---
library_name: transformers
base_model: ivrit-ai/whisper-large-v3-turbo
tags:
- generated_from_trainer
model-index:
- name: whisper-large-v3-turbo-ivrit-ai-coursera-fine-tuned
  results: []
datasets:
- imvladikon/hebrew_speech_coursera
---

<!-- 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-v3-turbo-ivrit-ai-coursera-fine-tuned

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.
It achieves the following results on the evaluation set:
- Loss: 0.2829

## Model description

This model created for my work for the Open University Of Israel.
[Here](https://colab.research.google.com/gist/zibib3/373bbc36c305899e29c1a91b9a834c97/.ipynb) you can see the notebook that used to
create this model, and [here](https://www.youtube.com/live/rEoG9vF0GAo) you can find me displaying the notebook.
I think that this model is useless becaus it has lower performance from its base model.



## 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: 5
- eval_batch_size: 5
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: constant_with_warmup
- lr_scheduler_warmup_ratio: 0.1
- lr_scheduler_warmup_steps: 500
- num_epochs: 10

### Training results

| Training Loss | Epoch  | Step  | Validation Loss |
|:-------------:|:------:|:-----:|:---------------:|
| 0.1907        | 0.1641 | 500   | 0.2266          |
| 0.2283        | 0.3283 | 1000  | 0.2217          |
| 0.2253        | 0.4924 | 1500  | 0.2154          |
| 0.2257        | 0.6566 | 2000  | 0.2080          |
| 0.2138        | 0.8207 | 2500  | 0.2102          |
| 0.2153        | 0.9849 | 3000  | 0.2056          |
| 0.1615        | 1.1490 | 3500  | 0.2128          |
| 0.1588        | 1.3132 | 4000  | 0.1677          |
| 0.1628        | 1.4773 | 4500  | 0.1656          |
| 0.168         | 1.6415 | 5000  | 0.1798          |
| 0.167         | 1.8056 | 5500  | 0.1710          |
| 0.1663        | 1.9698 | 6000  | 0.1828          |
| 0.1297        | 2.1339 | 6500  | 0.1722          |
| 0.1196        | 2.2981 | 7000  | 0.1762          |
| 0.1336        | 2.4622 | 7500  | 0.1779          |
| 0.1258        | 2.6264 | 8000  | 0.1821          |
| 0.1275        | 2.7905 | 8500  | 0.1796          |
| 0.1331        | 2.9547 | 9000  | 0.1786          |
| 0.0988        | 3.1188 | 9500  | 0.1982          |
| 0.0933        | 3.2830 | 10000 | 0.1888          |
| 0.0963        | 3.4471 | 10500 | 0.1927          |
| 0.0946        | 3.6113 | 11000 | 0.1979          |
| 0.1018        | 3.7754 | 11500 | 0.2031          |
| 0.1027        | 3.9396 | 12000 | 0.1971          |
| 0.0795        | 4.1037 | 12500 | 0.2016          |
| 0.0698        | 4.2679 | 13000 | 0.2017          |
| 0.0736        | 4.4320 | 13500 | 0.2058          |
| 0.0747        | 4.5962 | 14000 | 0.2033          |
| 0.0768        | 4.7603 | 14500 | 0.2057          |
| 0.0801        | 4.9245 | 15000 | 0.2076          |
| 0.067         | 5.0886 | 15500 | 0.2196          |
| 0.0539        | 5.2528 | 16000 | 0.2185          |
| 0.0563        | 5.4169 | 16500 | 0.2220          |
| 0.0594        | 5.5811 | 17000 | 0.2265          |
| 0.0651        | 5.7452 | 17500 | 0.2176          |
| 0.0655        | 5.9094 | 18000 | 0.2227          |
| 0.0533        | 6.0735 | 18500 | 0.2387          |
| 0.0441        | 6.2377 | 19000 | 0.2334          |
| 0.0474        | 6.4018 | 19500 | 0.2343          |
| 0.0506        | 6.5660 | 20000 | 0.2387          |
| 0.0504        | 6.7301 | 20500 | 0.2373          |
| 0.0502        | 6.8943 | 21000 | 0.2318          |
| 0.0441        | 7.0584 | 21500 | 0.2524          |
| 0.0375        | 7.2226 | 22000 | 0.2533          |
| 0.0379        | 7.3867 | 22500 | 0.2491          |
| 0.0382        | 7.5509 | 23000 | 0.2635          |
| 0.0427        | 7.7150 | 23500 | 0.2506          |
| 0.0439        | 7.8792 | 24000 | 0.2430          |
| 0.043         | 8.0433 | 24500 | 0.2575          |
| 0.0296        | 8.2075 | 25000 | 0.2617          |
| 0.0309        | 8.3716 | 25500 | 0.2797          |
| 0.0366        | 8.5358 | 26000 | 0.2689          |
| 0.0351        | 8.6999 | 26500 | 0.2687          |
| 0.0384        | 8.8641 | 27000 | 0.2643          |
| 0.0365        | 9.0282 | 27500 | 0.2688          |
| 0.0265        | 9.1924 | 28000 | 0.2903          |
| 0.0299        | 9.3565 | 28500 | 0.2742          |
| 0.0347        | 9.5207 | 29000 | 0.2754          |
| 0.0311        | 9.6848 | 29500 | 0.2744          |
| 0.0345        | 9.8490 | 30000 | 0.2829          |


### Framework versions

- Transformers 4.48.1
- Pytorch 2.6.0+cu124
- Datasets 3.4.1
- Tokenizers 0.21.1