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2480be8e
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2480be8e
编写于
9月 10, 2021
作者:
H
Hui Zhang
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
timer info for st,u2 kaldi
上级
28a0a641
变更
2
隐藏空白更改
内联
并排
Showing
2 changed file
with
62 addition
and
56 deletion
+62
-56
deepspeech/exps/u2_kaldi/model.py
deepspeech/exps/u2_kaldi/model.py
+31
-28
deepspeech/exps/u2_st/model.py
deepspeech/exps/u2_st/model.py
+31
-28
未找到文件。
deepspeech/exps/u2_kaldi/model.py
浏览文件 @
2480be8e
...
@@ -32,6 +32,7 @@ from deepspeech.io.dataloader import BatchDataLoader
...
@@ -32,6 +32,7 @@ from deepspeech.io.dataloader import BatchDataLoader
from
deepspeech.models.u2
import
U2Model
from
deepspeech.models.u2
import
U2Model
from
deepspeech.training.optimizer
import
OptimizerFactory
from
deepspeech.training.optimizer
import
OptimizerFactory
from
deepspeech.training.scheduler
import
LRSchedulerFactory
from
deepspeech.training.scheduler
import
LRSchedulerFactory
from
deepspeech.training.timer
import
Timer
from
deepspeech.training.trainer
import
Trainer
from
deepspeech.training.trainer
import
Trainer
from
deepspeech.utils
import
ctc_utils
from
deepspeech.utils
import
ctc_utils
from
deepspeech.utils
import
error_rate
from
deepspeech.utils
import
error_rate
...
@@ -190,35 +191,37 @@ class U2Trainer(Trainer):
...
@@ -190,35 +191,37 @@ class U2Trainer(Trainer):
logger
.
info
(
f
"Train Total Examples:
{
len
(
self
.
train_loader
.
dataset
)
}
"
)
logger
.
info
(
f
"Train Total Examples:
{
len
(
self
.
train_loader
.
dataset
)
}
"
)
while
self
.
epoch
<
self
.
config
.
training
.
n_epoch
:
while
self
.
epoch
<
self
.
config
.
training
.
n_epoch
:
self
.
model
.
train
()
with
Timer
(
"Epoch-Train Time Cost: {}"
):
try
:
self
.
model
.
train
()
data_start_time
=
time
.
time
()
try
:
for
batch_index
,
batch
in
enumerate
(
self
.
train_loader
):
dataload_time
=
time
.
time
()
-
data_start_time
msg
=
"Train: Rank: {}, "
.
format
(
dist
.
get_rank
())
msg
+=
"epoch: {}, "
.
format
(
self
.
epoch
)
msg
+=
"step: {}, "
.
format
(
self
.
iteration
)
msg
+=
"batch : {}/{}, "
.
format
(
batch_index
+
1
,
len
(
self
.
train_loader
))
msg
+=
"lr: {:>.8f}, "
.
format
(
self
.
lr_scheduler
())
msg
+=
"data time: {:>.3f}s, "
.
format
(
dataload_time
)
self
.
train_batch
(
batch_index
,
batch
,
msg
)
data_start_time
=
time
.
time
()
data_start_time
=
time
.
time
()
except
Exception
as
e
:
for
batch_index
,
batch
in
enumerate
(
self
.
train_loader
):
logger
.
error
(
e
)
dataload_time
=
time
.
time
()
-
data_start_time
raise
e
msg
=
"Train: Rank: {}, "
.
format
(
dist
.
get_rank
())
msg
+=
"epoch: {}, "
.
format
(
self
.
epoch
)
total_loss
,
num_seen_utts
=
self
.
valid
()
msg
+=
"step: {}, "
.
format
(
self
.
iteration
)
if
dist
.
get_world_size
()
>
1
:
msg
+=
"batch : {}/{}, "
.
format
(
batch_index
+
1
,
num_seen_utts
=
paddle
.
to_tensor
(
num_seen_utts
)
len
(
self
.
train_loader
))
# the default operator in all_reduce function is sum.
msg
+=
"lr: {:>.8f}, "
.
format
(
self
.
lr_scheduler
())
dist
.
all_reduce
(
num_seen_utts
)
msg
+=
"data time: {:>.3f}s, "
.
format
(
dataload_time
)
total_loss
=
paddle
.
to_tensor
(
total_loss
)
self
.
train_batch
(
batch_index
,
batch
,
msg
)
dist
.
all_reduce
(
total_loss
)
data_start_time
=
time
.
time
()
cv_loss
=
total_loss
/
num_seen_utts
except
Exception
as
e
:
cv_loss
=
float
(
cv_loss
)
logger
.
error
(
e
)
else
:
raise
e
cv_loss
=
total_loss
/
num_seen_utts
with
Timer
(
"Eval Time Cost: {}"
):
total_loss
,
num_seen_utts
=
self
.
valid
()
if
dist
.
get_world_size
()
>
1
:
num_seen_utts
=
paddle
.
to_tensor
(
num_seen_utts
)
# the default operator in all_reduce function is sum.
dist
.
all_reduce
(
num_seen_utts
)
total_loss
=
paddle
.
to_tensor
(
total_loss
)
dist
.
all_reduce
(
total_loss
)
cv_loss
=
total_loss
/
num_seen_utts
cv_loss
=
float
(
cv_loss
)
else
:
cv_loss
=
total_loss
/
num_seen_utts
logger
.
info
(
logger
.
info
(
'Epoch {} Val info val_loss {}'
.
format
(
self
.
epoch
,
cv_loss
))
'Epoch {} Val info val_loss {}'
.
format
(
self
.
epoch
,
cv_loss
))
...
