whisper-large-v3-cv-capes-fs024-IEEE-pt
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: 0.1045
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: 5e-06
- train_batch_size: 128
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 256
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 1000
Training results
Training Loss | Epoch | Step | Validation Loss |
---|---|---|---|
0.1585 | 0.2315 | 50 | 0.1324 |
0.0913 | 0.4630 | 100 | 0.1042 |
0.0758 | 0.6944 | 150 | 0.0975 |
0.0732 | 0.9259 | 200 | 0.0940 |
0.0522 | 1.1574 | 250 | 0.0931 |
0.0536 | 1.3889 | 300 | 0.0919 |
0.0505 | 1.6204 | 350 | 0.0915 |
0.0491 | 1.8519 | 400 | 0.0913 |
0.0426 | 2.0833 | 450 | 0.0930 |
0.0358 | 2.3148 | 500 | 0.0933 |
0.036 | 2.5463 | 550 | 0.0934 |
0.0347 | 2.7778 | 600 | 0.0934 |
0.0344 | 3.0093 | 650 | 0.0935 |
0.0271 | 3.2407 | 700 | 0.0976 |
0.0269 | 3.4722 | 750 | 0.0988 |
0.027 | 3.7037 | 800 | 0.1005 |
0.0278 | 3.9352 | 850 | 0.0992 |
0.0228 | 4.1667 | 900 | 0.1037 |
0.0214 | 4.3981 | 950 | 0.1042 |
0.0225 | 4.6296 | 1000 | 0.1045 |
Framework versions
- Transformers 4.50.2
- Pytorch 2.5.1+cu124
- Datasets 3.6.0
- Tokenizers 0.21.2
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Base model
openai/whisper-large-v3