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metadata
language:
  - ar
license: apache-2.0
base_model: openai/whisper-large
tags:
  - whisper-event
  - generated_from_trainer
datasets:
  - mozilla-foundation/common_voice_11_0
metrics:
  - wer
model-index:
  - name: Whisper Small ar - Mohammed Bakheet
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Common Voice 11.0
          type: mozilla-foundation/common_voice_11_0
          config: ar
          split: test
          args: ar
        metrics:
          - name: Wer
            type: wer
            value: 12.614980289093298

Whisper Small ar - Mohammed Bakheet

نموذج كلام للتعرف على الصوت، هذا النموذج يتميز بدقة عالية في التعرف على الصوت باللغة العربية.

This model is a fine-tuned version of openai/whisper-large on the Common Voice 11.0 dataset. It achieves the following results on the evaluation set:

  • Loss: 0.1921
  • Wer: 12.6150

Model description

This model is a fine-tuned version of openai/whisper-large on the Common Voice 11.0 dataset. It achieves 12.61 WER. Data augmentation can be implemented to further improve the model performance.

Training and evaluation data

More information needed

Training procedure

This model is trained on the Common Voice 11.0 dataset. It's trained on 64 cores CPU, Nvidia A100 GPU with 48 VRAM, and 100GB Disk space. The GPU utilization reached 100%. Please check the training hyperparameters below.

Training hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 1e-05
  • train_batch_size: 4
  • eval_batch_size: 4
  • seed: 42
  • gradient_accumulation_steps: 16
  • total_train_batch_size: 64
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 500
  • training_steps: 2000
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer
0.1952 1.6630 1000 0.1843 14.0098
0.0339 3.3261 2000 0.1921 12.6150

Framework versions

  • Transformers 4.43.3
  • Pytorch 2.2.0
  • Datasets 2.20.0
  • Tokenizers 0.19.1