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metadata
language:
  - ga
  - en
license: apache-2.0
base_model: openai/whisper-small
tags:
  - generated_from_trainer
datasets:
  - ymoslem/IWSLT2023-GA-EN
  - ymoslem/FLEURS-GA-EN
  - ymoslem/BitesizeIrish-GA-EN
  - ymoslem/SpokenWords-GA-EN-MTed
metrics:
  - bleu
  - wer
model-index:
  - name: Whisper Medium GA-EN Speech Translation
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: IWSLT-2023, FLEURS, BiteSize, and SpokenWords
          type: ymoslem/IWSLT2023-GA-EN
        metrics:
          - name: Bleu
            type: bleu
            value: 27.06
          - name: Wer
            type: wer
            value: 73.4804142278253

Whisper Medium GA-EN Speech Translation

This model is a fine-tuned version of openai/whisper-small on the IWSLT-2023, FLEURS, BiteSize, and SpokenWords dataset. It achieves the following results on the evaluation set:

  • Loss: 1.2998
  • Bleu: 27.06
  • Chrf: 47.61
  • Wer: 73.4804

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: 0.0001
  • train_batch_size: 8
  • eval_batch_size: 8
  • seed: 42
  • gradient_accumulation_steps: 2
  • total_train_batch_size: 16
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 0.03
  • training_steps: 2000
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Bleu Chrf Validation Loss Wer
2.5227 0.05 100 1.05 12.82 2.4253 343.2238
2.4775 0.11 200 10.04 24.39 2.0665 95.2724
2.114 0.16 300 8.79 28.6 1.9792 141.9181
1.9813 0.22 400 17.5 33.84 1.7596 82.8906
1.6979 0.27 500 13.89 33.51 1.6820 115.0383
1.7157 0.32 600 18.54 36.44 1.5795 91.4003
1.3845 0.38 700 19.51 39.03 1.4989 88.7888
1.3803 0.43 800 25.18 40.96 1.4176 69.5182
1.1 0.49 900 28.98 44.78 1.3666 65.9613
1.1843 0.54 1000 27.59 45.91 1.3298 70.4638
1.1317 0.59 1100 1.5018 20.22 41.14 86.9878
1.071 0.65 1200 1.4600 20.67 40.43 85.6371
1.1542 0.7 1300 1.4114 26.84 43.76 69.5182
1.0729 0.76 1400 1.4056 22.98 42.65 78.0729
0.8747 0.81 1500 1.3537 24.65 44.89 73.4804
0.8626 0.86 1600 1.3391 28.0 46.03 68.7978
0.7643 0.92 1700 1.3250 27.23 45.31 70.3287
0.6971 0.97 1800 1.2795 30.05 48.28 65.5110
0.3055 1.02 1900 1.2994 27.41 47.91 71.1842
0.2801 1.08 2000 1.2998 27.06 47.61 73.4804

Framework versions

  • Transformers 4.39.3
  • Pytorch 2.2.1+cu121
  • Datasets 2.18.0
  • Tokenizers 0.15.2