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5ea181b7
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5ea181b7
编写于
4月 14, 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
fix train logitc
上级
b5bbfc5e
变更
7
隐藏空白更改
内联
并排
Showing
7 changed file
with
81 addition
and
72 deletion
+81
-72
deepspeech/exps/deepspeech2/model.py
deepspeech/exps/deepspeech2/model.py
+5
-5
deepspeech/exps/u2/bin/train.py
deepspeech/exps/u2/bin/train.py
+6
-1
deepspeech/exps/u2/model.py
deepspeech/exps/u2/model.py
+38
-33
deepspeech/training/trainer.py
deepspeech/training/trainer.py
+22
-24
deepspeech/utils/checkpoint.py
deepspeech/utils/checkpoint.py
+9
-7
examples/aishell/s1/conf/conformer.yaml
examples/aishell/s1/conf/conformer.yaml
+0
-1
examples/tiny/s1/conf/conformer.yaml
examples/tiny/s1/conf/conformer.yaml
+1
-1
未找到文件。
deepspeech/exps/deepspeech2/model.py
浏览文件 @
5ea181b7
...
@@ -45,9 +45,10 @@ class DeepSpeech2Trainer(Trainer):
...
@@ -45,9 +45,10 @@ class DeepSpeech2Trainer(Trainer):
def
__init__
(
self
,
config
,
args
):
def
__init__
(
self
,
config
,
args
):
super
().
__init__
(
config
,
args
)
super
().
__init__
(
config
,
args
)
def
train_batch
(
self
,
batch_data
):
def
train_batch
(
self
,
batch_data
,
msg
):
start
=
time
.
time
()
self
.
model
.
train
()
self
.
model
.
train
()
start
=
time
.
time
()
loss
=
self
.
model
(
*
batch_data
)
loss
=
self
.
model
(
*
batch_data
)
loss
.
backward
()
loss
.
backward
()
layer_tools
.
print_grads
(
self
.
model
,
print_func
=
None
)
layer_tools
.
print_grads
(
self
.
model
,
print_func
=
None
)
...
@@ -59,10 +60,8 @@ class DeepSpeech2Trainer(Trainer):
...
@@ -59,10 +60,8 @@ class DeepSpeech2Trainer(Trainer):
losses_np
=
{
losses_np
=
{
'train_loss'
:
float
(
loss
),
'train_loss'
:
float
(
loss
),
}
}
msg
=
"Train: Rank: {}, "
.
format
(
dist
.
get_rank
())
msg
+=
"epoch: {}, "
.
format
(
self
.
epoch
)
msg
+=
"step: {}, "
.
format
(
self
.
iteration
)
msg
+=
"time: {:>.3f}s, "
.
format
(
iteration_time
)
msg
+=
"time: {:>.3f}s, "
.
format
(
iteration_time
)
msg
+=
"batch size: {}, "
.
format
(
self
.
config
.
data
.
batch_size
)
msg
+=
', '
.
join
(
'{}: {:>.6f}'
.
format
(
k
,
v
)
msg
+=
', '
.
join
(
'{}: {:>.6f}'
.
format
(
k
,
v
)
for
k
,
v
in
losses_np
.
items
())
for
k
,
v
in
losses_np
.
items
())
self
.
logger
.
info
(
msg
)
self
.
logger
.
info
(
msg
)
...
@@ -71,6 +70,7 @@ class DeepSpeech2Trainer(Trainer):
...
@@ -71,6 +70,7 @@ class DeepSpeech2Trainer(Trainer):
for
k
,
v
in
losses_np
.
items
():
for
k
,
v
in
losses_np
.
items
():
self
.
visualizer
.
add_scalar
(
"train/{}"
.
format
(
k
),
v
,
self
.
visualizer
.
add_scalar
(
"train/{}"
.
format
(
k
),
v
,
self
.
iteration
)
self
.
iteration
)
self
.
iteration
+=
1
@
mp_tools
.
rank_zero_only
@
mp_tools
.
rank_zero_only
@
paddle
.
no_grad
()
@
paddle
.
no_grad
()
...
...
deepspeech/exps/u2/bin/train.py
浏览文件 @
5ea181b7
...
@@ -13,6 +13,8 @@
...
