---
license: apache-2.0
base_model: openai/whisper-small
tags:
- audio
- automatic-speech-recognition
datasets:
- mozilla-foundation/common_voice_16_1
metrics:
- wer
widget:
  - example_title: Sample 1
    src: sample_ar.mp3
model-index:
- name: whisper-small-ar-v1
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: mozilla-foundation/common_voice_16_1
      type: mozilla-foundation/common_voice_16_1
      config: ar
      split: test
      args: ar
    metrics:
    - name: Wer
      type: wer
      value: 158.15321276282899
language:
- ar
library_name: transformers
pipeline_tag: automatic-speech-recognition
---



# whisper-small-ar-v1

This model is for Arabic automatic speech recognition (ASR). It is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Arabic portion of the `mozilla-foundation/common_voice_16_1` dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3354
- Wer: 158.1532

## Model description

Whisper model fine-tuned on Arabic data, following the [official tutorial](https://huggingface.co/blog/fine-tune-whisper).

## Intended uses & limitations

The model is not fully trained yet. Hence, it is not intended for professional use.

## Training and evaluation data

Training Data: CommonVoice (v16.1) Arabic train + validation splits  
Validation Data: CommonVoice (v16.1) Arabic test split

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 4000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer      |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.2742        | 0.82  | 1000 | 0.3790          | 275.2463 |
| 0.1625        | 1.65  | 2000 | 0.3353          | 228.5252 |
| 0.1002        | 2.47  | 3000 | 0.3311          | 238.8858 |
| 0.0751        | 3.3   | 4000 | 0.3354          | 158.1532 |


### Framework versions

- Transformers 4.37.2
- Pytorch 2.2.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.2