---

library_name: transformers language:

• fa license: apache-2.0 base_model: openai/whisper-large-v3 tags:
• generated_from_trainer datasets:
• nezamisafa/ASR_fa_v1 metrics:
• wer model-index:
• name: whisper-large-v3-persian results:
  • task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: ASR_fa_v1 type: nezamisafa/ASR_fa_v1 args: 'config: fa, split: test' metrics:
    • name: Wer type: wer value: 8.7299744601811

---

whisper-large-v3-persian

This model is a fine-tuned version of openai/whisper-large-v3 on the ASR_fa_v1 dataset. It achieves the following results on the evaluation set:

• Loss: 0.0858
• Wer: 8.7300

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: 16
• eval_batch_size: 8
• seed: 42
• optimizer: Use OptimizerNames.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: 5000
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
0.1501	0.5970	1000	0.1537	17.1059
0.081	1.1940	2000	0.1156	12.6248
0.0766	1.7910	3000	0.0965	11.1969
0.0313	2.3881	4000	0.0877	9.3975
0.0263	2.9851	5000	0.0858	8.7300

Framework versions

• Transformers 4.52.4
• Pytorch 2.7.0+cu126
• Datasets 3.6.0
• Tokenizers 0.21.1