whisper-small-dari
This model is a fine-tuned version of openai/whisper-small on the audiofolder dataset. It achieves the following results on the evaluation set:
- Loss: 0.3171
- Wer: 30.1313
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: 64
- eval_batch_size: 64
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 128
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED 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: 0.1
- num_epochs: 10
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| No log | 1.0 | 7 | 0.6818 | 48.0304 |
| 3.2896 | 2.0 | 14 | 0.4796 | 38.9772 |
| 1.7258 | 3.0 | 21 | 0.3972 | 35.3836 |
| 1.7258 | 4.0 | 28 | 0.3604 | 32.1355 |
| 1.1070 | 5.0 | 35 | 0.3359 | 30.6151 |
| 0.8097 | 6.0 | 42 | 0.3274 | 31.2370 |
| 0.8097 | 7.0 | 49 | 0.3196 | 29.9931 |
| 0.6274 | 8.0 | 56 | 0.3194 | 29.9240 |
| 0.5292 | 9.0 | 63 | 0.3163 | 29.5784 |
| 0.4368 | 10.0 | 70 | 0.3171 | 30.1313 |
Framework versions
- Transformers 5.14.1
- Pytorch 2.10.0+cu128
- Datasets 5.0.1
- Tokenizers 0.22.2