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UPDATE

Use an updated fine tunned version Sagicc/whisper-large-v3-sr-cmb with new 50+ hours of dataset.

Whisper Large v3 Sr

This model is a fine-tuned version of openai/whisper-large-v3 on Serbian Mozilla/Common Voice 13 and Google/Fleurs datasets. It achieves the following results on the evaluation set:

  • Loss: 0.1628
  • Wer Ortho: 0.1635
  • Wer: 0.0556

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 1e-05
  • train_batch_size: 2
  • eval_batch_size: 8
  • seed: 42
  • gradient_accumulation_steps: 8
  • total_train_batch_size: 16
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 50
  • training_steps: 1500
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer Ortho Wer
0.0567 1.34 500 0.1512 0.1676 0.0717
0.0256 2.67 1000 0.1482 0.1585 0.0610
0.0114 4.01 1500 0.1628 0.1635 0.0556

Framework versions

  • Transformers 4.35.0
  • Pytorch 2.0.1+cu117
  • Datasets 2.14.5
  • Tokenizers 0.14.1
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Finetuned from

Datasets used to train Sagicc/whisper-large-v3-sr-combined

Collection including Sagicc/whisper-large-v3-sr-combined

Evaluation results