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---
language:
- ara
license: apache-2.0
base_model: openai/whisper-small
tags:
- hf-asr-leaderboard
- generated_from_trainer
datasets:
- uoseftalaat/GP
metrics:
- wer
model-index:
- name: Whisper Small for quran recognition
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Quran_requiters
type: uoseftalaat/GP
config: default
split: test
args: 'config: default, split: train'
metrics:
- name: Wer
type: wer
value: 3.369434416365824
---
<!-- 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 Small for quran recognition
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Quran_requiters dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0183
- Wer: 3.3694
## 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: 4000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.0026 | 3.24 | 1000 | 0.0205 | 4.4868 |
| 0.0003 | 6.47 | 2000 | 0.0180 | 3.3522 |
| 0.0003 | 6.49 | 2005 | 0.0180 | 3.3522 |
| 0.0003 | 6.5 | 2010 | 0.0180 | 3.3522 |
| 0.0001 | 9.71 | 3000 | 0.0180 | 3.2663 |
| 0.0 | 12.94 | 4000 | 0.0183 | 3.3694 |
### Framework versions
- Transformers 4.40.0.dev0
- Pytorch 2.1.2
- Datasets 2.17.1
- Tokenizers 0.15.1
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