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DeepSpeech

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DeepSpeech
提交 d064c819 编写于 作者: X xiongxinlei
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update the speaker verification model, test=doc

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demos/speaker_verification/README.md
浏览文件 @ d064c819
...@@ -30,6 +30,11 @@ wget -c https://paddlespeech.bj.bcebos.com/vector/audio/85236145389.wav ...@@ -30,6 +30,11 @@ wget -c https://paddlespeech.bj.bcebos.com/vector/audio/85236145389.wav
paddlespeech vector --task spk --input vec.job paddlespeech vector --task spk --input vec.job
echo -e "demo2 85236145389.wav \n demo3 85236145389.wav" | paddlespeech vector --task spk 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: Usage:
...@@ -38,6 +43,7 @@ wget -c https://paddlespeech.bj.bcebos.com/vector/audio/85236145389.wav ...@@ -38,6 +43,7 @@ wget -c https://paddlespeech.bj.bcebos.com/vector/audio/85236145389.wav
``` ```
Arguments: Arguments:
- `input`(required): Audio file to recognize. - `input`(required): Audio file to recognize.
- `task` (required): Specify `vector` task. Default `spk`。
- `model`: Model type of vector task. Default: `ecapatdnn_voxceleb12`. - `model`: Model type of vector task. Default: `ecapatdnn_voxceleb12`.
- `sample_rate`: Sample rate of the model. Default: `16000`. - `sample_rate`: Sample rate of the model. Default: `16000`.
- `config`: Config of vector task. Use pretrained model when it is None. Default: `None`. - `config`: Config of vector task. Use pretrained model when it is None. Default: `None`.
...@@ -47,45 +53,45 @@ wget -c https://paddlespeech.bj.bcebos.com/vector/audio/85236145389.wav ...@@ -47,45 +53,45 @@ wget -c https://paddlespeech.bj.bcebos.com/vector/audio/85236145389.wav
Output: Output:
```bash ```bash
demo [ -5.749211 9.505463 -8.200284 -5.2075014 5.3940268 demo [ 1.4217498 5.626253 -5.342073 1.1773866 3.308055
-3.04878 1.611095 10.127234 -10.534177 -15.821609 1.756596 5.167894 10.80636 -3.8226728 -5.6141334
1.2032688 -0.35080156 1.2629458 -12.643498 -2.5758228 2.623845 -0.8072968 1.9635103 -7.3128724 0.01103897
-11.343508 2.3385992 -8.719341 14.213509 15.404744 -9.723131 0.6619743 -6.976803 10.213478 7.494748
-0.39327756 6.338786 2.688887 8.7104025 17.469526 2.9105635 3.8949256 3.7999806 7.1061673 16.905321
-8.77959 7.0576906 4.648855 -1.3089896 -23.294737 -7.1493764 8.733103 3.4230042 -4.831653 -11.403367
8.013747 13.891729 -9.926753 5.655307 -5.9422326 11.232214 7.1274667 -4.2828417 2.452362 -5.130748
-22.842539 0.6293588 -18.46266 -10.811862 9.8192625 -18.177666 -2.6116815 -11.000337 -6.7314315 1.6564683
3.0070958 3.8072643 -2.3861165 3.0821571 -14.739942 0.7618269 1.1253023 -2.083836 4.725744 -8.782597
1.7594414 -0.6485091 4.485623 2.0207152 7.264915 -3.539873 3.814236 5.1420674 2.162061 4.096431
-6.40137 23.63524 2.9711294 -22.708025 9.93719 -6.4162116 12.747448 1.9429878 -15.152943 6.417416
20.354511 -10.324688 -0.700492 -8.783211 -5.27593 16.097002 -9.716668 -1.9920526 -3.3649497 -1.871939
15.999649 3.3004563 12.747926 15.429879 4.7849145 11.567354 3.69788 11.258265 7.442363 9.183411
