finetune_v1
This model is a fine-tuned version of openai/whisper-large-v3 on the None dataset. It achieves the following results on the evaluation set:
- Loss: 1.8789
- Wer: 115.1042
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: 4
- eval_batch_size: 2
- seed: 42
- distributed_type: multi-GPU
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 300
- training_steps: 2400
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.0 | 300.0 | 300 | 0.1414 | 50.0 |
| 0.0 | 600.0 | 600 | 0.3828 | 28.125 |
| 0.0 | 900.0 | 900 | 0.7280 | 97.9167 |
| 0.0 | 1200.0 | 1200 | 1.1172 | 126.5625 |
| 0.0 | 1500.0 | 1500 | 1.4219 | 125.5208 |
| 0.0 | 1800.0 | 1800 | 1.6904 | 119.7917 |
| 0.0 | 2100.0 | 2100 | 1.9209 | 115.1042 |
| 0.0 | 2400.0 | 2400 | 1.8789 | 115.1042 |
Framework versions
- Transformers 4.41.2
- Pytorch 2.2.0
- Datasets 2.20.0
- Tokenizers 0.19.1
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Base model
openai/whisper-large-v3