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whisper-large-v2-eu-train

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metadata
language:
  - eu
license: apache-2.0
base_model: openai/whisper-large-v2
tags:
  - whisper-event
  - generated_from_trainer
datasets:
  - mozilla-foundation/common_voice_13_0
metrics:
  - wer
model-index:
  - name: Whisper Large-V2 Basque
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: mozilla-foundation/common_voice_13_0 eu
          type: mozilla-foundation/common_voice_13_0
          config: eu
          split: validation
          args: eu
        metrics:
          - name: Wer
            type: wer
            value: 12.627697515565494

Whisper Large-V2 Basque

This model is a fine-tuned version of openai/whisper-large-v2 on the mozilla-foundation/common_voice_13_0 eu dataset. It achieves the following results on the evaluation set:

  • Loss: 0.4121
  • Wer: 12.6277

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: 1e-05
  • train_batch_size: 32
  • eval_batch_size: 16
  • seed: 42
  • gradient_accumulation_steps: 2
  • total_train_batch_size: 64
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 500
  • training_steps: 20000
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer
0.1098 5.85 1000 0.2495 16.6354
0.022 11.7 2000 0.2733 14.6306
0.0089 17.54 3000 0.3075 13.9697
0.0056 23.39 4000 0.3206 14.0724
0.0053 29.24 5000 0.3314 13.7944
0.0037 35.09 6000 0.3376 13.7480
0.0027 40.94 7000 0.3492 13.6815
0.0023 46.78 8000 0.3455 13.8488
0.002 52.63 9000 0.3500 13.5123
0.0009 58.48 10000 0.3590 13.2967
0.0016 64.33 11000 0.3675 13.4679
0.0007 70.18 12000 0.3785 13.2685
0.0008 76.02 13000 0.3822 13.3652
0.0004 81.87 14000 0.3929 13.3148
0.0006 87.72 15000 0.3880 13.1032
0.0002 93.57 16000 0.4005 12.6982
0.0002 99.42 17000 0.4004 13.1516
0.0001 105.26 18000 0.4140 12.8735
0.0001 111.11 19000 0.4131 12.5128
0.0001 116.96 20000 0.4121 12.6277

Framework versions

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