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End of training
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metadata
license: apache-2.0
base_model: openai/whisper-small
tags:
  - generated_from_trainer
datasets:
  - common_voice_11_0
metrics:
  - wer
model-index:
  - name: whisper-small-en-nonnative
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: common_voice_11_0
          type: common_voice_11_0
          config: en
          split: test
          args: en
        metrics:
          - name: Wer
            type: wer
            value: 40.75993091537133

whisper-small-en-nonnative

This model is a fine-tuned version of openai/whisper-small on the common_voice_11_0 dataset. It achieves the following results on the evaluation set:

  • Loss: 0.4024
  • Wer: 40.7599

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: linear
  • lr_scheduler_warmup_steps: 500
  • training_steps: 10000
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer
0.2992 0.2 1000 0.4468 29.3898
0.2918 0.39 2000 0.4240 27.1733
0.3078 0.59 3000 0.4173 28.4974
0.2711 0.79 4000 0.4027 24.5538
0.2813 0.98 5000 0.4029 28.6413
0.1416 1.18 6000 0.4078 25.9931
0.1399 1.38 7000 0.4078 28.8140
0.1478 1.57 8000 0.4070 31.3759
0.1479 1.77 9000 0.4033 33.5636
0.1266 1.97 10000 0.4024 40.7599

Framework versions

  • Transformers 4.37.2
  • Pytorch 2.2.0+cu121
  • Datasets 2.16.1
  • Tokenizers 0.15.1