# speaker = str(batch['speakers'][b]).replace('-', '_')
if streaming:
utt_id = str(batch['utt_ids'][b]) + '_0000000_0000001'
else:
utt_id = str(batch['utt_ids'][b])
f_ref.write(ref + '\n')
f_hyp.write(hyp + '\n')
logger.debug('utt-id: %s' % utt_id)
logger.debug('Ref: %s' % ref)
logger.debug('Hyp: %s' % hyp)
logger.debug('-' * 150)
if not streaming:
list_of_references += [[ref.split(' ')]]
hypotheses += [hyp.split(' ')]
n_sentence += 1
# Compute sentence-level BLEU
if fine_grained:
s_bleu_b = sentence_bleu([ref.split(' ')],
hyp.split(' '))
s_bleu += s_bleu_b * 100
xlen_bin = (batch['xlens'][b] // 200 + 1) * 200
if xlen_bin in s_bleu_dist.keys():
s_bleu_dist[xlen_bin] += [s_bleu_b / 100]
else:
s_bleu_dist[xlen_bin] = [s_bleu_b / 100]
if progressbar:
pbar.update(1)
if is_new_epoch:
break
if progressbar:
pbar.close()
# Reset data counters
dataset.reset()
c_bleu = corpus_bleu(list_of_references, hypotheses) * 100
if not streaming and fine_grained:
s_bleu /= n_sentence
for len_bin, s_bleus in sorted(s_bleu_dist.items(),
key=lambda x: x[0]):
logger.info(' sentence-level BLEU (%s): %.2f %% (%d)' %
(dataset.set, sum(s_bleus) / len(s_bleus), len_bin))
logger.debug('Corpus-level BLEU (%s): %.2f %%' % (dataset.set, c_bleu))
return c_bleu
def main():
args = parse_args_train(sys.argv[1:])
args_init = copy.deepcopy(args)
args_teacher = copy.deepcopy(args)
# Load a conf file
if args.resume:
conf = load_config(os.path.join(os.path.dirname(args.resume), 'conf.yml'))
for k, v in conf.items():
if k != 'resume':
setattr(args, k, v)
recog_params = vars(args)
args = compute_susampling_factor(args)
# Load dataset
batch_size = args.batch_size * args.n_gpus if args.n_gpus >= 1 else args.batch_size
train_set = Dataset(corpus=args.corpus,
tsv_path=args.train_set,
tsv_path_sub1=args.train_set_sub1,
tsv_path_sub2=args.train_set_sub2,
dict_path=args.dict,
dict_path_sub1=args.dict_sub1,
dict_path_sub2=args.dict_sub2,
nlsyms=args.nlsyms,
unit=args.unit,
unit_sub1=args.unit_sub1,
unit_sub2=args.unit_sub2,
wp_model=args.wp_model,
wp_model_sub1=args.wp_model_sub1,
wp_model_sub2=args.wp_model_sub2,
batch_size=batch_size,
n_epochs=args.n_epochs,
min_n_frames=args.min_n_frames,
max_n_frames=args.max_n_frames,
shuffle_bucket=args.shuffle_bucket,
sort_by='input',
short2long=args.sort_short2long,
sort_stop_epoch=args.sort_stop_epoch,
dynamic_batching=args.dynamic_batching,
ctc=args.ctc_weight > 0,
ctc_sub1=args.ctc_weight_sub1 > 0,
ctc_sub2=args.ctc_weight_sub2 > 0,
subsample_factor=args.subsample_factor,
subsample_factor_sub1=args.subsample_factor_sub1,
subsample_factor_sub2=args.subsample_factor_sub2,
discourse_aware=args.discourse_aware)
dev_set = Dataset(corpus=args.corpus,
tsv_path=args.dev_set,
tsv_path_sub1=args.dev_set_sub1,
tsv_path_sub2=args.dev_set_sub2,
dict_path=args.dict,
dict_path_sub1=args.dict_sub1,
dict_path_sub2=args.dict_sub2,
nlsyms=args.nlsyms,
unit=args.unit,
unit_sub1=args.unit_sub1,
unit_sub2=args.unit_sub2,
wp_model=args.wp_model,
wp_model_sub1=args.wp_model_sub1,
wp_model_sub2=args.wp_model_sub2,
batch_size=batch_size,
min_n_frames=args.min_n_frames,
max_n_frames=args.max_n_frames,
ctc=args.ctc_weight > 0,
ctc_sub1=args.ctc_weight_sub1 > 0,
ctc_sub2=args.ctc_weight_sub2 > 0,
subsample_factor=args.subsample_factor,
subsample_factor_sub1=args.subsample_factor_sub1,
subsample_factor_sub2=args.subsample_factor_sub2)
eval_sets = [Dataset(corpus=args.corpus,
tsv_path=s,
dict_path=args.dict,
nlsyms=args.nlsyms,
unit=args.unit,
wp_model=args.wp_model,
batch_size=1,
is_test=True) for s in args.eval_sets]
args.vocab = train_set.vocab
args.vocab_sub1 = train_set.vocab_sub1
args.vocab_sub2 = train_set.vocab_sub2
args.input_dim = train_set.input_dim
# Set save path
if args.resume:
save_path = os.path.dirname(args.resume)
dir_name = os.path.basename(save_path)
else:
dir_name = set_asr_model_name(args)
if args.mbr_training:
assert args.asr_init
save_path = mkdir_join(os.path.dirname(args.asr_init), dir_name)
else:
save_path = mkdir_join(args.model_save_dir, '_'.join(
os.path.basename(args.train_set).split('.')[:-1]), dir_name)
save_path = set_save_path(save_path) # avoid overwriting
# Set logger
set_logger(os.path.join(save_path, 'train.log'), stdout=args.stdout)
# Load a LM conf file for LM fusion & LM initialization
if not args.resume and args.external_lm:
lm_conf = load_config(os.path.join(os.path.dirname(args.external_lm), 'conf.yml'))
args.lm_conf = argparse.Namespace()
for k, v in lm_conf.items():
setattr(args.lm_conf, k, v)
assert args.unit == args.lm_conf.unit
assert args.vocab == args.lm_conf.vocab
# Model setting
model = Speech2Text(args, save_path, train_set.idx2token[0])
if not args.resume:
# Save the conf file as a yaml file
save_config(vars(args), os.path.join(save_path, 'conf.yml'))
if args.external_lm:
save_config(args.lm_conf, os.path.join(save_path, 'conf_lm.yml'))
# Save the nlsyms, dictionary, and wp_model
if args.nlsyms:
shutil.copy(args.nlsyms, os.path.join(save_path, 'nlsyms.txt'))
for sub in ['', '_sub1', '_sub2']:
if getattr(args, 'dict' + sub):
shutil.copy(getattr(args, 'dict' + sub), os.path.join(save_path, 'dict' + sub + '.txt'))
if getattr(args, 'unit' + sub) == 'wp':
shutil.copy(getattr(args, 'wp_model' + sub), os.path.join(save_path, 'wp' + sub + '.model'))
for k, v in sorted(vars(args).items(), key=lambda x: x[0]):
logger.info('%s: %s' % (k, str(v)))
# Count total parameters
for n in sorted(list(model.num_params_dict.keys())):
n_params = model.num_params_dict[n]
logger.info("%s %d" % (n, n_params))
logger.info("Total %.2f M parameters" % (model.total_parameters / 1000000))
logger.info(model)
# Initialize with pre-trained model's parameters
if args.asr_init:
# Load the ASR model (full model)
conf_init = load_config(os.path.join(os.path.dirname(args.asr_init), 'conf.yml'))
for k, v in conf_init.items():
setattr(args_init, k, v)
model_init = Speech2Text(args_init)
load_checkpoint(args.asr_init, model_init)
# Overwrite parameters
param_dict = dict(model_init.named_parameters())
for n, p in model.named_parameters():
if n in param_dict.keys() and p.size() == param_dict[n].size():
if args.asr_init_enc_only and 'enc' not in n:
continue
p.data = param_dict[n].data
logger.info('Overwrite %s' % n)
# Set optimizer
resume_epoch = 0
if args.resume:
resume_epoch = int(args.resume.split('-')[-1])
optimizer = set_optimizer(model, 'sgd' if resume_epoch > args.convert_to_sgd_epoch else args.optimizer,
args.lr, args.weight_decay)
else:
optimizer = set_optimizer(model, args.optimizer, args.lr, args.weight_decay)
# Wrap optimizer by learning rate scheduler
is_transformer = 'former' in args.enc_type or 'former' in args.dec_type
optimizer = LRScheduler(optimizer, args.lr,
decay_type=args.lr_decay_type,
decay_start_epoch=args.lr_decay_start_epoch,
decay_rate=args.lr_decay_rate,
decay_patient_n_epochs=args.lr_decay_patient_n_epochs,
early_stop_patient_n_epochs=args.early_stop_patient_n_epochs,
lower_better=args.metric not in ['accuracy', 'bleu'],
warmup_start_lr=args.warmup_start_lr,
warmup_n_steps=args.warmup_n_steps,
model_size=getattr(args, 'transformer_d_model', 0),
factor=args.lr_factor,
noam=args.optimizer == 'noam',
save_checkpoints_topk=10 if is_transformer else 1)
if args.resume:
# Restore the last saved model
load_checkpoint(args.resume, model, optimizer)
# Resume between convert_to_sgd_epoch -1 and convert_to_sgd_epoch
if resume_epoch == args.convert_to_sgd_epoch:
optimizer.convert_to_sgd(model, args.lr, args.weight_decay,
decay_type='always', decay_rate=0.5)
# Load the teacher ASR model
teacher = None
if args.teacher:
assert os.path.isfile(args.teacher), 'There is no checkpoint.'
