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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-mmc-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-mmc-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: 4.0536
- Cer: 0.9382
- Wer: 0.9965
## 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 |
|:-------------:|:-------:|:-----:|:---------------:|:------:|:------:|
| 7.5593 | 0.1705 | 100 | 3.7574 | 1.0 | 1.0 |
| 3.7617 | 0.3410 | 200 | 3.6406 | 1.0 | 1.0 |
| 3.7935 | 0.5115 | 300 | 3.7703 | 1.0000 | 1.0 |
| 3.7515 | 0.6820 | 400 | 3.4756 | 0.9938 | 0.9993 |
| 3.7554 | 0.8525 | 500 | 3.5054 | 0.9932 | 0.9998 |
| 3.7684 | 1.0222 | 600 | 3.4862 | 0.9932 | 1.0 |
| 3.7297 | 1.1927 | 700 | 3.5204 | 0.9933 | 1.0 |
| 3.7861 | 1.3632 | 800 | 3.6136 | 0.9932 | 1.0 |
| 3.7326 | 1.5337 | 900 | 3.6448 | 0.9932 | 1.0 |
| 3.7333 | 1.7042 | 1000 | 3.5650 | 0.9932 | 1.0 |
| 3.7813 | 1.8747 | 1100 | 3.5611 | 0.9932 | 0.9998 |
| 3.6991 | 2.0443 | 1200 | 4.0765 | 0.9932 | 1.0 |
| 3.783 | 2.2148 | 1300 | 3.8187 | 0.9932 | 1.0 |
| 3.812 | 2.3853 | 1400 | 3.8855 | 0.9932 | 1.0 |
| 3.7223 | 2.5558 | 1500 | 3.8915 | 0.9932 | 0.9998 |
| 3.7504 | 2.7263 | 1600 | 3.8943 | 0.9932 | 1.0 |
| 3.7417 | 2.8968 | 1700 | 3.9197 | 0.9930 | 1.0 |
| 3.7336 | 3.0665 | 1800 | 3.5882 | 0.9938 | 1.0 |
| 3.7272 | 3.2370 | 1900 | 3.5592 | 0.9938 | 1.0 |
| 3.7303 | 3.4075 | 2000 | 3.5324 | 0.9856 | 1.0 |
| 3.7643 | 3.5780 | 2100 | 3.5002 | 0.9847 | 1.0 |
| 3.7024 | 3.7485 | 2200 | 3.4924 | 0.9931 | 0.9998 |
| 3.7144 | 3.9190 | 2300 | 3.5628 | 0.9932 | 1.0 |
| 3.6976 | 4.0887 | 2400 | 3.4476 | 0.9930 | 1.0 |
| 3.7041 | 4.2592 | 2500 | 3.4516 | 0.9932 | 1.0 |
| 3.708 | 4.4297 | 2600 | 3.4573 | 0.9932 | 1.0 |
| 3.6759 | 4.6002 | 2700 | 3.4665 | 0.9938 | 1.0 |
| 3.668 | 4.7707 | 2800 | 3.4816 | 0.9932 | 1.0 |
| 3.6309 | 4.9412 | 2900 | 3.4814 | 0.9930 | 1.0 |
| 3.6628 | 5.1108 | 3000 | 4.2411 | 0.9938 | 1.0 |
| 3.6599 | 5.2813 | 3100 | 4.2083 | 0.9932 | 0.9998 |
| 3.5753 | 5.4518 | 3200 | 4.0952 | 0.9890 | 1.0 |
| 3.6248 | 5.6223 | 3300 | 4.5791 | 0.9938 | 1.0 |
| 3.5746 | 5.7928 | 3400 | 4.4709 | 0.9939 | 1.0 |
| 3.5916 | 5.9633 | 3500 | 4.0641 | 0.9864 | 0.9986 |
| 3.5661 | 6.1330 | 3600 | 4.0738 | 0.9938 | 1.0 |
| 3.5995 | 6.3035 | 3700 | 3.8767 | 0.9863 | 1.0 |
| 3.5785 | 6.4740 | 3800 | 4.2056 | 0.9864 | 0.9984 |
| 3.6001 | 6.6445 | 3900 | 4.2901 | 0.9932 | 1.0 |
| 3.5385 | 6.8150 | 4000 | 4.2071 | 0.9819 | 1.0 |
| 3.5545 | 6.9855 | 4100 | 4.0290 | 0.9903 | 0.9991 |
