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README.md CHANGED
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  ---
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- library_name: transformers
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- tags: []
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ---
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- # Model Card for Model ID
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- ## Model Details
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- [More Information Needed]
 
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  ---
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+ license: cc-by-nc-4.0
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+ base_model: facebook/mms-1b-all
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+ tags:
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+ - generated_from_trainer
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+ datasets:
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+ - audiofolder
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+ metrics:
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+ - wer
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+ model-index:
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+ - name: nhi_heldout-speaker-exp_GGN505_mms-1b-nhi-adapterft
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+ results:
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+ - task:
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+ name: Automatic Speech Recognition
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+ type: automatic-speech-recognition
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+ dataset:
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+ name: audiofolder
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+ type: audiofolder
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+ config: default
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+ split: test
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+ args: default
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+ metrics:
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+ - name: Wer
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+ type: wer
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+ value: 0.35643954312721543
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  ---
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+ <!-- This model card has been generated automatically according to the information the Trainer had access to. You
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+ should probably proofread and complete it, then remove this comment. -->
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+
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+ # nhi_heldout-speaker-exp_GGN505_mms-1b-nhi-adapterft
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+
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+ This model is a fine-tuned version of [facebook/mms-1b-all](https://huggingface.co/facebook/mms-1b-all) on the audiofolder dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.5168
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+ - Wer: 0.3564
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+ - Cer: 0.0963
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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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+ The following hyperparameters were used during training:
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+ - learning_rate: 0.001
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+ - train_batch_size: 16
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+ - eval_batch_size: 32
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+ - seed: 42
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+ - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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+ - lr_scheduler_type: linear
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+ - lr_scheduler_warmup_steps: 100
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+ - num_epochs: 100
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+ - mixed_precision_training: Native AMP
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss | Wer | Cer |
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+ |:-------------:|:-------:|:-----:|:---------------:|:------:|:------:|
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+ | 1.0628 | 1.3072 | 200 | 0.6601 | 0.6203 | 0.1672 |
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+ | 0.8658 | 2.6144 | 400 | 0.5592 | 0.5526 | 0.1436 |
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+ | 0.7815 | 3.9216 | 600 | 0.5179 | 0.5286 | 0.1345 |
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+ | 0.7157 | 5.2288 | 800 | 0.5074 | 0.5108 | 0.1281 |
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+ | 0.6834 | 6.5359 | 1000 | 0.4945 | 0.4801 | 0.1259 |
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+ | 0.6746 | 7.8431 | 1200 | 0.4748 | 0.4699 | 0.1229 |
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+ | 0.6337 | 9.1503 | 1400 | 0.4826 | 0.4655 | 0.1198 |
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+ | 0.6218 | 10.4575 | 1600 | 0.4772 | 0.4584 | 0.1205 |
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+ | 0.5927 | 11.7647 | 1800 | 0.4740 | 0.4490 | 0.1155 |
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+ | 0.5927 | 13.0719 | 2000 | 0.4637 | 0.4293 | 0.1121 |
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+ | 0.5776 | 14.3791 | 2200 | 0.4621 | 0.4423 | 0.1153 |
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+ | 0.5432 | 15.6863 | 2400 | 0.4752 | 0.4289 | 0.1131 |
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+ | 0.5259 | 16.9935 | 2600 | 0.4516 | 0.3966 | 0.1059 |
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+ | 0.5152 | 18.3007 | 2800 | 0.4510 | 0.4210 | 0.1109 |
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+ | 0.4901 | 19.6078 | 3000 | 0.4602 | 0.4191 | 0.1095 |
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+ | 0.4926 | 20.9150 | 3200 | 0.4607 | 0.4057 | 0.1068 |
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+ | 0.4742 | 22.2222 | 3400 | 0.4569 | 0.3856 | 0.1037 |
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+ | 0.4813 | 23.5294 | 3600 | 0.4538 | 0.4163 | 0.1080 |
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+ | 0.4598 | 24.8366 | 3800 | 0.4672 | 0.4147 | 0.1098 |