conf_teacher = load_config(os.path.join(os.path.dirname(args.teacher), 'conf.yml'))
for k, v in conf_teacher.items():
setattr(args_teacher, k, v)
# Setting for knowledge distillation
args_teacher.ss_prob = 0
args.lsm_prob = 0
teacher = Speech2Text(args_teacher)
load_checkpoint(args.teacher, teacher)
# Load the teacher LM
teacher_lm = None
if args.teacher_lm:
assert os.path.isfile(args.teacher_lm), 'There is no checkpoint.'
conf_lm = load_config(os.path.join(os.path.dirname(args.teacher_lm), 'conf.yml'))
args_lm = argparse.Namespace()
for k, v in conf_lm.items():
setattr(args_lm, k, v)
teacher_lm = build_lm(args_lm)
load_checkpoint(args.teacher_lm, teacher_lm)
# GPU setting
use_apex = args.train_dtype in ["O0", "O1", "O2", "O3"]
amp = None
if args.n_gpus >= 1:
model.cudnn_setting(deterministic=not (is_transformer or args.cudnn_benchmark),
benchmark=not is_transformer and args.cudnn_benchmark)
model.cuda()
# Mix precision training setting
if use_apex:
from apex import amp
model, optimizer.optimizer = amp.initialize(model, optimizer.optimizer,
opt_level=args.train_dtype)
from neural_sp.models.seq2seq.decoders.ctc import CTC
amp.register_float_function(CTC, "loss_fn")
# NOTE: see https://github.com/espnet/espnet/pull/1779
amp.init()
if args.resume:
load_checkpoint(args.resume, amp=amp)
model = CustomDataParallel(model, device_ids=list(range(0, args.n_gpus)))
if teacher is not None:
teacher.cuda()
if teacher_lm is not None:
teacher_lm.cuda()
else:
model = CPUWrapperASR(model)
# Set process name
logger.info('PID: %s' % os.getpid())
logger.info('USERNAME: %s' % os.uname()[1])
logger.info('#GPU: %d' % torch.cuda.device_count())
setproctitle(args.job_name if args.job_name else dir_name)
# Set reporter
reporter = Reporter(save_path)
if args.mtl_per_batch:
# NOTE: from easier to harder tasks
tasks = []
if 1 - args.bwd_weight - args.ctc_weight - args.sub1_weight - args.sub2_weight > 0:
tasks += ['ys']
if args.bwd_weight > 0:
tasks = ['ys.bwd'] + tasks
if args.ctc_weight > 0:
tasks = ['ys.ctc'] + tasks
if args.mbr_ce_weight > 0:
tasks = ['ys.mbr'] + tasks
for sub in ['sub1', 'sub2']:
if getattr(args, 'train_set_' + sub):
if getattr(args, sub + '_weight') - getattr(args, 'ctc_weight_' + sub) > 0:
tasks = ['ys_' + sub] + tasks
if getattr(args, 'ctc_weight_' + sub) > 0:
tasks = ['ys_' + sub + '.ctc'] + tasks
else:
tasks = ['all']
start_time_train = time.time()
start_time_epoch = time.time()
start_time_step = time.time()
accum_n_steps = 0
n_steps = optimizer.n_steps * args.accum_grad_n_steps
epoch_detail_prev = 0
for ep in range(resume_epoch, args.n_epochs):
pbar_epoch = tqdm(total=len(train_set))
session_prev = None
for batch_train, is_new_epoch in train_set:
# Compute loss in the training set
if args.discourse_aware and batch_train['sessions'][0] != session_prev:
model.module.reset_session()
session_prev = batch_train['sessions'][0]
accum_n_steps += 1
# Change mini-batch depending on task
if accum_n_steps == 1:
loss_train = 0 # moving average over gradient accumulation
for task in tasks:
loss, observation = model(batch_train, task,
teacher=teacher, teacher_lm=teacher_lm)
reporter.add(observation)
if use_apex:
with amp.scale_loss(loss, optimizer.optimizer) as scaled_loss:
scaled_loss.backward()
else:
loss.backward()
loss.detach() # Trancate the graph
loss_train = (loss_train * (accum_n_steps - 1) + loss.item()) / accum_n_steps
if accum_n_steps >= args.accum_grad_n_steps or is_new_epoch:
if args.clip_grad_norm > 0:
total_norm = torch.nn.utils.clip_grad_norm_(
model.module.parameters(), args.clip_grad_norm)
reporter.add_tensorboard_scalar('total_norm', total_norm)
optimizer.step()
optimizer.zero_grad()
accum_n_steps = 0
# NOTE: parameters are forcibly updated at the end of every epoch
del loss
pbar_epoch.update(len(batch_train['utt_ids']))
reporter.add_tensorboard_scalar('learning_rate', optimizer.lr)
# NOTE: loss/acc/ppl are already added in the model
reporter.step()
n_steps += 1
# NOTE: n_steps is different from the step counter in Noam Optimizer
if n_steps % args.print_step == 0:
# Compute loss in the dev set
batch_dev = iter(dev_set).next(batch_size=1 if 'transducer' in args.dec_type else None)[0]
# Change mini-batch depending on task
for task in tasks:
loss, observation = model(batch_dev, task, is_eval=True)
reporter.add(observation, is_eval=True)
loss_dev = loss.item()
del loss
reporter.step(is_eval=True)
duration_step = time.time() - start_time_step
if args.input_type == 'speech':
xlen = max(len(x) for x in batch_train['xs'])
ylen = max(len(y) for y in batch_train['ys'])
elif args.input_type == 'text':
xlen = max(len(x) for x in batch_train['ys'])
ylen = max(len(y) for y in batch_train['ys_sub1'])
logger.info("step:%d(ep:%.2f) loss:%.3f(%.3f)/lr:%.7f/bs:%d/xlen:%d/ylen:%d (%.2f min)" %
(n_steps, optimizer.n_epochs + train_set.epoch_detail,
loss_train, loss_dev,
optimizer.lr, len(batch_train['utt_ids']),
xlen, ylen, duration_step / 60))
start_time_step = time.time()
# Save fugures of loss and accuracy
if n_steps % (args.print_step * 10) == 0:
reporter.snapshot()
model.module.plot_attention()
model.module.plot_ctc()
# Ealuate model every 0.1 epoch during MBR training
if args.mbr_training:
if int(train_set.epoch_detail * 10) != int(epoch_detail_prev * 10):
# dev
evaluate([model.module], dev_set, recog_params, args,
int(train_set.epoch_detail * 10) / 10, logger)
# Save the model
optimizer.save_checkpoint(
model, save_path, remove_old=False, amp=amp,
epoch_detail=train_set.epoch_detail)
epoch_detail_prev = train_set.epoch_detail
if is_new_epoch:
break
# Save checkpoint and evaluate model per epoch
duration_epoch = time.time() - start_time_epoch
logger.info('========== EPOCH:%d (%.2f min) ==========' %
(optimizer.n_epochs + 1, duration_epoch / 60))
if optimizer.n_epochs + 1 < args.eval_start_epoch:
optimizer.epoch() # lr decay
reporter.epoch() # plot
# Save the model
optimizer.save_checkpoint(
model, save_path, remove_old=not is_transformer and args.remove_old_checkpoints, amp=amp)
else:
start_time_eval = time.time()
# dev
metric_dev = evaluate([model.module], dev_set, recog_params, args,
optimizer.n_epochs + 1, logger)
optimizer.epoch(metric_dev) # lr decay
reporter.epoch(metric_dev, name=args.metric) # plot
if optimizer.is_topk or is_transformer:
# Save the model
optimizer.save_checkpoint(
model, save_path, remove_old=not is_transformer and args.remove_old_checkpoints, amp=amp)
# test
if optimizer.is_topk:
for eval_set in eval_sets:
evaluate([model.module], eval_set, recog_params, args,
optimizer.n_epochs, logger)
duration_eval = time.time() - start_time_eval
logger.info('Evaluation time: %.2f min' % (duration_eval / 60))
# Early stopping
if optimizer.is_early_stop:
break
# Convert to fine-tuning stage
if optimizer.n_epochs == args.convert_to_sgd_epoch:
optimizer.convert_to_sgd(model, args.lr, args.weight_decay,
decay_type='always', decay_rate=0.5)
if optimizer.n_epochs >= args.n_epochs:
break
# if args.ss_prob > 0:
# model.module.scheduled_sampling_trigger()
start_time_step = time.time()
start_time_epoch = time.time()
duration_train = time.time() - start_time_train
logger.info('Total time: %.2f hour' % (duration_train / 3600))
reporter.tf_writer.close()
pbar_epoch.close()
return save_path
def eval_char(models, dataloader, recog_params, epoch,
recog_dir=None, streaming=False, progressbar=False, task_idx=0):
"""Evaluate the character-level model by WER & CER.
