sudoping01 / maliba-asr-v2

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Model's Last Updated: March 18 2025

Introduction of maliba-asr-v2

Model Details of maliba-asr-v2

maliba-asr-v2

This model is a fine-tuned version of openai/whisper-large-v2 on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 0.3245
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: 0.001
  • train_batch_size: 32
  • eval_batch_size: 32
  • seed: 42
  • gradient_accumulation_steps: 4
  • total_train_batch_size: 128
  • optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 50
  • num_epochs: 4
  • mixed_precision_training: Native AMP
Training results
Training Loss Epoch Step Validation Loss
0.3987 1.0 531 0.4138
0.3376 2.0 1062 0.3620
0.2863 3.0 1593 0.3361
0.2484 4.0 2124 0.3245
Framework versions
  • PEFT 0.14.1.dev0
  • Transformers 4.50.0.dev0
  • Pytorch 2.5.1+cu124
  • Datasets 3.2.0
  • Tokenizers 0.21.0

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