(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 cli 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
wget -c https://paddlespeech.bj.bcebos.com/vector/audio/123456789.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 [ -1.3251206 7.8606825 -4.620626 0.3000721 2.2648535 -1.1931441 3.0647137 7.673595 -6.0044727 -12.02426 -1.9496069 3.1269536 1.618838 -7.6383104 -1.2299773 -12.338331 2.1373026 -5.3957124 9.717328 5.6752305 3.7805123 3.0597172 3.429692 8.97601 13.174125 -0.53132284 8.9424715 4.46511 -4.4262476 -9.726503 8.399328 7.2239175 -7.435854 2.9441683 -4.3430395 -13.886965 -1.6346735 -10.9027405 -5.311245 3.8007221 3.8976038 -2.1230774 -2.3521194 4.151031 -7.4048667 0.13911647 2.4626107 4.9664545 0.9897574 5.4839754 -3.3574002 10.1340065 -0.6120171 -10.403095 4.6007543 16.00935 -7.7836914 -4.1945305 -6.9368606 1.1789556 11.490801 4.2380238 9.550931 8.375046 7.5089145 -0.65707296 -0.30051577 2.8406055 3.0828028 0.730817 6.148354 0.13766119 -13.424735 -7.7461405 -2.3227983 -8.305252 2.9879124 -10.995229 0.15211068 -2.3820348 -1.7984174 8.495629 -5.8522367 -3.755498 0.6989711 -5.2702994 -2.6188622 -1.8828466 -4.64665 14.078544 -0.5495333 10.579158 -3.2160501 9.349004 -4.381078 -11.675817 -2.8630207 4.5721755 2.246612 -4.574342 1.8610188 2.3767874 5.6257877 -9.784078 0.64967257 -1.4579505 0.4263264 -4.9211264 -2.454784 3.4869802 -0.42654222 8.341269 1.356552 7.0966883 -13.102829 8.016734 -7.1159344 1.8699781 0.208721 14.699384 -1.025278 -2.6107233 -2.5082312 8.427193 6.9138527 -6.2912464 0.6157366 2.489688 -3.4668267 9.921763 11.200815 -0.1966403 7.4916005 -0.62312716 -0.25848144 -9.947997 -0.9611041 1.1649219 -2.1907122 -1.5028487 -0.51926106 15.165954 2.4649463 -0.9980445 7.4416637 -2.0768049 3.5896823 -7.3055434 -7.5620847 4.323335 0.0804418 -6.56401 -2.3148053 -1.7642345 -2.4708817 -7.675618 -9.548878 -1.0177554 0.16986446 2.5877135 -1.8752296 -0.36614323 -6.0493784 -2.3965611 -5.9453387 0.9424033 -13.155974 -7.457801 0.14658108 -3.742797 5.8414927 -1.2872906 5.5694313 12.57059 1.0939219 2.2142086 1.9181576 6.9914207 -5.888139 3.1409824 -2.003628 2.4434285 9.973139 5.03668 2.0051203 2.8615603 5.860224 2.9176188 -1.6311141 2.0292206 -4.070415 -6.831437 ]
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Python API
from paddlespeech.cli.vector 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: [ -1.3251206 7.8606825 -4.620626 0.3000721 2.2648535 -1.1931441 3.0647137 7.673595 -6.0044727 -12.02426 -1.9496069 3.1269536 1.618838 -7.6383104 -1.2299773 -12.338331 2.1373026 -5.3957124 9.717328 5.6752305 3.7805123 3.0597172 3.429692 8.97601 13.174125 -0.53132284 8.9424715 4.46511 -4.4262476 -9.726503 8.399328 7.2239175 -7.435854 2.9441683 -4.3430395 -13.886965 -1.6346735 -10.9027405 -5.311245 3.8007221 3.8976038 -2.1230774 -2.3521194 4.151031 -7.4048667 0.13911647 2.4626107 4.9664545 0.9897574 5.4839754 -3.3574002 10.1340065 -0.6120171 -10.403095 4.6007543 16.00935 -7.7836914 -4.1945305 -6.9368606 1.1789556 11.490801 4.2380238 9.550931 8.375046 7.5089145 -0.65707296 -0.30051577 2.8406055 3.0828028 0.730817 6.148354 0.13766119 -13.424735 -7.7461405 -2.3227983 -8.305252 2.9879124 -10.995229 0.15211068 -2.3820348 -1.7984174 8.495629 -5.8522367 -3.755498 0.6989711 -5.2702994 -2.6188622 -1.8828466 -4.64665 14.078544 -0.5495333 10.579158 -3.2160501 9.349004 -4.381078 -11.675817 -2.8630207 4.5721755 2.246612 -4.574342 1.8610188 2.3767874 5.6257877 -9.784078 0.64967257 -1.4579505 0.4263264 -4.9211264 -2.454784 3.4869802 -0.42654222 8.341269 1.356552 7.0966883 -13.102829 8.016734 -7.1159344 1.8699781 0.208721 14.699384 -1.025278 -2.6107233 -2.5082312 8.427193 6.9138527 -6.2912464 0.6157366 2.489688 -3.4668267 9.921763 11.200815 -0.1966403 7.4916005 -0.62312716 -0.25848144 -9.947997 -0.9611041 1.1649219 -2.1907122 -1.5028487 -0.51926106 15.165954 2.4649463 -0.9980445 7.4416637 -2.0768049 3.5896823 -7.3055434 -7.5620847 4.323335 0.0804418 -6.56401 -2.3148053 -1.7642345 -2.4708817 -7.675618 -9.548878 -1.0177554 0.16986446 2.5877135 -1.8752296 -0.36614323 -6.0493784 -2.3965611 -5.9453387 0.9424033 -13.155974 -7.457801 0.14658108 -3.742797 5.8414927 -1.2872906 5.5694313 12.57059 1.0939219 2.2142086 1.9181576 6.9914207 -5.888139 3.1409824 -2.003628 2.4434285 9.973139 5.03668 2.0051203 2.8615603 5.860224 2.9176188 -1.6311141 2.0292206 -4.070415 -6.831437 ] # get the test embedding Test embedding Result: [ 2.5247195 5.119042 -4.335273 4.4583654 5.047907 3.5059214 1.6159848 0.49364898 -11.6899185 -3.1014526 -5.6589785 -0.42684984 2.674276 -11.937654 6.2248464 -10.776924 -5.694543 1.112041 1.5709964 1.0961034 1.3976512 2.324352 1.339981 5.279319 13.734659 -2.5753925 13.651442 -2.2357535 5.1575427 -3.251567 1.4023279 6.1191974 -6.0845175 -1.3646189 -2.6789894 -15.220778 9.779349 -9.411551 -6.388947 6.8313975 -9.245996 0.31196198 2.5509644 -4.413065 6.1649427 6.793837 2.6328635 8.620976 3.4832475 0.52491665 2.9115407 5.8392377 0.6702376 -3.2726715 2.6694255 16.91701 -5.5811176 0.23362345 -4.5573606 -11.801059 14.728292 -0.5198082 -3.999922 7.0927105 -7.0459595 -5.4389 -0.46420583 -5.1085467 10.376568 -8.889225 -0.37705845 -1.659806 2.6731026 -7.1909504 1.4608804 -2.163136 -0.17949677 4.0241547 0.11319201 0.601279 2.039692 3.1910992 -11.649526 -8.121584 -4.8707457 0.3851982 1.4231744 -2.3321972 0.99332285 14.121717 5.899413 0.7384519 -17.760096 10.555021 4.1366534 -0.3391071 -0.20792882 3.208204 0.8847948 -8.721497 -6.432868 13.006379 4.8956 -9.155822 -1.9441519 5.7815638 -2.066733 10.425042 -0.8802383 -2.4314315 -9.869258 0.35095334 -5.3549943 2.1076174 -8.290468 8.4433365 -4.689333 9.334139 -2.172678 -3.0250976 8.394216 -3.2110903 -7.93868 2.3960824 -2.3213403 -1.4963245 -3.476059 4.132903 -10.893354 4.362673 -0.45456508 10.258634 -1.1655927 -6.7799754 0.22885278 -4.399287 2.333433 -4.84745 -4.2752337 -1.3577863 -1.0685898 9.505196 7.3062205 0.08708266 12.927811 -9.57974 1.3936648 -1.9444873 5.776769 15.251903 10.6118355 -1.4903594 -9.535318 -3.6553776 -1.6699586 -0.5933151 7.600357 -4.8815503 -8.698617 -15.855757 0.25632986 -7.2235737 0.9506656 0.7128582 -9.051738 8.74869 -1.6426028 -6.5762258 2.506905 -6.7431564 5.129912 -12.189555 -3.6435068 12.068113 -6.0059533 -2.3535995 2.9014351 22.3082 -1.5563312 13.193291 2.7583609 -7.468798 1.3407065 -4.599617 -6.2345777 10.7689295 7.137627 5.099476 0.3473359 9.647881 -2.0484571 -5.8549366 ] # get the score between enroll and test Eembeddings Score: 0.45332613587379456
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 |