---
language:
- ar
license: apache-2.0
base_model: openai/whisper-large-v3
tags:
- hf-asr-leaderboard
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_11_0
metrics:
- wer
model-index:
- name: Whisper Large Ar - Rami
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: Common Voice 11.0
      type: mozilla-foundation/common_voice_11_0
      config: default
      split: None
      args: 'config: hi, split: test'
    metrics:
    - name: Wer
      type: wer
      value: 103.42342342342343
---

<!-- 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 Large Ar - Rami

This model is a fine-tuned version of [openai/whisper-large-v3](https://huggingface.co./openai/whisper-large-v3) on the Common Voice 11.0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1317
- Wer: 103.4234

## 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: 500
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer      |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.2845        | 0.16  | 100  | 0.2153          | 30.0541  |
| 0.1417        | 0.32  | 200  | 0.1466          | 53.8018  |
| 0.1446        | 0.48  | 300  | 0.1388          | 64.7568  |
| 0.1326        | 0.64  | 400  | 0.1371          | 128.7568 |
| 0.13          | 0.8   | 500  | 0.1317          | 103.4234 |


### Framework versions

- Transformers 4.39.0.dev0
- Pytorch 2.1.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2