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体验新版 GitCode,发现更多精彩内容 >>
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2a095db2
编写于
4月 07, 2022
作者:
X
xiongxinlei
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
remove unuse content in readme, test=doc
上级
d064c819
变更
2
隐藏空白更改
内联
并排
Showing
2 changed file
with
0 addition
and
113 deletion
+0
-113
demos/speaker_verification/README.md
demos/speaker_verification/README.md
+0
-59
demos/speaker_verification/README_cn.md
demos/speaker_verification/README_cn.md
+0
-54
未找到文件。
demos/speaker_verification/README.md
浏览文件 @
2a095db2
...
...
@@ -30,11 +30,6 @@ wget -c https://paddlespeech.bj.bcebos.com/vector/audio/85236145389.wav
paddlespeech vector
--task
spk
--input
vec.job
echo
-e
"demo2 85236145389.wav
\n
demo3 85236145389.wav"
| paddlespeech vector
--task
spk
paddlespeech vector
--task
score
--input
"./85236145389.wav ./123456789.wav"
echo
-e
"demo4 85236145389.wav 85236145389.wav
\n
demo5 85236145389.wav 123456789.wav"
>
vec.job
paddlespeech vector
--task
score
--input
vec.job
```
Usage:
...
...
@@ -108,17 +103,6 @@ wget -c https://paddlespeech.bj.bcebos.com/vector/audio/85236145389.wav
audio_file
=
'./85236145389.wav'
,
device
=
paddle
.
get_device
())
print
(
'Audio embedding Result:
\n
{}'
.
format
(
audio_emb
))
test_emb
=
vector_executor
(
model
=
'ecapatdnn_voxceleb12'
,
sample_rate
=
16000
,
config
=
None
,
# Set `config` and `ckpt_path` to None to use pretrained model.
ckpt_path
=
None
,
audio_file
=
'./123456789.wav'
,
device
=
paddle
.
get_device
())
print
(
'Test embedding Result:
\n
{}'
.
format
(
test_emb
))
score
=
vector_executor
.
get_embeddings_score
(
audio_emb
,
test_emb
)
print
(
f
"Eembeddings Score:
{
score
}
"
)
```
Output:
...
...
@@ -165,49 +149,6 @@ wget -c https://paddlespeech.bj.bcebos.com/vector/audio/85236145389.wav
-3
.3925855 5.079156 7.759716 4.677565 5.8457737
2.402413 7.7071047 3.9711342
-6
.390043 6.1268735
-3
.7760346
-11
.118123
]
# get the test embedding
Test embedding Result:
[
-1
.902964 2.0690894
-8
.034194 3.5472693 0.18089125
6.9085927 1.4097427
-1
.9487704
-10
.021278
-0
.20755845
-8
.04332 4.344489 2.3200977
-14
.306299 5.184692
-11
.55602
-3
.8497238 0.6444722 1.2833948 2.6766639
0.5878921 0.7946299 1.7207596 2.5791872 14.998469
-1
.3385371 15.031221
-0
.8006958 1.99287
-9
.52007
2.435466 4.003221
-4
.33817
-4
.898601
-5
.304714
-18
.033886 10.790787
-12
.784645
-5
.641755 2.9761686
-10
.566622 1.4839455 6.152458
-5
.7195854 2.8603241
6.112133 8.489869 5.5958056 1.2836679
-1
.2293907
0.89927405 7.0288725
-2
.854029
-0
.9782962 5.8255906
14.905906
-5
.025907 0.7866458
-4
.2444224
-16
.354029
10.521315 0.9604709
-3
.3257897 7.144871
-13
.592733
-8
.568869
-1
.7953678 0.26313916 10.916714
-6
.9374123
1.857403
-6
.2746415 2.8154466
-7
.2338667
-2
.293357
-0
.05452765 5.4287076 5.0849075
-6
.690375
-1
.6183422
3.654291 0.94352573
-9
.200294
-5
.4749465
-3
.5235846
1.3420814 4.240421
-2
.772944
-2
.8451524 16.311104
4.2969875
-1
.762936
-12
.5758915 8.595198
-0
.8835239
-1
.5708797 1.568961 1.1413603 3.5032008
-0
.45251232
-6
.786333 16.89443 5.3366146
-8
.789056 0.6355629
3.2579517
-3
.328322 7.5969577 0.66025066
-6
.550468
-9
.148656 2.020372
-0
.4615173 1.1965656
-3
.8764873
11.6562195
-6
.0750933 12.182899 3.2218833 0.81969476
5.570001
-3
.8459578
-7
.205299 7.9262037
-7
.6611166
-5
.249467
-2
.2671914 7.2658715
-13
.298164 4.821147
-2
.7263982 11.691089
-3
.8918593
-2
.838112
-1
.0336838
-3
.8034165 2.8536487
-5
.60398
-1
.1972581 1.3455094
-3
.4903061 2.2408795 5.5010734
-3
.970756 11.99696
-7
.8858757 0.43160373
-5
.5059714 4.3426995 16.322706
11.635366 0.72157705
-9
.245714
-3
.91465
-4
.449838
-1
.5716927 7.713747
-2
.2430465
-6
.198303
-13
.481864
2.8156567
-5
.7812386 5.1456156 2.7289324
-14
.505571
13.270688 3.448231
-7
.0659585 4.5886116
-4
.466099
-0
.296428
-11
.463529
-2
.6076477 14.110243
-6
.9725137
-1
.9962958 2.7119343 19.391657 0.01961198 14.607133
-1
.6695905
-4
.391516 1.3131028
-6
.670972
-5
.888604
12.0612335 5.9285784 3.3715196 1.492534 10.723728
-0
.95514804
-12
.085431
]
# get the score between enroll and test
Eembeddings Score: 0.4292638301849365
```
### 4.Pretrained Models
...
