---
language:
- ar
license: apache-2.0
base_model: openai/whisper-medium
tags:
- generated_from_trainer
datasets:
- arabic_speech_corpus
model-index:
- name: Whisper Medium Arabic
  results: []
metrics:
- wer
library_name: transformers
pipeline_tag: automatic-speech-recognition
---

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[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/abdelrahmanabosteet/Graduation_project/runs/uszjncge)
# Whisper Medium Arabic

This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the Arabic Speech Corpus dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.0794
- eval_wer: 5.4226
- eval_runtime: 200.1714
- eval_samples_per_second: 0.5
- eval_steps_per_second: 0.5
- epoch: 5.7143
- step: 250

## 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: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 400

### Framework versions

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