learning_rate: 3e-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: linear
lr_scheduler_warmup_steps: 1250
num_epochs: 12
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
1.0345	0.1416	500	1.4197	109.5730
0.8638	0.2832	1000	1.2463	80.6058
0.8131	0.4248	1500	1.1927	89.7440
0.7539	0.5664	2000	1.1132	86.8466
0.7429	0.7080	2500	1.0692	78.1367
0.7524	0.8496	3000	1.0429	67.9277
0.6429	0.9912	3500	0.9951	79.6665
0.5777	1.1328	4000	0.9834	70.1924
0.5681	1.2744	4500	0.9818	63.9629
0.509	1.4160	5000	0.9620	60.5350
0.5353	1.5576	5500	0.9432	65.0628
0.4851	1.6992	6000	0.9316	58.2249
0.501	1.8408	6500	0.9281	67.6438
0.5861	1.9824	7000	0.9029	60.6275
0.3368	2.1240	7500	0.9508	59.1756
0.3633	2.2656	8000	0.9263	55.8807
0.3257	2.4073	8500	0.9449	57.2272
0.361	2.5489	9000	0.9355	61.9156
0.337	2.6905	9500	0.9372	58.0384
0.3408	2.8321	10000	0.9153	56.0965
0.3362	2.9737	10500	0.9213	57.5663
0.196	3.1153	11000	0.9820	57.2597
0.1815	3.2569	11500	1.0006	55.0728
0.178	3.3985	12000	1.0060	57.1818
0.2	3.5401	12500	0.9999	54.7241
0.1896	3.6817	13000	0.9970	54.0346
0.1881	3.8233	13500	0.9912	56.3187
0.2182	3.9649	14000	0.9785	55.4233
0.0855	4.1065	14500	1.0754	54.8181
0.1033	4.2481	15000	1.0664	55.2594
0.0969	4.3897	15500	1.0600	55.6406
0.0985	4.5313	16000	1.0799	57.9232
0.094	4.6729	16500	1.1025	56.3755
0.1026	4.8145	17000	1.0939	54.8814
0.1029	4.9561	17500	1.0987	54.8765
0.0444	5.0977	18000	1.1591	54.4142
0.047	5.2393	18500	1.1655	57.5711
0.0519	5.3809	19000	1.1609	55.7461
0.0479	5.5225	19500	1.1737	54.6381
0.046	5.6641	20000	1.1773	55.1053
0.0448	5.8057	20500	1.1837	54.6413
0.0422	5.9473	21000	1.1810	55.3535
0.0247	6.0889	21500	1.2235	54.7095
0.0267	6.2305	22000	1.2358	54.3655
0.0222	6.3721	22500	1.2313	55.2367
0.0281	6.5137	23000	1.2455	54.3380
0.0231	6.6553	23500	1.2549	53.6096
0.0278	6.7969	24000	1.2543	53.9308
0.0276	6.9385	24500	1.2500	53.9389
0.0121	7.0801	25000	1.2829	53.9535
0.0164	7.2218	25500	1.2841	53.6031
0.0164	7.3634	26000	1.2826	55.3000
0.0139	7.5050	26500	1.2754	53.8237
0.0141	7.6466	27000	1.3143	54.2244
0.0157	7.7882	27500	1.2910	53.2981
0.0155	7.9298	28000	1.2914	54.4580
0.0083	8.0714	28500	1.3115	53.1066
0.0082	8.2130	29000	1.3224	53.2072
0.0088	8.3546	29500	1.3260	54.2455
0.008	8.4962	30000	1.3389	52.7579
0.0065	8.6378	30500	1.3449	54.0897
0.0086	8.7794	31000	1.3381	54.0719
0.0111	8.9210	31500	1.3331	53.9843
0.005	9.0626	32000	1.3531	53.3403
0.0033	9.2042	32500	1.3539	53.9535
0.0041	9.3458	33000	1.3654	52.9055
0.0027	9.4874	33500	1.3526	53.0628
0.0038	9.6290	34000	1.3802	52.8730
0.0054	9.7706	34500	1.3724	53.2235
0.003	9.9122	35000	1.3734	53.4635
0.0024	10.0538	35500	1.3829	52.5421
0.002	10.1954	36000	1.3788	52.9347
0.0034	10.3370	36500	1.3749	52.8146
0.0013	10.4786	37000	1.3951	52.4269
0.0014	10.6202	37500	1.3997	52.6865
0.0015	10.7618	38000	1.3900	52.6589
0.0013	10.9034	38500	1.4116	52.4513
0.0014	11.0450	39000	1.4101	52.2014
0.0032	11.1866	39500	1.4156	52.5113
0.0005	11.3282	40000	1.4236	52.3182
0.0003	11.4698	40500	1.4343	52.2890
0.0002	11.6114	41000	1.4376	52.2063
0.0003	11.7530	41500	1.4428	52.1998
0.0002	11.8946	42000	1.4392	52.2874

Framework versions

Transformers 4.54.0.dev0
Pytorch 2.6.0+cu124
Datasets 3.6.0
Tokenizers 0.21.2

samil24/whisper-large-telephone-v1

whisper-large-telephone-v1

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

samil24/
whisper-large-telephone-v1