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---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- wer
model-index:
- name: model_broadclass_onSet0.1
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. -->
# model_broadclass_onSet0.1
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1129
- 0 Precision: 1.0
- 0 Recall: 1.0
- 0 F1-score: 1.0
- 0 Support: 31
- 1 Precision: 0.9259
- 1 Recall: 1.0
- 1 F1-score: 0.9615
- 1 Support: 25
- 2 Precision: 1.0
- 2 Recall: 0.9259
- 2 F1-score: 0.9615
- 2 Support: 27
- 3 Precision: 1.0
- 3 Recall: 1.0
- 3 F1-score: 1.0
- 3 Support: 15
- Accuracy: 0.9796
- Macro avg Precision: 0.9815
- Macro avg Recall: 0.9815
- Macro avg F1-score: 0.9808
- Macro avg Support: 98
- Weighted avg Precision: 0.9811
- Weighted avg Recall: 0.9796
- Weighted avg F1-score: 0.9796
- Weighted avg Support: 98
- Wer: 0.0859
- Mtrix: [[0, 1, 2, 3], [0, 31, 0, 0, 0], [1, 0, 25, 0, 0], [2, 0, 2, 25, 0], [3, 0, 0, 0, 15]]
## 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: 0.0003
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 200
- num_epochs: 80
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | 0 Precision | 0 Recall | 0 F1-score | 0 Support | 1 Precision | 1 Recall | 1 F1-score | 1 Support | 2 Precision | 2 Recall | 2 F1-score | 2 Support | 3 Precision | 3 Recall | 3 F1-score | 3 Support | Accuracy | Macro avg Precision | Macro avg Recall | Macro avg F1-score | Macro avg Support | Weighted avg Precision | Weighted avg Recall | Weighted avg F1-score | Weighted avg Support | Wer | Mtrix |
|:-------------:|:-----:|:----:|:---------------:|:-----------:|:--------:|:----------:|:---------:|:-----------:|:--------:|:----------:|:---------:|:-----------:|:--------:|:----------:|:---------:|:-----------:|:--------:|:----------:|:---------:|:--------:|:-------------------:|:----------------:|:------------------:|:-----------------:|:----------------------:|:-------------------:|:---------------------:|:--------------------:|:------:|:---------------------------------------------------------------------------------------:|
| 2.343 | 4.16 | 100 | 2.2083 | 0.3163 | 1.0 | 0.4806 | 31 | 0.0 | 0.0 | 0.0 | 25 | 0.0 | 0.0 | 0.0 | 27 | 0.0 | 0.0 | 0.0 | 15 | 0.3163 | 0.0791 | 0.25 | 0.1202 | 98 | 0.1001 | 0.3163 | 0.1520 | 98 | 0.9847 | [[0, 1, 2, 3], [0, 31, 0, 0, 0], [1, 25, 0, 0, 0], [2, 27, 0, 0, 0], [3, 15, 0, 0, 0]] |
| 2.2769 | 8.33 | 200 | 2.1649 | 0.3163 | 1.0 | 0.4806 | 31 | 0.0 | 0.0 | 0.0 | 25 | 0.0 | 0.0 | 0.0 | 27 | 0.0 | 0.0 | 0.0 | 15 | 0.3163 | 0.0791 | 0.25 | 0.1202 | 98 | 0.1001 | 0.3163 | 0.1520 | 98 | 0.9847 | [[0, 1, 2, 3], [0, 31, 0, 0, 0], [1, 25, 0, 0, 0], [2, 27, 0, 0, 0], [3, 15, 0, 0, 0]] |
