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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-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-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: 0.5506 |
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- Wer: 0.3355 |
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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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| 3.4326 | 1.0 | 500 | 1.5832 | 1.0063 | |
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| 0.8235 | 2.01 | 1000 | 0.5310 | 0.5134 | |
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| 0.4224 | 3.01 | 1500 | 0.4488 | 0.4461 | |
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| 0.2978 | 4.02 | 2000 | 0.4243 | 0.4191 | |
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| 0.232 | 5.02 | 2500 | 0.4532 | 0.4149 | |
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| 0.1902 | 6.02 | 3000 | 0.4732 | 0.3912 | |
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| 0.1628 | 7.03 | 3500 | 0.4807 | 0.3868 | |
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| 0.1437 | 8.03 | 4000 | 0.5295 | 0.3670 | |
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| 0.1241 | 9.04 | 4500 | 0.4602 | 0.3810 | |
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| 0.1206 | 10.04 | 5000 | 0.4691 | 0.3783 | |
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| 0.0984 | 11.04 | 5500 | 0.4500 | 0.3710 | |
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| 0.0929 | 12.05 | 6000 | 0.5247 | 0.3550 | |
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| 0.0914 | 13.05 | 6500 | 0.5546 | 0.3821 | |
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| 0.0742 | 14.06 | 7000 | 0.4874 | 0.3646 | |
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| 0.0729 | 15.06 | 7500 | 0.5327 | 0.3934 | |
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| 0.0663 | 16.06 | 8000 | 0.5769 | 0.3661 | |
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| 0.0575 | 17.07 | 8500 | 0.5191 | 0.3524 | |
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| 0.0588 | 18.07 | 9000 | 0.5155 | 0.3360 | |
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| 0.0456 | 19.08 | 9500 | 0.5135 | 0.3539 | |
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| 0.0444 | 20.08 | 10000 | 0.5380 | 0.3603 | |
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| 0.0419 | 21.08 | 10500 | 0.5275 | 0.3467 | |
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| 0.0366 | 22.09 | 11000 | 0.5072 | 0.3487 | |
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| 0.0331 | 23.09 | 11500 | 0.5450 | 0.3437 | |
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| 0.0345 | 24.1 | 12000 | 0.5138 | 0.3431 | |
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| 0.029 | 25.1 | 12500 | 0.5067 | 0.3413 | |
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| 0.0274 | 26.1 | 13000 | 0.5421 | 0.3422 | |
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| 0.0243 | 27.11 | 13500 | 0.5456 | 0.3392 | |
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| 0.0226 | 28.11 | 14000 | 0.5665 | 0.3368 | |
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| 0.0216 | 29.12 | 14500 | 0.5506 | 0.3355 | |
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### Framework versions |
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- Transformers 4.20.0 |
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- Pytorch 1.11.0+cu113 |
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- Datasets 1.13.3 |
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- Tokenizers 0.12.1 |
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