Setosm/whisper-base-ea_5hrsd

./whisper-base-ea_5hr

This model is a fine-tuned version of openai/whisper-base on the Afrispeech-200 dataset. It achieves the following results on the evaluation set:

• Loss: 0.7154
• Wer Ortho: 0.2898
• Wer: 0.2319
• Cer: 0.1088
• Precision: 0.8599
• Recall: 0.8590
• F1: 0.8585

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: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: constant_with_warmup
• lr_scheduler_warmup_steps: 50
• training_steps: 500
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer Ortho	Wer	Cer	Precision	Recall	F1
0.9173	0.2364	100	0.9484	0.3210	0.2633	0.1182	0.8389	0.8419	0.8398
0.8464	0.4728	200	0.8059	0.2924	0.2312	0.1026	0.8519	0.8523	0.8515
0.7615	0.7092	300	0.7548	0.2925	0.2340	0.1048	0.8557	0.8556	0.8550
0.7301	0.9456	400	0.7282	0.2877	0.2288	0.1030	0.8603	0.8613	0.8602
0.5936	1.1820	500	0.7154	0.2898	0.2319	0.1088	0.8599	0.8590	0.8585

Framework versions

• Transformers 4.51.3
• Pytorch 2.6.0+cu124
• Datasets 3.6.0
• Tokenizers 0.21.1