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43b52082
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43b52082
编写于
7月 01, 2021
作者:
H
Hui Zhang
提交者:
GitHub
7月 01, 2021
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差异文件
Merge pull request #629 from PaddlePaddle/align
ctc alignment
上级
717fe1e4
4c9a1f6d
变更
11
隐藏空白更改
内联
并排
Showing
11 changed file
with
397 addition
and
22 deletion
+397
-22
deepspeech/exps/u2/model.py
deepspeech/exps/u2/model.py
+80
-2
deepspeech/utils/ctc_utils.py
deepspeech/utils/ctc_utils.py
+22
-16
deepspeech/utils/text_grid.py
deepspeech/utils/text_grid.py
+127
-0
deepspeech/utils/utility.py
deepspeech/utils/utility.py
+19
-0
examples/aishell/s1/local/align.sh
examples/aishell/s1/local/align.sh
+43
-0
examples/aishell/s1/run.sh
examples/aishell/s1/run.sh
+6
-1
examples/librispeech/s1/local/align.sh
examples/librispeech/s1/local/align.sh
+43
-0
examples/librispeech/s1/run.sh
examples/librispeech/s1/run.sh
+5
-0
examples/tiny/s1/local/align.sh
examples/tiny/s1/local/align.sh
+43
-0
examples/tiny/s1/run.sh
examples/tiny/s1/run.sh
+7
-1
tools/Makefile
tools/Makefile
+2
-2
未找到文件。
deepspeech/exps/u2/model.py
浏览文件 @
43b52082
...
@@ -34,9 +34,12 @@ from deepspeech.models.u2 import U2Model
...
@@ -34,9 +34,12 @@ from deepspeech.models.u2 import U2Model
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.trainer
import
Trainer
from
deepspeech.training.trainer
import
Trainer
from
deepspeech.utils
import
ctc_utils
from
deepspeech.utils
import
error_rate
from
deepspeech.utils
import
error_rate
from
deepspeech.utils
import
layer_tools
from
deepspeech.utils
import
layer_tools
from
deepspeech.utils
import
mp_tools
from
deepspeech.utils
import
mp_tools
from
deepspeech.utils
import
text_grid
from
deepspeech.utils
import
utility
from
deepspeech.utils.log
import
Log
from
deepspeech.utils.log
import
Log
logger
=
Log
(
__name__
).
getlog
()
logger
=
Log
(
__name__
).
getlog
()
...
@@ -278,7 +281,15 @@ class U2Trainer(Trainer):
...
@@ -278,7 +281,15 @@ class U2Trainer(Trainer):
shuffle
=
False
,
shuffle
=
False
,
drop_last
=
False
,
drop_last
=
False
,
collate_fn
=
SpeechCollator
.
from_config
(
config
))
collate_fn
=
SpeechCollator
.
from_config
(
config
))
logger
.
info
(
"Setup train/valid/test Dataloader!"
)
# return text token id
config
.
collator
.
keep_transcription_text
=
False
self
.
align_loader
=
DataLoader
(
test_dataset
,
batch_size
=
config
.
decoding
.
batch_size
,
shuffle
=
False
,
drop_last
=
False
,
collate_fn
=
SpeechCollator
.
from_config
(
config
))
logger
.
info
(
"Setup train/valid/test/align Dataloader!"
)
def
setup_model
(
self
):
def
setup_model
(
self
):
config
=
self
.
config
config
=
self
.
config
...
@@ -353,7 +364,7 @@ class U2Tester(U2Trainer):
...
@@ -353,7 +364,7 @@ class U2Tester(U2Trainer):
decoding_chunk_size
=-
1
,
# decoding chunk size. Defaults to -1.
decoding_chunk_size
=-
1
,
# decoding chunk size. Defaults to -1.
# <0: for decoding, use full chunk.
# <0: for decoding, use full chunk.
# >0: for decoding, use fixed chunk size as set.
# >0: for decoding, use fixed chunk size as set.
# 0: used for training, it's prohibited here.
# 0: used for training, it's prohibited here.
num_decoding_left_chunks
=-
1
,
# number of left chunks for decoding. Defaults to -1.
num_decoding_left_chunks
=-
1
,
# number of left chunks for decoding. Defaults to -1.
simulate_streaming
=
False
,
# simulate streaming inference. Defaults to False.
simulate_streaming
=
False
,
# simulate streaming inference. Defaults to False.
))
))
...
