--- license: cc-by-nc-4.0 base_model: facebook/mms-1b-all tags: - generated_from_trainer datasets: - audiofolder metrics: - wer model-index: - name: nhi_heldout-speaker-exp_GGN505_mms-1b-nhi-adapterft results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: audiofolder type: audiofolder config: default split: test args: default metrics: - name: Wer type: wer value: 0.35643954312721543 --- # nhi_heldout-speaker-exp_GGN505_mms-1b-nhi-adapterft This model is a fine-tuned version of [facebook/mms-1b-all](https://huggingface.co/facebook/mms-1b-all) on the audiofolder dataset. It achieves the following results on the evaluation set: - Loss: 0.5168 - Wer: 0.3564 - Cer: 0.0963 ## 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: 16 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - 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 | |:-------------:|:-------:|:-----:|:---------------:|:------:|:------:| | 1.0628 | 1.3072 | 200 | 0.6601 | 0.6203 | 0.1672 | | 0.8658 | 2.6144 | 400 | 0.5592 | 0.5526 | 0.1436 | | 0.7815 | 3.9216 | 600 | 0.5179 | 0.5286 | 0.1345 | | 0.7157 | 5.2288 | 800 | 0.5074 | 0.5108 | 0.1281 | | 0.6834 | 6.5359 | 1000 | 0.4945 | 0.4801 | 0.1259 | | 0.6746 | 7.8431 | 1200 | 0.4748 | 0.4699 | 0.1229 | | 0.6337 | 9.1503 | 1400 | 0.4826 | 0.4655 | 0.1198 | | 0.6218 | 10.4575 | 1600 | 0.4772 | 0.4584 | 0.1205 | | 0.5927 | 11.7647 | 1800 | 0.4740 | 0.4490 | 0.1155 | | 0.5927 | 13.0719 | 2000 | 0.4637 | 0.4293 | 0.1121 | | 0.5776 | 14.3791 | 2200 | 0.4621 | 0.4423 | 0.1153 | | 0.5432 | 15.6863 | 2400 | 0.4752 | 0.4289 | 0.1131 | | 0.5259 | 16.9935 | 2600 | 0.4516 | 0.3966 | 0.1059 | | 0.5152 | 18.3007 | 2800 | 0.4510 | 0.4210 | 0.1109 | | 0.4901 | 19.6078 | 3000 | 0.4602 | 0.4191 | 0.1095 | | 0.4926 | 20.9150 | 3200 | 0.4607 | 0.4057 | 0.1068 | | 0.4742 | 22.2222 | 3400 | 0.4569 | 0.3856 | 0.1037 | | 0.4813 | 23.5294 | 3600 | 0.4538 | 0.4163 | 0.1080 | | 0.4598 | 24.8366 | 3800 | 0.4672 | 0.4147 | 0.1098 | | 0.4418 | 26.1438 | 4000 | 0.4656 | 0.4013 | 0.1054 | | 0.4561 | 27.4510 | 4200 | 0.4737 | 0.4002 | 0.1054 | | 0.4358 | 28.7582 | 4400 | 0.4560 | 0.3931 | 0.1059 | | 0.4343 | 30.0654 | 4600 | 0.4644 | 0.3954 | 0.1062 | | 0.4148 | 31.3725 | 4800 | 0.4510 | 0.3966 | 0.1072 | | 0.4208 | 32.6797 | 5000 | 0.4687 | 0.3840 | 0.1026 | | 0.4208 | 33.9869 | 5200 | 0.4805 | 0.3856 | 0.1062 | | 0.4189 | 35.2941 | 5400 | 0.4624 | 0.3765 | 0.1032 | | 0.3899 | 36.6013 | 5600 | 0.4741 | 0.3844 | 0.1051 | | 0.3835 | 37.9085 | 5800 | 0.4721 | 0.3876 | 0.1040 | | 0.4017 | 39.2157 | 6000 | 0.4733 | 0.3915 | 0.1056 | | 0.3928 | 40.5229 | 6200 | 0.4644 | 0.3742 | 0.1029 | | 0.373 | 41.8301 | 6400 | 0.4628 | 0.3848 | 0.1027 | | 0.372 | 43.1373 | 6600 | 0.4805 | 0.3899 | 0.1034 | | 0.3473 | 44.4444 | 6800 | 0.4637 | 0.3769 | 0.1012 | | 0.3419 | 45.7516 | 7000 | 0.4687 | 0.3753 | 0.1008 | | 0.3607 | 47.0588 | 7200 | 0.4642 | 0.3777 | 0.1007 | | 0.3474 | 48.3660 | 7400 | 0.4610 | 0.3690 | 0.0989 | | 0.3464 | 49.6732 | 7600 | 0.4631 | 0.3757 | 0.1001 | | 0.3398 | 50.9804 | 7800 | 0.4571 | 0.3588 | 0.0982 | | 0.3182 | 52.2876 | 8000 | 0.4868 | 0.3659 | 0.0999 | | 0.3158 | 53.5948 | 8200 | 0.4821 | 0.3718 | 0.1004 | | 0.3368 | 54.9020 | 8400 | 0.4712 | 0.3777 | 0.1015 | | 0.3312 | 56.2092 | 8600 | 0.4918 | 0.3820 | 0.1022 | | 0.3175 | 57.5163 | 8800 | 0.4969 | 0.3761 | 0.1023 | | 0.3081 | 58.8235 | 9000 | 0.4717 | 0.3742 | 0.0996 | | 0.3144 | 60.1307 | 9200 | 0.4901 | 0.3753 | 0.1029 | | 0.3085 | 61.4379 | 9400 | 0.4793 | 0.3655 | 0.0992 | | 0.3033 | 62.7451 | 9600 | 0.4726 | 0.3623 | 0.0985 | | 0.291 | 64.0523 | 9800 | 0.4792 | 0.3750 | 0.1015 | | 0.3022 | 65.3595 | 10000 | 0.4942 | 0.3761 | 0.1018 | | 0.2949 | 66.6667 | 10200 | 0.5000 | 0.3809 | 0.1028 | | 0.2744 | 67.9739 | 10400 | 0.5011 | 0.3773 | 0.1010 | | 0.2648 | 69.2810 | 10600 | 0.5171 | 0.3809 | 0.1028 | | 0.2793 | 70.5882 | 10800 | 0.5050 | 0.3738 | 0.1004 | | 0.2664 | 71.8954 | 11000 | 0.4973 | 0.3663 | 0.1000 | | 0.2515 | 73.2026 | 11200 | 0.5010 | 0.3675 | 0.1004 | | 0.2421 | 74.5098 | 11400 | 0.5130 | 0.3560 | 0.0973 | | 0.2638 | 75.8170 | 11600 | 0.5044 | 0.3679 | 0.0995 | | 0.2444 | 77.1242 | 11800 | 0.4933 | 0.3627 | 0.0986 | | 0.2399 | 78.4314 | 12000 | 0.4950 | 0.3620 | 0.0984 | | 0.2532 | 79.7386 | 12200 | 0.4971 | 0.3588 | 0.0974 | | 0.242 | 81.0458 | 12400 | 0.5043 | 0.3718 | 0.1012 | | 0.2351 | 82.3529 | 12600 | 0.5112 | 0.3722 | 0.0990 | | 0.2344 | 83.6601 | 12800 | 0.4991 | 0.3651 | 0.0986 | | 0.2274 | 84.9673 | 13000 | 0.5089 | 0.3533 | 0.0959 | | 0.2394 | 86.2745 | 13200 | 0.5069 | 0.3588 | 0.0973 | | 0.2336 | 87.5817 | 13400 | 0.5152 | 0.3631 | 0.0983 | | 0.2323 | 88.8889 | 13600 | 0.5168 | 0.3600 | 0.0975 | | 0.2427 | 90.1961 | 13800 | 0.5017 | 0.3620 | 0.0979 | | 0.2296 | 91.5033 | 14000 | 0.5121 | 0.3596 | 0.0975 | | 0.2289 | 92.8105 | 14200 | 0.5106 | 0.3545 | 0.0956 | | 0.2153 | 94.1176 | 14400 | 0.5133 | 0.3584 | 0.0959 | | 0.244 | 95.4248 | 14600 | 0.5134 | 0.3553 | 0.0959 | | 0.2277 | 96.7320 | 14800 | 0.5166 | 0.3580 | 0.0964 | | 0.2224 | 98.0392 | 15000 | 0.5136 | 0.3568 | 0.0963 | | 0.2218 | 99.3464 | 15200 | 0.5168 | 0.3564 | 0.0963 | ### Framework versions - Transformers 4.41.2 - Pytorch 2.4.0 - Datasets 3.2.0 - Tokenizers 0.19.1