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whisper-large-eu

Automatic Speech Recognition
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metadata
library_name: transformers
language:
  - eu
license: apache-2.0
base_model: openai/whisper-large-v3
tags:
  - whisper-event
  - generated_from_trainer
datasets:
  - mozilla-foundation/common_voice_17_0
metrics:
  - wer
model-index:
  - name: Whisper Large 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: 7.215361500971087

Whisper Large Basque

This model is a fine-tuned version of openai/whisper-large-v3 on the mozilla-foundation/common_voice_17_0 eu dataset. It achieves the following results on the evaluation set:

  • Loss: 0.1259
  • Wer: 7.2154

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: 4.375e-06
  • train_batch_size: 16
  • eval_batch_size: 8
  • 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: 10000
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer
0.2208 0.05 500 0.2592 20.6915
0.1489 0.1 1000 0.1971 14.6827
0.1973 0.15 1500 0.1747 12.3777
0.1353 1.0296 2000 0.1527 10.7195
0.1065 1.0796 2500 0.1456 9.8694
0.106 1.1296 3000 0.1362 9.0925
0.0718 2.0092 3500 0.1326 8.5428
0.0683 2.0592 4000 0.1343 8.4851
0.0482 2.1092 4500 0.1336 8.1049
0.0548 2.1592 5000 0.1316 7.9244
0.0282 3.0388 5500 0.1391 7.8182
0.025 3.0888 6000 0.1425 7.9409
0.0274 3.1388 6500 0.1391 7.7311
0.0155 4.0184 7000 0.1492 7.6972
0.0189 4.0684 7500 0.1517 7.6569
0.0139 4.1184 8000 0.1539 7.6267
0.0141 4.1684 8500 0.1550 7.5424
0.0368 5.048 9000 0.1259 7.2154

Framework versions

  • Transformers 4.46.0.dev0
  • Pytorch 2.4.1+cu121
  • Datasets 3.0.2.dev0
  • Tokenizers 0.20.0