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--- |
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library_name: transformers |
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language: |
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- sq |
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license: mit |
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base_model: openai/whisper-large-v3-turbo |
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tags: |
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- generated_from_trainer |
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datasets: |
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- Kushtrim/audioshqip |
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metrics: |
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- wer |
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model-index: |
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- name: Whisper Large v3 Turbo Shqip |
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results: |
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- task: |
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type: automatic-speech-recognition |
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name: Automatic Speech Recognition |
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dataset: |
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name: Audio Shqip 50 orë |
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type: Kushtrim/audioshqip |
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args: 'config: sq, split: test' |
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metrics: |
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- type: wer |
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value: 26.29520403254481 |
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name: Wer |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# Whisper Large v3 Turbo Shqip |
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This model is a fine-tuned version of [openai/whisper-large-v3-turbo](https://huggingface.co/openai/whisper-large-v3-turbo) on the Audio Shqip 50 orë dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.5501 |
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- Wer: 26.2952 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 1e-05 |
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- train_batch_size: 4 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 16 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_steps: 500 |
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- training_steps: 10000 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Wer | |
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|:-------------:|:-------:|:-----:|:---------------:|:-------:| |
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| 0.5589 | 0.5363 | 500 | 0.5765 | 40.2773 | |
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| 0.391 | 1.0727 | 1000 | 0.4633 | 32.9234 | |
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| 0.3557 | 1.6090 | 1500 | 0.4209 | 32.8188 | |
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| 0.2288 | 2.1453 | 2000 | 0.4132 | 30.0056 | |
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| 0.237 | 2.6817 | 2500 | 0.4012 | 29.9073 | |
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| 0.1776 | 3.2180 | 3000 | 0.4055 | 30.2650 | |
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| 0.1838 | 3.7544 | 3500 | 0.4034 | 29.6501 | |
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| 0.1328 | 4.2907 | 4000 | 0.4109 | 29.3719 | |
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| 0.1301 | 4.8270 | 4500 | 0.4052 | 28.7716 | |
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| 0.1034 | 5.3634 | 5000 | 0.4231 | 27.3180 | |
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| 0.0845 | 5.8997 | 5500 | 0.4296 | 27.5167 | |
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| 0.0857 | 6.4360 | 6000 | 0.4526 | 26.9750 | |
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| 0.0526 | 6.9724 | 6500 | 0.4550 | 27.2343 | |
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| 0.0436 | 7.5087 | 7000 | 0.4833 | 27.2824 | |
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| 0.0284 | 8.0451 | 7500 | 0.4983 | 26.5734 | |
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| 0.0328 | 8.5814 | 8000 | 0.5043 | 26.8244 | |
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| 0.0164 | 9.1177 | 8500 | 0.5225 | 26.5441 | |
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| 0.0171 | 9.6541 | 9000 | 0.5318 | 26.2659 | |
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| 0.019 | 10.1904 | 9500 | 0.5473 | 26.3182 | |
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| 0.0253 | 10.7267 | 10000 | 0.5501 | 26.2952 | |
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### Framework versions |
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- Transformers 4.45.2 |
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- Pytorch 2.5.1+cu121 |
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- Datasets 3.1.0 |
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- Tokenizers 0.20.3 |
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