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alakxender/whisper-large-dv-a40

🤗 Hugging Face 来源automatic-speech-recognitionapache-2.01.5B 参数6.2 GBsafetensors✓ 3 个校验和今天更新
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在你的模型文件夹旁边运行它。它会制作种子、将文件与 Hugging Face 比对，然后提交。你只需开始做种，并粘贴你账户中的密钥。它只读取你的文件，绝不修改。如果愿意，可以先阅读脚本。

curl -fsSL https://pirateface.co/package.sh | bash -s -- --repo alakxender/whisper-large-dv-a40 ./model-folder
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This model is a fine-tuned version of openai/whisper-large-v3 on an unknown dataset. It achieves the following results on the evaluation set:

  • Loss: 0.0252
  • Wer: 2.0163
  • Wer Ortho: 15.2648

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: 32
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: constant_with_warmup
  • lr_scheduler_warmup_steps: 50
  • training_steps: 4000
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer Wer Ortho
0.0426 0.0354 500 0.0501 3.6048 24.5780
0.0293 0.0709 1000 0.0367 2.6889 21.3792
0.0251 0.1063 1500 0.0317 2.3869 17.6751
0.0244 0.1418 2000 0.0296 2.2782 16.7890
0.0209 0.1772 2500 0.0284 2.2486 16.2831
0.0205 0.2126 3000 0.0254 1.9749 14.9776
0.0234 0.2481 3500 0.0261 2.1892 15.1784
0.0229 0.2835 4000 0.0252 2.0163 15.2648

Framework versions

  • Transformers 4.41.0.dev0
  • Pytorch 2.3.0+cu121
  • Datasets 2.19.0
  • Tokenizers 0.19.1