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metadata
language:
  - eu
license: apache-2.0
base_model: openai/whisper-base
tags:
  - whisper-event
  - generated_from_trainer
datasets:
  - mozilla-foundation/common_voice_16_1
metrics:
  - wer
model-index:
  - name: Whisper Base 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: 16.17652806002814

Whisper Base Basque

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

  • Loss: 0.5038
  • Wer: 16.1765

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: 2.5e-05
  • train_batch_size: 128
  • eval_batch_size: 64
  • seed: 42
  • gradient_accumulation_steps: 2
  • 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
0.0225 10.0 1000 0.3059 19.0812
0.0037 20.0 2000 0.3530 18.4618
0.0012 30.0 3000 0.3724 17.9332
0.0004 40.0 4000 0.4025 17.8951
0.0002 50.0 5000 0.4245 17.8951
0.0001 60.0 6000 0.4459 17.9772
0.0001 70.0 7000 0.4665 18.0163
0.0 80.0 8000 0.4882 18.1081
0.0003 90.0 9000 0.3803 16.3807
0.0001 100.0 10000 0.4047 16.2293
0.0001 110.0 11000 0.4207 16.2420
0.0001 120.0 12000 0.4353 16.2879
0.0 130.0 13000 0.4502 16.3700
0.0 140.0 14000 0.4653 16.5087
0.0 150.0 15000 0.4805 16.4393
0.0 160.0 16000 0.4964 16.4941
0.0 170.0 17000 0.5128 16.5107
0.0 180.0 18000 0.5285 16.6377
0.0 190.0 19000 0.5457 16.6572
0.0102 200.0 20000 0.4229 18.1902
0.0 210.0 21000 0.4498 16.2117
0.0 220.0 22000 0.4646 16.2146
0.0 230.0 23000 0.4754 16.1961
0.0 240.0 24000 0.4853 16.1863
0.0 250.0 25000 0.4946 16.1912
0.0 260.0 26000 0.5038 16.1765
0.0 270.0 27000 0.5133 16.2215
0.0 280.0 28000 0.5228 16.2224
0.0 290.0 29000 0.5326 16.2557
0.0 300.0 30000 0.5427 16.2420
0.0 310.0 31000 0.5525 16.2635
0.0 320.0 32000 0.5624 16.2957
0.0 330.0 33000 0.5706 16.3299
0.0 340.0 34000 0.5798 16.3534
0.0 350.0 35000 0.5880 16.3495
0.0 360.0 36000 0.5948 16.3622
0.0 370.0 37000 0.6005 16.3934
0.0 380.0 38000 0.6045 16.3876
0.0 390.0 39000 0.6074 16.4325
0.0 400.0 40000 0.6085 16.4315

Framework versions

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