@@ -13,6 +13,8 @@
# limitations under the License.
# limitations under the License.
"""Trainer for U2 model."""
"""Trainer for U2 model."""
import
os
import
cProfile
from
paddle
import
distributed
as
dist
from
paddle
import
distributed
as
dist
from
deepspeech.utils.utility
import
print_arguments
from
deepspeech.utils.utility
import
print_arguments
...
@@ -52,4 +54,7 @@ if __name__ == "__main__":
...
@@ -52,4 +54,7 @@ if __name__ == "__main__":
with
open
(
args
.
dump_config
,
'w'
)
as
f
:
with
open
(
args
.
dump_config
,
'w'
)
as
f
:
print
(
config
,
file
=
f
)
print
(
config
,
file
=
f
)
main
(
config
,
args
)
# Setting for profiling
pr
=
cProfile
.
Profile
()
pr
.
runcall
(
main
,
config
,
args
)
pr
.
dump_stats
(
os
.
path
.
join
(
'.'
,
'train.profile'
))
deepspeech/exps/u2/model.py
浏览文件 @
5ea181b7
...
@@ -80,54 +80,60 @@ class U2Trainer(Trainer):
...
@@ -80,54 +80,60 @@ class U2Trainer(Trainer):
self
.
model
.
train
()
self
.
model
.
train
()
start
=
time
.
time
()
start
=
time
.
time
()
loss
,
attention_loss
,
ctc_loss
=
self
.
model
(
*
batch_data
)
loss
,
attention_loss
,
ctc_loss
=
self
.
model
(
*
batch_data
)
# loss div by `batch_size * accum_grad`
loss
/=
train_conf
.
accum_grad
loss
.
backward
()
loss
.
backward
()
layer_tools
.
print_grads
(
self
.
model
,
print_func
=
None
)
layer_tools
.
print_grads
(
self
.
model
,
print_func
=
None
)
if
self
.
iteration
%
train_conf
.
accum_grad
==
0
:
losses_np
=
{
'train_loss'
:
float
(
loss
)
*
train_conf
.
accum_grad
,
'train_att_loss'
:
float
(
attention_loss
),
'train_ctc_loss'
:
float
(
ctc_loss
),
}
if
(
self
.
iteration
+
1
)
%
train_conf
.
accum_grad
==
0
:
if
dist
.
get_rank
()
==
0
and
self
.
visualizer
:
for
k
,
v
in
losses_np
.
items
():
self
.
visualizer
.
add_scalar
(
"train/{}"
.
format
(
k
),
v
,
self
.
iteration
)
self
.
optimizer
.
step
()
self
.
optimizer
.
step
()
self
.
optimizer
.
clear_grad
()
self
.
optimizer
.
clear_grad
()
self
.
lr_scheduler
.
step
()
self
.
lr_scheduler
.
step
()
self
.
iteration
+=
1
iteration_time
=
time
.
time
()
-
start
iteration_time
=
time
.
time
()
-
start
losses_np
=
{
if
(
self
.
iteration
+
1
)
%
train_conf
.
log_interval
==
0
:
'train_loss'
:
float
(
loss
),
msg
+=
"time: {:>.3f}s, "
.
format
(
iteration_time
)
'train_att_loss'
:
float
(
attention_loss
),
msg
+=
"batch size: {}, "
.
format
(
self
.
config
.
data
.
batch_size
)
'train_ctc_loss'
:
float
(
ctc_loss
),
msg
+=
"accum: {}, "
.
format
(
train_conf
.
accum_grad
)
}
msg
+=
', '
.
join
(
'{}: {:>.6f}'
.
format
(
k
,
v
)
msg
+=
"time: {:>.3f}s, "
.
format
(
iteration_time
)
for
k
,
v
in
losses_np
.
items
())
msg
+=
"batch size: {}, "
.
format
(
self
.
config
.
data
.
batch_size
)
msg
+=
"accum: {}, "
.
format
(
train_conf
.
accum_grad
)
msg
+=
', '
.
join
(
'{}: {:>.6f}'
.
format
(
k
,
v
)
for
k
,
v
in
losses_np
.
items
())
if
self
.
iteration
%
train_conf
.
log_interval
==
0
:
self
.
logger
.
info
(
msg
)
self
.
logger
.
info
(
msg
)
# display
if
dist
.
get_rank
()
==
0
and
self
.
visualizer
:
for
k
,
v
in
losses_np
.
items
():
self
.
visualizer
.
add_scalar
(
"train/{}"
.
format
(
k
),
v
,
self
.
iteration
)
def
train
(
self
):
def
train
(
self
):
"""The training process.
