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--- |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: wavlm-large-timit-punctuation |
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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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# wavlm-large-timit-punctuation |
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This model is a fine-tuned version of [microsoft/wavlm-large](https://huggingface.co/microsoft/wavlm-large) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.3368 |
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- Wer: 0.2601 |
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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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| 5.2379 | 1.0 | 500 | 3.1228 | 1.0 | |
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| 2.5847 | 2.01 | 1000 | 1.1550 | 0.9147 | |
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| 1.0034 | 3.01 | 1500 | 0.5856 | 0.5180 | |
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| 0.5868 | 4.02 | 2000 | 0.4238 | 0.4229 | |
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| 0.3892 | 5.02 | 2500 | 0.3356 | 0.3665 | |
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| 0.2926 | 6.02 | 3000 | 0.3196 | 0.3360 | |
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| 0.2294 | 7.03 | 3500 | 0.3046 | 0.3170 | |
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| 0.1976 | 8.03 | 4000 | 0.3032 | 0.3111 | |
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| 0.1644 | 9.04 | 4500 | 0.2946 | 0.2954 | |
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| 0.1574 | 10.04 | 5000 | 0.3211 | 0.2998 | |
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| 0.1391 | 11.04 | 5500 | 0.2986 | 0.2922 | |
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| 0.1124 | 12.05 | 6000 | 0.2948 | 0.2837 | |
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| 0.1003 | 13.05 | 6500 | 0.2928 | 0.2788 | |
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| 0.1031 | 14.06 | 7000 | 0.3230 | 0.2805 | |
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| 0.0901 | 15.06 | 7500 | 0.3081 | 0.2749 | |
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| 0.0842 | 16.06 | 8000 | 0.3075 | 0.2726 | |
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| 0.0809 | 17.07 | 8500 | 0.3215 | 0.2717 | |
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| 0.0747 | 18.07 | 9000 | 0.3272 | 0.2721 | |
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| 0.0735 | 19.08 | 9500 | 0.3242 | 0.2684 | |
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| 0.0631 | 20.08 | 10000 | 0.3216 | 0.2640 | |
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| 0.0632 | 21.08 | 10500 | 0.3149 | 0.2646 | |
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| 0.0625 | 22.09 | 11000 | 0.3196 | 0.2630 | |
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| 0.0611 | 23.09 | 11500 | 0.3244 | 0.2638 | |
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| 0.0532 | 24.1 | 12000 | 0.3271 | 0.2641 | |
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| 0.0503 | 25.1 | 12500 | 0.3368 | 0.2636 | |
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| 0.0534 | 26.1 | 13000 | 0.3393 | 0.2627 | |
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| 0.049 | 27.11 | 13500 | 0.3389 | 0.2626 | |
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| 0.0441 | 28.11 | 14000 | 0.3375 | 0.2605 | |
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| 0.0522 | 29.12 | 14500 | 0.3368 | 0.2601 | |
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### Framework versions |
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- Transformers 4.19.2 |
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- Pytorch 1.8.2+cu111 |
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- Datasets 1.17.0 |
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- Tokenizers 0.11.6 |
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