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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-ref
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-ref
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: 0.6490
- Accuracy: 0.8396
- Precision: 0.8518
- Recall: 0.8396
- F1: 0.8378
- Binary: 0.8887
## 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
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | Binary |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:------:|
| No log | 0.13 | 50 | 4.2247 | 0.0647 | 0.0139 | 0.0647 | 0.0177 | 0.3345 |
| No log | 0.27 | 100 | 3.9116 | 0.0930 | 0.0338 | 0.0930 | 0.0338 | 0.3598 |
| No log | 0.4 | 150 | 3.6537 | 0.1523 | 0.0800 | 0.1523 | 0.0856 | 0.4030 |
| No log | 0.54 | 200 | 3.4519 | 0.1860 | 0.1524 | 0.1860 | 0.1277 | 0.4245 |
| No log | 0.67 | 250 | 3.2675 | 0.3315 | 0.2378 | 0.3315 | 0.2500 | 0.5302 |
| No log | 0.81 | 300 | 3.0858 | 0.3450 | 0.2487 | 0.3450 | 0.2596 | 0.5395 |
| No log | 0.94 | 350 | 2.9341 | 0.3625 | 0.2613 | 0.3625 | 0.2730 | 0.5524 |
| 3.6847 | 1.08 | 400 | 2.7592 | 0.4461 | 0.3862 | 0.4461 | 0.3690 | 0.6132 |
| 3.6847 | 1.21 | 450 | 2.5895 | 0.5027 | 0.4694 | 0.5027 | 0.4387 | 0.6509 |
| 3.6847 | 1.35 | 500 | 2.4411 | 0.5566 | 0.5189 | 0.5566 | 0.4930 | 0.6887 |
| 3.6847 | 1.48 | 550 | 2.3212 | 0.5593 | 0.5286 | 0.5593 | 0.4985 | 0.6910 |
| 3.6847 | 1.62 | 600 | 2.1863 | 0.5903 | 0.5494 | 0.5903 | 0.5344 | 0.7135 |
| 3.6847 | 1.75 | 650 | 2.0742 | 0.6092 | 0.5808 | 0.6092 | 0.5618 | 0.7267 |
| 3.6847 | 1.89 | 700 | 1.9542 | 0.6442 | 0.6075 | 0.6442 | 0.5985 | 0.7512 |
| 2.5893 | 2.02 | 750 | 1.8513 | 0.6739 | 0.6664 | 0.6739 | 0.6306 | 0.7720 |
| 2.5893 | 2.16 | 800 | 1.7673 | 0.6806 | 0.6703 | 0.6806 | 0.6424 | 0.7755 |
| 2.5893 | 2.29 | 850 | 1.6589 | 0.7075 | 0.6837 | 0.7075 | 0.6696 | 0.7956 |
| 2.5893 | 2.43 | 900 | 1.5751 | 0.7035 | 0.6882 | 0.7035 | 0.6704 | 0.7933 |
| 2.5893 | 2.56 | 950 | 1.5010 | 0.7426 | 0.7286 | 0.7426 | 0.7164 | 0.8206 |
| 2.5893 | 2.7 | 1000 | 1.4422 | 0.7385 | 0.7346 | 0.7385 | 0.7169 | 0.8173 |
| 2.5893 | 2.83 | 1050 | 1.3884 | 0.7426 | 0.7328 | 0.7426 | 0.7170 | 0.8202 |
