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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-timit-demo-google-colab |
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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-timit-demo-google-colab |
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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: 0.4770 |
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- Wer: 0.3360 |
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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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| 3.6401 | 1.0 | 500 | 2.4138 | 1.0 | |
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| 0.9717 | 2.01 | 1000 | 0.6175 | 0.5531 | |
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| 0.4393 | 3.01 | 1500 | 0.4309 | 0.4414 | |
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| 0.2976 | 4.02 | 2000 | 0.4167 | 0.4162 | |
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| 0.2345 | 5.02 | 2500 | 0.4273 | 0.3927 | |
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| 0.1919 | 6.02 | 3000 | 0.3983 | 0.3886 | |
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| 0.1565 | 7.03 | 3500 | 0.5581 | 0.3928 | |
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| 0.1439 | 8.03 | 4000 | 0.4509 | 0.3821 | |
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| 0.1266 | 9.04 | 4500 | 0.4733 | 0.3774 | |
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| 0.1091 | 10.04 | 5000 | 0.4755 | 0.3808 | |
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| 0.1001 | 11.04 | 5500 | 0.4435 | 0.3689 | |
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| 0.0911 | 12.05 | 6000 | 0.4962 | 0.3897 | |
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| 0.0813 | 13.05 | 6500 | 0.5031 | 0.3622 | |
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| 0.0729 | 14.06 | 7000 | 0.4853 | 0.3597 | |
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| 0.0651 | 15.06 | 7500 | 0.5180 | 0.3577 | |
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| 0.0608 | 16.06 | 8000 | 0.5251 | 0.3630 | |
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| 0.0592 | 17.07 | 8500 | 0.4915 | 0.3591 | |
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| 0.0577 | 18.07 | 9000 | 0.4724 | 0.3656 | |
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| 0.0463 | 19.08 | 9500 | 0.4536 | 0.3546 | |
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| 0.0475 | 20.08 | 10000 | 0.5107 | 0.3546 | |
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| 0.0464 | 21.08 | 10500 | 0.4829 | 0.3464 | |
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| 0.0369 | 22.09 | 11000 | 0.4844 | 0.3448 | |
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| 0.0327 | 23.09 | 11500 | 0.4865 | 0.3437 | |
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| 0.0337 | 24.1 | 12000 | 0.4825 | 0.3488 | |
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| 0.0271 | 25.1 | 12500 | 0.4824 | 0.3445 | |
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| 0.0236 | 26.1 | 13000 | 0.4747 | 0.3397 | |
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| 0.0243 | 27.11 | 13500 | 0.4840 | 0.3397 | |
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| 0.0226 | 28.11 | 14000 | 0.4716 | 0.3354 | |
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| 0.0235 | 29.12 | 14500 | 0.4770 | 0.3360 | |
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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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