whisper-small-mn-2

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: 0.7259
• Wer: 40.8783
• Cer: 13.9617

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: 16
• 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: 15000
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer	Cer
0.0839	4.26	1000	0.4647	45.7286	16.0020
0.0093	8.51	2000	0.5434	43.9753	15.2446
0.0044	12.77	3000	0.6009	43.6257	15.1717
0.0029	17.02	4000	0.6166	43.0031	14.7578
0.002	21.28	5000	0.6390	42.6098	14.7286
0.001	25.53	6000	0.6558	41.7468	14.3516
0.0021	29.79	7000	0.6714	42.3039	14.4589
0.0003	34.04	8000	0.6791	41.0586	13.9506
0.0001	38.3	9000	0.6949	41.3808	14.1670
0.0013	42.55	10000	0.6875	41.4682	14.2983
0.0001	46.81	11000	0.6937	40.9165	13.9549
0.0001	51.06	12000	0.7092	40.9275	13.9549
0.0	55.32	13000	0.7190	40.9657	13.9703
0.0	59.57	14000	0.7259	40.8783	13.9617
0.0	63.83	15000	0.7292	40.8838	13.9274

Framework versions

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