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wav2vec2-large-xlsr-arabic-demo-17-july

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wav2vec2
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arabic-asr-unique-words-model

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  1. README.md +93 -13
  2. model.safetensors +1 -1
README.md CHANGED
@@ -22,7 +22,7 @@ model-index:
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  metrics:
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  - name: Wer
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  type: wer
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- value: 1.0415944540727904
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  ---
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  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -32,8 +32,8 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the audiofolder dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.0426
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- - Wer: 1.0416
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  ## Model description
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@@ -68,16 +68,96 @@ The following hyperparameters were used during training:
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  | Training Loss | Epoch | Step | Validation Loss | Wer |
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  |:-------------:|:-------:|:----:|:---------------:|:------:|
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- | 6.7188 | 2.7397 | 100 | 5.2691 | 1.0 |
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- | 3.2081 | 5.4795 | 200 | 3.1517 | 1.0 |
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- | 2.6748 | 8.2192 | 300 | 2.5799 | 1.0 |
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- | 1.5772 | 10.9589 | 400 | 1.3468 | 1.0962 |
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- | 0.5164 | 13.6986 | 500 | 0.3235 | 1.3683 |
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- | 0.3366 | 16.4384 | 600 | 0.1103 | 1.1464 |
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- | 0.2755 | 19.1781 | 700 | 0.1065 | 1.1334 |
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- | 0.2133 | 21.9178 | 800 | 0.0651 | 1.0828 |
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- | 0.1861 | 24.6575 | 900 | 0.0509 | 1.0503 |
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- | 0.1765 | 27.3973 | 1000 | 0.0426 | 1.0416 |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ### Framework versions
 
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  metrics:
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  - name: Wer
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  type: wer
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+ value: 0.03302203188689954
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  ---
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  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
 
