Fine-tune 資訊

• 原始模型: openai/whisper-small
• 使用音訊數量: 5044
• 使用音訊總長: 2.87 小時
• 音訊平均長度: 2.05 秒
• GPU: NVIDIA H100 PCIe x 1
• 訓練時間: 00:25:30
• 模型大小: 0.90 GB
• 訓練參數:
  • batch size: 14
  • eval batch size: 7
  • gradient checkpointing: False
  • fp16: False
  • bf16: True

Fine-tuned Whisper model for Legislative Yuan of Taiwan

• Loss: 0.0416
• Wer: 88.6288

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

• learning_rate: 1e-05
• train_batch_size: 14
• eval_batch_size: 7
• seed: 42
• 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: 500
• training_steps: 5000

Training results

Framework versions

• Transformers 4.51.3
• Pytorch 2.5.1
• Datasets 3.5.0
• Tokenizers 0.21.1