| 3.5021 | 7.1552 | 4200 | 3.6264 | 0.9868 | 0.9984 |
| 3.5614 | 7.3257 | 4300 | 3.6253 | 0.9932 | 1.0 |
| 3.5401 | 7.4962 | 4400 | 4.2398 | 0.9877 | 0.9998 |
| 3.5546 | 7.6667 | 4500 | 3.5696 | 0.9866 | 1.0 |
| 3.5081 | 7.8372 | 4600 | 3.6183 | 0.9776 | 1.0 |
| 3.5486 | 8.0068 | 4700 | 3.8877 | 0.9809 | 1.0 |
| 3.4921 | 8.1773 | 4800 | 3.9502 | 0.9809 | 1.0 |
| 3.4263 | 8.3478 | 4900 | 3.8585 | 0.9876 | 0.9998 |
| 3.4313 | 8.5183 | 5000 | 4.0835 | 0.9759 | 0.9991 |
| 3.4208 | 8.6888 | 5100 | 3.7700 | 0.9689 | 1.0 |
| 3.392 | 8.8593 | 5200 | 3.8942 | 0.9761 | 1.0 |
| 3.3797 | 9.0290 | 5300 | 3.7086 | 0.9662 | 1.0 |
| 3.3889 | 9.1995 | 5400 | 3.8059 | 0.9463 | 0.9998 |
| 3.3904 | 9.3700 | 5500 | 3.5559 | 0.9681 | 1.0 |
| 3.3925 | 9.5405 | 5600 | 3.5007 | 0.9711 | 1.0 |
| 3.4002 | 9.7110 | 5700 | 3.8154 | 0.9651 | 0.9998 |
| 3.3646 | 9.8815 | 5800 | 3.7271 | 0.9651 | 0.9974 |
| 3.3481 | 10.0512 | 5900 | 3.6049 | 0.9682 | 1.0 |
| 3.3493 | 10.2217 | 6000 | 3.6880 | 0.9645 | 1.0 |
| 3.3517 | 10.3922 | 6100 | 3.6931 | 0.9594 | 0.9995 |
| 3.3389 | 10.5627 | 6200 | 3.6221 | 0.9658 | 0.9998 |
| 3.351 | 10.7332 | 6300 | 3.5192 | 0.9643 | 0.9988 |
| 3.3732 | 10.9037 | 6400 | 3.8387 | 0.9562 | 0.9933 |
| 3.3355 | 11.0733 | 6500 | 3.5540 | 0.9679 | 1.0 |
| 3.3241 | 11.2438 | 6600 | 3.9117 | 0.9568 | 1.0 |
| 3.325 | 11.4143 | 6700 | 3.4597 | 0.9684 | 0.9998 |
| 3.3182 | 11.5848 | 6800 | 3.7552 | 0.9560 | 0.9944 |
| 3.3009 | 11.7553 | 6900 | 3.7510 | 0.9550 | 0.9875 |
| 3.3061 | 11.9258 | 7000 | 3.7513 | 0.9502 | 0.9998 |
| 3.2736 | 12.0955 | 7100 | 4.0637 | 0.9594 | 0.9972 |
| 3.3196 | 12.2660 | 7200 | 4.0105 | 0.9431 | 0.9991 |
| 3.2558 | 12.4365 | 7300 | 3.8223 | 0.9509 | 0.9998 |
| 3.2786 | 12.6070 | 7400 | 3.9672 | 0.9482 | 0.9993 |
| 3.3058 | 12.7775 | 7500 | 3.9256 | 0.9558 | 0.9993 |
| 3.2352 | 12.9480 | 7600 | 3.8248 | 0.9556 | 0.9849 |
| 3.2395 | 13.1176 | 7700 | 3.7745 | 0.9544 | 0.9884 |
| 3.2468 | 13.2882 | 7800 | 3.7510 | 0.9536 | 0.9882 |
| 3.2628 | 13.4587 | 7900 | 3.5977 | 0.9438 | 0.9868 |
| 3.2254 | 13.6292 | 8000 | 3.7842 | 0.9462 | 1.0009 |
| 3.26 | 13.7997 | 8100 | 3.6298 | 0.9616 | 0.9949 |
| 3.2314 | 13.9702 | 8200 | 3.5676 | 0.9558 | 0.9865 |
| 3.2138 | 14.1398 | 8300 | 3.6229 | 0.9651 | 0.9998 |
| 3.2246 | 14.3103 | 8400 | 4.0188 | 0.9614 | 0.9972 |
| 3.206 | 14.4808 | 8500 | 4.0960 | 0.9445 | 0.9886 |
| 3.1805 | 14.6513 | 8600 | 3.5612 | 0.9658 | 0.9981 |
| 3.2015 | 14.8218 | 8700 | 3.5860 | 0.9499 | 0.9974 |
| 3.2258 | 14.9923 | 8800 | 3.8274 | 0.9608 | 0.9986 |
