jmshd/whisper-uz

Whisper base uz - Jamshid Ahmadov

This model is a fine-tuned version of Whisper Base on an Common Voice dataset. It achieves the following results on the evaluation set:

• Loss: 0.1652
• Wer: 14.0135

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 1e-05
• train_batch_size: 8
• eval_batch_size: 8
• seed: 42
• gradient_accumulation_steps: 2
• total_train_batch_size: 16
• optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: linear
• lr_scheduler_warmup_steps: 500
• training_steps: 2000
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
0.0346	0.5714	500	0.1719	14.7950
0.0348	1.1429	1000	0.1703	14.2490
0.0327	1.7143	1500	0.1672	14.1848
0.02	2.2857	2000	0.1652	14.0135

Framework versions

• Transformers 4.50.3
• Pytorch 2.5.1+cu121
• Datasets 3.5.0
• Tokenizers 0.21.0