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mms-1B_all_BIG_C_Bemba_100hr_v1

Automatic Speech Recognition
Transformers
Safetensors
Bemba (Zambia)
wav2vec2
Generated from Trainer
Eval Results
Inference Endpoints
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mms-1B_all_BIG_C_Bemba_100hr_v1 / README.md
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metadata
library_name: transformers
language:
  - bem
license: cc-by-nc-4.0
base_model: facebook/mms-1b-all
tags:
  - generated_from_trainer
datasets:
  - BIG_C/Bemba
metrics:
  - wer
model-index:
  - name: facebook/mms-1b-all
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: BIG_C
          type: BIG_C/Bemba
        metrics:
          - name: Wer
            type: wer
            value: 0.4170597821425508

facebook/mms-1b-all

This model is a fine-tuned version of facebook/mms-1b-all on the BIG_C dataset. It achieves the following results on the evaluation set:

  • Loss: 0.3475
  • Model Preparation Time: 0.0112
  • Wer: 0.4171
  • Cer: 0.0777

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: 4
  • eval_batch_size: 8
  • seed: 42
  • gradient_accumulation_steps: 4
  • total_train_batch_size: 16
  • 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 Model Preparation Time Wer Cer
0.9189 1.0 3096 0.5510 0.0112 0.4860 0.1248
0.5994 2.0 6192 0.5258 0.0112 0.4870 0.1264
0.5721 3.0 9288 0.5079 0.0112 0.4638 0.1264
0.5573 4.0 12384 0.4963 0.0112 0.4410 0.1128
0.5442 5.0 15480 0.4938 0.0112 0.4449 0.1202
0.5347 6.0 18576 0.4837 0.0112 0.4348 0.1165
0.5261 7.0 21672 0.4795 0.0112 0.4205 0.1092
0.5203 8.0 24768 0.4791 0.0112 0.4237 0.1076
0.5132 9.0 27864 0.4745 0.0112 0.4159 0.1073
0.5073 10.0 30960 0.4696 0.0112 0.4162 0.1072
0.5037 11.0 34056 0.4696 0.0112 0.4227 0.1136
0.4974 12.0 37152 0.4702 0.0112 0.4042 0.1047
0.4924 13.0 40248 0.4761 0.0112 0.3916 0.1028
0.4873 14.0 43344 0.4617 0.0112 0.3977 0.1046
0.4846 15.0 46440 0.4756 0.0112 0.3926 0.1025
0.4798 16.0 49536 0.4614 0.0112 0.3980 0.1066
0.4767 17.0 52632 0.4606 0.0112 0.3940 0.1031
0.473 18.0 55728 0.4712 0.0112 0.3919 0.1030
0.4685 19.0 58824 0.4586 0.0112 0.3913 0.1032
0.4656 20.0 61920 0.4713 0.0112 0.3892 0.1034
0.4618 21.0 65016 0.4627 0.0112 0.3854 0.1020
0.4577 22.0 68112 0.4565 0.0112 0.3880 0.1015
0.4554 23.0 71208 0.4593 0.0112 0.3862 0.1034
0.4523 24.0 74304 0.4624 0.0112 0.3818 0.1015
0.4486 25.0 77400 0.4638 0.0112 0.3855 0.1024
0.446 26.0 80496 0.4574 0.0112 0.3929 0.1029
0.4426 27.0 83592 0.4662 0.0112 0.3851 0.1033
0.4407 28.0 86688 0.4633 0.0112 0.3898 0.1032
0.4378 29.0 89784 0.4614 0.0112 0.3800 0.1017
0.4337 30.0 92880 0.4595 0.0112 0.3809 0.1009
0.4312 31.0 95976 0.4681 0.0112 0.3841 0.1046
0.4277 32.0 99072 0.4575 0.0112 0.3749 0.0999
0.4257 33.0 102168 0.4583 0.0112 0.3760 0.1021
0.4224 34.0 105264 0.4566 0.0112 0.3830 0.1012
0.4205 35.0 108360 0.4718 0.0112 0.3698 0.1004
0.4159 36.0 111456 0.4642 0.0112 0.3734 0.1008
0.4141 37.0 114552 0.4601 0.0112 0.3802 0.1024
0.4118 38.0 117648 0.4665 0.0112 0.3718 0.1004
0.408 39.0 120744 0.4665 0.0112 0.3745 0.1041
0.4062 40.0 123840 0.4743 0.0112 0.3708 0.0994
0.4033 41.0 126936 0.4700 0.0112 0.3720 0.1003
0.4017 42.0 130032 0.4755 0.0112 0.3841 0.1057
0.3982 43.0 133128 0.4798 0.0112 0.3671 0.0984
0.3966 44.0 136224 0.4738 0.0112 0.3752 0.1010
0.3944 45.0 139320 0.4749 0.0112 0.3727 0.1010
0.3923 46.0 142416 0.4702 0.0112 0.3792 0.1033
0.389 47.0 145512 0.4723 0.0112 0.3714 0.1014
0.3874 48.0 148608 0.4750 0.0112 0.3733 0.1004
0.3851 49.0 151704 0.4708 0.0112 0.3747 0.1024
0.3831 50.0 154800 0.4768 0.0112 0.3688 0.0996
0.3803 51.0 157896 0.4807 0.0112 0.3673 0.0996
0.379 52.0 160992 0.4823 0.0112 0.3648 0.0991
0.3772 53.0 164088 0.4809 0.0112 0.3723 0.1002
0.3741 54.0 167184 0.4807 0.0112 0.3705 0.0996
0.3726 55.0 170280 0.4866 0.0112 0.3669 0.0990
0.371 56.0 173376 0.4793 0.0112 0.3738 0.1027
0.3695 57.0 176472 0.4789 0.0112 0.3768 0.1025
0.3668 58.0 179568 0.4774 0.0112 0.3681 0.1015
0.3656 59.0 182664 0.4783 0.0112 0.3684 0.1000
0.3639 60.0 185760 0.4848 0.0112 0.3714 0.1002
0.3615 61.0 188856 0.4893 0.0112 0.3721 0.0996
0.3596 62.0 191952 0.4808 0.0112 0.3654 0.1008

Framework versions

  • Transformers 4.47.0.dev0
  • Pytorch 2.1.0+cu118
  • Datasets 3.1.0
  • Tokenizers 0.20.1