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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_v5 |
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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-ft30ep_v5 |
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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 None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.4763 |
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- Wer: 0.3322 |
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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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| 3.9596 | 0.87 | 500 | 3.1237 | 1.0 | |
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| 2.5388 | 1.73 | 1000 | 1.1689 | 0.9184 | |
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| 1.0448 | 2.6 | 1500 | 0.6106 | 0.5878 | |
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| 0.6793 | 3.46 | 2000 | 0.4912 | 0.5200 | |
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| 0.5234 | 4.33 | 2500 | 0.4529 | 0.4798 | |
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| 0.4368 | 5.19 | 3000 | 0.4239 | 0.4543 | |
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| 0.3839 | 6.06 | 3500 | 0.4326 | 0.4339 | |
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| 0.3315 | 6.92 | 4000 | 0.4265 | 0.4173 | |
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| 0.2878 | 7.79 | 4500 | 0.4304 | 0.4068 | |
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| 0.25 | 8.65 | 5000 | 0.4130 | 0.3940 | |
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| 0.242 | 9.52 | 5500 | 0.4310 | 0.3938 | |
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| 0.2182 | 10.38 | 6000 | 0.4204 | 0.3843 | |
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| 0.2063 | 11.25 | 6500 | 0.4449 | 0.3816 | |
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| 0.2099 | 12.11 | 7000 | 0.4016 | 0.3681 | |
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| 0.1795 | 12.98 | 7500 | 0.4027 | 0.3647 | |
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| 0.1604 | 13.84 | 8000 | 0.4294 | 0.3664 | |
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| 0.1683 | 14.71 | 8500 | 0.4412 | 0.3661 | |
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| 0.1452 | 15.57 | 9000 | 0.4484 | 0.3588 | |
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| 0.1491 | 16.44 | 9500 | 0.4508 | 0.3515 | |
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| 0.1388 | 17.3 | 10000 | 0.4240 | 0.3518 | |
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| 0.1399 | 18.17 | 10500 | 0.4605 | 0.3513 | |
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| 0.1265 | 19.03 | 11000 | 0.4412 | 0.3485 | |
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| 0.1137 | 19.9 | 11500 | 0.4520 | 0.3467 | |
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| 0.106 | 20.76 | 12000 | 0.4873 | 0.3426 | |
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| 0.1243 | 21.63 | 12500 | 0.4456 | 0.3396 | |
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| 0.1055 | 22.49 | 13000 | 0.4819 | 0.3406 | |
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| 0.1124 | 23.36 | 13500 | 0.4613 | 0.3391 | |
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| 0.1064 | 24.22 | 14000 | 0.4842 | 0.3430 | |
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| 0.0875 | 25.09 | 14500 | 0.4661 | 0.3348 | |
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| 0.086 | 25.95 | 15000 | 0.4724 | 0.3371 | |
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| 0.0842 | 26.82 | 15500 | 0.4982 | 0.3381 | |
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| 0.0834 | 27.68 | 16000 | 0.4856 | 0.3337 | |
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| 0.0918 | 28.55 | 16500 | 0.4783 | 0.3344 | |
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| 0.0773 | 29.41 | 17000 | 0.4763 | 0.3322 | |
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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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