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whisper-base-cv17-hu

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metadata
language:
  - hu
license: apache-2.0
base_model: openai/whisper-small
tags:
  - generated_from_trainer
datasets:
  - common_voice_11_0
metrics:
  - wer
model-index:
  - name: Whisper Base Hu CV17
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Common Voice 17.0
          type: common_voice_11_0
          config: hu
          split: None
          args: hu
        metrics:
          - name: Wer
            type: wer
            value: 8.132226504595316

Whisper Base Hu CV17

This model is a fine-tuned version of openai/whisper-small on the Common Voice 17.0 dataset. It achieves the following results on the evaluation set:

  • Loss: 0.1148
  • Wer Ortho: 8.9576
  • Wer: 8.1322

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: 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: 500
  • training_steps: 5000
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer Ortho Wer
0.5156 0.3298 250 0.5620 52.2010 49.0898
0.3906 0.6596 500 0.4262 43.7131 40.2668
0.3276 0.9894 750 0.3243 33.3915 30.2728
0.2161 1.3193 1000 0.2639 27.8152 24.9778
0.2009 1.6491 1250 0.2314 24.8705 21.9923
0.1835 1.9789 1500 0.1938 21.4922 18.8260
0.0973 2.3087 1750 0.1748 18.8396 16.0243
0.0963 2.6385 2000 0.1600 17.0240 14.8651
0.0913 2.9683 2250 0.1414 14.0853 12.1198
0.046 3.2982 2500 0.1374 13.2000 11.4468
0.0447 3.6280 2750 0.1306 12.5677 10.9191
0.0409 3.9578 3000 0.1216 11.1436 9.8251
0.0173 4.2876 3250 0.1205 10.4812 9.2292
0.0165 4.6174 3500 0.1180 10.2343 9.0898
0.0152 4.9472 3750 0.1149 9.6200 8.5562
0.0061 5.2770 4000 0.1149 9.1021 8.1589
0.0056 5.6069 4250 0.1144 9.2406 8.2864
0.006 5.9367 4500 0.1138 9.0630 8.1559
0.0036 6.2665 4750 0.1148 9.0148 8.1737
0.0033 6.5963 5000 0.1148 8.9576 8.1322

Framework versions

  • Transformers 4.41.1
  • Pytorch 2.2.1+cu121
  • Datasets 2.19.1
  • Tokenizers 0.19.1