--- license: cc-by-nc-4.0 tags: - generated_from_trainer base_model: facebook/mms-1b-all metrics: - wer model-index: - name: wav2vec2-large-mms-1b-livvi-karelian-CodeSwitching results: [] --- # wav2vec2-large-mms-1b-livvi-karelian-CodeSwitching This model is a fine-tuned version of [facebook/mms-1b-all](https://huggingface.co/facebook/mms-1b-all) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4475 - Wer: 0.4337 - Cer: 0.0962 ## 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: 0.0007 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 100 - training_steps: 10000 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | Cer | |:-------------:|:-------:|:-----:|:---------------:|:------:|:------:| | 4.5669 | 0.9070 | 100 | 0.7117 | 0.7585 | 0.1988 | | 0.687 | 1.8141 | 200 | 0.5073 | 0.6150 | 0.1476 | | 0.5978 | 2.7211 | 300 | 0.4575 | 0.5917 | 0.1373 | | 0.5536 | 3.6281 | 400 | 0.4288 | 0.5484 | 0.1297 | | 0.524 | 4.5351 | 500 | 0.4283 | 0.5639 | 0.1295 | | 0.4979 | 5.4422 | 600 | 0.4172 | 0.5474 | 0.1262 | | 0.4791 | 6.3492 | 700 | 0.4048 | 0.5311 | 0.1207 | | 0.4809 | 7.2562 | 800 | 0.4005 | 0.5217 | 0.1204 | | 0.4434 | 8.1633 | 900 | 0.3877 | 0.5122 | 0.1171 | | 0.437 | 9.0703 | 1000 | 0.3845 | 0.5105 | 0.1163 | | 0.4251 | 9.9773 | 1100 | 0.3829 | 0.5037 | 0.1146 | | 0.3866 | 10.8844 | 1200 | 0.3638 | 0.4861 | 0.1114 | | 0.3956 | 11.7914 | 1300 | 0.3726 | 0.4922 | 0.1128 | | 0.3796 | 12.6984 | 1400 | 0.3644 | 0.4922 | 0.1102 | | 0.3698 | 13.6054 | 1500 | 0.3646 | 0.4800 | 0.1091 | | 0.3657 | 14.5125 | 1600 | 0.3664 | 0.4780 | 0.1088 | | 0.3506 | 15.4195 | 1700 | 0.3609 | 0.4740 | 0.1087 | | 0.3476 | 16.3265 | 1800 | 0.3542 | 0.4770 | 0.1077 | | 0.3461 | 17.2336 | 1900 | 0.3639 | 0.4760 | 0.1070 | | 0.3194 | 18.1406 | 2000 | 0.3556 | 0.4733 | 0.1054 | | 0.3191 | 19.0476 | 2100 | 0.3639 | 0.4692 | 0.1055 | | 0.3076 | 19.9546 | 2200 | 0.3565 | 0.4614 | 0.1034 | | 0.3056 | 20.8617 | 2300 | 0.3597 | 0.4652 | 0.1047 | | 0.3068 | 21.7687 | 2400 | 0.3620 | 0.4740 | 0.1048 | | 0.2913 | 22.6757 | 2500 | 0.3535 | 0.4601 | 0.1033 | | 0.2875 | 23.5828 | 2600 | 0.3610 | 0.4672 | 0.1035 | | 0.2845 | 24.4898 | 2700 | 0.3586 | 0.4560 | 0.1020 | | 0.2793 | 25.3968 | 2800 | 0.3633 | 0.4587 | 0.1028 | | 0.273 | 26.3039 | 2900 | 0.3638 | 0.4614 | 0.1028 | | 0.2682 | 27.2109 | 3000 | 0.3725 | 0.4699 | 0.1029 | | 0.2549 | 28.1179 | 3100 | 0.3669 | 0.4540 | 0.1015 | | 0.2535 | 29.0249 | 3200 | 0.3630 | 0.4604 | 0.1026 | | 0.2464 | 29.9320 | 3300 | 0.3601 | 0.4601 | 0.1023 | | 0.2517 | 30.8390 | 3400 | 0.3680 | 0.4621 | 0.1024 | | 0.2424 | 31.7460 | 3500 | 0.3773 | 0.4597 | 0.1028 | | 0.2455 | 32.6531 | 3600 | 0.3706 | 0.4614 | 0.1019 | | 0.2311 | 33.5601 | 3700 | 0.3667 | 0.4530 | 0.0992 | | 0.2318 | 34.4671 | 3800 | 0.3750 | 0.4597 | 0.1022 | | 0.2233 | 35.3741 | 3900 | 0.3643 | 0.4662 | 0.1027 | | 0.2246 | 36.2812 | 4000 | 0.3737 | 0.4570 | 0.1013 | | 0.2187 | 37.1882 | 4100 | 0.3864 | 0.4547 | 0.1001 | | 0.2199 | 38.0952 | 4200 | 0.3925 | 0.4418 | 0.0980 | | 0.2167 | 39.0023 | 4300 | 0.3808 | 0.4533 | 0.0992 | | 0.2131 | 39.9093 | 4400 | 0.3861 | 0.4506 | 0.0996 | | 0.2044 | 40.8163 | 4500 | 0.3771 | 0.4581 | 0.1005 | | 0.1981 | 41.7234 | 4600 | 0.3759 | 0.4611 | 0.1021 | | 0.2013 | 42.6304 | 4700 | 0.3817 | 0.4641 | 0.1028 | | 0.198 | 43.5374 | 4800 | 0.3786 | 0.4479 | 0.0990 | | 0.1905 | 44.4444 | 4900 | 0.3889 | 0.4465 | 0.0976 | | 0.1829 | 45.3515 | 5000 | 0.3895 | 0.4496 | 0.0982 | | 0.1912 | 46.2585 | 5100 | 0.3894 | 0.4459 | 0.0983 | | 0.1879 | 47.1655 | 5200 | 0.3986 | 0.4486 | 0.0980 | | 0.1817 | 48.0726 | 5300 | 0.3865 | 0.4476 | 0.0996 | | 0.1803 | 48.9796 | 5400 | 0.4024 | 0.4408 | 0.0989 | | 0.1752 | 49.8866 | 5500 | 0.3871 | 0.4493 | 0.0994 | | 0.1755 | 50.7937 | 5600 | 0.3969 | 0.4442 | 0.0983 | | 0.1794 | 51.7007 | 5700 | 0.4069 | 0.4364 | 0.0969 | | 0.1739 | 52.6077 | 5800 | 0.4048 | 0.4357 | 0.0981 | | 0.1691 | 53.5147 | 5900 | 0.4082 | 0.4469 | 0.0982 | | 0.1648 | 54.4218 | 6000 | 0.4227 | 0.4422 | 0.0984 | | 0.1748 | 55.3288 | 6100 | 0.4027 | 0.4435 | 0.0990 | | 0.1668 | 56.2358 | 6200 | 0.4311 | 0.4438 | 0.0990 | | 0.1617 | 57.1429 | 6300 | 0.4116 | 0.4472 | 0.0988 | | 0.1617 | 58.0499 | 6400 | 0.4051 | 0.4432 | 0.0974 | | 0.1625 | 58.9569 | 6500 | 0.4146 | 0.4374 | 0.0975 | | 0.1594 | 59.8639 | 6600 | 0.4108 | 0.4435 | 0.0988 | | 0.1668 | 60.7710 | 6700 | 0.4095 | 0.4327 | 0.0969 | | 0.1512 | 61.6780 | 6800 | 0.4172 | 0.4378 | 0.0966 | | 0.15 | 62.5850 | 6900 | 0.4135 | 0.4422 | 0.0982 | | 0.1537 | 63.4921 | 7000 | 0.4326 | 0.4401 | 0.0980 | | 0.151 | 64.3991 | 7100 | 0.4255 | 0.4411 | 0.0980 | | 0.152 | 65.3061 | 7200 | 0.4236 | 0.4378 | 0.0976 | | 0.1468 | 66.2132 | 7300 | 0.4207 | 0.4526 | 0.0996 | | 0.1497 | 67.1202 | 7400 | 0.4160 | 0.4398 | 0.0980 | | 0.1509 | 68.0272 | 7500 | 0.4210 | 0.4401 | 0.0975 | | 0.138 | 68.9342 | 7600 | 0.4224 | 0.4469 | 0.0984 | | 0.1435 | 69.8413 | 7700 | 0.4217 | 0.4425 | 0.0968 | | 0.1465 | 70.7483 | 7800 | 0.4280 | 0.4354 | 0.0964 | | 0.1367 | 71.6553 | 7900 | 0.4260 | 0.4405 | 0.0978 | | 0.1445 | 72.5624 | 8000 | 0.4253 | 0.4394 | 0.0980 | | 0.1379 | 73.4694 | 8100 | 0.4250 | 0.4347 | 0.0968 | | 0.1385 | 74.3764 | 8200 | 0.4303 | 0.4391 | 0.0975 | | 0.1334 | 75.2834 | 8300 | 0.4377 | 0.4357 | 0.0968 | | 0.1372 | 76.1905 | 8400 | 0.4423 | 0.4323 | 0.0957 | | 0.1329 | 77.0975 | 8500 | 0.4281 | 0.4334 | 0.0967 | | 0.1322 | 78.0045 | 8600 | 0.4366 | 0.4313 | 0.0956 | | 0.1327 | 78.9116 | 8700 | 0.4377 | 0.4344 | 0.0960 | | 0.1313 | 79.8186 | 8800 | 0.4405 | 0.4361 | 0.0964 | | 0.1315 | 80.7256 | 8900 | 0.4401 | 0.4350 | 0.0959 | | 0.134 | 81.6327 | 9000 | 0.4370 | 0.4364 | 0.0963 | | 0.1351 | 82.5397 | 9100 | 0.4389 | 0.4364 | 0.0962 | | 0.1289 | 83.4467 | 9200 | 0.4504 | 0.4384 | 0.0972 | | 0.1308 | 84.3537 | 9300 | 0.4447 | 0.4334 | 0.0963 | | 0.1321 | 85.2608 | 9400 | 0.4509 | 0.4334 | 0.0965 | | 0.1296 | 86.1678 | 9500 | 0.4489 | 0.4323 | 0.0960 | | 0.1277 | 87.0748 | 9600 | 0.4489 | 0.4374 | 0.0963 | | 0.1245 | 87.9819 | 9700 | 0.4475 | 0.4344 | 0.0965 | | 0.1267 | 88.8889 | 9800 | 0.4459 | 0.4323 | 0.0952 | | 0.1259 | 89.7959 | 9900 | 0.4457 | 0.4320 | 0.0959 | | 0.1247 | 90.7029 | 10000 | 0.4475 | 0.4337 | 0.0962 | ### Framework versions - Transformers 4.41.0.dev0 - Pytorch 2.2.2 - Datasets 2.19.0 - Tokenizers 0.19.1