whisper-base-zh

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

• Loss: 0.3717
• Cer: 15.8574

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: 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: 100
• training_steps: 4000
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Cer
0.5054	0.25	100	0.4976	21.1112
0.4526	0.5	200	0.4336	18.1724
0.4131	0.75	300	0.4105	20.9577
0.3772	1.0	400	0.3952	17.7166
0.297	1.25	500	0.3872	17.8054
0.2837	1.5	600	0.3798	18.3740
0.2801	1.75	700	0.3747	15.5887
0.2776	2.0	800	0.3677	16.4739
0.1981	2.25	900	0.3697	17.1169
0.2198	2.5	1000	0.3662	16.7474
0.2133	2.75	1100	0.3624	15.8334
0.2015	3.0	1200	0.3597	15.9798
0.1597	3.25	1300	0.3633	15.7902
0.1796	3.5	1400	0.3611	16.7834
0.145	3.75	1500	0.3607	16.6947
0.1581	4.0	1600	0.3602	16.2005
0.1235	4.25	1700	0.3639	14.9530
0.1118	4.5	1800	0.3674	15.3344
0.1266	4.75	1900	0.3654	15.3728
0.1214	5.0	2000	0.3644	15.3248
0.0911	5.25	2100	0.3678	15.8238
0.0969	5.5	2200	0.3703	15.8046
0.0956	5.75	2300	0.3717	15.8574

Framework versions

• Transformers 4.46.2
• Pytorch 2.5.1+cu121
• Datasets 3.1.0
• Tokenizers 0.20.3