Wav2vec2-xlsr-Shemo
This model is a fine-tuned version of ehcalabres/wav2vec2-lg-xlsr-en-speech-emotion-recognition on the minoosh/shEMO dataset. It achieves the following results on the evaluation set:
- Loss: 0.9168
- Accuracy: 0.7267
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: 0.003
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 30
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 1.1825 | 1.0 | 150 | 1.1383 | 0.6267 |
| 1.3392 | 2.0 | 300 | 1.4398 | 0.5533 |
| 1.2058 | 3.0 | 450 | 1.1194 | 0.6300 |
| 1.0984 | 4.0 | 600 | 1.2049 | 0.6200 |
| 1.0033 | 5.0 | 750 | 1.0080 | 0.6500 |
| 0.9694 | 6.0 | 900 | 0.9878 | 0.6367 |
| 0.8506 | 7.0 | 1050 | 0.8965 | 0.7033 |
| 0.8068 | 8.0 | 1200 | 0.9359 | 0.6833 |
| 0.7674 | 9.0 | 1350 | 1.1235 | 0.6333 |
| 0.7817 | 10.0 | 1500 | 0.8682 | 0.6900 |
| 0.7172 | 11.0 | 1650 | 0.8289 | 0.7067 |
| 0.6989 | 12.0 | 1800 | 0.9318 | 0.7000 |
| 0.6127 | 13.0 | 1950 | 0.8712 | 0.6967 |
| 0.6311 | 14.0 | 2100 | 0.8965 | 0.7133 |
| 0.5901 | 15.0 | 2250 | 0.9008 | 0.7267 |
| 0.5667 | 16.0 | 2400 | 1.0093 | 0.7200 |
| 0.5652 | 17.0 | 2550 | 0.9032 | 0.7300 |
| 0.565 | 18.0 | 2700 | 0.9317 | 0.7267 |
| 0.5705 | 19.0 | 2850 | 1.0134 | 0.7133 |
| 0.4984 | 20.0 | 3000 | 0.9432 | 0.7367 |
| 0.5207 | 21.0 | 3150 | 0.9368 | 0.6933 |
| 0.5005 | 22.0 | 3300 | 0.9746 | 0.7033 |
| 0.5055 | 23.0 | 3450 | 1.0437 | 0.7133 |
| 0.4867 | 24.0 | 3600 | 1.0052 | 0.7067 |
| 0.5315 | 25.0 | 3750 | 0.9689 | 0.7200 |
| 0.4755 | 26.0 | 3900 | 0.8962 | 0.7367 |
| 0.5083 | 27.0 | 4050 | 0.9319 | 0.7300 |
| 0.4661 | 28.0 | 4200 | 0.9301 | 0.7233 |
| 0.4536 | 29.0 | 4350 | 0.9370 | 0.7267 |
| 0.4693 | 30.0 | 4500 | 0.9168 | 0.7267 |
Framework versions
- Transformers 4.29.2
- Pytorch 2.0.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3