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cdc85209
编写于
11月 30, 2021
作者:
H
huangyuxin
浏览文件
操作
浏览文件
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电子邮件补丁
差异文件
add the infer
上级
e9798498
变更
1
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Showing
1 changed file
with
212 addition
and
27 deletion
+212
-27
paddlespeech/cli/s2t/infer.py
paddlespeech/cli/s2t/infer.py
+212
-27
未找到文件。
paddlespeech/cli/s2t/infer.py
浏览文件 @
cdc85209
...
...
@@ -13,17 +13,24 @@
# limitations under the License.
import
argparse
import
os
import
sys
from
typing
import
List
from
typing
import
Optional
from
typing
import
Union
import
soundfile
import
paddle
from
..executor
import
BaseExecutor
from
..utils
import
cli_register
from
..utils
import
download_and_decompress
from
..utils
import
logger
from
..utils
import
MODEL_HOME
from
paddlespeech.cli.executor
import
BaseExecutor
from
paddlespeech.cli.utils
import
cli_register
from
paddlespeech.cli.utils
import
download_and_decompress
from
paddlespeech.cli.utils
import
logger
from
paddlespeech.cli.utils
import
MODEL_HOME
from
paddlespeech.s2t.exps.u2.config
import
get_cfg_defaults
from
paddlespeech.s2t.frontend.featurizer.text_featurizer
import
TextFeaturizer
from
paddlespeech.s2t.io.collator
import
SpeechCollator
from
paddlespeech.s2t.transform.transformation
import
Transformation
from
paddlespeech.s2t.utils.dynamic_import
import
dynamic_import
from
paddlespeech.s2t.utils.utility
import
UpdateConfig
__all__
=
[
'S2TExecutor'
]
...
...
@@ -33,9 +40,44 @@ pretrained_models = {
'https://paddlespeech.bj.bcebos.com/s2t/wenetspeech/conformer.model.tar.gz'
,
'md5'
:
'54e7a558a6e020c2f5fb224874943f97'
,
'cfg_path'
:
'conf/conformer.yaml'
,
'ckpt_path'
:
'exp/conformer/checkpoints/wenetspeech'
,
}
}
model_alias
=
{
"ds2_offline"
:
"paddlespeech.s2t.models.ds2:DeepSpeech2Model"
,
"ds2_online"
:
"paddlespeech.s2t.models.ds2_online:DeepSpeech2ModelOnline"
,
"conformer"
:
"paddlespeech.s2t.models.u2:U2Model"
,
"transformer"
:
"paddlespeech.s2t.models.u2:U2Model"
,
"wenetspeech"
:
"paddlespeech.s2t.models.u2:U2Model"
,
}
pretrain_model_alias
=
{
"ds2_online_zn"
:
[
"https://paddlespeech.bj.bcebos.com/s2t/aishell/asr0/aishell_ds2_online_cer8.00_release.tar.gz"
,
""
,
""
],
"ds2_offline_zn"
:
[
"https://paddlespeech.bj.bcebos.com/s2t/aishell/asr0/ds2.model.tar.gz"
,
""
,
""
],
"transformer_zn"
:
[
"https://paddlespeech.bj.bcebos.com/s2t/aishell/asr1/transformer.model.tar.gz"
,
""
,
""
],
"conformer_zn"
:
[
"https://paddlespeech.bj.bcebos.com/s2t/wenetspeech/conformer.model.tar.gz"
,
""
,
""
],
"wenetspeech_zn"
:
[
"https://paddlespeech.bj.bcebos.com/s2t/wenetspeech/conformer.model.tar.gz"
,
"conf/conformer.yaml"
,
"exp/conformer/checkpoints/wenetspeech"
],
}
@
cli_register
(
name
=
'paddlespeech.s2t'
,
description
=
'Speech to text infer command.'
)
...
...
@@ -63,7 +105,10 @@ class S2TExecutor(BaseExecutor):
default
=
None
,
help
=
'Checkpoint file of model.'
)
self
.
parser
.
add_argument
(
'--input'
,
type
=
str
,
help
=
'Audio file to recognize.'
)
'--input'
,
type
=
str
,
default
=
"../Downloads/asr-demo-1.wav"
,
help
=
'Audio file to recognize.'
)
self
.
parser
.
add_argument
(
'--device'
,
type
=
str
,
...
...
@@ -80,8 +125,10 @@ class S2TExecutor(BaseExecutor):
res_path
=
os
.
path
.
join
(
MODEL_HOME
,
tag
)
decompressed_path
=
download_and_decompress
(
pretrained_models
[
tag
],
res_path
)
decompressed_path
=
os
.
path
.
abspath
(
decompressed_path
)
logger
.
info
(
'Use pretrained model stored in: {}'
.
format
(
decompressed_path
))
return
decompressed_path
def
_init_from_path
(
self
,
...
...