5.6699696 -2.3826702 10.605882 3.9112158 3.1500628 4.5281515 -1.2417862 4.3959084 6.6727695 5.8898783
15.859915 -2.1832209 -23.908653 -6.4799504 -4.5365124 7.627124 -0.66919386 -11.889693 -9.208865 -7.4274073
-9.224193 14.568347 -10.568833 4.982321 -4.342062 -3.7776625 6.917234 -9.848748 -2.0944717 -5.135116
0.0914714 12.645902 -5.74285 -3.2141201 -2.7173362 0.49563864 9.317534 -5.9141874 -1.8098574 -0.11738578
-6.680575 0.4757669 -5.035051 -6.7964664 16.865469 -7.169265 -1.0578263 -5.7216787 -5.1173844 16.137651
-11.54324 7.681869 0.44475392 9.708182 -8.932846 -4.473626 7.6624317 -0.55381083 9.631587 -6.4704556
0.4123232 -4.361452 1.3948607 9.511665 0.11667654 -8.548508 4.3716145 -0.79702514 4.478997 -2.9758704
2.9079323 6.049952 9.275183 -18.078873 6.2983274 3.272176 2.8382776 5.134597 -9.190781 -0.5657382
-0.7500531 -2.725033 -7.6027865 3.3404543 2.990815 -4.8745747 2.3165567 -5.984303 -2.1798875 0.35541576
4.010979 11.000591 -2.8873312 7.1352735 -16.79663 -0.31784213 9.493548 2.1144536 4.358092 -12.089823
18.495346 -14.293832 7.89578 2.2714825 22.976387 8.451689 -7.925461 4.6242585 4.4289427 18.692003
-4.875734 -3.0836344 -2.9999814 13.751918 6.448228 -2.6204622 -5.149185 -0.35821092 8.488551 4.981496
-11.924197 2.171869 2.0423572 -6.173772 10.778437 -9.32683 -2.2544234 6.6417594 1.2119585 10.977129
25.77281 -4.9495463 14.57806 0.3044315 2.6132357 16.555033 3.3238444 9.551863 -1.6676947 -0.79539716
-7.591999 -2.076944 9.025118 1.7834753 -3.1799617 -8.605674 -0.47356385 2.6741948 -5.359179 -2.6673796
-4.9401326 23.465864 5.1685796 -9.018578 9.037825 0.66607 15.443222 4.740594 -3.4725387 11.592567
-4.4150195 6.859591 -12.274467 -0.88911164 5.186309 -2.054497 1.7361217 -8.265324 -9.30447 5.4068313
-3.9988663 -13.638606 -9.925445 -0.06329413 -3.6709652 -1.5180256 -7.746615 -6.089606 0.07112726 -0.34904733
-12.397416 -12.719869 -1.395601 2.1150916 5.7381287 -8.649895 -9.998958 -2.564841 -0.53999114 2.601808
-4.4691963 -3.82819 -0.84233856 -1.1604277 -13.490127 -0.31927416 -1.8815292 -2.07215 -3.4105783 -8.2998085
8.731719 -20.778936 -11.495662 5.8033476 -4.752041 1.483641 -15.365992 -8.288208 3.8847756 -3.4876456
10.833007 -6.717991 4.504732 13.4244375 1.1306485 7.3629923 0.4657332 3.132599 12.438889 -1.8337058
7.3435574 1.400918 14.704036 -9.501399 7.2315617 4.532936 2.7264361 10.145339 -6.521951 2.897153
-6.417456 1.3333273 11.872697 -0.30664724 8.8845 -3.3925855 5.079156 7.759716 4.677565 5.8457737
6.5569253 4.7948146 0.03662816 -8.704245 6.224871 2.402413 7.7071047 3.9711342 -6.390043 6.1268735
-3.2701402 -11.508579 ] -3.7760346 -11.118123 ]
``` ```
- Python API - Python API
...@@ -97,56 +103,111 @@ wget -c https://paddlespeech.bj.bcebos.com/vector/audio/85236145389.wav ...@@ -97,56 +103,111 @@ wget -c https://paddlespeech.bj.bcebos.com/vector/audio/85236145389.wav
audio_emb = vector_executor( audio_emb = vector_executor(
model='ecapatdnn_voxceleb12', model='ecapatdnn_voxceleb12',
sample_rate=16000, sample_rate=16000,
config=None, config=None, # Set `config` and `ckpt_path` to None to use pretrained model.
