---
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 - 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 base AR - BH

This model is a fine-tuned version of [openai/whisper-base](https://huggingface.co/openai/whisper-base) on the quran-ayat-speech-to-text-segments dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0171
- Wer: 0.2407

## 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: 16
- eval_batch_size: 8
- 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    |
|:-------------:|:-------:|:----:|:---------------:|:------:|
| 0.0105        | 2.6667  | 100  | 0.0154          | 0.2514 |
| 0.0021        | 5.3333  | 200  | 0.0171          | 0.2407 |
| 0.0022        | 8.0     | 300  | 0.0200          | 0.2475 |
| 0.0026        | 10.6667 | 400  | 0.0215          | 0.2847 |
| 0.0027        | 13.3333 | 500  | 0.0237          | 0.2929 |
| 0.0014        | 16.0    | 600  | 0.0216          | 0.2548 |
| 0.0001        | 18.6667 | 700  | 0.0210          | 0.2386 |


### Framework versions

- Transformers 4.46.3
- Pytorch 2.4.0
- Datasets 3.1.0
- Tokenizers 0.20.3