whisper-base

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

• eval_loss: 1.0027
• eval_wer: 0.3287
• eval_runtime: 19.3097
• eval_samples_per_second: 5.852
• eval_steps_per_second: 0.207
• epoch: 47.5333
• step: 380

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: 32
• eval_batch_size: 32
• seed: 42
• gradient_accumulation_steps: 2
• total_train_batch_size: 64
• optimizer: Use adafactor and the args are: No additional optimizer arguments
• lr_scheduler_type: linear
• lr_scheduler_warmup_steps: 100
• training_steps: 1000

Framework versions

• Transformers 4.51.0
• Pytorch 2.8.0+cu129
• Datasets 3.6.0
• Tokenizers 0.21.4