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0d0b5811
编写于
8月 25, 2021
作者:
H
huangyuxin
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
add static_forward_online and static_forward_offline
上级
92617f08
变更
3
隐藏空白更改
内联
并排
Showing
3 changed file
with
79 addition
and
174 deletion
+79
-174
deepspeech/exps/deepspeech2/model.py
deepspeech/exps/deepspeech2/model.py
+78
-155
deepspeech/models/ds2/deepspeech2.py
deepspeech/models/ds2/deepspeech2.py
+1
-1
deepspeech/models/ds2_online/deepspeech2.py
deepspeech/models/ds2_online/deepspeech2.py
+0
-18
未找到文件。
deepspeech/exps/deepspeech2/model.py
浏览文件 @
0d0b5811
...
@@ -12,6 +12,7 @@
...
@@ -12,6 +12,7 @@
# See the License for the specific language governing permissions and
# See the License for the specific language governing permissions and
# limitations under the License.
# limitations under the License.
"""Contains DeepSpeech2 and DeepSpeech2Online model."""
"""Contains DeepSpeech2 and DeepSpeech2Online model."""
import
os
import
time
import
time
from
collections
import
defaultdict
from
collections
import
defaultdict
from
pathlib
import
Path
from
pathlib
import
Path
...
@@ -398,40 +399,10 @@ class DeepSpeech2Tester(DeepSpeech2Trainer):
...
@@ -398,40 +399,10 @@ class DeepSpeech2Tester(DeepSpeech2Trainer):
self
.
output_dir
=
output_dir
self
.
output_dir
=
output_dir
class
DeepSpeech2ExportTester
(
DeepSpeech2Trainer
):
class
DeepSpeech2ExportTester
(
DeepSpeech2Tester
):
@
classmethod
def
params
(
cls
,
config
:
Optional
[
CfgNode
]
=
None
)
->
CfgNode
:
# testing config
default
=
CfgNode
(
dict
(
alpha
=
2.5
,
# Coef of LM for beam search.
beta
=
0.3
,
# Coef of WC for beam search.
cutoff_prob
=
1.0
,
# Cutoff probability for pruning.
cutoff_top_n
=
40
,
# Cutoff number for pruning.
lang_model_path
=
'models/lm/common_crawl_00.prune01111.trie.klm'
,
# Filepath for language model.
decoding_method
=
'ctc_beam_search'
,
# Decoding method. Options: ctc_beam_search, ctc_greedy
error_rate_type
=
'wer'
,
# Error rate type for evaluation. Options `wer`, 'cer'
num_proc_bsearch
=
8
,
# # of CPUs for beam search.
beam_size
=
500
,
# Beam search width.
batch_size
=
128
,
# decoding batch size
))
if
config
is
not
None
:
config
.
merge_from_other_cfg
(
default
)
return
default
def
__init__
(
self
,
config
,
args
):
def
__init__
(
self
,
config
,
args
):
super
().
__init__
(
config
,
args
)
super
().
__init__
(
config
,
args
)
def
ordid2token
(
self
,
texts
,
texts_len
):
""" ord() id to chr() chr """
trans
=
[]
for
text
,
n
in
zip
(
texts
,
texts_len
):
n
=
n
.
numpy
().
item
()
ids
=
text
[:
n
]
trans
.
append
(
''
.
join
([
chr
(
i
)
for
i
in
ids
]))
return
trans
def
compute_metrics
(
self
,
def
compute_metrics
(
self
,
utts
,
utts
,
audio
,
audio
,
...
@@ -447,9 +418,48 @@ class DeepSpeech2ExportTester(DeepSpeech2Trainer):
...
