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ctaguchi
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ssc-bxk-model

Automatic Speech Recognition
Transformers
Safetensors
wav2vec2
Generated from Trainer
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  2. model.safetensors +1 -1
README.md ADDED
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+ ---
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+ library_name: transformers
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+ license: apache-2.0
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+ base_model: facebook/wav2vec2-xls-r-300m
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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: ssc-bxk-model
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+ results: []
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+ ---
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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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+
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+ # ssc-bxk-model
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+
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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 an unknown dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 1.4388
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+ - Cer: 0.2469
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+ - Wer: 0.8253
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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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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: 16
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+ - eval_batch_size: 16
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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: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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+ - lr_scheduler_type: linear
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+ - lr_scheduler_warmup_steps: 100
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+ - num_epochs: 30
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+ - mixed_precision_training: Native AMP
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss | Cer | Wer |
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+ |:-------------:|:-------:|:-----:|:---------------:|:------:|:------:|
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+ | 4.6605 | 0.2882 | 100 | 3.0343 | 0.9955 | 1.0 |
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+ | 2.9398 | 0.5764 | 200 | 2.9498 | 0.9919 | 0.9998 |
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+ | 2.6066 | 0.8646 | 300 | 2.6204 | 0.7305 | 1.0 |
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+ | 1.927 | 1.1527 | 400 | 1.6190 | 0.4561 | 0.9996 |
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+ | 1.5776 | 1.4409 | 500 | 1.5378 | 0.4264 | 0.9989 |
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+ | 1.438 | 1.7291 | 600 | 1.2293 | 0.3542 | 0.9624 |
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+ | 1.3485 | 2.0173 | 700 | 1.1700 | 0.3398 | 0.9502 |
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+ | 1.2258 | 2.3055 | 800 | 1.1301 | 0.3231 | 0.9289 |
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+ | 1.2223 | 2.5937 | 900 | 1.1717 | 0.4102 | 0.9601 |
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+ | 1.2276 | 2.8818 | 1000 | 1.0739 | 0.3068 | 0.9080 |
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+ | 1.1002 | 3.1700 | 1100 | 1.0304 | 0.3090 | 0.9174 |
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+ | 1.1105 | 3.4582 | 1200 | 0.9805 | 0.3034 | 0.9022 |
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+ | 1.0848 | 3.7464 | 1300 | 1.0215 | 0.3204 | 0.9146 |
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+ | 1.0122 | 4.0346 | 1400 | 1.0029 | 0.2903 | 0.8837 |
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+ | 0.9599 | 4.3228 | 1500 | 0.9633 | 0.2795 | 0.8630 |
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+ | 0.9635 | 4.6110 | 1600 | 1.0093 | 0.3015 | 0.8924 |
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+ | 0.9947 | 4.8991 | 1700 | 1.0203 | 0.2732 | 0.8813 |
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+ | 0.9357 | 5.1873 | 1800 | 1.0053 | 0.2866 | 0.8841 |
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+ | 0.8969 | 5.4755 | 1900 | 0.9909 | 0.2936 | 0.8892 |
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+ | 0.8773 | 5.7637 | 2000 | 0.9700 | 0.3065 | 0.8843 |
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+ | 0.8913 | 6.0519 | 2100 | 0.9634 | 0.2909 | 0.8829 |
