---
language:
- ar
license: apache-2.0
base_model: openai/whisper-large-v3
tags:
- hf-asr-leaderboard
- generated_from_trainer
datasets:
- whitefox123/tashkeel
metrics:
- wer
model-index:
- name: Whisper large - tuned
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: CLARtts
      type: whitefox123/tashkeel
      config: default
      split: None
      args: 'config: ar, split: test'
    metrics:
    - name: Wer
      type: wer
      value: 217.9099099099099
---

<!-- 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 - tuned

This model is a fine-tuned version of [openai/whisper-large-v3](https://huggingface.co/openai/whisper-large-v3) on the CLARtts dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1346
- Wer: 217.9099

## 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: 500
- training_steps: 3125
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer      |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.0843        | 1.6   | 1000 | 0.1141          | 248.9730 |
| 0.024         | 3.2   | 2000 | 0.1194          | 274.9189 |
| 0.0108        | 4.8   | 3000 | 0.1346          | 217.9099 |


### Framework versions

- Transformers 4.38.0.dev0
- Pytorch 2.1.0+cu118
- Datasets 2.17.0
- Tokenizers 0.15.2