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---
library_name: transformers
license: apache-2.0
base_model: facebook/wav2vec2-base
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- f1
model-index:
- name: wav2vec2_ASV_deepfake_audio_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. -->
# wav2vec2_ASV_deepfake_audio_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: 0.5628
- Accuracy: 0.8999
- Precision: 0.9057
- F1: 0.8612
- Tp: 181
- Tn: 16068
- Fn: 1800
- Fp: 8
- Auc Roc: 0.9372
## 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: 5e-05
- train_batch_size: 100
- eval_batch_size: 100
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 400
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | F1 | Tp | Tn | Fn | Fp | Auc Roc |
|:-------------:|:------:|:----:|:---------------:|:--------:|:---------:|:------:|:---:|:-----:|:----:|:---:|:-------:|
| 0.693 | 0.1143 | 10 | 0.6628 | 0.8854 | 0.8117 | 0.8385 | 23 | 15964 | 1958 | 112 | 0.5001 |
| 0.6589 | 0.2286 | 20 | 0.4915 | 0.8903 | 0.7926 | 0.8386 | 0 | 16076 | 1981 | 0 | 0.5030 |
| 0.5546 | 0.3429 | 30 | 0.3825 | 0.8865 | 0.8231 | 0.8406 | 39 | 15969 | 1942 | 107 | 0.5748 |
| 0.3566 | 0.4571 | 40 | 0.3403 | 0.8909 | 0.8620 | 0.8419 | 28 | 16059 | 1953 | 17 | 0.6201 |
| 0.2115 | 0.5714 | 50 | 0.3617 | 0.8923 | 0.8908 | 0.8442 | 43 | 16070 | 1938 | 6 | 0.7028 |
| 0.1636 | 0.6857 | 60 | 0.3428 | 0.8958 | 0.8756 | 0.8586 | 182 | 15993 | 1799 | 83 | 0.7968 |
| 0.1415 | 0.8 | 70 | 0.3899 | 0.8925 | 0.9015 | 0.8440 | 41 | 16075 | 1940 | 1 | 0.6722 |
| 0.11 | 0.9143 | 80 | 0.3756 | 0.8930 | 0.9024 | 0.8452 | 50 | 16075 | 1931 | 1 | 0.7490 |
| 0.1041 | 1.0286 | 90 | 0.3885 | 0.8960 | 0.9006 | 0.8526 | 110 | 16069 | 1871 | 7 | 0.6362 |
| 0.0888 | 1.1429 | 100 | 0.3484 | 0.8995 | 0.8936 | 0.8630 | 207 | 16036 | 1774 | 40 | 0.8231 |
| 0.0669 | 1.2571 | 110 | 0.3386 | 0.9049 | 0.9040 | 0.8734 | 299 | 16041 | 1682 | 35 | 0.8354 |
| 0.0552 | 1.3714 | 120 | 0.4530 | 0.8942 | 0.9055 | 0.8480 | 71 | 16076 | 1910 | 0 | 0.8554 |
| 0.071 | 1.4857 | 130 | 0.4327 | 0.8963 | 0.8937 | 0.8545 | 128 | 16057 | 1853 | 19 | 0.8543 |
| 0.0665 | 1.6 | 140 | 0.4547 | 0.8947 | 0.9045 | 0.8491 | 80 | 16075 | 1901 | 1 | 0.8065 |
| 0.054 | 1.7143 | 150 | 0.3210 | 0.9148 | 0.9064 | 0.8970 | 592 | 15926 | 1389 | 150 | 0.8851 |
| 0.0575 | 1.8286 | 160 | 0.4901 | 0.8934 | 0.9012 | 0.8462 | 58 | 16074 | 1923 | 2 | 0.7591 |
| 0.0437 | 1.9429 | 170 | 0.4849 | 0.8979 | 0.9036 | 0.8568 | 144 | 16069 | 1837 | 7 | 0.6435 |
| 0.0471 | 2.0571 | 180 | 0.3822 | 0.9071 | 0.9103 | 0.8767 | 324 | 16056 | 1657 | 20 | 0.9277 |
