--- library_name: transformers license: cc-by-nc-4.0 base_model: facebook/mms-1b-all tags: - generated_from_trainer metrics: - wer model-index: - name: bambara-mms-20-hours-oza75bambara-asr-hf results: [] --- [Visualize in Weights & Biases](https://wandb.ai/asr-africa-research-team/ASR%20Africa/runs/zhcmcfyn) # bambara-mms-20-hours-oza75bambara-asr-hf This model is a fine-tuned version of [facebook/mms-1b-all](https://huggingface.co/facebook/mms-1b-all) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.0263 - Wer: 0.4776 - Cer: 0.2282 ## 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: 16 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 50 ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | Cer | |:-------------:|:-------:|:-----:|:---------------:|:------:|:------:| | 1.5836 | 0.4167 | 500 | 1.3427 | 0.7568 | 0.3696 | | 1.4893 | 0.8333 | 1000 | 1.3016 | 0.7671 | 0.3789 | | 1.4043 | 1.25 | 1500 | 1.1957 | 0.7125 | 0.3471 | | 1.377 | 1.6667 | 2000 | 1.1697 | 0.7138 | 0.3396 | | 1.3016 | 2.0833 | 2500 | 1.1237 | 0.6892 | 0.3352 | | 1.2902 | 2.5 | 3000 | 1.1183 | 0.6653 | 0.3203 | | 1.2622 | 2.9167 | 3500 | 1.0832 | 0.6598 | 0.3265 | | 1.1992 | 3.3333 | 4000 | 1.0645 | 0.6525 | 0.3101 | | 1.2156 | 3.75 | 4500 | 1.0081 | 0.6232 | 0.3013 | | 1.1711 | 4.1667 | 5000 | 1.0372 | 0.6438 | 0.3067 | | 1.148 | 4.5833 | 5500 | 1.0092 | 0.6264 | 0.2950 | | 1.1433 | 5.0 | 6000 | 1.0095 | 0.6200 | 0.2958 | | 1.1042 | 5.4167 | 6500 | 1.0094 | 0.6265 | 0.3055 | | 1.0931 | 5.8333 | 7000 | 0.9810 | 0.6115 | 0.2890 | | 1.0745 | 6.25 | 7500 | 0.9724 | 0.6200 | 0.2899 | | 1.0488 | 6.6667 | 8000 | 0.9712 | 0.5995 | 0.2814 | | 1.0608 | 7.0833 | 8500 | 0.9923 | 0.6103 | 0.2938 | | 1.0132 | 7.5 | 9000 | 0.9634 | 0.5933 | 0.2852 | | 1.0209 | 7.9167 | 9500 | 0.9360 | 0.5860 | 0.2786 | | 0.9742 | 8.3333 | 10000 | 0.9615 | 0.6037 | 0.2950 | | 0.9778 | 8.75 | 10500 | 0.9622 | 0.5878 | 0.2831 | | 0.9595 | 9.1667 | 11000 | 0.9613 | 0.5834 | 0.2734 | | 0.9298 | 9.5833 | 11500 | 0.9635 | 0.5758 | 0.2707 | | 0.9464 | 10.0 | 12000 | 0.9312 | 0.5862 | 0.2719 | | 0.8894 | 10.4167 | 12500 | 1.0033 | 0.5756 | 0.2821 | | 0.9103 | 10.8333 | 13000 | 0.9104 | 0.5697 | 0.2733 | | 0.8705 | 11.25 | 13500 | 0.9330 | 0.5572 | 0.2626 | | 0.8669 | 11.6667 | 14000 | 0.9328 | 0.5581 | 0.2684 | | 0.8551 | 12.0833 | 14500 | 0.9438 | 0.5747 | 0.2721 | | 0.8189 | 12.5 | 15000 | 0.9608 | 0.5536 | 0.2617 | | 0.8239 | 12.9167 | 15500 | 0.9347 | 0.5511 | 0.2664 | | 0.7917 | 13.3333 | 16000 | 0.9275 | 0.5422 | 0.2617 | | 0.7978 | 13.75 | 16500 | 0.9308 | 0.5539 | 0.2663 | | 0.783 | 14.1667 | 17000 | 0.9918 | 0.5460 | 0.2586 | | 0.7641 | 14.5833 | 17500 | 0.9407 | 0.5579 | 0.2663 | | 0.7743 | 15.0 | 18000 | 0.9389 | 0.5487 | 0.2650 | | 0.7221 | 15.4167 | 18500 | 0.9997 | 0.5353 | 0.2564 | | 0.7293 | 15.8333 | 19000 | 0.9576 | 0.5547 | 0.2643 | | 0.7073 | 16.25 | 19500 | 0.9581 | 0.5441 | 0.2585 | | 0.696 | 16.6667 | 20000 | 0.9683 | 0.5376 | 0.2538 | | 0.6889 | 17.0833 | 20500 | 1.0302 | 0.5393 | 0.2580 | | 0.6594 | 17.5 | 21000 | 1.0046 | 0.5283 | 0.2479 | | 0.6618 | 17.9167 | 21500 | 0.9584 | 0.5340 | 0.2557 | | 0.637 | 18.3333 | 22000 | 0.9824 | 0.5215 | 0.2466 | | 0.6391 | 18.75 | 22500 | 1.0197 | 0.5219 | 0.2478 | | 0.6243 | 19.1667 | 23000 | 0.9848 | 0.5316 | 0.2517 | | 0.6038 | 19.5833 | 23500 | 1.0653 | 0.5180 | 0.2462 | | 0.6181 | 20.0 | 24000 | 0.9717 | 0.5252 | 0.2516 | | 0.5748 | 20.4167 | 24500 | 1.0184 | 0.5163 | 0.2473 | | 0.5895 | 20.8333 | 25000 | 1.0450 | 0.5275 | 0.2509 | | 0.5541 | 21.25 | 25500 | 1.0550 | 0.5271 | 0.2478 | | 0.5444 | 21.6667 | 26000 | 1.0680 | 0.5168 | 0.2505 | | 0.5562 | 22.0833 | 26500 | 1.0354 | 0.5331 | 0.2522 | | 0.5186 | 22.5 | 27000 | 1.1237 | 0.5165 | 0.2452 | | 0.5349 | 22.9167 | 27500 | 1.0960 | 0.5065 | 0.2450 | | 0.4976 | 23.3333 | 28000 | 1.0851 | 0.5164 | 0.2462 | | 0.4993 | 23.75 | 28500 | 1.1112 | 0.5139 | 0.2443 | | 0.4936 | 24.1667 | 29000 | 1.1059 | 0.5124 | 0.2427 | | 0.475 | 24.5833 | 29500 | 1.1079 | 0.5180 | 0.2458 | | 0.4787 | 25.0 | 30000 | 1.0897 | 0.5120 | 0.2435 | | 0.4469 | 25.4167 | 30500 | 1.1467 | 0.5095 | 0.2508 | | 0.4548 | 25.8333 | 31000 | 1.1535 | 0.5208 | 0.2482 | | 0.4414 | 26.25 | 31500 | 1.1973 | 0.5155 | 0.2460 | | 0.416 | 26.6667 | 32000 | 1.1981 | 0.5191 | 0.2471 | | 0.4317 | 27.0833 | 32500 | 1.2177 | 0.5108 | 0.2416 | | 0.4014 | 27.5 | 33000 | 1.2161 | 0.5173 | 0.2482 | | 0.4063 | 27.9167 | 33500 | 1.1857 | 0.5129 | 0.2457 | | 0.3817 | 28.3333 | 34000 | 1.3049 | 0.5098 | 0.2444 | | 0.3828 | 28.75 | 34500 | 1.1981 | 0.5180 | 0.2467 | | 0.381 | 29.1667 | 35000 | 1.2967 | 0.5086 | 0.2410 | | 0.3592 | 29.5833 | 35500 | 1.3089 | 0.5088 | 0.2433 | | 0.3709 | 30.0 | 36000 | 1.2478 | 0.5070 | 0.2394 | | 0.3422 | 30.4167 | 36500 | 1.3559 | 0.5067 | 0.2418 | | 0.3442 | 30.8333 | 37000 | 1.3932 | 0.5066 | 0.2428 | | 0.3304 | 31.25 | 37500 | 1.3619 | 0.5113 | 0.2454 | | 0.3278 | 31.6667 | 38000 | 1.3764 | 0.5068 | 0.2439 | | 0.3208 | 32.0833 | 38500 | 1.3906 | 