zagibest/whisper-small-custom-data

Whisper Small MN with custom data - Zagi

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

• Loss: 0.0917
• Wer: 9.3784

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: 500
• training_steps: 4000

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
0.0653	0.61	500	0.1102	13.5820
0.054	1.21	1000	0.1002	11.9380
0.0523	1.82	1500	0.0966	11.5903
0.0366	2.43	2000	0.0954	10.9710
0.0168	3.03	2500	0.0909	10.3866
0.0204	3.64	3000	0.0912	9.7817
0.0067	4.25	3500	0.0910	9.4936
0.0078	4.85	4000	0.0917	9.3784

Framework versions

• Transformers 4.39.0
• Pytorch 2.0.1+cu117
• Datasets 2.18.0
• Tokenizers 0.15.2