whisper-base-hac
This model is a fine-tuned version of openai/whisper-base on the razhan/DOLMA-speech hawrami dataset. It achieves the following results on the evaluation set:
- Loss: 0.3272
- Wer: 0.4792
- Cer: 0.1039
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: 192
- eval_batch_size: 128
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- total_train_batch_size: 384
- total_eval_batch_size: 256
- optimizer: Use 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: 1
- num_epochs: 5.0
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Cer |
|---|---|---|---|---|---|
| 1.9468 | 1.0 | 27 | 0.9999 | 0.9633 | 0.3797 |
| 0.6132 | 2.0 | 54 | 0.4681 | 0.6078 | 0.1469 |
| 0.3976 | 3.0 | 81 | 0.3668 | 0.5161 | 0.1128 |
| 0.3485 | 4.0 | 108 | 0.3360 | 0.4889 | 0.1065 |
| 0.3292 | 5.0 | 135 | 0.3272 | 0.4792 | 0.1039 |
Framework versions
- Transformers 4.49.0.dev0
- Pytorch 2.6.0+cu124
- Datasets 3.2.0
- Tokenizers 0.21.0