---
library_name: transformers
language:
- multilingual
license: apache-2.0
base_model: openai/whisper-Large
tags:
- generated_from_trainer
datasets:
- AhmedBadawy11/high_quality_ds
metrics:
- wer
model-index:
- name: Whisper Large UAE
  results: []
---

<!-- 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 UAE

This model is a fine-tuned version of [openai/whisper-Large](https://huggingface.co/openai/whisper-Large) on the high_quality_ds dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0000
- Wer: 0.0261

## 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: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- 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    |
|:-------------:|:-------:|:----:|:---------------:|:------:|
| 0.0024        | 15.8730 | 1000 | 0.0048          | 0.1303 |
| 0.0001        | 31.7460 | 2000 | 0.0001          | 0.0261 |
| 0.0           | 47.6190 | 3000 | 0.0001          | 0.0261 |
| 0.0           | 63.4921 | 4000 | 0.0000          | 0.0261 |


### Framework versions

- Transformers 4.48.0
- Pytorch 2.5.1+cu121
- Datasets 3.2.0
- Tokenizers 0.21.0