---
library_name: transformers
language:
- ar
license: mit
base_model: openai/whisper-large-v3-turbo
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: turboardj0.2
  results: []
---

<!-- 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. -->

# turboardj0.2

This model is a fine-tuned version of [openai/whisper-large-v3-turbo](https://huggingface.co/openai/whisper-large-v3-turbo) on the test dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1442
- Wer: 8.7712

## 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: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 5000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Wer     |
|:-------------:|:------:|:----:|:---------------:|:-------:|
| 0.2254        | 0.8873 | 1000 | 0.1703          | 12.7173 |
| 0.114         | 1.7746 | 2000 | 0.1476          | 10.4795 |
| 0.0556        | 2.6619 | 3000 | 0.1418          | 9.0360  |
| 0.0253        | 3.5492 | 4000 | 0.1485          | 8.4765  |
| 0.0117        | 4.4366 | 5000 | 0.1442          | 8.7712  |


### Framework versions

- Transformers 4.45.2
- Pytorch 2.4.1+cu121
- Datasets 3.0.1
- Tokenizers 0.20.1