whisper-small-dv-full
This model is a fine-tuned version of openai/whisper-small on the arrow dataset. It achieves the following results on the evaluation set:
- eval_loss: 0.0273
- eval_wer_ortho: 15.8343
- eval_wer: 2.3726
- eval_runtime: 11624.8972
- eval_samples_per_second: 3.774
- eval_steps_per_second: 0.079
- epoch: 0.6569
- step: 4000
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- distributed_type: multi-GPU
- num_devices: 3
- total_train_batch_size: 48
- total_eval_batch_size: 48
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: constant_with_warmup
- lr_scheduler_warmup_steps: 50
- training_steps: 4000
- mixed_precision_training: Native AMP
Framework versions
- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 3.5.0
- Tokenizers 0.21.1