whisper-finetuned-IEMOCAP
This model is a fine-tuned version of BurningFang/finetuned_whisper_for_speech_emotion_recognition_optimised on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 2.6875
- Accuracy: 0.5476
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: 3e-05
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- 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_steps: 0.1
- num_epochs: 5
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 5.4490 | 1.0 | 402 | 1.3078 | 0.5146 |
| 4.1216 | 2.0 | 804 | 1.3081 | 0.5177 |
| 1.7227 | 3.0 | 1206 | 1.5997 | 0.5327 |
| 0.5641 | 4.0 | 1608 | 2.3515 | 0.5389 |
| 0.2490 | 5.0 | 2010 | 2.6875 | 0.5476 |
Framework versions
- Transformers 5.9.0
- Pytorch 2.11.0+cu128
- Datasets 4.8.5
- Tokenizers 0.22.2
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