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w2v-bert-2.0-Fleurs_AMMI_AFRIVOICE_LRSC-ln-5hrs-v1

Automatic Speech Recognition
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wav2vec2-bert
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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-Fleurs_AMMI_AFRIVOICE_LRSC-ln-5hrs-v1
    results: []

w2v-bert-2.0-Fleurs_AMMI_AFRIVOICE_LRSC-ln-5hrs-v1

This model is a fine-tuned version of facebook/w2v-bert-2.0 on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 0.6643
  • Wer: 0.2469
  • Cer: 0.0788

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: 8
  • eval_batch_size: 4
  • seed: 42
  • gradient_accumulation_steps: 2
  • total_train_batch_size: 16
  • 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
1.8874 0.9949 98 0.6403 0.5429 0.1657
0.4899 2.0 197 0.4921 0.3300 0.1001
0.3892 2.9949 295 0.4608 0.3314 0.1019
0.3259 4.0 394 0.4729 0.3080 0.0942
0.2863 4.9949 492 0.4495 0.3156 0.0951
0.2333 6.0 591 0.4269 0.2624 0.0808
0.2059 6.9949 689 0.4365 0.2609 0.0839
0.1722 8.0 788 0.4346 0.2552 0.0825
0.1551 8.9949 886 0.4134 0.2468 0.0766
0.1318 10.0 985 0.4794 0.2631 0.0811
0.1189 10.9949 1083 0.5191 0.2530 0.0796
0.1004 12.0 1182 0.5311 0.2689 0.0794
0.0959 12.9949 1280 0.5502 0.2535 0.0778
0.0831 14.0 1379 0.5060 0.2476 0.0757
0.0679 14.9949 1477 0.5023 0.2517 0.0830
0.0617 16.0 1576 0.5279 0.2403 0.0757
0.0562 16.9949 1674 0.6012 0.2411 0.0761
0.0496 18.0 1773 0.6263 0.2423 0.0755
0.0442 18.9949 1871 0.5991 0.2581 0.0794
0.0401 20.0 1970 0.6323 0.2412 0.0762
0.0329 20.9949 2068 0.6417 0.2326 0.0735
0.0266 22.0 2167 0.6279 0.2381 0.0756
0.0255 22.9949 2265 0.5834 0.2470 0.0772
0.0214 24.0 2364 0.6781 0.2364 0.0735
0.0217 24.9949 2462 0.6253 0.2398 0.0752
0.0163 26.0 2561 0.6940 0.2427 0.0813
0.0363 26.9949 2659 0.6632 0.2363 0.0756
0.0182 28.0 2758 0.6094 0.2363 0.0766
0.014 28.9949 2856 0.6928 0.2438 0.0770
0.0157 30.0 2955 0.6863 0.2422 0.0768
0.0121 30.9949 3053 0.6643 0.2469 0.0788

Framework versions

  • Transformers 4.46.3
  • Pytorch 2.1.0+cu118
  • Datasets 3.1.0
  • Tokenizers 0.20.3