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library_name: transformers
license: apache-2.0
base_model: facebook/wav2vec2-xls-r-300m
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: ssc-meh-model
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# ssc-meh-model
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.
It achieves the following results on the evaluation set:
- Loss: 1.8542
- Cer: 0.3340
- Wer: 0.8345
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Cer | Wer |
|:-------------:|:-------:|:-----:|:---------------:|:------:|:------:|
| 5.7202 | 0.1905 | 100 | 2.9202 | 1.0 | 1.0 |
| 2.9539 | 0.3810 | 200 | 2.7751 | 1.0 | 1.0 |
| 2.9096 | 0.5714 | 300 | 2.7931 | 1.0 | 1.0 |
| 2.9036 | 0.7619 | 400 | 2.7883 | 1.0 | 1.0 |
| 2.8563 | 0.9524 | 500 | 2.9839 | 1.0 | 1.0 |
| 2.7445 | 1.1429 | 600 | 2.5705 | 1.0 | 1.0 |
| 2.6738 | 1.3333 | 700 | 2.4914 | 0.9969 | 0.9992 |
| 2.5821 | 1.5238 | 800 | 2.2302 | 0.9231 | 0.9753 |
| 2.4371 | 1.7143 | 900 | 2.0914 | 0.8529 | 1.0 |
| 2.2815 | 1.9048 | 1000 | 1.8929 | 0.7330 | 0.9997 |
| 2.1294 | 2.0952 | 1100 | 1.8964 | 0.6461 | 0.9673 |
| 1.9156 | 2.2857 | 1200 | 1.8122 | 0.7197 | 0.9931 |
| 1.8408 | 2.4762 | 1300 | 1.7276 | 0.5993 | 0.9602 |
| 1.7506 | 2.6667 | 1400 | 1.6561 | 0.6534 | 0.9657 |
| 1.7086 | 2.8571 | 1500 | 1.7104 | 0.4768 | 0.9349 |
| 1.5834 | 3.0476 | 1600 | 1.4368 | 0.4460 | 0.9084 |
| 1.5731 | 3.2381 | 1700 | 1.4594 | 0.4292 | 0.9379 |
| 1.5743 | 3.4286 | 1800 | 1.4713 | 0.4183 | 0.9731 |
| 1.4766 | 3.6190 | 1900 | 1.3967 | 0.4334 | 0.9114 |
| 1.4799 | 3.8095 | 2000 | 1.5286 | 0.4260 | 0.9550 |
| 1.4973 | 4.0 | 2100 | 1.7988 | 0.4115 | 1.1627 |
| 1.3498 | 4.1905 | 2200 | 1.4739 | 0.4768 | 0.9209 |
| 1.3642 | 4.3810 | 2300 | 1.3685 | 0.4241 | 0.9290 |
| 1.361 | 4.5714 | 2400 | 1.3578 | 0.4705 | 0.9097 |
| 1.3747 | 4.7619 | 2500 | 1.4424 | 0.4560 | 0.9060 |
| 1.3728 | 4.9524 | 2600 | 1.5908 | 0.3978 | 0.9865 |
| 1.2524 | 5.1429 | 2700 | 1.3019 | 0.3814 | 0.9119 |
| 1.2532 | 5.3333 | 2800 | 1.3969 | 0.3960 | 0.9056 |
| 1.2527 | 5.5238 | 2900 | 1.2484 | 0.3659 | 0.9150 |
| 1.2347 | 5.7143 | 3000 | 1.5303 | 0.4045 | 1.0358 |
| 1.2531 | 5.9048 | 3100 | 1.3037 | 0.3827 | 0.8702 |
| 1.2506 | 6.0952 | 3200 | 1.4484 | 0.3815 | 0.9621 |
| 1.1295 | 6.2857 | 3300 | 1.4467 | 0.3843 | 0.9590 |