@@ -447,9 +418,48 @@ class DeepSpeech2ExportTester(DeepSpeech2Trainer):
vocab_list
=
self
.
test_loader
.
collate_fn
.
vocab_list
vocab_list
=
self
.
test_loader
.
collate_fn
.
vocab_list
batch_size
=
self
.
config
.
decoding
.
batch_size
if
self
.
args
.
model_type
==
"online"
:
output_probs_branch
,
output_lens_branch
=
self
.
static_forward_online
(
audio
,
audio_len
)
elif
self
.
args
.
model_type
==
"offline"
:
output_probs_branch
,
output_lens_branch
=
self
.
static_forward_offline
(
audio
,
audio_len
)
else
:
raise
Exception
(
"wrong model type"
)
self
.
predictor
.
clear_intermediate_tensor
()
self
.
predictor
.
try_shrink_memory
()
self
.
model
.
decoder
.
init_decode
(
cfg
.
alpha
,
cfg
.
beta
,
cfg
.
lang_model_path
,
vocab_list
,
cfg
.
decoding_method
)
result_transcripts
=
self
.
model
.
decoder
.
decode_probs
(
output_probs_branch
.
numpy
(),
output_lens_branch
,
vocab_list
,
cfg
.
decoding_method
,
cfg
.
lang_model_path
,
cfg
.
alpha
,
cfg
.
beta
,
cfg
.
beam_size
,
cfg
.
cutoff_prob
,
cfg
.
cutoff_top_n
,
cfg
.
num_proc_bsearch
)
output_prob_list
=
[]
target_transcripts
=
self
.
ordid2token
(
texts
,
texts_len
)
for
utt
,
target
,
result
in
zip
(
utts
,
target_transcripts
,
result_transcripts
):
errors
,
len_ref
=
errors_func
(
target
,
result
)
errors_sum
+=
errors
len_refs
+=
len_ref
num_ins
+=
1
if
fout
:
fout
.
write
(
utt
+
" "
+
result
+
"
\n
"
)
logger
.
info
(
"
\n
Target Transcription: %s
\n
Output Transcription: %s"
%
(
target
,
result
))
logger
.
info
(
"Current error rate [%s] = %f"
%
(
cfg
.
error_rate_type
,
error_rate_func
(
target
,
result
)))
return
dict
(
errors_sum
=
errors_sum
,
len_refs
=
len_refs
,
num_ins
=
num_ins
,
error_rate
=
errors_sum
/
len_refs
,
error_rate_type
=
cfg
.
error_rate_type
)
def
static_forward_online
(
self
,
audio
,
audio_len
):
output_probs_list
=
[]
output_lens_list
=
[]
output_lens_list
=
[]
decoder_chunk_size
=
8
decoder_chunk_size
=
8
subsampling_rate
=
self
.
model
.
encoder
.
conv
.
subsampling_rate
subsampling_rate
=
self
.
model
.
encoder
.
conv
.
subsampling_rate
...
@@ -459,15 +469,18 @@ class DeepSpeech2ExportTester(DeepSpeech2Trainer):
...
@@ -459,15 +469,18 @@ class DeepSpeech2ExportTester(DeepSpeech2Trainer):
)
*
subsampling_rate
+
receptive_field_length
)
*
subsampling_rate
+
receptive_field_length
x_batch
=
audio
.
numpy
()
x_batch
=
audio
.
numpy
()
batch_size
=
x_batch
.
shape
[
0
]
x_len_batch
=
audio_len
.
numpy
().
astype
(
np
.
int64
)
x_len_batch
=
audio_len
.
numpy
().
astype
(
np
.
int64
)
max_len_batch
=
x_batch
.
shape
[
1
]
max_len_batch
=
x_batch
.
shape
[
1
]
batch_padding_len
=
chunk_stride
-
(
batch_padding_len
=
chunk_stride
-
(
max_len_batch
-
chunk_size
max_len_batch
-
chunk_size
)
%
chunk_stride
# The length of padding for the batch
)
%
chunk_stride
# The length of padding for the batch
x_list
=
np
.
split
(
x_batch
,
x_batch
.
shape
[
0
]
,
axis
=
0
)
x_list
=
np
.
split
(
x_batch
,
batch_size
,
axis
=
0
)
x_len_list
=
np
.
split
(
x_len_batch
,
x_batch
.
shape
[
0
],
axis
=
0
)
x_len_list
=
np
.
split
(
x_len_batch
,
x_batch
.
shape
[
0
],
axis
=
0
)
for
x
,
x_len
in
zip
(
x_list
,
x_len_list
):
for
x
,
x_len
in
zip
(
x_list
,
x_len_list
):
self
.
autolog
.
times
.
start
()
self
.
autolog
.
times
.
stamp
()
assert
(
chunk_size
<=
x_len
[
0
])
assert
(
chunk_size
<=
x_len
[
0
])
eouts_chunk_list
=
[]
eouts_chunk_list
=
[]
...
