sudoping01 / whisereer-v0

huggingface.co
Total runs: 2
24-hour runs: 0
7-day runs: 0
30-day runs: 0
Model's Last Updated: August 12 2025

Introduction of whisereer-v0

Model Details of whisereer-v0

whisereer-v0

This model is a fine-tuned version of openai/whisper-large-v2 on an unknown dataset. It achieves the following results on the evaluation set:

  • Loss: 1.8428
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: 8
  • eval_batch_size: 8
  • seed: 42
  • gradient_accumulation_steps: 4
  • total_train_batch_size: 32
  • 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: 6
  • mixed_precision_training: Native AMP
Training results
Training Loss Epoch Step Validation Loss
1.4502 1.0 427 1.7092
1.2366 2.0 854 1.6973
1.1277 3.0 1281 1.6775
0.9028 4.0 1708 1.6519
0.7469 5.0 2135 1.7480
0.5263 5.9877 2556 1.8428
Framework versions
  • PEFT 0.15.2
  • Transformers 4.51.3
  • Pytorch 2.5.1+cu124
  • Datasets 3.5.1
  • Tokenizers 0.21.1

Runs of sudoping01 whisereer-v0 on huggingface.co

2
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3-day runs
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7-day runs
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https://huggingface.co/sudoping01/whisereer-v0

whisereer-v0 install

whisereer-v0 is an open source model from GitHub that offers a free installation service, and any user can find whisereer-v0 on GitHub to install. At the same time, huggingface.co provides the effect of whisereer-v0 install, users can directly use whisereer-v0 installed effect in huggingface.co for debugging and trial. It also supports api for free installation.

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https://huggingface.co/sudoping01/whisereer-v0

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