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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_da0.3 |
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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_da0.3 |
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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: 2.2956 |
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- Wer: 0.1811 |
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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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| 21.7228 | 17.86 | 250 | 3.7139 | 1.0 | |
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| 3.1757 | 35.71 | 500 | 3.1149 | 1.0 | |
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| 2.8079 | 53.57 | 750 | 1.7093 | 1.0038 | |
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| 0.301 | 71.43 | 1000 | 1.4764 | 0.2443 | |
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| 0.0637 | 89.29 | 1250 | 1.7091 | 0.2200 | |
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| 0.0311 | 107.14 | 1500 | 1.7022 | 0.2055 | |
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| 0.021 | 125.0 | 1750 | 2.0876 | 0.2016 | |
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| 0.0154 | 142.86 | 2000 | 2.2718 | 0.1974 | |
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| 0.0114 | 160.71 | 2250 | 2.1541 | 0.1845 | |
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| 0.0089 | 178.57 | 2500 | 2.2868 | 0.1854 | |
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| 0.0074 | 196.43 | 2750 | 2.3831 | 0.1914 | |
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| 0.0062 | 214.29 | 3000 | 2.2381 | 0.1841 | |
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| 0.0054 | 232.14 | 3250 | 2.4147 | 0.1824 | |
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| 0.0056 | 250.0 | 3500 | 2.2956 | 0.1811 | |
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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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