m-aliabbas1/whisper-base-urdu

Whisper Base Urdu - Ali Abbas

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

• Loss: 0.2081
• Wer: 34.3796

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
0.4518	0.3521	100	0.4355	63.5879
0.3641	0.7042	200	0.3525	55.6127
0.3096	1.0563	300	0.3102	50.6367
0.2726	1.4085	400	0.2806	47.1325
0.2466	1.7606	500	0.2573	44.3656
0.2203	2.1127	600	0.2406	41.8900
0.1953	2.4648	700	0.2288	40.7312
0.1953	2.8169	800	0.2183	38.2804
0.1561	3.1690	900	0.2124	37.6545
0.1657	3.5211	1000	0.2066	36.4864
0.1597	3.8732	1100	0.2016	35.3277
0.1282	4.2254	1200	0.1998	35.3741
0.1363	4.5775	1300	0.1963	35.4919
0.1299	4.9296	1400	0.1941	34.4260
0.1203	5.2817	1500	0.1946	33.9892
0.1147	5.6338	1600	0.1929	34.2308
0.1073	5.9859	1700	0.1909	33.5957
0.0968	6.3380	1800	0.1926	33.7847
0.0941	6.6901	1900	0.1930	33.6050
0.0899	7.0423	2000	0.1918	33.8218
0.0834	7.3944	2100	0.1934	33.3199
0.0796	7.7465	2200	0.1937	33.9396
0.0673	8.0986	2300	0.1947	33.1960
0.078	8.4507	2400	0.1962	33.9055
0.0695	8.8028	2500	0.1952	34.1689
0.0649	9.1549	2600	0.1976	33.0906
0.0611	9.5070	2700	0.1981	33.9024
0.0616	9.8592	2800	0.1986	33.6235
0.0584	10.2113	2900	0.2010	34.0139
0.0548	10.5634	3000	0.2017	33.8095
0.0558	10.9155	3100	0.2020	34.3641
0.0489	11.2676	3200	0.2033	34.2525
0.0488	11.6197	3300	0.2045	34.3888
0.0491	11.9718	3400	0.2048	34.1751
0.0465	12.3239	3500	0.2066	34.5252
0.0431	12.6761	3600	0.2067	34.1441
0.0444	13.0282	3700	0.2072	34.1255
0.0425	13.3803	3800	0.2079	34.4415
0.0446	13.7324	3900	0.2081	34.1689
0.0414	14.0845	4000	0.2081	34.3796

Framework versions

• Transformers 4.50.0.dev0
• Pytorch 2.6.0+cu124
• Datasets 3.4.0
• Tokenizers 0.21.1