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@ -30,6 +30,11 @@ wget -c https://paddlespeech.bj.bcebos.com/vector/audio/85236145389.wav
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paddlespeech vector --task spk --input vec.job
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echo -e "demo2 85236145389.wav \n demo3 85236145389.wav" | paddlespeech vector --task spk
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paddlespeech vector --task score --input "./85236145389.wav ./123456789.wav"
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echo -e "demo4 85236145389.wav 85236145389.wav \n demo5 85236145389.wav 123456789.wav" > vec.job
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paddlespeech vector --task score --input vec.job
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```
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Usage:
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@ -38,6 +43,7 @@ wget -c https://paddlespeech.bj.bcebos.com/vector/audio/85236145389.wav
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```
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Arguments:
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- `input`(required): Audio file to recognize.
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- `task` (required): Specify `vector` task. Default `spk`。
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- `model`: Model type of vector task. Default: `ecapatdnn_voxceleb12`.
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- `sample_rate`: Sample rate of the model. Default: `16000`.
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- `config`: Config of vector task. Use pretrained model when it is None. Default: `None`.
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@ -97,17 +103,29 @@ wget -c https://paddlespeech.bj.bcebos.com/vector/audio/85236145389.wav
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audio_emb = vector_executor(
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model='ecapatdnn_voxceleb12',
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sample_rate=16000,
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config=None,
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config=None, # Set `config` and `ckpt_path` to None to use pretrained model.
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ckpt_path=None,
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audio_file='./85236145389.wav',
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force_yes=False,
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device=paddle.get_device())
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print('Audio embedding Result: \n{}'.format(audio_emb))
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test_emb = vector_executor(
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model='ecapatdnn_voxceleb12',
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sample_rate=16000,
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config=None, # Set `config` and `ckpt_path` to None to use pretrained model.
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ckpt_path=None,
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audio_file='./123456789.wav',
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device=paddle.get_device())
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print('Test embedding Result: \n{}'.format(test_emb))
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score = vector_executor.get_embeddings_score(audio_emb, test_emb)
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print(f"Eembeddings Score: {score}")
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```
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Output:
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Output:
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```bash
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# Vector Result:
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Audio embedding Result:
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[ -5.749211 9.505463 -8.200284 -5.2075014 5.3940268
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-3.04878 1.611095 10.127234 -10.534177 -15.821609
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1.2032688 -0.35080156 1.2629458 -12.643498 -2.5758228
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@ -147,6 +165,49 @@ wget -c https://paddlespeech.bj.bcebos.com/vector/audio/85236145389.wav
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-6.417456 1.3333273 11.872697 -0.30664724 8.8845
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6.5569253 4.7948146 0.03662816 -8.704245 6.224871
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-3.2701402 -11.508579 ]
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# get the test embedding
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Test embedding Result:
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[ -1.9617152 4.2184057 -5.4289927 3.8006616 7.400566
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12.844175 1.4330423 0.4860911 -15.927942 -13.081303
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-4.585545 2.378477 5.5894523 -13.060747 18.578707
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-9.107497 -9.904055 0.7032993 0.7945765 -1.4118854
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-6.4434266 -2.7688267 5.4320455 2.9636188 23.857662
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-4.797293 22.821133 -1.6718386 0.80379957 -10.28131
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-1.0586771 5.840774 -11.794188 0.9715659 -10.794272
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-9.9839325 11.916608 -19.614918 -7.38727 12.361765
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-15.568076 3.796782 1.4648503 -9.617965 1.8912128
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5.5519567 4.1027875 9.565811 1.6652825 -0.06557167
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7.3765106 6.91407 -3.4179301 4.676896 2.4507313
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21.415924 -1.5271066 0.7630236 -15.634208 -24.682417
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12.035311 1.9669697 -13.733474 11.616938 -16.630692
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-16.287516 -7.4265285 -6.4809394 5.4794173 -8.481719
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2.0745668 -7.50969 1.8279544 -15.189501 -4.000386
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-1.5209727 6.975059 4.518711 3.0962887 -6.8465433
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1.3825562 7.6983547 -9.399815 -7.3269534 -2.6540608
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1.3231711 5.0338726 -5.9562182 -10.437971 19.123528
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12.213971 -2.8820174 -20.65914 15.071251 8.114322
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-4.045127 7.5128584 -3.3306584 6.822803 -0.05004288
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-4.4368496 18.926466 14.04377 -5.9657135 4.714744
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10.24277 -3.848245 14.494125 5.3582125 -6.30404
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-14.122616 2.1969411 -5.90989 9.3047 -8.431231
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10.438023 -11.987487 20.954517 -4.279951 -0.3756797
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13.041809 -6.051407 -10.529183 3.7894943 -1.6330183
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6.743382 -0.19549051 7.315633 -19.438568 0.6115422
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4.5697403 2.1208212 0.52282465 -6.9142766 -5.8893275
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0.5135903 0.92921656 -3.0571883 -7.4849505 2.2382743
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-3.0478394 0.08785366 6.810543 -5.1137877 15.182398
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-6.9418297 -8.922732 -2.4528694 7.324874 19.77244
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13.997188 -5.08692 -14.329076 -6.1807523 -1.8777156
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-3.6879017 6.3892293 -3.78877 -13.009837 -16.838747
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-4.1660237 -7.4346085 0.5579437 -2.8482168 -13.509024
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9.329142 8.1292095 -8.064337 -4.002228 -18.78694
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7.7969575 -13.585645 -5.8225474 15.266658 -8.57028
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-7.449079 2.2094946 28.004955 -3.0901644 11.932054
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-1.5897936 -4.826059 6.9232755 -11.169697 -5.235409
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11.251503 2.105524 4.0860977 -0.5384147 19.023642
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1.6203141 -10.608387 ]
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# get the score between enroll and test
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Eembeddings Score: 0.3965281546115875
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```
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### 4.Pretrained Models
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