--- library_name: transformers license: cc-by-nc-4.0 base_model: facebook/mms-1b-all tags: - generated_from_trainer metrics: - wer model-index: - name: mms-1b-all-lin-Fleurs_AMMI_AFRIVOICE_LRSC-1hrs-v1 results: [] --- # mms-1b-all-lin-Fleurs_AMMI_AFRIVOICE_LRSC-1hrs-v1 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: 1.7464 - Wer: 0.4690 - Cer: 0.1857 ## 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: 0.001 - train_batch_size: 8 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 16 - optimizer: Use OptimizerNames.ADAMW_TORCH 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: 100 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | Cer | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:| | 20.2969 | 1.0 | 20 | 15.4344 | 6.0492 | 1.9074 | | 8.6598 | 2.0 | 40 | 4.6574 | 1.0 | 0.9999 | | 4.0919 | 3.0 | 60 | 3.6494 | 1.0177 | 0.8475 | | 3.5479 | 4.0 | 80 | 3.1117 | 0.9919 | 0.7177 | | 2.9834 | 5.0 | 100 | 2.9935 | 0.9976 | 0.6736 | | 2.9875 | 6.0 | 120 | 2.6889 | 0.9079 | 0.6285 | | 2.4481 | 7.0 | 140 | 2.5176 | 0.8753 | 0.5858 | | 2.1694 | 8.0 | 160 | 2.3393 | 0.8359 | 0.5096 | | 1.9925 | 9.0 | 180 | 2.1131 | 0.8073 | 0.4976 | | 1.7516 | 10.0 | 200 | 2.1095 | 0.7797 | 0.4831 | | 1.5563 | 11.0 | 220 | 1.9102 | 0.7538 | 0.4248 | | 1.3814 | 12.0 | 240 | 1.8076 | 0.7351 | 0.4033 | | 1.2278 | 13.0 | 260 | 1.7135 | 0.7027 | 0.3646 | | 1.0993 | 14.0 | 280 | 1.6182 | 0.6884 | 0.3347 | | 0.992 | 15.0 | 300 | 1.5669 | 0.6584 | 0.3120 | | 0.8873 | 16.0 | 320 | 1.5504 | 0.6491 | 0.2943 | | 1.0836 | 17.0 | 340 | 1.5024 | 0.6229 | 0.2854 | | 0.9552 | 18.0 | 360 | 1.4542 | 0.6003 | 0.2668 | | 0.6556 | 19.0 | 380 | 1.4784 | 0.5956 | 0.2609 | | 0.6097 | 20.0 | 400 | 1.4696 | 0.5847 | 0.2499 | | 0.5452 | 21.0 | 420 | 1.4980 | 0.5749 | 0.2443 | | 0.5073 | 22.0 | 440 | 1.4862 | 0.5674 | 0.2359 | | 0.5055 | 23.0 | 460 | 1.4599 | 0.5463 | 0.2297 | | 0.4414 | 24.0 | 480 | 1.4955 | 0.5574 | 0.2279 | | 0.4092 | 25.0 | 500 | 1.4877 | 0.5516 | 0.2280 | | 0.3752 | 26.0 | 520 | 1.4439 | 0.5471 | 0.2241 | | 0.3616 | 27.0 | 540 | 1.4631 | 0.5346 | 0.2196 | | 0.3228 | 28.0 | 560 | 1.4857 | 0.5285 | 0.2142 | | 0.3132 | 29.0 | 580 | 1.4594 | 0.5257 | 0.2156 | | 0.2877 | 30.0 | 600 | 1.5222 | 0.5288 | 0.2123 | | 0.2993 | 31.0 | 620 | 1.5179 | 0.5294 | 0.2132 | | 0.2699 | 32.0 | 640 | 1.5192 | 0.5233 | 0.2130 | | 0.2635 | 33.0 | 660 | 1.5113 | 0.5144 | 0.2090 | | 0.2331 | 34.0 | 680 | 1.5547 | 0.5217 | 0.2098 | | 0.2374 | 35.0 | 700 | 1.5334 | 0.5034 | 0.2026 | | 0.2317 | 36.0 | 720 | 1.5067 | 0.5143 | 0.2057 | | 0.2121 | 37.0 | 740 | 1.5589 | 0.5067 | 0.2051 | | 0.2023 | 38.0 | 760 | 1.5842 | 0.5053 | 0.2048 | | 0.2002 | 39.0 | 780 | 1.6043 | 0.5167 | 0.2033 | | 0.1981 | 40.0 | 800 | 1.5686 | 0.5096 | 0.2049 | | 0.1902 | 41.0 | 820 | 1.6279 | 0.5056 | 0.2026 | | 0.1743 | 42.0 | 840 | 1.5759 | 0.5089 | 0.2022 | | 0.1892 | 43.0 | 860 | 1.6137 | 0.5065 | 0.1999 | | 0.1641 | 44.0 | 880 | 1.6219 | 0.4974 | 0.1981 | | 0.4117 | 45.0 | 900 | 1.5925 | 0.4973 | 0.1964 | | 0.1497 | 46.0 | 920 | 1.6325 | 0.4967 | 0.1973 | | 0.1592 | 47.0 | 940 | 1.5893 | 0.4992 | 0.1976 | | 0.1399 | 48.0 | 960 | 1.6137 | 0.4985 | 0.1980 | | 0.145 | 49.0 | 980 | 1.5947 | 0.4862 | 0.1967 | | 0.129 | 50.0 | 1000 | 1.6438 | 0.4972 | 0.1977 | | 0.1198 | 51.0 | 1020 | 1.6853 | 0.5019 | 0.1970 | | 0.1266 | 52.0 | 1040 | 1.6279 | 0.4993 | 0.1967 | | 0.1176 | 53.0 | 1060 | 1.6381 | 0.4965 | 0.1973 | | 0.1157 | 54.0 | 1080 | 1.6612 | 0.4891 | 0.1942 | | 0.1126 | 55.0 | 1100 | 1.6502 | 0.4867 | 0.1946 | | 0.1078 | 56.0 | 1120 | 1.7070 | 0.4874 | 0.1939 | | 0.1074 | 57.0 | 1140 | 1.6772 | 0.4939 | 0.1961 | | 0.1145 | 58.0 | 1160 | 1.7170 | 0.4863 | 0.1927 | | 0.0998 | 59.0 | 1180 | 1.7114 | 0.4850 | 0.1923 | | 0.1024 | 60.0 | 1200 | 1.7010 | 0.4838 | 0.1932 | | 0.1089 | 61.0 | 1220 | 1.6970 | 0.4762 | 0.1897 | | 0.0886 | 62.0 | 1240 | 1.7086 | 0.4762 | 0.1898 | | 0.0971 | 63.0 | 1260 | 1.6991 | 0.4776 | 0.1901 | | 0.0932 | 64.0 | 1280 | 1.6583 | 0.4761 | 0.1889 | | 0.0847 | 65.0 | 1300 | 1.7442 | 0.4809 | 0.1887 | | 0.0913 | 66.0 | 1320 | 1.7359 | 0.4772 | 0.1910 | | 0.0892 | 67.0 | 1340 | 1.6782 | 0.4797 | 0.1902 | | 0.0818 | 68.0 | 1360 | 1.7167 | 0.4844 | 0.1920 | | 0.0857 | 69.0 | 1380 | 1.7230 | 0.4797 | 0.1897 | | 0.0878 | 70.0 | 1400 | 1.6981 | 0.4860 | 0.1928 | | 0.0739 | 71.0 | 1420 | 1.7313 | 0.4735 | 0.1862 | | 0.0757 | 72.0 | 1440 | 1.7053 | 0.4767 | 0.1883 | | 0.0716 | 73.0 | 1460 | 1.7432 | 0.4792 | 0.1884 | | 0.075 | 74.0 | 1480 | 1.7191 | 0.4770 | 0.1878 | | 0.0666 | 75.0 | 1500 | 1.7157 | 0.4760 | 0.1895 | | 0.0701 | 76.0 | 1520 | 1.7501 | 0.4779 | 0.1873 | | 0.0743 | 77.0 | 1540 | 1.7318 | 0.4750 | 0.1873 | | 0.0676 | 78.0 | 1560 | 1.7164 | 0.4736 | 0.1867 | | 0.062 | 79.0 | 1580 | 1.7338 | 0.4658 | 0.1851 | | 0.0637 | 80.0 | 1600 | 1.7325 | 0.4730 | 0.1876 | | 0.0697 | 81.0 | 1620 | 1.7165 | 0.4693 | 0.1861 | | 0.0584 | 82.0 | 1640 | 1.7529 | 0.4700 | 0.1863 | | 0.0597 | 83.0 | 1660 | 1.7759 | 0.4687 | 0.1850 | | 0.0658 | 84.0 | 1680 | 1.7439 | 0.4683 | 0.1860 | | 0.0555 | 85.0 | 1700 | 1.7395 | 0.4721 | 0.1870 | | 0.0617 | 86.0 | 1720 | 1.7525 | 0.4685 | 0.1855 | | 0.0641 | 87.0 | 1740 | 1.7330 | 0.4705 | 0.1849 | | 0.0542 | 88.0 | 1760 | 1.7356 | 0.4704 | 0.1862 | | 0.0582 | 89.0 | 1780 | 1.7464 | 0.4690 | 0.1857 | ### Framework versions - Transformers 4.48.1 - Pytorch 2.5.1+cu124 - Datasets 3.2.0 - Tokenizers 0.21.0