"""The training process control by step."""
It includes forward/backward/update and periodical validation and
saving.
"""
# !!!IMPORTANT!!!
# !!!IMPORTANT!!!
# Try to export the model by script, if fails, we should refine
# Try to export the model by script, if fails, we should refine
# the code to satisfy the script export requirements
# the code to satisfy the script export requirements
# script_model = paddle.jit.to_static(self.model)
# script_model = paddle.jit.to_static(self.model)
# script_model_path = str(self.checkpoint_dir / 'init')
# script_model_path = str(self.checkpoint_dir / 'init')
# paddle.jit.save(script_model, script_model_path)
# paddle.jit.save(script_model, script_model_path)
from_scratch
=
self
.
resume_or_scratch
()
from_scratch
=
self
.
resume_or_scratch
()
if
from_scratch
:
# save init model, i.e. 0 epoch
self
.
save
(
tag
=
'init'
)
self
.
lr_scheduler
.
step
(
self
.
iteration
)
if
self
.
parallel
:
self
.
train_loader
.
batch_sampler
.
set_epoch
(
self
.
epoch
)
self
.
logger
.
info
(
self
.
logger
.
info
(
f
"Train Total Examples:
{
len
(
self
.
train_loader
.
dataset
)
}
"
)
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
:
try
:
try
:
data_start_time
=
time
.
time
()
data_start_time
=
time
.
time
()
for
batch
in
self
.
train_loader
:
for
batch
in
self
.
train_loader
:
...
@@ -135,19 +141,18 @@ class U2Trainer(Trainer):
...
@@ -135,19 +141,18 @@ class U2Trainer(Trainer):
msg
=
"Train: Rank: {}, "
.
format
(
dist
.
get_rank
())
msg
=
"Train: Rank: {}, "
.
format
(
dist
.
get_rank
())
msg
+=
"epoch: {}, "
.
format
(
self
.
epoch
)
msg
+=
"epoch: {}, "
.
format
(
self
.
epoch
)
msg
+=
"step: {}, "
.
format
(
self
.
iteration
)
msg
+=
"step: {}, "
.
format
(
self
.
iteration
)
msg
+=
"lr: {
}, "
.
foram
t
(
self
.
lr_scheduler
())
msg
+=
"lr: {
:>.8f}, "
.
forma
t
(
self
.
lr_scheduler
())
msg
+=
"dataloader time: {:>.3f}s, "
.
format
(
dataload_time
)
msg
+=
"dataloader time: {:>.3f}s, "
.
format
(
dataload_time
)
self
.
iteration
+=
1
self
.
train_batch
(
batch
,
msg
)
self
.
train_batch
(
batch
,
msg
)
data_start_time
=
time
.
time
()
data_start_time
=
time
.
time
()
except
Exception
as
e
:
except
Exception
as
e
:
self
.
logger
.
error
(
e
)
self
.
logger
.
error
(
e
)
raise
e
raise
e
self
.
valid
()
self
.
valid
()
self
.
save
()
self
.
save
()
self
.
new_epoch
()
self
.
new_epoch
()
@
mp_tools
.
rank_zero_only
@
mp_tools
.
rank_zero_only
@
paddle
.
no_grad
()
@
paddle
.
no_grad
()
def
valid
(
self
):
def
valid
(
self
):
...
@@ -263,12 +268,12 @@ class U2Trainer(Trainer):
...
@@ -263,12 +268,12 @@ class U2Trainer(Trainer):
lr_scheduler
=
paddle
.
optimizer
.
lr
.
ExponentialDecay
(
lr_scheduler
=
paddle
.
optimizer
.
lr
.
