---
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: 0.0101
- Wer: 0.1150
- Cer: 0.0408

## 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: 16
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- 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: 20
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer    | Cer    |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|
| 0.0101        | 1.0   | 157  | 0.0101          | 0.1162 | 0.0413 |
| 0.008         | 2.0   | 314  | 0.0099          | 0.1148 | 0.0406 |
| 0.0055        | 3.0   | 471  | 0.0100          | 0.1140 | 0.0384 |
| 0.0079        | 4.0   | 628  | 0.0103          | 0.1155 | 0.0401 |
| 0.0033        | 5.0   | 785  | 0.0117          | 0.1146 | 0.0409 |
| 0.0023        | 6.0   | 942  | 0.0121          | 0.1184 | 0.0402 |
| 0.0011        | 7.0   | 1099 | 0.0133          | 0.1204 | 0.0425 |
| 0.001         | 8.0   | 1256 | 0.0138          | 0.1166 | 0.0402 |


### Framework versions

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