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+ | 0.4418 | 26.1438 | 4000 | 0.4656 | 0.4013 | 0.1054 |
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+ | 0.4561 | 27.4510 | 4200 | 0.4737 | 0.4002 | 0.1054 |
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+ | 0.4358 | 28.7582 | 4400 | 0.4560 | 0.3931 | 0.1059 |
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+ | 0.4343 | 30.0654 | 4600 | 0.4644 | 0.3954 | 0.1062 |
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+ | 0.4148 | 31.3725 | 4800 | 0.4510 | 0.3966 | 0.1072 |
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+ | 0.4208 | 32.6797 | 5000 | 0.4687 | 0.3840 | 0.1026 |
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+ | 0.4208 | 33.9869 | 5200 | 0.4805 | 0.3856 | 0.1062 |
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+ | 0.4189 | 35.2941 | 5400 | 0.4624 | 0.3765 | 0.1032 |
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+ | 0.3899 | 36.6013 | 5600 | 0.4741 | 0.3844 | 0.1051 |
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+ | 0.3835 | 37.9085 | 5800 | 0.4721 | 0.3876 | 0.1040 |
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+ | 0.4017 | 39.2157 | 6000 | 0.4733 | 0.3915 | 0.1056 |
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+ | 0.3928 | 40.5229 | 6200 | 0.4644 | 0.3742 | 0.1029 |
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+ | 0.373 | 41.8301 | 6400 | 0.4628 | 0.3848 | 0.1027 |
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+ | 0.372 | 43.1373 | 6600 | 0.4805 | 0.3899 | 0.1034 |
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+ | 0.3473 | 44.4444 | 6800 | 0.4637 | 0.3769 | 0.1012 |
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+ | 0.3419 | 45.7516 | 7000 | 0.4687 | 0.3753 | 0.1008 |
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+ | 0.3607 | 47.0588 | 7200 | 0.4642 | 0.3777 | 0.1007 |
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+ | 0.3474 | 48.3660 | 7400 | 0.4610 | 0.3690 | 0.0989 |
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+ | 0.3464 | 49.6732 | 7600 | 0.4631 | 0.3757 | 0.1001 |
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+ | 0.3398 | 50.9804 | 7800 | 0.4571 | 0.3588 | 0.0982 |
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+ | 0.3182 | 52.2876 | 8000 | 0.4868 | 0.3659 | 0.0999 |
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+ | 0.3158 | 53.5948 | 8200 | 0.4821 | 0.3718 | 0.1004 |
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+ | 0.3368 | 54.9020 | 8400 | 0.4712 | 0.3777 | 0.1015 |
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+ | 0.3312 | 56.2092 | 8600 | 0.4918 | 0.3820 | 0.1022 |
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+ | 0.3175 | 57.5163 | 8800 | 0.4969 | 0.3761 | 0.1023 |
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+ | 0.3081 | 58.8235 | 9000 | 0.4717 | 0.3742 | 0.0996 |
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+ | 0.3144 | 60.1307 | 9200 | 0.4901 | 0.3753 | 0.1029 |
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+ | 0.3085 | 61.4379 | 9400 | 0.4793 | 0.3655 | 0.0992 |
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+ | 0.3033 | 62.7451 | 9600 | 0.4726 | 0.3623 | 0.0985 |
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+ | 0.291 | 64.0523 | 9800 | 0.4792 | 0.3750 | 0.1015 |
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+ | 0.3022 | 65.3595 | 10000 | 0.4942 | 0.3761 | 0.1018 |
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+ | 0.2949 | 66.6667 | 10200 | 0.5000 | 0.3809 | 0.1028 |
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+ | 0.2744 | 67.9739 | 10400 | 0.5011 | 0.3773 | 0.1010 |
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+ | 0.2648 | 69.2810 | 10600 | 0.5171 | 0.3809 | 0.1028 |
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+ | 0.2793 | 70.5882 | 10800 | 0.5050 | 0.3738 | 0.1004 |
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+ | 0.2664 | 71.8954 | 11000 | 0.4973 | 0.3663 | 0.1000 |
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+ | 0.2515 | 73.2026 | 11200 | 0.5010 | 0.3675 | 0.1004 |
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+ | 0.2421 | 74.5098 | 11400 | 0.5130 | 0.3560 | 0.0973 |
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+ | 0.2638 | 75.8170 | 11600 | 0.5044 | 0.3679 | 0.0995 |
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+ | 0.2444 | 77.1242 | 11800 | 0.4933 | 0.3627 | 0.0986 |
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+ | 0.2399 | 78.4314 | 12000 | 0.4950 | 0.3620 | 0.0984 |
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+ | 0.2532 | 79.7386 | 12200 | 0.4971 | 0.3588 | 0.0974 |
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+ | 0.242 | 81.0458 | 12400 | 0.5043 | 0.3718 | 0.1012 |
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+ | 0.2351 | 82.3529 | 12600 | 0.5112 | 0.3722 | 0.0990 |
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+ | 0.2344 | 83.6601 | 12800 | 0.4991 | 0.3651 | 0.0986 |
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+ | 0.2274 | 84.9673 | 13000 | 0.5089 | 0.3533 | 0.0959 |
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+ | 0.2394 | 86.2745 | 13200 | 0.5069 | 0.3588 | 0.0973 |
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+ | 0.2336 | 87.5817 | 13400 | 0.5152 | 0.3631 | 0.0983 |
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+ | 0.2323 | 88.8889 | 13600 | 0.5168 | 0.3600 | 0.0975 |
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+ | 0.2427 | 90.1961 | 13800 | 0.5017 | 0.3620 | 0.0979 |
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+ | 0.2296 | 91.5033 | 14000 | 0.5121 | 0.3596 | 0.0975 |
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+ | 0.2289 | 92.8105 | 14200 | 0.5106 | 0.3545 | 0.0956 |
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+ | 0.2153 | 94.1176 | 14400 | 0.5133 | 0.3584 | 0.0959 |
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+ | 0.244 | 95.4248 | 14600 | 0.5134 | 0.3553 | 0.0959 |
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+ | 0.2277 | 96.7320 | 14800 | 0.5166 | 0.3580 | 0.0964 |
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+ | 0.2224 | 98.0392 | 15000 | 0.5136 | 0.3568 | 0.0963 |
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+ | 0.2218 | 99.3464 | 15200 | 0.5168 | 0.3564 | 0.0963 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.41.2
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+ - Pytorch 2.4.0
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+ - Datasets 3.2.0
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+ - Tokenizers 0.19.1
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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