---
language:
- ar
license: apache-2.0
base_model: openai/whisper-large-v3
tags:
- hf-asr-leaderboard
- generated_from_trainer
datasets:
- ahishamm/QURANICWhisperDataset
metrics:
- wer
model-index:
- name: QURANIC Whisper Large V3 - 10000
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: QURANICWhisperDataset
      type: ahishamm/QURANICWhisperDataset
      args: 'config: ar, split: train'
    metrics:
    - name: Wer
      type: wer
      value: 99.93905329450803
---

<!-- 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. -->

# QURANIC Whisper Large V3 - 10000

This model is a fine-tuned version of [openai/whisper-large-v3](https://huggingface.co./openai/whisper-large-v3) on the QURANICWhisperDataset dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2528
- Wer: 99.9391

## 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: 8
- 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: 10000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Wer      |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.0907        | 2.0   | 1000  | 0.1326          | 107.4287 |
| 0.0545        | 4.0   | 2000  | 0.1366          | 156.4231 |
| 0.0211        | 6.0   | 3000  | 0.1515          | 245.3308 |
| 0.0076        | 8.0   | 4000  | 0.1627          | 330.6630 |
| 0.0031        | 10.0  | 5000  | 0.1788          | 170.7794 |
| 0.0035        | 12.0  | 6000  | 0.1947          | 107.0630 |
| 0.0006        | 14.0  | 7000  | 0.2107          | 98.0091  |
| 0.0           | 16.0  | 8000  | 0.2208          | 97.8533  |
| 0.0           | 18.0  | 9000  | 0.2426          | 99.7833  |
| 0.0           | 20.0  | 10000 | 0.2528          | 99.9391  |


### Framework versions

- Transformers 4.39.2
- Pytorch 2.2.0
- Datasets 2.18.0
- Tokenizers 0.15.1