Args:
models (list): models to evaluate
dataloader (torch.utils.data.DataLoader): evaluation dataloader
recog_params (dict):
epoch (int):
recog_dir (str):
streaming (bool): streaming decoding for the session-level evaluation
progressbar (bool): visualize the progressbar
task_idx (int): the index of the target task in interest
0: main task
1: sub task
2: sub sub task
Returns:
wer (float): Word error rate
cer (float): Character error rate
"""
if recog_dir is None:
recog_dir = 'decode_' + dataloader.set + '_ep' + str(epoch) + '_beam' + str(recog_params['recog_beam_width'])
recog_dir += '_lp' + str(recog_params['recog_length_penalty'])
recog_dir += '_cp' + str(recog_params['recog_coverage_penalty'])
recog_dir += '_' + str(recog_params['recog_min_len_ratio']) + '_' + str(recog_params['recog_max_len_ratio'])
recog_dir += '_lm' + str(recog_params['recog_lm_weight'])
ref_trn_path = mkdir_join(models[0].save_path, recog_dir, 'ref.trn')
hyp_trn_path = mkdir_join(models[0].save_path, recog_dir, 'hyp.trn')
else:
ref_trn_path = mkdir_join(recog_dir, 'ref.trn')
hyp_trn_path = mkdir_join(recog_dir, 'hyp.trn')
wer, cer = 0, 0
n_sub_w, n_ins_w, n_del_w = 0, 0, 0
n_sub_c, n_ins_c, n_del_c = 0, 0, 0
n_word, n_char = 0, 0
n_streamable, quantity_rate, n_utt = 0, 0, 0
last_success_frame_ratio = 0
# Reset data counter
dataloader.reset(recog_params['recog_batch_size'])
if progressbar:
pbar = tqdm(total=len(dataloader))
if task_idx == 0:
task = 'ys'
elif task_idx == 1:
task = 'ys_sub1'
elif task_idx == 2:
task = 'ys_sub2'
elif task_idx == 3:
task = 'ys_sub3'
with codecs.open(hyp_trn_path, 'w', encoding='utf-8') as f_hyp, \
codecs.open(ref_trn_path, 'w', encoding='utf-8') as f_ref:
while True:
batch, is_new_epoch = dataloader.next(recog_params['recog_batch_size'])
if streaming or recog_params['recog_chunk_sync']:
best_hyps_id, _ = models[0].decode_streaming(
batch['xs'], recog_params, dataloader.idx2token[0],
exclude_eos=True)
else:
best_hyps_id, _ = models[0].decode(
batch['xs'], recog_params,
idx2token=dataloader.idx2token[task_idx] if progressbar else None,
exclude_eos=True,
refs_id=batch['ys'] if task_idx == 0 else batch['ys_sub' + str(task_idx)],
utt_ids=batch['utt_ids'],
speakers=batch['sessions' if dataloader.corpus == 'swbd' else 'speakers'],
task=task,
ensemble_models=models[1:] if len(models) > 1 else [])
for b in range(len(batch['xs'])):
ref = batch['text'][b]
hyp = dataloader.idx2token[task_idx](best_hyps_id[b])
# Truncate the first and last spaces for the char_space unit
if len(hyp) > 0 and hyp[0] == ' ':
hyp = hyp[1:]
if len(hyp) > 0 and hyp[-1] == ' ':
hyp = hyp[:-1]
# Write to trn
speaker = str(batch['speakers'][b]).replace('-', '_')
if streaming:
utt_id = str(batch['utt_ids'][b]) + '_0000000_0000001'
else:
utt_id = str(batch['utt_ids'][b])
f_ref.write(ref + ' (' + speaker + '-' + utt_id + ')\n')
f_hyp.write(hyp + ' (' + speaker + '-' + utt_id + ')\n')
logger.debug('utt-id: %s' % utt_id)
logger.debug('Ref: %s' % ref)
logger.debug('Hyp: %s' % hyp)
logger.debug('-' * 150)
if not streaming:
if ('char' in dataloader.unit and 'nowb' not in dataloader.unit) or (task_idx > 0 and dataloader.unit_sub1 == 'char'):
# Compute WER
wer_b, sub_b, ins_b, del_b = compute_wer(ref=ref.split(' '),
hyp=hyp.split(' '),
normalize=False)
wer += wer_b
n_sub_w += sub_b
n_ins_w += ins_b
n_del_w += del_b
n_word += len(ref.split(' '))
# NOTE: sentence error rate for Chinese
# Compute CER
if dataloader.corpus == 'csj':
ref = ref.replace(' ', '')
hyp = hyp.replace(' ', '')
cer_b, sub_b, ins_b, del_b = compute_wer(ref=list(ref),
hyp=list(hyp),
normalize=False)
cer += cer_b
n_sub_c += sub_b
n_ins_c += ins_b
n_del_c += del_b
n_char += len(ref)
if models[0].streamable():
n_streamable += 1
else:
last_success_frame_ratio += models[0].last_success_frame_ratio()
quantity_rate += models[0].quantity_rate()
n_utt += 1
if progressbar:
pbar.update(1)
if is_new_epoch:
break
if progressbar:
pbar.close()
# Reset data counters
dataloader.reset()
if not streaming:
if ('char' in dataloader.unit and 'nowb' not in dataloader.unit) or (task_idx > 0 and dataloader.unit_sub1 == 'char'):
wer /= n_word
n_sub_w /= n_word
n_ins_w /= n_word
n_del_w /= n_word
else:
wer = n_sub_w = n_ins_w = n_del_w = 0
cer /= n_char
n_sub_c /= n_char
n_ins_c /= n_char
n_del_c /= n_char
if n_utt - n_streamable > 0:
last_success_frame_ratio /= (n_utt - n_streamable)
n_streamable /= n_utt
quantity_rate /= n_utt
logger.debug('WER (%s): %.2f %%' % (dataloader.set, wer))
logger.debug('SUB: %.2f / INS: %.2f / DEL: %.2f' % (n_sub_w, n_ins_w, n_del_w))
logger.debug('CER (%s): %.2f %%' % (dataloader.set, cer))
logger.debug('SUB: %.2f / INS: %.2f / DEL: %.2f' % (n_sub_c, n_ins_c, n_del_c))
logger.info('Streamability (%s): %.2f %%' % (dataloader.set, n_streamable * 100))
logger.info('Quantity rate (%s): %.2f %%' % (dataloader.set, quantity_rate * 100))
logger.info('Last success frame ratio (%s): %.2f %%' % (dataloader.set, last_success_frame_ratio))
return wer, cer
def main():
args = parse()
args_init = copy.deepcopy(args)
args_teacher = copy.deepcopy(args)
# Load a conf file
if args.resume:
conf = load_config(
os.path.join(os.path.dirname(args.resume), 'conf.yml'))
for k, v in conf.items():
if k != 'resume':
setattr(args, k, v)
recog_params = vars(args)
# Compute subsampling factor
subsample_factor = 1
subsample_factor_sub1 = 1
subsample_factor_sub2 = 1
subsample = [int(s) for s in args.subsample.split('_')]