| 2.5893 | 2.97 | 1100 | 1.3253 | 0.7466 | 0.7319 | 0.7466 | 0.7218 | 0.8225 |
| 1.9357 | 3.1 | 1150 | 1.2850 | 0.7507 | 0.7492 | 0.7507 | 0.7297 | 0.8257 |
| 1.9357 | 3.24 | 1200 | 1.2297 | 0.7736 | 0.7781 | 0.7736 | 0.7541 | 0.8429 |
| 1.9357 | 3.37 | 1250 | 1.2131 | 0.7722 | 0.7738 | 0.7722 | 0.7528 | 0.8406 |
| 1.9357 | 3.51 | 1300 | 1.1359 | 0.7830 | 0.7835 | 0.7830 | 0.7652 | 0.8489 |
| 1.9357 | 3.64 | 1350 | 1.0756 | 0.8019 | 0.7958 | 0.8019 | 0.7870 | 0.8621 |
| 1.9357 | 3.78 | 1400 | 1.0650 | 0.7992 | 0.7994 | 0.7992 | 0.7826 | 0.8602 |
| 1.9357 | 3.91 | 1450 | 1.0384 | 0.7925 | 0.7841 | 0.7925 | 0.7731 | 0.8555 |
| 1.5532 | 4.05 | 1500 | 1.0125 | 0.7951 | 0.7957 | 0.7951 | 0.7794 | 0.8565 |
| 1.5532 | 4.18 | 1550 | 0.9956 | 0.7978 | 0.8071 | 0.7978 | 0.7844 | 0.8598 |
| 1.5532 | 4.32 | 1600 | 1.0085 | 0.7749 | 0.7802 | 0.7749 | 0.7600 | 0.8415 |
| 1.5532 | 4.45 | 1650 | 0.9397 | 0.7965 | 0.8091 | 0.7965 | 0.7850 | 0.8580 |
| 1.5532 | 4.59 | 1700 | 0.9449 | 0.7911 | 0.7945 | 0.7911 | 0.7751 | 0.8538 |
| 1.5532 | 4.72 | 1750 | 0.9208 | 0.7898 | 0.7909 | 0.7898 | 0.7731 | 0.8527 |
| 1.5532 | 4.86 | 1800 | 0.9147 | 0.7884 | 0.8127 | 0.7884 | 0.7797 | 0.8522 |
| 1.5532 | 4.99 | 1850 | 0.8418 | 0.8127 | 0.8136 | 0.8127 | 0.8020 | 0.8691 |
| 1.3035 | 5.12 | 1900 | 0.8513 | 0.8100 | 0.8227 | 0.8100 | 0.8033 | 0.8674 |
| 1.3035 | 5.26 | 1950 | 0.8372 | 0.8154 | 0.8232 | 0.8154 | 0.8088 | 0.8717 |
| 1.3035 | 5.39 | 2000 | 0.8166 | 0.8181 | 0.8246 | 0.8181 | 0.8102 | 0.8735 |
| 1.3035 | 5.53 | 2050 | 0.7987 | 0.8261 | 0.8414 | 0.8261 | 0.8208 | 0.8778 |
| 1.3035 | 5.66 | 2100 | 0.7924 | 0.8181 | 0.8347 | 0.8181 | 0.8143 | 0.8730 |
| 1.3035 | 5.8 | 2150 | 0.7732 | 0.8140 | 0.8273 | 0.8140 | 0.8092 | 0.8708 |
| 1.3035 | 5.93 | 2200 | 0.7636 | 0.8261 | 0.8410 | 0.8261 | 0.8222 | 0.8802 |
| 1.1281 | 6.07 | 2250 | 0.7663 | 0.8154 | 0.8275 | 0.8154 | 0.8070 | 0.8716 |
| 1.1281 | 6.2 | 2300 | 0.7494 | 0.8356 | 0.8498 | 0.8356 | 0.8305 | 0.8846 |
| 1.1281 | 6.34 | 2350 | 0.7347 | 0.8356 | 0.8466 | 0.8356 | 0.8329 | 0.8848 |
| 1.1281 | 6.47 | 2400 | 0.7434 | 0.8235 | 0.8391 | 0.8235 | 0.8212 | 0.8771 |
| 1.1281 | 6.61 | 2450 | 0.7393 | 0.8302 | 0.8422 | 0.8302 | 0.8248 | 0.8814 |
