---
library_name: transformers
language:
- ar
license: apache-2.0
base_model: openai/whisper-base
tags:
- generated_from_trainer
datasets:
- RetaSy/quranic_audio_dataset
metrics:
- wer
model-index:
- name: Whisper Base Ar - GPTeam
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: quranic_audio_dataset
      type: RetaSy/quranic_audio_dataset
      args: 'config: ar, split: test'
    metrics:
    - name: Wer
      type: wer
      value: 29.20499342969777
---

<!-- 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 Base Ar - GPTeam

This model is a fine-tuned version of [openai/whisper-base](https://huggingface.co/openai/whisper-base) on the quranic_audio_dataset dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0527
- Wer: 29.2050

## 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: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 4000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Wer     |
|:-------------:|:-------:|:----:|:---------------:|:-------:|
| 0.0771        | 2.9240  | 1000 | 0.0722          | 34.2806 |
| 0.0183        | 5.8480  | 2000 | 0.0553          | 30.8476 |
| 0.0062        | 8.7719  | 3000 | 0.0527          | 30.7654 |
| 0.0023        | 11.6959 | 4000 | 0.0527          | 29.2050 |


### Framework versions

- Transformers 4.49.0
- Pytorch 2.5.1+cu124
- Datasets 3.3.2
- Tokenizers 0.21.0