if args.conv_poolings and 'conv' in args.enc_type:
for p in args.conv_poolings.split('_'):
subsample_factor *= int(p.split(',')[0].replace('(', ''))
else:
subsample_factor = np.prod(subsample)
if args.train_set_sub1:
if args.conv_poolings and 'conv' in args.enc_type:
subsample_factor_sub1 = subsample_factor * np.prod(
subsample[:args.enc_n_layers_sub1 - 1])
else:
subsample_factor_sub1 = subsample_factor
if args.train_set_sub2:
if args.conv_poolings and 'conv' in args.enc_type:
subsample_factor_sub2 = subsample_factor * np.prod(
subsample[:args.enc_n_layers_sub2 - 1])
else:
subsample_factor_sub2 = subsample_factor
# Set save path
if args.resume:
save_path = os.path.dirname(args.resume)
dir_name = os.path.basename(save_path)
else:
dir_name = set_asr_model_name(args, subsample_factor)
save_path = mkdir_join(
args.model_save_dir,
'_'.join(os.path.basename(args.train_set).split('.')[:-1]),
dir_name)
save_path = set_save_path(save_path) # avoid overwriting
# Set logger
logger = set_logger(os.path.join(save_path, 'train.log'),
key='training',
stdout=args.stdout)
# for multi-GPUs
if args.n_gpus > 1:
logger.info("Batch size is automatically reduced from %d to %d" %
(args.batch_size, args.batch_size // 2))
args.batch_size //= 2
skip_thought = 'skip' in args.enc_type
# Load dataset
train_set = Dataset(corpus=args.corpus,
tsv_path=args.train_set,
tsv_path_sub1=args.train_set_sub1,
tsv_path_sub2=args.train_set_sub2,
dict_path=args.dict,
dict_path_sub1=args.dict_sub1,
dict_path_sub2=args.dict_sub2,
nlsyms=args.nlsyms,
unit=args.unit,
unit_sub1=args.unit_sub1,
unit_sub2=args.unit_sub2,
wp_model=args.wp_model,
wp_model_sub1=args.wp_model_sub1,
wp_model_sub2=args.wp_model_sub2,
batch_size=args.batch_size * args.n_gpus,
n_epochs=args.n_epochs,
min_n_frames=args.min_n_frames,
max_n_frames=args.max_n_frames,
sort_by='input',
short2long=True,
sort_stop_epoch=args.sort_stop_epoch,
dynamic_batching=args.dynamic_batching,
ctc=args.ctc_weight > 0,
ctc_sub1=args.ctc_weight_sub1 > 0,
ctc_sub2=args.ctc_weight_sub2 > 0,
subsample_factor=subsample_factor,
subsample_factor_sub1=subsample_factor_sub1,
subsample_factor_sub2=subsample_factor_sub2,
discourse_aware=args.discourse_aware,
skip_thought=skip_thought)
dev_set = Dataset(corpus=args.corpus,
tsv_path=args.dev_set,
tsv_path_sub1=args.dev_set_sub1,
tsv_path_sub2=args.dev_set_sub2,
dict_path=args.dict,
dict_path_sub1=args.dict_sub1,
dict_path_sub2=args.dict_sub2,
nlsyms=args.nlsyms,
unit=args.unit,
unit_sub1=args.unit_sub1,
unit_sub2=args.unit_sub2,
wp_model=args.wp_model,
wp_model_sub1=args.wp_model_sub1,
wp_model_sub2=args.wp_model_sub2,
batch_size=args.batch_size * args.n_gpus,
min_n_frames=args.min_n_frames,
max_n_frames=args.max_n_frames,
ctc=args.ctc_weight > 0,
ctc_sub1=args.ctc_weight_sub1 > 0,
ctc_sub2=args.ctc_weight_sub2 > 0,
subsample_factor=subsample_factor,
subsample_factor_sub1=subsample_factor_sub1,
subsample_factor_sub2=subsample_factor_sub2,
discourse_aware=args.discourse_aware,
skip_thought=skip_thought)
eval_sets = []
for s in args.eval_sets:
eval_sets += [
Dataset(corpus=args.corpus,
tsv_path=s,
dict_path=args.dict,
nlsyms=args.nlsyms,
unit=args.unit,
wp_model=args.wp_model,
batch_size=1,
discourse_aware=args.discourse_aware,
skip_thought=skip_thought,
is_test=True)
]
args.vocab = train_set.vocab
args.vocab_sub1 = train_set.vocab_sub1
args.vocab_sub2 = train_set.vocab_sub2
args.input_dim = train_set.input_dim
# Load a LM conf file for LM fusion & LM initialization
if not args.resume and (args.lm_fusion or args.lm_init):
if args.lm_fusion:
lm_conf = load_config(
os.path.join(os.path.dirname(args.lm_fusion), 'conf.yml'))
elif args.lm_init:
lm_conf = load_config(
os.path.join(os.path.dirname(args.lm_init), 'conf.yml'))
args.lm_conf = argparse.Namespace()
for k, v in lm_conf.items():
setattr(args.lm_conf, k, v)
assert args.unit == args.lm_conf.unit
assert args.vocab == args.lm_conf.vocab
# Model setting
model = Speech2Text(args, save_path) if not skip_thought else SkipThought(
args, save_path)
if args.resume:
# Set optimizer
epoch = int(args.resume.split('-')[-1])
optimizer = set_optimizer(
model, 'sgd' if epoch > conf['convert_to_sgd_epoch'] else
conf['optimizer'], conf['lr'], conf['weight_decay'])
# Wrap optimizer by learning rate scheduler
noam = 'transformer' in conf['enc_type'] or conf[
'dec_type'] == 'transformer'
optimizer = LRScheduler(
optimizer,
conf['lr'],
decay_type=conf['lr_decay_type'],
decay_start_epoch=conf['lr_decay_start_epoch'],
decay_rate=conf['lr_decay_rate'],
decay_patient_n_epochs=conf['lr_decay_patient_n_epochs'],
early_stop_patient_n_epochs=conf['early_stop_patient_n_epochs'],
warmup_start_lr=conf['warmup_start_lr'],
warmup_n_steps=conf['warmup_n_steps'],
model_size=conf['d_model'],
factor=conf['lr_factor'],
noam=noam)
# Restore the last saved model
model, optimizer = load_checkpoint(model,
args.resume,
optimizer,
resume=True)
# Resume between convert_to_sgd_epoch -1 and convert_to_sgd_epoch
if epoch == conf['convert_to_sgd_epoch']:
optimizer.convert_to_sgd(model,
'sgd',
args.lr,
conf['weight_decay'],
decay_type='always',
decay_rate=0.5)
else:
# Save the conf file as a yaml file
save_config(vars(args), os.path.join(save_path, 'conf.yml'))
if args.lm_fusion:
save_config(args.lm_conf, os.path.join(save_path, 'conf_lm.yml'))
# Save the nlsyms, dictionar, and wp_model
if args.nlsyms:
