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Whisper-med-ml

This model is a fine-tuned version of openai/whisper-medium on the datasets: IMASC, MSC, OpenSLR Malayalam Train split, Festvox Malayalam .

It achieves the following results on the validation set : OpenSLR-Test

  • Loss: 0.0318
  • Wer: 14.7300

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
  • train_batch_size: 16
  • eval_batch_size: 8
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 1000
  • training_steps: 6000
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer
0.0599 0.4 1000 0.0910 42.4981
0.0341 0.79 2000 0.0584 30.0572
0.0183 1.19 3000 0.0439 23.1650
0.0147 1.58 4000 0.0363 18.7360
0.0107 1.98 5000 0.0322 16.4220
0.0032 2.37 6000 0.0318 14.7300

Framework versions

  • Transformers 4.37.2
  • Pytorch 2.1.1+cu121
  • Datasets 2.16.1
  • Tokenizers 0.15.1
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Finetuned from

Datasets used to train vrclc/Whisper-med-ml

Space using vrclc/Whisper-med-ml 1

Evaluation results