whisper-small-CV-Fleurs-lg-1hrs-v1

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

• Loss: 1.5981
• Wer: 0.8787
• Cer: 0.3264

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: 4
• eval_batch_size: 2
• seed: 42
• gradient_accumulation_steps: 2
• total_train_batch_size: 8
• optimizer: Use OptimizerNames.ADAMW_HF with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: linear
• lr_scheduler_warmup_ratio: 0.1
• num_epochs: 100
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer	Cer
5.6051	1.0	66	3.5145	1.4569	0.6891
2.4999	2.0	132	2.0266	1.1776	0.4738
1.5987	3.0	198	1.6438	1.0026	0.3884
1.1473	4.0	264	1.4459	0.9933	0.3876
0.7941	5.0	330	1.3575	1.0070	0.3922
0.5137	6.0	396	1.3371	1.0377	0.4016
0.2992	7.0	462	1.3442	1.0814	0.4432
0.1564	8.0	528	1.3580	1.1707	0.4875
0.0918	9.0	594	1.3761	1.0757	0.4521
0.0594	10.0	660	1.3911	1.1061	0.4463
0.0421	11.0	726	1.4346	1.0884	0.4501
0.0307	12.0	792	1.4065	1.1398	0.4760
0.0244	13.0	858	1.4255	0.9986	0.3835
0.0166	14.0	924	1.4470	0.9827	0.4068
0.0113	15.0	990	1.4665	1.0230	0.4303
0.0129	16.0	1056	1.4771	1.0429	0.4329
0.0081	17.0	1122	1.4891	0.9374	0.3507
0.0078	18.0	1188	1.4732	0.9948	0.3844
0.0043	19.0	1254	1.4902	0.9031	0.3429
0.0015	20.0	1320	1.4938	0.8725	0.3073
0.0014	21.0	1386	1.5089	0.8962	0.3254
0.0006	22.0	1452	1.5250	0.8717	0.3070
0.0005	23.0	1518	1.5295	0.8920	0.3174
0.0004	24.0	1584	1.5374	0.9067	0.3286
0.0004	25.0	1650	1.5447	0.9138	0.3367
0.0004	26.0	1716	1.5517	0.9078	0.3349
0.0003	27.0	1782	1.5572	0.8969	0.3329
0.0003	28.0	1848	1.5627	0.9181	0.3504
0.0003	29.0	1914	1.5684	0.9089	0.3423
0.0003	30.0	1980	1.5735	0.9190	0.3513
0.0003	31.0	2046	1.5790	0.8971	0.3363
0.0002	32.0	2112	1.5846	0.8979	0.3365
0.0002	33.0	2178	1.5893	0.8793	0.3235
0.0002	34.0	2244	1.5948	0.8864	0.3316
0.0002	35.0	2310	1.5981	0.8787	0.3264

Framework versions

• Transformers 4.48.1
• Pytorch 2.5.1+cu124
• Datasets 3.2.0
• Tokenizers 0.21.0