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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: 0.5436 |
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- Wer: 0.3401 |
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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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| 3.5276 | 1.0 | 500 | 1.9983 | 1.0066 | |
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| 0.8606 | 2.01 | 1000 | 0.5323 | 0.5220 | |
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| 0.4339 | 3.01 | 1500 | 0.4697 | 0.4512 | |
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| 0.3026 | 4.02 | 2000 | 0.4342 | 0.4266 | |
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| 0.2297 | 5.02 | 2500 | 0.5001 | 0.4135 | |
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| 0.1939 | 6.02 | 3000 | 0.4350 | 0.3897 | |
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| 0.1613 | 7.03 | 3500 | 0.4740 | 0.3883 | |
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| 0.1452 | 8.03 | 4000 | 0.4289 | 0.3825 | |
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| 0.1362 | 9.04 | 4500 | 0.4721 | 0.3927 | |
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| 0.1146 | 10.04 | 5000 | 0.4707 | 0.3730 | |
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| 0.1061 | 11.04 | 5500 | 0.4470 | 0.3701 | |
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| 0.0947 | 12.05 | 6000 | 0.4694 | 0.3722 | |
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| 0.0852 | 13.05 | 6500 | 0.5222 | 0.3733 | |
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| 0.0741 | 14.06 | 7000 | 0.4881 | 0.3657 | |
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| 0.069 | 15.06 | 7500 | 0.4957 | 0.3677 | |
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| 0.0679 | 16.06 | 8000 | 0.5241 | 0.3634 | |
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| 0.0618 | 17.07 | 8500 | 0.5091 | 0.3564 | |
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| 0.0576 | 18.07 | 9000 | 0.5055 | 0.3557 | |
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| 0.0493 | 19.08 | 9500 | 0.5013 | 0.3515 | |
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| 0.0469 | 20.08 | 10000 | 0.5506 | 0.3530 | |
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| 0.044 | 21.08 | 10500 | 0.5564 | 0.3528 | |
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| 0.0368 | 22.09 | 11000 | 0.5213 | 0.3509 | |
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| 0.0355 | 23.09 | 11500 | 0.5707 | 0.3495 | |
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| 0.0357 | 24.1 | 12000 | 0.5558 | 0.3483 | |
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| 0.0285 | 25.1 | 12500 | 0.5613 | 0.3455 | |
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| 0.0285 | 26.1 | 13000 | 0.5533 | 0.3480 | |
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| 0.0266 | 27.11 | 13500 | 0.5526 | 0.3462 | |
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| 0.0249 | 28.11 | 14000 | 0.5488 | 0.3429 | |
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| 0.0237 | 29.12 | 14500 | 0.5436 | 0.3401 | |
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### Framework versions |
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- Transformers 4.17.0 |
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- Pytorch 1.11.0+cu115 |
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- Datasets 1.18.3 |
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- Tokenizers 0.12.1 |
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