whisper-small-en / README.md
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metadata
language:
  - en
license: apache-2.0
tags:
  - whisper-event
  - generated_from_trainer
datasets:
  - mozilla-foundation/common_voice_11_0
metrics:
  - wer
model-index:
  - name: Whisper Small English
    results:
      - task:
          type: automatic-speech-recognition
          name: Automatic Speech Recognition
        dataset:
          name: mozilla-foundation/common_voice_11_0 en
          type: mozilla-foundation/common_voice_11_0
          config: en
          split: test
          args: en
        metrics:
          - type: wer
            value: 13.058509783761204
            name: Wer
      - task:
          type: automatic-speech-recognition
          name: Automatic Speech Recognition
        dataset:
          name: google/fleurs
          type: google/fleurs
          config: en_us
          split: test
        metrics:
          - type: wer
            value: 9.27
            name: WER

Whisper Small English

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

  • Loss: 0.3269
  • Wer: 13.0585

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: 32
  • eval_batch_size: 16
  • 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
0.1537 0.1 1000 0.4405 17.9276
0.2378 0.2 2000 0.4009 15.9888
0.1709 0.3 3000 0.3852 15.4953
0.2792 0.4 4000 0.3699 14.8758
0.2172 0.5 5000 0.3577 14.2660
0.3616 0.6 6000 0.4042 18.1846
0.2456 0.7 7000 0.3375 13.3091
0.2505 0.8 8000 0.3395 13.6227
0.2563 0.9 9000 0.3305 13.1408
0.2395 1.0 10000 0.3269 13.0585

Framework versions

  • Transformers 4.28.0.dev0
  • Pytorch 2.0.0+cu117
  • Datasets 2.11.1.dev0
  • Tokenizers 0.13.2