This model is a fine-tuned version of openai/whisper-medium on the fsicoli/cv17-fleurs-coraa-mls-ted-alcaim-cf-cdc-lapsbm-lapsmail-sydney-lingualibre-voxforge-tatoeba default dataset. It achieves the following results on the evaluation set:

Loss: 0.9306
Wer: 0.1101

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: 5e-06
train_batch_size: 8
eval_batch_size: 8
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 10000
num_epochs: 10.0
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
0.4423	0.2	20000	0.4723	0.1633
0.4963	0.39	40000	0.4921	0.1547
0.3853	0.59	60000	0.5099	0.1470
0.37	0.79	80000	0.4753	0.1439
0.3615	0.98	100000	0.5074	0.1386
0.2394	1.18	120000	0.4858	0.1341
0.227	1.38	140000	0.5758	0.1323
0.2461	1.57	160000	0.5067	0.1322
0.2078	1.77	180000	0.5087	0.1291
0.2138	1.97	200000	0.5201	0.1273
0.1188	2.16	220000	0.6359	0.1265
0.1009	2.36	240000	0.6229	0.1253
0.1394	2.56	260000	0.5734	0.1231
0.1383	2.75	280000	0.5914	0.1213
0.1332	2.95	300000	0.6174	0.1212
0.0634	3.15	320000	0.6461	0.1190
0.0667	3.34	340000	0.6330	0.1211
0.0546	3.54	360000	0.6927	0.1190
0.1029	3.74	380000	0.6777	0.1184
0.0664	3.93	400000	0.6367	0.1161
0.0665	4.13	420000	0.7467	0.1171
0.0695	4.33	440000	0.7332	0.1164
0.0708	4.52	460000	0.7141	0.1171
0.0695	4.72	480000	0.6869	0.1169
0.0758	4.92	500000	0.7360	0.1153
0.061	5.11	520000	0.7594	0.1161
0.0804	5.31	540000	0.7640	0.1158
0.0963	5.51	560000	0.7848	0.1157
0.0815	5.7	580000	0.7635	0.1145
0.0794	5.9	600000	0.7566	0.1134
0.0907	6.1	620000	0.8152	0.1147
0.0664	6.29	640000	0.8405	0.1123
0.0654	6.49	660000	0.8278	0.1119
0.0652	6.69	680000	0.8267	0.1134
0.1043	6.88	700000	0.8254	0.1122
0.0383	7.08	720000	0.8719	0.1122
0.0461	7.28	740000	0.8640	0.1130
0.0791	7.47	760000	0.8990	0.1122
0.0587	7.67	780000	0.9107	0.1122
0.0578	7.87	800000	0.9060	0.1124
0.0218	8.06	820000	0.8845	0.1111
0.0125	8.26	840000	0.9072	0.1112
0.0172	8.46	860000	0.8899	0.1107
0.0204	8.65	880000	0.9149	0.1108
0.0145	8.85	900000	0.9097	0.1103
0.0146	9.05	920000	0.9084	0.1107
0.0166	9.24	940000	0.9053	0.1103
0.0177	9.44	960000	0.9193	0.1100
0.0157	9.64	980000	0.9212	0.1101
0.0096	9.83	1000000	0.9313	0.1103

Framework versions

Transformers 4.39.0.dev0
Pytorch 2.2.1+cu121
Datasets 2.18.1.dev0
Tokenizers 0.15.0

fsicoli/whisper-medium-pt-3000h

whisper-medium-pt-3000h

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

fsicoli/
whisper-medium-pt-3000h