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--- |
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tags: |
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- "es" |
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- "robust-speech-event" |
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- generated_from_trainer |
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datasets: |
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- common_voice |
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model-index: |
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- name: wav2vec2-large-xls-r-300m-spanish-small-v3 |
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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-large-xls-r-300m-spanish-small-v3 |
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This model is a fine-tuned version of [jhonparra18/wav2vec2-large-xls-r-300m-spanish-custom](https://huggingface.co/jhonparra18/wav2vec2-large-xls-r-300m-spanish-custom) on the common_voice dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.3986 |
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- Wer: 0.1980 |
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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: 0.0004 |
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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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- gradient_accumulation_steps: 2 |
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- total_train_batch_size: 16 |
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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: 500 |
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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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| 0.2372 | 0.26 | 400 | 0.3011 | 0.2660 | |
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| 0.3413 | 0.53 | 800 | 0.3559 | 0.3228 | |
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| 0.3598 | 0.79 | 1200 | 0.3753 | 0.3400 | |
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| 0.3529 | 1.05 | 1600 | 0.3385 | 0.2979 | |
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| 0.3133 | 1.32 | 2000 | 0.3559 | 0.3056 | |
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| 0.3158 | 1.58 | 2400 | 0.3364 | 0.2994 | |
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| 0.3092 | 1.85 | 2800 | 0.3210 | 0.2876 | |
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| 0.2919 | 2.11 | 3200 | 0.3460 | 0.3010 | |
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| 0.2666 | 2.37 | 3600 | 0.3543 | 0.3036 | |
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| 0.2819 | 2.64 | 4000 | 0.3477 | 0.2959 | |
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| 0.283 | 2.9 | 4400 | 0.3492 | 0.2968 | |
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| 0.2484 | 3.16 | 4800 | 0.3647 | 0.2993 | |
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| 0.2371 | 3.43 | 5200 | 0.3601 | 0.2942 | |
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| 0.2382 | 3.69 | 5600 | 0.3656 | 0.3019 | |
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| 0.2425 | 3.96 | 6000 | 0.3379 | 0.2873 | |
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| 0.2092 | 4.22 | 6400 | 0.3385 | 0.2736 | |
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| 0.2171 | 4.48 | 6800 | 0.3503 | 0.2889 | |
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| 0.2185 | 4.75 | 7200 | 0.3289 | 0.2727 | |
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| 0.2236 | 5.01 | 7600 | 0.3447 | 0.2771 | |
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| 0.1882 | 5.27 | 8000 | 0.3586 | 0.2860 | |
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| 0.1986 | 5.54 | 8400 | 0.3404 | 0.2829 | |
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| 0.2055 | 5.8 | 8800 | 0.3561 | 0.2869 | |
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| 0.196 | 6.06 | 9200 | 0.3633 | 0.2811 | |
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| 0.1748 | 6.33 | 9600 | 0.3703 | 0.2818 | |
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| 0.1758 | 6.59 | 10000 | 0.3525 | 0.2816 | |
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| 0.1819 | 6.86 | 10400 | 0.3581 | 0.2765 | |
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| 0.1715 | 7.12 | 10800 | 0.3480 | 0.2628 | |
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| 0.1606 | 7.38 | 11200 | 0.3490 | 0.2703 | |
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| 0.1632 | 7.65 | 11600 | 0.3461 | 0.2706 | |
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| 0.1638 | 7.91 | 12000 | 0.3458 | 0.2673 | |
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| 0.1552 | 8.17 | 12400 | 0.3646 | 0.2732 | |
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| 0.154 | 8.44 | 12800 | 0.3706 | 0.2726 | |
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| 0.1512 | 8.7 | 13200 | 0.3609 | 0.2683 | |
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| 0.149 | 8.97 | 13600 | 0.3610 | 0.2668 | |
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| 0.1357 | 9.23 | 14000 | 0.3693 | 0.2740 | |
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| 0.1375 | 9.49 | 14400 | 0.3677 | 0.2625 | |
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| 0.1391 | 9.76 | 14800 | 0.3795 | 0.2762 | |
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| 0.1378 | 10.02 | 15200 | 0.3541 | 0.2592 | |
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| 0.1197 | 10.28 | 15600 | 0.3562 | 0.2507 | |
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| 0.1259 | 10.55 | 16000 | 0.3612 | 0.2584 | |
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| 0.1266 | 10.81 | 16400 | 0.3470 | 0.2527 | |
