wav2vec2-base-finetuned-ks
This model is a fine-tuned version of facebook/wav2vec2-base on the None dataset. It achieves the following results on the evaluation set:
- Loss: 1.1376
- Accuracy: 0.8210
- F1: 0.8209
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: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|---|---|---|---|---|---|
| 1.3731 | 0.99 | 35 | 1.3532 | 0.3767 | 0.2859 |
| 1.3039 | 2.0 | 71 | 1.2740 | 0.4237 | 0.3434 |
| 1.2185 | 2.99 | 106 | 1.1573 | 0.5020 | 0.4423 |
| 1.0887 | 4.0 | 142 | 1.1107 | 0.5013 | 0.4389 |
| 1.0183 | 4.99 | 177 | 1.0801 | 0.5610 | 0.5348 |
| 0.8625 | 6.0 | 213 | 0.9364 | 0.6373 | 0.6285 |
| 0.7487 | 6.99 | 248 | 0.9735 | 0.6048 | 0.5867 |
| 0.6151 | 8.0 | 284 | 0.8946 | 0.6698 | 0.6735 |
| 0.5081 | 8.99 | 319 | 0.8748 | 0.6797 | 0.6855 |
| 0.4559 | 10.0 | 355 | 0.8701 | 0.6850 | 0.6832 |
| 0.4347 | 10.99 | 390 | 0.8887 | 0.7003 | 0.7040 |
| 0.2845 | 12.0 | 426 | 0.8715 | 0.7129 | 0.7145 |
| 0.275 | 12.99 | 461 | 0.8846 | 0.7268 | 0.7263 |
| 0.2301 | 14.0 | 497 | 0.8651 | 0.7261 | 0.7324 |
| 0.1657 | 14.99 | 532 | 0.8573 | 0.7473 | 0.7473 |
| 0.1593 | 16.0 | 568 | 0.8472 | 0.7420 | 0.7443 |
| 0.1398 | 16.99 | 603 | 0.7433 | 0.7825 | 0.7829 |
| 0.1318 | 18.0 | 639 | 0.7989 | 0.7739 | 0.7768 |
| 0.1425 | 18.99 | 674 | 0.7967 | 0.7759 | 0.7788 |
| 0.1116 | 20.0 | 710 | 0.8969 | 0.7659 | 0.7650 |
| 0.0716 | 20.99 | 745 | 0.9783 | 0.7434 | 0.7480 |
| 0.0909 | 22.0 | 781 | 0.9413 | 0.7593 | 0.7626 |
| 0.0691 | 22.99 | 816 | 0.9298 | 0.7832 | 0.7832 |
| 0.068 | 24.0 | 852 | 0.9522 | 0.7725 | 0.7744 |
| 0.0416 | 24.99 | 887 | 0.9624 | 0.7686 | 0.7746 |
| 0.0569 | 26.0 | 923 | 0.9376 | 0.7832 | 0.7832 |
| 0.0369 | 26.99 | 958 | 1.0163 | 0.7845 | 0.7843 |
| 0.0482 | 28.0 | 994 | 1.0013 | 0.7931 | 0.7895 |
| 0.0497 | 28.99 | 1029 | 1.1005 | 0.7725 | 0.7713 |
| 0.0427 | 30.0 | 1065 | 1.0346 | 0.7891 | 0.7901 |
| 0.0252 | 30.99 | 1100 | 1.0611 | 0.7871 | 0.7883 |
| 0.0268 | 32.0 | 1136 | 1.0436 | 0.7944 | 0.7962 |
| 0.022 | 32.99 | 1171 | 1.0217 | 0.8031 | 0.8012 |
| 0.0127 | 34.0 | 1207 | 1.0936 | 0.7971 | 0.7969 |
| 0.0153 | 34.99 | 1242 | 1.0777 | 0.8097 | 0.8055 |
| 0.0062 | 36.0 | 1278 | 1.2379 | 0.7699 | 0.7751 |
| 0.0081 | 36.99 | 1313 | 1.0697 | 0.7977 | 0.7987 |
| 0.0072 | 38.0 | 1349 | 1.1284 | 0.7997 | 0.8001 |
| 0.0105 | 38.99 | 1384 | 1.0593 | 0.8137 | 0.8136 |
| 0.0102 | 40.0 | 1420 | 1.0805 | 0.8130 | 0.8126 |
| 0.0088 | 40.99 | 1455 | 1.1237 | 0.8110 | 0.8115 |
| 0.0073 | 42.0 | 1491 | 1.0980 | 0.8170 | 0.8167 |
| 0.0046 | 42.99 | 1526 | 1.1584 | 0.8044 | 0.8049 |
| 0.0061 | 44.0 | 1562 | 1.1517 | 0.8110 | 0.8114 |
| 0.0021 | 44.99 | 1597 | 1.1564 | 0.8064 | 0.8074 |
| 0.0073 | 46.0 | 1633 | 1.1214 | 0.8183 | 0.8183 |
| 0.002 | 46.99 | 1668 | 1.1376 | 0.8210 | 0.8209 |
| 0.0064 | 48.0 | 1704 | 1.1283 | 0.8210 | 0.8208 |
| 0.0072 | 48.99 | 1739 | 1.1271 | 0.8203 | 0.8201 |
| 0.0019 | 49.3 | 1750 | 1.1273 | 0.8203 | 0.8201 |
Framework versions
- Transformers 4.36.2
- Pytorch 2.1.1+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0