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--- |
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license: apache-2.0 |
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base_model: facebook/wav2vec2-base |
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tags: |
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- generated_from_trainer |
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metrics: |
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- wer |
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model-index: |
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- name: w2v2-base-pretrained_lr1e-4_at0.8_da0.4 |
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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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# w2v2-base-pretrained_lr1e-4_at0.8_da0.4 |
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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: 2.1115 |
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- Wer: 0.1952 |
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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: 32 |
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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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- training_steps: 3500 |
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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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| 21.2157 | 13.16 | 250 | 4.1634 | 1.0 | |
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| 3.2337 | 26.32 | 500 | 3.1231 | 1.0 | |
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| 3.0575 | 39.47 | 750 | 3.0466 | 1.0 | |
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| 2.0739 | 52.63 | 1000 | 1.0677 | 0.6284 | |
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| 0.1758 | 65.79 | 1250 | 1.3711 | 0.3170 | |
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| 0.0675 | 78.95 | 1500 | 1.6521 | 0.2268 | |
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| 0.0355 | 92.11 | 1750 | 1.7313 | 0.2332 | |
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| 0.0209 | 105.26 | 2000 | 1.9720 | 0.2114 | |
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| 0.0162 | 118.42 | 2250 | 1.7569 | 0.2085 | |
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| 0.0099 | 131.58 | 2500 | 2.1623 | 0.1944 | |
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| 0.0071 | 144.74 | 2750 | 2.2067 | 0.1922 | |
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| 0.0066 | 157.89 | 3000 | 2.1246 | 0.1944 | |
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| 0.0059 | 171.05 | 3250 | 2.1484 | 0.1922 | |
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| 0.0045 | 184.21 | 3500 | 2.1115 | 0.1952 | |
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### Framework versions |
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- Transformers 4.35.0 |
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- Pytorch 2.0.0 |
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- Datasets 2.14.6 |
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- Tokenizers 0.14.1 |
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