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whisper-small-ur

This model is a fine-tuned version of openai/whisper-small on the Urdu dataset. It achieves the following results on the evaluation set:

  • Loss: 0.4843
  • Wer: 33.3110

Training and evaluation data

Dataset included two rows; transcription & audio. The model was prepared using a dataset of 6500 rows. Train-test split was applied, 82% training (5324) and 18% testing (1176).

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 5e-05
  • train_batch_size: 8
  • eval_batch_size: 8
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 500
  • training_steps: 1200
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer
0.5907 0.6 400 0.6646 44.5644
0.2862 1.2 800 0.5806 38.1544
0.251 1.8 1200 0.4843 33.3110

Framework versions

  • Transformers 4.36.0.dev0
  • Pytorch 2.1.0+cu118
  • Datasets 2.14.6
  • Tokenizers 0.14.1
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