@@ -498,6 +509,73 @@ class U2Tester(U2Trainer):
...
@@ -498,6 +509,73 @@ class U2Tester(U2Trainer):
except
KeyboardInterrupt
:
except
KeyboardInterrupt
:
sys
.
exit
(
-
1
)
sys
.
exit
(
-
1
)
@
paddle
.
no_grad
()
def
align
(
self
):
if
self
.
config
.
decoding
.
batch_size
>
1
:
logger
.
fatal
(
'alignment mode must be running with batch_size == 1'
)
sys
.
exit
(
1
)
# xxx.align
assert
self
.
args
.
result_file
and
self
.
args
.
result_file
.
endswith
(
'.align'
)
self
.
model
.
eval
()
logger
.
info
(
f
"Align Total Examples:
{
len
(
self
.
align_loader
.
dataset
)
}
"
)
stride_ms
=
self
.
align_loader
.
collate_fn
.
stride_ms
token_dict
=
self
.
align_loader
.
collate_fn
.
vocab_list
with
open
(
self
.
args
.
result_file
,
'w'
)
as
fout
:
# one example in batch
for
i
,
batch
in
enumerate
(
self
.
align_loader
):
key
,
feat
,
feats_length
,
target
,
target_length
=
batch
# 1. Encoder
encoder_out
,
encoder_mask
=
self
.
model
.
_forward_encoder
(
feat
,
feats_length
)
# (B, maxlen, encoder_dim)
maxlen
=
encoder_out
.
size
(
1
)
ctc_probs
=
self
.
model
.
ctc
.
log_softmax
(
encoder_out
)
# (1, maxlen, vocab_size)
# 2. alignment
ctc_probs
=
ctc_probs
.
squeeze
(
0
)
target
=
target
.
squeeze
(
0
)
alignment
=
ctc_utils
.
forced_align
(
ctc_probs
,
target
)
logger
.
info
(
"align ids"
,
key
[
0
],
alignment
)
fout
.
write
(
'{} {}
\n
'
.
format
(
key
[
0
],
alignment
))
# 3. gen praat
# segment alignment
align_segs
=
text_grid
.
segment_alignment
(
alignment
)
logger
.
info
(
"align tokens"
,
key
[
0
],
align_segs
)
# IntervalTier, List["start end token\n"]
subsample
=
utility
.
get_subsample
(
self
.
config
)
tierformat
=
text_grid
.
align_to_tierformat
(
align_segs
,
subsample
,
token_dict
)
# write tier
align_output_path
=
os
.
path
.
join
(
os
.
path
.
dirname
(
self
.
args
.
result_file
),
"align"
)
tier_path
=
os
.
path
.
join
(
align_output_path
,
key
[
0
]
+
".tier"
)
with
open
(
tier_path
,
'w'
)
as
f
:
f
.
writelines
(
tierformat
)
# write textgrid
textgrid_path
=
os
.
path
.
join
(
align_output_path
,
key
[
0
]
+
".TextGrid"
)
second_per_frame
=
1.
/
(
1000.
/
stride_ms
)
# 25ms window, 10ms stride
second_per_example
=
(
len
(
alignment
)
+
1
)
*
subsample
*
second_per_frame
text_grid
.
generate_textgrid
(
maxtime
=
second_per_example
,
intervals
=
tierformat
,
output
=
textgrid_path
)
def
run_align
(
self
):
self
.
resume_or_scratch
()
try
:
self
.
align
()
except
KeyboardInterrupt
:
sys
.
exit
(
-
1
)
def
load_inferspec
(
self
):
def
load_inferspec
(
self
):
"""infer model and input spec.
"""infer model and input spec.
...
...
deepspeech/utils/ctc_utils.py
浏览文件 @
43b52082
...
@@ -38,21 +38,23 @@ def remove_duplicates_and_blank(hyp: List[int], blank_id=0) -> List[int]:
...
