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wav2vec2-xls-r-300m-Fleurs_AMMI_AFRIVOICE_LRSC-ln-20hrs-v2

Automatic Speech Recognition
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wav2vec2
Generated from Trainer
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metadata
library_name: transformers
license: apache-2.0
base_model: facebook/wav2vec2-xls-r-300m
tags:
  - generated_from_trainer
metrics:
  - wer
model-index:
  - name: wav2vec2-xls-r-300m-Fleurs_AMMI_AFRIVOICE_LRSC-ln-20hrs-v2
    results: []

wav2vec2-xls-r-300m-Fleurs_AMMI_AFRIVOICE_LRSC-ln-20hrs-v2

This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 0.3763
  • Wer: 0.2252
  • Cer: 0.0740

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: 1e-05
  • train_batch_size: 4
  • eval_batch_size: 2
  • seed: 42
  • 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
  • num_epochs: 100
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer Cer
5.6396 1.0 1423 2.8770 1.0 1.0
2.8585 2.0 2846 2.7805 1.0 1.0
2.131 3.0 4269 1.0970 0.7618 0.2105
1.2082 4.0 5692 0.7337 0.5150 0.1455
0.9577 5.0 7115 0.5965 0.4144 0.1220
0.8269 6.0 8538 0.5259 0.3800 0.1142
0.7495 7.0 9961 0.5030 0.3515 0.1058
0.69 8.0 11384 0.4540 0.3472 0.1020
0.6471 9.0 12807 0.4356 0.3291 0.0990
0.6132 10.0 14230 0.4083 0.3299 0.0997
0.5859 11.0 15653 0.4029 0.3063 0.0924
0.5537 12.0 17076 0.4057 0.3201 0.0992
0.5426 13.0 18499 0.3984 0.2917 0.0894
0.5156 14.0 19922 0.3756 0.2850 0.0869
0.5007 15.0 21345 0.3751 0.2812 0.0870
0.485 16.0 22768 0.3957 0.2712 0.0842
0.4757 17.0 24191 0.3705 0.2714 0.0842
0.4596 18.0 25614 0.3626 0.2612 0.0813
0.4478 19.0 27037 0.3639 0.2689 0.0834
0.44 20.0 28460 0.3683 0.2620 0.0816
0.4272 21.0 29883 0.3550 0.2632 0.0846
0.4175 22.0 31306 0.3603 0.2543 0.0804
0.4015 23.0 32729 0.3432 0.2544 0.0803
0.3977 24.0 34152 0.3496 0.2519 0.0792
0.3904 25.0 35575 0.3661 0.2452 0.0773
0.3786 26.0 36998 0.3655 0.2463 0.0782
0.3711 27.0 38421 0.3467 0.2463 0.0790
0.3631 28.0 39844 0.3537 0.2463 0.0783
0.3593 29.0 41267 0.3609 0.2361 0.0756
0.3464 30.0 42690 0.3335 0.2531 0.0820
0.3458 31.0 44113 0.3588 0.2365 0.0750
0.3402 32.0 45536 0.3510 0.2352 0.0751
0.3329 33.0 46959 0.3464 0.2362 0.0762
0.3307 34.0 48382 0.3471 0.2340 0.0762
0.3199 35.0 49805 0.3741 0.2374 0.0765
0.3185 36.0 51228 0.3385 0.2390 0.0767
0.3137 37.0 52651 0.3572 0.2317 0.0743
0.3059 38.0 54074 0.3745 0.2294 0.0734
0.3024 39.0 55497 0.3968 0.2299 0.0741
0.2958 40.0 56920 0.3469 0.2317 0.0756
0.296 41.0 58343 0.3302 0.2495 0.0823
0.2927 42.0 59766 0.3747 0.2261 0.0730
0.2842 43.0 61189 0.3799 0.2216 0.0719
0.2754 44.0 62612 0.3530 0.2602 0.0988
0.2781 45.0 64035 0.3907 0.2237 0.0726
0.2656 46.0 65458 0.3523 0.2397 0.0824
0.2649 47.0 66881 0.3621 0.2289 0.0762
0.2605 48.0 68304 0.3946 0.2259 0.0727
0.2633 49.0 69727 0.3852 0.2233 0.0737
0.2623 50.0 71150 0.3821 0.2247 0.0731
0.2544 51.0 72573 0.3742 0.2226 0.0723
0.2512 52.0 73996 0.3686 0.2229 0.0731
0.2522 53.0 75419 0.3763 0.2252 0.0740

Framework versions

  • Transformers 4.46.1
  • Pytorch 2.1.0+cu118
  • Datasets 3.1.0
  • Tokenizers 0.20.1