@@ -93,56 +140,194 @@ class S2TExecutor(BaseExecutor):
Init model and other resources from a specific path.
"""
if
cfg_path
is
None
or
ckpt_path
is
None
:
res_path
=
self
.
_get_pretrained_path
(
model_type
+
'_'
+
lang
)
# wenetspeech_zh
cfg_path
=
os
.
path
.
join
(
res_path
,
'conf/conformer.yaml'
)
ckpt_path
=
os
.
path
.
join
(
res_path
,
'exp/conformer/checkpoints/wenetspeech.pdparams'
)
tag
=
model_type
+
'_'
+
lang
res_path
=
self
.
_get_pretrained_path
(
tag
)
# wenetspeech_zh
self
.
cfg_path
=
os
.
path
.
join
(
res_path
,
pretrained_models
[
tag
][
'cfg_path'
])
self
.
ckpt_path
=
os
.
path
.
join
(
res_path
,
pretrained_models
[
tag
][
'ckpt_path'
])
logger
.
info
(
res_path
)
logger
.
info
(
cfg_path
)
logger
.
info
(
ckpt_path
)
logger
.
info
(
self
.
cfg_path
)
logger
.
info
(
self
.
ckpt_path
)
else
:
self
.
cfg_path
=
os
.
path
.
abspath
(
cfg_path
)
self
.
ckpt_path
=
os
.
path
.
abspath
(
ckpt_path
)
res_path
=
os
.
path
.
dirname
(
os
.
path
.
dirname
(
os
.
path
.
abspath
(
self
.
cfg_path
)))
# Init body.
pass
os
.
chdir
(
res_path
)
#Init body.
parser_args
=
self
.
parser_args
paddle
.
set_device
(
parser_args
.
device
)
self
.
config
=
get_cfg_defaults
()
self
.
config
.
merge_from_file
(
self
.
cfg_path
)
self
.
config
.
decoding
.
decoding_method
=
"attention_rescoring"
#self.config.freeze()
model_conf
=
self
.
config
.
model
logger
.
info
(
model_conf
)
with
UpdateConfig
(
model_conf
):
if
parser_args
.
model
==
"ds2_online"
or
parser_args
.
model
==
"ds2_offline"
:
self
.
config
.
collator
.
vocab_filepath
=
os
.
path
.
join
(
res_path
,
self
.
config
.
collator
.
vocab_filepath
)
self
.
config
.
collator
.
vocab_filepath
=
os
.
path
.
join
(
res_path
,
self
.
config
.
collator
.
cmvn_path
)
self
.
collate_fn_test
=
SpeechCollator
.
from_config
(
self
.
config
)
model_conf
.
feat_size
=
self
.
collate_fn_test
.
feature_size
model_conf
.
dict_size
=
self
.
text_feature
.
vocab_size
elif
parser_args
.
model
==
"conformer"
or
parser_args
.
model
==
"transformer"
or
parser_args
.
model
==
"wenetspeech"
:
self
.
config
.
collator
.
vocab_filepath
=
os
.
path
.
join
(
res_path
,
self
.
config
.
collator
.
vocab_filepath
)
self
.
text_feature
=
TextFeaturizer
(
unit_type
=
self
.
config
.
collator
.
unit_type
,
vocab_filepath
=
self
.
config
.
collator
.
vocab_filepath
,
spm_model_prefix
=
self
.
config
.
collator
.
spm_model_prefix
)
model_conf
.
input_dim
=
self
.
config
.
collator
.
feat_dim
model_conf
.
output_dim
=
self
.
text_feature
.
vocab_size
else
:
raise
Exception
(
"wrong type"
)
model_class
=
dynamic_import
(
parser_args
.
model
,
model_alias
)
model
=
model_class
.
from_config
(
model_conf
)
self
.
model
=
model
self
.
model
.
eval
()
# load model
params_path
=
self
.
ckpt_path
+
".pdparams"
model_dict
=
paddle
.
load
(
params_path
)
self
.
model
.
set_state_dict
(
model_dict
)
def
preprocess
(
self
,
input
:
Union
[
str
,
os
.
PathLike
]):
"""
Input preprocess and return paddle.Tensor stored in self.input.
Input content can be a text(t2s), a file(s2t, cls) or a streaming(not supported yet).