@@ -38,21 +38,23 @@ def remove_duplicates_and_blank(hyp: List[int], blank_id=0) -> List[int]:
new_hyp
:
List
[
int
]
=
[]
new_hyp
:
List
[
int
]
=
[]
cur
=
0
cur
=
0
while
cur
<
len
(
hyp
):
while
cur
<
len
(
hyp
):
# add non-blank into new_hyp
if
hyp
[
cur
]
!=
blank_id
:
if
hyp
[
cur
]
!=
blank_id
:
new_hyp
.
append
(
hyp
[
cur
])
new_hyp
.
append
(
hyp
[
cur
])
# skip repeat label
prev
=
cur
prev
=
cur
while
cur
<
len
(
hyp
)
and
hyp
[
cur
]
==
hyp
[
prev
]:
while
cur
<
len
(
hyp
)
and
hyp
[
cur
]
==
hyp
[
prev
]:
cur
+=
1
cur
+=
1
return
new_hyp
return
new_hyp
def
insert_blank
(
label
:
np
.
ndarray
,
blank_id
:
int
=
0
):
def
insert_blank
(
label
:
np
.
ndarray
,
blank_id
:
int
=
0
)
->
np
.
ndarray
:
"""Insert blank token between every two label token.
"""Insert blank token between every two label token.
"abcdefg" -> "-a-b-c-d-e-f-g-"
"abcdefg" -> "-a-b-c-d-e-f-g-"
Args:
Args:
label ([np.ndarray]): label ids, (L).
label ([np.ndarray]): label ids,
List[int],
(L).
blank_id (int, optional): blank id. Defaults to 0.
blank_id (int, optional): blank id. Defaults to 0.
Returns:
Returns:
...
@@ -61,13 +63,13 @@ def insert_blank(label: np.ndarray, blank_id: int=0):
...
@@ -61,13 +63,13 @@ def insert_blank(label: np.ndarray, blank_id: int=0):
label
=
np
.
expand_dims
(
label
,
1
)
#[L, 1]
label
=
np
.
expand_dims
(
label
,
1
)
#[L, 1]
blanks
=
np
.
zeros
((
label
.
shape
[
0
],
1
),
dtype
=
np
.
int64
)
+
blank_id
blanks
=
np
.
zeros
((
label
.
shape
[
0
],
1
),
dtype
=
np
.
int64
)
+
blank_id
label
=
np
.
concatenate
([
blanks
,
label
],
axis
=
1
)
#[L, 2]
label
=
np
.
concatenate
([
blanks
,
label
],
axis
=
1
)
#[L, 2]
label
=
label
.
reshape
(
-
1
)
#[2L]
label
=
label
.
reshape
(
-
1
)
#[2L]
, -l-l-l
label
=
np
.
append
(
label
,
label
[
0
])
#[2L + 1]
label
=
np
.
append
(
label
,
label
[
0
])
#[2L + 1]
, -l-l-l-
return
label
return
label
def
forced_align
(
ctc_probs
:
paddle
.
Tensor
,
y
:
paddle
.
Tensor
,
def
forced_align
(
ctc_probs
:
paddle
.
Tensor
,
y
:
paddle
.
Tensor
,
blank_id
=
0
)
->
list
:
blank_id
=
0
)
->
List
[
int
]
:
"""ctc forced alignment.
"""ctc forced alignment.
https://distill.pub/2017/ctc/
https://distill.pub/2017/ctc/
...
@@ -77,23 +79,25 @@ def forced_align(ctc_probs: paddle.Tensor, y: paddle.Tensor,
...
@@ -77,23 +79,25 @@ def forced_align(ctc_probs: paddle.Tensor, y: paddle.Tensor,
y (paddle.Tensor): label id sequence tensor, 1d tensor (L)
y (paddle.Tensor): label id sequence tensor, 1d tensor (L)
blank_id (int): blank symbol index
blank_id (int): blank symbol index
Returns:
Returns:
paddle.Tensor
: best alignment result, (T).
List[int]
: best alignment result, (T).