@@ -536,38 +549,40 @@ class DeepSpeech2ExportTester(DeepSpeech2Trainer):
...
@@ -536,38 +549,40 @@ class DeepSpeech2ExportTester(DeepSpeech2Trainer):
output_state_c_handle
=
self
.
predictor
.
get_output_handle
(
output_state_c_handle
=
self
.
predictor
.
get_output_handle
(
output_names
[
3
])
output_names
[
3
])
self
.
predictor
.
run
()
self
.
predictor
.
run
()
output_chunk_prob
=
output_handle
.
copy_to_cpu
()
output_chunk_prob
s
=
output_handle
.
copy_to_cpu
()
output_chunk_lens
=
output_lens_handle
.
copy_to_cpu
()
output_chunk_lens
=
output_lens_handle
.
copy_to_cpu
()
chunk_state_h_box
=
output_state_h_handle
.
copy_to_cpu
()
chunk_state_h_box
=
output_state_h_handle
.
copy_to_cpu
()
chunk_state_c_box
=
output_state_c_handle
.
copy_to_cpu
()
chunk_state_c_box
=
output_state_c_handle
.
copy_to_cpu
()
output_chunk_prob
=
paddle
.
to_tensor
(
output_chunk_prob
)
output_chunk_prob
s
=
paddle
.
to_tensor
(
output_chunk_probs
)
output_chunk_lens
=
paddle
.
to_tensor
(
output_chunk_lens
)
output_chunk_lens
=
paddle
.
to_tensor
(
output_chunk_lens
)
probs_chunk_list
.
append
(
output_chunk_prob
)
probs_chunk_list
.
append
(
output_chunk_prob
s
)
probs_chunk_lens_list
.
append
(
output_chunk_lens
)
probs_chunk_lens_list
.
append
(
output_chunk_lens
)
output_prob
=
paddle
.
concat
(
probs_chunk_list
,
axis
=
1
)
output_prob
s
=
paddle
.
concat
(
probs_chunk_list
,
axis
=
1
)
output_lens
=
paddle
.
add_n
(
probs_chunk_lens_list
)
output_lens
=
paddle
.
add_n
(
probs_chunk_lens_list
)
output_prob
_padding_len
=
max_len_batch
+
batch_padding_len
-
output_prob
.
shape
[
output_prob
s_padding_len
=
max_len_batch
+
batch_padding_len
-
output_probs
.
shape
[
1
]
1
]
output_prob_padding
=
paddle
.
zeros
(
output_prob
s
_padding
=
paddle
.
zeros
(
(
1
,
output_prob
_padding_len
,
output_prob
.
shape
[
2
]),
(
1
,
output_prob
s_padding_len
,
output_probs
.
shape
[
2
]),
dtype
=
"float32"
)
# The prob padding for a piece of utterance
dtype
=
"float32"
)
# The prob padding for a piece of utterance
output_prob
=
paddle
.
concat
(
output_prob
s
=
paddle
.
concat
(
[
output_prob
,
output_prob
_padding
],
axis
=
1
)
[
output_prob
s
,
output_probs
_padding
],
axis
=
1
)
output_prob
_list
.
append
(
output_prob
)
output_prob
s_list
.
append
(
output_probs
)
output_lens_list
.
append
(
output_lens
)
output_lens_list
.
append
(
output_lens
)
output_prob_branch
=
paddle
.
concat
(
output_prob_list
,
axis
=
0
)
self
.
autolog
.
times
.
stamp
()
self
.
autolog
.
times
.
stamp
()
self
.
autolog
.
times
.
end
()
output_probs_branch
=
paddle
.
concat
(
output_probs_list
,
axis
=
0
)
output_lens_branch
=
paddle
.
concat
(
output_lens_list
,
axis
=
0
)
output_lens_branch
=
paddle
.
concat
(
output_lens_list
,
axis
=
0
)
"""
return
output_probs_branch
,
output_lens_branch
def
static_forward_offline
(
self
,
audio
,
audio_len
):
x
=
audio
.
numpy
()
x
=
audio
.
numpy
()
x_len
=
audio_len
.
numpy
().
astype
(
np
.
int64
)
x_len
=
audio_len
.
numpy
().
astype
(
np
.
