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distill-ar

Automatic Speech Recognition
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Arabic
whisper
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distill-ar / README.md
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metadata
language:
  - ar
license: mit
base_model: distil-whisper/distil-large-v2
tags:
  - generated_from_trainer
datasets:
  - nadsoft/Jordan-Audio
metrics:
  - wer
model-index:
  - name: Hamsa distill alfa
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: nadsoft/Jordan-Audio
          type: nadsoft/Jordan-Audio
        metrics:
          - name: Wer
            type: wer
            value: 45.223367697594504

Hamsa distill alfa

This model is a fine-tuned version of distil-whisper/distil-large-v2 on the nadsoft/Jordan-Audio dataset. It achieves the following results on the evaluation set:

  • Loss: 0.9732
  • Wer Ortho: 47.5105
  • Wer: 45.2234

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: 0.0002
  • train_batch_size: 16
  • eval_batch_size: 16
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: constant_with_warmup
  • lr_scheduler_warmup_steps: 50
  • training_steps: 8000
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer Ortho Wer
0.2094 7.04 2000 0.8198 48.5575 46.3918
0.0883 14.08 4000 0.9112 47.4174 44.6048
0.0662 21.13 6000 0.9644 46.8125 44.6277
0.0496 28.17 8000 0.9732 47.5105 45.2234

Framework versions

  • Transformers 4.35.0
  • Pytorch 2.1.0+cu118
  • Datasets 2.14.6
  • Tokenizers 0.14.1