"""
"""
y_insert_blank
=
insert_blank
(
y
,
blank_id
)
y_insert_blank
=
insert_blank
(
y
,
blank_id
)
#(2L+1)
log_alpha
=
paddle
.
zeros
(
log_alpha
=
paddle
.
zeros
(
(
ctc_probs
.
size
(
0
),
len
(
y_insert_blank
)))
#(T, 2L+1)
(
ctc_probs
.
size
(
0
),
len
(
y_insert_blank
)))
#(T, 2L+1)
log_alpha
=
log_alpha
-
float
(
'inf'
)
# log of zero
log_alpha
=
log_alpha
-
float
(
'inf'
)
# log of zero
# TODO(Hui Zhang): zeros not support paddle.int16
state_path
=
(
paddle
.
zeros
(
state_path
=
(
paddle
.
zeros
(
(
ctc_probs
.
size
(
0
),
len
(
y_insert_blank
)),
dtype
=
paddle
.
int
16
)
-
1
(
ctc_probs
.
size
(
0
),
len
(
y_insert_blank
)),
dtype
=
paddle
.
int
32
)
-
1
)
# state path
)
# state path
, Tuple((T, 2L+1))
# init start state
# init start state
log_alpha
[
0
,
0
]
=
ctc_probs
[
0
][
y_insert_blank
[
0
]]
# Sb
# TODO(Hui Zhang): VarBase.__getitem__() not support np.int64
log_alpha
[
0
,
1
]
=
ctc_probs
[
0
][
y_insert_blank
[
1
]]
# Snb
log_alpha
[
0
,
0
]
=
ctc_probs
[
0
][
int
(
y_insert_blank
[
0
])]
# State-b, Sb
log_alpha
[
0
,
1
]
=
ctc_probs
[
0
][
int
(
y_insert_blank
[
1
])]
# State-nb, Snb
for
t
in
range
(
1
,
ctc_probs
.
size
(
0
)):
for
t
in
range
(
1
,
ctc_probs
.
size
(
0
)):
# T
for
s
in
range
(
len
(
y_insert_blank
)):
for
s
in
range
(
len
(
y_insert_blank
)):
# 2L+1
if
y_insert_blank
[
s
]
==
blank_id
or
s
<
2
or
y_insert_blank
[
if
y_insert_blank
[
s
]
==
blank_id
or
s
<
2
or
y_insert_blank
[
s
]
==
y_insert_blank
[
s
-
2
]:
s
]
==
y_insert_blank
[
s
-
2
]:
candidates
=
paddle
.
to_tensor
(
candidates
=
paddle
.
to_tensor
(
...
@@ -106,11 +110,13 @@ def forced_align(ctc_probs: paddle.Tensor, y: paddle.Tensor,
...
@@ -106,11 +110,13 @@ def forced_align(ctc_probs: paddle.Tensor, y: paddle.Tensor,
log_alpha
[
t
-
1
,
s
-
2
],
log_alpha
[
t
-
1
,
s
-
2
],
])
])
prev_state
=
[
s
,
s
-
1
,
s
-
2
]
prev_state
=
[
s
,
s
-
1
,
s
-
2
]
log_alpha
[
t
,
s
]
=
paddle
.
max
(
candidates
)
+
ctc_probs
[
t
][
# TODO(Hui Zhang): VarBase.__getitem__() not support np.int64
y_insert_blank
[
s
]]
log_alpha
[
t
,
s
]
=
paddle
.
max
(
candidates
)
+
ctc_probs
[
t
][
int
(
y_insert_blank
[
s
])]
state_path
[
t
,
s
]
=
prev_state
[
paddle
.
argmax
(
candidates
)]
state_path
[
t
,
s
]
=
prev_state
[
paddle
.
argmax
(
candidates
)]
state_seq
=
-
1
*
paddle
.
ones
((
ctc_probs
.
size
(
0
),
1
),
dtype
=
paddle
.
int16
)
# TODO(Hui Zhang): zeros not support paddle.int16
state_seq
=
-
1
*
paddle
.
ones
((
ctc_probs
.
size
(
0
),
1
),
dtype
=
paddle
.
int32
)
candidates
=
paddle
.
to_tensor
([
candidates
=
paddle
.
to_tensor
([
log_alpha
[
-
1
,
len
(
y_insert_blank
)
-
1
],
# Sb
log_alpha
[
-
1
,
len
(
y_insert_blank
)
-
1
],
# Sb
...
