whisper-small

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

• Loss: 0.3925
• Wer Strict(with punctuation): 13.6333
• Wer Ortho: 8.6149
• Punctuation Penalty: 5.0184

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: 4
• total_train_batch_size: 64
• 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: cosine
• lr_scheduler_warmup_steps: 20
• training_steps: 300
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer Strict(with punctuation)	Wer Ortho	Punctuation Penalty
0.8871	1.2346	50	0.3234	12.9390	7.8269	5.1121
0.2165	2.4691	100	0.3421	13.4197	8.3745	5.0452
0.0508	3.7037	150	0.3664	13.5132	8.4279	5.0852
0.0203	4.9383	200	0.3925	13.6333	8.6149	5.0184

Framework versions

• Transformers 4.46.0
• Pytorch 2.10.0+cu128
• Datasets 3.5.0
• Tokenizers 0.20.3