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| 0.1199 | 11.07 | 16800 | 0.3721 | 0.2571 | |
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| 0.1157 | 11.34 | 17200 | 0.3734 | 0.2571 | |
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| 0.1107 | 11.6 | 17600 | 0.3730 | 0.2589 | |
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| 0.1148 | 11.87 | 18000 | 0.3648 | 0.2536 | |
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| 0.1095 | 12.13 | 18400 | 0.3746 | 0.2521 | |
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| 0.1047 | 12.39 | 18800 | 0.3566 | 0.2530 | |
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| 0.1043 | 12.66 | 19200 | 0.3794 | 0.2545 | |
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| 0.1066 | 12.92 | 19600 | 0.3548 | 0.2439 | |
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| 0.0974 | 13.18 | 20000 | 0.3702 | 0.2461 | |
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| 0.0978 | 13.45 | 20400 | 0.3721 | 0.2492 | |
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| 0.095 | 13.71 | 20800 | 0.3599 | 0.2467 | |
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| 0.0963 | 13.97 | 21200 | 0.3650 | 0.2402 | |
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| 0.0902 | 14.24 | 21600 | 0.3689 | 0.2459 | |
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| 0.0898 | 14.5 | 22000 | 0.3832 | 0.2452 | |
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| 0.0865 | 14.77 | 22400 | 0.3982 | 0.2436 | |
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| 0.0911 | 15.03 | 22800 | 0.3785 | 0.2398 | |
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| 0.0793 | 15.29 | 23200 | 0.3731 | 0.2396 | |
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| 0.0806 | 15.56 | 23600 | 0.3626 | 0.2372 | |
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| 0.0789 | 15.82 | 24000 | 0.3707 | 0.2356 | |
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| 0.0779 | 16.08 | 24400 | 0.3850 | 0.2368 | |
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| 0.078 | 16.35 | 24800 | 0.3831 | 0.2363 | |
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| 0.0732 | 16.61 | 25200 | 0.3947 | 0.2287 | |
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| 0.0733 | 16.88 | 25600 | 0.3928 | 0.2374 | |
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| 0.0721 | 17.14 | 26000 | 0.3943 | 0.2324 | |
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| 0.0676 | 17.4 | 26400 | 0.3793 | 0.2311 | |
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| 0.0682 | 17.67 | 26800 | 0.3958 | 0.2257 | |
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| 0.0714 | 17.93 | 27200 | 0.3890 | 0.2322 | |
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| 0.0673 | 18.19 | 27600 | 0.3872 | 0.2229 | |
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| 0.0613 | 18.46 | 28000 | 0.3828 | 0.2226 | |
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| 0.0621 | 18.72 | 28400 | 0.3812 | 0.2214 | |
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| 0.0622 | 18.98 | 28800 | 0.3919 | 0.2212 | |
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| 0.0576 | 19.25 | 29200 | 0.4000 | 0.2205 | |
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| 0.0581 | 19.51 | 29600 | 0.3953 | 0.2203 | |
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| 0.0573 | 19.78 | 30000 | 0.3947 | 0.2190 | |
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| 0.0576 | 20.04 | 30400 | 0.3909 | 0.2156 | |
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| 0.0551 | 20.3 | 30800 | 0.4178 | 0.2153 | |
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| 0.0525 | 20.57 | 31200 | 0.3935 | 0.2152 | |
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| 0.0522 | 20.83 | 31600 | 0.4054 | 0.2151 | |
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| 0.0519 | 21.09 | 32000 | 0.3877 | 0.2135 | |
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| 0.0479 | 21.36 | 32400 | 0.4119 | 0.2107 | |
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| 0.0472 | 21.62 | 32800 | 0.3967 | 0.2091 | |
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| 0.048 | 21.89 | 33200 | 0.3812 | 0.2057 | |
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| 0.0458 | 22.15 | 33600 | 0.3931 | 0.2043 | |
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| 0.0459 | 22.41 | 34000 | 0.3937 | 0.2049 | |
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| 0.0448 | 22.68 | 34400 | 0.3900 | 0.2056 | |
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| 0.0432 | 22.94 | 34800 | 0.4050 | 0.2049 | |
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| 0.0425 | 23.2 | 35200 | 0.3985 | 0.2014 | |
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| 0.0415 | 23.47 | 35600 | 0.3976 | 0.2013 | |
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| 0.0403 | 23.73 | 36000 | 0.4031 | 0.2018 | |
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| 0.04 | 23.99 | 36400 | 0.3996 | 0.2000 | |
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| 0.039 | 24.26 | 36800 | 0.3977 | 0.1993 | |
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| 0.0406 | 24.52 | 37200 | 0.3967 | 0.2000 | |
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| 0.0391 | 24.79 | 37600 | 0.3986 | 0.1980 | |
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### Framework versions |
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- Transformers 4.17.0.dev0 |
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- Pytorch 1.10.2+cu102 |
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- Datasets 1.18.2.dev0 |
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- Tokenizers 0.11.0 |
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