noflm
update model card README.md
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metadata
language:
  - ja
license: other
tags:
  - whisper-event
  - generated_from_trainer
datasets:
  - Elite35P-Server/EliteVoiceProject
metrics:
  - wer
model-index:
  - name: Whisper Small Japanese Elite
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Elite35P-Server/EliteVoiceProject youtube
          type: Elite35P-Server/EliteVoiceProject
          config: youtube
          split: test
          args: youtube
        metrics:
          - name: Wer
            type: wer
            value: 31.536388140161726

Whisper Small Japanese Elite

This model is a fine-tuned version of openai/whisper-small on the Elite35P-Server/EliteVoiceProject youtube dataset. It achieves the following results on the evaluation set:

  • Loss: 1.1596
  • Wer: 31.5364

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: 1e-05
  • train_batch_size: 16
  • eval_batch_size: 8
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: constant_with_warmup
  • lr_scheduler_warmup_steps: 100
  • training_steps: 10000
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer
0.0003 52.0 1000 0.8053 28.8410
0.0 105.0 2000 0.8636 28.5714
0.0 157.0 3000 0.9056 28.0323
0.0 210.0 4000 0.9414 28.8410
0.0 263.0 5000 0.9842 31.2668
0.0 315.0 6000 1.0223 31.2668
0.0 368.0 7000 1.0677 31.2668
0.0 421.0 8000 1.1079 31.2668
0.0 473.0 9000 1.1468 31.5364
0.0 526.0 10000 1.1596 31.5364

Framework versions

  • Transformers 4.26.0.dev0
  • Pytorch 1.13.1+cu117
  • Datasets 2.8.1.dev0
  • Tokenizers 0.13.2