ExponentialDecay
(
learning_rate
=
optim_conf
.
lr
,
learning_rate
=
optim_conf
.
lr
,
gamma
=
scheduler_conf
.
lr_decay
,
gamma
=
scheduler_conf
.
lr_decay
,
verbose
=
Tru
e
)
verbose
=
Fals
e
)
elif
scheduler_type
==
'warmuplr'
:
elif
scheduler_type
==
'warmuplr'
:
lr_scheduler
=
WarmupLR
(
lr_scheduler
=
WarmupLR
(
learning_rate
=
optim_conf
.
lr
,
learning_rate
=
optim_conf
.
lr
,
warmup_steps
=
scheduler_conf
.
warmup_steps
,
warmup_steps
=
scheduler_conf
.
warmup_steps
,
verbose
=
Tru
e
)
verbose
=
Fals
e
)
else
:
else
:
raise
ValueError
(
f
"Not support scheduler:
{
scheduler_type
}
"
)
raise
ValueError
(
f
"Not support scheduler:
{
scheduler_type
}
"
)
...
...
deepspeech/training/trainer.py
浏览文件 @
5ea181b7
...
@@ -127,7 +127,7 @@ class Trainer():
...
@@ -127,7 +127,7 @@ class Trainer():
dist
.
init_parallel_env
()
dist
.
init_parallel_env
()
@
mp_tools
.
rank_zero_only
@
mp_tools
.
rank_zero_only
def
save
(
self
,
infos
=
None
):
def
save
(
self
,
tag
=
None
,
infos
=
None
):
"""Save checkpoint (model parameters and optimizer states).
"""Save checkpoint (model parameters and optimizer states).
"""
"""
if
infos
is
None
:
if
infos
is
None
:
...
@@ -136,8 +136,9 @@ class Trainer():
...
@@ -136,8 +136,9 @@ class Trainer():
"epoch"
:
self
.
epoch
,
"epoch"
:
self
.
epoch
,
"lr"
:
self
.
optimizer
.
get_lr
(),
"lr"
:
self
.
optimizer
.
get_lr
(),
}
}
checkpoint
.
save_parameters
(
self
.
checkpoint_dir
,
self
.
iteration
,
checkpoint
.
save_parameters
(
self
.
checkpoint_dir
,
self
.
iteration
self
.
model
,
self
.
optimizer
,
infos
)
if
tag
is
None
else
tag
,
self
.
model
,
self
.
optimizer
,
infos
)
def
resume_or_scratch
(
self
):
def
resume_or_scratch
(
self
):
"""Resume from latest checkpoint at checkpoints in the output
"""Resume from latest checkpoint at checkpoints in the output
...
@@ -146,6 +147,7 @@ class Trainer():
...
@@ -146,6 +147,7 @@ class Trainer():
If ``args.checkpoint_path`` is not None, load the checkpoint, else
If ``args.checkpoint_path`` is not None, load the checkpoint, else
resume training.
resume training.
"""
"""
scratch
=
None
infos
=
checkpoint
.
load_parameters
(
infos
=
checkpoint
.
load_parameters
(
self
.
model
,
self
.
model
,
self
.
optimizer
,
self
.
optimizer
,
...
@@ -155,44 +157,41 @@ class Trainer():
...
@@ -155,44 +157,41 @@ class Trainer():
# restore from ckpt
# restore from ckpt
self
.
iteration
=
infos
[
"step"
]
self
.
iteration
=
infos
[
"step"
]
self
.
epoch
=
infos
[
"epoch"
]
self
.
epoch
=
infos
[
"epoch"
]
self
.
lr_scheduler
.
step
(
self
.
iteration
)
scratch
=
False
if
self
.
parallel
:
self
.
train_loader
.
batch_sampler
.
set_epoch
(
self
.
epoch
)
return
False
else
:
else
:
# from scratch, epoch and iteration init with zero
scratch
=
True
# save init model, i.e. 0 epoch
self
.
save
()
return
scratch
# self.epoch start from 1.
self
.
new_epoch
()
return
True
def
new_epoch
(
self
):
def
new_epoch
(
self
):
"""Reset the train loader
and increment ``epoch`
`.
"""Reset the train loader
seed and increment `epoch
`.
"""
"""
self
.
epoch
+=
1
if
self
.
parallel
:
if
self
.
parallel
:
# batch sampler epoch start from 0
self
.
train_loader
.
batch_sampler
.
set_epoch
(
self
.
epoch
)
self
.
train_loader
.
batch_sampler
.
set_epoch
(
self
.
epoch
)
self
.
epoch
+=
1
def
train
(
self
):
def
train
(
self
):
"""The training process.
