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---
base_model: anderloh/Hugginhface-master-wav2vec-pretreined-5-class-train-test
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: wav2vec2-5Class-Validation-Mic
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. -->
# wav2vec2-5Class-Validation-Mic
This model is a fine-tuned version of [anderloh/Hugginhface-master-wav2vec-pretreined-5-class-train-test](https://huggingface.co/anderloh/Hugginhface-master-wav2vec-pretreined-5-class-train-test) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5967
- Accuracy: 0.4057
## 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: 128
- eval_batch_size: 128
- seed: 0
- gradient_accumulation_steps: 4
- total_train_batch_size: 512
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 150.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| No log | 0.92 | 3 | 1.6032 | 0.3203 |
| No log | 1.85 | 6 | 1.6029 | 0.3203 |
| No log | 2.77 | 9 | 1.6024 | 0.3203 |
| No log | 4.0 | 13 | 1.6015 | 0.3025 |
| No log | 4.92 | 16 | 1.6005 | 0.3025 |
| No log | 5.85 | 19 | 1.5994 | 0.2811 |
| No log | 6.77 | 22 | 1.5981 | 0.2705 |
| No log | 8.0 | 26 | 1.5959 | 0.2562 |
| No log | 8.92 | 29 | 1.5941 | 0.2384 |
| No log | 9.85 | 32 | 1.5923 | 0.2206 |
| No log | 10.77 | 35 | 1.5902 | 0.2384 |
| No log | 12.0 | 39 | 1.5872 | 0.2384 |
| No log | 12.92 | 42 | 1.5848 | 0.2491 |
| No log | 13.85 | 45 | 1.5822 | 0.2633 |
| No log | 14.77 | 48 | 1.5797 | 0.2633 |
| No log | 16.0 | 52 | 1.5768 | 0.2384 |
| No log | 16.92 | 55 | 1.5747 | 0.2278 |
| No log | 17.85 | 58 | 1.5729 | 0.2278 |
| No log | 18.77 | 61 | 1.5713 | 0.2313 |
| No log | 20.0 | 65 | 1.5694 | 0.2313 |
| No log | 20.92 | 68 | 1.5681 | 0.2313 |
| No log | 21.85 | 71 | 1.5670 | 0.2313 |
| No log | 22.77 | 74 | 1.5666 | 0.2313 |
| No log | 24.0 | 78 | 1.5666 | 0.2313 |
| No log | 24.92 | 81 | 1.5672 | 0.2313 |
| No log | 25.85 | 84 | 1.5685 | 0.2313 |
| No log | 26.77 | 87 | 1.5707 | 0.2313 |
| No log | 28.0 | 91 | 1.5751 | 0.2313 |
| No log | 28.92 | 94 | 1.5796 | 0.2313 |
| No log | 29.85 | 97 | 1.5857 | 0.2313 |
| 1.5332 | 30.77 | 100 | 1.5937 | 0.2313 |
| 1.5332 | 32.0 | 104 | 1.6070 | 0.2313 |
| 1.5332 | 32.92 | 107 | 1.6198 | 0.2313 |
| 1.5332 | 33.85 | 110 | 1.6357 | 0.2313 |
| 1.5332 | 34.77 | 113 | 1.6535 | 0.2313 |
