whisper-large-v3-sna-waxal

• Loss: 0.4227
• Wer: 0.3182
• Cer: 0.0851

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

• learning_rate: 0.001
• train_batch_size: 8
• eval_batch_size: 8
• seed: 42
• gradient_accumulation_steps: 2
• total_train_batch_size: 16
• 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: linear
• lr_scheduler_warmup_steps: 100
• training_steps: 1000
• mixed_precision_training: Native AMP

Training results

Framework versions

• PEFT 0.20.0
• Transformers 5.14.1
• Pytorch 2.10.0+cu128
• Datasets 5.0.1
• Tokenizers 0.22.2