(简体中文|English)
Speaker Verification, refers to the problem of getting a speaker embedding from an audio.
This demo is an implementation to extract speaker embedding from a specific audio file. It can be done by a single command or a few lines in python using PaddleSpeech.
see installation.
You can choose one way from easy, meduim and hard to install paddlespeech.
The input of this demo should be a WAV file(.wav), and the sample rate must be the same as the model.
Here are sample files for this demo that can be downloaded:
wget -c https://paddlespeech.bj.bcebos.com/vector/audio/85236145389.wav-
Command Line(Recommended)
paddlespeech vector --task spk --input 85236145389.wav echo -e "demo1 85236145389.wav" > vec.job 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:
paddlespeech vector --help
Arguments:
input(required): Audio file to recognize.task(required): Specifyvectortask. Defaultspk。model: Model type of vector task. Default:ecapatdnn_voxceleb12.sample_rate: Sample rate of the model. Default:16000.config: Config of vector task. Use pretrained model when it is None. Default:None.ckpt_path: Model checkpoint. Use pretrained model when it is None. Default:None.device: Choose device to execute model inference. Default: default device of paddlepaddle in current environment.
Output:
demo [ 1.4217498 5.626253 -5.342073 1.1773866 3.308055 1.756596 5.167894 10.80636 -3.8226728 -5.6141334 2.623845 -0.8072968 1.9635103 -7.3128724 0.01103897 -9.723131 0.6619743 -6.976803 10.213478 7.494748 2.9105635 3.8949256 3.7999806 7.1061673 16.905321 -7.1493764 8.733103 3.4230042 -4.831653 -11.403367 11.232214 7.1274667 -4.2828417 2.452362 -5.130748 -18.177666 -2.6116815 -11.000337 -6.7314315 1.6564683 0.7618269 1.1253023 -2.083836 4.725744 -8.782597 -3.539873 3.814236 5.1420674 2.162061 4.096431 -6.4162116 12.747448 1.9429878 -15.152943 6.417416 16.097002 -9.716668 -1.9920526 -3.3649497 -1.871939 11.567354 3.69788 11.258265 7.442363 9.183411 4.5281515 -1.2417862 4.3959084 6.6727695 5.8898783 7.627124 -0.66919386 -11.889693 -9.208865 -7.4274073 -3.7776625 6.917234 -9.848748 -2.0944717 -5.135116 0.49563864 9.317534 -5.9141874 -1.8098574 -0.11738578 -7.169265 -1.0578263 -5.7216787 -5.1173844 16.137651 -4.473626 7.6624317 -0.55381083 9.631587 -6.4704556 -8.548508 4.3716145 -0.79702514 4.478997 -2.9758704 3.272176 2.8382776 5.134597 -9.190781 -0.5657382 -4.8745747 2.3165567 -5.984303 -2.1798875 0.35541576 -0.31784213 9.493548 2.1144536 4.358092 -12.089823 8.451689 -7.925461 4.6242585 4.4289427 18.692003 -2.6204622 -5.149185 -0.35821092 8.488551 4.981496 -9.32683 -2.2544234 6.6417594 1.2119585 10.977129 16.555033 3.3238444 9.551863 -1.6676947 -0.79539716 -8.605674 -0.47356385 2.6741948 -5.359179 -2.6673796 0.66607 15.443222 4.740594 -3.4725387 11.592567 -2.054497 1.7361217 -8.265324 -9.30447 5.4068313 -1.5180256 -7.746615 -6.089606 0.07112726 -0.34904733 -8.649895 -9.998958 -2.564841 -0.53999114 2.601808 -0.31927416 -1.8815292 -2.07215 -3.4105783 -8.2998085 1.483641 -15.365992 -8.288208 3.8847756 -3.4876456 7.3629923 0.4657332 3.132599 12.438889 -1.8337058 4.532936 2.7264361 10.145339 -6.521951 2.897153 -3.3925855 5.079156 7.759716 4.677565 5.8457737 2.402413 7.7071047 3.9711342 -6.390043 6.1268735 -3.7760346 -11.118123 ]
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Python API
import paddle from paddlespeech.cli import VectorExecutor vector_executor = VectorExecutor() audio_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='./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 range [0, 1] score = vector_executor.get_embeddings_score(audio_emb, test_emb) print(f"Eembeddings Score: {score}")
Output：
# Vector Result: Audio embedding Result: [ 1.4217498 5.626253 -5.342073 1.1773866 3.308055 1.756596 5.167894 10.80636 -3.8226728 -5.6141334 2.623845 -0.8072968 1.9635103 -7.3128724 0.01103897 -9.723131 0.6619743 -6.976803 10.213478 7.494748 2.9105635 3.8949256 3.7999806 7.1061673 16.905321 -7.1493764 8.733103 3.4230042 -4.831653 -11.403367 11.232214 7.1274667 -4.2828417 2.452362 -5.130748 -18.177666 -2.6116815 -11.000337 -6.7314315 1.6564683 0.7618269 1.1253023 -2.083836 4.725744 -8.782597 -3.539873 3.814236 5.1420674 2.162061 4.096431 -6.4162116 12.747448 1.9429878 -15.152943 6.417416 16.097002 -9.716668 -1.9920526 -3.3649497 -1.871939 11.567354 3.69788 11.258265 7.442363 9.183411 4.5281515 -1.2417862 4.3959084 6.6727695 5.8898783 7.627124 -0.66919386 -11.889693 -9.208865 -7.4274073 -3.7776625 6.917234 -9.848748 -2.0944717 -5.135116 0.49563864 9.317534 -5.9141874 -1.8098574 -0.11738578 -7.169265 -1.0578263 -5.7216787 -5.1173844 16.137651 -4.473626 7.6624317 -0.55381083 9.631587 -6.4704556 -8.548508 4.3716145 -0.79702514 4.478997 -2.9758704 3.272176 2.8382776 5.134597 -9.190781 -0.5657382 -4.8745747 2.3165567 -5.984303 -2.1798875 0.35541576 -0.31784213 9.493548 2.1144536 4.358092 -12.089823 8.451689 -7.925461 4.6242585 4.4289427 18.692003 -2.6204622 -5.149185 -0.35821092 8.488551 4.981496 -9.32683 -2.2544234 6.6417594 1.2119585 10.977129 16.555033 3.3238444 9.551863 -1.6676947 -0.79539716 -8.605674 -0.47356385 2.6741948 -5.359179 -2.6673796 0.66607 15.443222 4.740594 -3.4725387 11.592567 -2.054497 1.7361217 -8.265324 -9.30447 5.4068313 -1.5180256 -7.746615 -6.089606 0.07112726 -0.34904733 -8.649895 -9.998958 -2.564841 -0.53999114 2.601808 -0.31927416 -1.8815292 -2.07215 -3.4105783 -8.2998085 1.483641 -15.365992 -8.288208 3.8847756 -3.4876456 7.3629923 0.4657332 3.132599 12.438889 -1.8337058 4.532936 2.7264361 10.145339 -6.521951 2.897153 -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
Here is a list of pretrained models released by PaddleSpeech that can be used by command and python API:
| Model | Sample Rate |
|---|---|
| ecapatdnn_voxceleb12 | 16k |