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+ | 0.819 | 6.3401 | 2200 | 0.9919 | 0.2963 | 0.8823 |
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+ | 0.8203 | 6.6282 | 2300 | 0.9623 | 0.2711 | 0.8655 |
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+ | 0.8374 | 6.9164 | 2400 | 0.9565 | 0.2723 | 0.8690 |
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+ | 0.7793 | 7.2046 | 2500 | 0.9639 | 0.2778 | 0.8531 |
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+ | 0.7874 | 7.4928 | 2600 | 0.9875 | 0.2762 | 0.8756 |
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+ | 0.7736 | 7.7810 | 2700 | 0.9054 | 0.2717 | 0.8355 |
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+ | 0.7649 | 8.0692 | 2800 | 0.9827 | 0.2746 | 0.8425 |
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+ | 0.7166 | 8.3573 | 2900 | 0.9269 | 0.2601 | 0.8293 |
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+ | 0.726 | 8.6455 | 3000 | 0.9668 | 0.2670 | 0.8520 |
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+ | 0.6946 | 8.9337 | 3100 | 0.9494 | 0.2612 | 0.8423 |
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+ | 0.6848 | 9.2219 | 3200 | 1.0094 | 0.2536 | 0.8348 |
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+ | 0.6637 | 9.5101 | 3300 | 0.9842 | 0.2664 | 0.8412 |
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+ | 0.6711 | 9.7983 | 3400 | 0.9495 | 0.2477 | 0.8239 |
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+ | 0.6417 | 10.0865 | 3500 | 0.9724 | 0.2724 | 0.8511 |
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+ | 0.6206 | 10.3746 | 3600 | 0.9884 | 0.2635 | 0.8373 |
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+ | 0.6172 | 10.6628 | 3700 | 1.0208 | 0.2652 | 0.8504 |
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+ | 0.6383 | 10.9510 | 3800 | 0.9368 | 0.2594 | 0.8343 |
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+ | 0.5773 | 11.2392 | 3900 | 1.0053 | 0.2598 | 0.8499 |
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+ | 0.561 | 11.5274 | 4000 | 0.9953 | 0.2579 | 0.8301 |
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+ | 0.5901 | 11.8156 | 4100 | 0.9727 | 0.2612 | 0.8338 |
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+ | 0.572 | 12.1037 | 4200 | 1.0612 | 0.2633 | 0.8458 |
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+ | 0.531 | 12.3919 | 4300 | 0.9773 | 0.2610 | 0.8441 |
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+ | 0.5407 | 12.6801 | 4400 | 0.9359 | 0.2546 | 0.8079 |
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+ | 0.5481 | 12.9683 | 4500 | 0.9912 | 0.2622 | 0.8423 |
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+ | 0.5121 | 13.2565 | 4600 | 1.0189 | 0.2435 | 0.8021 |
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+ | 0.4812 | 13.5447 | 4700 | 1.0280 | 0.2644 | 0.8283 |
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+ | 0.4957 | 13.8329 | 4800 | 1.0750 | 0.2621 | 0.8418 |
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+ | 0.4883 | 14.1210 | 4900 | 1.0284 | 0.2574 | 0.8182 |
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+ | 0.4452 | 14.4092 | 5000 | 1.0755 | 0.2676 | 0.8336 |
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+ | 0.4784 | 14.6974 | 5100 | 1.0643 | 0.2469 | 0.8194 |
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+ | 0.4816 | 14.9856 | 5200 | 1.0728 | 0.2671 | 0.8410 |
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+ | 0.4237 | 15.2738 | 5300 | 1.0982 | 0.2646 | 0.8460 |
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+ | 0.4245 | 15.5620 | 5400 | 1.0886 | 0.2393 | 0.8038 |
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+ | 0.4351 | 15.8501 | 5500 | 1.0750 | 0.2522 | 0.8471 |
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+ | 0.4229 | 16.1383 | 5600 | 1.1751 | 0.2555 | 0.8288 |
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+ | 0.4111 | 16.4265 | 5700 | 1.1463 | 0.2584 | 0.8357 |
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+ | 0.3947 | 16.7147 | 5800 | 1.1446 | 0.2586 | 0.8290 |
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+ | 0.4146 | 17.0029 | 5900 | 1.1675 | 0.2507 | 0.8256 |
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+ | 0.3463 | 17.2911 | 6000 | 1.1274 | 0.2540 | 0.8386 |
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+ | 0.3682 | 17.5793 | 6100 | 1.1349 | 0.2604 | 0.8278 |
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+ | 0.361 | 17.8674 | 6200 | 1.1877 | 0.2584 | 0.8449 |
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+ | 0.3672 | 18.1556 | 6300 | 1.1213 | 0.2570 | 0.8324 |
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+ | 0.3533 | 18.4438 | 6400 | 1.0914 | 0.2516 | 0.8079 |
