Teamim-medium_WeightDecay-0.005_Augmented__date-08-04-2025

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he-cantillation

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

• Loss: 2.8408
• Wer: 94.6548
• Avg Precision Exact: 0.0941
• Avg Recall Exact: 0.1218
• Avg F1 Exact: 0.1041
• Avg Precision Letter Shift: 0.1168
• Avg Recall Letter Shift: 0.1531
• Avg F1 Letter Shift: 0.1297
• Avg Precision Word Level: 0.1354
• Avg Recall Word Level: 0.1752
• Avg F1 Word Level: 0.1488
• Avg Precision Word Shift: 0.2611
• Avg Recall Word Shift: 0.3573
• Avg F1 Word Shift: 0.2941
• Precision Median Exact: 0.0513
• Recall Median Exact: 0.0769
• F1 Median Exact: 0.0615
• Precision Max Exact: 1.0
• Recall Max Exact: 1.0
• F1 Max Exact: 1.0
• Precision Min Exact: 0.0
• Recall Min Exact: 0.0
• F1 Min Exact: 0.0
• Precision Min Letter Shift: 0.0
• Recall Min Letter Shift: 0.0
• F1 Min Letter Shift: 0.0
• Precision Min Word Level: 0.0
• Recall Min Word Level: 0.0
• F1 Min Word Level: 0.0
• Precision Min Word Shift: 0.0
• Recall Min Word Shift: 0.0
• F1 Min Word Shift: 0.0

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: 2
• 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: linear
• lr_scheduler_warmup_steps: 1000
• training_steps: 20000
• mixed_precision_training: Native AMP

Training results

Framework versions

• Transformers 4.49.0
• Pytorch 2.6.0+cu126
• Datasets 2.12.0
• Tokenizers 0.20.1