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whisper-base-eu-cv17_0

Automatic Speech Recognition
Transformers
TensorBoard
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Basque
whisper
whisper-event
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metadata
library_name: transformers
language:
  - eu
license: apache-2.0
base_model: openai/whisper-base
tags:
  - whisper-event
  - generated_from_trainer
datasets:
  - mozilla-foundation/common_voice_17_0
metrics:
  - wer
model-index:
  - name: Whisper Base Basque
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: mozilla-foundation/common_voice_17_0 eu
          type: mozilla-foundation/common_voice_17_0
          config: eu
          split: test
          args: eu
        metrics:
          - name: Wer
            type: wer
            value: 13.316721023122872

Whisper Base Basque

This model is a fine-tuned version of openai/whisper-base on the mozilla-foundation/common_voice_17_0 eu dataset. It achieves the following results on the evaluation set:

  • Loss: 0.4594
  • Wer: 13.3167

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: 128
  • eval_batch_size: 64
  • seed: 42
  • optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
  • 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
0.0638 4.6948 1000 0.2553 17.4365
0.0058 9.3897 2000 0.2922 16.3024
0.0063 14.0845 3000 0.3181 16.5021
0.0047 18.7793 4000 0.3290 16.3152
0.0038 23.4742 5000 0.3358 15.9698
0.002 28.1690 6000 0.3449 15.7408
0.0028 32.8638 7000 0.3470 15.6089
0.0015 37.5587 8000 0.3582 15.5576
0.0019 42.2535 9000 0.3658 15.3157
0.0013 46.9484 10000 0.3606 15.0619
0.0018 51.6432 11000 0.3786 15.2415
0.0008 56.3380 12000 0.3760 15.0867
0.0016 61.0329 13000 0.3736 15.1114
0.0005 65.7277 14000 0.3750 14.5022
0.0019 70.4225 15000 0.3899 15.2433
0.0005 75.1174 16000 0.3851 15.0207
0.0007 79.8122 17000 0.3922 14.8613
0.0003 84.5070 18000 0.3889 14.4628
0.0 89.2019 19000 0.3941 14.0056
0.0 93.8967 20000 0.4005 13.8591
0.0 98.5915 21000 0.4057 13.7097
0.0 103.2864 22000 0.4110 13.6044
0.0 107.9812 23000 0.4165 13.5274
0.0 112.6761 24000 0.4225 13.4871
0.0 117.3709 25000 0.4290 13.4120
0.0 122.0657 26000 0.4361 13.4633
0.0 126.7606 27000 0.4438 13.4358
0.0 131.4554 28000 0.4515 13.3506
0.0 136.1502 29000 0.4594 13.3167
0.0 140.8451 30000 0.4672 13.4386
0.0 145.5399 31000 0.4750 13.4441
0.0 150.2347 32000 0.4827 13.4248
0.0 154.9296 33000 0.4891 13.4294
0.0 159.6244 34000 0.4952 13.4221
0.0 164.3192 35000 0.5003 13.3634
0.0 169.0141 36000 0.5041 13.3561
0.0 173.7089 37000 0.5069 13.3579
0.0 178.4038 38000 0.5091 13.3479
0.0 183.0986 39000 0.5103 13.3396

Framework versions

  • Transformers 4.52.3
  • Pytorch 2.6.0+cu124
  • Datasets 3.6.0
  • Tokenizers 0.21.1