| 1.5332 | 36.0 | 117 | 1.6803 | 0.2313 |
| 1.5332 | 36.92 | 120 | 1.7035 | 0.2313 |
| 1.5332 | 37.85 | 123 | 1.7277 | 0.2313 |
| 1.5332 | 38.77 | 126 | 1.7509 | 0.2313 |
| 1.5332 | 40.0 | 130 | 1.7757 | 0.2313 |
| 1.5332 | 40.92 | 133 | 1.7878 | 0.2313 |
| 1.5332 | 41.85 | 136 | 1.7966 | 0.2313 |
| 1.5332 | 42.77 | 139 | 1.8039 | 0.2313 |
| 1.5332 | 44.0 | 143 | 1.8047 | 0.2349 |
| 1.5332 | 44.92 | 146 | 1.8001 | 0.2491 |
| 1.5332 | 45.85 | 149 | 1.7924 | 0.2456 |
| 1.5332 | 46.77 | 152 | 1.7863 | 0.2562 |
| 1.5332 | 48.0 | 156 | 1.7770 | 0.2633 |
| 1.5332 | 48.92 | 159 | 1.7693 | 0.2705 |
| 1.5332 | 49.85 | 162 | 1.7656 | 0.2776 |
| 1.5332 | 50.77 | 165 | 1.7619 | 0.2918 |
| 1.5332 | 52.0 | 169 | 1.7609 | 0.3025 |
| 1.5332 | 52.92 | 172 | 1.7629 | 0.3060 |
| 1.5332 | 53.85 | 175 | 1.7646 | 0.3096 |
| 1.5332 | 54.77 | 178 | 1.7646 | 0.3132 |
| 1.5332 | 56.0 | 182 | 1.7650 | 0.3132 |
| 1.5332 | 56.92 | 185 | 1.7623 | 0.3238 |
| 1.5332 | 57.85 | 188 | 1.7614 | 0.3310 |
| 1.5332 | 58.77 | 191 | 1.7595 | 0.3345 |
| 1.5332 | 60.0 | 195 | 1.7589 | 0.3345 |
| 1.5332 | 60.92 | 198 | 1.7556 | 0.3381 |
| 1.2887 | 61.85 | 201 | 1.7556 | 0.3381 |
| 1.2887 | 62.77 | 204 | 1.7508 | 0.3416 |
| 1.2887 | 64.0 | 208 | 1.7468 | 0.3452 |
| 1.2887 | 64.92 | 211 | 1.7416 | 0.3452 |
| 1.2887 | 65.85 | 214 | 1.7356 | 0.3452 |
| 1.2887 | 66.77 | 217 | 1.7274 | 0.3559 |
| 1.2887 | 68.0 | 221 | 1.7196 | 0.3594 |
| 1.2887 | 68.92 | 224 | 1.7133 | 0.3630 |
| 1.2887 | 69.85 | 227 | 1.7103 | 0.3630 |
| 1.2887 | 70.77 | 230 | 1.7120 | 0.3630 |
| 1.2887 | 72.0 | 234 | 1.7099 | 0.3665 |
| 1.2887 | 72.92 | 237 | 1.7038 | 0.3701 |
| 1.2887 | 73.85 | 240 | 1.6975 | 0.3737 |
| 1.2887 | 74.77 | 243 | 1.6929 | 0.3772 |
| 1.2887 | 76.0 | 247 | 1.6884 | 0.3808 |
| 1.2887 | 76.92 | 250 | 1.6822 | 0.3879 |
| 1.2887 | 77.85 | 253 | 1.6749 | 0.3879 |
| 1.2887 | 78.77 | 256 | 1.6709 | 0.3915 |
| 1.2887 | 80.0 | 260 | 1.6645 | 0.3915 |
| 1.2887 | 80.92 | 263 | 1.6606 | 0.3915 |
| 1.2887 | 81.85 | 266 | 1.6586 | 0.3915 |
| 1.2887 | 82.77 | 269 | 1.6515 | 0.3915 |
| 1.2887 | 84.0 | 273 | 1.6471 | 0.3950 |
| 1.2887 | 84.92 | 276 | 1.6459 | 0.3950 |
| 1.2887 | 85.85 | 279 | 1.6428 | 0.3950 |
| 1.2887 | 86.77 | 282 | 1.6446 | 0.3950 |
| 1.2887 | 88.0 | 286 | 1.6454 | 0.3950 |
| 1.2887 | 88.92 | 289 | 1.6433 | 0.3950 |
| 1.2887 | 89.85 | 292 | 1.6395 | 0.3950 |
