metadata
language:
- ar
license: apache-2.0
base_model: openai/whisper-medium
tags:
- generated_from_trainer
datasets:
- tarteel-ai/EA-UD
- tarteel-ai/everyayah
metrics:
- wer
model-index:
- name: Whisper Medium ar-quran
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 17.0
type: tarteel-ai/EA-UD
metrics:
- name: Wer
type: wer
value: 0.15533980582524273
Whisper Medium ar-quran
This model is a fine-tuned version of openai/whisper-medium on the Common Voice 17.0 dataset. It achieves the following results on the evaluation set:
- Loss: 1.0757
- Wer: 0.1553
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_ratio: 0.04
- training_steps: 15000
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.0377 | 1.0 | 15000 | 1.0757 | 0.1553 |
Framework versions
- Transformers 4.42.0.dev0
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
Citation
@misc{deepdml/whisper-medium-ar-quran-mix,
title={Fine-tuned Whisper medium ASR model for speech recognition in Arabic},
author={Jimenez, David},
howpublished={\url{https://huggingface.co/deepdml/whisper-medium-ar-quran-mix}},
year={2025}
}