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deepdml/whisper-large-v3-turbo-ta-mix-norm

sourceHugging Facemitupdated 7mo agoView on Hugging Face
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Whisper Turbo ta

This model is a fine-tuned version of openai/whisper-large-v3-turbo on the Common Voice 17.0 dataset. It achieves the following results on the evaluation set:

  • —Loss: 0.1246
  • —Wer: 28.7424
  • —Cer: 4.7569

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: 16
  • —evalbatchsize: 16
  • —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
0.22070.055610000.238848.52709.7063
0.13060.111120000.212243.23408.2086
0.10750.166730000.201041.04427.7213
0.12790.222240000.190638.83117.2164
0.10840.277850000.182037.95916.9916
0.07070.333360000.170135.26566.3098
0.06760.388970000.161134.96286.1673
0.07530.444480000.157134.13845.9429
0.07140.590000.157333.83186.0002
0.08320.5556100000.157233.63095.8641
0.06630.6111110000.148832.94695.6721
0.04880.6667120000.147532.04265.5184
0.04620.7222130000.146631.67815.3493
0.03620.7778140000.144131.15125.2179
0.05280.8333150000.130229.97774.9914
0.05070.8889160000.130429.80514.9342
0.04510.9444170000.127529.51914.8730
0.04251.0180000.124628.74244.7569

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-large-v3-turbo-ta-mix-norm,
      title={Fine-tuned Whisper turbo ASR model for speech recognition in Tamil},
      author={Jimenez, David},
      howpublished={\url{https://huggingface.co/deepdml/whisper-large-v3-turbo-ta-mix-norm}},
      year={2026}
    }