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wav2vec2-large-xlrs-korean-v5

Automatic Speech Recognition
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metadata
license: apache-2.0
tags:
  - generated_from_trainer
base_model: facebook/wav2vec2-xls-r-300m
datasets:
  - zeroth_korean
metrics:
  - wer
model-index:
  - name: wav2vec2-large-xlrs-korean-v5
    results:
      - task:
          type: automatic-speech-recognition
          name: Automatic Speech Recognition
        dataset:
          name: zeroth_korean
          type: zeroth_korean
          config: clean
          split: None
          args: clean
        metrics:
          - type: wer
            value: 0.2433368468604126
            name: Wer

wav2vec2-large-xlrs-korean-v5

This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the zeroth_korean dataset. It achieves the following results on the evaluation set:

  • Loss: 0.1300
  • Wer: 0.2433

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: 0.0001
  • train_batch_size: 32
  • eval_batch_size: 8
  • seed: 42
  • gradient_accumulation_steps: 2
  • total_train_batch_size: 64
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 1000
  • num_epochs: 30
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer
5.1453 1.4368 500 3.1530 1.0
2.4287 2.8736 1000 0.6084 0.8317
0.5556 4.3103 1500 0.3414 0.6165
0.3929 5.7471 2000 0.2729 0.5386
0.3211 7.1839 2500 0.2294 0.4794
0.281 8.6207 3000 0.2052 0.4298
0.2483 10.0575 3500 0.1911 0.4061
0.2243 11.4943 4000 0.1685 0.3873
0.2023 12.9310 4500 0.1627 0.3524
0.188 14.3678 5000 0.1572 0.3272
0.1784 15.8046 5500 0.1495 0.3131
0.1677 17.2414 6000 0.1424 0.2881
0.1533 18.6782 6500 0.1418 0.2709
0.1501 20.1149 7000 0.1387 0.2822
0.1402 21.5517 7500 0.1401 0.2697
0.1353 22.9885 8000 0.1367 0.2643
0.133 24.4253 8500 0.1337 0.2578
0.1254 25.8621 9000 0.1355 0.2560
0.1262 27.2989 9500 0.1339 0.2474
0.121 28.7356 10000 0.1300 0.2433

Framework versions

  • Transformers 4.41.0
  • Pytorch 2.3.0+cu121
  • Datasets 2.19.1
  • Tokenizers 0.19.1