| 1.2887 | 90.77 | 295 | 1.6372 | 0.3950 |
| 1.2887 | 92.0 | 299 | 1.6350 | 0.3950 |
| 1.1159 | 92.92 | 302 | 1.6332 | 0.3986 |
| 1.1159 | 93.85 | 305 | 1.6306 | 0.3986 |
| 1.1159 | 94.77 | 308 | 1.6296 | 0.3986 |
| 1.1159 | 96.0 | 312 | 1.6273 | 0.3986 |
| 1.1159 | 96.92 | 315 | 1.6257 | 0.3986 |
| 1.1159 | 97.85 | 318 | 1.6229 | 0.4021 |
| 1.1159 | 98.77 | 321 | 1.6211 | 0.4021 |
| 1.1159 | 100.0 | 325 | 1.6199 | 0.4021 |
| 1.1159 | 100.92 | 328 | 1.6203 | 0.4021 |
| 1.1159 | 101.85 | 331 | 1.6201 | 0.4021 |
| 1.1159 | 102.77 | 334 | 1.6200 | 0.3986 |
| 1.1159 | 104.0 | 338 | 1.6153 | 0.4021 |
| 1.1159 | 104.92 | 341 | 1.6125 | 0.4057 |
| 1.1159 | 105.85 | 344 | 1.6099 | 0.4057 |
| 1.1159 | 106.77 | 347 | 1.6073 | 0.4057 |
| 1.1159 | 108.0 | 351 | 1.6028 | 0.4057 |
| 1.1159 | 108.92 | 354 | 1.6007 | 0.4057 |
| 1.1159 | 109.85 | 357 | 1.6002 | 0.4057 |
| 1.1159 | 110.77 | 360 | 1.6003 | 0.4057 |
| 1.1159 | 112.0 | 364 | 1.6025 | 0.4057 |
| 1.1159 | 112.92 | 367 | 1.6049 | 0.4021 |
| 1.1159 | 113.85 | 370 | 1.6071 | 0.4021 |
| 1.1159 | 114.77 | 373 | 1.6078 | 0.4021 |
| 1.1159 | 116.0 | 377 | 1.6086 | 0.4021 |
| 1.1159 | 116.92 | 380 | 1.6080 | 0.4021 |
| 1.1159 | 117.85 | 383 | 1.6063 | 0.4021 |
| 1.1159 | 118.77 | 386 | 1.6059 | 0.4021 |
| 1.1159 | 120.0 | 390 | 1.6057 | 0.4021 |
| 1.1159 | 120.92 | 393 | 1.6052 | 0.4021 |
| 1.1159 | 121.85 | 396 | 1.6048 | 0.4021 |
| 1.1159 | 122.77 | 399 | 1.6036 | 0.4021 |
| 1.0195 | 124.0 | 403 | 1.6036 | 0.4021 |
| 1.0195 | 124.92 | 406 | 1.6032 | 0.4021 |
| 1.0195 | 125.85 | 409 | 1.6019 | 0.4021 |
| 1.0195 | 126.77 | 412 | 1.6004 | 0.4021 |
| 1.0195 | 128.0 | 416 | 1.5979 | 0.4021 |
| 1.0195 | 128.92 | 419 | 1.5969 | 0.4021 |
| 1.0195 | 129.85 | 422 | 1.5966 | 0.4021 |
| 1.0195 | 130.77 | 425 | 1.5965 | 0.4021 |
| 1.0195 | 132.0 | 429 | 1.5959 | 0.4057 |
| 1.0195 | 132.92 | 432 | 1.5960 | 0.4057 |
| 1.0195 | 133.85 | 435 | 1.5960 | 0.4057 |
| 1.0195 | 134.77 | 438 | 1.5962 | 0.4057 |
| 1.0195 | 136.0 | 442 | 1.5966 | 0.4057 |
| 1.0195 | 136.92 | 445 | 1.5967 | 0.4057 |
| 1.0195 | 137.85 | 448 | 1.5967 | 0.4057 |
| 1.0195 | 138.46 | 450 | 1.5967 | 0.4057 |
### Framework versions
- Transformers 4.39.0.dev0
- Pytorch 2.2.1+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2
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