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: 0.3943
  • Wer: 11.3391

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

Training results

Training Loss Epoch Step Validation Loss Wer
0.0355 4.01 1000 0.2616 14.8224
0.0079 9.01 2000 0.2777 13.5202
0.0041 14.01 3000 0.2764 12.7364
0.0047 19.0 4000 0.2932 12.6939
0.004 24.0 5000 0.2969 12.7992
0.0019 29.0 6000 0.3066 12.6008
0.004 33.01 7000 0.2973 12.6696
0.0007 38.01 8000 0.3253 12.2686
0.0006 43.01 9000 0.3391 12.5319
0.0009 48.01 10000 0.3303 12.2767
0.0004 53.0 11000 0.3383 12.0195
0.0003 58.0 12000 0.3398 11.7441
0.0005 63.0 13000 0.3396 11.8778
0.0001 67.01 14000 0.3544 11.6469
0.0 72.01 15000 0.3752 11.4160
0.0 77.01 16000 0.3860 11.3411
0.0 82.01 17000 0.3943 11.3391
0.0 87.0 18000 0.4013 11.3532
0.0 92.0 19000 0.4063 11.3613
0.0 97.0 20000 0.4086 11.3512

Framework versions

  • Transformers 4.33.0.dev0
  • Pytorch 2.0.1+cu117
  • Datasets 2.14.4
  • Tokenizers 0.13.3
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