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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-tcrs |
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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-tcrs |
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This model is a fine-tuned version of [facebook/wav2vec2-large-lv60](https://huggingface.co/facebook/wav2vec2-large-lv60) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 2.9550 |
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- Wer: 1.0657 |
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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: 1 |
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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: 100 |
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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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| 13.6613 | 3.38 | 500 | 3.2415 | 1.0 | |
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| 2.9524 | 6.76 | 1000 | 3.0199 | 1.0 | |
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| 2.9425 | 10.14 | 1500 | 3.0673 | 1.0 | |
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| 2.9387 | 13.51 | 2000 | 3.0151 | 1.0 | |
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| 2.9384 | 16.89 | 2500 | 3.0320 | 1.0 | |
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| 2.929 | 20.27 | 3000 | 2.9691 | 1.0 | |
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| 2.9194 | 23.65 | 3500 | 2.9596 | 1.0 | |
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| 2.9079 | 27.03 | 4000 | 2.9279 | 1.0 | |
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| 2.8957 | 30.41 | 4500 | 2.9647 | 1.0 | |
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| 2.8385 | 33.78 | 5000 | 2.8114 | 1.0193 | |
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| 2.6546 | 37.16 | 5500 | 2.6744 | 1.0983 | |
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| 2.5866 | 40.54 | 6000 | 2.6192 | 1.1071 | |
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| 2.5475 | 43.92 | 6500 | 2.5777 | 1.0950 | |
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| 2.5177 | 47.3 | 7000 | 2.5845 | 1.1220 | |
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| 2.482 | 50.68 | 7500 | 2.5730 | 1.1264 | |
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| 2.4343 | 54.05 | 8000 | 2.5722 | 1.0955 | |
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| 2.3754 | 57.43 | 8500 | 2.5781 | 1.1353 | |
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| 2.3055 | 60.81 | 9000 | 2.6177 | 1.0972 | |
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| 2.2446 | 64.19 | 9500 | 2.6351 | 1.1027 | |
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| 2.1625 | 67.57 | 10000 | 2.6924 | 1.0756 | |
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| 2.1078 | 70.95 | 10500 | 2.6817 | 1.0795 | |
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| 2.0366 | 74.32 | 11000 | 2.7629 | 1.0657 | |
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| 1.9899 | 77.7 | 11500 | 2.7972 | 1.0845 | |
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| 1.9309 | 81.08 | 12000 | 2.8450 | 1.0734 | |
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| 1.8861 | 84.46 | 12500 | 2.8703 | 1.0668 | |
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| 1.8437 | 87.84 | 13000 | 2.9308 | 1.0917 | |
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| 1.8192 | 91.22 | 13500 | 2.9298 | 1.0701 | |
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| 1.7952 | 94.59 | 14000 | 2.9488 | 1.0685 | |
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| 1.7745 | 97.97 | 14500 | 2.9550 | 1.0657 | |
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### Framework versions |
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- Transformers 4.11.3 |
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- Pytorch 1.9.1 |
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- Datasets 1.18.3 |
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- Tokenizers 0.10.3 |
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