...
deepspeech/utils/text_grid.py
0 → 100644
浏览文件 @
43b52082
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from
typing
import
Dict
from
typing
import
List
from
typing
import
Text
import
textgrid
def
segment_alignment
(
alignment
:
List
[
int
],
blank_id
=
0
)
->
List
[
List
[
int
]]:
"""segment ctc alignment ids by continuous blank and repeat label.
Args:
alignment (List[int]): ctc alignment id sequence.
e.g. [0, 0, 0, 1, 1, 1, 2, 0, 0, 3]
blank_id (int, optional): blank id. Defaults to 0.
Returns:
List[List[int]]: token align, segment aligment id sequence.
e.g. [[0, 0, 0, 1, 1, 1], [2], [0, 0, 3]]
"""
# convert alignment to a praat format, which is a doing phonetics
# by computer and helps analyzing alignment
align_segs
=
[]
# get frames level duration for each token
start
=
0
end
=
0
while
end
<
len
(
alignment
):
while
end
<
len
(
alignment
)
and
alignment
[
end
]
==
blank_id
:
# blank
end
+=
1
if
end
==
len
(
alignment
):
align_segs
[
-
1
].
extend
(
alignment
[
start
:])
break
end
+=
1
while
end
<
len
(
alignment
)
and
alignment
[
end
-
1
]
==
alignment
[
end
]:
# repeat label
end
+=
1
align_segs
.
append
(
alignment
[
start
:
end
])
start
=
end
return
align_segs
def
align_to_tierformat
(
align_segs
:
List
[
List
[
int
]],
subsample
:
int
,
token_dict
:
Dict
[
int
,
Text
],
blank_id
=
0
)
->
List
[
Text
]:
"""Generate textgrid.Interval format from alignment segmentations.
Args:
align_segs (List[List[int]]): segmented ctc alignment ids.
subsample (int): 25ms frame_length, 10ms hop_length, 1/subsample
token_dict (Dict[int, Text]): int -> str map.
Returns:
List[Text]: list of textgrid.Interval text, str(start, end, text).
"""
hop_length
=
10
# ms
second_ms
=
1000
# ms
frame_per_second
=
second_ms
/
hop_length
# 25ms frame_length, 10ms hop_length
second_per_frame
=
1.0
/
frame_per_second
begin
=
0
duration
=
0
tierformat
=
[]
for
idx
,
tokens
in
enumerate
(
align_segs
):
token_len
=
len
(
tokens
)
token
=
tokens
[
-
1
]
# time duration in second
duration
=
token_len
*
subsample
*
second_per_frame
if
idx
<
len
(
align_segs
)
-
1
:
print
(
f
"
{
begin
:.
2
f
}
{
begin
+
duration
:.
2
f
}
{
token_dict
[
token
]
}
"
)
tierformat
.
append
(
f
"
{
begin
:.
2
f
}
{
begin
+
duration
:.
2
f
}
{
token_dict
[
token
]
}
\n
"
)
else
:
for
i
in
tokens
:
if
i
!=
blank_id
:
token
=
i
break
print
(
f
"
{
begin
:.
2
f
}
{
begin
+
duration
:.
2
f
}
{
token_dict
[
token
]
}
"
)
tierformat
.
append
(
f
"
{
begin
:.
2
f
}
{
begin
+
duration
:.
2
f
}
{
token_dict
[
token
]
}
\n
"
)
begin
=
begin
+
duration
return
tierformat
def
generate_textgrid
(
maxtime
:
float
,
intervals
:
List
[
Text
],
output
:
Text
,
name
:
Text
=
'ali'
)
->
None
:
"""Create alignment textgrid file.
Args:
maxtime (float): audio duartion.
intervals (List[Text]): ctc output alignment. e.g. "start-time end-time word" per item.
output (Text): textgrid filepath.
name (Text, optional): tier or layer name. Defaults to 'ali'.
"""
# Download Praat: https://www.fon.hum.uva.nl/praat/
avg_interval
=
maxtime
/
(
len
(
intervals
)
+
1
)
print
(
f
"average second/token:
{
avg_interval
}
"
)
margin
=
0.0001
tg
=
textgrid
.
TextGrid
(
maxTime
=
maxtime
)
tier
=
textgrid
.