shutil.copy(args.nlsyms, os.path.join(save_path, 'nlsyms.txt'))
for sub in ['', '_sub1', '_sub2']:
if getattr(args, 'dict' + sub):
shutil.copy(getattr(args, 'dict' + sub),
os.path.join(save_path, 'dict' + sub + '.txt'))
if getattr(args, 'unit' + sub) == 'wp':
shutil.copy(getattr(args, 'wp_model' + sub),
os.path.join(save_path, 'wp' + sub + '.model'))
for k, v in sorted(vars(args).items(), key=lambda x: x[0]):
logger.info('%s: %s' % (k, str(v)))
# Count total parameters
for n in sorted(list(model.num_params_dict.keys())):
n_params = model.num_params_dict[n]
logger.info("%s %d" % (n, n_params))
logger.info("Total %.2f M parameters" %
(model.total_parameters / 1000000))
logger.info(model)
# Initialize with pre-trained model's parameters
if args.asr_init and os.path.isfile(args.asr_init):
# Load the ASR model
conf_init = load_config(
os.path.join(os.path.dirname(args.asr_init), 'conf.yml'))
for k, v in conf_init.items():
setattr(args_init, k, v)
model_init = Speech2Text(args_init)
model_init = load_checkpoint(model_init, args.asr_init)[0]
# Overwrite parameters
only_enc = (args.enc_n_layers != args_init.enc_n_layers) or (
args.unit != args_init.unit) or args_init.ctc_weight == 1
param_dict = dict(model_init.named_parameters())
for n, p in model.named_parameters():
if n in param_dict.keys() and p.size() == param_dict[n].size():
if only_enc and 'enc' not in n:
continue
if args.lm_fusion_type == 'cache' and 'output' in n:
continue
p.data = param_dict[n].data
logger.info('Overwrite %s' % n)
# Set optimizer
optimizer = set_optimizer(model, args.optimizer, args.lr,
args.weight_decay)
# Wrap optimizer by learning rate scheduler
noam = 'transformer' in args.enc_type or args.dec_type == 'transformer'
optimizer = LRScheduler(
optimizer,
args.lr,
decay_type=args.lr_decay_type,
decay_start_epoch=args.lr_decay_start_epoch,
decay_rate=args.lr_decay_rate,
decay_patient_n_epochs=args.lr_decay_patient_n_epochs,
early_stop_patient_n_epochs=args.early_stop_patient_n_epochs,
warmup_start_lr=args.warmup_start_lr,
warmup_n_steps=args.warmup_n_steps,
model_size=args.d_model,
factor=args.lr_factor,
noam=noam)
# Load the teacher ASR model
teacher = None
if args.teacher and os.path.isfile(args.teacher):
conf_teacher = load_config(
os.path.join(os.path.dirname(args.teacher), 'conf.yml'))
for k, v in conf_teacher.items():
setattr(args_teacher, k, v)
# Setting for knowledge distillation
args_teacher.ss_prob = 0
args.lsm_prob = 0
teacher = Speech2Text(args_teacher)
teacher = load_checkpoint(teacher, args.teacher)[0]
# Load the teacher LM
teacher_lm = None
if args.teacher_lm and os.path.isfile(args.teacher_lm):
conf_lm = load_config(
os.path.join(os.path.dirname(args.teacher_lm), 'conf.yml'))
args_lm = argparse.Namespace()
for k, v in conf_lm.items():
setattr(args_lm, k, v)
teacher_lm = build_lm(args_lm)
teacher_lm = load_checkpoint(teacher_lm, args.teacher_lm)[0]
# GPU setting
if args.n_gpus >= 1:
torch.backends.cudnn.benchmark = True
model = CustomDataParallel(model,
device_ids=list(range(0, args.n_gpus)))
model.cuda()
if teacher is not None:
teacher.cuda()
if teacher_lm is not None:
teacher_lm.cuda()
# Set process name
logger.info('PID: %s' % os.getpid())
logger.info('USERNAME: %s' % os.uname()[1])
setproctitle(args.job_name if args.job_name else dir_name)
# Set reporter
reporter = Reporter(save_path)
if args.mtl_per_batch:
# NOTE: from easier to harder tasks
tasks = []
if 1 - args.bwd_weight - args.ctc_weight - args.sub1_weight - args.sub2_weight > 0:
tasks += ['ys']
if args.bwd_weight > 0:
tasks = ['ys.bwd'] + tasks
if args.ctc_weight > 0:
tasks = ['ys.ctc'] + tasks
for sub in ['sub1', 'sub2']:
if getattr(args, 'train_set_' + sub):
if getattr(args, sub + '_weight') - getattr(
args, 'ctc_weight_' + sub) > 0:
tasks = ['ys_' + sub] + tasks
if getattr(args, 'ctc_weight_' + sub) > 0:
tasks = ['ys_' + sub + '.ctc'] + tasks
else:
tasks = ['all']
start_time_train = time.time()
start_time_epoch = time.time()
start_time_step = time.time()
pbar_epoch = tqdm(total=len(train_set))
accum_n_tokens = 0
while True:
# Compute loss in the training set
batch_train, is_new_epoch = train_set.next()
accum_n_tokens += sum([len(y) for y in batch_train['ys']])
# Change mini-batch depending on task
for task in tasks:
if skip_thought:
loss, reporter = model(batch_train['ys'],
ys_prev=batch_train['ys_prev'],
ys_next=batch_train['ys_next'],
reporter=reporter)
else:
loss, reporter = model(batch_train,
reporter,
task,
teacher=teacher,
teacher_lm=teacher_lm)
loss.backward()
loss.detach() # Trancate the graph
if args.accum_grad_n_tokens == 0 or accum_n_tokens >= args.accum_grad_n_tokens:
if args.clip_grad_norm > 0:
total_norm = torch.nn.utils.clip_grad_norm_(
model.module.parameters(), args.clip_grad_norm)
reporter.add_tensorboard_scalar('total_norm', total_norm)
optimizer.step()
optimizer.zero_grad()
accum_n_tokens = 0
loss_train = loss.item()
del loss
reporter.add_tensorboard_scalar('learning_rate', optimizer.lr)
# NOTE: loss/acc/ppl are already added in the model
reporter.step()
if optimizer.n_steps % args.print_step == 0:
# Compute loss in the dev set
batch_dev = dev_set.next()[0]
# Change mini-batch depending on task
for task in tasks:
if skip_thought:
loss, reporter = model(batch_dev['ys'],
ys_prev=batch_dev['ys_prev'],
ys_next=batch_dev['ys_next'],
reporter=reporter,
is_eval=True)
else:
loss, reporter = model(batch_dev,
reporter,
task,
is_eval=True)
loss_dev = loss.item()