| 1.1507 | 6.4762 | 3400 | 1.3074 | 0.4033 | 0.8888 |
| 1.146 | 6.6667 | 3500 | 1.6379 | 0.4109 | 1.0024 |
| 1.1812 | 6.8571 | 3600 | 1.3674 | 0.4137 | 0.9033 |
| 1.063 | 7.0476 | 3700 | 1.2695 | 0.3719 | 0.9200 |
| 1.0615 | 7.2381 | 3800 | 1.2816 | 0.3645 | 0.9137 |
| 1.0779 | 7.4286 | 3900 | 1.3308 | 0.3772 | 0.9202 |
| 1.059 | 7.6190 | 4000 | 1.1988 | 0.3983 | 0.8730 |
| 1.0706 | 7.8095 | 4100 | 1.2735 | 0.3771 | 0.8901 |
| 1.1306 | 8.0 | 4200 | 1.3397 | 0.4057 | 0.8777 |
| 0.965 | 8.1905 | 4300 | 1.4966 | 0.3934 | 1.0157 |
| 1.0081 | 8.3810 | 4400 | 1.2140 | 0.3899 | 0.8674 |
| 1.0052 | 8.5714 | 4500 | 1.2490 | 0.3873 | 0.8774 |
| 1.0262 | 8.7619 | 4600 | 1.2793 | 0.3705 | 0.8633 |
| 0.9791 | 8.9524 | 4700 | 1.1578 | 0.3542 | 0.8501 |
| 0.8975 | 9.1429 | 4800 | 1.1994 | 0.3646 | 0.8731 |
| 0.9043 | 9.3333 | 4900 | 1.1890 | 0.3426 | 0.8523 |
| 0.9641 | 9.5238 | 5000 | 1.2537 | 0.3604 | 0.9406 |
| 0.9526 | 9.7143 | 5100 | 1.2081 | 0.3549 | 0.8456 |
| 0.905 | 9.9048 | 5200 | 1.2440 | 0.3614 | 0.9021 |
| 0.9099 | 10.0952 | 5300 | 1.3497 | 0.3703 | 0.9114 |
| 0.8435 | 10.2857 | 5400 | 1.1731 | 0.3861 | 0.8658 |
| 0.8914 | 10.4762 | 5500 | 1.3744 | 0.3745 | 0.9399 |
| 0.879 | 10.6667 | 5600 | 1.2298 | 0.3559 | 0.8850 |
| 0.8754 | 10.8571 | 5700 | 1.3239 | 0.3508 | 0.9467 |
| 0.8348 | 11.0476 | 5800 | 1.2166 | 0.3551 | 0.8572 |
| 0.781 | 11.2381 | 5900 | 1.2808 | 0.3511 | 0.9238 |
| 0.8081 | 11.4286 | 6000 | 1.2105 | 0.3496 | 0.8970 |
| 0.8023 | 11.6190 | 6100 | 1.1924 | 0.3434 | 0.8323 |
| 0.8236 | 11.8095 | 6200 | 1.2141 | 0.3453 | 0.9049 |
| 0.892 | 12.0 | 6300 | 1.2798 | 0.3720 | 0.9378 |
| 0.7549 | 12.1905 | 6400 | 1.1700 | 0.3318 | 0.8809 |
| 0.7428 | 12.3810 | 6500 | 1.3049 | 0.3628 | 0.8782 |
| 0.765 | 12.5714 | 6600 | 1.2451 | 0.3449 | 0.9348 |
| 0.7747 | 12.7619 | 6700 | 1.3216 | 0.3714 | 0.8654 |
| 0.7448 | 12.9524 | 6800 | 1.3266 | 0.3633 | 0.8783 |
| 0.6694 | 13.1429 | 6900 | 1.2095 | 0.3411 | 0.8247 |
| 0.6949 | 13.3333 | 7000 | 1.3466 | 0.3589 | 0.8935 |
| 0.721 | 13.5238 | 7100 | 1.2278 | 0.3478 | 0.8737 |
| 0.7298 | 13.7143 | 7200 | 1.2689 | 0.3533 | 0.9153 |
| 0.6928 | 13.9048 | 7300 | 1.2013 | 0.3433 | 0.8368 |
| 0.7061 | 14.0952 | 7400 | 1.3039 | 0.3781 | 0.8541 |
| 0.6477 | 14.2857 | 7500 | 1.2427 | 0.3407 | 0.8845 |
| 0.6752 | 14.4762 | 7600 | 1.3106 | 0.3536 | 0.8776 |