ckpt_path=None, ckpt_path=None,
audio_file='./85236145389.wav', audio_file='./85236145389.wav',
force_yes=False,
device=paddle.get_device()) device=paddle.get_device())
print('Audio embedding Result: \n{}'.format(audio_emb)) 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: Output：
```bash ```bash
# Vector Result: # Vector Result:
[ -5.749211 9.505463 -8.200284 -5.2075014 5.3940268 Audio embedding Result:
-3.04878 1.611095 10.127234 -10.534177 -15.821609 [ 1.4217498 5.626253 -5.342073 1.1773866 3.308055
1.2032688 -0.35080156 1.2629458 -12.643498 -2.5758228 1.756596 5.167894 10.80636 -3.8226728 -5.6141334
-11.343508 2.3385992 -8.719341 14.213509 15.404744 2.623845 -0.8072968 1.9635103 -7.3128724 0.01103897
-0.39327756 6.338786 2.688887 8.7104025 17.469526 -9.723131 0.6619743 -6.976803 10.213478 7.494748
-8.77959 7.0576906 4.648855 -1.3089896 -23.294737 2.9105635 3.8949256 3.7999806 7.1061673 16.905321
8.013747 13.891729 -9.926753 5.655307 -5.9422326 -7.1493764 8.733103 3.4230042 -4.831653 -11.403367
-22.842539 0.6293588 -18.46266 -10.811862 9.8192625 11.232214 7.1274667 -4.2828417 2.452362 -5.130748
3.0070958 3.8072643 -2.3861165 3.0821571 -14.739942 -18.177666 -2.6116815 -11.000337 -6.7314315 1.6564683
1.7594414 -0.6485091 4.485623 2.0207152 7.264915 0.7618269 1.1253023 -2.083836 4.725744 -8.782597
-6.40137 23.63524 2.9711294 -22.708025 9.93719 -3.539873 3.814236 5.1420674 2.162061 4.096431
20.354511 -10.324688 -0.700492 -8.783211 -5.27593 -6.4162116 12.747448 1.9429878 -15.152943 6.417416
15.999649 3.3004563 12.747926 15.429879 4.7849145 16.097002 -9.716668 -1.9920526 -3.3649497 -1.871939
5.6699696 -2.3826702 10.605882 3.9112158 3.1500628 11.567354 3.69788 11.258265 7.442363 9.183411
15.859915 -2.1832209 -23.908653 -6.4799504 -4.5365124 4.5281515 -1.2417862 4.3959084 6.6727695 5.8898783
-9.224193 14.568347 -10.568833 4.982321 -4.342062 7.627124 -0.66919386 -11.889693 -9.208865 -7.4274073
0.0914714 12.645902 -5.74285 -3.2141201 -2.7173362 -3.7776625 6.917234 -9.848748 -2.0944717 -5.135116
-6.680575 0.4757669 -5.035051 -6.7964664 16.865469 0.49563864 9.317534 -5.9141874 -1.8098574 -0.11738578
-11.54324 7.681869 0.44475392 9.708182 -8.932846 -7.169265 -1.0578263 -5.7216787 -5.1173844 16.137651
0.4123232 -4.361452 1.3948607 9.511665 0.11667654 -4.473626 7.6624317 -0.55381083 9.631587 -6.4704556
2.9079323 6.049952 9.275183 -18.078873 6.2983274 -8.548508 4.3716145 -0.79702514 4.478997 -2.9758704
-0.7500531 -2.725033 -7.6027865 3.3404543 2.990815 3.272176 2.8382776 5.134597 -9.190781 -0.5657382
4.010979 11.000591 -2.8873312 7.1352735 -16.79663 -4.8745747 2.3165567 -5.984303 -2.1798875 0.35541576
18.495346 -14.293832 7.89578 2.2714825 22.976387 -0.31784213 9.493548 2.1144536 4.358092 -12.089823
-4.875734 -3.0836344 -2.9999814 13.751918 6.448228 8.451689 -7.925461 4.6242585 4.4289427 18.692003
