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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-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: 0.5218 |
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- Wer: 0.3434 |
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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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| 3.5634 | 1.0 | 500 | 2.0727 | 1.0096 | |
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| 0.9357 | 2.01 | 1000 | 0.6623 | 0.5634 | |
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| 0.4536 | 3.01 | 1500 | 1.4421 | 0.4829 | |
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| 0.3044 | 4.02 | 2000 | 0.4361 | 0.4363 | |
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| 0.2369 | 5.02 | 2500 | 0.5098 | 0.4495 | |
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| 0.1994 | 6.02 | 3000 | 0.4741 | 0.3711 | |
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| 0.1699 | 7.03 | 3500 | 0.4652 | 0.3898 | |
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| 0.1499 | 8.03 | 4000 | 0.4151 | 0.3949 | |
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| 0.1308 | 9.04 | 4500 | 0.4685 | 0.3838 | |
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| 0.1234 | 10.04 | 5000 | 0.5076 | 0.3794 | |
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| 0.1055 | 11.04 | 5500 | 0.4492 | 0.3790 | |
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| 0.0953 | 12.05 | 6000 | 0.4726 | 0.3679 | |
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| 0.0863 | 13.05 | 6500 | 0.4797 | 0.3717 | |
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| 0.0816 | 14.06 | 7000 | 0.4725 | 0.3655 | |
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| 0.0842 | 15.06 | 7500 | 0.5181 | 0.3405 | |
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| 0.0661 | 16.06 | 8000 | 0.5315 | 0.3510 | |
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| 0.0593 | 17.07 | 8500 | 0.5024 | 0.3668 | |
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| 0.0624 | 18.07 | 9000 | 0.5374 | 0.3663 | |
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| 0.0535 | 19.08 | 9500 | 0.4861 | 0.3517 | |
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| 0.0524 | 20.08 | 10000 | 0.4812 | 0.3574 | |
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| 0.0461 | 21.08 | 10500 | 0.4976 | 0.3431 | |
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| 0.0363 | 22.09 | 11000 | 0.5062 | 0.3476 | |
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| 0.0351 | 23.09 | 11500 | 0.5094 | 0.3479 | |
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| 0.0327 | 24.1 | 12000 | 0.5291 | 0.3455 | |
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| 0.0319 | 25.1 | 12500 | 0.5209 | 0.3460 | |
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| 0.0268 | 26.1 | 13000 | 0.5173 | 0.3481 | |
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| 0.0263 | 27.11 | 13500 | 0.5362 | 0.3486 | |
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| 0.0234 | 28.11 | 14000 | 0.5333 | 0.3444 | |
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| 0.0237 | 29.12 | 14500 | 0.5218 | 0.3434 | |
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### Framework versions |
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- Transformers 4.17.0 |
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- Pytorch 1.12.1+cu113 |
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- Datasets 1.18.3 |
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- Tokenizers 0.13.0 |
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