"""The training process control by epoch."""
"""
from_scratch
=
self
.
resume_or_scratch
()
from_scratch
=
self
.
resume_or_scratch
()
if
from_scratch
:
# save init model, i.e. 0 epoch
self
.
save
(
tag
=
'init'
)
self
.
lr_scheduler
.
step
(
self
.
iteration
)
if
self
.
parallel
:
self
.
train_loader
.
batch_sampler
.
set_epoch
(
self
.
epoch
)
self
.
logger
.
info
(
self
.
logger
.
info
(
f
"Train Total Examples:
{
len
(
self
.
train_loader
.
dataset
)
}
"
)
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
:
try
:
try
:
data_start_time
=
time
.
time
()
data_start_time
=
time
.
time
()
for
batch
in
self
.
train_loader
:
for
batch
in
self
.
train_loader
:
dataload_time
=
time
.
time
()
-
data_start_time
dataload_time
=
time
.
time
()
-
data_start_time
# iteration start from 1.
self
.
iteration
+=
1
msg
=
"Train: Rank: {}, "
.
format
(
dist
.
get_rank
())
msg
=
"Train: Rank: {}, "
.
format
(
dist
.
get_rank
())
msg
+=
"epoch: {}, "
.
format
(
self
.
epoch
)
msg
+=
"epoch: {}, "
.
format
(
self
.
epoch
)
msg
+=
"step: {}, "
.
format
(
self
.
iteration
)
msg
+=
"step: {}, "
.
format
(
self
.
iteration
)
msg
+=
"lr: {:>.8f}, "
.
format
(
self
.
lr_scheduler
())
msg
+=
"dataloader time: {:>.3f}s, "
.
format
(
dataload_time
)
msg
+=
"dataloader time: {:>.3f}s, "
.
format
(
dataload_time
)
self
.
train_batch
(
batch
,
msg
)
self
.
train_batch
(
batch
,
msg
)
data_start_time
=
time
.
time
()
data_start_time
=
time
.
time
()
...
@@ -202,7 +201,6 @@ class Trainer():
...
@@ -202,7 +201,6 @@ class Trainer():
self
.
valid
()
self
.
valid
()
self
.
save
()
self
.
save
()
# lr control by epoch
self
.
lr_scheduler
.
step
()
self
.
lr_scheduler
.
step
()
self
.
new_epoch
()
self
.
new_epoch
()
...
...
deepspeech/utils/checkpoint.py
浏览文件 @
5ea181b7
...
@@ -16,6 +16,7 @@ import os
...
@@ -16,6 +16,7 @@ import os
import
logging
import
logging
import
re
import
re
import
json
import
json
from
typing
import
Union
import
paddle
import
paddle
from
paddle
import
distributed
as
dist
from
paddle
import
distributed
as
dist
...
@@ -79,7 +80,7 @@ def load_parameters(model,
...
@@ -79,7 +80,7 @@ def load_parameters(model,
configs
=
{}
configs
=
{}
if
checkpoint_path
is
not
None
:
if
checkpoint_path
is
not
None
:
iteration
=
int
(
os
.
path
.
basename
(
checkpoint_path
).
split
(
":"
)[
-
1
])
tag
=
os
.
path
.
basename
(
checkpoint_path
).
split
(
":"
)[
-
1
]
elif
checkpoint_dir
is
not
None
:
elif
checkpoint_dir
is
not
None
:
iteration
=
_load_latest_checkpoint
(
checkpoint_dir
)
iteration
=
_load_latest_checkpoint
(
checkpoint_dir
)
if
iteration
==
-
1
:
if
iteration
==
-
1
:
...
@@ -113,14 +114,14 @@ def load_parameters(model,
...
@@ -113,14 +114,14 @@ def load_parameters(model,
@
mp_tools
.
rank_zero_only
@
mp_tools
.
rank_zero_only
def
save_parameters
(
checkpoint_dir
:
str
,
def
save_parameters
(
checkpoint_dir
:
str
,
iteration
:
int
,
tag_or_iteration
:
Union
[
int
,
str
]
,
model
:
paddle
.
nn
.
