whisper-small-CV-Fleurs-lg-313hrs-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:

• Cer: 0.0803
• Loss: 0.7644
• Wer: 0.2848

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 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: 30
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Cer	Validation Loss	Wer
0.9959	1.0	30487	0.4780	0.5805	1.0507
0.2917	2.0	60974	0.8109	0.4413	1.4684
0.1945	3.0	91461	0.1449	0.4016	0.4604
0.139	4.0	121948	0.0933	0.3980	0.3740
0.0986	5.0	152435	0.0910	0.4101	0.3542
0.0698	6.0	182922	0.0935	0.4245	0.3447
0.0515	7.0	213409	0.0824	0.4571	0.3246
0.0412	8.0	243896	0.0843	0.4769	0.3185
0.0362	9.0	274383	0.0812	0.4909	0.3106
0.0339	10.0	304870	0.0819	0.5001	0.3119
0.0301	11.0	335357	0.0848	0.5248	0.3142
0.0243	12.0	365844	0.0843	0.5246	0.3045
0.0199	13.0	396331	0.0801	0.5518	0.3004
0.0167	14.0	426818	0.0857	0.5877	0.3085
0.0143	15.0	457305	0.0806	0.5835	0.3024
0.0124	16.0	487792	0.0819	0.5982	0.2995
0.011	17.0	518279	0.0845	0.5933	0.3022
0.0099	18.0	548766	0.0866	0.6195	0.2996
0.0088	19.0	579253	0.0825	0.6577	0.2966
0.0079	20.0	609740	0.0843	0.6416	0.2991
0.0073	21.0	640227	0.0810	0.6536	0.2938
0.0065	22.0	670714	0.0829	0.6708	0.2990
0.006	23.0	701201	0.0867	0.6726	0.2978
0.0056	24.0	731688	0.0819	0.6944	0.2921
0.0053	25.0	762175	0.0824	0.6845	0.2942
0.0049	26.0	792662	0.0840	0.6856	0.2926
0.0046	27.0	823149	0.0829	0.6926	0.2914
0.0042	28.0	853636	0.0832	0.7022	0.2866
0.004	29.0	884123	0.0800	0.7230	0.2898
0.0037	30.0	914610	0.0824	0.7287	0.2925
0.0034	31.0	945097	0.0801	0.7363	0.2860
0.0033	32.0	975584	0.0803	0.7497	0.2866
0.0032	33.0	1006071	0.0814	0.7478	0.2827
0.0029	34.0	1036558	0.0791	0.7292	0.2845
0.0028	35.0	1067045	0.0829	0.7657	0.2891
0.0026	36.0	1097532	0.0803	0.7644	0.2848

Framework versions

• Transformers 4.47.0
• Pytorch 2.1.0+cu118
• Datasets 3.2.0
• Tokenizers 0.21.0