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whisper-tiny-en-US

Automatic Speech Recognition
Transformers
PyTorch
whisper
generated_from_trainer
Eval Results
Inference Endpoints
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metadata
license: apache-2.0
base_model: openai/whisper-tiny
tags:
  - generated_from_trainer
datasets:
  - PolyAI/minds14
metrics:
  - wer
model-index:
  - name: whisper-tiny-en-US
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: PolyAI/minds14
          type: PolyAI/minds14
          config: en-AU
          split: train
          args: en-AU
        metrics:
          - name: Wer
            type: wer
            value: 0.1655499720826354

whisper-tiny-en-US

This model is a fine-tuned version of openai/whisper-tiny on the PolyAI/minds14 dataset. It achieves the following results on the evaluation set:

  • Loss: 0.4245
  • Wer Ortho: 0.1714
  • Wer: 0.1655

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: 16
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: constant_with_warmup
  • lr_scheduler_warmup_steps: 5
  • training_steps: 400

Training results

Training Loss Epoch Step Validation Loss Wer Ortho Wer
No log 0.36 10 3.1022 0.3282 0.1960
No log 0.71 20 1.6867 0.2399 0.1865
2.9245 1.07 30 0.6685 0.2332 0.1982
2.9245 1.43 40 0.4912 0.2017 0.1848
0.6297 1.79 50 0.4243 0.1865 0.1753
0.6297 2.14 60 0.3895 0.1801 0.1689
0.6297 2.5 70 0.3678 0.1769 0.1669
0.3045 2.86 80 0.3570 0.1746 0.1689
0.3045 3.21 90 0.3496 0.1720 0.1647
0.1949 3.57 100 0.3451 0.1746 0.1661
0.1949 3.93 110 0.3407 0.1804 0.1700
0.1949 4.29 120 0.3439 0.1778 0.1695
0.1099 4.64 130 0.3501 0.1743 0.1689
0.1099 5.0 140 0.3488 0.1737 0.1667
0.0583 5.36 150 0.3554 0.1778 0.1697
0.0583 5.71 160 0.3595 0.1708 0.1628
0.0583 6.07 170 0.3514 0.1746 0.1661
0.032 6.43 180 0.3672 0.1755 0.1683
0.032 6.79 190 0.3676 0.1676 0.1602
0.0146 7.14 200 0.3791 0.1658 0.1600
0.0146 7.5 210 0.3825 0.1676 0.1625
0.0146 7.86 220 0.3799 0.1702 0.1650
0.0084 8.21 230 0.3827 0.1702 0.1655
0.0084 8.57 240 0.3869 0.1778 0.1714
0.0043 8.93 250 0.3951 0.1740 0.1686
0.0043 9.29 260 0.3958 0.1720 0.1672
0.0043 9.64 270 0.3968 0.1758 0.1706
0.003 10.0 280 0.3978 0.1725 0.1672
0.003 10.36 290 0.4012 0.1734 0.1681
0.0023 10.71 300 0.4068 0.1728 0.1678
0.0023 11.07 310 0.4097 0.1752 0.1697
0.0023 11.43 320 0.4113 0.1746 0.1692
0.0018 11.79 330 0.4120 0.1737 0.1681
0.0018 12.14 340 0.4141 0.1740 0.1683
0.0016 12.5 350 0.4172 0.1731 0.1678
0.0016 12.86 360 0.4193 0.1740 0.1681
0.0016 13.21 370 0.4197 0.1731 0.1672
0.0014 13.57 380 0.4215 0.1731 0.1672
0.0014 13.93 390 0.4228 0.1720 0.1664
0.0012 14.29 400 0.4245 0.1714 0.1655

Framework versions

  • Transformers 4.31.0.dev0
  • Pytorch 2.0.1+cu118
  • Datasets 2.13.1
  • Tokenizers 0.13.3