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--- |
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language: |
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- tr |
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license: apache-2.0 |
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base_model: facebook/wav2vec2-xls-r-300m |
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tags: |
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- generated_from_trainer |
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datasets: |
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- mozilla-foundation/common_voice_17 |
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model-index: |
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- name: 'Wav2Vec2-XLS-TR ' |
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results: [] |
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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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should probably proofread and complete it, then remove this comment. --> |
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# Wav2Vec2-XLS-TR |
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This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the Common Voice 17 dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.5348 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 1e-05 |
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- train_batch_size: 32 |
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- eval_batch_size: 32 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_ratio: 0.1 |
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- num_epochs: 30 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:-------:|:-----:|:---------------:| |
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| 21.9538 | 0.3446 | 500 | 12.1320 | |
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| 7.2139 | 0.6892 | 1000 | 5.5372 | |
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| 4.9229 | 1.0338 | 1500 | 4.4389 | |
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| 4.0539 | 1.3784 | 2000 | 3.7210 | |
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| 3.5019 | 1.7229 | 2500 | 3.3480 | |
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| 3.2272 | 2.0675 | 3000 | 3.1302 | |
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| 2.9163 | 2.4121 | 3500 | 2.5875 | |
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| 1.902 | 2.7567 | 4000 | 1.5471 | |
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| 1.2034 | 3.1013 | 4500 | 1.1319 | |
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| 0.9389 | 3.4459 | 5000 | 1.0412 | |
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| 0.7599 | 3.7905 | 5500 | 0.8486 | |
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| 0.6418 | 4.1351 | 6000 | 0.7179 | |
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| 0.569 | 4.4797 | 6500 | 0.7109 | |
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| 0.5248 | 4.8243 | 7000 | 0.6470 | |
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| 0.4752 | 5.1688 | 7500 | 0.6298 | |
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| 0.4461 | 5.5134 | 8000 | 0.6198 | |
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| 0.4187 | 5.8580 | 8500 | 0.6224 | |
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| 0.3935 | 6.2026 | 9000 | 0.6116 | |
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| 0.3756 | 6.5472 | 9500 | 0.5536 | |
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| 0.3597 | 6.8918 | 10000 | 0.5263 | |
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| 0.3483 | 7.2364 | 10500 | 0.5179 | |
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| 0.3283 | 7.5810 | 11000 | 0.5054 | |
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| 0.3204 | 7.9256 | 11500 | 0.5200 | |
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| 0.3031 | 8.2702 | 12000 | 0.4984 | |
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| 0.2986 | 8.6147 | 12500 | 0.4846 | |
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| 0.2936 | 8.9593 | 13000 | 0.4984 | |
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| 0.2789 | 9.3039 | 13500 | 0.4888 | |
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| 0.2724 | 9.6485 | 14000 | 0.4654 | |
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| 0.2718 | 9.9931 | 14500 | 0.4553 | |
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| 0.2533 | 10.3377 | 15000 | 0.4506 | |
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| 0.2498 | 10.6823 | 15500 | 0.4983 | |
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| 0.2501 | 11.0269 | 16000 | 0.4835 | |
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| 0.2384 | 11.3715 | 16500 | 0.4749 | |
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| 0.2371 | 11.7161 | 17000 | 0.4877 | |
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| 0.2319 | 12.0606 | 17500 | 0.4806 | |
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| 0.2235 | 12.4052 | 18000 | 0.4874 | |
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| 0.2218 | 12.7498 | 18500 | 0.4565 | |
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| 0.2193 | 13.0944 | 19000 | 0.4733 | |
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| 0.2154 | 13.4390 | 19500 | 0.4747 | |
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| 0.2157 | 13.7836 | 20000 | 0.4694 | |
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| 0.2067 | 14.1282 | 20500 | 0.4848 | |
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| 0.2044 | 14.4728 | 21000 | 0.5092 | |
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| 0.2023 | 14.8174 | 21500 | 0.4752 | |
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| 0.1975 | 15.1620 | 22000 | 0.4852 | |
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| 0.1903 | 15.5065 | 22500 | 0.4891 | |
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| 0.1922 | 15.8511 | 23000 | 0.4825 | |
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| 0.1901 | 16.1957 | 23500 | 0.4836 | |
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| 0.1862 | 16.5403 | 24000 | 0.4838 | |
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| 0.1842 | 16.8849 | 24500 | 0.4897 | |
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| 0.1837 | 17.2295 | 25000 | 0.4920 | |
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| 0.1782 | 17.5741 | 25500 | 0.4937 | |
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| 0.1775 | 17.9187 | 26000 | 0.4868 | |
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| 0.1738 | 18.2633 | 26500 | 0.5107 | |
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| 0.1739 | 18.6079 | 27000 | 0.4943 | |
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| 0.1722 | 18.9524 | 27500 | 0.4740 | |
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| 0.1691 | 19.2970 | 28000 | 0.4965 | |
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| 0.1684 | 19.6416 | 28500 | 0.4834 | |
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| 0.1646 | 19.9862 | 29000 | 0.5187 | |
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| 0.1637 | 20.3308 | 29500 | 0.4979 | |
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| 0.1625 | 20.6754 | 30000 | 0.4945 | |
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| 0.1608 | 21.0200 | 30500 | 0.5149 | |
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| 0.1567 | 21.3646 | 31000 | 0.5030 | |
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| 0.1571 | 21.7092 | 31500 | 0.5013 | |
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| 0.1589 | 22.0538 | 32000 | 0.5269 | |
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| 0.1524 | 22.3983 | 32500 | 0.5191 | |
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| 0.1491 | 22.7429 | 33000 | 0.5200 | |
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| 0.1521 | 23.0875 | 33500 | 0.5206 | |
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| 0.149 | 23.4321 | 34000 | 0.5214 | |
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| 0.1505 | 23.7767 | 34500 | 0.5255 | |
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| 0.1516 | 24.1213 | 35000 | 0.5168 | |
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| 0.1479 | 24.4659 | 35500 | 0.5431 | |
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| 0.1476 | 24.8105 | 36000 | 0.5352 | |
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| 0.1433 | 25.1551 | 36500 | 0.5281 | |
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| 0.1464 | 25.4997 | 37000 | 0.5207 | |
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| 0.1444 | 25.8442 | 37500 | 0.5254 | |
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| 0.1466 | 26.1888 | 38000 | 0.5163 | |
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| 0.1424 | 26.5334 | 38500 | 0.5200 | |
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| 0.1375 | 26.8780 | 39000 | 0.5188 | |
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| 0.1428 | 27.2226 | 39500 | 0.5315 | |
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| 0.1376 | 27.5672 | 40000 | 0.5323 | |
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| 0.139 | 27.9118 | 40500 | 0.5370 | |
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| 0.1437 | 28.2564 | 41000 | 0.5426 | |
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| 0.138 | 28.6010 | 41500 | 0.5263 | |
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| 0.1382 | 28.9456 | 42000 | 0.5286 | |
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| 0.139 | 29.2901 | 42500 | 0.5302 | |
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| 0.139 | 29.6347 | 43000 | 0.5334 | |
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| 0.1356 | 29.9793 | 43500 | 0.5348 | |
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### Framework versions |
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- Transformers 4.41.2 |
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- Pytorch 2.3.1+cu121 |
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- Datasets 2.20.0 |
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- Tokenizers 0.19.1 |
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