| --- |
| language: |
| - tr |
| license: apache-2.0 |
| base_model: openai/whisper-large |
| tags: |
| - generated_from_trainer |
| datasets: |
| - custom |
| metrics: |
| - wer |
| model-index: |
| - name: Whisper large tr - baki |
| results: |
| - task: |
| name: Automatic Speech Recognition |
| type: automatic-speech-recognition |
| dataset: |
| name: custom |
| type: custom |
| args: 'config: tr, split: test' |
| metrics: |
| - name: Wer |
| type: wer |
| value: 90.93493367024637 |
| --- |
| |
| <!-- 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 large tr - baki |
|
|
| This model is a fine-tuned version of [openai/whisper-large](https://huggingface.co/openai/whisper-large) on the custom dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 2.0105 |
| - Wer: 90.9349 |
|
|
| ## 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 |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| - lr_scheduler_type: linear |
| - lr_scheduler_warmup_steps: 40 |
| - training_steps: 300 |
| - mixed_precision_training: Native AMP |
| |
| ### Training results |
| |
| | Training Loss | Epoch | Step | Validation Loss | Wer | |
| |:-------------:|:------:|:----:|:---------------:|:--------:| |
| | 2.1523 | 0.9615 | 100 | 2.1371 | 117.2773 | |
| | 1.5102 | 1.9231 | 200 | 1.9995 | 93.6829 | |
| | 1.1534 | 2.8846 | 300 | 2.0105 | 90.9349 | |
| |
| |
| ### Framework versions |
| |
| - Transformers 4.42.3 |
| - Pytorch 2.1.0+cu118 |
| - Datasets 2.20.0 |
| - Tokenizers 0.19.1 |
| |