...
demos/speaker_verification/README_cn.md
浏览文件 @
2a095db2
...
...
@@ -106,17 +106,6 @@ wget -c https://paddlespeech.bj.bcebos.com/vector/audio/85236145389.wav
audio_file
=
'./85236145389.wav'
,
device
=
paddle
.
get_device
())
print
(
'Audio embedding Result:
\n
{}'
.
format
(
audio_emb
))
test_emb
=
vector_executor
(
model
=
'ecapatdnn_voxceleb12'
,
sample_rate
=
16000
,
config
=
None
,
# Set `config` and `ckpt_path` to None to use pretrained model.
ckpt_path
=
None
,
audio_file
=
'./123456789.wav'
,
device
=
paddle
.
get_device
())
print
(
'Test embedding Result:
\n
{}'
.
format
(
test_emb
))
score
=
vector_executor
.
get_embeddings_score
(
audio_emb
,
test_emb
)
print
(
f
"Eembeddings Score:
{
score
}
"
)
```
输出:
...
...
@@ -162,49 +151,6 @@ wget -c https://paddlespeech.bj.bcebos.com/vector/audio/85236145389.wav
-3
.3925855 5.079156 7.759716 4.677565 5.8457737
2.402413 7.7071047 3.9711342
-6
.390043 6.1268735
-3
.7760346
-11
.118123
]
# get the test embedding
Test embedding Result:
[
-1
.902964 2.0690894
-8
.034194 3.5472693 0.18089125
6.9085927 1.4097427
-1
.9487704
-10
.021278
-0
.20755845
-8
.04332 4.344489 2.3200977
-14
.306299 5.184692
-11
.55602
-3
.8497238 0.6444722 1.2833948 2.6766639
0.5878921 0.7946299 1.7207596 2.5791872 14.998469
-1
.3385371 15.031221
-0
.8006958 1.99287
-9
.52007
2.435466 4.003221
-4
.33817
-4
.898601
-5
.304714
-18
.033886 10.790787
-12
.784645
-5
.641755 2.9761686
-10
.566622 1.4839455 6.152458
-5
.7195854 2.8603241
6.112133 8.489869 5.5958056 1.2836679
-1
.2293907
0.89927405 7.0288725
-2
.854029
-0
.9782962 5.8255906
14.905906
-5
.025907 0.7866458
-4
.2444224
-16
.354029
10.521315 0.9604709
-3
.3257897 7.144871
-13
.592733
-8
.568869
-1
.7953678 0.26313916 10.916714
-6
.9374123
1.857403
-6
.2746415 2.8154466
-7
.2338667
-2
.293357
-0
.05452765 5.4287076 5.0849075
-6
.690375
-1
.6183422
3.654291 0.94352573
-9
.200294
-5
.4749465
-3
.5235846
1.3420814 4.240421
-2
.772944
-2
.8451524 16.311104
4.2969875
-1
.762936
-12
.5758915 8.595198
-0
.8835239
-1
.5708797 1.568961 1.1413603 3.5032008
-0
.45251232
-6
.786333 16.89443 5.3366146
-8
.789056 0.6355629
3.2579517
-3
.328322 7.5969577 0.66025066
-6
.550468
-9
.148656 2.020372
-0
.4615173 1.1965656
-3
.8764873
11.6562195
-6
.0750933 12.182899 3.2218833 0.81969476
5.570001
-3
.8459578
-7
.205299 7.9262037
-7
.6611166
-5
.249467
-2
.2671914 7.2658715
-13
.298164 4.821147
-2
.7263982 11.691089
-3
.8918593
-2
.838112
-1
.0336838
-3
.8034165 2.8536487
-5
.60398
-1
.1972581 1.3455094
-3
.4903061 2.2408795 5.5010734
-3
.970756 11.99696
-7
.8858757 0.43160373
-5
.5059714 4.3426995 16.322706
11.635366 0.72157705
-9
.245714
-3
.91465
-4
.449838
-1
.5716927 7.713747
-2
.2430465
-6
.198303
-13
.481864
2.8156567
-5
.7812386 5.1456156 2.7289324
-14
.505571
13.270688 3.448231
-7
.0659585 4.5886116
-4
.466099
-0
.296428
-11
.463529
-2
.6076477 14.110243
-6
.9725137
-1
.9962958 2.7119343 19.391657 0.01961198 14.607133
-1
.6695905
-4
.391516 1.3131028
-6
.670972
-5
.888604
12.0612335 5.9285784 3.3715196 1.492534 10.723728
-0
.95514804
-12
.085431
]
# get the score between enroll and test
Eembeddings Score: 0.4292638301849365
```
### 4.预训练模型
...
...
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