whisper-medium-eu / README.md
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metadata
library_name: transformers
license: apache-2.0
base_model: openai/whisper-medium
tags:
  - whisper-event
  - generated_from_trainer
datasets:
  - asierhv/composite_corpus_eu_v2.1
language:
  - eu
metrics:
  - wer
model-index:
  - name: Whisper Medium Basque
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Common Voice 17.0
          type: mozilla-foundation/common_voice_17_0
          config: eu
          split: test
          args:
            language: eu
        metrics:
          - name: Test WER
            type: wer
            value: 7.97
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: asierhv/composite_corpus_eu_v2.1
          type: asierhv/composite_corpus_eu_v2.1
        metrics:
          - name: Wer
            type: wer
            value: 9.98410769374591

Whisper Medium Basque

This model is a fine-tuned version of openai/whisper-medium on the asierhv/composite_corpus_eu_v2.1 dataset. It achieves the following results on the evaluation set:

  • Loss: 0.2191
  • Wer: 9.9841
  • Wer on Common Voice 17.0, test split: 7.97

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: 6.25e-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: 8000
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer
0.3412 0.0625 500 0.4570 28.2182
0.1462 0.125 1000 0.3524 19.9402
0.2495 0.1875 1500 0.3077 17.5236
0.2617 0.25 2000 0.2811 16.5841
0.1646 0.3125 2500 0.2726 13.8917
0.0934 0.375 3000 0.2533 14.0273
0.1016 0.4375 3500 0.2331 12.1623
0.1454 0.5 4000 0.2299 11.5546
0.1502 0.5625 4500 0.2333 12.4007
0.0916 0.625 5000 0.2271 10.9657
0.0914 0.6875 5500 0.2343 10.5029
0.1093 0.75 6000 0.2191 9.9841
0.0948 0.8125 6500 0.2215 10.5357
0.0744 0.875 7000 0.2108 11.2368
0.1269 0.9375 7500 0.2158 10.0028
0.1408 1.0 8000 0.2141 10.1290

Framework versions

  • Transformers 4.49.0.dev0
  • Pytorch 2.6.0+cu124
  • Datasets 3.2.1.dev0
  • Tokenizers 0.21.0