---
library_name: transformers
language:
- ar
license: apache-2.0
base_model: openai/whisper-small
tags:
- generated_from_trainer
datasets:
- UBC-NLP/Casablanca
- fixie-ai/common_voice_17_0
- deepdml/Tunisian_MSA
- google/fleurs
- ymoslem/MediaSpeech
metrics:
- wer
model-index:
- name: Whisper Small ar
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: Common Voice 17.0
      type: UBC-NLP/Casablanca
    metrics:
    - name: Wer
      type: wer
      value: 27.151539633866435
---
<!-- 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

This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 17.0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3340
- Wer: 27.1515
- Cer: 7.9929

## 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: 32
- eval_batch_size: 32
- seed: 42
- 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_ratio: 0.04
- training_steps: 18000

### Training results

| Training Loss | Epoch  | Step  | Validation Loss | Wer     | Cer     |
|:-------------:|:------:|:-----:|:---------------:|:-------:|:-------:|
| 0.5511        | 0.0556 | 1000  | 0.4263          | 38.7837 | 12.1682 |
| 0.2273        | 0.1111 | 2000  | 0.3858          | 34.5513 | 10.7969 |
| 0.1023        | 0.1667 | 3000  | 0.3663          | 33.5690 | 10.3863 |
| 0.0545        | 0.2222 | 4000  | 0.3567          | 31.5786 | 9.2661  |
| 0.043         | 0.2778 | 5000  | 0.3421          | 31.7236 | 9.3731  |
| 0.0254        | 0.3333 | 6000  | 0.3316          | 30.0600 | 9.0426  |
| 0.0219        | 0.3889 | 7000  | 0.3269          | 29.6451 | 8.7922  |
| 0.0177        | 0.4444 | 8000  | 0.3258          | 29.2705 | 8.7774  |
| 0.0209        | 0.5    | 9000  | 0.3157          | 28.5177 | 8.5056  |
| 0.0212        | 0.5556 | 10000 | 0.3105          | 28.9345 | 8.4034  |
| 0.0093        | 0.6111 | 11000 | 0.3111          | 27.8052 | 8.1165  |
| 0.012         | 0.6667 | 12000 | 0.3158          | 27.7042 | 8.2345  |
| 0.0124        | 0.7222 | 13000 | 0.3119          | 27.0304 | 7.9191  |
| 0.005         | 1.0393 | 14000 | 0.3392          | 27.5739 | 8.0862  |
| 0.008         | 1.0949 | 15000 | 0.3334          | 27.3590 | 7.9829  |
| 0.0049        | 1.1504 | 16000 | 0.3451          | 27.2911 | 8.0076  |
| 0.0032        | 1.206  | 17000 | 0.3468          | 27.0873 | 7.9693  |
| 0.0031        | 1.2616 | 18000 | 0.3340          | 27.1515 | 7.9929  |


### Framework versions

- Transformers 4.48.0.dev0
- Pytorch 2.5.1+cu121
- Datasets 3.6.0
- Tokenizers 0.21.0

## Citation

Please cite the model using the following BibTeX entry:

```bibtex
@misc{deepdml/whisper-small-ar-mix-norm,
      title={Fine-tuned Whisper small ASR model for speech recognition in Arabic},
      author={Jimenez, David},
      howpublished={\url{https://huggingface.co/deepdml/whisper-small-ar-mix-norm}},
      year={2026}
    }
```