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deepdml/whisper-tiny-ar-mix-norm

sourceHugging Faceapache-2.0updated 7mo agoView on Hugging Face
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Model Card

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Whisper Tiny ar

This model is a fine-tuned version of openai/whisper-tiny on the Common Voice 17.0 dataset. It achieves the following results on the evaluation set:

  • Loss: 0.5962
  • Wer: 52.3622
  • Cer: 18.7245

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
  • trainbatchsize: 64
  • evalbatchsize: 64
  • seed: 42
  • optimizer: Use adamwtorch with betas=(0.9,0.999) and epsilon=1e-08 and optimizerargs=No additional optimizer arguments
  • lrschedulertype: linear
  • lrschedulerwarmup_ratio: 0.04
  • training_steps: 18000

Training results

Training LossEpochStepValidation LossWerCer
1.12340.055610000.810667.099425.5658
0.89570.111120000.723462.531023.3911
0.71140.166730000.687159.767521.8722
0.63460.222240000.663758.197620.7336
0.49610.277850000.654557.740420.7048
0.43540.333360000.647356.794820.2061
0.39240.388970000.632555.840020.0139
0.34660.444480000.627455.417620.1441
0.29790.590000.620654.699719.6005
0.30990.5556100000.615054.016619.3231
0.26810.6111110000.612053.598019.1106
0.23830.6667120000.611353.557619.4238
0.25820.7222130000.606052.751518.7573
0.15430.7778140000.601852.617518.4895
0.23560.8333150000.602352.990218.9782
0.20310.8889160000.598452.516518.8550
0.24370.9444170000.595152.492618.7514
0.22691.0180000.596252.362218.7245

Framework versions

  • Transformers 4.48.0.dev0
  • Pytorch 2.5.1+cu121
  • Datasets 3.6.0
  • Tokenizers 0.21.0

Citation

Please cite the model using the following BibTeX entry:

bibtex
@misc{deepdml/whisper-tiny-ar-mix-norm,
      title={Fine-tuned Whisper tiny ASR model for speech recognition in Arabic},
      author={Jimenez, David},
      howpublished={\url{https://huggingface.co/deepdml/whisper-tiny-ar-mix-norm}},
      year={2026}
    }