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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: wav2vec2-base-cynthia-timit |
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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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# wav2vec2-base-cynthia-timit |
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This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.4888 |
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- Wer: 0.3315 |
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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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| 3.7674 | 1.0 | 500 | 2.8994 | 1.0 | |
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| 1.3538 | 2.01 | 1000 | 0.5623 | 0.5630 | |
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| 0.5416 | 3.01 | 1500 | 0.4595 | 0.4765 | |
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| 0.3563 | 4.02 | 2000 | 0.4435 | 0.4328 | |
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| 0.2869 | 5.02 | 2500 | 0.4035 | 0.4145 | |
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| 0.2536 | 6.02 | 3000 | 0.4090 | 0.3945 | |
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| 0.2072 | 7.03 | 3500 | 0.4188 | 0.3809 | |
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| 0.1825 | 8.03 | 4000 | 0.4139 | 0.3865 | |
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| 0.1754 | 9.04 | 4500 | 0.4320 | 0.3763 | |
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| 0.1477 | 10.04 | 5000 | 0.4668 | 0.3699 | |
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| 0.1418 | 11.04 | 5500 | 0.4439 | 0.3683 | |
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| 0.1207 | 12.05 | 6000 | 0.4419 | 0.3678 | |
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| 0.115 | 13.05 | 6500 | 0.4606 | 0.3786 | |
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| 0.1022 | 14.06 | 7000 | 0.4403 | 0.3610 | |
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| 0.1019 | 15.06 | 7500 | 0.4966 | 0.3609 | |
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| 0.0898 | 16.06 | 8000 | 0.4675 | 0.3586 | |
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| 0.0824 | 17.07 | 8500 | 0.4844 | 0.3583 | |
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| 0.0737 | 18.07 | 9000 | 0.4801 | 0.3534 | |
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| 0.076 | 19.08 | 9500 | 0.4945 | 0.3529 | |
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| 0.0627 | 20.08 | 10000 | 0.4700 | 0.3417 | |
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| 0.0723 | 21.08 | 10500 | 0.4630 | 0.3449 | |
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| 0.0597 | 22.09 | 11000 | 0.5164 | 0.3456 | |
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| 0.0566 | 23.09 | 11500 | 0.4957 | 0.3401 | |
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| 0.0453 | 24.1 | 12000 | 0.5032 | 0.3419 | |
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| 0.0492 | 25.1 | 12500 | 0.5391 | 0.3387 | |
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| 0.0524 | 26.1 | 13000 | 0.5057 | 0.3348 | |
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| 0.0381 | 27.11 | 13500 | 0.5098 | 0.3331 | |
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| 0.0402 | 28.11 | 14000 | 0.5087 | 0.3353 | |
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| 0.0358 | 29.12 | 14500 | 0.4888 | 0.3315 | |
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### Framework versions |
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- Transformers 4.11.3 |
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- Pytorch 1.10.0+cu111 |
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- Datasets 1.13.3 |
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- Tokenizers 0.10.3 |
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