del loss
# NOTE: this makes training slow
# Compute WER/CER regardless of the output unit (greedy decoding)
# best_hyps_id, _, _ = model.module.decode(
# batch_dev['xs'], recog_params, dev_set.idx2token[0], exclude_eos=True)
# cer = 0.
# ref_n_words, ref_n_chars = 0, 0
# for b in range(len(batch_dev['xs'])):
# ref = batch_dev['text'][b]
# hyp = dev_set.idx2token[0](best_hyps_id[b])
# cer += editdistance.eval(hyp, ref)
# ref_n_words += len(ref.split())
# ref_n_chars += len(ref)
# wer = cer / ref_n_words
# cer /= ref_n_chars
# reporter.add_tensorboard_scalar('dev/WER', wer)
# reporter.add_tensorboard_scalar('dev/CER', cer)
# logger.info('WER (dev)', wer)
# logger.info('CER (dev)', cer)
reporter.step(is_eval=True)
duration_step = time.time() - start_time_step
if args.input_type == 'speech':
xlen = max(len(x) for x in batch_train['xs'])
ylen = max(len(y) for y in batch_train['ys'])
elif args.input_type == 'text':
xlen = max(len(x) for x in batch_train['ys'])
ylen = max(len(y) for y in batch_train['ys_sub1'])
logger.info(
"step:%d(ep:%.2f) loss:%.3f(%.3f)/lr:%.7f/bs:%d/xlen:%d/ylen:%d (%.2f min)"
%
(optimizer.n_steps, optimizer.n_epochs +
train_set.epoch_detail, loss_train, loss_dev, optimizer.lr,
len(batch_train['utt_ids']), xlen, ylen, duration_step / 60))
start_time_step = time.time()
pbar_epoch.update(len(batch_train['utt_ids']))
# Save fugures of loss and accuracy
if optimizer.n_steps % (args.print_step * 10) == 0:
reporter.snapshot()
model.module.plot_attention()
# Save checkpoint and evaluate model per epoch
if is_new_epoch:
duration_epoch = time.time() - start_time_epoch
logger.info('========== EPOCH:%d (%.2f min) ==========' %
(optimizer.n_epochs + 1, duration_epoch / 60))
if optimizer.n_epochs + 1 < args.eval_start_epoch:
optimizer.epoch() # lr decay
reporter.epoch() # plot
# Save the model
save_checkpoint(model,
save_path,
optimizer,
optimizer.n_epochs,
remove_old_checkpoints=not noam)
else:
start_time_eval = time.time()
# dev
metric_dev = eval_epoch([model.module], dev_set, recog_params,
args, optimizer.n_epochs + 1, logger)
optimizer.epoch(metric_dev) # lr decay
reporter.epoch(metric_dev) # plot
if optimizer.is_best:
# Save the model
save_checkpoint(model,
save_path,
optimizer,
optimizer.n_epochs,
remove_old_checkpoints=not noam)
# test
for eval_set in eval_sets:
eval_epoch([model.module], eval_set, recog_params,
args, optimizer.n_epochs, logger)
# start scheduled sampling
if args.ss_prob > 0:
model.module.scheduled_sampling_trigger()
duration_eval = time.time() - start_time_eval
logger.info('Evaluation time: %.2f min' % (duration_eval / 60))
# Early stopping
if optimizer.is_early_stop:
break
# Convert to fine-tuning stage
if optimizer.n_epochs == args.convert_to_sgd_epoch:
optimizer.convert_to_sgd(model,
'sgd',
args.lr,
args.weight_decay,
decay_type='always',
decay_rate=0.5)
pbar_epoch = tqdm(total=len(train_set))
if optimizer.n_epochs == args.n_epochs:
break
start_time_step = time.time()
start_time_epoch = time.time()
duration_train = time.time() - start_time_train
logger.info('Total time: %.2f hour' % (duration_train / 3600))
reporter.tf_writer.close()
pbar_epoch.close()
return save_path
def main():
# Load configuration
args, recog_params, dir_name = parse_args_eval(sys.argv[1:])
# Setting for logging
if os.path.isfile(os.path.join(args.recog_dir, 'plot.log')):
os.remove(os.path.join(args.recog_dir, 'plot.log'))
set_logger(os.path.join(args.recog_dir, 'plot.log'),
stdout=args.recog_stdout)
for i, s in enumerate(args.recog_sets):
# Load dataset
dataset = Dataset(
corpus=args.corpus,
tsv_path=s,
dict_path=os.path.join(dir_name, 'dict.txt'),
dict_path_sub1=os.path.join(dir_name, 'dict_sub1.txt') if
os.path.isfile(os.path.join(dir_name, 'dict_sub1.txt')) else False,
nlsyms=args.nlsyms,
wp_model=os.path.join(dir_name, 'wp.model'),
unit=args.unit,
unit_sub1=args.unit_sub1,
batch_size=args.recog_batch_size,
is_test=True)
if i == 0:
# Load the ASR model
model = Speech2Text(args, dir_name)
epoch = int(args.recog_model[0].split('-')[-1])
if args.recog_n_average > 1:
# Model averaging for Transformer
model = average_checkpoints(model,
args.recog_model[0],
n_average=args.recog_n_average)
else:
load_checkpoint(args.recog_model[0], model)
if not args.recog_unit:
args.recog_unit = args.unit
logger.info('recog unit: %s' % args.recog_unit)
logger.info('epoch: %d' % epoch)
logger.info('batch size: %d' % args.recog_batch_size)
# GPU setting
if args.recog_n_gpus >= 1:
model.cudnn_setting(deterministic=True, benchmark=False)
model.cuda()
save_path = mkdir_join(args.recog_dir, 'ctc_probs')
# Clean directory
if save_path is not None and os.path.isdir(save_path):
shutil.rmtree(save_path)
os.mkdir(save_path)
while True:
batch, is_new_epoch = dataset.next(
recog_params['recog_batch_size'])
best_hyps_id, _ = model.decode(batch['xs'], recog_params)
# Get CTC probs
ctc_probs, topk_ids, xlens = model.get_ctc_probs(batch['xs'],
temperature=1,
topk=min(
100,
model.vocab))
# NOTE: ctc_probs: '[B, T, topk]'
for b in range(len(batch['xs'])):
tokens = dataset.idx2token[0](best_hyps_id[b],
return_list=True)
spk = batch['speakers'][b]
plot_ctc_probs(
ctc_probs[b, :xlens[b]],
topk_ids[b],
subsample_factor=args.subsample_factor,
spectrogram=batch['xs'][b][:, :dataset.input_dim],
save_path=mkdir_join(save_path, spk,
batch['utt_ids'][b] + '.png'),
figsize=(20, 8))
hyp = ' '.join(tokens)
logger.info('utt-id: %s' % batch['utt_ids'][b])
logger.info('Ref: %s' % batch['text'][b].lower())
logger.info('Hyp: %s' % hyp)
logger.info('-' * 50)
if is_new_epoch:
break
def main():
args = parse()
# Load a conf file
if args.resume:
conf = load_config(
os.path.join(os.path.dirname(args.resume), 'conf.yml'))
for k, v in conf.items():
if k != 'resume':
setattr(args, k, v)
# Set save path
if args.resume:
save_path = os.path.dirname(args.resume)
dir_name = os.path.basename(save_path)
else:
dir_name = set_lm_name(args)
save_path = mkdir_join(
args.model_save_dir,
'_'.join(os.path.basename(args.train_set).split('.')[:-1]),
dir_name)
save_path = set_save_path(save_path) # avoid overwriting
# Set logger
logger = set_logger(os.path.join(save_path, 'train.log'),
key='training',
stdout=args.stdout)
# Load dataset
train_set = Dataset(corpus=args.corpus,
tsv_path=args.train_set,
dict_path=args.dict,
nlsyms=args.nlsyms,
unit=args.unit,
wp_model=args.wp_model,
batch_size=args.batch_size * args.n_gpus,
n_epochs=args.n_epochs,
min_n_tokens=args.min_n_tokens,
bptt=args.bptt,
backward=args.backward,
serialize=args.serialize)
dev_set = Dataset(corpus=args.corpus,
tsv_path=args.dev_set,
dict_path=args.dict,
nlsyms=args.nlsyms,
unit=args.unit,
wp_model=args.wp_model,
batch_size=args.batch_size * args.n_gpus,
bptt=args.bptt,
backward=args.backward,
serialize=args.serialize)
eval_sets = []
for s in args.eval_sets:
eval_sets += [
Dataset(corpus=args.corpus,
tsv_path=s,
dict_path=args.dict,
nlsyms=args.nlsyms,
unit=args.unit,
wp_model=args.wp_model,
batch_size=1,
bptt=args.bptt,
backward=args.backward,
serialize=args.serialize)
]
args.vocab = train_set.vocab
# Model setting
model = build_lm(args, save_path)
if args.resume:
