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w2v-bert-2.0-BIG_C-AMMI-BEMBA_SPEECH_CORPUS-BEMBA-189hrs-V1

Automatic Speech Recognition
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wav2vec2-bert
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w2v-bert-2.0-BIG_C-AMMI-BEMBA_SPEECH_CORPUS-BEMBA-189hrs-V1 / README.md
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metadata
base_model: facebook/w2v-bert-2.0
library_name: transformers
license: mit
metrics:
  - wer
tags:
  - generated_from_trainer
model-index:
  - name: w2v-bert-2.0-BIG_C-AMMI-BEMBA_SPEECH_CORPUS-BEMBA-189hrs-V1
    results: []

w2v-bert-2.0-BIG_C-AMMI-BEMBA_SPEECH_CORPUS-BEMBA-189hrs-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: 0.7377
  • Wer: 0.2954
  • Cer: 0.0681

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-06
  • train_batch_size: 2
  • eval_batch_size: 1
  • seed: 42
  • gradient_accumulation_steps: 2
  • total_train_batch_size: 4
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: cosine
  • lr_scheduler_warmup_ratio: 0.025
  • num_epochs: 100

Training results

Training Loss Epoch Step Cer Validation Loss Wer
1.0463 1.0 22932 0.0921 0.3791 0.4291
0.4544 2.0 45864 0.0737 0.3468 0.3564
0.4096 3.0 68796 0.0671 0.3095 0.3097
0.3795 4.0 91728 0.0674 0.2922 0.3008
0.358 5.0 114660 0.0662 0.2915 0.2962
0.3387 6.0 137592 0.0636 0.2844 0.2832
0.3203 7.0 160524 0.0622 0.2843 0.2761
0.3 8.0 183456 0.0636 0.2880 0.2864
0.2778 9.0 206388 0.0630 0.2906 0.2782
0.2543 10.0 229320 0.0649 0.2986 0.2863
0.2312 11.0 252252 0.0643 0.3220 0.2829
0.2082 12.0 275184 0.0644 0.3376 0.2836
0.1864 13.0 298116 0.0653 0.3579 0.2832
0.167 14.0 321048 0.0641 0.3896 0.2836
0.1498 15.0 343980 0.0653 0.4124 0.2902
0.1351 16.0 366912 0.0649 0.4565 0.2852
0.1216 17.0 389844 0.0671 0.4517 0.2967
0.1102 18.0 412776 0.4959 0.2912 0.0659
0.0999 19.0 435708 0.5536 0.2909 0.0652
0.091 20.0 458640 0.5782 0.2932 0.0667
0.0828 21.0 481572 0.6136 0.2949 0.0663
0.0752 22.0 504504 0.6310 0.2900 0.0662
0.0679 23.0 527436 0.6588 0.2925 0.0659
0.0614 24.0 550368 0.6938 0.2945 0.0671
0.0559 25.0 573300 0.7247 0.2959 0.0667
0.0499 26.0 596232 0.7278 0.2927 0.0663
0.045 27.0 619164 0.7377 0.2954 0.0681

Framework versions

  • Transformers 4.45.2
  • Pytorch 2.1.0+cu118
  • Datasets 3.0.1
  • Tokenizers 0.20.0