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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-vios-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-vios-google-colab |
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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: 0.5647 |
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- Wer: 0.4970 |
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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: 16 |
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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: 32 |
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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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| 7.7292 | 2.0 | 500 | 3.4159 | 1.0 | |
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| 3.0762 | 4.0 | 1000 | 1.3005 | 0.9615 | |
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| 0.8812 | 6.0 | 1500 | 0.4664 | 0.4740 | |
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| 0.5076 | 8.0 | 2000 | 0.4101 | 0.4180 | |
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| 0.4075 | 10.0 | 2500 | 0.3815 | 0.3802 | |
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| 0.3724 | 12.0 | 3000 | 0.3785 | 0.3741 | |
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| 0.3762 | 14.0 | 3500 | 0.4404 | 0.3766 | |
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| 0.4541 | 16.0 | 4000 | 0.4671 | 0.3822 | |
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| 0.6391 | 18.0 | 4500 | 0.5643 | 0.4200 | |
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| 0.7681 | 20.0 | 5000 | 0.6564 | 0.5214 | |
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| 0.8131 | 22.0 | 5500 | 0.5786 | 0.4934 | |
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| 0.7448 | 24.0 | 6000 | 0.5561 | 0.4920 | |
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| 0.7337 | 26.0 | 6500 | 0.5631 | 0.4964 | |
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| 0.7359 | 28.0 | 7000 | 0.5647 | 0.4968 | |
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| 0.7397 | 30.0 | 7500 | 0.5647 | 0.4970 | |
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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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