vrclc/Malasar_Medium_DTF

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leenag/Malasar_Medium

This model is a fine-tuned version of openai/whisper-medium on the Spoken Bible Corpus: Malasar dataset. It achieves the following results on the evaluation set:

• —Loss: 0.5907
• —Wer: 50.2570

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: 32
• —evalbatchsize: 16
• —seed: 42
• —optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• —lrschedulertype: linear
• —lrschedulerwarmup_steps: 500
• —training_steps: 2000
• —mixedprecisiontraining: Native AMP

Training results

Framework versions

• —Transformers 4.41.2
• —Pytorch 2.0.1+cu117
• —Datasets 2.16.0
• —Tokenizers 0.19.1

Citation

@misc{multistage2024,
      title={Multistage Fine-tuning Strategies for Automatic Speech Recognition in Low-resource Languages}, 
      author={Leena G Pillai, Kavya Manohar, Basil K Raju, Elizabeth Sherly},
      year={2024},
      eprint={2411.04573},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2411.04573}, 
}