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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
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
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: 0.8841
- Accuracy: 0.8015
- Precision: 0.8244
- Recall: 0.8015
- F1: 0.7954
- Binary: 0.8613
## 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 | 0.17 | 50 | 4.2299 | 0.0680 | 0.0274 | 0.0680 | 0.0275 | 0.3352 |
| No log | 0.35 | 100 | 3.9259 | 0.0631 | 0.0167 | 0.0631 | 0.0191 | 0.3408 |
| No log | 0.52 | 150 | 3.6877 | 0.1117 | 0.0535 | 0.1117 | 0.0606 | 0.3738 |
| No log | 0.69 | 200 | 3.4911 | 0.1650 | 0.0958 | 0.1650 | 0.1017 | 0.4119 |
| No log | 0.86 | 250 | 3.3070 | 0.2233 | 0.1497 | 0.2233 | 0.1517 | 0.4541 |
| 3.8291 | 1.04 | 300 | 3.1733 | 0.2354 | 0.1675 | 0.2354 | 0.1635 | 0.4619 |
| 3.8291 | 1.21 | 350 | 3.0042 | 0.3034 | 0.2792 | 0.3034 | 0.2378 | 0.5087 |
| 3.8291 | 1.38 | 400 | 2.8631 | 0.3519 | 0.3257 | 0.3519 | 0.2808 | 0.5434 |
| 3.8291 | 1.55 | 450 | 2.7172 | 0.3981 | 0.4132 | 0.3981 | 0.3454 | 0.5765 |
| 3.8291 | 1.73 | 500 | 2.5629 | 0.4733 | 0.4595 | 0.4733 | 0.4250 | 0.6299 |
| 3.8291 | 1.9 | 550 | 2.4316 | 0.4927 | 0.4601 | 0.4927 | 0.4352 | 0.6420 |
| 2.9187 | 2.07 | 600 | 2.3206 | 0.5218 | 0.5337 | 0.5218 | 0.4800 | 0.6638 |
| 2.9187 | 2.24 | 650 | 2.1817 | 0.5801 | 0.5746 | 0.5801 | 0.5292 | 0.7039 |
| 2.9187 | 2.42 | 700 | 2.0852 | 0.5631 | 0.5165 | 0.5631 | 0.5089 | 0.6927 |
| 2.9187 | 2.59 | 750 | 1.9932 | 0.5752 | 0.5836 | 0.5752 | 0.5310 | 0.7005 |
| 2.9187 | 2.76 | 800 | 1.8989 | 0.6189 | 0.6288 | 0.6189 | 0.5850 | 0.7318 |
| 2.9187 | 2.93 | 850 | 1.8100 | 0.6529 | 0.6389 | 0.6529 | 0.6181 | 0.7563 |
| 2.291 | 3.11 | 900 | 1.7138 | 0.6650 | 0.7058 | 0.6650 | 0.6360 | 0.7641 |
| 2.291 | 3.28 | 950 | 1.6582 | 0.6869 | 0.7094 | 0.6869 | 0.6585 | 0.7786 |
| 2.291 | 3.45 | 1000 | 1.5810 | 0.7039 | 0.7509 | 0.7039 | 0.6886 | 0.7913 |
| 2.291 | 3.62 | 1050 | 1.5116 | 0.7306 | 0.7799 | 0.7306 | 0.7263 | 0.8100 |
| 2.291 | 3.8 | 1100 | 1.4638 | 0.7039 | 0.7450 | 0.7039 | 0.6850 | 0.7920 |
| 2.291 | 3.97 | 1150 | 1.4173 | 0.7233 | 0.7744 | 0.7233 | 0.7099 | 0.8056 |
| 1.8674 | 4.14 | 1200 | 1.4021 | 0.6869 | 0.7375 | 0.6869 | 0.6707 | 0.7794 |
| 1.8674 | 4.31 | 1250 | 1.3271 | 0.7282 | 0.7796 | 0.7282 | 0.7240 | 0.8090 |
| 1.8674 | 4.49 | 1300 | 1.2851 | 0.7403 | 0.7903 | 0.7403 | 0.7305 | 0.8175 |
| 1.8674 | 4.66 | 1350 | 1.2666 | 0.7257 | 0.7796 | 0.7257 | 0.7162 | 0.8066 |
