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w2v-bert-2.0_BIG-C_corpus_Bemba_1hr_v1

Automatic Speech Recognition
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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: w2v-bert-2.0_BIG-C_corpus_Bemba_1hr_v1
    results: []

w2v-bert-2.0_BIG-C_corpus_Bemba_1hr_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: 0.5722
  • Wer: 0.5985
  • Cer: 0.1196

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: 16
  • eval_batch_size: 8
  • seed: 42
  • gradient_accumulation_steps: 2
  • total_train_batch_size: 32
  • 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
7.0053 1.0 15 6.3978 1.0085 1.1686
5.7304 2.0 30 4.1429 1.0 0.9477
3.6107 3.0 45 3.1577 1.0 0.9999
3.087 4.0 60 2.8811 1.0 0.9799
2.8587 5.0 75 2.7459 1.0 0.9090
2.7163 6.0 90 2.4646 1.0 0.8254
2.3267 7.0 105 1.9810 0.9998 0.6766
1.7199 8.0 120 1.3007 0.9820 0.3001
1.1524 9.0 135 0.9403 0.8640 0.2081
0.9037 10.0 150 0.8880 0.7630 0.1692
0.7707 11.0 165 0.7744 0.7416 0.1788
0.6817 12.0 180 0.7403 0.6391 0.1462
0.6124 13.0 195 0.7595 0.6170 0.1406
0.5606 14.0 210 0.7323 0.6665 0.1565
0.5283 15.0 225 0.7329 0.7097 0.1781
0.4703 16.0 240 0.7322 0.6011 0.1402
0.5413 17.0 255 0.7942 0.7116 0.1545
0.531 18.0 270 0.8518 0.6595 0.1535
0.5132 19.0 285 0.8821 0.6633 0.1442
0.4961 20.0 300 0.7836 0.6450 0.1478
0.5584 21.0 315 0.9809 0.6544 0.1546
0.7199 22.0 330 0.9238 0.7732 0.2111
0.8428 23.0 345 0.8865 0.7223 0.1874
0.9216 24.0 360 1.3912 0.9975 0.6157
1.1638 25.0 375 1.1943 0.7590 0.1808
1.0508 26.0 390 1.1233 0.9919 0.4404

Framework versions

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