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whisper-small-ga2en-v1.8-r

Automatic Speech Recognition
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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 Small GA-EN Speech Translation Raw + warmup_ratio=0.01
    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: 30.07
          - name: Wer
            type: wer
            value: 66.32147681224674

Whisper Small GA-EN Speech Translation Raw + warmup_ratio=0.01

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: 1.7281
  • Bleu: 30.07
  • Chrf: 46.7
  • Wer: 66.3215

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: 64
  • eval_batch_size: 64
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_ratio: 0.03
  • training_steps: 3000
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Bleu Chrf Wer
2.1013 0.2155 100 1.8575 8.31 24.75 116.5241
1.5495 0.4310 200 1.5721 14.29 33.25 105.0428
1.3385 0.6466 300 1.5329 20.71 38.26 86.9428
1.071 0.8621 400 1.4540 20.37 39.28 85.1418
0.4771 1.0776 500 1.4936 18.05 39.17 93.8316
0.4685 1.2931 600 1.5303 24.36 39.36 75.6866
0.4477 1.5086 700 1.5242 22.93 42.01 80.3242
0.4238 1.7241 800 1.5052 26.32 43.01 68.8879
0.3802 1.9397 900 1.5171 25.94 41.44 73.7956
0.1429 2.1552 1000 1.5741 28.83 43.83 65.4660
0.1607 2.3707 1100 1.6029 27.67 43.2 64.9257
0.1513 2.5862 1200 1.6130 28.61 44.28 66.1864
0.137 2.8017 1300 1.6087 21.97 40.99 89.4642
0.112 3.0172 1400 1.6146 28.74 44.01 65.9613
0.0717 3.2328 1500 1.6156 27.3 42.78 70.0585
0.0596 3.4483 1600 1.6381 27.31 45.58 69.6983
0.064 3.6638 1700 1.6262 29.73 45.88 65.9163
0.0642 3.8793 1800 1.6798 30.78 46.13 68.2575
0.0335 4.0948 1900 1.6854 29.55 45.06 67.8523
0.0366 4.3103 2000 1.6963 28.83 44.42 68.8879
0.036 4.5259 2100 1.7062 28.05 43.79 69.6983
0.0259 4.7414 2200 1.7279 28.75 45.25 68.3926
0.0353 4.9569 2300 1.7084 29.7 46.13 66.3665
0.0138 5.1724 2400 1.6906 30.81 46.26 64.1603
0.0156 5.3879 2500 1.7135 29.09 45.94 67.4471
0.0133 5.6034 2600 1.7311 29.86 45.61 65.5110
0.0161 5.8190 2700 1.7067 29.5 45.22 67.0869
0.0098 6.0345 2800 1.7038 30.32 46.6 65.3309
0.008 6.25 2900 1.7261 29.88 46.41 66.8167
0.0045 6.4655 3000 1.7281 30.07 46.7 66.3215

Framework versions

  • Transformers 4.41.2
  • Pytorch 2.2.0+cu121
  • Datasets 2.19.2
  • Tokenizers 0.19.1