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  This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the audiofolder dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.0192
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+ - Wer: 0.0330
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  ## Model description
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  | Training Loss | Epoch | Step | Validation Loss | Wer |
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  |:-------------:|:-------:|:----:|:---------------:|:------:|
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+ | 7.5233 | 0.3300 | 100 | 5.9510 | 1.0 |
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+ | 3.4 | 0.6601 | 200 | 3.3562 | 1.0 |
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+ | 2.8077 | 0.9901 | 300 | 2.7972 | 1.0 |
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+ | 1.3772 | 1.3201 | 400 | 1.0323 | 0.8612 |
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+ | 0.6436 | 1.6502 | 500 | 0.3189 | 0.4047 |
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+ | 0.4996 | 1.9802 | 600 | 0.1811 | 0.3562 |
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+ | 0.4313 | 2.3102 | 700 | 0.2062 | 0.3067 |
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+ | 0.4207 | 2.6403 | 800 | 0.2273 | 0.3495 |
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+ | 0.4021 | 2.9703 | 900 | 0.1336 | 0.2045 |
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+ | 0.339 | 3.3003 | 1000 | 0.1281 | 0.1873 |
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+ | 0.3848 | 3.6304 | 1100 | 0.1254 | 0.1969 |
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+ | 0.33 | 3.9604 | 1200 | 0.1392 | 0.2297 |
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+ | 0.3039 | 4.2904 | 1300 | 0.1041 | 0.1704 |
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+ | 0.287 | 4.6205 | 1400 | 0.1037 | 0.1818 |
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+ | 0.2526 | 4.9505 | 1500 | 0.1156 | 0.2215 |
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+ | 0.2806 | 5.2805 | 1600 | 0.1125 | 0.1780 |
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+ | 0.2742 | 5.6106 | 1700 | 0.0892 | 0.1472 |
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+ | 0.2715 | 5.9406 | 1800 | 0.0884 | 0.1540 |
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+ | 0.2282 | 6.2706 | 1900 | 0.0906 | 0.1569 |
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+ | 0.2558 | 6.6007 | 2000 | 0.0894 | 0.1505 |
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+ | 0.2267 | 6.9307 | 2100 | 0.0889 | 0.1927 |
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+ | 0.2188 | 7.2607 | 2200 | 0.0796 | 0.1318 |
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+ | 0.2347 | 7.5908 | 2300 | 0.1093 | 0.1779 |
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+ | 0.223 | 7.9208 | 2400 | 0.0802 | 0.1305 |
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+ | 0.2069 | 8.2508 | 2500 | 0.0831 | 0.1381 |
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+ | 0.1939 | 8.5809 | 2600 | 0.0748 | 0.1252 |
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+ | 0.1857 | 8.9109 | 2700 | 0.0691 | 0.1182 |
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+ | 0.1671 | 9.2409 | 2800 | 0.0628 | 0.1009 |
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+ | 0.1822 | 9.5710 | 2900 | 0.0687 | 0.1101 |
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+ | 0.1823 | 9.9010 | 3000 | 0.0642 | 0.1048 |
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+ | 0.2006 | 10.2310 | 3100 | 0.0821 | 0.1360 |
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+ | 0.1686 | 10.5611 | 3200 | 0.0750 | 0.1175 |
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+ | 0.1702 | 10.8911 | 3300 | 0.0810 | 0.1366 |
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+ | 0.1824 | 11.2211 | 3400 | 0.1038 | 0.1736 |
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+ | 0.1794 | 11.5512 | 3500 | 0.0711 | 0.1145 |
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+ | 0.1834 | 11.8812 | 3600 | 0.0695 | 0.1130 |
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+ | 0.1601 | 12.2112 | 3700 | 0.0620 | 0.1000 |
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+ | 0.138 | 12.5413 | 3800 | 0.0593 | 0.1074 |
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+ | 0.1394 | 12.8713 | 3900 | 0.0542 | 0.0873 |
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+ | 0.1636 | 13.2013 | 4000 | 0.0661 | 0.1210 |
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+ | 0.1272 | 13.5314 | 4100 | 0.0575 | 0.0966 |
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+ | 0.1428 | 13.8614 | 4200 | 0.0665 | 0.0992 |
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+ | 0.2048 | 14.1914 | 4300 | 0.0592 | 0.1110 |
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+ | 0.1789 | 14.5215 | 4400 | 0.0544 | 0.0892 |
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+ | 0.181 | 14.8515 | 4500 | 0.0534 | 0.0934 |
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+ | 0.2337 | 15.1815 | 4600 | 0.0560 | 0.0931 |
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+ | 0.2261 | 15.5116 | 4700 | 0.0565 | 0.0985 |
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+ | 0.2106 | 15.8416 | 4800 | 0.0473 | 0.0808 |
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+ | 0.2219 | 16.1716 | 4900 | 0.0520 | 0.0875 |
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+ | 0.2647 | 16.5017 | 5000 | 0.0607 | 0.0962 |
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+ | 0.2376 | 16.8317 | 5100 | 0.0500 | 0.0859 |
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+ | 0.2211 | 17.1617 | 5200 | 0.0478 | 0.0752 |
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+ | 0.229 | 17.4917 | 5300 | 0.0574 | 0.0928 |
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+ | 0.1958 | 17.8218 | 5400 | 0.0481 | 0.0772 |
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+ | 0.1646 | 18.1518 | 5500 | 0.0432 | 0.0715 |
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+ | 0.1949 | 18.4818 | 5600 | 0.0483 | 0.0787 |
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+ | 0.2152 | 18.8119 | 5700 | 0.0489 | 0.0822 |
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+ | 0.1749 | 19.1419 | 5800 | 0.0422 | 0.0746 |
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+ | 0.1663 | 19.4719 | 5900 | 0.0421 | 0.0766 |
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+ | 0.1684 | 19.8020 | 6000 | 0.0367 | 0.0592 |
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+ | 0.1484 | 20.1320 | 6100 | 0.0374 | 0.0599 |
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+ | 0.1501 | 20.4620 | 6200 | 0.0358 | 0.0613 |
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+ | 0.1426 | 20.7921 | 6300 | 0.0388 | 0.0650 |
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+ | 0.1422 | 21.1221 | 6400 | 0.0355 | 0.0594 |
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+ | 0.1343 | 21.4521 | 6500 | 0.0352 | 0.0585 |
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+ | 0.1275 | 21.7822 | 6600 | 0.0305 | 0.0511 |
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+ | 0.1235 | 22.1122 | 6700 | 0.0329 | 0.0562 |
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+ | 0.1033 | 22.4422 | 6800 | 0.0301 | 0.0499 |
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+ | 0.1064 | 22.7723 | 6900 | 0.0310 | 0.0502 |
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+ | 0.1173 | 23.1023 | 7000 | 0.0280 | 0.0479 |
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+ | 0.114 | 23.4323 | 7100 | 0.0310 | 0.0501 |
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+ | 0.1183 | 23.7624 | 7200 | 0.0293 | 0.0499 |
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+ | 0.0891 | 24.0924 | 7300 | 0.0287 | 0.0488 |
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+ | 0.091 | 24.4224 | 7400 | 0.0273 | 0.0473 |
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+ | 0.1043 | 24.7525 | 7500 | 0.0269 | 0.0467 |
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+ | 0.0887 | 25.0825 | 7600 | 0.0253 | 0.0434 |
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+ | 0.0806 | 25.4125 | 7700 | 0.0253 | 0.0448 |
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+ | 0.0902 | 25.7426 | 7800 | 0.0244 | 0.0414 |
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+ | 0.0705 | 26.0726 | 7900 | 0.0239 | 0.0402 |
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+ | 0.0885 | 26.4026 | 8000 | 0.0226 | 0.0364 |
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+ | 0.0889 | 26.7327 | 8100 | 0.0230 | 0.0395 |
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+ | 0.0835 | 27.0627 | 8200 | 0.0222 | 0.0373 |
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+ | 0.0762 | 27.3927 | 8300 | 0.0216 | 0.0362 |
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+ | 0.0609 | 27.7228 | 8400 | 0.0211 | 0.0360 |
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+ | 0.0592 | 28.0528 | 8500 | 0.0204 | 0.0353 |
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+ | 0.0683 | 28.3828 | 8600 | 0.0212 | 0.0363 |
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+ | 0.072 | 28.7129 | 8700 | 0.0198 | 0.0341 |
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+ | 0.0653 | 29.0429 | 8800 | 0.0198 | 0.0338 |
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+ | 0.0588 | 29.3729 | 8900 | 0.0193 | 0.0333 |
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+ | 0.065 | 29.7030 | 9000 | 0.0192 | 0.0330 |
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  ### Framework versions
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