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wavlm-basic_n-f-n_8batch_5sec_0.0001lr_unfrozen
This model is a fine-tuned version of microsoft/wavlm-large on the None dataset. It achieves the following results on the evaluation set:
- Loss: 1.0704
- Accuracy: 0.7333
- F1: 0.7308
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: 0.0001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.003
- num_epochs: 1000
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|---|---|---|---|---|---|
| 2.3031 | 0.98 | 24 | 2.3002 | 0.1667 | 0.1148 |
| 2.2766 | 2.0 | 49 | 2.2805 | 0.15 | 0.0930 |
| 2.2298 | 2.98 | 73 | 2.0679 | 0.2333 | 0.1421 |
| 1.9839 | 4.0 | 98 | 1.8757 | 0.25 | 0.1380 |
| 1.7495 | 4.98 | 122 | 1.5981 | 0.4 | 0.3370 |
| 1.5318 | 6.0 | 147 | 1.4640 | 0.45 | 0.3698 |
| 1.2765 | 6.98 | 171 | 1.3181 | 0.5167 | 0.4437 |
| 1.261 | 8.0 | 196 | 1.0905 | 0.5833 | 0.5429 |
| 1.078 | 8.98 | 220 | 1.0944 | 0.55 | 0.5244 |
| 0.9116 | 10.0 | 245 | 0.8228 | 0.6167 | 0.5603 |
| 0.8973 | 10.98 | 269 | 0.8632 | 0.5833 | 0.5266 |
| 0.8033 | 12.0 | 294 | 0.9061 | 0.65 | 0.6398 |
| 0.7183 | 12.98 | 318 | 0.8047 | 0.7 | 0.6877 |
| 0.7526 | 14.0 | 343 | 0.6695 | 0.7333 | 0.7176 |
| 0.6381 | 14.98 | 367 | 0.7510 | 0.7833 | 0.7788 |
| 0.5266 | 16.0 | 392 | 0.6154 | 0.8 | 0.7901 |
| 0.4485 | 16.98 | 416 | 0.8614 | 0.75 | 0.7359 |
| 0.5123 | 18.0 | 441 | 1.0848 | 0.65 | 0.6306 |
| 0.4094 | 18.98 | 465 | 0.6748 | 0.7667 | 0.7680 |
| 0.3114 | 20.0 | 490 | 0.7406 | 0.75 | 0.7389 |
| 0.2668 | 20.98 | 514 | 0.8419 | 0.75 | 0.7424 |
Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3
