dhaval0108/whisper-large-v3-turbo-finetuned-3e5-lr

ap-u2ve4vNEqG8dHywtw4QDVP

This model is a fine-tuned version of openai/whisper-large-v3-turbo on the None dataset. It achieves the following results on the evaluation set:

• Loss: 0.3547
• Model Preparation Time: 0.0073
• Wer: 0.1169

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: 3e-05
• train_batch_size: 8
• eval_batch_size: 8
• seed: 42
• gradient_accumulation_steps: 8
• total_train_batch_size: 64
• 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: 400
• num_epochs: 16
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Model Preparation Time	Wer
0.4516	1.0	250	0.2822	0.0073	0.1329
0.1981	2.0	500	0.2433	0.0073	0.1390
0.1437	3.0	750	0.2299	0.0073	0.1183
0.1073	4.0	1000	0.2392	0.0073	0.1261
0.0825	5.0	1250	0.2526	0.0073	0.1339
0.063	6.0	1500	0.2545	0.0073	0.1262
0.0496	7.0	1750	0.2777	0.0073	0.1244
0.0375	8.0	2000	0.2883	0.0073	0.1308
0.0289	9.0	2250	0.2757	0.0073	0.1129
0.0216	10.0	2500	0.3027	0.0073	0.1218
0.0153	11.0	2750	0.3247	0.0073	0.1199
0.0117	12.0	3000	0.3193	0.0073	0.1147
0.0059	13.0	3250	0.3347	0.0073	0.1158
0.0023	14.0	3500	0.3520	0.0073	0.1178
0.0011	15.0	3750	0.3593	0.0073	0.1160
0.0007	15.9365	3984	0.3547	0.0073	0.1169

Framework versions

• Transformers 4.48.3
• Pytorch 2.5.1+cu124
• Datasets 3.2.0
• Tokenizers 0.21.2