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--- |
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library_name: transformers |
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base_model: ivrit-ai/whisper-large-v3-turbo |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: whisper-large-v3-turbo-ivrit-ai-coursera-fine-tuned |
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results: [] |
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datasets: |
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- imvladikon/hebrew_speech_coursera |
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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-ivrit-ai-coursera-fine-tuned |
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This model is a fine-tuned version of [ivrit-ai/whisper-large-v3-turbo](https://huggingface.co/ivrit-ai/whisper-large-v3-turbo) on the dataset imvladikon/hebrew_speech_coursera. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2829 |
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## Model description |
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This model created for my work for the Open University Of Israel. |
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[Here](https://colab.research.google.com/gist/zibib3/373bbc36c305899e29c1a91b9a834c97/.ipynb) you can see the notebook that used to |
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create this model, and [here](https://www.youtube.com/live/rEoG9vF0GAo) you can find me displaying the notebook. |
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I think that this model is useless becaus it has lower performance from its base model. |
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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: 5 |
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- eval_batch_size: 5 |
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- seed: 42 |
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- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
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- lr_scheduler_type: constant_with_warmup |
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- lr_scheduler_warmup_ratio: 0.1 |
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- lr_scheduler_warmup_steps: 500 |
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- num_epochs: 10 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:------:|:-----:|:---------------:| |
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| 0.1907 | 0.1641 | 500 | 0.2266 | |
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| 0.2283 | 0.3283 | 1000 | 0.2217 | |
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| 0.2253 | 0.4924 | 1500 | 0.2154 | |
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| 0.2257 | 0.6566 | 2000 | 0.2080 | |
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| 0.2138 | 0.8207 | 2500 | 0.2102 | |
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| 0.2153 | 0.9849 | 3000 | 0.2056 | |
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| 0.1615 | 1.1490 | 3500 | 0.2128 | |
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| 0.1588 | 1.3132 | 4000 | 0.1677 | |
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| 0.1628 | 1.4773 | 4500 | 0.1656 | |
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| 0.168 | 1.6415 | 5000 | 0.1798 | |
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| 0.167 | 1.8056 | 5500 | 0.1710 | |
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| 0.1663 | 1.9698 | 6000 | 0.1828 | |
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| 0.1297 | 2.1339 | 6500 | 0.1722 | |
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| 0.1196 | 2.2981 | 7000 | 0.1762 | |
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| 0.1336 | 2.4622 | 7500 | 0.1779 | |
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| 0.1258 | 2.6264 | 8000 | 0.1821 | |
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| 0.1275 | 2.7905 | 8500 | 0.1796 | |
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| 0.1331 | 2.9547 | 9000 | 0.1786 | |
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| 0.0988 | 3.1188 | 9500 | 0.1982 | |
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| 0.0933 | 3.2830 | 10000 | 0.1888 | |
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| 0.0963 | 3.4471 | 10500 | 0.1927 | |
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| 0.0946 | 3.6113 | 11000 | 0.1979 | |
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| 0.1018 | 3.7754 | 11500 | 0.2031 | |
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| 0.1027 | 3.9396 | 12000 | 0.1971 | |
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| 0.0795 | 4.1037 | 12500 | 0.2016 | |
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| 0.0698 | 4.2679 | 13000 | 0.2017 | |
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| 0.0736 | 4.4320 | 13500 | 0.2058 | |
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| 0.0747 | 4.5962 | 14000 | 0.2033 | |
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| 0.0768 | 4.7603 | 14500 | 0.2057 | |
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| 0.0801 | 4.9245 | 15000 | 0.2076 | |
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| 0.067 | 5.0886 | 15500 | 0.2196 | |
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| 0.0539 | 5.2528 | 16000 | 0.2185 | |
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| 0.0563 | 5.4169 | 16500 | 0.2220 | |
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| 0.0594 | 5.5811 | 17000 | 0.2265 | |
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| 0.0651 | 5.7452 | 17500 | 0.2176 | |
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| 0.0655 | 5.9094 | 18000 | 0.2227 | |
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| 0.0533 | 6.0735 | 18500 | 0.2387 | |
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| 0.0441 | 6.2377 | 19000 | 0.2334 | |
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| 0.0474 | 6.4018 | 19500 | 0.2343 | |
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| 0.0506 | 6.5660 | 20000 | 0.2387 | |
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| 0.0504 | 6.7301 | 20500 | 0.2373 | |
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| 0.0502 | 6.8943 | 21000 | 0.2318 | |
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| 0.0441 | 7.0584 | 21500 | 0.2524 | |
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| 0.0375 | 7.2226 | 22000 | 0.2533 | |
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| 0.0379 | 7.3867 | 22500 | 0.2491 | |
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| 0.0382 | 7.5509 | 23000 | 0.2635 | |
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| 0.0427 | 7.7150 | 23500 | 0.2506 | |
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| 0.0439 | 7.8792 | 24000 | 0.2430 | |
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| 0.043 | 8.0433 | 24500 | 0.2575 | |
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| 0.0296 | 8.2075 | 25000 | 0.2617 | |
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| 0.0309 | 8.3716 | 25500 | 0.2797 | |
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| 0.0366 | 8.5358 | 26000 | 0.2689 | |
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| 0.0351 | 8.6999 | 26500 | 0.2687 | |
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| 0.0384 | 8.8641 | 27000 | 0.2643 | |
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| 0.0365 | 9.0282 | 27500 | 0.2688 | |
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| 0.0265 | 9.1924 | 28000 | 0.2903 | |
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| 0.0299 | 9.3565 | 28500 | 0.2742 | |
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| 0.0347 | 9.5207 | 29000 | 0.2754 | |
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| 0.0311 | 9.6848 | 29500 | 0.2744 | |
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| 0.0345 | 9.8490 | 30000 | 0.2829 | |
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### Framework versions |
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- Transformers 4.48.1 |
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- Pytorch 2.6.0+cu124 |
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- Datasets 3.4.1 |
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- Tokenizers 0.21.1 |
