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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: XLS-R_Finetuned |
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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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# XLS-R_Finetuned |
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This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2280 |
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- Wer: 0.1725 |
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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: 0.00024 |
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- train_batch_size: 1 |
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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: 2 |
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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: 800 |
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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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| 6.0094 | 0.32 | 500 | 3.5637 | 1.0 | |
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| 3.3935 | 0.64 | 1000 | 2.6589 | 1.0 | |
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| 1.5455 | 0.95 | 1500 | 0.7979 | 0.8225 | |
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| 0.9065 | 1.27 | 2000 | 0.5392 | 0.6244 | |
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| 0.7891 | 1.59 | 2500 | 0.3554 | 0.4551 | |
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| 0.7118 | 1.91 | 3000 | 0.3682 | 0.4608 | |
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| 0.6061 | 2.23 | 3500 | 0.3384 | 0.4416 | |
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| 0.5536 | 2.54 | 4000 | 0.2987 | 0.4042 | |
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| 0.547 | 2.86 | 4500 | 0.2892 | 0.3892 | |
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| 0.4841 | 3.18 | 5000 | 0.2890 | 0.3690 | |
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| 0.4434 | 3.5 | 5500 | 0.2605 | 0.3636 | |
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| 0.4542 | 3.81 | 6000 | 0.2932 | 0.3773 | |
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| 0.4171 | 4.13 | 6500 | 0.2768 | 0.3550 | |
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| 0.3697 | 4.45 | 7000 | 0.2443 | 0.3382 | |
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| 0.3776 | 4.77 | 7500 | 0.2572 | 0.3366 | |
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| 0.3448 | 5.09 | 8000 | 0.2267 | 0.3006 | |
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| 0.3285 | 5.4 | 8500 | 0.2377 | 0.3023 | |
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| 0.3165 | 5.72 | 9000 | 0.2344 | 0.2888 | |
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| 0.3194 | 6.04 | 9500 | 0.2228 | 0.2699 | |
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| 0.2737 | 6.36 | 10000 | 0.2201 | 0.2754 | |
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| 0.2986 | 6.68 | 10500 | 0.2413 | 0.2850 | |
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| 0.2836 | 6.99 | 11000 | 0.2117 | 0.2629 | |
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| 0.2467 | 7.31 | 11500 | 0.2408 | 0.2877 | |
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| 0.2577 | 7.63 | 12000 | 0.2134 | 0.2448 | |
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| 0.2503 | 7.95 | 12500 | 0.2260 | 0.2600 | |
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| 0.2371 | 8.26 | 13000 | 0.2081 | 0.2379 | |
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| 0.2303 | 8.58 | 13500 | 0.2322 | 0.2668 | |
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| 0.213 | 8.9 | 14000 | 0.2339 | 0.2586 | |
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| 0.2029 | 9.22 | 14500 | 0.2300 | 0.2704 | |
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| 0.2146 | 9.54 | 15000 | 0.2321 | 0.2533 | |
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| 0.2044 | 9.85 | 15500 | 0.2393 | 0.2685 | |
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| 0.2008 | 10.17 | 16000 | 0.2193 | 0.2467 | |
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| 0.182 | 10.49 | 16500 | 0.2323 | 0.2611 | |
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| 0.2 | 10.81 | 17000 | 0.2188 | 0.2537 | |
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| 0.1855 | 11.13 | 17500 | 0.2436 | 0.2523 | |
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| 0.1745 | 11.44 | 18000 | 0.2351 | 0.2473 | |
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| 0.1705 | 11.76 | 18500 | 0.2556 | 0.2663 | |
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| 0.1745 | 12.08 | 19000 | 0.2189 | 0.2229 | |
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| 0.1641 | 12.4 | 19500 | 0.2192 | 0.2342 | |
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| 0.1546 | 12.71 | 20000 | 0.2432 | 0.2228 | |
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| 0.1661 | 13.03 | 20500 | 0.2323 | 0.2242 | |
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| 0.1436 | 13.35 | 21000 | 0.2554 | 0.2496 | |
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| 0.1443 | 13.67 | 21500 | 0.2195 | 0.2026 | |
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| 0.151 | 13.99 | 22000 | 0.2400 | 0.2201 | |
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| 0.1333 | 14.3 | 22500 | 0.2181 | 0.2235 | |
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| 0.137 | 14.62 | 23000 | 0.2400 | 0.2254 | |
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| 0.1303 | 14.94 | 23500 | 0.2265 | 0.2088 | |
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| 0.1386 | 15.26 | 24000 | 0.2330 | 0.2152 | |
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| 0.1325 | 15.58 | 24500 | 0.2328 | 0.2127 | |
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| 0.1227 | 15.89 | 25000 | 0.2375 | 0.2077 | |
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| 0.1196 | 16.21 | 25500 | 0.2394 | 0.2144 | |
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| 0.1197 | 16.53 | 26000 | 0.2591 | 0.2171 | |
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| 0.1122 | 16.85 | 26500 | 0.2383 | 0.2066 | |
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| 0.1093 | 17.16 | 27000 | 0.2254 | 0.2042 | |
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| 0.105 | 17.48 | 27500 | 0.2330 | 0.2008 | |
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| 0.0982 | 17.8 | 28000 | 0.2317 | 0.1902 | |
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| 0.1072 | 18.12 | 28500 | 0.2332 | 0.1971 | |
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| 0.1033 | 18.44 | 29000 | 0.2313 | 0.1923 | |
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| 0.0982 | 18.75 | 29500 | 0.2344 | 0.1934 | |
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| 0.103 | 19.07 | 30000 | 0.2295 | 0.1902 | |
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| 0.0945 | 19.39 | 30500 | 0.2352 | 0.1976 | |
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| 0.0892 | 19.71 | 31000 | 0.2414 | 0.1920 | |
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| 0.1003 | 20.03 | 31500 | 0.2300 | 0.1879 | |
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| 0.0861 | 20.34 | 32000 | 0.2215 | 0.1778 | |
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| 0.0845 | 20.66 | 32500 | 0.2321 | 0.1866 | |
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| 0.0858 | 20.98 | 33000 | 0.2311 | 0.1850 | |
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| 0.0785 | 21.3 | 33500 | 0.2341 | 0.1874 | |
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| 0.0786 | 21.61 | 34000 | 0.2322 | 0.1916 | |
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| 0.0793 | 21.93 | 34500 | 0.2358 | 0.1846 | |
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| 0.0772 | 22.25 | 35000 | 0.2234 | 0.1770 | |
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| 0.0786 | 22.57 | 35500 | 0.2180 | 0.1758 | |
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| 0.0747 | 22.89 | 36000 | 0.2269 | 0.1830 | |
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| 0.0734 | 23.2 | 36500 | 0.2320 | 0.1860 | |
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| 0.067 | 23.52 | 37000 | 0.2324 | 0.1797 | |
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| 0.0733 | 23.84 | 37500 | 0.2324 | 0.1772 | |
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| 0.0701 | 24.16 | 38000 | 0.2293 | 0.1737 | |
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| 0.0691 | 24.48 | 38500 | 0.2303 | 0.1750 | |
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| 0.0613 | 24.79 | 39000 | 0.2280 | 0.1725 | |
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### Framework versions |
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- Transformers 4.24.0 |
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- Pytorch 1.12.1+cu113 |
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- Datasets 2.6.1 |
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- Tokenizers 0.13.1 |
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