-11.924197 2.171869 2.0423572 -6.173772 10.778437 -2.6204622 -5.149185 -0.35821092 8.488551 4.981496
25.77281 -4.9495463 14.57806 0.3044315 2.6132357 -9.32683 -2.2544234 6.6417594 1.2119585 10.977129
-7.591999 -2.076944 9.025118 1.7834753 -3.1799617 16.555033 3.3238444 9.551863 -1.6676947 -0.79539716
-4.9401326 23.465864 5.1685796 -9.018578 9.037825 -8.605674 -0.47356385 2.6741948 -5.359179 -2.6673796
-4.4150195 6.859591 -12.274467 -0.88911164 5.186309 0.66607 15.443222 4.740594 -3.4725387 11.592567
-3.9988663 -13.638606 -9.925445 -0.06329413 -3.6709652 -2.054497 1.7361217 -8.265324 -9.30447 5.4068313
-12.397416 -12.719869 -1.395601 2.1150916 5.7381287 -1.5180256 -7.746615 -6.089606 0.07112726 -0.34904733
-4.4691963 -3.82819 -0.84233856 -1.1604277 -13.490127 -8.649895 -9.998958 -2.564841 -0.53999114 2.601808
8.731719 -20.778936 -11.495662 5.8033476 -4.752041 -0.31927416 -1.8815292 -2.07215 -3.4105783 -8.2998085
10.833007 -6.717991 4.504732 13.4244375 1.1306485 1.483641 -15.365992 -8.288208 3.8847756 -3.4876456
7.3435574 1.400918 14.704036 -9.501399 7.2315617 7.3629923 0.4657332 3.132599 12.438889 -1.8337058
-6.417456 1.3333273 11.872697 -0.30664724 8.8845 4.532936 2.7264361 10.145339 -6.521951 2.897153
6.5569253 4.7948146 0.03662816 -8.704245 6.224871 -3.3925855 5.079156 7.759716 4.677565 5.8457737
-3.2701402 -11.508579 ] 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 ### 4.Pretrained Models
......
demos/speaker_verification/README_cn.md
浏览文件 @ d064c819
...@@ -29,6 +29,11 @@ wget -c https://paddlespeech.bj.bcebos.com/vector/audio/85236145389.wav ...@@ -29,6 +29,11 @@ wget -c https://paddlespeech.bj.bcebos.com/vector/audio/85236145389.wav
paddlespeech vector --task spk --input vec.job paddlespeech vector --task spk --input vec.job
echo -e "demo2 85236145389.wav \n demo3 85236145389.wav" | paddlespeech vector --task spk 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
``` ```
使用方法： 使用方法：
...@@ -37,6 +42,7 @@ wget -c https://paddlespeech.bj.bcebos.com/vector/audio/85236145389.wav ...@@ -37,6 +42,7 @@ wget -c https://paddlespeech.bj.bcebos.com/vector/audio/85236145389.wav
``` ```
参数： 参数：
- `input`(必须输入)：用于识别的音频文件。 - `input`(必须输入)：用于识别的音频文件。
- `task` (必须输入): 用于指定 `vector` 处理的具体任务，默认是 `spk`。
- `model`：声纹任务的模型，默认值：`ecapatdnn_voxceleb12`。 - `model`：声纹任务的模型，默认值：`ecapatdnn_voxceleb12`。
- `sample_rate`：音频采样率，默认值：`16000`。 - `sample_rate`：音频采样率，默认值：`16000`。
- `config`：声纹任务的参数文件，若不设置则使用预训练模型中的默认配置，默认值：`None`。 - `config`：声纹任务的参数文件，若不设置则使用预训练模型中的默认配置，默认值：`None`。
...@@ -45,45 +51,45 @@ wget -c https://paddlespeech.bj.bcebos.com/vector/audio/85236145389.wav ...@@ -45,45 +51,45 @@ wget -c https://paddlespeech.bj.bcebos.com/vector/audio/85236145389.wav
输出： 输出：
```bash ```bash
demo [ -5.749211 9.505463 -8.200284 -5.2075014 5.3940268 demo [ 1.4217498 5.626253 -5.342073 1.1773866 3.308055