IntervalTier
(
name
=
name
,
maxTime
=
maxtime
)
i
=
0
for
dur
in
intervals
:
s
,
e
,
text
=
dur
.
split
()
tier
.
add
(
minTime
=
float
(
s
)
+
margin
,
maxTime
=
float
(
e
),
mark
=
text
)
tg
.
append
(
tier
)
tg
.
write
(
output
)
print
(
"successfully generator textgrid {}."
.
format
(
output
))
deepspeech/utils/utility.py
浏览文件 @
43b52082
...
@@ -79,3 +79,22 @@ def log_add(args: List[int]) -> float:
...
@@ -79,3 +79,22 @@ def log_add(args: List[int]) -> float:
a_max
=
max
(
args
)
a_max
=
max
(
args
)
lsp
=
math
.
log
(
sum
(
math
.
exp
(
a
-
a_max
)
for
a
in
args
))
lsp
=
math
.
log
(
sum
(
math
.
exp
(
a
-
a_max
)
for
a
in
args
))
return
a_max
+
lsp
return
a_max
+
lsp
def
get_subsample
(
config
):
"""Subsample rate from config.
Args:
config (yacs.config.CfgNode): yaml config
Returns:
int: subsample rate.
"""
input_layer
=
config
[
"model"
][
"encoder_conf"
][
"input_layer"
]
assert
input_layer
in
[
"conv2d"
,
"conv2d6"
,
"conv2d8"
]
if
input_layer
==
"conv2d"
:
return
4
elif
input_layer
==
"conv2d6"
:
return
6
elif
input_layer
==
"conv2d8"
:
return
8
examples/aishell/s1/local/align.sh
0 → 100755
浏览文件 @
43b52082
#! /usr/bin/env bash
if
[
$#
!=
2
]
;
then
echo
"usage:
${
0
}
config_path ckpt_path_prefix"
exit
-1
fi
ngpu
=
$(
echo
$CUDA_VISIBLE_DEVICES
|
awk
-F
","
'{print NF}'
)
echo
"using
$ngpu
gpus..."
device
=
gpu
if
[
ngpu
==
0
]
;
then
device
=
cpu
fi
config_path
=
$1
ckpt_prefix
=
$2
ckpt_name
=
$(
basename
${
ckpt_prefxi
}
)
mkdir
-p
exp
batch_size
=
1
output_dir
=
${
ckpt_prefix
}
mkdir
-p
${
output_dir
}
# align dump in `result_file`
# .tier, .TextGrid dump in `dir of result_file`
python3
-u
${
BIN_DIR
}
/alignment.py
\
--device
${
device
}
\
--nproc
1
\
--config
${
config_path
}
\
--result_file
${
output_dir
}
/
${
type
}
.align
\
--checkpoint_path
${
ckpt_prefix
}
\
--opts
decoding.batch_size
${
batch_size
}
if
[
$?
-ne
0
]
;
then
echo
"Failed in ctc alignment!"
exit
1
fi
exit
0
examples/aishell/s1/run.sh
浏览文件 @
43b52082
...
@@ -30,10 +30,15 @@ fi
...
