CasperMuz/whisper-base-sna-cleaned-duration-sortagrad

Whisper Medium Shona - Cleaned Data Duration SortaGrad

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

• Loss: 0.4571
• Wer: 40.7137

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 adamw_torch_fused with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: linear
• lr_scheduler_warmup_steps: 200
• training_steps: 2000
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
0.8798	0.2398	200	0.9193	63.5934
0.6167	0.4796	400	0.6583	50.7509
0.5866	0.7194	600	0.5679	49.3659
0.5302	0.9592	800	0.5203	45.3494
0.4440	1.1990	1000	0.4967	43.8557
0.4213	1.4388	1200	0.4814	42.0742
0.4077	1.6787	1400	0.4709	42.3322
0.4016	1.9185	1600	0.4619	41.7158
0.3498	2.1583	1800	0.4586	40.7164
0.3882	2.3981	2000	0.4571	40.7137

Framework versions

• Transformers 5.13.1
• Pytorch 2.12.0+cu130
• Datasets 5.0.0
• Tokenizers 0.22.2