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--- |
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library_name: transformers |
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tags: |
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- generated_from_trainer |
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datasets: |
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- common_voice_17_0 |
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metrics: |
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- wer |
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model-index: |
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- name: wav2vec2-gpt2-enc-dec |
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results: |
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- task: |
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name: Automatic Speech Recognition |
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type: automatic-speech-recognition |
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dataset: |
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name: common_voice_17_0 |
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type: common_voice_17_0 |
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config: cs |
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split: train+validation |
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args: cs |
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metrics: |
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- name: Wer |
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type: wer |
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value: 0.7337164750957854 |
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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-gpt2-enc-dec |
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This model is a fine-tuned version of [](https://huggingface.co./) on the common_voice_17_0 dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.3169 |
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- Wer: 0.7337 |
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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: 4 |
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- eval_batch_size: 4 |
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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: 0.08 |
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- num_epochs: 20 |
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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 | Wer | |
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|:-------------:|:-------:|:------:|:---------------:|:------:| |
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| 0.7683 | 0.2744 | 2000 | 0.7136 | 1.0624 | |
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| 0.6998 | 0.5488 | 4000 | 0.6563 | 1.1959 | |
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| 0.6798 | 0.8232 | 6000 | 0.6385 | 1.2165 | |
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| 0.6563 | 1.0975 | 8000 | 0.6162 | 1.1502 | |
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| 0.6385 | 1.3719 | 10000 | 0.6097 | 1.2121 | |
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| 0.6509 | 1.6463 | 12000 | 0.6009 | 1.1585 | |
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| 0.6365 | 1.9207 | 14000 | 0.5816 | 1.1171 | |
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| 0.6148 | 2.1951 | 16000 | 0.5811 | 1.1667 | |
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| 0.6081 | 2.4695 | 18000 | 0.5620 | 1.0605 | |
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| 0.6116 | 2.7439 | 20000 | 0.5596 | 1.1314 | |
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| 0.5875 | 3.0182 | 22000 | 0.5496 | 1.1081 | |
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| 0.5831 | 3.2926 | 24000 | 0.5347 | 1.0435 | |
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| 0.5798 | 3.5670 | 26000 | 0.5363 | 1.0736 | |
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| 0.6004 | 3.8414 | 28000 | 0.5234 | 1.0120 | |
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| 0.5525 | 4.1158 | 30000 | 0.5096 | 0.9784 | |
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| 0.5616 | 4.3902 | 32000 | 0.5074 | 0.9910 | |
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| 0.5623 | 4.6646 | 34000 | 0.5054 | 0.9849 | |
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| 0.549 | 4.9389 | 36000 | 0.5028 | 0.9962 | |
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| 0.5334 | 5.2133 | 38000 | 0.4876 | 0.9537 | |
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| 0.5482 | 5.4877 | 40000 | 0.4865 | 0.9414 | |
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| 0.5277 | 5.7621 | 42000 | 0.4886 | 0.9789 | |
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| 0.5126 | 6.0365 | 44000 | 0.4769 | 0.9494 | |
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| 0.5246 | 6.3109 | 46000 | 0.4690 | 0.9278 | |
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| 0.5255 | 6.5853 | 48000 | 0.4688 | 0.9455 | |
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| 0.5156 | 6.8597 | 50000 | 0.4597 | 0.9236 | |
