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---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- wer
model-index:
- name: model_broadclass_onSet2
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_onSet2
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: 0.5931
- 0 Precision: 1.0
- 0 Recall: 0.9615
- 0 F1-score: 0.9804
- 0 Support: 26
- 1 Precision: 0.9730
- 1 Recall: 0.9231
- 1 F1-score: 0.9474
- 1 Support: 39
- 2 Precision: 1.0
- 2 Recall: 1.0
- 2 F1-score: 1.0
- 2 Support: 19
- 3 Precision: 0.8125
- 3 Recall: 1.0
- 3 F1-score: 0.8966
- 3 Support: 13
- Accuracy: 0.9588
- Macro avg Precision: 0.9464
- Macro avg Recall: 0.9712
- Macro avg F1-score: 0.9561
- Macro avg Support: 97
- Weighted avg Precision: 0.9640
- Weighted avg Recall: 0.9588
- Weighted avg F1-score: 0.9597
- Weighted avg Support: 97
- Wer: 0.6924
- Mtrix: [[0, 1, 2, 3], [0, 25, 1, 0, 0], [1, 0, 36, 0, 3], [2, 0, 0, 19, 0], [3, 0, 0, 0, 13]]
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:-----------:|:--------:|:----------:|:---------:|:-----------:|:--------:|:----------:|:---------:|:-----------:|:--------:|:----------:|:---------:|:-----------:|:--------:|:----------:|:---------:|:--------:|:-------------------:|:----------------:|:------------------:|:-----------------:|:----------------------:|:-------------------:|:---------------------:|:--------------------:|:------:|:---------------------------------------------------------------------------------------:|
| 2.3566 | 4.16 | 100 | 2.1836 | 0.2680 | 1.0 | 0.4228 | 26 | 0.0 | 0.0 | 0.0 | 39 | 0.0 | 0.0 | 0.0 | 19 | 0.0 | 0.0 | 0.0 | 13 | 0.2680 | 0.0670 | 0.25 | 0.1057 | 97 | 0.0718 | 0.2680 | 0.1133 | 97 | 0.9869 | [[0, 1, 2, 3], [0, 26, 0, 0, 0], [1, 39, 0, 0, 0], [2, 19, 0, 0, 0], [3, 13, 0, 0, 0]] |
| 2.2923 | 8.33 | 200 | 2.1159 | 0.2680 | 1.0 | 0.4228 | 26 | 0.0 | 0.0 | 0.0 | 39 | 0.0 | 0.0 | 0.0 | 19 | 0.0 | 0.0 | 0.0 | 13 | 0.2680 | 0.0670 | 0.25 | 0.1057 | 97 | 0.0718 | 0.2680 | 0.1133 | 97 | 0.9869 | [[0, 1, 2, 3], [0, 26, 0, 0, 0], [1, 39, 0, 0, 0], [2, 19, 0, 0, 0], [3, 13, 0, 0, 0]] |
| 1.9868 | 12.49 | 300 | 1.9923 | 0.2680 | 1.0 | 0.4228 | 26 | 0.0 | 0.0 | 0.0 | 39 | 0.0 | 0.0 | 0.0 | 19 | 0.0 | 0.0 | 0.0 | 13 | 0.2680 | 0.0670 | 0.25 | 0.1057 | 97 | 0.0718 | 0.2680 | 0.1133 | 97 | 0.9869 | [[0, 1, 2, 3], [0, 26, 0, 0, 0], [1, 39, 0, 0, 0], [2, 19, 0, 0, 0], [3, 13, 0, 0, 0]] |
