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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-xlsr-persian-50p |
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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-xlsr-persian-50p |
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This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.6846 |
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- Wer: 0.4339 |
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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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- gradient_accumulation_steps: 2 |
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- total_train_batch_size: 16 |
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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: 500 |
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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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| No log | 1.05 | 250 | 3.2104 | 1.0 | |
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| 3.2437 | 2.11 | 500 | 2.9131 | 1.0 | |
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| 3.2437 | 3.16 | 750 | 1.0335 | 0.7303 | |
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| 1.4382 | 4.22 | 1000 | 0.8335 | 0.6155 | |
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| 1.4382 | 5.27 | 1250 | 0.7640 | 0.5904 | |
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| 0.6923 | 6.33 | 1500 | 0.6923 | 0.5468 | |
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| 0.6923 | 7.38 | 1750 | 0.6627 | 0.5238 | |
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| 0.5137 | 8.44 | 2000 | 0.6606 | 0.5112 | |
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| 0.5137 | 9.49 | 2250 | 0.6600 | 0.5125 | |
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| 0.4258 | 10.55 | 2500 | 0.6337 | 0.4939 | |
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| 0.4258 | 11.6 | 2750 | 0.6454 | 0.4851 | |
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| 0.362 | 12.66 | 3000 | 0.6481 | 0.4793 | |
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| 0.362 | 13.71 | 3250 | 0.6487 | 0.4801 | |
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| 0.3179 | 14.77 | 3500 | 0.6602 | 0.4668 | |
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| 0.3179 | 15.82 | 3750 | 0.6757 | 0.4683 | |
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| 0.2861 | 16.88 | 4000 | 0.6544 | 0.4591 | |
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| 0.2861 | 17.93 | 4250 | 0.6659 | 0.4634 | |
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| 0.2529 | 18.99 | 4500 | 0.6311 | 0.4556 | |
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| 0.2529 | 20.04 | 4750 | 0.6574 | 0.4525 | |
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| 0.235 | 21.1 | 5000 | 0.7019 | 0.4462 | |
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| 0.235 | 22.15 | 5250 | 0.6783 | 0.4426 | |
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| 0.2203 | 23.21 | 5500 | 0.6789 | 0.4361 | |
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| 0.2203 | 24.26 | 5750 | 0.6779 | 0.4336 | |
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| 0.2014 | 25.32 | 6000 | 0.6805 | 0.4406 | |
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| 0.2014 | 26.37 | 6250 | 0.6918 | 0.4407 | |
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| 0.1957 | 27.43 | 6500 | 0.6919 | 0.4360 | |
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| 0.1957 | 28.48 | 6750 | 0.6795 | 0.4332 | |
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| 0.1837 | 29.53 | 7000 | 0.6846 | 0.4339 | |
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### Framework versions |
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- Transformers 4.11.3 |
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- Pytorch 1.10.0+cu113 |
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- Datasets 1.18.3 |
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- Tokenizers 0.10.3 |
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