---
library_name: transformers
language:
- ar
license: apache-2.0
base_model: openai/whisper-base
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: Whisper base AR - BA
  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. -->

# Whisper base AR - BA

This model is a fine-tuned version of [openai/whisper-base](https://huggingface.co/openai/whisper-base) on the quran-ayat-speech-to-text dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0853
- Wer: 0.1969

## 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: 0.0001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- 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
- num_epochs: 15
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch   | Step  | Validation Loss | Wer    |
|:-------------:|:-------:|:-----:|:---------------:|:------:|
| 2.0599        | 0.5858  | 1000  | 0.0910          | 0.2075 |
| 1.6156        | 1.1716  | 2000  | 0.0921          | 0.1917 |
| 1.5706        | 1.7575  | 3000  | 0.0891          | 0.1953 |
| 1.3401        | 2.3433  | 4000  | 0.0880          | 0.1882 |
| 1.2238        | 2.9291  | 5000  | 0.0865          | 0.1886 |
| 1.0654        | 3.5149  | 6000  | 0.0860          | 0.1922 |
| 1.0904        | 4.1008  | 7000  | 0.0859          | 0.2000 |
| 1.2607        | 4.6866  | 8000  | 0.0872          | 0.1882 |
| 1.147         | 5.2724  | 9000  | 0.0870          | 0.1944 |
| 1.1237        | 5.8582  | 10000 | 0.0856          | 0.1905 |
| 1.0093        | 6.4441  | 11000 | 0.0849          | 0.2001 |
| 0.9993        | 7.0299  | 12000 | 0.0839          | 0.1888 |
| 0.8718        | 7.6157  | 13000 | 0.0844          | 0.1894 |
| 0.8877        | 8.2015  | 14000 | 0.0838          | 0.1908 |
| 0.8187        | 8.7873  | 15000 | 0.0843          | 0.1957 |
| 0.8235        | 9.3732  | 16000 | 0.0838          | 0.1975 |
| 0.7972        | 9.9590  | 17000 | 0.0835          | 0.1911 |
| 0.8203        | 10.5448 | 18000 | 0.0844          | 0.1866 |
| 0.8593        | 11.1306 | 19000 | 0.0843          | 0.1916 |
| 0.8279        | 11.7165 | 20000 | 0.0840          | 0.1905 |
| 0.806         | 12.3023 | 21000 | 0.0827          | 0.1897 |
| 0.8343        | 12.8881 | 22000 | 0.0832          | 0.1891 |
| 0.7252        | 13.4739 | 23000 | 0.0830          | 0.1845 |
| 0.7685        | 14.0598 | 24000 | 0.0830          | 0.1919 |
| 0.7085        | 14.6456 | 25000 | 0.0829          | 0.1975 |


### Framework versions

- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1