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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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metrics: |
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- wer |
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model-index: |
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- name: hubert-base-libri-demo-feature_extractor_not_frozen_v3_25epochs_check |
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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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# hubert-base-libri-demo-feature_extractor_not_frozen_v3_25epochs_check |
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This model is a fine-tuned version of [facebook/hubert-base-ls960](https://huggingface.co/facebook/hubert-base-ls960) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1231 |
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- Wer: 0.1112 |
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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: 0.00015 |
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- train_batch_size: 64 |
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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: 3000 |
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- num_epochs: 25 |
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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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| 4.3342 | 1.12 | 500 | 3.4935 | 1.0000 | |
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| 2.8802 | 2.24 | 1000 | 3.5637 | 1.0000 | |
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| 2.1866 | 3.36 | 1500 | 0.7219 | 0.6232 | |
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| 0.6141 | 4.48 | 2000 | 0.2954 | 0.3238 | |
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| 0.3328 | 5.61 | 2500 | 0.1810 | 0.2212 | |
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| 0.2251 | 6.73 | 3000 | 0.1377 | 0.1640 | |
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| 0.1861 | 7.85 | 3500 | 0.1270 | 0.1473 | |
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| 0.1671 | 8.97 | 4000 | 0.1173 | 0.1372 | |
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| 0.1496 | 10.09 | 4500 | 0.1218 | 0.1322 | |
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| 0.117 | 11.21 | 5000 | 0.1180 | 0.1268 | |
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| 0.1182 | 12.33 | 5500 | 0.1255 | 0.1257 | |
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| 0.0948 | 13.45 | 6000 | 0.1215 | 0.1221 | |
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| 0.0935 | 14.57 | 6500 | 0.1233 | 0.1217 | |
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| 0.0873 | 15.7 | 7000 | 0.1124 | 0.1209 | |
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| 0.0798 | 16.82 | 7500 | 0.1172 | 0.1185 | |
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| 0.0752 | 17.94 | 8000 | 0.1197 | 0.1171 | |
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| 0.0747 | 19.06 | 8500 | 0.1252 | 0.1171 | |
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| 0.0775 | 20.18 | 9000 | 0.1209 | 0.1149 | |
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| 0.0665 | 21.3 | 9500 | 0.1180 | 0.1133 | |
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| 0.0657 | 22.42 | 10000 | 0.1240 | 0.1122 | |
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| 0.0606 | 23.54 | 10500 | 0.1222 | 0.1110 | |
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| 0.0581 | 24.66 | 11000 | 0.1231 | 0.1112 | |
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### Framework versions |
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- Transformers 4.30.0.dev0 |
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- Pytorch 2.0.1 |
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- Datasets 2.12.1.dev0 |
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- Tokenizers 0.13.3 |
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