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---
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: 106.18181818181817
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# 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: 3.2714
- Wer: 106.1818
## 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: 8
- 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 |
|:-------------:|:-------:|:----:|:---------------:|:--------:|
| 0.2223 | 5.2083 | 500 | 2.8313 | 106.8364 |
| 0.0126 | 10.4167 | 1000 | 3.2714 | 106.1818 |
### Framework versions
- Transformers 4.42.0.dev0
- Pytorch 2.3.0+cu121
- Datasets 2.19.2
- Tokenizers 0.19.1