---
library_name: transformers
language:
- ar
license: apache-2.0
base_model: openai/whisper-large
tags:
- automatic-speech-recognition
- arabic
- generated_from_trainer
datasets:
- audiofolder
metrics:
- wer
model-index:
- name: Whisper Large Informal Arabic
  results:
  - task:
      type: automatic-speech-recognition
      name: Automatic Speech Recognition
    dataset:
      name: Informal Arabic
      type: audiofolder
      config: default
      split: None
      args: default
    metrics:
    - type: wer
      value: 24.96401151631478
      name: Wer
---

<!-- 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 Large Informal Arabic

This model is a fine-tuned version of [openai/whisper-large](https://huggingface.co/openai/whisper-large) on the Informal Arabic dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5264
- Wer: 24.9640
- Cer: 8.1265

## 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: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 16
- 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: 250
- training_steps: 2500
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Wer     | Cer    |
|:-------------:|:-------:|:----:|:---------------:|:-------:|:------:|
| 0.0054        | 13.1611 | 500  | 0.4210          | 27.3153 | 9.2113 |
| 0.0002        | 26.3221 | 1000 | 0.4803          | 24.9640 | 7.9997 |
| 0.0001        | 39.4832 | 1500 | 0.5063          | 24.6881 | 7.9997 |
| 0.0001        | 52.6443 | 2000 | 0.5200          | 24.7001 | 8.0326 |
| 0.0001        | 65.8054 | 2500 | 0.5264          | 24.9640 | 8.1265 |


### Framework versions

- Transformers 4.50.3
- Pytorch 2.6.0+cu124
- Datasets 3.5.0
- Tokenizers 0.21.1