Layer
,
model
:
paddle
.
nn
.
Layer
,
optimizer
:
Optimizer
=
None
,
optimizer
:
Optimizer
=
None
,
infos
:
dict
=
None
):
infos
:
dict
=
None
):
"""Checkpoint the latest trained model parameters.
"""Checkpoint the latest trained model parameters.
Args:
Args:
checkpoint_dir (str): the directory where checkpoint is saved.
checkpoint_dir (str): the directory where checkpoint is saved.
iteration (int
): the latest iteration(step or epoch) number.
tag_or_iteration (int or str
): the latest iteration(step or epoch) number.
model (Layer): model to be checkpointed.
model (Layer): model to be checkpointed.
optimizer (Optimizer, optional): optimizer to be checkpointed.
optimizer (Optimizer, optional): optimizer to be checkpointed.
Defaults to None.
Defaults to None.
...
@@ -128,7 +129,8 @@ def save_parameters(checkpoint_dir: str,
...
@@ -128,7 +129,8 @@ def save_parameters(checkpoint_dir: str,
Returns:
Returns:
None
None
"""
"""
checkpoint_path
=
os
.
path
.
join
(
checkpoint_dir
,
"{}"
.
format
(
iteration
))
checkpoint_path
=
os
.
path
.
join
(
checkpoint_dir
,
"{}"
.
format
(
tag_or_iteration
))
model_dict
=
model
.
state_dict
()
model_dict
=
model
.
state_dict
()
params_path
=
checkpoint_path
+
".pdparams"
params_path
=
checkpoint_path
+
".pdparams"
...
@@ -142,10 +144,10 @@ def save_parameters(checkpoint_dir: str,
...
@@ -142,10 +144,10 @@ def save_parameters(checkpoint_dir: str,
logger
.
info
(
"Saved optimzier state to {}"
.
format
(
optimizer_path
))
logger
.
info
(
"Saved optimzier state to {}"
.
format
(
optimizer_path
))
info_path
=
re
.
sub
(
'.pdparams$'
,
'.json'
,
params_path
)
info_path
=
re
.
sub
(
'.pdparams$'
,
'.json'
,
params_path
)
if
infos
is
None
:
infos
=
{}
if
infos
is
None
else
infos
infos
=
{}
with
open
(
info_path
,
'w'
)
as
fout
:
with
open
(
info_path
,
'w'
)
as
fout
:
data
=
json
.
dumps
(
infos
)
data
=
json
.
dumps
(
infos
)
fout
.
write
(
data
)
fout
.
write
(
data
)
_save_checkpoint
(
checkpoint_dir
,
iteration
)
if
isinstance
(
tag_or_iteration
,
int
):
_save_checkpoint
(
checkpoint_dir
,
tag_or_iteration
)
examples/aishell/s1/conf/conformer.yaml
浏览文件 @
5ea181b7
...
@@ -6,7 +6,6 @@ data:
...
@@ -6,7 +6,6 @@ data:
vocab_filepath
:
data/vocab.txt
vocab_filepath
:
data/vocab.txt
unit_type
:
'
char'
unit_type
:
'
char'
spm_model_prefix
:
'
'
spm_model_prefix
:
'
'
mean_std_filepath
:
"
"
augmentation_config
:
conf/augmentation.json
augmentation_config
:
conf/augmentation.json
batch_size
:
64
batch_size
:
64
min_input_len
:
0.5
min_input_len
:
0.5
...
...
examples/tiny/s1/conf/conformer.yaml
浏览文件 @
5ea181b7
...
@@ -12,7 +12,7 @@ data:
...
@@ -12,7 +12,7 @@ data:
min_input_len
:
0.5
min_input_len
:
0.5
max_input_len
:
20.0
max_input_len
:
20.0
min_output_len
:
0.0
min_output_len
:
0.0
max_output_len
:
400
max_output_len
:
400
.0
min_output_input_ratio
:
0.05
min_output_input_ratio
:
0.05
max_output_input_ratio
:
10.0
max_output_input_ratio
:
10.0
raw_wav
:
True
# use raw_wav or kaldi feature
raw_wav
:
True
# use raw_wav or kaldi feature
...
...
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