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Whisper Medium et

This model is a fine-tuned version of openai/whisper-medium on the ERR2020, Common Voice 11.0, FLEURS dataset. It achieves the following results on the evaluation set:

  • Loss: 0.4288
  • Wer: 29.7203

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-06
  • 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: 5000
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer
0.4018 0.1 500 0.5518 39.3951
0.2654 0.2 1000 0.4611 34.3929
0.2121 0.3 1500 0.4346 32.0582
0.1752 0.4 2000 0.4247 31.1926
0.1337 0.5 2500 0.4216 30.3364
0.1281 0.6 3000 0.4219 30.0745
0.1127 0.7 3500 0.4252 29.7388
0.1254 0.8 4000 0.4276 29.8928
0.1035 0.9 4500 0.4292 29.7634
0.1114 1.0 5000 0.4288 29.7203

Framework versions

  • Transformers 4.26.0.dev0
  • Pytorch 1.13.0+cu117
  • Datasets 2.7.1.dev0
  • Tokenizers 0.13.2
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Dataset used to train rristo/whisper-medium-et

Evaluation results