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W2V2-Bert_DigitalUmuganda_Afrivoice_Shona_5hr_v1

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wav2vec2-bert
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W2V2-Bert_DigitalUmuganda_Afrivoice_Shona_5hr_v1 / README.md
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metadata
library_name: transformers
license: mit
base_model: facebook/w2v-bert-2.0
tags:
  - generated_from_trainer
metrics:
  - wer
model-index:
  - name: W2V2-Bert_DigitalUmuganda_Afrivoice_Shona_5hr_v1
    results: []

W2V2-Bert_DigitalUmuganda_Afrivoice_Shona_5hr_v1

This model is a fine-tuned version of facebook/w2v-bert-2.0 on an unknown dataset. It achieves the following results on the evaluation set:

  • Loss: 0.5836
  • Wer: 0.3300
  • Cer: 0.0669

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: 5e-05
  • train_batch_size: 4
  • eval_batch_size: 8
  • seed: 42
  • gradient_accumulation_steps: 2
  • total_train_batch_size: 8
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: cosine
  • lr_scheduler_warmup_steps: 500
  • num_epochs: 100
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer Cer
4.2445 1.0 110 2.8442 1.0 0.9853
1.099 2.0 220 0.2941 0.3722 0.0597
0.2699 3.0 330 0.2436 0.2851 0.0489
0.2172 4.0 440 0.2659 0.3090 0.0562
0.1991 5.0 550 0.2614 0.3125 0.0542
0.1848 6.0 660 0.2632 0.2933 0.0506
0.1544 7.0 770 0.2559 0.2831 0.0517
0.1371 8.0 880 0.2605 0.2746 0.0485
0.118 9.0 990 0.2669 0.3128 0.0502
0.0982 10.0 1100 0.2901 0.3135 0.0506
0.0832 11.0 1210 0.2899 0.2728 0.0477
0.0726 12.0 1320 0.2902 0.2791 0.0479
0.0619 13.0 1430 0.3287 0.2893 0.0477
0.0479 14.0 1540 0.3254 0.2664 0.0462
0.0405 15.0 1650 0.3244 0.3025 0.0473
0.0311 16.0 1760 0.3584 0.2753 0.0460
0.0279 17.0 1870 0.3913 0.2748 0.0474
0.0242 18.0 1980 0.3918 0.2678 0.0445
0.0235 19.0 2090 0.3669 0.2761 0.0475
0.0213 20.0 2200 0.3855 0.2631 0.0460
0.016 21.0 2310 0.4096 0.2748 0.0475
0.0154 22.0 2420 0.4276 0.2916 0.0488
0.0127 23.0 2530 0.3918 0.2649 0.0452
0.0115 24.0 2640 0.4195 0.2778 0.0472
0.0105 25.0 2750 0.4143 0.2726 0.0463
0.0086 26.0 2860 0.3923 0.2748 0.0468
0.0111 27.0 2970 0.4108 0.2708 0.0461
0.0107 28.0 3080 0.4169 0.2698 0.0469
0.0083 29.0 3190 0.4363 0.2659 0.0448
0.0085 30.0 3300 0.4340 0.2649 0.0459

Framework versions

  • Transformers 4.44.1
  • Pytorch 2.2.0+cu121
  • Datasets 2.21.0
  • Tokenizers 0.19.1