| 0.0377 | 2.1714 | 190 | 0.5301 | 0.8928 | 0.8962 | 0.8450 | 49 | 16072 | 1932 | 4 | 0.9112 |
| 0.0327 | 2.2857 | 200 | 0.5534 | 0.8920 | 0.9036 | 0.8426 | 30 | 16076 | 1951 | 0 | 0.8755 |
| 0.0522 | 2.4 | 210 | 0.2332 | 0.9260 | 0.9192 | 0.9162 | 865 | 15856 | 1116 | 220 | 0.9448 |
| 0.0449 | 2.5143 | 220 | 0.3034 | 0.9102 | 0.9104 | 0.8835 | 397 | 16038 | 1584 | 38 | 0.9453 |
| 0.0338 | 2.6286 | 230 | 0.4001 | 0.9018 | 0.9072 | 0.8654 | 218 | 16066 | 1763 | 10 | 0.9153 |
| 0.0337 | 2.7429 | 240 | 0.4761 | 0.8973 | 0.9056 | 0.8552 | 130 | 16073 | 1851 | 3 | 0.8789 |
| 0.0347 | 2.8571 | 250 | 0.5613 | 0.8921 | 0.9037 | 0.8429 | 32 | 16076 | 1949 | 0 | 0.9068 |
| 0.0301 | 2.9714 | 260 | 0.4896 | 0.8967 | 0.9025 | 0.8540 | 121 | 16070 | 1860 | 6 | 0.9480 |
| 0.0208 | 3.0857 | 270 | 0.5223 | 0.8983 | 0.9053 | 0.8575 | 149 | 16071 | 1832 | 5 | 0.9471 |
| 0.0197 | 3.2 | 280 | 0.5003 | 0.9024 | 0.9068 | 0.8669 | 232 | 16063 | 1749 | 13 | 0.9445 |
| 0.0167 | 3.3143 | 290 | 0.4328 | 0.9087 | 0.9123 | 0.8796 | 351 | 16057 | 1630 | 19 | 0.9561 |
| 0.0235 | 3.4286 | 300 | 0.3612 | 0.9097 | 0.9115 | 0.8821 | 380 | 16047 | 1601 | 29 | 0.9596 |
| 0.0207 | 3.5429 | 310 | 0.3538 | 0.9158 | 0.9169 | 0.8934 | 498 | 16038 | 1483 | 38 | 0.9591 |
| 0.0192 | 3.6571 | 320 | 0.4185 | 0.9145 | 0.9171 | 0.8907 | 465 | 16049 | 1516 | 27 | 0.9404 |
| 0.0176 | 3.7714 | 330 | 0.6594 | 0.8926 | 0.9017 | 0.8443 | 43 | 16075 | 1938 | 1 | 0.8734 |
| 0.0174 | 3.8857 | 340 | 0.5727 | 0.8995 | 0.9073 | 0.8600 | 170 | 16072 | 1811 | 4 | 0.9276 |
| 0.021 | 4.0 | 350 | 0.5943 | 0.8937 | 0.8988 | 0.8471 | 65 | 16072 | 1916 | 4 | 0.9460 |
| 0.02 | 4.1143 | 360 | 0.5183 | 0.8982 | 0.9040 | 0.8574 | 149 | 16069 | 1832 | 7 | 0.9507 |
| 0.015 | 4.2286 | 370 | 0.5329 | 0.8980 | 0.9037 | 0.8570 | 146 | 16069 | 1835 | 7 | 0.9477 |
| 0.0139 | 4.3429 | 380 | 0.5545 | 0.8967 | 0.9017 | 0.8541 | 122 | 16069 | 1859 | 7 | 0.9438 |
| 0.0103 | 4.4571 | 390 | 0.5638 | 0.8969 | 0.9021 | 0.8546 | 126 | 16069 | 1855 | 7 | 0.9403 |
| 0.0099 | 4.5714 | 400 | 0.5094 | 0.9030 | 0.9078 | 0.8679 | 241 | 16064 | 1740 | 12 | 0.9419 |
| 0.0121 | 4.6857 | 410 | 0.5066 | 0.9049 | 0.9099 | 0.8717 | 275 | 16064 | 1706 | 12 | 0.9406 |
| 0.0122 | 4.8 | 420 | 0.5700 | 0.8992 | 0.9047 | 0.8596 | 168 | 16068 | 1813 | 8 | 0.9326 |
| 0.0155 | 4.9143 | 430 | 0.5628 | 0.8999 | 0.9057 | 0.8612 | 181 | 16068 | 1800 | 8 | 0.9372 |
### Framework versions
- Transformers 4.44.1
- Pytorch 2.2.1+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1
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