Fine-tune 資訊
- 原始模型:
openai/whisper-large-v3
- 使用音訊數量: 22318
- 使用音訊總長: 11.74 小時
- 音訊平均長度: 1.89 秒
- GPU:
NVIDIA H100 PCIe
x 1 - 訓練時間: 02:50:20
- 模型大小: 5.75 GB
- 訓練參數:
- batch size: 80
- eval batch size: 40
- gradient checkpointing: True
- fp16: False
- bf16: True
Fine-tuned Whisper model for Legislative Yuan of Taiwan
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: 0.0188
- Wer: 72.0212
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: 80
- eval_batch_size: 40
- 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: 50
- training_steps: 500
Training results
Training Loss | Epoch | Step | Validation Loss | Wer |
---|---|---|---|---|
0.0227 | 0.3584 | 100 | 0.0204 | 74.5344 |
0.0188 | 0.7168 | 200 | 0.0194 | 72.9145 |
0.0148 | 1.0753 | 300 | 0.0190 | 72.1575 |
0.0157 | 1.4337 | 400 | 0.0191 | 72.0969 |
0.0149 | 1.7921 | 500 | 0.0188 | 72.0212 |
Framework versions
- Transformers 4.51.3
- Pytorch 2.5.1
- Datasets 3.5.0
- Tokenizers 0.21.1
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Base model
openai/whisper-large-v3