Sagicc/whisper-base-sr

Whisper Base Sr

This model is a fine-tuned version of openai/whisper-base. It achieves the following results on the evaluation set:

• Loss: 0.3129
• Wer Ortho: 0.3801
• Wer: 0.2789

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: 16
• eval_batch_size: 8
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_steps: 50
• training_steps: 4000
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer Ortho	Wer
0.4839	0.03	500	0.4684	0.5407	0.4170
0.4084	0.05	1000	0.3948	0.4578	0.3559
0.3873	0.08	1500	0.3690	0.4276	0.3260
0.3562	0.11	2000	0.3450	0.4129	0.3117
0.3233	0.13	2500	0.3293	0.3935	0.2912
0.313	0.16	3000	0.3232	0.3887	0.2861
0.3062	0.19	3500	0.3158	0.3866	0.2851
0.3154	0.22	4000	0.3129	0.3801	0.2789

Framework versions

• Transformers 4.37.2
• Pytorch 2.0.1+cu117
• Datasets 2.16.1
• Tokenizers 0.15.1