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| 0.5135 | 7.1340 | 52000 | 0.4581 | 0.9245 | |
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| 0.5041 | 7.4084 | 54000 | 0.4488 | 0.9067 | |
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| 0.505 | 7.6828 | 56000 | 0.4456 | 0.9050 | |
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| 0.5151 | 7.9572 | 58000 | 0.4441 | 0.9053 | |
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| 0.4953 | 8.2316 | 60000 | 0.4374 | 0.8990 | |
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| 0.4815 | 8.5060 | 62000 | 0.4338 | 0.8987 | |
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| 0.506 | 8.7804 | 64000 | 0.4295 | 0.8897 | |
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| 0.4847 | 9.0547 | 66000 | 0.4273 | 0.8922 | |
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| 0.4763 | 9.3291 | 68000 | 0.4206 | 0.8782 | |
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| 0.4822 | 9.6035 | 70000 | 0.4180 | 0.8790 | |
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| 0.4728 | 9.8779 | 72000 | 0.4131 | 0.8673 | |
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| 0.4645 | 10.1523 | 74000 | 0.4086 | 0.8708 | |
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| 0.4696 | 10.4267 | 76000 | 0.4087 | 0.8741 | |
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| 0.4664 | 10.7011 | 78000 | 0.4020 | 0.8547 | |
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| 0.4677 | 10.9754 | 80000 | 0.3974 | 0.8503 | |
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| 0.4482 | 11.2498 | 82000 | 0.3907 | 0.8410 | |
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| 0.4518 | 11.5242 | 84000 | 0.3890 | 0.8451 | |
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| 0.4638 | 11.7986 | 86000 | 0.3855 | 0.8407 | |
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| 0.4325 | 12.0730 | 88000 | 0.3825 | 0.8328 | |
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| 0.4421 | 12.3474 | 90000 | 0.3742 | 0.8134 | |
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| 0.4366 | 12.6218 | 92000 | 0.3751 | 0.8303 | |
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| 0.4412 | 12.8961 | 94000 | 0.3671 | 0.8120 | |
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| 0.4297 | 13.1705 | 96000 | 0.3639 | 0.8035 | |
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| 0.4267 | 13.4449 | 98000 | 0.3651 | 0.8131 | |
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| 0.437 | 13.7193 | 100000 | 0.3575 | 0.7947 | |
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| 0.4373 | 13.9937 | 102000 | 0.3561 | 0.8010 | |
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| 0.4182 | 14.2681 | 104000 | 0.3514 | 0.7860 | |
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| 0.4141 | 14.5425 | 106000 | 0.3490 | 0.7874 | |
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| 0.4117 | 14.8168 | 108000 | 0.3466 | 0.7833 | |
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| 0.4116 | 15.0912 | 110000 | 0.3446 | 0.7753 | |
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| 0.4074 | 15.3656 | 112000 | 0.3411 | 0.7696 | |
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| 0.4086 | 15.6400 | 114000 | 0.3394 | 0.7770 | |
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| 0.4189 | 15.9144 | 116000 | 0.3358 | 0.7666 | |
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| 0.3972 | 16.1888 | 118000 | 0.3332 | 0.7608 | |
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| 0.3911 | 16.4632 | 120000 | 0.3328 | 0.7603 | |
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| 0.4062 | 16.7375 | 122000 | 0.3296 | 0.7496 | |
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| 0.4 | 17.0119 | 124000 | 0.3279 | 0.7537 | |
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| 0.388 | 17.2863 | 126000 | 0.3262 | 0.7469 | |
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| 0.3929 | 17.5607 | 128000 | 0.3251 | 0.7471 | |
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| 0.4036 | 17.8351 | 130000 | 0.3237 | 0.7455 | |
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| 0.392 | 18.1095 | 132000 | 0.3217 | 0.7417 | |
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| 0.3916 | 18.3839 | 134000 | 0.3207 | 0.7406 | |
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| 0.3934 | 18.6583 | 136000 | 0.3190 | 0.7375 | |
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| 0.3805 | 18.9326 | 138000 | 0.3188 | 0.7384 | |
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| 0.3778 | 19.2070 | 140000 | 0.3178 | 0.7365 | |
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| 0.3795 | 19.4814 | 142000 | 0.3173 | 0.7337 | |
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| 0.3841 | 19.7558 | 144000 | 0.3169 | 0.7337 | |
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### Framework versions |
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- Transformers 4.45.2 |
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- Pytorch 2.4.1+cu121 |
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- Datasets 3.0.1 |
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- Tokenizers 0.20.0 |
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