---
base_model: openai/whisper-small
datasets:
- fleurs
language:
- ar
license: apache-2.0
metrics:
- wer
tags:
- hf-asr-leaderboard
- generated_from_trainer
model-index:
- name: Whisper Small - Chee Li
  results:
  - task:
      type: automatic-speech-recognition
      name: Automatic Speech Recognition
    dataset:
      name: Google Fleurs
      type: fleurs
      config: ar_eg
      split: None
      args: 'config: ar split: test'
    metrics:
    - type: wer
      value: 35.462500000000006
      name: Wer
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# Whisper Small - Chee Li

This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Google Fleurs dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4791
- Wer: 35.4625

## 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     |
|:-------------:|:-------:|:----:|:---------------:|:-------:|
| 0.0147        | 6.6667  | 1000 | 0.3868          | 34.5125 |
| 0.0009        | 13.3333 | 2000 | 0.4417          | 36.6375 |
| 0.0004        | 20.0    | 3000 | 0.4693          | 35.5625 |
| 0.0003        | 26.6667 | 4000 | 0.4791          | 35.4625 |


### Framework versions

- Transformers 4.44.0
- Pytorch 2.3.1+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1