int64
)
input_names
=
self
.
predictor
.
get_input_names
()
input_names
=
self
.
predictor
.
get_input_names
()
audio_handle
=
self
.
predictor
.
get_input_handle
(
input_names
[
0
])
audio_handle
=
self
.
predictor
.
get_input_handle
(
input_names
[
0
])
audio_len_handle
=
self
.
predictor
.
get_input_handle
(
input_names
[
1
])
audio_len_handle
=
self
.
predictor
.
get_input_handle
(
input_names
[
1
])
h_box_handle = self.predictor.get_input_handle(input_names[2])
c_box_handle = self.predictor.get_input_handle(input_names[3])
audio_handle
.
reshape
(
x
.
shape
)
audio_handle
.
reshape
(
x
.
shape
)
audio_handle
.
copy_from_cpu
(
x
)
audio_handle
.
copy_from_cpu
(
x
)
...
@@ -575,100 +590,21 @@ class DeepSpeech2ExportTester(DeepSpeech2Trainer):
...
@@ -575,100 +590,21 @@ class DeepSpeech2ExportTester(DeepSpeech2Trainer):
audio_len_handle
.
reshape
(
x_len
.
shape
)
audio_len_handle
.
reshape
(
x_len
.
shape
)
audio_len_handle
.
copy_from_cpu
(
x_len
)
audio_len_handle
.
copy_from_cpu
(
x_len
)
init_state_h_box = np.zeros((self.config.model.num_rnn_layers, audio.shape[0], self.config.model.rnn_layer_size), dtype=np.float32)
self
.
autolog
.
times
.
start
()
init_state_c_box = np.zeros((self.config.model.num_rnn_layers, audio.shape[0], self.config.model.rnn_layer_size), dtype=np.float32)
self
.
autolog
.
times
.
stamp
()
h_box_handle.reshape(init_state_h_box.shape)
h_box_handle.copy_from_cpu(init_state_h_box)
c_box_handle.reshape(init_state_c_box.shape)
c_box_handle.copy_from_cpu(init_state_c_box)
#self.autolog.times.start()
#self.autolog.times.stamp()
self
.
predictor
.
run
()
self
.
predictor
.
run
()
self
.
autolog
.
times
.
stamp
()
self
.
autolog
.
times
.
stamp
()
self
.
autolog
.
times
.
end
()
output_names
=
self
.
predictor
.
get_output_names
()
output_names
=
self
.
predictor
.
get_output_names
()
output_handle
=
self
.
predictor
.
get_output_handle
(
output_names
[
0
])
output_handle
=
self
.
predictor
.
get_output_handle
(
output_names
[
0
])
output_lens_handle
=
self
.
predictor
.
get_output_handle
(
output_names
[
1
])
output_lens_handle
=
self
.
predictor
.
get_output_handle
(
output_names
[
1
])
output_state_h_handle = self.predictor.get_output_handle(output_names[2])
output_probs
=
output_handle
.
copy_to_cpu
()
output_state_c_handle = self.predictor.get_output_handle(output_names[3])
output_prob = output_handle.copy_to_cpu()
output_lens
=
output_lens_handle
.
copy_to_cpu
()
output_lens
=
output_lens_handle
.
copy_to_cpu
()
output_stata_h_box = output_state_h_handle.copy_to_cpu()
output_probs_branch
=
paddle
.
to_tensor
(
output_probs
)
output_stata_c_box = output_state_c_handle.copy_to_cpu()
output_prob_branch = paddle.to_tensor(output_prob)
output_lens_branch
=
paddle
.
to_tensor
(
output_lens
)
output_lens_branch
=
paddle
.
to_tensor
(
output_lens
)
"""
return
output_probs_branch
,
output_lens_branch
result_transcripts
=
self
.
model
.
decode_by_probs
(
output_prob_branch
,
output_lens_branch
,
vocab_list
,
decoding_method
=
cfg
.
decoding_method
,
lang_model_path
=
cfg
.
lang_model_path
,
beam_alpha
=
cfg
.
alpha
,
beam_beta
=
cfg
.
beta
,
beam_size
=
cfg
.
beam_size
,
cutoff_prob
=
cfg
.
cutoff_prob
,
cutoff_top_n
=
cfg
.