...
deepspeech/exps/u2_st/model.py
浏览文件 @
2480be8e
...
@@ -38,6 +38,7 @@ from deepspeech.io.sampler import SortagradDistributedBatchSampler
...
@@ -38,6 +38,7 @@ from deepspeech.io.sampler import SortagradDistributedBatchSampler
from
deepspeech.models.u2_st
import
U2STModel
from
deepspeech.models.u2_st
import
U2STModel
from
deepspeech.training.gradclip
import
ClipGradByGlobalNormWithLog
from
deepspeech.training.gradclip
import
ClipGradByGlobalNormWithLog
from
deepspeech.training.scheduler
import
WarmupLR
from
deepspeech.training.scheduler
import
WarmupLR
from
deepspeech.training.timer
import
Timer
from
deepspeech.training.trainer
import
Trainer
from
deepspeech.training.trainer
import
Trainer
from
deepspeech.utils
import
bleu_score
from
deepspeech.utils
import
bleu_score
from
deepspeech.utils
import
ctc_utils
from
deepspeech.utils
import
ctc_utils
...
@@ -207,35 +208,37 @@ class U2STTrainer(Trainer):
...
@@ -207,35 +208,37 @@ class U2STTrainer(Trainer):
logger
.
info
(
f
"Train Total Examples:
{
len
(
self
.
train_loader
.
dataset
)
}
"
)
logger
.
info
(
f
"Train Total Examples:
{
len
(
self
.
train_loader
.
dataset
)
}
"
)
while
self
.
epoch
<
self
.
config
.
training
.
n_epoch
:
while
self
.
epoch
<
self
.
config
.
training
.
n_epoch
:
self
.
model
.
train
()
with
Timer
(
"Epoch-Train Time Cost: {}"
):
try
:
self
.
model
.
train
()
data_start_time
=
time
.
time
()
try
:
for
batch_index
,
batch
in
enumerate
(
self
.
train_loader
):
dataload_time
=
time
.
time
()
-
data_start_time
msg
=
"Train: Rank: {}, "
.
format
(
dist
.
get_rank
())
msg
+=
"epoch: {}, "
.
format
(
self
.
epoch
)
msg
+=
"step: {}, "
.
format
(
self
.
iteration
)
msg
+=
"batch : {}/{}, "
.
format
(
batch_index
+
1
,
len
(
self
.
train_loader
))
msg
+=
"lr: {:>.8f}, "
.
format
(
self
.
lr_scheduler
())
msg
+=
"data time: {:>.3f}s, "
.
format
(
dataload_time
)
self
.
train_batch
(
batch_index
,
batch
,
msg
)
data_start_time
=
time
.
time
()
data_start_time
=
time
.
time
()
except
Exception
as
e
:
for
batch_index
,
batch
in
enumerate
(
self
.
train_loader
):
logger
.
error
(
e
)
dataload_time
=
time
.
time
()
-
data_start_time
raise
e
msg
=
"Train: Rank: {}, "
.
format
(
dist
.
get_rank
())
msg
+=
"epoch: {}, "
.
format
(
self
.
epoch
)
total_loss
,
num_seen_utts
=
self
.
valid
()
msg
+=
"step: {}, "
.
format
(
self
.
iteration
)
if
dist
.
get_world_size
()
>
1
:
msg
+=
"batch : {}/{}, "
.
format
(
batch_index
+
1
,
num_seen_utts
=
paddle
.
to_tensor
(
num_seen_utts
)
len
(
self
.
train_loader
))
# the default operator in all_reduce function is sum.
msg
+=
"lr: {:>.8f}, "
.
format
(
self
.
lr_scheduler
())
dist
.
all_reduce
(
num_seen_utts
)
msg
+=
"data time: {:>.3f}s, "
.
format
(
dataload_time
)
total_loss
=
paddle
.
to_tensor
(
total_loss
)
self
.
train_batch
(
batch_index
,
batch
,
msg
)
dist
.
all_reduce
(
total_loss
)
data_start_time
=
time
.
time
()
cv_loss
=
total_loss
/
num_seen_utts
except
Exception
as
e
:
cv_loss
=
float
(
cv_loss
)
logger
.
error
(
e
)
else
:
raise
e
cv_loss
=
total_loss
/
num_seen_utts
with
Timer
(
"Eval Time Cost: {}"
):
total_loss
,
num_seen_utts
=
self
.
valid
()
if
dist
.
get_world_size
()
>
1
:
num_seen_utts
=
paddle
.
to_tensor
(
num_seen_utts
)
# the default operator in all_reduce function is sum.
dist
.
all_reduce
(
num_seen_utts
)
total_loss
=
paddle
.
to_tensor
(
total_loss
)
dist
.
all_reduce
(
total_loss
)
cv_loss
=
total_loss
/
num_seen_utts
cv_loss
=
float
(
cv_loss
)
else
:
cv_loss
=
total_loss
/
num_seen_utts
logger
.
info
(
logger
.
info
(
'Epoch {} Val info val_loss {}'
.
format
(
self
.
epoch
,
cv_loss
))
'Epoch {} Val info val_loss {}'
.
format
(
self
.
epoch
,
cv_loss
))
...
...
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