whisper-largev3-egy-v1

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

• Loss: 0.5546
• Wer: 82.4230
• Cer: 64.5239

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: 16
• 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: cosine
• lr_scheduler_warmup_steps: 2000
• num_epochs: 15

Training results

Training Loss	Epoch	Step	Validation Loss	Wer	Cer
0.2956	0.3742	1500	0.3018	63.6348	47.5770
0.2867	0.7483	3000	0.2841	93.2416	79.6830
0.213	1.1225	4500	0.2759	97.8988	85.8519
0.227	1.4966	6000	0.2677	91.6266	74.8936
0.2212	1.8708	7500	0.2617	93.4321	79.6175
0.1611	2.2449	9000	0.2667	96.8099	78.9492
0.1589	2.6191	10500	0.2643	100.5175	76.5414
0.1697	2.9933	12000	0.2622	84.5441	67.4836
0.1071	3.3674	13500	0.2855	96.4829	81.3859
0.1136	3.7416	15000	0.2814	99.5195	75.9088
0.0637	4.1157	16500	0.3122	83.2163	64.5333
0.0692	4.4899	18000	0.3137	90.7196	76.6609
0.0687	4.8641	19500	0.3179	91.4560	75.3464
0.0333	5.2382	21000	0.3506	90.0088	71.7452
0.0366	5.6124	22500	0.3571	86.4889	69.5759
0.0379	5.9865	24000	0.3554	88.3228	63.6308
0.0174	6.3607	25500	0.3917	91.0494	75.0314
0.0176	6.7348	27000	0.3937	84.2740	64.2078
0.0075	7.1090	28500	0.4261	83.2419	65.5333
0.0092	7.4832	30000	0.4228	89.9832	69.3133
0.0096	7.8573	31500	0.4328	86.2415	71.8841
0.0042	8.2315	33000	0.4482	83.1168	64.2068
0.0045	8.6056	34500	0.4502	86.0169	67.4873
0.005	8.9798	36000	0.4580	83.4096	65.3331
0.0027	9.3540	37500	0.4667	82.2837	60.9284
0.0027	9.7281	39000	0.4718	84.5469	63.7325
0.0012	10.1023	40500	0.4819	80.8678	62.0516
0.0012	10.4764	42000	0.4911	81.6951	59.3660
0.0013	10.8506	43500	0.4950	78.6415	60.5107
0.0005	11.2247	45000	0.5076	81.8117	62.4285
0.0005	11.5989	46500	0.5055	78.8746	59.2439
0.0008	11.9731	48000	0.5101	79.2244	59.3010
0.0006	12.3472	49500	0.5234	81.2459	63.5160
0.0002	12.7214	51000	0.5310	83.3272	65.2183
0.0001	13.0955	52500	0.5380	83.5347	65.1864
0.0001	13.4697	54000	0.5450	81.4990	63.0972
0.0001	13.8439	55500	0.5491	82.4230	64.9317
0.0001	14.2180	57000	0.5523	82.3889	64.7414
0.0001	14.5922	58500	0.5544	82.5510	64.7718
0.0001	14.9663	60000	0.5546	82.4230	64.5239

Framework versions

• Transformers 4.51.3
• Pytorch 2.7.1+cu128
• Datasets 3.6.0
• Tokenizers 0.21.4