whisper-large-v3-igbo

This model is a fine-tuned version of openai/whisper-large-v3 on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 0.6021
• Wer: 51.26

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: 16
• seed: 42
• gradient_accumulation_steps: 2
• total_train_batch_size: 32
• optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: cosine
• lr_scheduler_warmup_steps: 500
• num_epochs: 15

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
2.0089	1.0661	500	0.9678	77.77
1.3785	2.1322	1000	0.7134	60.19
1.1897	3.1983	1500	0.6503	55.73
1.0523	4.2644	2000	0.6214	52.91
0.9796	5.3305	2500	0.6098	52.23
0.9334	6.3966	3000	0.6042	52.11
0.9120	7.4627	3500	0.6021	51.26
0.8714	8.5288	4000	0.6032	51.65
0.8599	9.5949	4500	0.6021	51.29
0.8597	10.6610	5000	0.6031	51.43
0.8562	11.7271	5500	0.6034	51.78
0.8612	12.7932	6000	0.6037	51.69
0.8595	13.8593	6500	0.6038	51.57
0.8415	14.9254	7000	0.6038	51.48

Framework versions

• Transformers 5.0.0
• Pytorch 2.11.0+cu128
• Datasets 4.0.0
• Tokenizers 0.22.2