@@ -30,10 +30,15 @@ fi
if
[
${
stage
}
-le
3
]
&&
[
${
stop_stage
}
-ge
3
]
;
then
if
[
${
stage
}
-le
3
]
&&
[
${
stop_stage
}
-ge
3
]
;
then
# test ckpt avg_n
# test ckpt avg_n
CUDA_VISIBLE_DEVICES
=
4
./local/test.sh
${
conf_path
}
exp/
${
ckpt
}
/checkpoints/
${
avg_ckpt
}
||
exit
-1
CUDA_VISIBLE_DEVICES
=
0
./local/test.sh
${
conf_path
}
exp/
${
ckpt
}
/checkpoints/
${
avg_ckpt
}
||
exit
-1
fi
fi
if
[
${
stage
}
-le
4
]
&&
[
${
stop_stage
}
-ge
4
]
;
then
if
[
${
stage
}
-le
4
]
&&
[
${
stop_stage
}
-ge
4
]
;
then
# ctc alignment of test data
CUDA_VISIBLE_DEVICES
=
0 ./local/align.sh
${
conf_path
}
exp/
${
ckpt
}
/checkpoints/
${
avg_ckpt
}
||
exit
-1
fi
if
[
${
stage
}
-le
5
]
&&
[
${
stop_stage
}
-ge
5
]
;
then
# export ckpt avg_n
# export ckpt avg_n
CUDA_VISIBLE_DEVICES
=
./local/export.sh
${
conf_path
}
exp/
${
ckpt
}
/checkpoints/
${
avg_ckpt
}
exp/
${
ckpt
}
/checkpoints/
${
avg_ckpt
}
.jit
CUDA_VISIBLE_DEVICES
=
./local/export.sh
${
conf_path
}
exp/
${
ckpt
}
/checkpoints/
${
avg_ckpt
}
exp/
${
ckpt
}
/checkpoints/
${
avg_ckpt
}
.jit
fi
fi
examples/librispeech/s1/local/align.sh
0 → 100755
浏览文件 @
43b52082
#! /usr/bin/env bash
if
[
$#
!=
2
]
;
then
echo
"usage:
${
0
}
config_path ckpt_path_prefix"
exit
-1
fi
ngpu
=
$(
echo
$CUDA_VISIBLE_DEVICES
|
awk
-F
","
'{print NF}'
)
echo
"using
$ngpu
gpus..."
device
=
gpu
if
[
ngpu
==
0
]
;
then
device
=
cpu
fi
config_path
=
$1
ckpt_prefix
=
$2
ckpt_name
=
$(
basename
${
ckpt_prefxi
}
)
mkdir
-p
exp
batch_size
=
1
output_dir
=
${
ckpt_prefix
}
mkdir
-p
${
output_dir
}
# align dump in `result_file`
# .tier, .TextGrid dump in `dir of result_file`
python3
-u
${
BIN_DIR
}
/alignment.py
\
--device
${
device
}
\
--nproc
1
\
--config
${
config_path
}
\
--result_file
${
output_dir
}
/
${
type
}
.align
\
--checkpoint_path
${
ckpt_prefix
}
\
--opts
decoding.batch_size
${
batch_size
}
if
[
$?
-ne
0
]
;
then
echo
"Failed in ctc alignment!"
exit
1
fi
exit
0
examples/librispeech/s1/run.sh
浏览文件 @
43b52082
...
@@ -33,6 +33,11 @@ if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then
...
@@ -33,6 +33,11 @@ if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then
fi
fi
if
[
${
stage
}
-le
4
]
&&
[
${
stop_stage
}
-ge
4
]
;
then
if
[
${
stage
}
-le
4
]
&&
[
${
stop_stage
}
-ge
4
]
;
then
# ctc alignment of test data
CUDA_VISIBLE_DEVICES
=
0 ./local/align.sh
${
conf_path
}
exp/
${
ckpt
}
/checkpoints/
${
avg_ckpt
}
||
exit
-1
fi
if
[
${
stage
}
-le
5
]
&&
[
${
stop_stage
}
-ge
5
]
;
then
# export ckpt avg_n
# export ckpt avg_n
CUDA_VISIBLE_DEVICES
=
./local/export.sh
${
conf_path
}
exp/
${
ckpt
}
/checkpoints/
${
avg_ckpt
}
exp/
${
ckpt
}
/checkpoints/
${
avg_ckpt
}
.jit
CUDA_VISIBLE_DEVICES
=
./local/export.sh
${
conf_path
}
exp/
${
ckpt
}
/checkpoints/
${
avg_ckpt
}
exp/
${
ckpt
}
/checkpoints/
${
avg_ckpt
}
.jit
fi
fi
examples/tiny/s1/local/align.sh
0 → 100755
浏览文件 @
43b52082
#! /usr/bin/env bash
if
[
$#
!=
2
]
;
then
echo
"usage:
${
0
}
config_path ckpt_path_prefix"
exit
-1
fi
ngpu
=
$(
echo
$CUDA_VISIBLE_DEVICES
|
awk
-F
","
'{print NF}'
)
echo
"using
$ngpu
gpus..."