| 0.6498 | 14.6667 | 7700 | 1.2447 | 0.3412 | 0.8615 |
| 0.6626 | 14.8571 | 7800 | 1.3016 | 0.3621 | 0.8733 |
| 0.5813 | 15.0476 | 7900 | 1.2265 | 0.3362 | 0.8301 |
| 0.6195 | 15.2381 | 8000 | 1.2933 | 0.3445 | 0.8307 |
| 0.5909 | 15.4286 | 8100 | 1.2376 | 0.3351 | 0.8288 |
| 0.6048 | 15.6190 | 8200 | 1.2648 | 0.3374 | 0.8197 |
| 0.6235 | 15.8095 | 8300 | 1.2515 | 0.3410 | 0.8602 |
| 0.6721 | 16.0 | 8400 | 1.2712 | 0.3422 | 0.8459 |
| 0.5156 | 16.1905 | 8500 | 1.3692 | 0.3527 | 0.8760 |
| 0.5312 | 16.3810 | 8600 | 1.3574 | 0.3438 | 0.8598 |
| 0.5859 | 16.5714 | 8700 | 1.2556 | 0.3398 | 0.8362 |
| 0.5454 | 16.7619 | 8800 | 1.3780 | 0.3574 | 0.8749 |
| 0.5552 | 16.9524 | 8900 | 1.2868 | 0.3461 | 0.8953 |
| 0.484 | 17.1429 | 9000 | 1.2784 | 0.3434 | 0.8335 |
| 0.5364 | 17.3333 | 9100 | 1.2629 | 0.3339 | 0.8294 |
| 0.5188 | 17.5238 | 9200 | 1.3447 | 0.3329 | 0.8361 |
| 0.4979 | 17.7143 | 9300 | 1.3755 | 0.3332 | 0.8532 |
| 0.5313 | 17.9048 | 9400 | 1.2748 | 0.3397 | 0.8486 |
| 0.5102 | 18.0952 | 9500 | 1.3303 | 0.3363 | 0.8214 |
| 0.4997 | 18.2857 | 9600 | 1.3119 | 0.3453 | 0.8571 |
| 0.475 | 18.4762 | 9700 | 1.4079 | 0.3446 | 0.8573 |
| 0.4989 | 18.6667 | 9800 | 1.3263 | 0.3335 | 0.8518 |
| 0.4915 | 18.8571 | 9900 | 1.3138 | 0.3410 | 0.8241 |
| 0.4469 | 19.0476 | 10000 | 1.3820 | 0.3309 | 0.8491 |
| 0.4391 | 19.2381 | 10100 | 1.3812 | 0.3344 | 0.8371 |
| 0.4481 | 19.4286 | 10200 | 1.3321 | 0.3434 | 0.8303 |
| 0.4482 | 19.6190 | 10300 | 1.3548 | 0.3356 | 0.8298 |
| 0.4592 | 19.8095 | 10400 | 1.3296 | 0.3405 | 0.8610 |
| 0.4886 | 20.0 | 10500 | 1.3636 | 0.3515 | 0.8321 |
| 0.4023 | 20.1905 | 10600 | 1.4382 | 0.3514 | 0.8467 |
| 0.4332 | 20.3810 | 10700 | 1.2829 | 0.3319 | 0.8508 |
| 0.4154 | 20.5714 | 10800 | 1.3922 | 0.3423 | 0.8313 |
| 0.4276 | 20.7619 | 10900 | 1.4043 | 0.3459 | 0.8460 |
| 0.4316 | 20.9524 | 11000 | 1.4012 | 0.3346 | 0.8272 |
| 0.351 | 21.1429 | 11100 | 1.4923 | 0.3404 | 0.8368 |
| 0.3941 | 21.3333 | 11200 | 1.4509 | 0.3438 | 0.8382 |
| 0.3883 | 21.5238 | 11300 | 1.4189 | 0.3359 | 0.8258 |
| 0.4208 | 21.7143 | 11400 | 1.4527 | 0.3411 | 0.8344 |
| 0.3843 | 21.9048 | 11500 | 1.5000 | 0.3474 | 0.8349 |
| 0.42 | 22.0952 | 11600 | 1.6168 | 0.3509 | 0.8666 |
| 0.3638 | 22.2857 | 11700 | 1.5645 | 0.3518 | 0.8581 |
| 0.3763 | 22.4762 | 11800 | 1.4347 | 0.3441 | 0.8419 |