-3.04878 1.611095 10.127234 -10.534177 -15.821609 1.756596 5.167894 10.80636 -3.8226728 -5.6141334
1.2032688 -0.35080156 1.2629458 -12.643498 -2.5758228 2.623845 -0.8072968 1.9635103 -7.3128724 0.01103897
-11.343508 2.3385992 -8.719341 14.213509 15.404744 -9.723131 0.6619743 -6.976803 10.213478 7.494748
-0.39327756 6.338786 2.688887 8.7104025 17.469526 2.9105635 3.8949256 3.7999806 7.1061673 16.905321
-8.77959 7.0576906 4.648855 -1.3089896 -23.294737 -7.1493764 8.733103 3.4230042 -4.831653 -11.403367
8.013747 13.891729 -9.926753 5.655307 -5.9422326 11.232214 7.1274667 -4.2828417 2.452362 -5.130748
-22.842539 0.6293588 -18.46266 -10.811862 9.8192625 -18.177666 -2.6116815 -11.000337 -6.7314315 1.6564683
3.0070958 3.8072643 -2.3861165 3.0821571 -14.739942 0.7618269 1.1253023 -2.083836 4.725744 -8.782597
1.7594414 -0.6485091 4.485623 2.0207152 7.264915 -3.539873 3.814236 5.1420674 2.162061 4.096431
-6.40137 23.63524 2.9711294 -22.708025 9.93719 -6.4162116 12.747448 1.9429878 -15.152943 6.417416
20.354511 -10.324688 -0.700492 -8.783211 -5.27593 16.097002 -9.716668 -1.9920526 -3.3649497 -1.871939
15.999649 3.3004563 12.747926 15.429879 4.7849145 11.567354 3.69788 11.258265 7.442363 9.183411
5.6699696 -2.3826702 10.605882 3.9112158 3.1500628 4.5281515 -1.2417862 4.3959084 6.6727695 5.8898783
15.859915 -2.1832209 -23.908653 -6.4799504 -4.5365124 7.627124 -0.66919386 -11.889693 -9.208865 -7.4274073
-9.224193 14.568347 -10.568833 4.982321 -4.342062 -3.7776625 6.917234 -9.848748 -2.0944717 -5.135116
0.0914714 12.645902 -5.74285 -3.2141201 -2.7173362 0.49563864 9.317534 -5.9141874 -1.8098574 -0.11738578
-6.680575 0.4757669 -5.035051 -6.7964664 16.865469 -7.169265 -1.0578263 -5.7216787 -5.1173844 16.137651
-11.54324 7.681869 0.44475392 9.708182 -8.932846 -4.473626 7.6624317 -0.55381083 9.631587 -6.4704556
0.4123232 -4.361452 1.3948607 9.511665 0.11667654 -8.548508 4.3716145 -0.79702514 4.478997 -2.9758704
2.9079323 6.049952 9.275183 -18.078873 6.2983274 3.272176 2.8382776 5.134597 -9.190781 -0.5657382
-0.7500531 -2.725033 -7.6027865 3.3404543 2.990815 -4.8745747 2.3165567 -5.984303 -2.1798875 0.35541576
4.010979 11.000591 -2.8873312 7.1352735 -16.79663 -0.31784213 9.493548 2.1144536 4.358092 -12.089823
18.495346 -14.293832 7.89578 2.2714825 22.976387 8.451689 -7.925461 4.6242585 4.4289427 18.692003
-4.875734 -3.0836344 -2.9999814 13.751918 6.448228 -2.6204622 -5.149185 -0.35821092 8.488551 4.981496
-11.924197 2.171869 2.0423572 -6.173772 10.778437 -9.32683 -2.2544234 6.6417594 1.2119585 10.977129
25.77281 -4.9495463 14.57806 0.3044315 2.6132357 16.555033 3.3238444 9.551863 -1.6676947 -0.79539716
-7.591999 -2.076944 9.025118 1.7834753 -3.1799617 -8.605674 -0.47356385 2.6741948 -5.359179 -2.6673796
-4.9401326 23.465864 5.1685796 -9.018578 9.037825 0.66607 15.443222 4.740594 -3.4725387 11.592567
