---
language:
- ara
license: apache-2.0
base_model: openai/whisper-base
tags:
- generated_from_trainer
datasets:
- google/fleurs
metrics:
- wer
model-index:
- name: Whisper Small Ar_Eg
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: Fleurs ar_eg
      type: google/fleurs
      config: ar_eg
      split: None
      args: 'config: ara, split: test'
    metrics:
    - name: Wer
      type: wer
      value: 23.1
---

<!-- 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_Eg

This model is a fine-tuned version of [openai/whisper-base](https://huggingface.co/openai/whisper-base) on the Fleurs ar_eg dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4820
- Wer: 23.1

## 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: 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     |
|:-------------:|:-------:|:----:|:---------------:|:-------:|
| 0.058         | 6.6667  | 1000 | 0.3934          | 23.6625 |
| 0.0014        | 13.3333 | 2000 | 0.4452          | 22.9875 |
| 0.0005        | 20.0    | 3000 | 0.4719          | 22.9375 |
| 0.0004        | 26.6667 | 4000 | 0.4820          | 23.1    |


### Framework versions

- Transformers 4.40.2
- Pytorch 2.1.2
- Datasets 2.19.1
- Tokenizers 0.19.1