# Set optimizer
epoch = int(args.resume.split('-')[-1])
optimizer = set_optimizer(
model, 'sgd' if epoch > conf['convert_to_sgd_epoch'] else
conf['optimizer'], conf['lr'], conf['weight_decay'])
# Wrap optimizer by learning rate scheduler
optimizer = LRScheduler(
optimizer,
conf['lr'],
decay_type=conf['lr_decay_type'],
decay_start_epoch=conf['lr_decay_start_epoch'],
decay_rate=conf['lr_decay_rate'],
decay_patient_n_epochs=conf['lr_decay_patient_n_epochs'],
early_stop_patient_n_epochs=conf['early_stop_patient_n_epochs'],
warmup_start_lr=conf['warmup_start_lr'],
warmup_n_steps=conf['warmup_n_steps'],
model_size=conf['d_model'],
factor=conf['lr_factor'],
noam=conf['lm_type'] == 'transformer')
# Restore the last saved model
model, optimizer = load_checkpoint(model,
args.resume,
optimizer,
resume=True)
# Resume between convert_to_sgd_epoch -1 and convert_to_sgd_epoch
if epoch == conf['convert_to_sgd_epoch']:
n_epochs = optimizer.n_epochs
n_steps = optimizer.n_steps
optimizer = set_optimizer(model, 'sgd', args.lr,
conf['weight_decay'])
optimizer = LRScheduler(optimizer,
args.lr,
decay_type='always',
decay_start_epoch=0,
decay_rate=0.5)
optimizer._epoch = n_epochs
optimizer._step = n_steps
logger.info('========== Convert to SGD ==========')
else:
# Save the conf file as a yaml file
save_config(vars(args), os.path.join(save_path, 'conf.yml'))
# Save the nlsyms, dictionar, and wp_model
if args.nlsyms:
shutil.copy(args.nlsyms, os.path.join(save_path, 'nlsyms.txt'))
shutil.copy(args.dict, os.path.join(save_path, 'dict.txt'))
if args.unit == 'wp':
shutil.copy(args.wp_model, os.path.join(save_path, 'wp.model'))
for k, v in sorted(vars(args).items(), key=lambda x: x[0]):
logger.info('%s: %s' % (k, str(v)))
# Count total parameters
for n in sorted(list(model.num_params_dict.keys())):
n_params = model.num_params_dict[n]
logger.info("%s %d" % (n, n_params))
logger.info("Total %.2f M parameters" %
(model.total_parameters / 1000000))
logger.info(model)
# Set optimizer
optimizer = set_optimizer(model, args.optimizer, args.lr,
args.weight_decay)
# Wrap optimizer by learning rate scheduler
optimizer = LRScheduler(
optimizer,
args.lr,
decay_type=args.lr_decay_type,
decay_start_epoch=args.lr_decay_start_epoch,
decay_rate=args.lr_decay_rate,
decay_patient_n_epochs=args.lr_decay_patient_n_epochs,
early_stop_patient_n_epochs=args.early_stop_patient_n_epochs,
warmup_start_lr=args.warmup_start_lr,
warmup_n_steps=args.warmup_n_steps,
model_size=args.d_model,
factor=args.lr_factor,
noam=args.lm_type == 'transformer')
# GPU setting
if args.n_gpus >= 1:
torch.backends.cudnn.benchmark = True
model = CustomDataParallel(model,
device_ids=list(range(0, args.n_gpus)))
model.cuda()
# Set process name
logger.info('PID: %s' % os.getpid())
logger.info('USERNAME: %s' % os.uname()[1])
setproctitle(args.job_name if args.job_name else dir_name)
# Set reporter
reporter = Reporter(save_path)
hidden = None
start_time_train = time.time()
start_time_epoch = time.time()
start_time_step = time.time()
pbar_epoch = tqdm(total=len(train_set))
accum_n_tokens = 0
while True:
# Compute loss in the training set
ys_train, is_new_epoch = train_set.next()
accum_n_tokens += sum([len(y) for y in ys_train])
optimizer.zero_grad()
loss, hidden, reporter = model(ys_train, hidden, reporter)
loss.backward()
loss.detach() # Trancate the graph
if args.accum_grad_n_tokens == 0 or accum_n_tokens >= args.accum_grad_n_tokens:
if args.clip_grad_norm > 0:
total_norm = torch.nn.utils.clip_grad_norm_(
model.module.parameters(), args.clip_grad_norm)
reporter.add_tensorboard_scalar('total_norm', total_norm)
optimizer.step()
optimizer.zero_grad()
accum_n_tokens = 0
loss_train = loss.item()
del loss
hidden = model.module.repackage_state(hidden)
reporter.add_tensorboard_scalar('learning_rate', optimizer.lr)
# NOTE: loss/acc/ppl are already added in the model
reporter.step()
if optimizer.n_steps % args.print_step == 0:
# Compute loss in the dev set
ys_dev = dev_set.next()[0]
loss, _, reporter = model(ys_dev, None, reporter, is_eval=True)
loss_dev = loss.item()
del loss
reporter.step(is_eval=True)
duration_step = time.time() - start_time_step
logger.info(
"step:%d(ep:%.2f) loss:%.3f(%.3f)/ppl:%.3f(%.3f)/lr:%.5f/bs:%d (%.2f min)"
% (optimizer.n_steps,
optimizer.n_epochs + train_set.epoch_detail, loss_train,
loss_dev, np.exp(loss_train), np.exp(loss_dev),
optimizer.lr, ys_train.shape[0], duration_step / 60))
start_time_step = time.time()
pbar_epoch.update(ys_train.shape[0] * (ys_train.shape[1] - 1))
# Save fugures of loss and accuracy
if optimizer.n_steps % (args.print_step * 10) == 0:
reporter.snapshot()
if args.lm_type == 'transformer':
model.module.plot_attention()
# Save checkpoint and evaluate model per epoch
if is_new_epoch:
duration_epoch = time.time() - start_time_epoch
logger.info('========== EPOCH:%d (%.2f min) ==========' %
(optimizer.n_epochs + 1, duration_epoch / 60))
if optimizer.n_epochs + 1 < args.eval_start_epoch:
optimizer.epoch() # lr decay
reporter.epoch() # plot
# Save the model
save_checkpoint(
model,
save_path,
optimizer,
optimizer.n_epochs,
remove_old_checkpoints=args.lm_type != 'transformer')
else:
start_time_eval = time.time()
# dev
ppl_dev, _ = eval_ppl([model.module],
dev_set,
batch_size=1,
bptt=args.bptt)
logger.info('PPL (%s, epoch:%d): %.2f' %
(dev_set.set, optimizer.n_epochs, ppl_dev))
optimizer.epoch(ppl_dev) # lr decay
reporter.epoch(ppl_dev, name='perplexity') # plot
if optimizer.is_best:
# Save the model
save_checkpoint(
model,
save_path,
optimizer,
optimizer.n_epochs,
remove_old_checkpoints=args.lm_type != 'transformer')
# test
ppl_test_avg = 0.
for eval_set in eval_sets:
ppl_test, _ = eval_ppl([model.module],
eval_set,
batch_size=1,
bptt=args.bptt)
logger.info(
'PPL (%s, epoch:%d): %.2f' %
(eval_set.set, optimizer.n_epochs, ppl_test))
ppl_test_avg += ppl_test
if len(eval_sets) > 0:
logger.info('PPL (avg., epoch:%d): %.2f' %
(optimizer.n_epochs,
ppl_test_avg / len(eval_sets)))
duration_eval = time.time() - start_time_eval
logger.info('Evaluation time: %.2f min' % (duration_eval / 60))
# Early stopping
if optimizer.is_early_stop:
break
# Convert to fine-tuning stage
if optimizer.n_epochs == args.convert_to_sgd_epoch:
n_epochs = optimizer.n_epochs
n_steps = optimizer.n_steps
optimizer = set_optimizer(model, 'sgd', args.lr,
args.weight_decay)
optimizer = LRScheduler(optimizer,
args.lr,
decay_type='always',
decay_start_epoch=0,
decay_rate=0.5)
optimizer._epoch = n_epochs
optimizer._step = n_steps
logger.info('========== Convert to SGD ==========')
pbar_epoch = tqdm(total=len(train_set))
if optimizer.n_epochs == args.n_epochs:
break
start_time_step = time.time()
start_time_epoch = time.time()
duration_train = time.time() - start_time_train
logger.info('Total time: %.2f hour' % (duration_train / 3600))
reporter.tf_writer.close()
pbar_epoch.close()
return save_path
def main():
# Load configuration
args, dir_name = parse_args_eval(sys.argv[1:])
# Setting for logging
if os.path.isfile(os.path.join(args.recog_dir, 'plot.log')):
os.remove(os.path.join(args.recog_dir, 'plot.log'))
set_logger(os.path.join(args.recog_dir, 'plot.log'),
stdout=args.recog_stdout)