| 1.1281 | 6.74 | 2500 | 0.7178 | 0.8221 | 0.8383 | 0.8221 | 0.8173 | 0.8749 |
| 1.1281 | 6.88 | 2550 | 0.6919 | 0.8410 | 0.8559 | 0.8410 | 0.8385 | 0.8885 |
| 1.0069 | 7.01 | 2600 | 0.7236 | 0.8248 | 0.8435 | 0.8248 | 0.8213 | 0.8779 |
| 1.0069 | 7.15 | 2650 | 0.7048 | 0.8315 | 0.8474 | 0.8315 | 0.8301 | 0.8822 |
| 1.0069 | 7.28 | 2700 | 0.6997 | 0.8275 | 0.8417 | 0.8275 | 0.8243 | 0.8787 |
| 1.0069 | 7.42 | 2750 | 0.6953 | 0.8329 | 0.8505 | 0.8329 | 0.8316 | 0.8830 |
| 1.0069 | 7.55 | 2800 | 0.6893 | 0.8275 | 0.8410 | 0.8275 | 0.8255 | 0.8783 |
| 1.0069 | 7.69 | 2850 | 0.6927 | 0.8261 | 0.8404 | 0.8261 | 0.8245 | 0.8794 |
| 1.0069 | 7.82 | 2900 | 0.6865 | 0.8288 | 0.8436 | 0.8288 | 0.8264 | 0.8802 |
| 1.0069 | 7.96 | 2950 | 0.6795 | 0.8383 | 0.8523 | 0.8383 | 0.8373 | 0.8869 |
| 0.9224 | 8.09 | 3000 | 0.6662 | 0.8356 | 0.8469 | 0.8356 | 0.8343 | 0.8854 |
| 0.9224 | 8.23 | 3050 | 0.6768 | 0.8342 | 0.8487 | 0.8342 | 0.8336 | 0.8849 |
| 0.9224 | 8.36 | 3100 | 0.6751 | 0.8329 | 0.8454 | 0.8329 | 0.8321 | 0.8840 |
| 0.9224 | 8.5 | 3150 | 0.6766 | 0.8315 | 0.8421 | 0.8315 | 0.8301 | 0.8830 |
| 0.9224 | 8.63 | 3200 | 0.6634 | 0.8302 | 0.8393 | 0.8302 | 0.8283 | 0.8821 |
| 0.9224 | 8.77 | 3250 | 0.6624 | 0.8329 | 0.8437 | 0.8329 | 0.8310 | 0.8834 |
| 0.9224 | 8.9 | 3300 | 0.6615 | 0.8342 | 0.8478 | 0.8342 | 0.8325 | 0.8849 |
| 0.8806 | 9.04 | 3350 | 0.6619 | 0.8356 | 0.8485 | 0.8356 | 0.8345 | 0.8853 |
| 0.8806 | 9.17 | 3400 | 0.6459 | 0.8423 | 0.8557 | 0.8423 | 0.8411 | 0.8906 |
| 0.8806 | 9.31 | 3450 | 0.6463 | 0.8437 | 0.8565 | 0.8437 | 0.8427 | 0.8915 |
| 0.8806 | 9.44 | 3500 | 0.6529 | 0.8423 | 0.8532 | 0.8423 | 0.8403 | 0.8900 |
| 0.8806 | 9.58 | 3550 | 0.6525 | 0.8369 | 0.8489 | 0.8369 | 0.8352 | 0.8868 |
| 0.8806 | 9.71 | 3600 | 0.6544 | 0.8383 | 0.8487 | 0.8383 | 0.8363 | 0.8872 |
| 0.8806 | 9.84 | 3650 | 0.6494 | 0.8410 | 0.8528 | 0.8410 | 0.8394 | 0.8896 |
| 0.8806 | 9.98 | 3700 | 0.6490 | 0.8396 | 0.8518 | 0.8396 | 0.8378 | 0.8887 |
### Framework versions
- Transformers 4.38.2
- Pytorch 2.3.0
- Datasets 2.19.1
- Tokenizers 0.15.1
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