sarpba/whisper-hu-small-finetuned

whisper-small-finetuned-hu

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

• Loss: 0.02658
• Wer: 0.08494

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: 2.5e-05
• train_batch_size: 32
• eval_batch_size: 32
• seed: 42
• distributed_type: multi-GPU
• num_devices: 2
• total_train_batch_size: 64
• total_eval_batch_size: 64
• 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
• num_epochs: 2
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
0.0559	0.0902	2000	0.0575	0.2634
0.0481	0.1804	4000	0.0488	0.1917
0.0415	0.2707	6000	0.0438	0.1329
0.0408	0.3609	8000	0.0408	0.1234
0.0393	0.4511	10000	0.0388	0.1173
0.0375	0.5413	12000	0.0372	0.1119
0.0342	0.6316	14000	0.0357	0.1101
0.0335	0.7218	16000	0.0349	0.1071
0.0323	0.8120	18000	0.0331	0.1037
0.0325	0.9022	20000	0.0326	0.1035
0.0305	0.9925	22000	0.0315	0.0974
0.02	1.0827	24000	0.0312	0.0992
0.0207	1.1729	26000	0.0310	0.0937
0.0203	1.2631	28000	0.0301	0.0941
0.0215	1.3534	30000	0.0296	0.0913
0.0199	1.4436	32000	0.0289	0.0911
0.0197	1.5338	34000	0.0285	0.0890
0.0187	1.6240	36000	0.0279	0.0887
0.0188	1.7143	38000	0.0276	0.0882
0.0186	1.8045	40000	0.0271	0.0856
0.0181	1.8947	42000	0.0266	0.0849
0.0176	1.9849	44000	0.0264	0.0863

Framework versions

• Transformers 4.47.0
• Pytorch 2.5.1+cu118
• Datasets 3.1.0
• Tokenizers 0.21.0