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vanilla-whisper-tiny_tat

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metadata
language:
  - zh
license: apache-2.0
base_model: openai/whisper-tiny
tags:
  - generated_from_trainer
datasets:
  - formospeech/tat_asr_aligned
metrics:
  - wer
model-index:
  - name: Whisper Tiny Taiwanese Condenser
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: TAT ASR Aligned
          type: formospeech/tat_asr_aligned
          args: 'config: taiwanese, split: test'
        metrics:
          - name: Wer
            type: wer
            value: 50.65108143376739

Whisper Tiny Taiwanese Condenser

This model is a fine-tuned version of openai/whisper-tiny on the TAT ASR Aligned dataset. It achieves the following results on the evaluation set:

  • Loss: 1.0814
  • Wer: 50.6511

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: 0.001
  • train_batch_size: 64
  • eval_batch_size: 32
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 1362
  • training_steps: 13620
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer
0.4229 0.9985 681 0.6344 65.8772
0.4529 1.9971 1362 0.7311 71.3135
0.3271 2.9956 2043 0.6324 64.7777
0.2257 3.9941 2724 0.5510 58.4141
0.1554 4.9927 3405 0.5350 57.6394
0.122 5.9912 4086 0.5911 56.8148
0.0935 6.9897 4767 0.6122 56.1179
0.0718 7.9883 5448 0.6492 54.7158
0.0588 8.9868 6129 0.6623 55.5599
0.0466 9.9853 6810 0.6883 56.9481
0.0349 10.9839 7491 0.7069 54.6770
0.0298 11.9824 8172 0.7441 54.2272
0.0215 12.9809 8853 0.7937 54.9491
0.0141 13.9795 9534 0.8062 52.9278
0.01 14.9780 10215 0.8717 53.2804
0.0067 15.9765 10896 0.9279 52.6029
0.0031 16.9751 11577 0.9783 52.0393
0.0012 17.9736 12258 1.0311 50.6622
0.0004 18.9721 12939 1.0574 50.7566
0.0001 19.9707 13620 1.0814 50.6511

Framework versions

  • Transformers 4.42.3
  • Pytorch 2.3.0+cu121
  • Datasets 2.20.0
  • Tokenizers 0.19.1