--- library_name: transformers language: - sn license: cc-by-nc-4.0 base_model: facebook/mms-300m tags: - generated_from_trainer datasets: - DigitalUmuganda_Afrivoice/Shona metrics: - wer model-index: - name: facebook/mms-300m results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: DigitalUmuganda type: DigitalUmuganda_Afrivoice/Shona metrics: - name: Wer type: wer value: 0.5175178580105934 --- # facebook/mms-300m This model is a fine-tuned version of [facebook/mms-300m](https://huggingface.co/facebook/mms-300m) on the DigitalUmuganda dataset. It achieves the following results on the evaluation set: - Loss: 0.9857 - Wer: 0.5175 - Cer: 0.1156 ## 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: 4 - total_train_batch_size: 32 - optimizer: Use 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 | |:-------------:|:-------:|:----:|:---------------:|:------:|:------:| | 54.8231 | 0.9818 | 27 | 5.5912 | 1.0 | 1.0 | | 16.0974 | 1.9727 | 54 | 3.2813 | 1.0 | 1.0 | | 12.3829 | 2.9636 | 81 | 2.9582 | 1.0 | 1.0 | | 11.6369 | 3.9909 | 109 | 2.9327 | 1.0 | 1.0 | | 11.78 | 4.9818 | 136 | 3.0289 | 1.0 | 1.0 | | 11.7294 | 5.9727 | 163 | 2.8970 | 1.0 | 1.0 | | 11.5829 | 6.9636 | 190 | 2.8451 | 1.0 | 1.0 | | 10.9847 | 7.9909 | 218 | 2.7870 | 1.0 | 1.0 | | 11.269 | 8.9818 | 245 | 2.6834 | 1.0 | 0.9652 | | 10.1931 | 9.9727 | 272 | 2.2750 | 1.0 | 0.8276 | | 8.6755 | 10.9636 | 299 | 1.8566 | 1.0 | 0.5798 | | 6.7633 | 11.9909 | 327 | 1.4861 | 1.0 | 0.4707 | | 5.7016 | 12.9818 | 354 | 1.2004 | 0.9976 | 0.3762 | | 4.6139 | 13.9727 | 381 | 0.9838 | 0.9509 | 0.2874 | | 3.7333 | 14.9636 | 408 | 0.8431 | 0.9037 | 0.2456 | | 2.9457 | 15.9909 | 436 | 0.6929 | 0.8081 | 0.1956 | | 2.4083 | 16.9818 | 463 | 0.5662 | 0.7055 | 0.1549 | | 1.8317 | 17.9727 | 490 | 0.5390 | 0.6683 | 0.1435 | | 1.4594 | 18.9636 | 517 | 0.4966 | 0.6362 | 0.1308 | | 1.1558 | 19.9909 | 545 | 0.4624 | 0.5875 | 0.1182 | | 0.9968 | 20.9818 | 572 | 0.4782 | 0.5759 | 0.1137 | | 0.8777 | 21.9727 | 599 | 0.4626 | 0.5409 | 0.1046 | | 0.7342 | 22.9636 | 626 | 0.4618 | 0.5367 | 0.1057 | | 0.6756 | 23.9909 | 654 | 0.4537 | 0.5375 | 0.1032 | | 0.5935 | 24.9818 | 681 | 0.4574 | 0.5195 | 0.0998 | | 0.5394 | 25.9727 | 708 | 0.4524 | 0.5122 | 0.0974 | | 0.4708 | 26.9636 | 735 | 0.4890 | 0.5034 | 0.0951 | | 0.4191 | 27.9909 | 763 | 0.4569 | 0.5058 | 0.0949 | | 0.4185 | 28.9818 | 790 | 0.5009 | 0.4964 | 0.0949 | | 0.4048 | 29.9727 | 817 | 0.5135 | 0.5163 | 0.0983 | | 0.3577 | 30.9636 | 844 | 0.4956 | 0.4942 | 0.0929 | | 0.3449 | 31.9909 | 872 | 0.4670 | 0.4757 | 0.0919 | | 0.3383 | 32.9818 | 899 | 0.4793 | 0.4903 | 0.0910 | | 0.3013 | 33.9727 | 926 | 0.5074 | 0.4920 | 0.0907 | | 0.2725 | 34.9636 | 953 | 0.5002 | 0.4842 | 0.0890 | | 0.2641 | 35.9909 | 981 | 0.5312 | 0.4723 | 0.0882 | | 0.2538 | 36.9818 | 1008 | 0.4744 | 0.4737 | 0.0863 | | 0.2392 | 37.9727 | 1035 | 0.5041 | 0.4621 | 0.0863 | | 0.2282 | 38.9636 | 1062 | 0.5037 | 0.4511 | 0.0848 | | 0.2088 | 39.9909 | 1090 | 0.4988 | 0.4655 | 0.0869 | | 0.2062 | 40.9818 | 1117 | 0.4873 | 0.4579 | 0.0842 | | 0.204 | 41.9727 | 1144 | 0.4689 | 0.4521 | 0.0836 | | 0.1856 | 42.9636 | 1171 | 0.5070 | 0.4555 | 0.0823 | | 0.1847 | 43.9909 | 1199 | 0.5058 | 0.4458 | 0.0826 | | 0.1899 | 44.9818 | 1226 | 0.4997 | 0.4334 | 0.0808 | | 0.1716 | 45.9727 | 1253 | 0.4995 | 0.4244 | 0.0796 | | 0.1772 | 46.9636 | 1280 | 0.4993 | 0.4399 | 0.0812 | | 0.1612 | 47.9909 | 1308 | 0.4982 | 0.4343 | 0.0800 | | 0.1645 | 48.9818 | 1335 | 0.4861 | 0.4321 | 0.0799 | | 0.1596 | 49.9727 | 1362 | 0.4963 | 0.4236 | 0.0788 | | 0.1544 | 50.9636 | 1389 | 0.5150 | 0.4358 | 0.0795 | | 0.1356 | 51.9909 | 1417 | 0.5069 | 0.4470 | 0.0808 | | 0.1445 | 52.9818 | 1444 | 0.5112 | 0.4343 | 0.0790 | | 0.1381 | 53.9727 | 1471 | 0.5201 | 0.4146 | 0.0760 | | 0.1355 | 54.9636 | 1498 | 0.4991 | 0.4110 | 0.0756 | | 0.1309 | 55.9909 | 1526 | 0.5260 | 0.4397 | 0.0798 | | 0.1359 | 56.9818 | 1553 | 0.5096 | 0.4285 | 0.0789 | | 0.1181 | 57.9727 | 1580 | 0.5013 | 0.4224 | 0.0767 | | 0.1227 | 58.9636 | 1607 | 0.5219 | 0.4125 | 0.0758 | | 0.1127 | 59.9909 | 1635 | 0.5043 | 0.4210 | 0.0759 | | 0.1083 | 60.9818 | 1662 | 0.4853 | 0.4010 | 0.0744 | | 0.1152 | 61.9727 | 1689 | 0.5032 | 0.4054 | 0.0740 | | 0.1142 | 62.9636 | 1716 | 0.5048 | 0.4086 | 0.0745 | | 0.0975 | 63.9909 | 1744 | 0.5218 | 0.3996 | 0.0723 | | 0.105 | 64.9818 | 1771 | 0.5210 | 0.4112 | 0.0740 | | 0.094 | 65.9727 | 1798 | 0.5418 | 0.4073 | 0.0727 | | 0.0987 | 66.9636 | 1825 | 0.5166 | 0.4008 | 0.0721 | | 0.0958 | 67.9909 | 1853 | 0.5008 | 0.4098 | 0.0722 | | 0.0936 | 68.9818 | 1880 | 0.5419 | 0.3988 | 0.0719 | | 0.0896 | 69.9727 | 1907 | 0.5570 | 0.4219 | 0.0747 | | 0.0853 | 70.9636 | 1934 | 0.5534 | 0.4117 | 0.0740 | | 0.0793 | 71.9909 | 1962 | 0.5557 | 0.4078 | 0.0726 | | 0.0805 | 72.9818 | 1989 | 0.5368 | 0.4018 | 0.0717 | | 0.0875 | 73.9727 | 2016 | 0.5476 | 0.4049 | 0.0741 | | 0.076 | 74.9636 | 2043 | 0.5561 | 0.4066 | 0.0729 | | 0.0703 | 75.9909 | 2071 | 0.5527 | 0.4052 | 0.0722 | | 0.0707 | 76.9818 | 2098 | 0.5543 | 0.3959 | 0.0713 | | 0.0665 | 77.9727 | 2125 | 0.5628 | 0.4003 | 0.0708 | | 0.0677 | 78.9636 | 2152 | 0.5413 | 0.3957 | 0.0699 | | 0.0638 | 79.9909 | 2180 | 0.5498 | 0.3988 | 0.0706 | | 0.0652 | 80.9818 | 2207 | 0.5507 | 0.3930 | 0.0699 | | 0.061 | 81.9727 | 2234 | 0.5259 | 0.3881 | 0.0682 | | 0.06 | 82.9636 | 2261 | 0.5397 | 0.3896 | 0.0684 | | 0.0564 | 83.9909 | 2289 | 0.5441 | 0.3842 | 0.0677 | | 0.0635 | 84.9818 | 2316 | 0.5372 | 0.3840 | 0.0678 | | 0.0514 | 85.9727 | 2343 | 0.5504 | 0.3816 | 0.0683 | | 0.0467 | 86.9636 | 2370 | 0.5573 | 0.3774 | 0.0674 | | 0.0485 | 87.9909 | 2398 | 0.5604 | 0.3811 | 0.0674 | | 0.0519 | 88.9818 | 2425 | 0.5459 | 0.3733 | 0.0665 | | 0.0514 | 89.9727 | 2452 | 0.5411 | 0.3799 | 0.0668 | | 0.0475 | 90.9636 | 2479 | 0.5369 | 0.3772 | 0.0664 | | 0.0434 | 91.9909 | 2507 | 0.5510 | 0.3850 | 0.0672 | | 0.0488 | 92.9818 | 2534 | 0.5488 | 0.3774 | 0.0659 | | 0.046 | 93.9727 | 2561 | 0.5443 | 0.3794 | 0.0663 | | 0.0463 | 94.9636 | 2588 | 0.5463 | 0.3806 | 0.0666 | | 0.0399 | 95.9909 | 2616 | 0.5500 | 0.3796 | 0.0663 | | 0.0401 | 96.9818 | 2643 | 0.5494 | 0.3769 | 0.0657 | | 0.0431 | 97.9727 | 2670 | 0.5516 | 0.375 | 0.0657 | | 0.0404 | 98.9636 | 2697 | 0.5523 | 0.3765 | 0.0659 | | 0.0409 | 99.0818 | 2700 | 0.5523 | 0.3765 | 0.0661 | ### Framework versions - Transformers 4.47.0.dev0 - Pytorch 2.1.0+cu118 - Datasets 3.0.2 - Tokenizers 0.20.1