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---
license: apache-2.0
base_model: facebook/wav2vec2-base
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: wav2vec2-classifier-aug-large
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-classifier-aug-large
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: 0.4814
- Accuracy: 0.8666
- Precision: 0.8790
- Recall: 0.8666
- F1: 0.8664
- Binary: 0.9089
## 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: 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
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | Binary |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:------:|
| No log | 0.1 | 50 | 3.8510 | 0.0606 | 0.0171 | 0.0606 | 0.0152 | 0.3357 |
| No log | 0.2 | 100 | 3.4088 | 0.0863 | 0.0528 | 0.0863 | 0.0366 | 0.3563 |
| No log | 0.29 | 150 | 3.1643 | 0.1105 | 0.0472 | 0.1105 | 0.0408 | 0.3740 |
| No log | 0.39 | 200 | 2.8623 | 0.2453 | 0.1221 | 0.2453 | 0.1447 | 0.4687 |
| No log | 0.49 | 250 | 2.5829 | 0.2763 | 0.1792 | 0.2763 | 0.1862 | 0.4920 |
| No log | 0.59 | 300 | 2.3355 | 0.3787 | 0.3219 | 0.3787 | 0.3034 | 0.5642 |
| No log | 0.69 | 350 | 2.0865 | 0.4744 | 0.4303 | 0.4744 | 0.4021 | 0.6301 |
| No log | 0.78 | 400 | 1.9045 | 0.5296 | 0.4587 | 0.5296 | 0.4649 | 0.6687 |
| No log | 0.88 | 450 | 1.6752 | 0.5472 | 0.5374 | 0.5472 | 0.4900 | 0.6829 |
| No log | 0.98 | 500 | 1.5273 | 0.6105 | 0.6019 | 0.6105 | 0.5709 | 0.7276 |
| 2.9976 | 1.08 | 550 | 1.3712 | 0.6536 | 0.6358 | 0.6536 | 0.6128 | 0.7567 |
| 2.9976 | 1.18 | 600 | 1.3239 | 0.6725 | 0.6797 | 0.6725 | 0.6389 | 0.7702 |
| 2.9976 | 1.27 | 650 | 1.1953 | 0.7170 | 0.7116 | 0.7170 | 0.6878 | 0.8024 |
| 2.9976 | 1.37 | 700 | 1.1213 | 0.7170 | 0.7116 | 0.7170 | 0.6834 | 0.8020 |
| 2.9976 | 1.47 | 750 | 1.0287 | 0.7291 | 0.7293 | 0.7291 | 0.7043 | 0.8106 |
| 2.9976 | 1.57 | 800 | 0.9258 | 0.7722 | 0.7818 | 0.7722 | 0.7572 | 0.8414 |
| 2.9976 | 1.67 | 850 | 0.8634 | 0.7722 | 0.7967 | 0.7722 | 0.7574 | 0.8415 |
| 2.9976 | 1.76 | 900 | 0.7849 | 0.7938 | 0.8226 | 0.7938 | 0.7874 | 0.8546 |
| 2.9976 | 1.86 | 950 | 0.8423 | 0.7601 | 0.7760 | 0.7601 | 0.7480 | 0.8321 |
| 2.9976 | 1.96 | 1000 | 0.7670 | 0.7830 | 0.8069 | 0.7830 | 0.7704 | 0.8488 |
| 1.686 | 2.06 | 1050 | 0.7352 | 0.7951 | 0.8024 | 0.7951 | 0.7832 | 0.8566 |
| 1.686 | 2.16 | 1100 | 0.7278 | 0.8019 | 0.8234 | 0.8019 | 0.7951 | 0.8627 |
| 1.686 | 2.25 | 1150 | 0.6867 | 0.8113 | 0.8241 | 0.8113 | 0.8059 | 0.8683 |
| 1.686 | 2.35 | 1200 | 0.6489 | 0.8167 | 0.8343 | 0.8167 | 0.8093 | 0.8722 |
| 1.686 | 2.45 | 1250 | 0.6217 | 0.8288 | 0.8454 | 0.8288 | 0.8242 | 0.8811 |
| 1.686 | 2.55 | 1300 | 0.6416 | 0.8113 | 0.8320 | 0.8113 | 0.8050 | 0.8678 |
| 1.686 | 2.65 | 1350 | 0.6517 | 0.8113 | 0.8254 | 0.8113 | 0.8055 | 0.8693 |
| 1.686 | 2.75 | 1400 | 0.6330 | 0.8140 | 0.8313 | 0.8140 | 0.8092 | 0.8710 |
| 1.686 | 2.84 | 1450 | 0.5905 | 0.8329 | 0.8575 | 0.8329 | 0.8339 | 0.8844 |
| 1.686 | 2.94 | 1500 | 0.5974 | 0.8329 | 0.8480 | 0.8329 | 0.8291 | 0.8840 |