| 1.7313 | 16.65 | 400 | 1.6081 | 0.2680 | 1.0 | 0.4228 | 26 | 0.0 | 0.0 | 0.0 | 39 | 0.0 | 0.0 | 0.0 | 19 | 0.0 | 0.0 | 0.0 | 13 | 0.2680 | 0.0670 | 0.25 | 0.1057 | 97 | 0.0718 | 0.2680 | 0.1133 | 97 | 0.9869 | [[0, 1, 2, 3], [0, 26, 0, 0, 0], [1, 39, 0, 0, 0], [2, 19, 0, 0, 0], [3, 13, 0, 0, 0]] |
| 1.6688 | 20.82 | 500 | 1.5971 | 0.2680 | 1.0 | 0.4228 | 26 | 0.0 | 0.0 | 0.0 | 39 | 0.0 | 0.0 | 0.0 | 19 | 0.0 | 0.0 | 0.0 | 13 | 0.2680 | 0.0670 | 0.25 | 0.1057 | 97 | 0.0718 | 0.2680 | 0.1133 | 97 | 0.9869 | [[0, 1, 2, 3], [0, 26, 0, 0, 0], [1, 39, 0, 0, 0], [2, 19, 0, 0, 0], [3, 13, 0, 0, 0]] |
| 1.5888 | 24.98 | 600 | 1.6098 | 0.2680 | 1.0 | 0.4228 | 26 | 0.0 | 0.0 | 0.0 | 39 | 0.0 | 0.0 | 0.0 | 19 | 0.0 | 0.0 | 0.0 | 13 | 0.2680 | 0.0670 | 0.25 | 0.1057 | 97 | 0.0718 | 0.2680 | 0.1133 | 97 | 0.9869 | [[0, 1, 2, 3], [0, 26, 0, 0, 0], [1, 39, 0, 0, 0], [2, 19, 0, 0, 0], [3, 13, 0, 0, 0]] |
| 1.5986 | 29.16 | 700 | 1.6984 | 0.2680 | 1.0 | 0.4228 | 26 | 0.0 | 0.0 | 0.0 | 39 | 0.0 | 0.0 | 0.0 | 19 | 0.0 | 0.0 | 0.0 | 13 | 0.2680 | 0.0670 | 0.25 | 0.1057 | 97 | 0.0718 | 0.2680 | 0.1133 | 97 | 0.9869 | [[0, 1, 2, 3], [0, 26, 0, 0, 0], [1, 39, 0, 0, 0], [2, 19, 0, 0, 0], [3, 13, 0, 0, 0]] |
| 1.5437 | 33.33 | 800 | 1.4933 | 0.2680 | 1.0 | 0.4228 | 26 | 0.0 | 0.0 | 0.0 | 39 | 0.0 | 0.0 | 0.0 | 19 | 0.0 | 0.0 | 0.0 | 13 | 0.2680 | 0.0670 | 0.25 | 0.1057 | 97 | 0.0718 | 0.2680 | 0.1133 | 97 | 0.9869 | [[0, 1, 2, 3], [0, 26, 0, 0, 0], [1, 39, 0, 0, 0], [2, 19, 0, 0, 0], [3, 13, 0, 0, 0]] |
| 1.1358 | 37.49 | 900 | 1.1118 | 0.2680 | 1.0 | 0.4228 | 26 | 0.0 | 0.0 | 0.0 | 39 | 0.0 | 0.0 | 0.0 | 19 | 0.0 | 0.0 | 0.0 | 13 | 0.2680 | 0.0670 | 0.25 | 0.1057 | 97 | 0.0718 | 0.2680 | 0.1133 | 97 | 0.9869 | [[0, 1, 2, 3], [0, 26, 0, 0, 0], [1, 39, 0, 0, 0], [2, 19, 0, 0, 0], [3, 13, 0, 0, 0]] |
| 0.983 | 41.65 | 1000 | 1.0538 | 0.3171 | 1.0 | 0.4815 | 26 | 1.0 | 0.0256 | 0.05 | 39 | 1.0 | 0.3158 | 0.4800 | 19 | 0.875 | 0.5385 | 0.6667 | 13 | 0.4124 | 0.7980 | 0.4700 | 0.4195 | 97 | 0.8002 | 0.4124 | 0.3325 | 97 | 0.9732 | [[0, 1, 2, 3], [0, 26, 0, 0, 0], [1, 37, 1, 0, 1], [2, 13, 0, 6, 0], [3, 6, 0, 0, 7]] |
| 0.96 | 45.82 | 1100 | 0.9324 | 0.4561 | 1.0 | 0.6265 | 26 | 1.0 | 0.3846 | 0.5556 | 39 | 1.0 | 0.6316 | 0.7742 | 19 | 1.0 | 1.0 | 1.0 | 13 | 0.6804 | 0.8640 | 0.7540 | 0.7391 | 97 | 0.8542 | 0.6804 | 0.6770 | 97 | 0.9510 | [[0, 1, 2, 3], [0, 26, 0, 0, 0], [1, 24, 15, 0, 0], [2, 7, 0, 12, 0], [3, 0, 0, 0, 13]] |
| 0.9569 | 49.98 | 1200 | 0.9106 | 0.52 | 1.0 | 0.6842 | 26 | 1.0 | 0.6410 | 0.7813 | 39 | 1.0 | 0.6316 | 0.7742 | 19 | 1.0 | 0.7692 | 0.8696 | 13 | 0.7526 | 0.88 | 0.7605 | 0.7773 | 97 | 0.8713 | 0.7526 | 0.7657 | 97 | 0.9343 | [[0, 1, 2, 3], [0, 26, 0, 0, 0], [1, 14, 25, 0, 0], [2, 7, 0, 12, 0], [3, 3, 0, 0, 10]] |