-4.4150195 6.859591 -12.274467 -0.88911164 5.186309 -2.054497 1.7361217 -8.265324 -9.30447 5.4068313
-3.9988663 -13.638606 -9.925445 -0.06329413 -3.6709652 -1.5180256 -7.746615 -6.089606 0.07112726 -0.34904733
-12.397416 -12.719869 -1.395601 2.1150916 5.7381287 -8.649895 -9.998958 -2.564841 -0.53999114 2.601808
-4.4691963 -3.82819 -0.84233856 -1.1604277 -13.490127 -0.31927416 -1.8815292 -2.07215 -3.4105783 -8.2998085
8.731719 -20.778936 -11.495662 5.8033476 -4.752041 1.483641 -15.365992 -8.288208 3.8847756 -3.4876456
10.833007 -6.717991 4.504732 13.4244375 1.1306485 7.3629923 0.4657332 3.132599 12.438889 -1.8337058
7.3435574 1.400918 14.704036 -9.501399 7.2315617 4.532936 2.7264361 10.145339 -6.521951 2.897153
-6.417456 1.3333273 11.872697 -0.30664724 8.8845 -3.3925855 5.079156 7.759716 4.677565 5.8457737
6.5569253 4.7948146 0.03662816 -8.704245 6.224871 2.402413 7.7071047 3.9711342 -6.390043 6.1268735
-3.2701402 -11.508579 ] -3.7760346 -11.118123 ]
``` ```
- Python API - Python API
...@@ -98,53 +104,107 @@ wget -c https://paddlespeech.bj.bcebos.com/vector/audio/85236145389.wav ...@@ -98,53 +104,107 @@ wget -c https://paddlespeech.bj.bcebos.com/vector/audio/85236145389.wav
config=None, # Set `config` and `ckpt_path` to None to use pretrained model. config=None, # Set `config` and `ckpt_path` to None to use pretrained model.
ckpt_path=None, ckpt_path=None,
audio_file='./85236145389.wav', audio_file='./85236145389.wav',
force_yes=False,
device=paddle.get_device()) device=paddle.get_device())
print('Audio embedding Result: \n{}'.format(audio_emb)) 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： 输出：
```bash ```bash
# Vector Result: # Vector Result:
[ -5.749211 9.505463 -8.200284 -5.2075014 5.3940268 Audio embedding Result:
-3.04878 1.611095 10.127234 -10.534177 -15.821609 [ 1.4217498 5.626253 -5.342073 1.1773866 3.308055
1.2032688 -0.35080156 1.2629458 -12.643498 -2.5758228 1.756596 5.167894 10.80636 -3.8226728 -5.6141334
-11.343508 2.3385992 -8.719341 14.213509 15.404744 2.623845 -0.8072968 1.9635103 -7.3128724 0.01103897
-0.39327756 6.338786 2.688887 8.7104025 17.469526 -9.723131 0.6619743 -6.976803 10.213478 7.494748
-8.77959 7.0576906 4.648855 -1.3089896 -23.294737 2.9105635 3.8949256 3.7999806 7.1061673 16.905321
8.013747 13.891729 -9.926753 5.655307 -5.9422326 -7.1493764 8.733103 3.4230042 -4.831653 -11.403367
-22.842539 0.6293588 -18.46266 -10.811862 9.8192625 11.232214 7.1274667 -4.2828417 2.452362 -5.130748
3.0070958 3.8072643 -2.3861165 3.0821571 -14.739942 -18.177666 -2.6116815 -11.000337 -6.7314315 1.6564683
1.7594414 -0.6485091 4.485623 2.0207152 7.264915 0.7618269 1.1253023 -2.083836 4.725744 -8.782597
-6.40137 23.63524 2.9711294 -22.708025 9.93719 -3.539873 3.814236 5.1420674 2.162061 4.096431
