whisper-base-hac-telegram
This model is a fine-tuned version of openai/whisper-base on the razhan/DOLMA-speech gilaki dataset. It achieves the following results on the evaluation set:
- Loss: 2.6806
- Wer: 1.0472
- Cer: 0.5468
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: 256
- eval_batch_size: 128
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- total_train_batch_size: 512
- 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: 100
- num_epochs: 5.0
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Cer |
|---|---|---|---|---|---|
| No log | 1.0 | 6 | 3.5224 | 1.1311 | 0.5560 |
| 2.4889 | 2.0 | 12 | 3.4807 | 1.0566 | 0.5018 |
| 2.4889 | 3.0 | 18 | 3.2108 | 1.0561 | 0.4986 |
| 2.3707 | 4.0 | 24 | 2.9445 | 1.0583 | 0.5155 |
| 2.0528 | 5.0 | 30 | 2.6806 | 1.0472 | 0.5468 |
Framework versions
- Transformers 4.49.0.dev0
- Pytorch 2.6.0+cu124
- Datasets 3.2.0
- Tokenizers 0.21.0