| 1.2582 | 3.04 | 1550 | 0.6449 | 0.8235 | 0.8430 | 0.8235 | 0.8192 | 0.8774 |
| 1.2582 | 3.14 | 1600 | 0.5734 | 0.8464 | 0.8633 | 0.8464 | 0.8449 | 0.8933 |
| 1.2582 | 3.24 | 1650 | 0.5771 | 0.8450 | 0.8641 | 0.8450 | 0.8440 | 0.8910 |
| 1.2582 | 3.33 | 1700 | 0.5133 | 0.8491 | 0.8619 | 0.8491 | 0.8466 | 0.8942 |
| 1.2582 | 3.43 | 1750 | 0.5608 | 0.8437 | 0.8621 | 0.8437 | 0.8419 | 0.8906 |
| 1.2582 | 3.53 | 1800 | 0.6194 | 0.8221 | 0.8446 | 0.8221 | 0.8197 | 0.8759 |
| 1.2582 | 3.63 | 1850 | 0.5060 | 0.8410 | 0.8527 | 0.8410 | 0.8381 | 0.8899 |
| 1.2582 | 3.73 | 1900 | 0.6035 | 0.8315 | 0.8528 | 0.8315 | 0.8262 | 0.8829 |
| 1.2582 | 3.82 | 1950 | 0.5269 | 0.8396 | 0.8542 | 0.8396 | 0.8376 | 0.8891 |
| 1.2582 | 3.92 | 2000 | 0.5115 | 0.8531 | 0.8638 | 0.8531 | 0.8489 | 0.8982 |
| 1.0473 | 4.02 | 2050 | 0.5209 | 0.8518 | 0.8688 | 0.8518 | 0.8497 | 0.8969 |
| 1.0473 | 4.12 | 2100 | 0.5327 | 0.8342 | 0.8530 | 0.8342 | 0.8326 | 0.8844 |
| 1.0473 | 4.22 | 2150 | 0.4859 | 0.8544 | 0.8694 | 0.8544 | 0.8527 | 0.8980 |
| 1.0473 | 4.31 | 2200 | 0.5414 | 0.8450 | 0.8648 | 0.8450 | 0.8402 | 0.8918 |
| 1.0473 | 4.41 | 2250 | 0.5982 | 0.8383 | 0.8545 | 0.8383 | 0.8355 | 0.8871 |
| 1.0473 | 4.51 | 2300 | 0.5458 | 0.8450 | 0.8562 | 0.8450 | 0.8421 | 0.8934 |
| 1.0473 | 4.61 | 2350 | 0.5115 | 0.8625 | 0.8753 | 0.8625 | 0.8601 | 0.9042 |
| 1.0473 | 4.71 | 2400 | 0.5226 | 0.8518 | 0.8671 | 0.8518 | 0.8491 | 0.8961 |
| 1.0473 | 4.8 | 2450 | 0.5058 | 0.8679 | 0.8807 | 0.8679 | 0.8661 | 0.9082 |
| 1.0473 | 4.9 | 2500 | 0.5442 | 0.8491 | 0.8647 | 0.8491 | 0.8461 | 0.8957 |
| 1.0473 | 5.0 | 2550 | 0.4810 | 0.8693 | 0.8816 | 0.8693 | 0.8680 | 0.9089 |
| 0.9144 | 5.1 | 2600 | 0.4729 | 0.8787 | 0.8918 | 0.8787 | 0.8769 | 0.9159 |
| 0.9144 | 5.2 | 2650 | 0.4981 | 0.8585 | 0.8686 | 0.8585 | 0.8564 | 0.9019 |
| 0.9144 | 5.29 | 2700 | 0.5505 | 0.8477 | 0.8629 | 0.8477 | 0.8464 | 0.8937 |
| 0.9144 | 5.39 | 2750 | 0.4829 | 0.8706 | 0.8859 | 0.8706 | 0.8701 | 0.9111 |
| 0.9144 | 5.49 | 2800 | 0.5203 | 0.8544 | 0.8690 | 0.8544 | 0.8520 | 0.9003 |
| 0.9144 | 5.59 | 2850 | 0.4907 | 0.8585 | 0.8730 | 0.8585 | 0.8568 | 0.9027 |
| 0.9144 | 5.69 | 2900 | 0.4710 | 0.8706 | 0.8801 | 0.8706 | 0.8686 | 0.9096 |
| 0.9144 | 5.78 | 2950 | 0.5062 | 0.8504 | 0.8647 | 0.8504 | 0.8490 | 0.8965 |
| 0.9144 | 5.88 | 3000 | 0.4455 | 0.8774 | 0.8914 | 0.8774 | 0.8777 | 0.9163 |
| 0.9144 | 5.98 | 3050 | 0.5032 | 0.8544 | 0.8718 | 0.8544 | 0.8541 | 0.8985 |
| 0.8213 | 6.08 | 3100 | 0.4735 | 0.8733 | 0.8895 | 0.8733 | 0.8718 | 0.9135 |
| 0.8213 | 6.18 | 3150 | 0.4743 | 0.8693 | 0.8880 | 0.8693 | 0.8679 | 0.9102 |
| 0.8213 | 6.27 | 3200 | 0.5357 | 0.8531 | 0.8720 | 0.8531 | 0.8492 | 0.8984 |
| 0.8213 | 6.37 | 3250 | 0.4820 | 0.8625 | 0.8783 | 0.8625 | 0.8601 | 0.9059 |
| 0.8213 | 6.47 | 3300 | 0.4732 | 0.8760 | 0.8897 | 0.8760 | 0.8755 | 0.9159 |
| 0.8213 | 6.57 | 3350 | 0.4814 | 0.8666 | 0.8790 | 0.8666 | 0.8664 | 0.9089 |
### Framework versions
- Transformers 4.38.2
- Pytorch 2.3.0
- Datasets 2.19.1
- Tokenizers 0.15.1
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