20.354511 -10.324688 -0.700492 -8.783211 -5.27593 -6.4162116 12.747448 1.9429878 -15.152943 6.417416
15.999649 3.3004563 12.747926 15.429879 4.7849145 16.097002 -9.716668 -1.9920526 -3.3649497 -1.871939
5.6699696 -2.3826702 10.605882 3.9112158 3.1500628 11.567354 3.69788 11.258265 7.442363 9.183411
15.859915 -2.1832209 -23.908653 -6.4799504 -4.5365124 4.5281515 -1.2417862 4.3959084 6.6727695 5.8898783
-9.224193 14.568347 -10.568833 4.982321 -4.342062 7.627124 -0.66919386 -11.889693 -9.208865 -7.4274073
0.0914714 12.645902 -5.74285 -3.2141201 -2.7173362 -3.7776625 6.917234 -9.848748 -2.0944717 -5.135116
-6.680575 0.4757669 -5.035051 -6.7964664 16.865469 0.49563864 9.317534 -5.9141874 -1.8098574 -0.11738578
-11.54324 7.681869 0.44475392 9.708182 -8.932846 -7.169265 -1.0578263 -5.7216787 -5.1173844 16.137651
0.4123232 -4.361452 1.3948607 9.511665 0.11667654 -4.473626 7.6624317 -0.55381083 9.631587 -6.4704556
2.9079323 6.049952 9.275183 -18.078873 6.2983274 -8.548508 4.3716145 -0.79702514 4.478997 -2.9758704
-0.7500531 -2.725033 -7.6027865 3.3404543 2.990815 3.272176 2.8382776 5.134597 -9.190781 -0.5657382
4.010979 11.000591 -2.8873312 7.1352735 -16.79663 -4.8745747 2.3165567 -5.984303 -2.1798875 0.35541576
18.495346 -14.293832 7.89578 2.2714825 22.976387 -0.31784213 9.493548 2.1144536 4.358092 -12.089823
-4.875734 -3.0836344 -2.9999814 13.751918 6.448228 8.451689 -7.925461 4.6242585 4.4289427 18.692003
-11.924197 2.171869 2.0423572 -6.173772 10.778437 -2.6204622 -5.149185 -0.35821092 8.488551 4.981496
25.77281 -4.9495463 14.57806 0.3044315 2.6132357 -9.32683 -2.2544234 6.6417594 1.2119585 10.977129
-7.591999 -2.076944 9.025118 1.7834753 -3.1799617 16.555033 3.3238444 9.551863 -1.6676947 -0.79539716
-4.9401326 23.465864 5.1685796 -9.018578 9.037825 -8.605674 -0.47356385 2.6741948 -5.359179 -2.6673796
-4.4150195 6.859591 -12.274467 -0.88911164 5.186309 0.66607 15.443222 4.740594 -3.4725387 11.592567
-3.9988663 -13.638606 -9.925445 -0.06329413 -3.6709652 -2.054497 1.7361217 -8.265324 -9.30447 5.4068313
-12.397416 -12.719869 -1.395601 2.1150916 5.7381287 -1.5180256 -7.746615 -6.089606 0.07112726 -0.34904733
-4.4691963 -3.82819 -0.84233856 -1.1604277 -13.490127 -8.649895 -9.998958 -2.564841 -0.53999114 2.601808
8.731719 -20.778936 -11.495662 5.8033476 -4.752041 -0.31927416 -1.8815292 -2.07215 -3.4105783 -8.2998085
10.833007 -6.717991 4.504732 13.4244375 1.1306485 1.483641 -15.365992 -8.288208 3.8847756 -3.4876456
7.3435574 1.400918 14.704036 -9.501399 7.2315617 7.3629923 0.4657332 3.132599 12.438889 -1.8337058
-6.417456 1.3333273 11.872697 -0.30664724 8.8845 4.532936 2.7264361 10.145339 -6.521951 2.897153
6.5569253 4.7948146 0.03662816 -8.704245 6.224871 -3.3925855 5.079156 7.759716 4.677565 5.8457737
-3.2701402 -11.508579 ] 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 ### 4.预训练模型
......