| 0.3637 | 22.6667 | 11900 | 1.6041 | 0.3466 | 0.8635 |
| 0.3717 | 22.8571 | 12000 | 1.5876 | 0.3400 | 0.8466 |
| 0.3398 | 23.0476 | 12100 | 1.5634 | 0.3378 | 0.8281 |
| 0.347 | 23.2381 | 12200 | 1.4949 | 0.3316 | 0.8112 |
| 0.3493 | 23.4286 | 12300 | 1.5127 | 0.3427 | 0.8347 |
| 0.338 | 23.6190 | 12400 | 1.5340 | 0.3458 | 0.8423 |
| 0.3456 | 23.8095 | 12500 | 1.5608 | 0.3492 | 0.8532 |
| 0.3829 | 24.0 | 12600 | 1.5481 | 0.3391 | 0.8569 |
| 0.331 | 24.1905 | 12700 | 1.5683 | 0.3323 | 0.8539 |
| 0.305 | 24.3810 | 12800 | 1.6475 | 0.3298 | 0.8240 |
| 0.3244 | 24.5714 | 12900 | 1.5566 | 0.3375 | 0.8227 |
| 0.3397 | 24.7619 | 13000 | 1.5676 | 0.3368 | 0.8393 |
| 0.3221 | 24.9524 | 13100 | 1.5839 | 0.3376 | 0.8391 |
| 0.2943 | 25.1429 | 13200 | 1.7326 | 0.3316 | 0.8413 |
| 0.3197 | 25.3333 | 13300 | 1.6815 | 0.3329 | 0.8492 |
| 0.3011 | 25.5238 | 13400 | 1.6816 | 0.3359 | 0.8492 |
| 0.3152 | 25.7143 | 13500 | 1.6790 | 0.3391 | 0.8683 |
| 0.3088 | 25.9048 | 13600 | 1.6628 | 0.3428 | 0.8819 |
| 0.336 | 26.0952 | 13700 | 1.7037 | 0.3340 | 0.8444 |
| 0.2778 | 26.2857 | 13800 | 1.7835 | 0.3479 | 0.8627 |
| 0.2775 | 26.4762 | 13900 | 1.7454 | 0.3359 | 0.8630 |
| 0.286 | 26.6667 | 14000 | 1.7595 | 0.3359 | 0.8395 |
| 0.2724 | 26.8571 | 14100 | 1.7468 | 0.3387 | 0.8408 |
| 0.2663 | 27.0476 | 14200 | 1.6998 | 0.3352 | 0.8452 |
| 0.2941 | 27.2381 | 14300 | 1.8233 | 0.3367 | 0.8526 |
| 0.2888 | 27.4286 | 14400 | 1.7692 | 0.3353 | 0.8241 |
| 0.2511 | 27.6190 | 14500 | 1.8221 | 0.3371 | 0.8425 |
| 0.2623 | 27.8095 | 14600 | 1.8068 | 0.3400 | 0.8403 |
| 0.2851 | 28.0 | 14700 | 1.8250 | 0.3398 | 0.8417 |
| 0.2557 | 28.1905 | 14800 | 1.8385 | 0.3405 | 0.8464 |
| 0.2844 | 28.3810 | 14900 | 1.8351 | 0.3359 | 0.8456 |
| 0.25 | 28.5714 | 15000 | 1.8201 | 0.3326 | 0.8261 |
| 0.267 | 28.7619 | 15100 | 1.8391 | 0.3376 | 0.8375 |
| 0.2409 | 28.9524 | 15200 | 1.8450 | 0.3371 | 0.8430 |
| 0.2263 | 29.1429 | 15300 | 1.8316 | 0.3356 | 0.8336 |
| 0.2579 | 29.3333 | 15400 | 1.8472 | 0.3338 | 0.8391 |
| 0.2424 | 29.5238 | 15500 | 1.8536 | 0.3328 | 0.8357 |
| 0.2612 | 29.7143 | 15600 | 1.8561 | 0.3341 | 0.8339 |
| 0.2644 | 29.9048 | 15700 | 1.8542 | 0.3340 | 0.8345 |
### Framework versions
- Transformers 4.57.2
- Pytorch 2.9.1+cu128
- Datasets 3.6.0
- Tokenizers 0.22.0
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