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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: wav2vec2-xlsr-1b-mecita-portuguese-all-text-a_coisa-os_morcegos |
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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-xlsr-1b-mecita-portuguese-all-text-a_coisa-os_morcegos |
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This model is a fine-tuned version of [jonatasgrosman/wav2vec2-xls-r-1b-portuguese](https://huggingface.co/jonatasgrosman/wav2vec2-xls-r-1b-portuguese) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1774 |
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- Wer: 0.0844 |
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- Cer: 0.0266 |
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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: 3e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- gradient_accumulation_steps: 2 |
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- total_train_batch_size: 32 |
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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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- num_epochs: 100 |
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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 | Cer | |
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|:-------------:|:-----:|:----:|:---------------:|:------:|:------:| |
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| 25.5905 | 1.0 | 79 | 0.4495 | 0.2580 | 0.0734 | |
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| 3.1482 | 2.0 | 158 | 0.2479 | 0.1204 | 0.0380 | |
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| 0.4247 | 3.0 | 237 | 0.2347 | 0.1025 | 0.0345 | |
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| 0.3136 | 4.0 | 316 | 0.2044 | 0.1017 | 0.0322 | |
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| 0.3136 | 5.0 | 395 | 0.1906 | 0.0930 | 0.0296 | |
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| 0.2985 | 6.0 | 474 | 0.2050 | 0.0963 | 0.0311 | |
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| 0.2413 | 7.0 | 553 | 0.2025 | 0.0971 | 0.0309 | |
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| 0.2267 | 8.0 | 632 | 0.2006 | 0.0885 | 0.0291 | |
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| 0.224 | 9.0 | 711 | 0.1991 | 0.0917 | 0.0291 | |
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| 0.224 | 10.0 | 790 | 0.1881 | 0.0885 | 0.0281 | |
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| 0.1864 | 11.0 | 869 | 0.1841 | 0.0893 | 0.0278 | |
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| 0.1951 | 12.0 | 948 | 0.1809 | 0.0895 | 0.0282 | |
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| 0.1794 | 13.0 | 1027 | 0.1923 | 0.0833 | 0.0280 | |
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| 0.1621 | 14.0 | 1106 | 0.1949 | 0.0857 | 0.0277 | |
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| 0.1621 | 15.0 | 1185 | 0.1929 | 0.0817 | 0.0266 | |
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| 0.1695 | 16.0 | 1264 | 0.1907 | 0.0839 | 0.0270 | |
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| 0.1528 | 17.0 | 1343 | 0.1839 | 0.0906 | 0.0286 | |
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| 0.1592 | 18.0 | 1422 | 0.1866 | 0.0903 | 0.0281 | |
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| 0.1519 | 19.0 | 1501 | 0.2031 | 0.0857 | 0.0275 | |
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| 0.1519 | 20.0 | 1580 | 0.1948 | 0.0860 | 0.0278 | |
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| 0.1257 | 21.0 | 1659 | 0.1850 | 0.0860 | 0.0262 | |
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| 0.1288 | 22.0 | 1738 | 0.1774 | 0.0844 | 0.0266 | |
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| 0.115 | 23.0 | 1817 | 0.1960 | 0.0844 | 0.0265 | |
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| 0.115 | 24.0 | 1896 | 0.1832 | 0.0825 | 0.0258 | |
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| 0.1223 | 25.0 | 1975 | 0.1920 | 0.0828 | 0.0261 | |
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| 0.1175 | 26.0 | 2054 | 0.1951 | 0.0803 | 0.0260 | |
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| 0.1051 | 27.0 | 2133 | 0.1996 | 0.0825 | 0.0266 | |
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| 0.1033 | 28.0 | 2212 | 0.2152 | 0.0847 | 0.0274 | |
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| 0.1033 | 29.0 | 2291 | 0.2082 | 0.0879 | 0.0277 | |
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| 0.0961 | 30.0 | 2370 | 0.2153 | 0.0855 | 0.0274 | |
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| 0.1003 | 31.0 | 2449 | 0.2044 | 0.0903 | 0.0288 | |
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| 0.1129 | 32.0 | 2528 | 0.2050 | 0.0855 | 0.0268 | |
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| 0.0939 | 33.0 | 2607 | 0.2028 | 0.0860 | 0.0271 | |
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| 0.0939 | 34.0 | 2686 | 0.2031 | 0.0847 | 0.0274 | |
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| 0.0846 | 35.0 | 2765 | 0.2046 | 0.0822 | 0.0269 | |
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| 0.083 | 36.0 | 2844 | 0.2094 | 0.0825 | 0.0265 | |
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| 0.0844 | 37.0 | 2923 | 0.2176 | 0.0820 | 0.0268 | |
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| 0.0829 | 38.0 | 3002 | 0.2082 | 0.0817 | 0.0267 | |
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| 0.0829 | 39.0 | 3081 | 0.2200 | 0.0893 | 0.0286 | |
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| 0.103 | 40.0 | 3160 | 0.2102 | 0.0841 | 0.0276 | |
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| 0.0728 | 41.0 | 3239 | 0.2143 | 0.0817 | 0.0271 | |
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| 0.079 | 42.0 | 3318 | 0.2131 | 0.0825 | 0.0265 | |
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### Framework versions |
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- Transformers 4.28.0 |
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- Pytorch 2.2.1+cu121 |
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- Datasets 2.17.0 |
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- Tokenizers 0.13.3 |
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