for i, s in enumerate(args.recog_sets):
# Load dataloader
dataloader = build_dataloader(
args=args,
tsv_path=s,
batch_size=1,
is_test=True,
first_n_utterances=args.recog_first_n_utt,
longform_max_n_frames=args.recog_longform_max_n_frames)
if i == 0:
# Load ASR model
model = Speech2Text(args, dir_name)
epoch = int(float(args.recog_model[0].split('-')[-1]) * 10) / 10
if args.recog_n_average > 1:
# Model averaging for Transformer
model = average_checkpoints(model,
args.recog_model[0],
n_average=args.recog_n_average)
else:
load_checkpoint(args.recog_model[0], model)
if not args.recog_unit:
args.recog_unit = args.unit
logger.info('recog unit: %s' % args.recog_unit)
logger.info('epoch: %d' % epoch)
logger.info('batch size: %d' % args.recog_batch_size)
# GPU setting
if args.recog_n_gpus >= 1:
model.cudnn_setting(deterministic=True, benchmark=False)
model.cuda()
save_path = mkdir_join(args.recog_dir, 'ctc_probs')
# Clean directory
if save_path is not None and os.path.isdir(save_path):
shutil.rmtree(save_path)
os.mkdir(save_path)
for batch in dataloader:
nbest_hyps_id, _ = model.decode(batch['xs'], args,
dataloader.idx2token[0])
best_hyps_id = [h[0] for h in nbest_hyps_id]
# Get CTC probs
ctc_probs, topk_ids, xlens = model.get_ctc_probs(batch['xs'],
temperature=1,
topk=min(
100,
model.vocab))
# NOTE: ctc_probs: '[B, T, topk]'
for b in range(len(batch['xs'])):
tokens = dataloader.idx2token[0](best_hyps_id[b],
return_list=True)
spk = batch['speakers'][b]
plot_ctc_probs(
ctc_probs[b, :xlens[b]],
topk_ids[b],
factor=args.subsample_factor,
spectrogram=batch['xs'][b][:, :dataloader.input_dim],
save_path=mkdir_join(save_path, spk,
batch['utt_ids'][b] + '.png'),
figsize=(20, 8))
hyp = ' '.join(tokens)
logger.info('utt-id: %s' % batch['utt_ids'][b])
logger.info('Ref: %s' % batch['text'][b].lower())
logger.info('Hyp: %s' % hyp)
logger.info('-' * 50)
def main():
# Load configuration
args, dir_name = parse_args_eval(sys.argv[1:])
# Setting for logging
if os.path.isfile(os.path.join(args.recog_dir, 'plot.log')):
os.remove(os.path.join(args.recog_dir, 'plot.log'))
set_logger(os.path.join(args.recog_dir, 'plot.log'), stdout=args.recog_stdout)
for i, s in enumerate(args.recog_sets):
# Load dataloader
dataloader = build_dataloader(args=args,
tsv_path=s,
batch_size=1,
is_test=True,
first_n_utterances=args.recog_first_n_utt,
longform_max_n_frames=args.recog_longform_max_n_frames)
if i == 0:
# Load ASR model
model = Speech2Text(args, dir_name)
epoch = int(float(args.recog_model[0].split('-')[-1]) * 10) / 10
if args.recog_n_average > 1:
# Model averaging for Transformer
model = average_checkpoints(model, args.recog_model[0],
n_average=args.recog_n_average)
else:
load_checkpoint(args.recog_model[0], model)
# Ensemble (different models)
ensemble_models = [model]
if len(args.recog_model) > 1:
for recog_model_e in args.recog_model[1:]:
conf_e = load_config(os.path.join(os.path.dirname(recog_model_e), 'conf.yml'))
args_e = copy.deepcopy(args)
for k, v in conf_e.items():
if 'recog' not in k:
setattr(args_e, k, v)
model_e = Speech2Text(args_e)
load_checkpoint(recog_model_e, model_e)
if args.recog_n_gpus >= 1:
model_e.cuda()
ensemble_models += [model_e]
# Load LM for shallow fusion
if not args.lm_fusion:
# first path
if args.recog_lm is not None and args.recog_lm_weight > 0:
conf_lm = load_config(os.path.join(os.path.dirname(args.recog_lm), 'conf.yml'))
args_lm = argparse.Namespace()
for k, v in conf_lm.items():
setattr(args_lm, k, v)
lm = build_lm(args_lm)
load_checkpoint(args.recog_lm, lm)
if args_lm.backward:
model.lm_bwd = lm
else:
model.lm_fwd = lm
# NOTE: only support for first path
if not args.recog_unit:
args.recog_unit = args.unit
logger.info('recog unit: %s' % args.recog_unit)
logger.info('recog oracle: %s' % args.recog_oracle)
logger.info('epoch: %d' % epoch)
logger.info('batch size: %d' % args.recog_batch_size)
logger.info('beam width: %d' % args.recog_beam_width)
logger.info('min length ratio: %.3f' % args.recog_min_len_ratio)
logger.info('max length ratio: %.3f' % args.recog_max_len_ratio)
logger.info('length penalty: %.3f' % args.recog_length_penalty)
logger.info('length norm: %s' % args.recog_length_norm)
logger.info('coverage penalty: %.3f' % args.recog_coverage_penalty)
logger.info('coverage threshold: %.3f' % args.recog_coverage_threshold)
logger.info('CTC weight: %.3f' % args.recog_ctc_weight)
logger.info('fist LM path: %s' % args.recog_lm)
logger.info('LM weight: %.3f' % args.recog_lm_weight)
logger.info('GNMT: %s' % args.recog_gnmt_decoding)
logger.info('forward-backward attention: %s' % args.recog_fwd_bwd_attention)
logger.info('resolving UNK: %s' % args.recog_resolving_unk)
logger.info('ensemble: %d' % (len(ensemble_models)))
logger.info('ASR decoder state carry over: %s' % (args.recog_asr_state_carry_over))
logger.info('LM state carry over: %s' % (args.recog_lm_state_carry_over))
logger.info('model average (Transformer): %d' % (args.recog_n_average))
# GPU setting
if args.recog_n_gpus >= 1:
model.cudnn_setting(deterministic=True, benchmark=False)
model.cuda()
save_path = mkdir_join(args.recog_dir, 'att_weights')
# Clean directory
if save_path is not None and os.path.isdir(save_path):
shutil.rmtree(save_path)
os.mkdir(save_path)
for batch in dataloader:
nbest_hyps_id, aws = model.decode(
batch['xs'], args, dataloader.idx2token[0],
exclude_eos=False,
refs_id=batch['ys'],
ensemble_models=ensemble_models[1:] if len(ensemble_models) > 1 else [],
speakers=batch['sessions'] if dataloader.corpus == 'swbd' else batch['speakers'])
best_hyps_id = [h[0] for h in nbest_hyps_id]
# Get CTC probs
ctc_probs, topk_ids = None, None
if args.ctc_weight > 0:
ctc_probs, topk_ids, xlens = model.get_ctc_probs(
batch['xs'], task='ys', temperature=1, topk=min(100, model.vocab))
# NOTE: ctc_probs: '[B, T, topk]'
ctc_probs_sub1, topk_ids_sub1 = None, None
if args.ctc_weight_sub1 > 0:
ctc_probs_sub1, topk_ids_sub1, xlens_sub1 = model.get_ctc_probs(
batch['xs'], task='ys_sub1', temperature=1, topk=min(100, model.vocab_sub1))
if model.bwd_weight > 0.5:
# Reverse the order
best_hyps_id = [hyp[::-1] for hyp in best_hyps_id]
aws = [[aw[0][:, ::-1]] for aw in aws]
for b in range(len(batch['xs'])):
tokens = dataloader.idx2token[0](best_hyps_id[b], return_list=True)
spk = batch['speakers'][b]
plot_attention_weights(
aws[b][0][:, :len(tokens)], tokens,
spectrogram=batch['xs'][b][:, :dataloader.input_dim] if args.input_type == 'speech' else None,
factor=args.subsample_factor,
ref=batch['text'][b].lower(),
save_path=mkdir_join(save_path, spk, batch['utt_ids'][b] + '.png'),
figsize=(20, 8),
ctc_probs=ctc_probs[b, :xlens[b]] if ctc_probs is not None else None,
ctc_topk_ids=topk_ids[b] if topk_ids is not None else None,
ctc_probs_sub1=ctc_probs_sub1[b, :xlens_sub1[b]] if ctc_probs_sub1 is not None else None,
ctc_topk_ids_sub1=topk_ids_sub1[b] if topk_ids_sub1 is not None else None)
if model.bwd_weight > 0.5:
hyp = ' '.join(tokens[::-1])
else:
hyp = ' '.join(tokens)
logger.info('utt-id: %s' % batch['utt_ids'][b])
logger.info('Ref: %s' % batch['text'][b].lower())
logger.info('Hyp: %s' % hyp)
logger.info('-' * 50)