cutoff_top_n
,
num_processes
=
cfg
.
num_proc_bsearch
)
#self.autolog.times.stamp()
#self.autolog.times.stamp()
#self.autolog.times.end()
target_transcripts
=
self
.
ordid2token
(
texts
,
texts_len
)
for
utt
,
target
,
result
in
zip
(
utts
,
target_transcripts
,
result_transcripts
):
errors
,
len_ref
=
errors_func
(
target
,
result
)
errors_sum
+=
errors
len_refs
+=
len_ref
num_ins
+=
1
if
fout
:
fout
.
write
(
utt
+
" "
+
result
+
"
\n
"
)
logger
.
info
(
"
\n
Target Transcription: %s
\n
Output Transcription: %s"
%
(
target
,
result
))
logger
.
info
(
"Current error rate [%s] = %f"
%
(
cfg
.
error_rate_type
,
error_rate_func
(
target
,
result
)))
return
dict
(
errors_sum
=
errors_sum
,
len_refs
=
len_refs
,
num_ins
=
num_ins
,
error_rate
=
errors_sum
/
len_refs
,
error_rate_type
=
cfg
.
error_rate_type
)
@
mp_tools
.
rank_zero_only
@
paddle
.
no_grad
()
def
test
(
self
):
logger
.
info
(
f
"Test Total Examples:
{
len
(
self
.
test_loader
.
dataset
)
}
"
)
#self.autolog = Autolog(
# batch_size=self.config.decoding.batch_size,
# model_name="deepspeech2",
# model_precision="fp32").getlog()
self
.
model
.
eval
()
cfg
=
self
.
config
error_rate_type
=
None
errors_sum
,
len_refs
,
num_ins
=
0.0
,
0
,
0
with
open
(
self
.
args
.
result_file
,
'w'
)
as
fout
:
for
i
,
batch
in
enumerate
(
self
.
test_loader
):
utts
,
audio
,
audio_len
,
texts
,
texts_len
=
batch
metrics
=
self
.
compute_metrics
(
utts
,
audio
,
audio_len
,
texts
,
texts_len
,
fout
)
errors_sum
+=
metrics
[
'errors_sum'
]
len_refs
+=
metrics
[
'len_refs'
]
num_ins
+=
metrics
[
'num_ins'
]
error_rate_type
=
metrics
[
'error_rate_type'
]
logger
.
info
(
"Error rate [%s] (%d/?) = %f"
%
(
error_rate_type
,
num_ins
,
errors_sum
/
len_refs
))
# logging
msg
=
"Test: "
msg
+=
"epoch: {}, "
.
format
(
self
.
epoch
)
msg
+=
"step: {}, "
.
format
(
self
.
iteration
)
msg
+=
"Final error rate [%s] (%d/%d) = %f"
%
(
error_rate_type
,
num_ins
,
num_ins
,
errors_sum
/
len_refs
)
logger
.
info
(
msg
)
#self.autolog.report()
def
run_test
(
self
):
def
run_test
(
self
):
try
:
try
:
...
@@ -676,19 +612,12 @@ class DeepSpeech2ExportTester(DeepSpeech2Trainer):
...
@@ -676,19 +612,12 @@ class DeepSpeech2ExportTester(DeepSpeech2Trainer):
except
KeyboardInterrupt
:
except
KeyboardInterrupt
:
exit
(
-
1
)
exit
(
-
1
)
def
run_export
(
self
):
try
:
self
.
export
()
except
KeyboardInterrupt
:
exit
(
-
1
)
def
setup
(
self
):
def
setup
(
self
):
"""Setup the experiment.
"""Setup the experiment.
"""
"""
paddle
.
set_device
(
self
.
args
.
device
)
paddle
.
set_device
(
self
.
args
.
device
)
self
.
setup_output_dir
()
self
.
setup_output_dir
()
#self.setup_checkpointer()
self
.
setup_dataloader
()
self
.
setup_dataloader
()
self
.
setup_model
()
self
.
setup_model
()
...
@@ -711,17 +640,11 @@ class DeepSpeech2ExportTester(DeepSpeech2Trainer):
...
@@ -711,17 +640,11 @@ class DeepSpeech2ExportTester(DeepSpeech2Trainer):
def
setup_model
(
self
):
def
setup_model
(
self
):
super
().
setup_model
()
super
().
setup_model
()
if
self
.
args
.
model_type
==
'online'
:
speedyspeech_config
=
inference
.
