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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: 0.5255 |
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- Wer: 0.3330 |
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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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| 3.5942 | 1.0 | 500 | 2.3849 | 1.0011 | |
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| 0.9765 | 2.01 | 1000 | 0.5907 | 0.5202 | |
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| 0.4424 | 3.01 | 1500 | 0.4547 | 0.4661 | |
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| 0.3008 | 4.02 | 2000 | 0.4194 | 0.4228 | |
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| 0.2316 | 5.02 | 2500 | 0.3933 | 0.4099 | |
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| 0.1921 | 6.02 | 3000 | 0.4532 | 0.3965 | |
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| 0.1561 | 7.03 | 3500 | 0.4315 | 0.3777 | |
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| 0.1378 | 8.03 | 4000 | 0.4463 | 0.3847 | |
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| 0.1222 | 9.04 | 4500 | 0.4402 | 0.3784 | |
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| 0.1076 | 10.04 | 5000 | 0.4253 | 0.3735 | |
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| 0.0924 | 11.04 | 5500 | 0.4844 | 0.3732 | |
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| 0.0866 | 12.05 | 6000 | 0.4758 | 0.3646 | |
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| 0.086 | 13.05 | 6500 | 0.6395 | 0.4594 | |
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| 0.0763 | 14.06 | 7000 | 0.4951 | 0.3647 | |
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| 0.0684 | 15.06 | 7500 | 0.4870 | 0.3577 | |
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| 0.0616 | 16.06 | 8000 | 0.5442 | 0.3591 | |
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| 0.0594 | 17.07 | 8500 | 0.5305 | 0.3606 | |
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| 0.0613 | 18.07 | 9000 | 0.5434 | 0.3546 | |
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| 0.0473 | 19.08 | 9500 | 0.4818 | 0.3532 | |
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| 0.0463 | 20.08 | 10000 | 0.5086 | 0.3514 | |
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| 0.042 | 21.08 | 10500 | 0.5017 | 0.3484 | |
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| 0.0365 | 22.09 | 11000 | 0.5129 | 0.3536 | |
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| 0.0336 | 23.09 | 11500 | 0.5411 | 0.3433 | |
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| 0.0325 | 24.1 | 12000 | 0.5307 | 0.3424 | |
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| 0.0282 | 25.1 | 12500 | 0.5261 | 0.3404 | |
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| 0.0245 | 26.1 | 13000 | 0.5306 | 0.3388 | |
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| 0.0257 | 27.11 | 13500 | 0.5242 | 0.3369 | |
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| 0.0234 | 28.11 | 14000 | 0.5216 | 0.3359 | |
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| 0.0221 | 29.12 | 14500 | 0.5255 | 0.3330 | |
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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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