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+ | 0.3433 | 18.7320 | 6500 | 1.1553 | 0.2489 | 0.8155 |
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+ | 0.3431 | 19.0202 | 6600 | 1.1193 | 0.2487 | 0.8201 |
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+ | 0.3138 | 19.3084 | 6700 | 1.1092 | 0.2462 | 0.8116 |
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+ | 0.329 | 19.5965 | 6800 | 1.1028 | 0.2475 | 0.8129 |
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+ | 0.3017 | 19.8847 | 6900 | 1.2623 | 0.2516 | 0.8200 |
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+ | 0.2905 | 20.1729 | 7000 | 1.1891 | 0.2584 | 0.8336 |
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+ | 0.2828 | 20.4611 | 7100 | 1.1295 | 0.2531 | 0.8081 |
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+ | 0.2975 | 20.7493 | 7200 | 1.1500 | 0.2608 | 0.8299 |
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+ | 0.2898 | 21.0375 | 7300 | 1.1343 | 0.2500 | 0.8263 |
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+ | 0.2694 | 21.3256 | 7400 | 1.2274 | 0.2489 | 0.8228 |
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+ | 0.2694 | 21.6138 | 7500 | 1.2690 | 0.2515 | 0.8272 |
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+ | 0.2672 | 21.9020 | 7600 | 1.2604 | 0.2574 | 0.8246 |
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+ | 0.2505 | 22.1902 | 7700 | 1.2891 | 0.2496 | 0.8192 |
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+ | 0.246 | 22.4784 | 7800 | 1.2759 | 0.2547 | 0.8198 |
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+ | 0.2651 | 22.7666 | 7900 | 1.2823 | 0.2520 | 0.8293 |
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+ | 0.2407 | 23.0548 | 8000 | 1.3137 | 0.2506 | 0.8329 |
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+ | 0.2312 | 23.3429 | 8100 | 1.3152 | 0.2587 | 0.8347 |
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+ | 0.2227 | 23.6311 | 8200 | 1.3156 | 0.2495 | 0.8159 |
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+ | 0.2152 | 23.9193 | 8300 | 1.3123 | 0.2484 | 0.8327 |
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+ | 0.2137 | 24.2075 | 8400 | 1.3881 | 0.2477 | 0.8210 |
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+ | 0.2096 | 24.4957 | 8500 | 1.3389 | 0.2525 | 0.8230 |
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+ | 0.2167 | 24.7839 | 8600 | 1.3801 | 0.2499 | 0.8249 |
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+ | 0.215 | 25.0720 | 8700 | 1.4032 | 0.2524 | 0.8301 |
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+ | 0.1984 | 25.3602 | 8800 | 1.4061 | 0.2495 | 0.8292 |
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+ | 0.2002 | 25.6484 | 8900 | 1.3790 | 0.2483 | 0.8311 |
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+ | 0.1921 | 25.9366 | 9000 | 1.3252 | 0.2464 | 0.8239 |
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+ | 0.185 | 26.2248 | 9100 | 1.4067 | 0.2482 | 0.8301 |
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+ | 0.1718 | 26.5130 | 9200 | 1.3845 | 0.2466 | 0.8173 |
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+ | 0.1881 | 26.8012 | 9300 | 1.3831 | 0.2474 | 0.8173 |
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+ | 0.1795 | 27.0893 | 9400 | 1.4146 | 0.2489 | 0.8150 |
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+ | 0.1655 | 27.3775 | 9500 | 1.4118 | 0.2482 | 0.8153 |
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+ | 0.1722 | 27.6657 | 9600 | 1.3973 | 0.2465 | 0.8208 |
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+ | 0.1736 | 27.9539 | 9700 | 1.3843 | 0.2476 | 0.8205 |
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+ | 0.1656 | 28.2421 | 9800 | 1.4340 | 0.2470 | 0.8256 |
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+ | 0.1644 | 28.5303 | 9900 | 1.4053 | 0.2461 | 0.8240 |
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+ | 0.1613 | 28.8184 | 10000 | 1.4068 | 0.2472 | 0.8217 |
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+ | 0.1639 | 29.1066 | 10100 | 1.4115 | 0.2443 | 0.8198 |
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+ | 0.1537 | 29.3948 | 10200 | 1.4257 | 0.2462 | 0.8219 |
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+ | 0.1549 | 29.6830 | 10300 | 1.4378 | 0.2464 | 0.8258 |
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+ | 0.1448 | 29.9712 | 10400 | 1.4388 | 0.2469 | 0.8253 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.57.2
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+ - Pytorch 2.9.1+cu128
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+ - Datasets 3.6.0
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+ - Tokenizers 0.22.0
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