whisper-large-turkish-v7

This model is a fine-tuned version of openai/whisper-large-v3 on the common_voice_17_0 dataset. It achieves the following results on the evaluation set:

• Loss: 0.2838
• Wer: 14.2172

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: 1000
• num_epochs: 12
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
0.1499	0.5171	1500	0.1817	15.9293
0.0816	1.0341	3000	0.1749	15.2271
0.0881	1.5512	4500	0.1813	17.0243
0.0476	2.0683	6000	0.1857	15.9072
0.0495	2.5853	7500	0.1798	14.8225
0.0304	3.1024	9000	0.1888	14.9279
0.0341	3.6194	10500	0.1955	14.8276
0.0204	4.1365	12000	0.2057	15.2850
0.0233	4.6536	13500	0.2065	14.9840
0.012	5.1706	15000	0.2173	14.7698
0.0146	5.6877	16500	0.2260	15.4890
0.0054	6.2048	18000	0.2402	14.6542
0.009	6.7218	19500	0.2383	14.9415
0.0038	7.2389	21000	0.2453	15.2748
0.0035	7.7559	22500	0.2443	15.0690
0.0028	8.2730	24000	0.2582	14.9143
0.0014	8.7901	25500	0.2548	14.3005
0.0004	9.3071	27000	0.2768	14.5080
0.0005	9.8242	28500	0.2707	13.9350
0.0009	10.3413	30000	0.2799	14.4433
0.0005	10.8583	31500	0.2773	14.3124
0.0001	11.3754	33000	0.2810	13.6340
0.0001	11.8925	34500	0.2838	14.2172

Framework versions

• Transformers 4.51.3
• Pytorch 2.6.0+cu124
• Datasets 3.6.0
• Tokenizers 0.21.4