---
library_name: transformers
language:
- ar
license: apache-2.0
base_model: openai/whisper-tiny
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: Whisper tiny AR - BH
  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 tiny AR - BH

This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co./openai/whisper-tiny) on the quran-ayat-speech-to-text dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0073
- Wer: 0.1246
- Cer: 0.0482

## 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: 5e-06
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Use 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    | Cer    |
|:-------------:|:------:|:----:|:---------------:|:------:|:------:|
| 0.0068        | 0.9801 | 37   | 0.0068          | 0.1158 | 0.0383 |
| 0.0071        | 1.9801 | 74   | 0.0067          | 0.1171 | 0.0394 |
| 0.0076        | 2.9801 | 111  | 0.0067          | 0.1222 | 0.0430 |
| 0.0062        | 3.9801 | 148  | 0.0067          | 0.1258 | 0.0409 |
| 0.005         | 4.9801 | 185  | 0.0068          | 0.1254 | 0.0406 |
| 0.0042        | 5.9801 | 222  | 0.0068          | 0.1242 | 0.0417 |
| 0.0055        | 6.9801 | 259  | 0.0070          | 0.1258 | 0.0425 |
| 0.0049        | 7.9801 | 296  | 0.0071          | 0.1240 | 0.0394 |


### Framework versions

- Transformers 4.47.0
- Pytorch 2.5.1+cu121
- Datasets 3.2.0
- Tokenizers 0.21.0