| 1.9687 | 12.49 | 300 | 1.8723 | 0.3163 | 1.0 | 0.4806 | 31 | 0.0 | 0.0 | 0.0 | 25 | 0.0 | 0.0 | 0.0 | 27 | 0.0 | 0.0 | 0.0 | 15 | 0.3163 | 0.0791 | 0.25 | 0.1202 | 98 | 0.1001 | 0.3163 | 0.1520 | 98 | 0.9847 | [[0, 1, 2, 3], [0, 31, 0, 0, 0], [1, 25, 0, 0, 0], [2, 27, 0, 0, 0], [3, 15, 0, 0, 0]] |
| 1.8046 | 16.65 | 400 | 1.6982 | 0.3163 | 1.0 | 0.4806 | 31 | 0.0 | 0.0 | 0.0 | 25 | 0.0 | 0.0 | 0.0 | 27 | 0.0 | 0.0 | 0.0 | 15 | 0.3163 | 0.0791 | 0.25 | 0.1202 | 98 | 0.1001 | 0.3163 | 0.1520 | 98 | 0.9847 | [[0, 1, 2, 3], [0, 31, 0, 0, 0], [1, 25, 0, 0, 0], [2, 27, 0, 0, 0], [3, 15, 0, 0, 0]] |
| 1.5645 | 20.82 | 500 | 1.5862 | 0.3163 | 1.0 | 0.4806 | 31 | 0.0 | 0.0 | 0.0 | 25 | 0.0 | 0.0 | 0.0 | 27 | 0.0 | 0.0 | 0.0 | 15 | 0.3163 | 0.0791 | 0.25 | 0.1202 | 98 | 0.1001 | 0.3163 | 0.1520 | 98 | 0.9847 | [[0, 1, 2, 3], [0, 31, 0, 0, 0], [1, 25, 0, 0, 0], [2, 27, 0, 0, 0], [3, 15, 0, 0, 0]] |
| 1.5322 | 24.98 | 600 | 1.5736 | 0.3163 | 1.0 | 0.4806 | 31 | 0.0 | 0.0 | 0.0 | 25 | 0.0 | 0.0 | 0.0 | 27 | 0.0 | 0.0 | 0.0 | 15 | 0.3163 | 0.0791 | 0.25 | 0.1202 | 98 | 0.1001 | 0.3163 | 0.1520 | 98 | 0.9847 | [[0, 1, 2, 3], [0, 31, 0, 0, 0], [1, 25, 0, 0, 0], [2, 27, 0, 0, 0], [3, 15, 0, 0, 0]] |
| 1.5468 | 29.16 | 700 | 1.4736 | 0.3163 | 1.0 | 0.4806 | 31 | 0.0 | 0.0 | 0.0 | 25 | 0.0 | 0.0 | 0.0 | 27 | 0.0 | 0.0 | 0.0 | 15 | 0.3163 | 0.0791 | 0.25 | 0.1202 | 98 | 0.1001 | 0.3163 | 0.1520 | 98 | 0.9847 | [[0, 1, 2, 3], [0, 31, 0, 0, 0], [1, 25, 0, 0, 0], [2, 27, 0, 0, 0], [3, 15, 0, 0, 0]] |
| 1.0542 | 33.33 | 800 | 1.0068 | 0.3163 | 1.0 | 0.4806 | 31 | 0.0 | 0.0 | 0.0 | 25 | 0.0 | 0.0 | 0.0 | 27 | 0.0 | 0.0 | 0.0 | 15 | 0.3163 | 0.0791 | 0.25 | 0.1202 | 98 | 0.1001 | 0.3163 | 0.1520 | 98 | 0.9847 | [[0, 1, 2, 3], [0, 31, 0, 0, 0], [1, 25, 0, 0, 0], [2, 27, 0, 0, 0], [3, 15, 0, 0, 0]] |
| 0.9664 | 37.49 | 900 | 0.9831 | 0.3483 | 1.0 | 0.5167 | 31 | 1.0 | 0.12 | 0.2143 | 25 | 1.0 | 0.0370 | 0.0714 | 27 | 0.8 | 0.2667 | 0.4 | 15 | 0.3980 | 0.7871 | 0.3559 | 0.3006 | 98 | 0.7632 | 0.3980 | 0.2990 | 98 | 0.9758 | [[0, 1, 2, 3], [0, 31, 0, 0, 0], [1, 21, 3, 0, 1], [2, 26, 0, 1, 0], [3, 11, 0, 0, 4]] |
| 0.9405 | 41.65 | 1000 | 0.9402 | 0.3827 | 1.0 | 0.5536 | 31 | 1.0 | 0.04 | 0.0769 | 25 | 1.0 | 0.4815 | 0.65 | 27 | 1.0 | 0.2 | 0.3333 | 15 | 0.4898 | 0.8457 | 0.4304 | 0.4035 | 98 | 0.8047 | 0.4898 | 0.4248 | 98 | 0.9630 | [[0, 1, 2, 3], [0, 31, 0, 0, 0], [1, 24, 1, 0, 0], [2, 14, 0, 13, 0], [3, 12, 0, 0, 3]] |
| 0.9341 | 45.82 | 1100 | 0.9330 | 0.5082 | 1.0 | 0.6739 | 31 | 0.9231 | 0.48 | 0.6316 | 25 | 1.0 | 0.6296 | 0.7727 | 27 | 0.8571 | 0.4 | 0.5455 | 15 | 0.6735 | 0.8221 | 0.6274 | 0.6559 | 98 | 0.8029 | 0.6735 | 0.6707 | 98 | 0.9497 | [[0, 1, 2, 3], [0, 31, 0, 0, 0], [1, 12, 12, 0, 1], [2, 9, 1, 17, 0], [3, 9, 0, 0, 6]] |
