Whisper Small Tajik
This model is a fine-tuned version of openai/whisper-small on the Google Fleurs dataset. It achieves the following results on the evaluation set:
- Loss: 0.4141
- Wer: 24.2606
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: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 2.7687 | 1.0 | 79 | 0.5778 | 39.6568 |
| 0.7193 | 2.0 | 158 | 0.3890 | 28.3568 |
| 0.3659 | 3.0 | 237 | 0.3611 | 26.0636 |
| 0.2021 | 4.0 | 316 | 0.3629 | 25.1068 |
| 0.1099 | 5.0 | 395 | 0.3740 | 25.3044 |
| 0.0597 | 6.0 | 474 | 0.3887 | 24.3081 |
| 0.0339 | 7.0 | 553 | 0.4005 | 24.6639 |
| 0.0213 | 8.0 | 632 | 0.4082 | 24.3239 |
| 0.0158 | 9.0 | 711 | 0.4131 | 24.2685 |
| 0.014 | 10.0 | 790 | 0.4141 | 24.2606 |
Framework versions
- Transformers 4.47.1
- Pytorch 2.5.1+cu121
- Datasets 3.2.0
- Tokenizers 0.21.0