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badrex / xlsr-he-adap-ar
Model's Last Updated: June 07 2024
automatic-speech-recognition
Introduction of xlsr-he-adap-ar
Model Details of xlsr-he-adap-ar
xlsr-he-adap-ar
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common_voice_17_0 dataset. It achieves the following results on the evaluation set:
- Loss: 1.2357
- Wer: 0.4911
- Cer: 0.1797
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 50
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Cer |
|---|---|---|---|---|---|
| 3.3397 | 0.8368 | 100 | 3.5569 | 1.0 | 1.0 |
| 3.1258 | 1.6736 | 200 | 3.4715 | 1.0 | 0.9998 |
| 1.5651 | 2.5105 | 300 | 1.5571 | 0.8937 | 0.4283 |
| 0.893 | 3.3473 | 400 | 1.2606 | 0.8439 | 0.3889 |
| 0.8221 | 4.1841 | 500 | 1.1361 | 0.8317 | 0.3575 |
| 0.6795 | 5.0209 | 600 | 1.0669 | 0.7839 | 0.3629 |
| 0.5132 | 5.8577 | 700 | 0.9873 | 0.7941 | 0.3168 |
| 0.537 | 6.6946 | 800 | 1.1638 | 0.8378 | 0.3449 |
| 0.5907 | 7.5314 | 900 | 0.9734 | 0.7733 | 0.3006 |
| 0.3733 | 8.3682 | 1000 | 1.0026 | 0.7387 | 0.2977 |
| 0.2651 | 9.2050 | 1100 | 1.1072 | 0.7123 | 0.2841 |
| 0.2781 | 10.0418 | 1200 | 0.9694 | 0.7036 | 0.2881 |
| 0.3623 | 10.8787 | 1300 | 1.1220 | 0.6823 | 0.2698 |
| 0.2877 | 11.7155 | 1400 | 1.2862 | 0.7280 | 0.3025 |
| 0.228 | 12.5523 | 1500 | 1.0958 | 0.7031 | 0.2823 |
| 0.2278 | 13.3891 | 1600 | 0.9645 | 0.6528 | 0.2410 |
| 0.2384 | 14.2259 | 1700 | 0.9532 | 0.6695 | 0.2538 |
| 0.1929 | 15.0628 | 1800 | 1.1704 | 0.6817 | 0.2612 |
| 0.1543 | 15.8996 | 1900 | 1.0379 | 0.6233 | 0.2369 |
| 0.3035 | 16.7364 | 2000 | 1.0761 | 0.6467 | 0.2476 |
| 0.1612 | 17.5732 | 2100 | 1.1896 | 0.6441 | 0.2504 |
| 0.1829 | 18.4100 | 2200 | 1.0596 | 0.5699 | 0.2106 |
| 0.2083 | 19.2469 | 2300 | 1.1206 | 0.5811 | 0.2192 |
| 0.1299 | 20.0837 | 2400 | 1.0621 | 0.6370 | 0.2403 |
| 0.186 | 20.9205 | 2500 | 1.1534 | 0.5943 | 0.2238 |
| 0.1404 | 21.7573 | 2600 | 1.1214 | 0.5948 | 0.2242 |
| 0.0893 | 22.5941 | 2700 | 1.1852 | 0.6472 | 0.2467 |
| 0.1458 | 23.4310 | 2800 | 1.0723 | 0.6131 | 0.2254 |
| 0.2173 | 24.2678 | 2900 | 1.0987 | 0.5902 | 0.2225 |
| 0.0952 | 25.1046 | 3000 | 1.1231 | 0.5953 | 0.2209 |
| 0.156 | 25.9414 | 3100 | 1.2038 | 0.5943 | 0.2259 |
| 0.1103 | 26.7782 | 3200 | 1.1634 | 0.6096 | 0.2348 |
| 0.0899 | 27.6151 | 3300 | 1.1886 | 0.5450 | 0.2022 |
| 0.1331 | 28.4519 | 3400 | 1.1969 | 0.5745 | 0.2183 |
| 0.1545 | 29.2887 | 3500 | 1.2478 | 0.5770 | 0.2205 |
| 0.1084 | 30.1255 | 3600 | 1.2240 | 0.5536 | 0.2072 |
| 0.1044 | 30.9623 | 3700 | 1.2254 | 0.5465 | 0.2033 |
| 0.0819 | 31.7992 | 3800 | 1.1872 | 0.5521 | 0.2047 |
| 0.0854 | 32.6360 | 3900 | 1.2303 | 0.5587 | 0.2107 |
| 0.1184 | 33.4728 | 4000 | 1.1831 | 0.5557 | 0.2077 |
| 0.0526 | 34.3096 | 4100 | 1.2022 | 0.5282 | 0.1961 |
| 0.0641 | 35.1464 | 4200 | 1.2088 | 0.5318 | 0.1930 |
| 0.1012 | 35.9833 | 4300 | 1.1910 | 0.5348 | 0.1993 |
| 0.051 | 36.8201 | 4400 | 1.1969 | 0.5140 | 0.1900 |
| 0.0691 | 37.6569 | 4500 | 1.2269 | 0.5008 | 0.1857 |
| 0.0602 | 38.4937 | 4600 | 1.2315 | 0.5058 | 0.1872 |
| 0.0835 | 39.3305 | 4700 | 1.2043 | 0.5140 | 0.1878 |
| 0.1145 | 40.1674 | 4800 | 1.2511 | 0.5084 | 0.1857 |
| 0.1396 | 41.0042 | 4900 | 1.2186 | 0.4987 | 0.1812 |
| 0.0756 | 41.8410 | 5000 | 1.2665 | 0.4977 | 0.1828 |
| 0.0519 | 42.6778 | 5100 | 1.2516 | 0.4997 | 0.1803 |
| 0.0675 | 43.5146 | 5200 | 1.2413 | 0.4972 | 0.1791 |
| 0.0564 | 44.3515 | 5300 | 1.2607 | 0.4881 | 0.1802 |
| 0.0803 | 45.1883 | 5400 | 1.2401 | 0.4947 | 0.1785 |
| 0.0567 | 46.0251 | 5500 | 1.2498 | 0.4835 | 0.1788 |
| 0.0687 | 46.8619 | 5600 | 1.2366 | 0.4865 | 0.1804 |
| 0.0766 | 47.6987 | 5700 | 1.2375 | 0.4875 | 0.1803 |
| 0.0891 | 48.5356 | 5800 | 1.2379 | 0.4901 | 0.1791 |
| 0.0517 | 49.3724 | 5900 | 1.2357 | 0.4911 | 0.1797 |
Framework versions
- Transformers 4.42.0.dev0
- Pytorch 2.3.1+cu121
- Datasets 2.19.2
- Tokenizers 0.19.1
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Updated:March 28 2026
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