---
language:
- ar
license: apache-2.0
base_model: openai/whisper-medium
tags:
- generated_from_trainer
datasets:
- Arbi-Houssem/comondov
metrics:
- wer
model-index:
- name: Whisper Tunisien
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: comondov
      type: Arbi-Houssem/comondov
      args: 'config: ar, split: test'
    metrics:
    - name: Wer
      type: wer
      value: 62.71186440677966
---

<!-- 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 Tunisien

This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the comondov dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7085
- Wer: 62.7119

## 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: 1000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer     |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.1296        | 50.0  | 200  | 1.5371          | 63.0200 |
| 0.0012        | 100.0 | 400  | 1.6181          | 61.4792 |
| 0.0005        | 150.0 | 600  | 1.6781          | 63.3282 |
| 0.0004        | 200.0 | 800  | 1.7005          | 61.9414 |
| 0.0003        | 250.0 | 1000 | 1.7085          | 62.7119 |


### Framework versions

- Transformers 4.41.0.dev0
- Pytorch 2.2.1+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1