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whisper-large-eu-cv16_1

Automatic Speech Recognition
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metadata
language:
  - eu
license: apache-2.0
base_model: openai/whisper-large
tags:
  - whisper-event
  - generated_from_trainer
datasets:
  - mozilla-foundation/common_voice_16_1
metrics:
  - wer
model-index:
  - name: Whisper Large Basque
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: mozilla-foundation/common_voice_16_1 eu
          type: mozilla-foundation/common_voice_16_1
          config: eu
          split: test
          args: eu
        metrics:
          - name: Wer
            type: wer
            value: 8.144442707519149

Whisper Large Basque

This model is a fine-tuned version of openai/whisper-large on the mozilla-foundation/common_voice_16_1 eu dataset. It achieves the following results on the evaluation set:

  • Loss: 0.4111
  • Wer: 8.1444

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
  • gradient_accumulation_steps: 8
  • total_train_batch_size: 256
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 500
  • training_steps: 40000
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer
0.004 10.04 1000 0.2314 10.6603
0.0028 20.08 2000 0.2480 10.2783
0.0027 30.11 3000 0.2492 10.0379
0.0005 40.15 4000 0.2753 9.3784
0.0016 50.19 5000 0.2489 9.3003
0.0006 60.23 6000 0.2599 9.0023
0.0011 70.26 7000 0.2606 8.9378
0.0005 80.3 8000 0.2723 8.9270
0.0001 90.34 9000 0.2764 8.5304
0.0011 100.38 10000 0.2668 8.8977
0.0001 110.41 11000 0.2856 8.3701
0.0 120.45 12000 0.3045 8.2890
0.0 130.49 13000 0.3149 8.2441
0.0 140.53 14000 0.3241 8.2285
0.0 150.56 15000 0.3336 8.2060
0.0 160.6 16000 0.3433 8.1601
0.0 170.64 17000 0.3537 8.1806
0.0 180.68 18000 0.3634 8.1874
0.0 190.72 19000 0.3738 8.1786
0.0 200.75 20000 0.3848 8.2441
0.0 210.79 21000 0.3952 8.2324
0.0 220.83 22000 0.4030 8.2480
0.0001 230.87 23000 0.2919 8.4268
0.0 240.9 24000 0.3137 8.1865
0.0 250.94 25000 0.3271 8.1884
0.0 260.98 26000 0.3378 8.1825
0.0 271.02 27000 0.3472 8.1865
0.0 281.05 28000 0.3556 8.2031
0.0 291.09 29000 0.3637 8.2099
0.0 301.13 30000 0.3710 8.1933
0.0 311.17 31000 0.3781 8.1874
0.0 321.2 32000 0.3845 8.1679
0.0 331.24 33000 0.3905 8.1601
0.0 341.28 34000 0.3971 8.1640
0.0 351.32 35000 0.4022 8.1611
0.0 361.36 36000 0.4046 8.1562
0.0 371.39 37000 0.4073 8.1523
0.0 381.43 38000 0.4093 8.1493
0.0 391.47 39000 0.4107 8.1513
0.0 401.51 40000 0.4111 8.1444

Framework versions

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