demos/speaker_verification/run.sh
浏览文件 @ d064c819
...@@ -2,5 +2,5 @@ ...@@ -2,5 +2,5 @@
wget -c https://paddlespeech.bj.bcebos.com/vector/audio/85236145389.wav wget -c https://paddlespeech.bj.bcebos.com/vector/audio/85236145389.wav
# asr # vector
paddlespeech vector --task spk --input ./85236145389.wav paddlespeech vector --task spk --input ./85236145389.wav
docs/source/released_model.md
浏览文件 @ d064c819
...@@ -80,7 +80,7 @@ PANN | ESC-50 |[pann-esc50](../../examples/esc50/cls0)|[esc50_cnn6.tar.gz](https ...@@ -80,7 +80,7 @@ PANN | ESC-50 |[pann-esc50](../../examples/esc50/cls0)|[esc50_cnn6.tar.gz](https
Model Type | Dataset| Example Link | Pretrained Models | Static Models Model Type | Dataset| Example Link | Pretrained Models | Static Models
:-------------:| :------------:| :-----: | :-----: | :-----: :-------------:| :------------:| :-----: | :-----: | :-----:
PANN | VoxCeleb| [voxceleb_ecapatdnn](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/examples/voxceleb/sv0) | [ecapatdnn.tar.gz](https://paddlespeech.bj.bcebos.com/vector/voxceleb/sv0_ecapa_tdnn_voxceleb12_ckpt_0_1_1.tar.gz) | - PANN | VoxCeleb| [voxceleb_ecapatdnn](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/examples/voxceleb/sv0) | [ecapatdnn.tar.gz](https://paddlespeech.bj.bcebos.com/vector/voxceleb/sv0_ecapa_tdnn_voxceleb12_ckpt_0_2_0.tar.gz) | -
## Punctuation Restoration Models ## Punctuation Restoration Models
Model Type | Dataset| Example Link | Pretrained Models Model Type | Dataset| Example Link | Pretrained Models
......
examples/voxceleb/sv0/RESULT.md
浏览文件 @ d064c819
...@@ -4,4 +4,4 @@ ...@@ -4,4 +4,4 @@
| Model | Number of Params | Release | Config | dim | Test set | Cosine | Cosine + S-Norm | | Model | Number of Params | Release | Config | dim | Test set | Cosine | Cosine + S-Norm |
| --- | --- | --- | --- | --- | --- | --- | ---- | | --- | --- | --- | --- | --- | --- | --- | ---- |
| ECAPA-TDNN | 85M | 0.1.1 | conf/ecapa_tdnn.yaml |192 | test | 1.15 | 1.06 | | ECAPA-TDNN | 85M | 0.2.0 | conf/ecapa_tdnn.yaml |192 | test | 1.02 | 0.95 |
paddlespeech/cli/vector/infer.py
浏览文件 @ d064c819
...@@ -42,9 +42,9 @@ pretrained_models = { ...@@ -42,9 +42,9 @@ pretrained_models = {
# "paddlespeech vector --task spk --model ecapatdnn_voxceleb12-16k --sr 16000 --input ./input.wav" # "paddlespeech vector --task spk --model ecapatdnn_voxceleb12-16k --sr 16000 --input ./input.wav"
"ecapatdnn_voxceleb12-16k": { "ecapatdnn_voxceleb12-16k": {
'url': 'url':
'https://paddlespeech.bj.bcebos.com/vector/voxceleb/sv0_ecapa_tdnn_voxceleb12_ckpt_0_1_1.tar.gz', 'https://paddlespeech.bj.bcebos.com/vector/voxceleb/sv0_ecapa_tdnn_voxceleb12_ckpt_0_2_0.tar.gz',
'md5': 'md5':
'a1c0dba7d4de997187786ff517d5b4ec', 'cc33023c54ab346cd318408f43fcaf95',
'cfg_path': 'cfg_path':
'conf/model.yaml', # the yaml config path 'conf/model.yaml', # the yaml config path
'ckpt_path': 'ckpt_path':
......
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