metadata

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

Whisper Small for quran recognition

This model is a fine-tuned version of 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 4.39.0.dev0
Pytorch 2.1.2
Datasets 2.17.1
Tokenizers 0.15.1