metadata

base_model: openai/whisper-small
datasets:
  - audiofolder
language:
  - ar
library_name: transformers
license: apache-2.0
metrics:
  - wer
tags:
  - generated_from_trainer
model-index:
  - name: quran-recitation-errors-test
    results:
      - task:
          type: automatic-speech-recognition
          name: Automatic Speech Recognition
        dataset:
          name: audiofolder
          type: audiofolder
          config: default
          split: train
          args: default
        metrics:
          - type: wer
            value: 9.619238476953909
            name: Wer

quran-recitation-errors-test

This model is a fine-tuned version of openai/whisper-small on the audiofolder dataset. It achieves the following results on the evaluation set:

Loss: 0.0732
Wer: 9.6192

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: 0.001
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: 100
training_steps: 500
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
0.7162	1.6949	100	0.7662	89.5792
0.5519	3.3898	200	0.5851	96.9940
0.3149	5.0847	300	0.2195	59.9198
0.0931	6.7797	400	0.1326	36.6733
0.0072	8.4746	500	0.0732	9.6192

Framework versions

Transformers 4.44.2
Pytorch 2.4.0+cu121
Datasets 2.21.0
Tokenizers 0.19.1