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--- |
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language: |
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- tr |
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tags: |
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- automatic-speech-recognition |
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- common_voice |
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- generated_from_trainer |
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datasets: |
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- common_voice |
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model-index: |
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- name: '' |
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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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# |
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This model is a fine-tuned version of [./checkpoint-10500](https://huggingface.co/./checkpoint-10500) on the COMMON_VOICE - TR dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.7540 |
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- Wer: 0.4647 |
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- Cer: 0.1318 |
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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: 0.0002 |
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- train_batch_size: 32 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.999,0.9999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 120.0 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Cer | Validation Loss | Wer | |
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|:-------------:|:------:|:-----:|:------:|:---------------:|:------:| |
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| 1.0779 | 4.59 | 500 | 0.2354 | 0.8260 | 0.7395 | |
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| 0.7573 | 9.17 | 1000 | 0.2100 | 0.7544 | 0.6960 | |
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| 0.8225 | 13.76 | 1500 | 0.2021 | 0.6867 | 0.6672 | |
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| 0.621 | 18.35 | 2000 | 0.1874 | 0.6824 | 0.6209 | |
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| 0.6362 | 22.94 | 2500 | 0.1904 | 0.6712 | 0.6286 | |
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| 0.624 | 27.52 | 3000 | 0.1820 | 0.6940 | 0.6116 | |
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| 0.4781 | 32.11 | 3500 | 0.1735 | 0.6966 | 0.5989 | |
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| 0.5685 | 36.7 | 4000 | 0.1769 | 0.6742 | 0.5971 | |
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| 0.4384 | 41.28 | 4500 | 0.1767 | 0.6904 | 0.5999 | |
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| 0.5509 | 45.87 | 5000 | 0.1692 | 0.6734 | 0.5641 | |
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| 0.3665 | 50.46 | 5500 | 0.1680 | 0.7018 | 0.5662 | |
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| 0.3914 | 55.05 | 6000 | 0.1631 | 0.7121 | 0.5552 | |
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| 0.2467 | 59.63 | 6500 | 0.1563 | 0.6657 | 0.5374 | |
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| 0.2576 | 64.22 | 7000 | 0.1554 | 0.6920 | 0.5316 | |
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| 0.2711 | 68.81 | 7500 | 0.1495 | 0.6900 | 0.5176 | |
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| 0.2626 | 73.39 | 8000 | 0.1454 | 0.6843 | 0.5043 | |
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| 0.1377 | 77.98 | 8500 | 0.1470 | 0.7383 | 0.5101 | |
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| 0.2005 | 82.57 | 9000 | 0.1430 | 0.7228 | 0.5045 | |
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| 0.1355 | 87.16 | 9500 | 0.1375 | 0.7231 | 0.4869 | |
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| 0.0431 | 91.74 | 10000 | 0.1350 | 0.7397 | 0.4749 | |
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| 0.0586 | 96.33 | 10500 | 0.1339 | 0.7360 | 0.4754 | |
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| 0.0896 | 100.92 | 11000 | 0.7187 | 0.4885 | 0.1398 | |
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| 0.183 | 105.5 | 11500 | 0.7310 | 0.4838 | 0.1392 | |
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| 0.0963 | 110.09 | 12000 | 0.7643 | 0.4759 | 0.1362 | |
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| 0.0437 | 114.68 | 12500 | 0.7525 | 0.4641 | 0.1328 | |
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| 0.1122 | 119.27 | 13000 | 0.7535 | 0.4651 | 0.1317 | |
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### Framework versions |
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- Transformers 4.17.0.dev0 |
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- Pytorch 1.10.2+cu102 |
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- Datasets 1.18.2.dev0 |
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- Tokenizers 0.11.0 |
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