| 3.174 | 15.1620 | 8900 | 3.7214 | 0.9559 | 0.9963 |
| 3.1546 | 15.3325 | 9000 | 4.3387 | 0.9361 | 1.0005 |
| 3.1583 | 15.5030 | 9100 | 4.3383 | 0.9453 | 0.9974 |
| 3.1766 | 15.6735 | 9200 | 4.0330 | 0.9290 | 0.9916 |
| 3.1202 | 15.8440 | 9300 | 3.9352 | 0.9368 | 0.9968 |
| 3.1504 | 16.0136 | 9400 | 4.3483 | 0.9471 | 0.9972 |
| 3.1646 | 16.1841 | 9500 | 4.2858 | 0.9494 | 0.9970 |
| 3.1224 | 16.3546 | 9600 | 3.7921 | 0.9543 | 0.9995 |
| 3.112 | 16.5251 | 9700 | 4.2156 | 0.9512 | 0.9986 |
| 3.1261 | 16.6957 | 9800 | 4.2245 | 0.9476 | 0.9965 |
| 3.0862 | 16.8662 | 9900 | 4.4306 | 0.9466 | 0.9970 |
| 3.1029 | 17.0358 | 10000 | 4.3931 | 0.9485 | 0.9956 |
| 3.1263 | 17.2063 | 10100 | 3.4666 | 0.9473 | 0.9944 |
| 3.075 | 17.3768 | 10200 | 3.6469 | 0.9464 | 0.9896 |
| 3.0742 | 17.5473 | 10300 | 3.8756 | 0.9450 | 0.9863 |
| 3.0918 | 17.7178 | 10400 | 3.8311 | 0.9480 | 0.9942 |
| 3.0716 | 17.8883 | 10500 | 3.5630 | 0.9487 | 0.9972 |
| 3.067 | 18.0580 | 10600 | 3.8665 | 0.9409 | 0.9879 |
| 3.0634 | 18.2285 | 10700 | 3.7174 | 0.9440 | 0.9942 |
| 3.0698 | 18.3990 | 10800 | 3.6759 | 0.9504 | 1.0 |
| 3.0336 | 18.5695 | 10900 | 4.0177 | 0.9330 | 0.9979 |
| 3.0657 | 18.7400 | 11000 | 4.3919 | 0.9405 | 0.9877 |
| 3.0139 | 18.9105 | 11100 | 3.5794 | 0.9500 | 0.9956 |
| 3.0357 | 19.0801 | 11200 | 3.8221 | 0.9331 | 0.9963 |
| 3.0356 | 19.2506 | 11300 | 3.7374 | 0.9423 | 0.9863 |
| 3.0324 | 19.4211 | 11400 | 3.6663 | 0.9516 | 0.9965 |
| 3.0064 | 19.5916 | 11500 | 4.1142 | 0.9504 | 0.9988 |
| 2.9911 | 19.7621 | 11600 | 3.7890 | 0.9558 | 0.9995 |
| 2.9757 | 19.9327 | 11700 | 4.1830 | 0.9411 | 0.9961 |
| 2.9811 | 20.1023 | 11800 | 4.2312 | 0.9315 | 0.9796 |
| 2.969 | 20.2728 | 11900 | 4.1445 | 0.9375 | 0.9905 |
| 3.0039 | 20.4433 | 12000 | 3.9386 | 0.9345 | 0.9807 |
| 2.9678 | 20.6138 | 12100 | 4.1448 | 0.9447 | 0.9937 |
| 2.9596 | 20.7843 | 12200 | 4.4927 | 0.9388 | 0.9956 |
| 2.9558 | 20.9548 | 12300 | 3.8670 | 0.9408 | 0.9884 |
| 2.9371 | 21.1245 | 12400 | 3.7713 | 0.9471 | 0.9954 |
| 2.9397 | 21.2950 | 12500 | 4.0556 | 0.9461 | 0.9914 |
| 2.9382 | 21.4655 | 12600 | 4.0336 | 0.9418 | 0.9842 |
| 2.9424 | 21.6360 | 12700 | 4.3215 | 0.9423 | 0.9833 |
| 2.9207 | 21.8065 | 12800 | 4.4437 | 0.9348 | 0.9800 |
| 2.9332 | 21.9770 | 12900 | 3.6444 | 0.9352 | 0.9872 |
| 2.9054 | 22.1466 | 13000 | 3.6124 | 0.9495 | 0.9963 |
| 2.8989 | 22.3171 | 13100 | 4.1020 | 0.9452 | 0.9845 |
| 2.9295 | 22.4876 | 13200 | 3.8291 | 0.9469 | 0.9921 |
| 2.8928 | 22.6581 | 13300 | 3.9756 | 0.9357 | 0.9819 |
| 2.9169 | 22.8286 | 13400 | 4.5840 | 0.9305 | 0.9998 |
