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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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datasets: |
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- common_voice |
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model-index: |
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- name: wav2vec2-large-xls-r-300m-spanish-custom |
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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-custom |
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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 common_voice dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.4426 |
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- Wer: 0.2117 |
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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: 0.0003 |
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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: 30 |
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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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| 4.2307 | 0.4 | 400 | 1.4431 | 0.9299 | |
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| 0.7066 | 0.79 | 800 | 0.5928 | 0.4836 | |
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| 0.4397 | 1.19 | 1200 | 0.4341 | 0.3730 | |
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| 0.3889 | 1.58 | 1600 | 0.4063 | 0.3499 | |
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| 0.3607 | 1.98 | 2000 | 0.3834 | 0.3235 | |
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| 0.2866 | 2.37 | 2400 | 0.3885 | 0.3163 | |
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| 0.2833 | 2.77 | 2800 | 0.3765 | 0.3140 | |
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| 0.2692 | 3.17 | 3200 | 0.3849 | 0.3132 | |
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| 0.2435 | 3.56 | 3600 | 0.3779 | 0.2984 | |
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| 0.2404 | 3.96 | 4000 | 0.3756 | 0.2934 | |
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| 0.2153 | 4.35 | 4400 | 0.3770 | 0.3075 | |
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| 0.2087 | 4.75 | 4800 | 0.3819 | 0.3022 | |
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| 0.1999 | 5.14 | 5200 | 0.3756 | 0.2959 | |
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| 0.1838 | 5.54 | 5600 | 0.3827 | 0.2858 | |
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| 0.1892 | 5.93 | 6000 | 0.3714 | 0.2999 | |
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| 0.1655 | 6.33 | 6400 | 0.3814 | 0.2812 | |
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| 0.1649 | 6.73 | 6800 | 0.3685 | 0.2727 | |
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| 0.1668 | 7.12 | 7200 | 0.3832 | 0.2825 | |
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| 0.1487 | 7.52 | 7600 | 0.3848 | 0.2788 | |
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| 0.152 | 7.91 | 8000 | 0.3810 | 0.2787 | |
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| 0.143 | 8.31 | 8400 | 0.3885 | 0.2856 | |
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| 0.1353 | 8.7 | 8800 | 0.4103 | 0.2827 | |
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| 0.1386 | 9.1 | 9200 | 0.4142 | 0.2874 | |
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| 0.1222 | 9.5 | 9600 | 0.3983 | 0.2830 | |
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| 0.1288 | 9.89 | 10000 | 0.4179 | 0.2781 | |
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| 0.1199 | 10.29 | 10400 | 0.4035 | 0.2789 | |
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| 0.1196 | 10.68 | 10800 | 0.4043 | 0.2746 | |
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| 0.1169 | 11.08 | 11200 | 0.4105 | 0.2753 | |
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| 0.1076 | 11.47 | 11600 | 0.4298 | 0.2686 | |
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| 0.1124 | 11.87 | 12000 | 0.4025 | 0.2704 | |
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| 0.1043 | 12.26 | 12400 | 0.4209 | 0.2659 | |
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| 0.0976 | 12.66 | 12800 | 0.4070 | 0.2672 | |
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| 0.1012 | 13.06 | 13200 | 0.4161 | 0.2720 | |
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| 0.0872 | 13.45 | 13600 | 0.4245 | 0.2697 | |
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| 0.0933 | 13.85 | 14000 | 0.4295 | 0.2684 | |
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| 0.0881 | 14.24 | 14400 | 0.4011 | 0.2650 | |
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| 0.0848 | 14.64 | 14800 | 0.3991 | 0.2675 | |
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| 0.0852 | 15.03 | 15200 | 0.4166 | 0.2617 | |
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| 0.0825 | 15.43 | 15600 | 0.4188 | 0.2639 | |
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| 0.081 | 15.83 | 16000 | 0.4181 | 0.2547 | |
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| 0.0753 | 16.22 | 16400 | 0.4103 | 0.2560 | |
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| 0.0747 | 16.62 | 16800 | 0.4017 | 0.2498 | |
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| 0.0761 | 17.01 | 17200 | 0.4159 | 0.2563 | |
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| 0.0711 | 17.41 | 17600 | 0.4112 | 0.2603 | |
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| 0.0698 | 17.8 | 18000 | 0.4335 | 0.2529 | |
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| 0.073 | 18.2 | 18400 | 0.4120 | 0.2512 | |
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| 0.0665 | 18.6 | 18800 | 0.4335 | 0.2496 | |
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| 0.0657 | 18.99 | 19200 | 0.4143 | 0.2468 | |
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| 0.0617 | 19.39 | 19600 | 0.4339 | 0.2435 | |
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| 0.06 | 19.78 | 20000 | 0.4179 | 0.2438 | |
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| 0.0613 | 20.18 | 20400 | 0.4251 | 0.2393 | |
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| 0.0583 | 20.57 | 20800 | 0.4347 | 0.2422 | |
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| 0.0562 | 20.97 | 21200 | 0.4246 | 0.2377 | |
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| 0.053 | 21.36 | 21600 | 0.4198 | 0.2338 | |
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| 0.0525 | 21.76 | 22000 | 0.4511 | 0.2427 | |
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| 0.0499 | 22.16 | 22400 | 0.4482 | 0.2353 | |
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| 0.0475 | 22.55 | 22800 | 0.4449 | 0.2329 | |
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| 0.0465 | 22.95 | 23200 | 0.4364 | 0.2320 | |
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| 0.0443 | 23.34 | 23600 | 0.4481 | 0.2304 | |
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| 0.0458 | 23.74 | 24000 | 0.4442 | 0.2267 | |
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| 0.0453 | 24.13 | 24400 | 0.4402 | 0.2261 | |
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| 0.0426 | 24.53 | 24800 | 0.4262 | 0.2232 | |
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| 0.0431 | 24.93 | 25200 | 0.4251 | 0.2210 | |
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| 0.0389 | 25.32 | 25600 | 0.4455 | 0.2232 | |
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| 0.039 | 25.72 | 26000 | 0.4372 | 0.2236 | |
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| 0.0378 | 26.11 | 26400 | 0.4236 | 0.2212 | |
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| 0.0348 | 26.51 | 26800 | 0.4359 | 0.2204 | |
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| 0.0361 | 26.9 | 27200 | 0.4248 | 0.2192 | |
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| 0.0356 | 27.3 | 27600 | 0.4397 | 0.2184 | |
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| 0.0325 | 27.7 | 28000 | 0.4367 | 0.2181 | |
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| 0.0313 | 28.09 | 28400 | 0.4477 | 0.2136 | |
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| 0.0306 | 28.49 | 28800 | 0.4533 | 0.2135 | |
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| 0.0314 | 28.88 | 29200 | 0.4410 | 0.2136 | |
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| 0.0307 | 29.28 | 29600 | 0.4457 | 0.2113 | |
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| 0.0309 | 29.67 | 30000 | 0.4426 | 0.2117 | |
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### Framework versions |
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- Transformers 4.16.0.dev0 |
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- Pytorch 1.10.1+cu102 |
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- Datasets 1.17.1.dev0 |
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- Tokenizers 0.11.0 |
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