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--- |
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license: apache-2.0 |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: hubert-base-timit-demo-google-colab-ft30ep_v4 |
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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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# hubert-base-timit-demo-google-colab-ft35ep |
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This model is a fine-tuned version of [facebook/hubert-base-ls960](https://huggingface.co/facebook/hubert-base-ls960) on the timit-asr dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.4602 |
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- Wer: 0.3466 |
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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: 0.0001 |
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- train_batch_size: 8 |
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- eval_batch_size: 8 |
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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_steps: 1000 |
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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 | Wer | |
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|:-------------:|:-----:|:-----:|:---------------:|:------:| |
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| 3.825 | 0.87 | 500 | 2.9521 | 1.0 | |
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| 2.431 | 1.73 | 1000 | 0.9760 | 0.8013 | |
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| 1.0089 | 2.6 | 1500 | 0.5934 | 0.5968 | |
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| 0.6859 | 3.46 | 2000 | 0.5132 | 0.5356 | |
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| 0.5302 | 4.33 | 2500 | 0.4506 | 0.4894 | |
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| 0.44 | 5.19 | 3000 | 0.4340 | 0.4670 | |
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| 0.3926 | 6.06 | 3500 | 0.4506 | 0.4528 | |
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| 0.3326 | 6.92 | 4000 | 0.4197 | 0.4486 | |
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| 0.2937 | 7.79 | 4500 | 0.4093 | 0.4193 | |
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| 0.2568 | 8.65 | 5000 | 0.4098 | 0.4229 | |
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| 0.2473 | 9.52 | 5500 | 0.4090 | 0.4141 | |
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| 0.2233 | 10.38 | 6000 | 0.4152 | 0.4125 | |
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| 0.2108 | 11.25 | 6500 | 0.4586 | 0.4189 | |
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| 0.2086 | 12.11 | 7000 | 0.4284 | 0.3969 | |
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| 0.1858 | 12.98 | 7500 | 0.4028 | 0.3946 | |
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| 0.1641 | 13.84 | 8000 | 0.4679 | 0.4002 | |
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| 0.1686 | 14.71 | 8500 | 0.4441 | 0.3936 | |
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| 0.1489 | 15.57 | 9000 | 0.4897 | 0.3828 | |
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| 0.1541 | 16.44 | 9500 | 0.4953 | 0.3783 | |
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| 0.1417 | 17.3 | 10000 | 0.4500 | 0.3758 | |
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| 0.1428 | 18.17 | 10500 | 0.4533 | 0.3796 | |
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| 0.1306 | 19.03 | 11000 | 0.4474 | 0.3792 | |
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| 0.1185 | 19.9 | 11500 | 0.4762 | 0.3743 | |
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| 0.1081 | 20.76 | 12000 | 0.4770 | 0.3699 | |
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| 0.1253 | 21.63 | 12500 | 0.4749 | 0.3629 | |
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| 0.1087 | 22.49 | 13000 | 0.4577 | 0.3534 | |
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| 0.1172 | 23.36 | 13500 | 0.4819 | 0.3525 | |
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| 0.1086 | 24.22 | 14000 | 0.4709 | 0.3623 | |
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| 0.089 | 25.09 | 14500 | 0.4852 | 0.3544 | |
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| 0.086 | 25.95 | 15000 | 0.4602 | 0.3555 | |
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| 0.086 | 26.82 | 15500 | 0.4861 | 0.3497 | |
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| 0.086 | 27.68 | 16000 | 0.4527 | 0.3473 | |
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| 0.0919 | 28.55 | 16500 | 0.4607 | 0.3487 | |
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| 0.0792 | 29.41 | 17000 | 0.4602 | 0.3466 | |
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### Framework versions |
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- Transformers 4.17.0 |
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- Pytorch 1.11.0+cu113 |
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- Datasets 1.18.3 |
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- Tokenizers 0.12.1 |
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