0.4952 | 0.2378 | | 0.3069 | 32.5 | 39000 | 1.4492 | 0.4996 | 0.2383 | | 0.3041 | 32.9167 | 39500 | 1.4623 | 0.5009 | 0.2395 | | 0.2938 | 33.3333 | 40000 | 1.4528 | 0.5022 | 0.2375 | | 0.2931 | 33.75 | 40500 | 1.4347 | 0.5000 | 0.2392 | | 0.2807 | 34.1667 | 41000 | 1.4737 | 0.4997 | 0.2382 | | 0.2735 | 34.5833 | 41500 | 1.4860 | 0.5050 | 0.2388 | | 0.2654 | 35.0 | 42000 | 1.5288 | 0.4930 | 0.2374 | | 0.2561 | 35.4167 | 42500 | 1.5550 | 0.4983 | 0.2376 | | 0.2626 | 35.8333 | 43000 | 1.4984 | 0.4931 | 0.2410 | | 0.2478 | 36.25 | 43500 | 1.5513 | 0.4964 | 0.2366 | | 0.2466 | 36.6667 | 44000 | 1.6438 | 0.4897 | 0.2377 | | 0.2417 | 37.0833 | 44500 | 1.5970 | 0.4918 | 0.2367 | | 0.2323 | 37.5 | 45000 | 1.5682 | 0.4945 | 0.2380 | | 0.2351 | 37.9167 | 45500 | 1.5746 | 0.5009 | 0.2380 | | 0.2213 | 38.3333 | 46000 | 1.6518 | 0.4922 | 0.2362 | | 0.2191 | 38.75 | 46500 | 1.6544 | 0.4918 | 0.2359 | | 0.2194 | 39.1667 | 47000 | 1.6425 | 0.4947 | 0.2370 | | 0.209 | 39.5833 | 47500 | 1.7225 | 0.4851 | 0.2337 | | 0.2114 | 40.0 | 48000 | 1.6719 | 0.4879 | 0.2331 | | 0.2011 | 40.4167 | 48500 | 1.7090 | 0.4902 | 0.2327 | | 0.1983 | 40.8333 | 49000 | 1.7086 | 0.4853 | 0.2321 | | 0.1969 | 41.25 | 49500 | 1.7467 | 0.4841 | 0.2310 | | 0.1896 | 41.6667 | 50000 | 1.7615 | 0.4843 | 0.2300 | | 0.1834 | 42.0833 | 50500 | 1.7834 | 0.4854 | 0.2325 | | 0.1831 | 42.5 | 51000 | 1.8245 | 0.4809 | 0.2289 | | 0.1827 | 42.9167 | 51500 | 1.8147 | 0.4832 | 0.2329 | | 0.1718 | 43.3333 | 52000 | 1.8478 | 0.4790 | 0.2289 | | 0.1734 | 43.75 | 52500 | 1.8383 | 0.4832 | 0.2305 | | 0.1675 | 44.1667 | 53000 | 1.9167 | 0.4862 | 0.2315 | | 0.1669 | 44.5833 | 53500 | 1.9083 | 0.4847 | 0.2303 | | 0.1682 | 45.0 | 54000 | 1.8658 | 0.4794 | 0.2275 | | 0.1633 | 45.4167 | 54500 | 1.9068 | 0.4811 | 0.2290 | | 0.1606 | 45.8333 | 55000 | 1.9087 | 0.4806 | 0.2279 | | 0.1519 | 46.25 | 55500 | 1.9602 | 0.4845 | 0.2299 | | 0.1538 | 46.6667 | 56000 | 1.9242 | 0.4803 | 0.2305 | | 0.1526 | 47.0833 | 56500 | 1.9976 | 0.4821 | 0.2302 | | 0.1519 | 47.5 | 57000 | 2.0323 | 0.4841 | 0.2305 | | 0.1525 | 47.9167 | 57500 | 1.9856 | 0.4802 | 0.2282 | | 0.1472 | 48.3333 | 58000 | 2.0402 | 0.4792 | 0.2295 | | 0.1479 | 48.75 | 58500 | 1.9940 | 0.4798 | 0.2282 | | 0.1408 | 49.1667 | 59000 | 2.0308 | 0.4784 | 0.2282 | | 0.1407 | 49.5833 | 59500 | 2.0308 | 0.4776 | 0.2282 | | 0.1468 | 50.0 | 60000 | 2.0263 | 0.4776 | 0.2282 | ### Framework versions - Transformers 4.45.1 - Pytorch 2.1.0+cu118 - Datasets 2.17.0 - Tokenizers 0.20.3