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w2vbert-lingala-sd2

Automatic Speech Recognition
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wav2vec2-bert
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metadata 
library_name: transformers
base_model: sulaimank/w2vbert-lingala-waxal-punct-v2
tags:
  - generated_from_trainer
model-index:
  - name: w2vbert-lingala-sd2
    results: []

w2vbert-lingala-sd2

This model is a fine-tuned version of sulaimank/w2vbert-lingala-waxal-punct-v2 on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 0.0545
  • Wer Keep: 0.0915
  • Cer Keep: 0.0214
  • Zindi Keep: 0.9436
  • Wer Strip: 0.0403
  • Zindi Strip: 0.9746
  • Zindi Lower: 0.9829

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: 3e-05
  • train_batch_size: 16
  • eval_batch_size: 16
  • seed: 42
  • gradient_accumulation_steps: 2
  • total_train_batch_size: 32
  • optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.98) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_ratio: 0.1
  • num_epochs: 8.0

Training results

Training Loss Epoch Step Cer Keep Validation Loss Wer Keep Wer Strip Zindi Keep Zindi Lower Zindi Strip
3.6428 0.1758 200 0.9997 3.2376 1.0 1.0 0.0001 0.0001 0.0001
1.6433 0.3516 400 0.0408 0.1868 0.1654 0.1120 0.8969 0.9459 0.9292
0.5927 0.5275 600 0.0336 0.1295 0.1356 0.0799 0.9154 0.9661 0.9494
0.5845 0.7033 800 0.0314 0.1090 0.1237 0.0728 0.9224 0.9662 0.9533
0.9751 0.8791 1000 0.0296 0.1028 0.1163 0.0651 0.9270 0.9686 0.9583
0.3809 1.0545 1200 0.0282 0.0974 0.1109 0.0594 0.9304 0.9718 0.9620
0.5085 1.2303 1400 0.0301 0.0975 0.1158 0.0632 0.9271 0.9684 0.9591
0.2797 1.4062 1600 0.0286 0.0970 0.1128 0.0606 0.9293 0.9714 0.9611
0.442 1.5820 1800 0.0274 0.0912 0.1092 0.0588 0.9317 0.9727 0.9624
0.6423 1.7578 2000 0.0272 0.0906 0.1108 0.0607 0.9310 0.9710 0.9613
0.2759 1.9336 2200 0.0274 0.0824 0.1080 0.0556 0.9323 0.9734 0.9643
0.3426 2.1090 2400 0.0262 0.0833 0.1049 0.0541 0.9344 0.9745 0.9653
0.1779 2.2848 2600 0.0269 0.0845 0.1073 0.0557 0.9329 0.9734 0.9642
0.3073 2.4607 2800 0.0265 0.0838 0.1065 0.0552 0.9335 0.9740 0.9646
0.3184 2.6365 3000 0.0267 0.0742 0.1074 0.0553 0.9330 0.9740 0.9645
0.3589 2.8123 3200 0.0262 0.0726 0.1045 0.0535 0.9346 0.9755 0.9656
0.3602 2.9881 3400 0.0254 0.0720 0.1028 0.0517 0.9359 0.9761 0.9669
0.259 3.1635 3600 0.0259 0.0754 0.1029 0.0516 0.9356 0.9764 0.9669
0.1736 3.3393 3800 0.0251 0.0722 0.1012 0.0492 0.9368 0.9783 0.9686
0.1477 3.5152 4000 0.0699 0.0992 0.0242 0.9383 0.0486 0.9690 0.9780
0.1615 3.6910 4200 0.0676 0.0994 0.0240 0.9383 0.0485 0.9692 0.9786
0.2897 3.8668 4400 0.0686 0.0984 0.0241 0.9388 0.0479 0.9695 0.9784
0.3471 4.0422 4600 0.0647 0.0993 0.0241 0.9383 0.0480 0.9695 0.9786
0.5524 4.2180 4800 0.0659 0.0994 0.0236 0.9385 0.0468 0.9703 0.9789
0.3344 4.3938 5000 0.0647 0.0984 0.0235 0.9390 0.0471 0.9702 0.9792
0.3536 4.5697 5200 0.0641 0.1005 0.0237 0.9379 0.0470 0.9702 0.9796
0.1587 4.7455 5400 0.0638 0.0961 0.0230 0.9405 0.0451 0.9713 0.9801
0.3888 4.9213 5600 0.0615 0.0958 0.0235 0.9404 0.0447 0.9715 0.9801
0.2487 5.0967 5800 0.0614 0.0975 0.0232 0.9397 0.0459 0.9709 0.9799
0.0854 5.2725 6000 0.0604 0.0950 0.0226 0.9412 0.0442 0.9720 0.9808
0.2548 5.4484 6200 0.0618 0.0948 0.0226 0.9413 0.0439 0.9722 0.9809
0.1518 5.6242 6400 0.0599 0.0951 0.0225 0.9412 0.0443 0.9719 0.9807
0.4898 5.8 6600 0.0586 0.0946 0.0223 0.9416 0.0431 0.9727 0.9812
0.2699 5.9758 6800 0.0572 0.0936 0.0222 0.9421 0.0420 0.9734 0.9820
0.316 6.1512 7000 0.0576 0.0942 0.0219 0.9420 0.0432 0.9727 0.9815
0.3514 6.3270 7200 0.0562 0.0921 0.0218 0.9431 0.0413 0.9738 0.9823
0.1803 6.5029 7400 0.0560 0.0922 0.0219 0.9429 0.0413 0.9739 0.9822
0.1222 6.6787 7600 0.0564 0.0931 0.0219 0.9425 0.0414 0.9738 0.9823
0.1351 6.8545 7800 0.0552 0.0917 0.0216 0.9433 0.0403 0.9744 0.9830
0.2344 7.0299 8000 0.0548 0.0917 0.0214 0.9434 0.0405 0.9744 0.9828
0.4722 7.2057 8200 0.0547 0.0911 0.0213 0.9438 0.0403 0.9745 0.9829
0.1888 7.3815 8400 0.0545 0.0912 0.0214 0.9437 0.0405 0.9744 0.9828
0.2479 7.5574 8600 0.0548 0.0920 0.0216 0.9432 0.0407 0.9743 0.9828
0.1695 7.7332 8800 0.0544 0.0919 0.0215 0.9433 0.0408 0.9742 0.9826
0.3146 7.9090 9000 0.0545 0.0915 0.0214 0.9436 0.0403 0.9746 0.9829

Framework versions

  • Transformers 4.57.6
  • Pytorch 2.13.0+cu130
  • Datasets 3.6.0
  • Tokenizers 0.22.2