| 1.8674 | 4.83 | 1400 | 1.2354 | 0.7379 | 0.7785 | 0.7379 | 0.7301 | 0.8158 |
| 1.5849 | 5.0 | 1450 | 1.1930 | 0.7451 | 0.7913 | 0.7451 | 0.7420 | 0.8201 |
| 1.5849 | 5.18 | 1500 | 1.1529 | 0.7549 | 0.8124 | 0.7549 | 0.7527 | 0.8277 |
| 1.5849 | 5.35 | 1550 | 1.1293 | 0.7621 | 0.8236 | 0.7621 | 0.7626 | 0.8328 |
| 1.5849 | 5.52 | 1600 | 1.0915 | 0.7646 | 0.8132 | 0.7646 | 0.7609 | 0.8345 |
| 1.5849 | 5.69 | 1650 | 1.1038 | 0.7549 | 0.8013 | 0.7549 | 0.7518 | 0.8277 |
| 1.5849 | 5.87 | 1700 | 1.0632 | 0.7670 | 0.8125 | 0.7670 | 0.7604 | 0.8362 |
| 1.379 | 6.04 | 1750 | 1.0175 | 0.7767 | 0.8151 | 0.7767 | 0.7708 | 0.8430 |
| 1.379 | 6.21 | 1800 | 0.9889 | 0.7791 | 0.8137 | 0.7791 | 0.7746 | 0.8447 |
| 1.379 | 6.38 | 1850 | 0.9825 | 0.7816 | 0.8255 | 0.7816 | 0.7793 | 0.8464 |
| 1.379 | 6.56 | 1900 | 0.9725 | 0.7864 | 0.8442 | 0.7864 | 0.7826 | 0.8498 |
| 1.379 | 6.73 | 1950 | 0.9357 | 0.7961 | 0.8341 | 0.7961 | 0.7943 | 0.8566 |
| 1.379 | 6.9 | 2000 | 0.9351 | 0.7888 | 0.8315 | 0.7888 | 0.7848 | 0.8515 |
| 1.2346 | 7.08 | 2050 | 0.9187 | 0.7888 | 0.8374 | 0.7888 | 0.7895 | 0.8515 |
| 1.2346 | 7.25 | 2100 | 0.9028 | 0.7840 | 0.8361 | 0.7840 | 0.7835 | 0.8481 |
| 1.2346 | 7.42 | 2150 | 0.8773 | 0.7961 | 0.8419 | 0.7961 | 0.7909 | 0.8566 |
| 1.2346 | 7.59 | 2200 | 0.8816 | 0.7985 | 0.8399 | 0.7985 | 0.8016 | 0.8583 |
| 1.2346 | 7.77 | 2250 | 0.8604 | 0.7913 | 0.8294 | 0.7913 | 0.7919 | 0.8532 |
| 1.2346 | 7.94 | 2300 | 0.8579 | 0.8010 | 0.8381 | 0.8010 | 0.8003 | 0.8600 |
| 1.1154 | 8.11 | 2350 | 0.8552 | 0.7985 | 0.8439 | 0.7985 | 0.7996 | 0.8583 |
| 1.1154 | 8.28 | 2400 | 0.8493 | 0.7985 | 0.8461 | 0.7985 | 0.8000 | 0.8583 |
| 1.1154 | 8.46 | 2450 | 0.8421 | 0.7985 | 0.8421 | 0.7985 | 0.8009 | 0.8583 |
| 1.1154 | 8.63 | 2500 | 0.8416 | 0.8010 | 0.8424 | 0.8010 | 0.8013 | 0.8600 |
| 1.1154 | 8.8 | 2550 | 0.8375 | 0.8010 | 0.8460 | 0.8010 | 0.8010 | 0.8600 |
| 1.1154 | 8.97 | 2600 | 0.8304 | 0.8058 | 0.8444 | 0.8058 | 0.8072 | 0.8633 |
| 1.0661 | 9.15 | 2650 | 0.8183 | 0.8107 | 0.8471 | 0.8107 | 0.8076 | 0.8667 |
| 1.0661 | 9.32 | 2700 | 0.8093 | 0.8131 | 0.8521 | 0.8131 | 0.8123 | 0.8684 |
| 1.0661 | 9.49 | 2750 | 0.8104 | 0.8155 | 0.8544 | 0.8155 | 0.8148 | 0.8701 |
| 1.0661 | 9.66 | 2800 | 0.8106 | 0.8204 | 0.8581 | 0.8204 | 0.8208 | 0.8735 |
| 1.0661 | 9.84 | 2850 | 0.8094 | 0.8131 | 0.8485 | 0.8131 | 0.8126 | 0.8684 |
### Framework versions
- Transformers 4.38.2
- Pytorch 2.3.0
- Datasets 2.19.1
- Tokenizers 0.15.1
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