This model is a fine-tuned version of openai/whisper-base on the Quranic Audio Dataset dataset. It achieves the following results on the evaluation set:

Loss: 0.0561
Wer: 29.2456

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 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: 4000
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
0.063	2.5974	1000	0.0768	37.1220
0.0175	5.1948	2000	0.0570	33.3666
0.0047	7.7922	3000	0.0561	29.2456
0.0017	10.3896	4000	0.0572	29.5447

Framework versions

Transformers 4.49.0
Pytorch 2.5.1+cu121
Datasets 3.3.2
Tokenizers 0.21.0

Mahmoud-Nasser/whisper-base-quranic

Whisper Base Quranic – Fine-tuned_Kaggle

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Mahmoud-Nasser/
whisper-base-quranic