| --- |
| license: apache-2.0 |
| tags: |
| - generated_from_trainer |
| model-index: |
| - name: Spoof_detection |
| results: [] |
| --- |
| |
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You |
| should probably proofread and complete it, then remove this comment. --> |
|
|
| # Spoof_detection |
| |
| This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on an unknown dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 1.7448 |
| - Wer: 0.1090 |
| |
| ## 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: 4 |
| - eval_batch_size: 8 |
| - seed: 42 |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| - lr_scheduler_type: linear |
| - lr_scheduler_warmup_steps: 1000 |
| - num_epochs: 30 |
| - mixed_precision_training: Native AMP |
|
|
| ### Training results |
|
|
| | Training Loss | Epoch | Step | Validation Loss | Wer | |
| |:-------------:|:-----:|:-----:|:---------------:|:------:| |
| | 95.9046 | 0.66 | 500 | 992.2993 | 0.6180 | |
| | 14.0322 | 1.33 | 1000 | 1.8873 | 0.1090 | |
| | 1.8659 | 1.99 | 1500 | 1.7827 | 0.1090 | |
| | 1.851 | 2.65 | 2000 | 1.8489 | 0.1090 | |
| | 1.8218 | 3.32 | 2500 | 1.8943 | 0.1090 | |
| | 1.8108 | 3.98 | 3000 | 1.9250 | 0.1090 | |
| | 1.8228 | 4.64 | 3500 | 1.7555 | 0.1090 | |
| | 1.832 | 5.31 | 4000 | 1.7837 | 0.1090 | |
| | 1.8403 | 5.97 | 4500 | 1.6644 | 0.1090 | |
| | 1.8292 | 6.63 | 5000 | 1.6906 | 0.1090 | |
| | 1.8223 | 7.29 | 5500 | 1.6966 | 0.1090 | |
| | 1.8007 | 7.96 | 6000 | 1.6951 | 0.1090 | |
| | 1.7986 | 8.62 | 6500 | 1.7436 | 0.1090 | |
| | 1.7933 | 9.28 | 7000 | 1.8169 | 0.1090 | |
| | 1.7861 | 9.95 | 7500 | 1.7209 | 0.1090 | |
| | 1.7843 | 10.61 | 8000 | 1.9379 | 0.1090 | |
| | 1.7743 | 11.27 | 8500 | 1.9834 | 0.1090 | |
| | 1.7721 | 11.94 | 9000 | 1.9279 | 0.1090 | |
| | 1.7719 | 12.6 | 9500 | 1.8187 | 0.1090 | |
| | 1.7616 | 13.26 | 10000 | 1.7804 | 0.1090 | |
| | 1.7638 | 13.93 | 10500 | 1.7884 | 0.1090 | |
| | 1.7651 | 14.59 | 11000 | 1.7476 | 0.1090 | |
| | 1.7603 | 15.25 | 11500 | 1.7570 | 0.1090 | |
| | 1.7543 | 15.92 | 12000 | 1.7356 | 0.1090 | |
| | 1.7556 | 16.58 | 12500 | 1.7140 | 0.1090 | |
| | 1.751 | 17.24 | 13000 | 1.7453 | 0.1090 | |
| | 1.75 | 17.9 | 13500 | 1.7648 | 0.1090 | |
| | 1.7492 | 18.57 | 14000 | 1.7338 | 0.1090 | |
| | 1.7484 | 19.23 | 14500 | 1.7093 | 0.1090 | |
| | 1.7461 | 19.89 | 15000 | 1.7393 | 0.1090 | |
| | 1.7429 | 20.56 | 15500 | 1.7605 | 0.1090 | |
| | 1.7446 | 21.22 | 16000 | 1.7782 | 0.1090 | |
| | 1.7435 | 21.88 | 16500 | 1.6749 | 0.1090 | |
| | 1.7392 | 22.55 | 17000 | 1.7468 | 0.1090 | |
| | 1.741 | 23.21 | 17500 | 1.7406 | 0.1090 | |
| | 1.7394 | 23.87 | 18000 | 1.7787 | 0.1090 | |
| | 1.739 | 24.54 | 18500 | 1.7969 | 0.1090 | |
| | 1.7341 | 25.2 | 19000 | 1.7490 | 0.1090 | |
| | 1.7371 | 25.86 | 19500 | 1.7783 | 0.1090 | |
| | 1.735 | 26.53 | 20000 | 1.7540 | 0.1090 | |
| | 1.7353 | 27.19 | 20500 | 1.7735 | 0.1090 | |
| | 1.7331 | 27.85 | 21000 | 1.7188 | 0.1090 | |
| | 1.7308 | 28.51 | 21500 | 1.7349 | 0.1090 | |
| | 1.7341 | 29.18 | 22000 | 1.7531 | 0.1090 | |
| | 1.7305 | 29.84 | 22500 | 1.7448 | 0.1090 | |
|
|
|
|
| ### Framework versions |
|
|
| - Transformers 4.17.0 |
| - Pytorch 1.10.0+cu102 |
| - Datasets 1.16.1 |
| - Tokenizers 0.12.1 |
|
|
