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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: librispeech-100h-supervised |
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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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# librispeech-100h-supervised |
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This model is a fine-tuned version of [facebook/wav2vec2-large-lv60](https://huggingface.co/facebook/wav2vec2-large-lv60) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0955 |
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- Wer: 0.0345 |
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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: 24 |
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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: 15 |
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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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| 4.8277 | 0.42 | 500 | 2.9071 | 1.0 | |
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| 2.0261 | 0.84 | 1000 | 0.3060 | 0.2496 | |
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| 0.2181 | 1.26 | 1500 | 0.1172 | 0.0873 | |
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| 0.1255 | 1.68 | 2000 | 0.0894 | 0.0637 | |
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| 0.0971 | 2.1 | 2500 | 0.0821 | 0.0560 | |
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| 0.078 | 2.52 | 3000 | 0.0751 | 0.0500 | |
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| 0.0706 | 2.94 | 3500 | 0.0721 | 0.0456 | |
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| 0.0609 | 3.36 | 4000 | 0.0755 | 0.0464 | |
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| 0.0572 | 3.78 | 4500 | 0.0705 | 0.0431 | |
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| 0.0528 | 4.2 | 5000 | 0.0715 | 0.0423 | |
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| 0.0481 | 4.62 | 5500 | 0.0691 | 0.0403 | |
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| 0.0471 | 5.04 | 6000 | 0.0743 | 0.0401 | |
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| 0.0412 | 5.46 | 6500 | 0.0757 | 0.0399 | |
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| 0.0416 | 5.88 | 7000 | 0.0688 | 0.0378 | |
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| 0.0391 | 6.3 | 7500 | 0.0704 | 0.0383 | |
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| 0.0367 | 6.72 | 8000 | 0.0742 | 0.0387 | |
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| 0.0349 | 7.14 | 8500 | 0.0732 | 0.0388 | |
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| 0.033 | 7.56 | 9000 | 0.0719 | 0.0374 | |
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| 0.0327 | 7.98 | 9500 | 0.0750 | 0.0369 | |
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| 0.0292 | 8.4 | 10000 | 0.0734 | 0.0368 | |
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| 0.0303 | 8.82 | 10500 | 0.0733 | 0.0365 | |
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| 0.0283 | 9.24 | 11000 | 0.0766 | 0.0357 | |
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| 0.0269 | 9.66 | 11500 | 0.0761 | 0.0350 | |
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| 0.0268 | 10.08 | 12000 | 0.0802 | 0.0359 | |
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| 0.0245 | 10.42 | 12500 | 0.0758 | 0.0354 | |
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| 0.023 | 10.84 | 13000 | 0.0775 | 0.0349 | |
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| 0.0186 | 11.26 | 13500 | 0.0817 | 0.0355 | |
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| 0.0176 | 11.68 | 14000 | 0.0853 | 0.0354 | |
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| 0.0163 | 12.1 | 14500 | 0.0880 | 0.0347 | |
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| 0.0156 | 12.52 | 15000 | 0.0864 | 0.0357 | |
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| 0.0141 | 12.94 | 15500 | 0.0897 | 0.0355 | |
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| 0.0134 | 13.36 | 16000 | 0.0915 | 0.0349 | |
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| 0.013 | 13.78 | 16500 | 0.0928 | 0.0350 | |
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| 0.0097 | 13.42 | 17000 | 0.0955 | 0.0345 | |
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### Framework versions |
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- Transformers 4.14.1 |
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- Pytorch 1.10.2 |
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- Datasets 1.18.2 |
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- Tokenizers 0.10.3 |
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