whisper-small-ar-2 / README.md
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metadata
license: apache-2.0
base_model: arun100/whisper-small-ar-1
tags:
  - whisper-event
  - generated_from_trainer
datasets:
  - google/fleurs
metrics:
  - wer
model-index:
  - name: Whisper Small Arabic
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: google/fleurs ar_eg
          type: google/fleurs
          config: ar_eg
          split: test
          args: ar_eg
        metrics:
          - name: Wer
            type: wer
            value: 28.809032414714096

Whisper Small Arabic

This model is a fine-tuned version of arun100/whisper-small-ar-1 on the google/fleurs ar_eg dataset. It achieves the following results on the evaluation set:

  • Loss: 0.4548
  • Wer: 28.8090

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: 5e-07
  • train_batch_size: 32
  • eval_batch_size: 32
  • seed: 42
  • gradient_accumulation_steps: 2
  • 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: 5000
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer
0.2414 52.0 500 0.3988 30.5694
0.0412 105.0 1000 0.4284 30.5694
0.0147 157.0 1500 0.4548 28.8090
0.0084 210.0 2000 0.4738 29.1125
0.0057 263.0 2500 0.4888 29.3553
0.0043 315.0 3000 0.5010 29.2218
0.0034 368.0 3500 0.5108 29.4889
0.0029 421.0 4000 0.5185 29.5010
0.0026 473.0 4500 0.5236 29.4889
0.0024 526.0 5000 0.5256 29.5375

Framework versions

  • Transformers 4.38.0.dev0
  • Pytorch 2.1.2+cu121
  • Datasets 2.16.2.dev0
  • Tokenizers 0.15.0