"""
pass
parser_args
=
self
.
parser_args
config
=
self
.
config
audio_file
=
input
#print("audio_file", audio_file)
logger
.
info
(
"audio_file"
+
audio_file
)
self
.
sr
=
config
.
collator
.
target_sample_rate
# Get the object for feature extraction
if
parser_args
.
model
==
"ds2_online"
or
parser_args
.
model
==
"ds2_offline"
:
audio
,
_
=
collate_fn_test
.
process_utterance
(
audio_file
=
audio_file
,
transcript
=
" "
)
audio_len
=
audio
.
shape
[
0
]
audio
=
paddle
.
to_tensor
(
audio
,
dtype
=
'float32'
)
self
.
audio_len
=
paddle
.
to_tensor
(
audio_len
)
self
.
audio
=
paddle
.
unsqueeze
(
audio
,
axis
=
0
)
self
.
vocab_list
=
collate_fn_test
.
vocab_list
logger
.
info
(
f
"audio feat shape:
{
self
.
audio
.
shape
}
"
)
elif
parser_args
.
model
==
"conformer"
or
parser_args
.
model
==
"transformer"
or
parser_args
.
model
==
"wenetspeech"
:
logger
.
info
(
"get the preprocess conf"
)
preprocess_conf
=
os
.
path
.
join
(
os
.
path
.
dirname
(
os
.
path
.
abspath
(
self
.
cfg_path
)),
"preprocess.yaml"
)
cmvn_path
:
data
/
mean_std
.
json
logger
.
info
(
preprocess_conf
)
preprocess_args
=
{
"train"
:
False
}
preprocessing
=
Transformation
(
preprocess_conf
)
audio
,
sample_rate
=
soundfile
.
read
(
audio_file
,
dtype
=
"int16"
,
always_2d
=
True
)
if
sample_rate
!=
self
.
sr
:
logger
.
error
(
f
"sample rate error:
{
sample_rate
}
, need
{
self
.
sr
}
"
)
sys
.
exit
(
-
1
)
audio
=
audio
[:,
0
]
logger
.
info
(
f
"audio shape:
{
audio
.
shape
}
"
)
# fbank
audio
=
preprocessing
(
audio
,
**
preprocess_args
)
self
.
audio_len
=
paddle
.
to_tensor
(
audio
.
shape
[
0
])
self
.
audio
=
paddle
.
to_tensor
(
audio
,
dtype
=
'float32'
).
unsqueeze
(
axis
=
0
)
logger
.
info
(
f
"audio feat shape:
{
self
.
audio
.
shape
}
"
)
else
:
raise
Exception
(
"wrong type"
)
@
paddle
.
no_grad
()
def
infer
(
self
):
"""
Model inference and result stored in self.output.
"""
cfg
=
self
.
config
.
decoding
parser_args
=
self
.
parser_args
audio
=
self
.
audio
audio_len
=
self
.
audio_len
if
parser_args
.
model
==
"ds2_online"
or
parser_args
.
model
==
"ds2_offline"
:
vocab_list
=
self
.
vocab_list
result_transcripts
=
self
.
model
.
decode
(
audio
,
audio_len
,
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
.
result_transcripts
=
result_transcripts
[
0
]
elif
parser_args
.
model
==
"conformer"
or
parser_args
.
model
==
"transformer"
or
parser_args
.
model
==
"wenetspeech"
:
text_feature
=
self
.
text_feature
result_transcripts
=
self
.
model
.
decode
(
audio
,
audio_len
,
text_feature
=
self
.
text_feature
,
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
,
ctc_weight
=
cfg
.
ctc_weight
,
decoding_chunk_size
=
cfg
.
decoding_chunk_size
,
num_decoding_left_chunks
=
cfg
.
num_decoding_left_chunks
,
simulate_streaming
=
cfg
.
simulate_streaming
)
self
.
result_transcripts
=
result_transcripts
[
0
][
0
]
else
:
raise
Exception
(
"invalid model name"
)
pass
def
postprocess
(
self
)
->
Union
[
str
,
os
.
PathLike
]:
"""
Output postprocess and return human-readable results such as texts and audio files.
"""
pas
s
return
self
.
result_transcript
s
def
execute
(
self
,
argv
:
List
[
str
])
->
bool
:
parser_args
=
self
.
parser
.
parse_args
(
argv
)
print
(
parser_args
)
self
.
parser_args
=
self
.
parser
.
parse_args
(
argv
)
model
=
parser_args
.
model
lang
=
parser_args
.
lang
config
=
parser_args
.
config
ckpt_path
=
parser_args
.
ckpt_path
audio_file
=
parser_args
.
input
device
=
parser_args
.
device
model
=
self
.
parser_args
.
model
lang
=
self
.
parser_args
.
lang
config
=
self
.
parser_args
.
config
ckpt_path
=
self
.
parser_args
.
ckpt_path
audio_file
=
os
.
path
.
abspath
(
self
.
parser_args
.
input
)
device
=
self
.
parser_args
.
device
try
:
self
.
_init_from_path
(
model
,
lang
,
config
,
ckpt_path
)
self
.
preprocess
(
audio_file
)
self
.
infer
()
res
=
self
.
postprocess
()
# Retrieve result of s2t.
print
(
res
)
logger
.
info
(
res
)
return
True
except
Exception
as
e
:
print
(
e
)
return
False
if
__name__
==
"__main__"
:
exe
=
S2TExecutor
()
exe
.
execute
(
''
)
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