| 0.943 | 54.16 | 1300 | 0.9142 | 0.7879 | 1.0 | 0.8814 | 26 | 1.0 | 0.8205 | 0.9014 | 39 | 1.0 | 0.9474 | 0.9730 | 19 | 0.9286 | 1.0 | 0.9630 | 13 | 0.9175 | 0.9291 | 0.9420 | 0.9297 | 97 | 0.9336 | 0.9175 | 0.9183 | 97 | 0.9242 | [[0, 1, 2, 3], [0, 26, 0, 0, 0], [1, 6, 32, 0, 1], [2, 1, 0, 18, 0], [3, 0, 0, 0, 13]] |
| 0.9177 | 58.33 | 1400 | 0.9201 | 0.7879 | 1.0 | 0.8814 | 26 | 1.0 | 0.7692 | 0.8696 | 39 | 1.0 | 1.0 | 1.0 | 19 | 0.8667 | 1.0 | 0.9286 | 13 | 0.9072 | 0.9136 | 0.9423 | 0.9199 | 97 | 0.9253 | 0.9072 | 0.9062 | 97 | 0.9197 | [[0, 1, 2, 3], [0, 26, 0, 0, 0], [1, 7, 30, 0, 2], [2, 0, 0, 19, 0], [3, 0, 0, 0, 13]] |
| 0.873 | 62.49 | 1500 | 0.8556 | 0.8387 | 1.0 | 0.9123 | 26 | 1.0 | 0.8718 | 0.9315 | 39 | 1.0 | 0.9474 | 0.9730 | 19 | 0.9286 | 1.0 | 0.9630 | 13 | 0.9381 | 0.9418 | 0.9548 | 0.9449 | 97 | 0.9472 | 0.9381 | 0.9387 | 97 | 0.9293 | [[0, 1, 2, 3], [0, 26, 0, 0, 0], [1, 4, 34, 0, 1], [2, 1, 0, 18, 0], [3, 0, 0, 0, 13]] |
| 0.798 | 66.65 | 1600 | 0.8133 | 0.8966 | 1.0 | 0.9455 | 26 | 1.0 | 0.8974 | 0.9459 | 39 | 1.0 | 1.0 | 1.0 | 19 | 0.9286 | 1.0 | 0.9630 | 13 | 0.9588 | 0.9563 | 0.9744 | 0.9636 | 97 | 0.9627 | 0.9588 | 0.9587 | 97 | 0.9071 | [[0, 1, 2, 3], [0, 26, 0, 0, 0], [1, 3, 35, 0, 1], [2, 0, 0, 19, 0], [3, 0, 0, 0, 13]] |
| 0.7299 | 70.82 | 1700 | 0.7332 | 1.0 | 0.9615 | 0.9804 | 26 | 0.9744 | 0.9744 | 0.9744 | 39 | 1.0 | 1.0 | 1.0 | 19 | 0.9286 | 1.0 | 0.9630 | 13 | 0.9794 | 0.9757 | 0.9840 | 0.9794 | 97 | 0.9801 | 0.9794 | 0.9795 | 97 | 0.8636 | [[0, 1, 2, 3], [0, 25, 1, 0, 0], [1, 0, 38, 0, 1], [2, 0, 0, 19, 0], [3, 0, 0, 0, 13]] |
| 0.6432 | 74.98 | 1800 | 0.6808 | 1.0 | 0.9615 | 0.9804 | 26 | 0.9730 | 0.9231 | 0.9474 | 39 | 1.0 | 1.0 | 1.0 | 19 | 0.8125 | 1.0 | 0.8966 | 13 | 0.9588 | 0.9464 | 0.9712 | 0.9561 | 97 | 0.9640 | 0.9588 | 0.9597 | 97 | 0.7758 | [[0, 1, 2, 3], [0, 25, 1, 0, 0], [1, 0, 36, 0, 3], [2, 0, 0, 19, 0], [3, 0, 0, 0, 13]] |
| 0.6067 | 79.16 | 1900 | 0.5931 | 1.0 | 0.9615 | 0.9804 | 26 | 0.9730 | 0.9231 | 0.9474 | 39 | 1.0 | 1.0 | 1.0 | 19 | 0.8125 | 1.0 | 0.8966 | 13 | 0.9588 | 0.9464 | 0.9712 | 0.9561 | 97 | 0.9640 | 0.9588 | 0.9597 | 97 | 0.6924 | [[0, 1, 2, 3], [0, 25, 1, 0, 0], [1, 0, 36, 0, 3], [2, 0, 0, 19, 0], [3, 0, 0, 0, 13]] |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.0+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2
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