| 2.876 | 22.9991 | 13500 | 4.3819 | 0.9394 | 0.9979 |
| 2.8265 | 23.1688 | 13600 | 3.9561 | 0.9325 | 0.9896 |
| 2.8623 | 23.3393 | 13700 | 4.1166 | 0.9376 | 0.9988 |
| 2.8554 | 23.5098 | 13800 | 4.4085 | 0.9337 | 0.9954 |
| 2.8668 | 23.6803 | 13900 | 4.1019 | 0.9307 | 0.9986 |
| 2.9283 | 23.8508 | 14000 | 3.6885 | 0.9426 | 0.9991 |
| 2.8797 | 24.0205 | 14100 | 4.2674 | 0.9378 | 1.0 |
| 2.861 | 24.1910 | 14200 | 4.0909 | 0.9388 | 0.9991 |
| 2.8567 | 24.3615 | 14300 | 4.6243 | 0.9376 | 0.9986 |
| 2.8289 | 24.5320 | 14400 | 4.4921 | 0.9323 | 0.9998 |
| 2.8416 | 24.7025 | 14500 | 4.2127 | 0.9405 | 0.9968 |
| 2.8427 | 24.8730 | 14600 | 3.6274 | 0.9422 | 0.9974 |
| 2.8449 | 25.0426 | 14700 | 3.7353 | 0.9424 | 0.9991 |
| 2.8254 | 25.2131 | 14800 | 4.1203 | 0.9357 | 0.9986 |
| 2.8269 | 25.3836 | 14900 | 4.2943 | 0.9405 | 0.9979 |
| 2.7912 | 25.5541 | 15000 | 4.2259 | 0.9344 | 0.9928 |
| 2.8326 | 25.7246 | 15100 | 4.5693 | 0.9380 | 0.9933 |
| 2.7957 | 25.8951 | 15200 | 4.6771 | 0.9349 | 0.9965 |
| 2.813 | 26.0648 | 15300 | 3.8681 | 0.9422 | 0.9993 |
| 2.7835 | 26.2353 | 15400 | 3.9947 | 0.9392 | 0.9998 |
| 2.8396 | 26.4058 | 15500 | 4.2958 | 0.9381 | 0.9984 |
| 2.787 | 26.5763 | 15600 | 4.6321 | 0.9280 | 0.9991 |
| 2.7925 | 26.7468 | 15700 | 3.9910 | 0.9375 | 0.9993 |
| 2.7928 | 26.9173 | 15800 | 4.0405 | 0.9379 | 0.9977 |
| 2.7983 | 27.0870 | 15900 | 4.2174 | 0.9355 | 0.9940 |
| 2.7935 | 27.2575 | 16000 | 4.0693 | 0.9364 | 0.9944 |
| 2.7497 | 27.4280 | 16100 | 4.0925 | 0.9370 | 0.9961 |
| 2.7529 | 27.5985 | 16200 | 4.1622 | 0.9353 | 0.9974 |
| 2.753 | 27.7690 | 16300 | 4.0096 | 0.9395 | 0.9974 |
| 2.8012 | 27.9395 | 16400 | 4.2769 | 0.9353 | 0.9968 |
| 2.7372 | 28.1091 | 16500 | 4.3867 | 0.9359 | 0.9993 |
| 2.749 | 28.2796 | 16600 | 4.1940 | 0.9388 | 0.9963 |
| 2.741 | 28.4501 | 16700 | 4.1484 | 0.9390 | 0.9984 |
| 2.7541 | 28.6206 | 16800 | 4.0567 | 0.9374 | 0.9988 |
| 2.7654 | 28.7911 | 16900 | 4.0987 | 0.9393 | 0.9974 |
| 2.7408 | 28.9616 | 17000 | 3.9829 | 0.9402 | 0.9991 |
| 2.7754 | 29.1313 | 17100 | 4.1334 | 0.9410 | 0.9977 |
| 2.7147 | 29.3018 | 17200 | 4.1019 | 0.9401 | 0.9991 |
| 2.739 | 29.4723 | 17300 | 4.1809 | 0.9395 | 0.9986 |
| 2.7449 | 29.6428 | 17400 | 4.1195 | 0.9385 | 0.9979 |
| 2.7468 | 29.8133 | 17500 | 4.0709 | 0.9377 | 0.9965 |
| 2.7354 | 29.9838 | 17600 | 4.0536 | 0.9382 | 0.9965 |
### Framework versions
- Transformers 4.57.2
- Pytorch 2.9.1+cu128
- Datasets 3.6.0
- Tokenizers 0.22.0
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