whisper-small-mn-12

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: 0.2949
• Wer: 32.3301
• Cer: 13.3493

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: 32
• eval_batch_size: 32
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_steps: 500
• training_steps: 25000
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer	Cer
0.3012	1.05	1000	0.3749	43.2379	17.6739
0.2171	2.11	2000	0.3012	36.7435	15.2029
0.1732	3.16	3000	0.2823	33.4225	13.7561
0.145	4.21	4000	0.2822	32.4995	13.2436
0.1159	5.27	5000	0.2949	32.3301	13.3493
0.0863	6.32	6000	0.3116	32.7234	13.3892
0.0685	7.38	7000	0.3343	32.4776	13.3077
0.0506	8.43	8000	0.3584	33.3952	13.7736
0.0336	9.48	9000	0.3861	33.7011	13.8493
0.0215	10.54	10000	0.4193	33.7011	14.0140
0.0141	11.59	11000	0.4463	34.0343	14.0298
0.0089	12.64	12000	0.4660	33.6137	13.8052
0.0057	13.7	13000	0.4913	33.9797	13.9849
0.0039	14.75	14000	0.5078	33.9906	14.0656
0.0033	15.81	15000	0.5244	33.7721	13.9192
0.0024	16.86	16000	0.5358	33.7612	13.7910
0.0018	17.91	17000	0.5469	33.6465	13.8468
0.0013	18.97	18000	0.5614	33.6683	13.7553
0.0014	20.02	19000	0.5707	33.6574	13.8884
0.0006	21.07	20000	0.5835	34.0671	14.0764
0.0007	22.13	21000	0.5927	33.9742	14.0772
0.0005	23.18	22000	0.5994	34.0398	14.0290
0.0004	24.24	23000	0.6067	33.9469	13.9217
0.0003	25.29	24000	0.6109	33.9688	13.9591
0.0003	26.34	25000	0.6130	33.8267	13.8360

Framework versions

• Transformers 4.26.0.dev0
• Pytorch 1.13.0+cu117
• Datasets 2.7.1.dev0
• Tokenizers 0.13.2