---
library_name: transformers
language:
- ar
license: apache-2.0
base_model: openai/whisper-Small
tags:
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_16_1
metrics:
- wer
model-index:
- name: Whisper Samll Ar
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: Common Voice 16.1
      type: mozilla-foundation/common_voice_16_1
      config: ar
      split: test
      args: 'config: ar, split: test'
    metrics:
    - name: Wer
      type: wer
      value: 44.497132774591975
---

<!-- 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 Samll Ar

This model is a fine-tuned version of [openai/whisper-Small](https://huggingface.co/openai/whisper-Small) on the Common Voice 16.1 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3402
- Wer: 44.4971

## 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: 16
- eval_batch_size: 8
- seed: 42
- 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: 4000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Wer     |
|:-------------:|:------:|:----:|:---------------:|:-------:|
| 0.3175        | 0.4122 | 1000 | 0.4157          | 50.2739 |
| 0.2705        | 0.8244 | 2000 | 0.3707          | 49.3769 |
| 0.1648        | 1.2366 | 3000 | 0.3512          | 45.7212 |
| 0.1485        | 1.6488 | 4000 | 0.3402          | 44.4971 |


### Framework versions

- Transformers 4.48.3
- Pytorch 2.5.1+cu124
- Datasets 3.2.0
- Tokenizers 0.21.0