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metadata
language:
  - eu
license: apache-2.0
base_model: openai/whisper-tiny
tags:
  - whisper-event
  - generated_from_trainer
datasets:
  - mozilla-foundation/common_voice_16_1
metrics:
  - wer
model-index:
  - name: Whisper Tiny 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: 19.094888228857275

Whisper Tiny Basque

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

  • Loss: 0.5146
  • Wer: 19.0949

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: 3.75e-05
  • train_batch_size: 256
  • eval_batch_size: 128
  • 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: 40000
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer
0.0426 10.0 1000 0.3451 23.2003
0.0077 20.0 2000 0.4123 22.6053
0.0013 30.0 3000 0.4288 21.1965
0.0004 40.0 4000 0.4538 21.1926
0.0003 50.0 5000 0.4757 21.1808
0.0206 60.0 6000 0.4172 22.2751
0.0003 70.0 7000 0.4374 19.5131
0.0002 80.0 8000 0.4547 19.5091
0.0001 90.0 9000 0.4697 19.5062
0.0001 100.0 10000 0.4853 19.5199
0.0001 110.0 11000 0.5009 19.5687
0.0 120.0 12000 0.5175 19.6586
0.0 130.0 13000 0.5348 19.7729
0.0 140.0 14000 0.5531 19.7847
0.0002 150.0 15000 0.4626 19.4730
0.0001 160.0 16000 0.4813 19.2199
0.0 170.0 17000 0.4932 19.1691
0.0 180.0 18000 0.5041 19.1291
0.0 190.0 19000 0.5146 19.0949
0.0 200.0 20000 0.5254 19.1232
0.0 210.0 21000 0.5369 19.1369
0.0 220.0 22000 0.5484 19.1125
0.0 230.0 23000 0.5606 19.1330
0.0 240.0 24000 0.5732 19.1965
0.0 250.0 25000 0.5864 19.2219
0.0 260.0 26000 0.6003 19.3108
0.0 270.0 27000 0.6140 19.3714
0.0034 280.0 28000 0.5536 20.6630
0.0 290.0 29000 0.5486 19.3391
0.0 300.0 30000 0.5591 19.3059
0.0 310.0 31000 0.5669 19.3137
0.0 320.0 32000 0.5737 19.3225
0.0 330.0 33000 0.5798 19.2883
0.0 340.0 34000 0.5856 19.2668
0.0 350.0 35000 0.5911 19.2346
0.0 360.0 36000 0.5962 19.2287
0.0 370.0 37000 0.6010 19.2326
0.0 380.0 38000 0.6050 19.2287
0.0 390.0 39000 0.6081 19.2375
0.0 400.0 40000 0.6095 19.1965

Framework versions

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