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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: wav2vec2-base-timit-demo-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-timit-demo-google-colab |
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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: 0.5313 |
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- Wer: 0.3317 |
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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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- 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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| 3.5823 | 1.0 | 500 | 1.8501 | 1.0236 | |
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| 0.8931 | 2.01 | 1000 | 0.5018 | 0.5196 | |
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| 0.4269 | 3.01 | 1500 | 0.4266 | 0.4461 | |
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| 0.2876 | 4.02 | 2000 | 0.4458 | 0.4359 | |
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| 0.2272 | 5.02 | 2500 | 0.4183 | 0.4146 | |
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| 0.1813 | 6.02 | 3000 | 0.4151 | 0.3945 | |
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| 0.1555 | 7.03 | 3500 | 0.4216 | 0.3881 | |
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| 0.1353 | 8.03 | 4000 | 0.4282 | 0.3824 | |
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| 0.1221 | 9.04 | 4500 | 0.4848 | 0.3845 | |
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| 0.1135 | 10.04 | 5000 | 0.5003 | 0.3818 | |
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| 0.0968 | 11.04 | 5500 | 0.5331 | 0.3738 | |
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| 0.09 | 12.05 | 6000 | 0.5082 | 0.3690 | |
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| 0.084 | 13.05 | 6500 | 0.4573 | 0.3634 | |
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| 0.0744 | 14.06 | 7000 | 0.4711 | 0.3705 | |
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| 0.0663 | 15.06 | 7500 | 0.4955 | 0.3634 | |
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| 0.0612 | 16.06 | 8000 | 0.4721 | 0.3558 | |
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| 0.0535 | 17.07 | 8500 | 0.4965 | 0.3654 | |
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| 0.0527 | 18.07 | 9000 | 0.5381 | 0.3592 | |
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| 0.0458 | 19.08 | 9500 | 0.5029 | 0.3498 | |
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| 0.0424 | 20.08 | 10000 | 0.5814 | 0.3547 | |
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| 0.042 | 21.08 | 10500 | 0.4893 | 0.3480 | |
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| 0.0373 | 22.09 | 11000 | 0.5047 | 0.3482 | |
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| 0.0333 | 23.09 | 11500 | 0.5235 | 0.3426 | |
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| 0.0306 | 24.1 | 12000 | 0.5165 | 0.3472 | |
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| 0.0293 | 25.1 | 12500 | 0.4988 | 0.3426 | |
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| 0.025 | 26.1 | 13000 | 0.5157 | 0.3382 | |
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| 0.0255 | 27.11 | 13500 | 0.5278 | 0.3412 | |
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| 0.022 | 28.11 | 14000 | 0.5401 | 0.3364 | |
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| 0.0195 | 29.12 | 14500 | 0.5313 | 0.3317 | |
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### Framework versions |
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- Transformers 4.35.2 |
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- Pytorch 2.1.0+cu121 |
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- Datasets 1.18.3 |
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- Tokenizers 0.15.1 |
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