Config
(
#inference_dir = "exp/deepspeech2_online/checkpoints/"
self
.
args
.
export_path
+
".pdmodel"
,
#inference_dir = "exp/deepspeech2_online_3rr_1fc_lr_decay0.91_lstm/checkpoints/"
self
.
args
.
export_path
+
".pdiparams"
)
#speedyspeech_config = inference.Config(
if
(
os
.
environ
[
'CUDA_VISIBLE_DEVICES'
].
strip
()
!=
''
):
# str(Path(inference_dir) / "avg_1.jit.pdmodel"),
# str(Path(inference_dir) / "avg_1.jit.pdiparams"))
speedyspeech_config
=
inference
.
Config
(
self
.
args
.
export_path
+
".pdmodel"
,
self
.
args
.
export_path
+
".pdiparams"
)
speedyspeech_config
.
enable_use_gpu
(
100
,
0
)
speedyspeech_config
.
enable_use_gpu
(
100
,
0
)
speedyspeech_config
.
enable_memory_optim
()
speedyspeech_config
.
enable_memory_optim
()
speedyspeech_predictor
=
inference
.
create_predictor
(
speedyspeech_predictor
=
inference
.
create_predictor
(
speedyspeech_config
)
speedyspeech_config
)
self
.
predictor
=
speedyspeech_predictor
self
.
predictor
=
speedyspeech_predictor
deepspeech/models/ds2/deepspeech2.py
浏览文件 @
0d0b5811
...
@@ -280,7 +280,7 @@ class DeepSpeech2InferModel(DeepSpeech2Model):
...
@@ -280,7 +280,7 @@ class DeepSpeech2InferModel(DeepSpeech2Model):
"""
"""
eouts
,
eouts_len
=
self
.
encoder
(
audio
,
audio_len
)
eouts
,
eouts_len
=
self
.
encoder
(
audio
,
audio_len
)
probs
=
self
.
decoder
.
softmax
(
eouts
)
probs
=
self
.
decoder
.
softmax
(
eouts
)
return
probs
return
probs
,
eouts_len
def
export
(
self
):
def
export
(
self
):
static_model
=
paddle
.
jit
.
to_static
(
static_model
=
paddle
.
jit
.
to_static
(
...
...
deepspeech/models/ds2_online/deepspeech2.py
浏览文件 @
0d0b5811
...
@@ -325,24 +325,6 @@ class DeepSpeech2ModelOnline(nn.Layer):
...
@@ -325,24 +325,6 @@ class DeepSpeech2ModelOnline(nn.Layer):
lang_model_path
,
beam_alpha
,
beam_beta
,
beam_size
,
cutoff_prob
,
lang_model_path
,
beam_alpha
,
beam_beta
,
beam_size
,
cutoff_prob
,
cutoff_top_n
,
num_processes
)
cutoff_top_n
,
num_processes
)
@
paddle
.
no_grad
()
def
decode_by_probs
(
self
,
probs
,
probs_len
,
vocab_list
,
decoding_method
,
lang_model_path
,
beam_alpha
,
beam_beta
,
beam_size
,
cutoff_prob
,
cutoff_top_n
,
num_processes
):
# init once
# decoders only accept string encoded in utf-8
self
.
decoder
.
init_decode
(
beam_alpha
=
beam_alpha
,
beam_beta
=
beam_beta
,
lang_model_path
=
lang_model_path
,
vocab_list
=
vocab_list
,
decoding_method
=
decoding_method
)
return
self
.
decoder
.
decode_probs
(
probs
.
numpy
(),
probs_len
,
vocab_list
,
decoding_method
,
lang_model_path
,
beam_alpha
,
beam_beta
,
beam_size
,
cutoff_prob
,
cutoff_top_n
,
num_processes
)
@
classmethod
@
classmethod
def
from_pretrained
(
cls
,
dataloader
,
config
,
checkpoint_path
):
def
from_pretrained
(
cls
,
dataloader
,
config
,
checkpoint_path
):
"""Build a DeepSpeech2Model model from a pretrained model.
"""Build a DeepSpeech2Model model from a pretrained model.
...
...
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