VMadalina/whisper-large-test-music

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Whisper Large Ro - VM3

This model is a fine-tuned version of openai/whisper-large on the None dataset. It achieves the following results on the evaluation set:

• —Loss: 0.1271
• —Wer: 19.1998

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: 2e-05
• —trainbatchsize: 32
• —evalbatchsize: 32
• —seed: 42
• —gradientaccumulationsteps: 2
• —totaltrainbatch_size: 64
• —optimizer: Use adamwtorch with betas=(0.9,0.999) and epsilon=1e-08 and optimizerargs=No additional optimizer arguments
• —lrschedulertype: linear
• —lrschedulerwarmup_steps: 100
• —training_steps: 500
• —mixedprecisiontraining: Native AMP

Training results

Framework versions

• —Transformers 4.50.1
• —Pytorch 2.6.0+cu124
• —Datasets 2.13.1
• —Tokenizers 0.21.1