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f2a42bd3
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f2a42bd3
编写于
4月 28, 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
more avg and test info
上级
c693bb08
变更
4
隐藏空白更改
内联
并排
Showing
4 changed file
with
51 addition
and
10 deletion
+51
-10
deepspeech/__init__.py
deepspeech/__init__.py
+1
-0
deepspeech/exps/u2/model.py
deepspeech/exps/u2/model.py
+27
-0
examples/aishell/s1/local/avg.sh
examples/aishell/s1/local/avg.sh
+2
-2
utils/avg_model.py
utils/avg_model.py
+21
-8
未找到文件。
deepspeech/__init__.py
浏览文件 @
f2a42bd3
...
...
@@ -125,6 +125,7 @@ if not hasattr(paddle, 'cat'):
def
item
(
x
:
paddle
.
Tensor
):
return
x
.
numpy
().
item
()
if
not
hasattr
(
paddle
.
Tensor
,
'item'
):
logger
.
warn
(
"override item of paddle.Tensor if exists or register, remove this when fixed!"
...
...
deepspeech/exps/u2/model.py
浏览文件 @
f2a42bd3
...
...
@@ -12,6 +12,8 @@
# See the License for the specific language governing permissions and
# limitations under the License.
"""Contains U2 model."""
import
json
import
os
import
sys
import
time
from
collections
import
defaultdict
...
...
@@ -439,6 +441,31 @@ class U2Tester(U2Trainer):
error_rate_type
,
num_ins
,
num_ins
,
errors_sum
/
len_refs
)
logger
.
info
(
msg
)
# test meta results
err_meta_path
=
os
.
path
.
splitext
(
self
.
args
.
checkpoint_path
)[
0
]
+
'.err'
err_type_str
=
"{}"
.
format
(
error_rate_type
)
with
open
(
err_meta_path
,
'w'
)
as
f
:
data
=
json
.
dumps
({
"epoch"
:
self
.
epoch
,
"step"
:
self
.
iteration
,
"rtf"
:
rtf
,
error_rate_type
:
errors_sum
/
len_refs
,
"dataset_hour"
:
(
num_frames
*
stride_ms
)
/
1000.0
/
3600.0
,
"process_hour"
:
num_time
/
1000.0
/
3600.0
,
"num_examples"
:
num_ins
,
"err_sum"
:
errors_sum
,
"ref_len"
:
len_refs
,
})
f
.
write
(
data
+
'
\n
'
)
def
run_test
(
self
):
self
.
resume_or_scratch
()
try
:
...
...
examples/aishell/s1/local/avg.sh
浏览文件 @
f2a42bd3
...
...
@@ -7,7 +7,7 @@ fi
ckpt_path
=
${
1
}
average_num
=
${
2
}
decode_checkpoint
=
${
ckpt_path
}
/avg_
${
average_num
}
.p
t
decode_checkpoint
=
${
ckpt_path
}
/avg_
${
average_num
}
.p
dparams
python3
-u
${
MAIN_ROOT
}
/utils/avg_model.py
\
--dst_model
${
decode_checkpoint
}
\
...
...
@@ -21,4 +21,4 @@ if [ $? -ne 0 ]; then
fi
exit
0
\ No newline at end of file
exit
0
utils/avg_model.py
浏览文件 @
f2a42bd3
...
...
@@ -21,14 +21,15 @@ import paddle
def
main
(
args
):
checkpoints
=
[]
val_scores
=
[]
beat_val_scores
=
[]
selected_epochs
=
[]
if
args
.
val_best
:
jsons
=
glob
.
glob
(
f
'
{
args
.
ckpt_dir
}
/[!train]*.json'
)
for
y
in
jsons
:
dic_json
=
json
.
load
(
y
)
loss
=
dic_json
[
'valid_loss'
]
with
open
(
y
,
'r'
)
as
f
:
dic_json
=
json
.
load
(
f
)
loss
=
dic_json
[
'val_loss'
]
epoch
=
dic_json
[
'epoch'
]
if
epoch
>=
args
.
min_epoch
and
epoch
<=
args
.
max_epoch
:
val_scores
.
append
((
epoch
,
loss
))
...
...
@@ -40,9 +41,11 @@ def main(args):
args
.
ckpt_dir
+
'/{}.pdparams'
.
format
(
int
(
epoch
))
for
epoch
in
sorted_val_scores
[:
args
.
num
,
0
]
]
print
(
"best val scores = "
+
str
(
sorted_val_scores
[:
args
.
num
,
1
]))
print
(
"selected epochs = "
+
str
(
sorted_val_scores
[:
args
.
num
,
0
].
astype
(
np
.
int64
)))
beat_val_scores
=
sorted_val_scores
[:
args
.
num
,
1
]
selected_epochs
=
sorted_val_scores
[:
args
.
num
,
0
].
astype
(
np
.
int64
)
print
(
"best val scores = "
+
str
(
beat_val_scores
))
print
(
"selected epochs = "
+
str
(
selected_epochs
))
else
:
path_list
=
glob
.
glob
(
f
'
{
args
.
ckpt_dir
}
/[!avg][!final]*.pdparams'
)
path_list
=
sorted
(
path_list
,
key
=
os
.
path
.
getmtime
)
...
...
@@ -64,11 +67,21 @@ def main(args):
# average
for
k
in
avg
.
keys
():
if
avg
[
k
]
is
not
None
:
avg
[
k
]
=
paddle
.
divide
(
avg
[
k
],
num
)
avg
[
k
]
/=
num
paddle
.
save
(
avg
,
args
.
dst_model
)
print
(
f
'Saving to
{
args
.
dst_model
}
'
)
meta_path
=
os
.
path
.
splitext
(
args
.
dst_model
)[
0
]
+
'.avg.json'
with
open
(
meta_path
,
'w'
)
as
f
:
data
=
json
.
dumps
({
"avg_ckpt"
:
args
.
dst_model
,
"ckpt"
:
path_list
,
"epoch"
:
selected_epochs
.
tolist
(),
"val_loss"
:
beat_val_scores
.
tolist
(),
})
f
.
write
(
data
+
"
\n
"
)
if
__name__
==
'__main__'
:
parser
=
argparse
.
ArgumentParser
(
description
=
'average model'
)
...
...
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