| 0.8769 | 49.98 | 1200 | 0.8662 | 0.6327 | 1.0 | 0.775 | 31 | 0.9565 | 0.88 | 0.9167 | 25 | 1.0 | 0.6296 | 0.7727 | 27 | 0.8889 | 0.5333 | 0.6667 | 15 | 0.7959 | 0.8695 | 0.7607 | 0.7828 | 98 | 0.8557 | 0.7959 | 0.7939 | 98 | 0.9442 | [[0, 1, 2, 3], [0, 31, 0, 0, 0], [1, 2, 22, 0, 1], [2, 9, 1, 17, 0], [3, 7, 0, 0, 8]] |
| 0.8122 | 54.16 | 1300 | 0.7951 | 0.9062 | 0.9355 | 0.9206 | 31 | 0.8519 | 0.92 | 0.8846 | 25 | 1.0 | 0.8519 | 0.92 | 27 | 0.9375 | 1.0 | 0.9677 | 15 | 0.9184 | 0.9239 | 0.9268 | 0.9232 | 98 | 0.9230 | 0.9184 | 0.9185 | 98 | 0.9348 | [[0, 1, 2, 3], [0, 29, 2, 0, 0], [1, 1, 23, 0, 1], [2, 2, 2, 23, 0], [3, 0, 0, 0, 15]] |
| 0.5747 | 58.33 | 1400 | 0.4843 | 1.0 | 1.0 | 1.0 | 31 | 0.96 | 0.96 | 0.96 | 25 | 1.0 | 0.9630 | 0.9811 | 27 | 0.9375 | 1.0 | 0.9677 | 15 | 0.9796 | 0.9744 | 0.9807 | 0.9772 | 98 | 0.9802 | 0.9796 | 0.9797 | 98 | 0.6732 | [[0, 1, 2, 3], [0, 31, 0, 0, 0], [1, 0, 24, 0, 1], [2, 0, 1, 26, 0], [3, 0, 0, 0, 15]] |
| 0.2794 | 62.49 | 1500 | 0.2062 | 1.0 | 1.0 | 1.0 | 31 | 0.96 | 0.96 | 0.96 | 25 | 1.0 | 0.9630 | 0.9811 | 27 | 0.9375 | 1.0 | 0.9677 | 15 | 0.9796 | 0.9744 | 0.9807 | 0.9772 | 98 | 0.9802 | 0.9796 | 0.9797 | 98 | 0.2236 | [[0, 1, 2, 3], [0, 31, 0, 0, 0], [1, 0, 24, 0, 1], [2, 0, 1, 26, 0], [3, 0, 0, 0, 15]] |
| 0.1654 | 66.65 | 1600 | 0.1573 | 1.0 | 0.9677 | 0.9836 | 31 | 0.9259 | 1.0 | 0.9615 | 25 | 1.0 | 0.9630 | 0.9811 | 27 | 1.0 | 1.0 | 1.0 | 15 | 0.9796 | 0.9815 | 0.9827 | 0.9816 | 98 | 0.9811 | 0.9796 | 0.9798 | 98 | 0.1303 | [[0, 1, 2, 3], [0, 30, 1, 0, 0], [1, 0, 25, 0, 0], [2, 0, 1, 26, 0], [3, 0, 0, 0, 15]] |
| 0.1092 | 70.82 | 1700 | 0.1451 | 1.0 | 0.9677 | 0.9836 | 31 | 0.8889 | 0.96 | 0.9231 | 25 | 1.0 | 0.9259 | 0.9615 | 27 | 0.9375 | 1.0 | 0.9677 | 15 | 0.9592 | 0.9566 | 0.9634 | 0.9590 | 98 | 0.9621 | 0.9592 | 0.9597 | 98 | 0.1056 | [[0, 1, 2, 3], [0, 30, 1, 0, 0], [1, 0, 24, 0, 1], [2, 0, 2, 25, 0], [3, 0, 0, 0, 15]] |
| 0.085 | 74.98 | 1800 | 0.1126 | 1.0 | 1.0 | 1.0 | 31 | 0.9259 | 1.0 | 0.9615 | 25 | 1.0 | 0.9259 | 0.9615 | 27 | 1.0 | 1.0 | 1.0 | 15 | 0.9796 | 0.9815 | 0.9815 | 0.9808 | 98 | 0.9811 | 0.9796 | 0.9796 | 98 | 0.0938 | [[0, 1, 2, 3], [0, 31, 0, 0, 0], [1, 0, 25, 0, 0], [2, 0, 2, 25, 0], [3, 0, 0, 0, 15]] |
| 0.0824 | 79.16 | 1900 | 0.1118 | 1.0 | 1.0 | 1.0 | 31 | 0.9259 | 1.0 | 0.9615 | 25 | 1.0 | 0.9259 | 0.9615 | 27 | 1.0 | 1.0 | 1.0 | 15 | 0.9796 | 0.9815 | 0.9815 | 0.9808 | 98 | 0.9811 | 0.9796 | 0.9796 | 98 | 0.0859 | [[0, 1, 2, 3], [0, 31, 0, 0, 0], [1, 0, 25, 0, 0], [2, 0, 2, 25, 0], [3, 0, 0, 0, 15]] |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.0+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2
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