---
language:
- ara
license: apache-2.0
base_model: openai/whisper-small
tags:
- hf-asr-leaderboard
- generated_from_trainer
datasets:
- AsemBadr/GP
metrics:
- wer
model-index:
- name: Whisper Small for Quran Recognition
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: Quran_Reciters
      type: AsemBadr/GP
      config: default
      split: test
      args: 'config: default, split: train'
    metrics:
    - name: Wer
      type: wer
      value: 3.4381983840467596
---

<!-- 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 Small for Quran Recognition

This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Quran_Reciters dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0180
- Wer: 3.4382

## 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: 2000

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.0268        | 1.62  | 500  | 0.0316          | 8.0626 |
| 0.002         | 3.24  | 1000 | 0.0205          | 4.3665 |
| 0.0005        | 4.85  | 1500 | 0.0178          | 3.4210 |
| 0.0002        | 6.47  | 2000 | 0.0180          | 3.4382 |


### Framework versions

- Transformers 4.40.0.dev0
- Pytorch 2.1.2
- Datasets 2.17.1
- Tokenizers 0.15.2