device
=
gpu
if
[
ngpu
==
0
]
;
then
device
=
cpu
fi
config_path
=
$1
ckpt_prefix
=
$2
ckpt_name
=
$(
basename
${
ckpt_prefxi
}
)
mkdir
-p
exp
batch_size
=
1
output_dir
=
${
ckpt_prefix
}
mkdir
-p
${
output_dir
}
# align dump in `result_file`
# .tier, .TextGrid dump in `dir of result_file`
python3
-u
${
BIN_DIR
}
/alignment.py
\
--device
${
device
}
\
--nproc
1
\
--config
${
config_path
}
\
--result_file
${
output_dir
}
/
${
type
}
.align
\
--checkpoint_path
${
ckpt_prefix
}
\
--opts
decoding.batch_size
${
batch_size
}
if
[
$?
-ne
0
]
;
then
echo
"Failed in ctc alignment!"
exit
1
fi
exit
0
examples/tiny/s1/run.sh
浏览文件 @
43b52082
...
@@ -34,6 +34,12 @@ if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then
...
@@ -34,6 +34,12 @@ if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then
fi
fi
if
[
${
stage
}
-le
4
]
&&
[
${
stop_stage
}
-ge
4
]
;
then
if
[
${
stage
}
-le
4
]
&&
[
${
stop_stage
}
-ge
4
]
;
then
# ctc alignment of test data
CUDA_VISIBLE_DEVICES
=
0 ./local/align.sh
${
conf_path
}
exp/
${
ckpt
}
/checkpoints/
${
avg_ckpt
}
||
exit
-1
fi
if
[
${
stage
}
-le
5
]
&&
[
${
stop_stage
}
-ge
5
]
;
then
# export ckpt avg_n
# export ckpt avg_n
CUDA_VISIBLE_DEVICES
=
./local/export.sh
${
conf_path
}
exp/
${
ckpt
}
/checkpoints/
${
avg_ckpt
}
exp/
${
ckpt
}
/checkpoints/
${
avg_ckpt
}
.jit
CUDA_VISIBLE_DEVICES
=
./local/export.sh
${
conf_path
}
exp/
${
ckpt
}
/checkpoints/
${
avg_ckpt
}
exp/
${
ckpt
}
/checkpoints/
${
avg_ckpt
}
.jit
fi
fi
tools/Makefile
浏览文件 @
43b52082
...
@@ -19,7 +19,7 @@ kenlm.done:
...
@@ -19,7 +19,7 @@ kenlm.done:
apt-get
install
-y
gcc-5 g++-5
&&
update-alternatives
--install
/usr/bin/gcc gcc /usr/bin/gcc-5 50
&&
update-alternatives
--install
/usr/bin/g++ g++ /usr/bin/g++-5 50
apt-get
install
-y
gcc-5 g++-5
&&
update-alternatives
--install
/usr/bin/gcc gcc /usr/bin/gcc-5 50
&&
update-alternatives
--install
/usr/bin/g++ g++ /usr/bin/g++-5 50
test
-d
kenlm
||
wget
-O
- https://kheafield.com/code/kenlm.tar.gz |
tar
xz
test
-d
kenlm
||
wget
-O
- https://kheafield.com/code/kenlm.tar.gz |
tar
xz
mkdir
-p
kenlm/build
&&
cd
kenlm/build
&&
cmake ..
&&
make
-j4
&&
make
install
mkdir
-p
kenlm/build
&&
cd
kenlm/build
&&
cmake ..
&&
make
-j4
&&
make
install
cd
kenlm
&&
python setup.py
install
source
venv/bin/activate
;
cd
kenlm
&&
python setup.py
install
touch
kenlm.done
touch
kenlm.done
sox.done
:
sox.done
:
...
@@ -32,4 +32,4 @@ sox.done:
...
@@ -32,4 +32,4 @@ sox.done:
soxbindings.done
:
soxbindings.done
:
test
-d
soxbindings
||
git clone https://github.com/pseeth/soxbindings.git
test
-d
soxbindings
||
git clone https://github.com/pseeth/soxbindings.git
source
venv/bin/activate
;
cd
soxbindings
&&
python setup.py
install
source
venv/bin/activate
;
cd
soxbindings
&&
python setup.py
install
touch
soxbindings.done
touch
soxbindings.done
\ No newline at end of file
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