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mms-300m_DigitalUmuganda_Afrivoice_Shona_10hr_v2

Automatic Speech Recognition
Transformers
Safetensors
Shona
wav2vec2
Generated from Trainer
Eval Results
Inference Endpoints
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mms-300m_DigitalUmuganda_Afrivoice_Shona_10hr_v2 / README.md
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metadata
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: 0.41041838767675787

facebook/mms-300m

This model is a fine-tuned version of facebook/mms-300m on the DigitalUmuganda dataset. It achieves the following results on the evaluation set:

  • Loss: 0.8585
  • Wer: 0.4104
  • Cer: 0.0907

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
33.7728 0.9955 55 3.2848 1.0 1.0
12.0407 1.9910 110 2.9063 1.0 1.0
11.7013 2.9864 165 2.8895 1.0 1.0
11.2973 4.0 221 2.8169 1.0 1.0
11.1719 4.9955 276 2.5708 1.0 0.8738
8.8604 5.9910 331 1.6831 1.0 0.5577
6.0356 6.9864 386 1.1403 0.9983 0.3583
4.3072 8.0 442 0.8494 0.9471 0.2649
3.3072 8.9955 497 0.6382 0.8545 0.1923
2.3828 9.9910 552 0.4925 0.6959 0.1443
1.7086 10.9864 607 0.3803 0.5268 0.1041
1.2672 12.0 663 0.3669 0.4825 0.0934
1.0349 12.9955 718 0.3209 0.4346 0.0825
0.8498 13.9910 773 0.3147 0.4300 0.0818
0.7259 14.9864 828 0.3151 0.4054 0.0742
0.6193 16.0 884 0.3132 0.4200 0.0748
0.5381 16.9955 939 0.3256 0.3895 0.0711
0.4792 17.9910 994 0.3441 0.3863 0.0699
0.4266 18.9864 1049 0.3323 0.3855 0.0692
0.3717 20.0 1105 0.3396 0.3871 0.0671
0.3503 20.9955 1160 0.3394 0.3772 0.0679
0.3254 21.9910 1215 0.3383 0.3868 0.0682
0.2808 22.9864 1270 0.3784 0.3808 0.0684
0.2534 24.0 1326 0.3449 0.3629 0.0652
0.2467 24.9955 1381 0.3540 0.3725 0.0664
0.2271 25.9910 1436 0.3677 0.3512 0.0630
0.2197 26.9864 1491 0.3623 0.3664 0.0645
0.2041 28.0 1547 0.3791 0.3950 0.0653
0.2013 28.9955 1602 0.3656 0.3515 0.0619
0.1877 29.9910 1657 0.3700 0.3533 0.0632
0.1763 30.9864 1712 0.3907 0.3594 0.0638
0.1651 32.0 1768 0.3825 0.3713 0.0652
0.1697 32.9955 1823 0.3777 0.3546 0.0624
0.1699 33.9910 1878 0.4016 0.3536 0.0631
0.1553 34.9864 1933 0.4027 0.3547 0.0620
0.1438 36.0 1989 0.4027 0.3444 0.0612
0.1526 36.9955 2044 0.4131 0.3510 0.0613
0.1388 37.9910 2099 0.3921 0.3594 0.0617
0.1334 38.9864 2154 0.3944 0.3519 0.0614
0.1269 40.0 2210 0.4067 0.3486 0.0611
0.1332 40.9955 2265 0.3779 0.3416 0.0594
0.1239 41.9910 2320 0.4081 0.3432 0.0603
0.1172 42.9864 2375 0.3946 0.3356 0.0587
0.1174 44.0 2431 0.4208 0.3333 0.0582
0.1168 44.9955 2486 0.3811 0.3297 0.0585
0.1118 45.9910 2541 0.4092 0.3406 0.0596
0.1061 46.9864 2596 0.4050 0.3248 0.0573
0.1019 48.0 2652 0.4025 0.3250 0.0572
0.0959 48.9955 2707 0.4168 0.3306 0.0579
0.102 49.9910 2762 0.4353 0.3300 0.0571
0.0967 50.9864 2817 0.4308 0.3307 0.0587
0.0953 52.0 2873 0.4258 0.3227 0.0567
0.0936 52.9955 2928 0.4012 0.3262 0.0555
0.0917 53.9910 2983 0.4252 0.3219 0.0549
0.0851 54.9864 3038 0.4243 0.3331 0.0570
0.0829 56.0 3094 0.4353 0.3224 0.0558
0.0761 56.9955 3149 0.4437 0.3299 0.0569
0.074 57.9910 3204 0.4178 0.3231 0.0551
0.0731 58.9864 3259 0.4297 0.3249 0.0560
0.0683 60.0 3315 0.4271 0.3215 0.0549
0.0698 60.9955 3370 0.4462 0.3197 0.0539
0.0722 61.9910 3425 0.4369 0.3157 0.0535
0.0654 62.9864 3480 0.4259 0.3208 0.0545
0.0653 64.0 3536 0.4431 0.3169 0.0544
0.0636 64.9955 3591 0.4397 0.3149 0.0535
0.0619 65.9910 3646 0.4410 0.3195 0.0549
0.0622 66.9864 3701 0.4306 0.3199 0.0545
0.0558 68.0 3757 0.4444 0.3099 0.0534
0.0595 68.9955 3812 0.4487 0.3123 0.0530
0.0558 69.9910 3867 0.4535 0.3110 0.0534
0.0487 70.9864 3922 0.4498 0.3105 0.0530
0.0512 72.0 3978 0.4411 0.3073 0.0524
0.0496 72.9955 4033 0.4345 0.3135 0.0533
0.0505 73.9910 4088 0.4520 0.3080 0.0521
0.0431 74.9864 4143 0.4432 0.3044 0.0518
0.0456 76.0 4199 0.4548 0.2995 0.0514
0.045 76.9955 4254 0.4503 0.2997 0.0505
0.0443 77.9910 4309 0.4552 0.3042 0.0517
0.0435 78.9864 4364 0.4646 0.2986 0.0505
0.0407 80.0 4420 0.4461 0.3075 0.0509
0.0396 80.9955 4475 0.4507 0.3031 0.0510
0.0401 81.9910 4530 0.4454 0.3023 0.0508
0.0352 82.9864 4585 0.4473 0.2991 0.0503
0.0328 84.0 4641 0.4550 0.3067 0.0515
0.037 84.9955 4696 0.4513 0.2971 0.0502
0.0358 85.9910 4751 0.4447 0.2955 0.0497
0.034 86.9864 4806 0.4504 0.2952 0.0496
0.0313 88.0 4862 0.4674 0.2965 0.0500
0.0269 88.9955 4917 0.4620 0.2967 0.0496
0.0304 89.9910 4972 0.4541 0.2927 0.0490
0.029 90.9864 5027 0.4503 0.2958 0.0492
0.0293 92.0 5083 0.4555 0.2935 0.0492
0.027 92.9955 5138 0.4598 0.2955 0.0491
0.0285 93.9910 5193 0.4629 0.2946 0.0492
0.0279 94.9864 5248 0.4582 0.2933 0.0490
0.0264 96.0 5304 0.4576 0.2916 0.0490
0.0258 96.9955 5359 0.4605 0.2933 0.0491
0.0247 97.9910 5414 0.4591 0.2948 0.0491
0.0226 98.9864 5469 0.4610 0.2952 0.0492
0.0226 99.5475 5500 0.4610 0.2945 0.0491

Framework versions

  • Transformers 4.47.0.dev0
  • Pytorch 2.1.0+cu118
  • Datasets 3.0.2
  • Tokenizers 0.20.1