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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: 0.5501 |
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- Wer: 0.3424 |
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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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| 3.5448 | 1.0 | 500 | 2.5044 | 1.0 | |
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| 1.0167 | 2.01 | 1000 | 0.5435 | 0.5278 | |
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| 0.4453 | 3.01 | 1500 | 0.4450 | 0.4534 | |
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| 0.3 | 4.02 | 2000 | 0.4401 | 0.4245 | |
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| 0.2304 | 5.02 | 2500 | 0.4146 | 0.4022 | |
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| 0.1889 | 6.02 | 3000 | 0.4241 | 0.3927 | |
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| 0.1573 | 7.03 | 3500 | 0.4545 | 0.3878 | |
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| 0.1363 | 8.03 | 4000 | 0.4936 | 0.3940 | |
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| 0.1213 | 9.04 | 4500 | 0.4964 | 0.3806 | |
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| 0.108 | 10.04 | 5000 | 0.4931 | 0.3826 | |
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| 0.0982 | 11.04 | 5500 | 0.5373 | 0.3778 | |
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| 0.0883 | 12.05 | 6000 | 0.4978 | 0.3733 | |
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| 0.0835 | 13.05 | 6500 | 0.5189 | 0.3728 | |
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| 0.0748 | 14.06 | 7000 | 0.4608 | 0.3692 | |
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| 0.068 | 15.06 | 7500 | 0.4827 | 0.3608 | |
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| 0.0596 | 16.06 | 8000 | 0.5022 | 0.3661 | |
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| 0.056 | 17.07 | 8500 | 0.5482 | 0.3646 | |
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| 0.0565 | 18.07 | 9000 | 0.5158 | 0.3573 | |
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| 0.0487 | 19.08 | 9500 | 0.4910 | 0.3513 | |
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| 0.0444 | 20.08 | 10000 | 0.5771 | 0.3580 | |
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| 0.045 | 21.08 | 10500 | 0.5160 | 0.3539 | |
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| 0.0363 | 22.09 | 11000 | 0.5367 | 0.3503 | |
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| 0.0313 | 23.09 | 11500 | 0.5773 | 0.3500 | |
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| 0.0329 | 24.1 | 12000 | 0.5683 | 0.3508 | |
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| 0.0297 | 25.1 | 12500 | 0.5355 | 0.3464 | |
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| 0.0272 | 26.1 | 13000 | 0.5317 | 0.3450 | |
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| 0.0256 | 27.11 | 13500 | 0.5602 | 0.3443 | |
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| 0.0242 | 28.11 | 14000 | 0.5586 | 0.3419 | |
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| 0.0239 | 29.12 | 14500 | 0.5501 | 0.3424 | |
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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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