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whisper-large-v2-tr-ft-26-03-26-encoder-only-25ksamples-simulated-data
alpcansoydas/whisper-large-v2-tr-ft-26-03-26-encoder-only-25ksamples-simulated-data
whisper-large-v2-tr-ft-26-03-26-encoder-only-25ksamples-simulated-data is a automatic speech recognition model from alpcansoydas. Use it when you need speech turned into text. It is set up for transformers. The card lists the license as apache-2.0.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Task
- Automatic Speech Recognition
- Library
- transformers
- License
- apache-2.0
- Model type
- whisper
- Access
- Public
- Created
- Mar 25, 2026
- Updated
- Mar 26, 2026
- SHA
- 67af7ecb
Base models
Model card
From the Hugging Face model README
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
Whisper for Turkish Call Centers
This model is a fine-tuned version of openai/whisper-large-v2 on the Custom turkish call center simulated data dataset. It achieves the following results on the evaluation set:
- Loss: 0.2845
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: 5e-06
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- 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: 500
- training_steps: 4500
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 0.6040 | 0.3554 | 250 | 0.3119 |
| 0.5851 | 0.7107 | 500 | 0.2891 |
| 0.5170 | 1.0654 | 750 | 0.2810 |
| 0.5519 | 1.4208 | 1000 | 0.2746 |
| 0.6250 | 1.7761 | 1250 | 0.2717 |
| 0.4590 | 2.1308 | 1500 | 0.2713 |
| 0.4245 | 2.4861 | 1750 | 0.2685 |
| 0.4649 | 2.8415 | 2000 | 0.2674 |
| 0.3951 | 3.1962 | 2250 | 0.2712 |
| 0.4086 | 3.5515 | 2500 | 0.2694 |
| 0.4263 | 3.9069 | 2750 | 0.2694 |
| 0.3639 | 4.2615 | 3000 | 0.2739 |
| 0.4839 | 4.6169 | 3250 | 0.2739 |
| 0.3619 | 4.9723 | 3500 | 0.2740 |
| 0.3577 | 5.3269 | 3750 | 0.2800 |
| 0.3624 | 5.6823 | 4000 | 0.2804 |
| 0.3432 | 6.0370 | 4250 | 0.2823 |
| 0.3676 | 6.3923 | 4500 | 0.2845 |
Framework versions
- Transformers 5.3.0
- Pytorch 2.10.0+cu128
- Datasets 4.8.4
- Tokenizers 0.22.2
- Task
- Automatic Speech Recognition
- Library
- transformers
- Type
- whisper
- License
- apache-2.0
- Languages
- tr
- Created
- Mar 25, 2026
- Updated
- Mar 26, 2026