---
library_name: transformers
language:
- ar
license: apache-2.0
base_model: openai/whisper-small
tags:
- whisper-event
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_11_0
metrics:
- wer
model-index:
- name: Whisper Small AR - Mohammed Bakheet
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: Common Voice 11.0
      type: mozilla-foundation/common_voice_11_0
      config: ar
      split: test
      args: ar
    metrics:
    - name: Wer
      type: wer
      value: 20.45616669795382
---

<!-- 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 Small AR - Mohammed Bakheet

This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 11.0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2601
- Wer: 20.4562

## 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: 8
- seed: 42
- gradient_accumulation_steps: 16
- 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
- training_steps: 5000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Wer     |
|:-------------:|:------:|:----:|:---------------:|:-------:|
| 0.5279        | 0.4158 | 500  | 0.3311          | 27.6591 |
| 0.2513        | 0.8316 | 1000 | 0.2866          | 24.5504 |
| 0.1673        | 1.2478 | 1500 | 0.2735          | 22.8928 |
| 0.1324        | 1.6635 | 2000 | 0.2645          | 21.8153 |
| 0.1138        | 2.0797 | 2500 | 0.2613          | 21.3816 |
| 0.064         | 2.4955 | 3000 | 0.2651          | 21.0006 |
| 0.0615        | 2.9113 | 3500 | 0.2601          | 20.4562 |


### Framework versions

- Transformers 4.46.2
- Pytorch 2.5.1+cu124
- Datasets 3.1.0
- Tokenizers 0.20.3