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---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- wer
model-index:
- name: model_broadclass_onSet2.1
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_onSet2.1
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: 0.1459
- 0 Precision: 0.9630
- 0 Recall: 1.0
- 0 F1-score: 0.9811
- 0 Support: 26
- 1 Precision: 1.0
- 1 Recall: 0.9231
- 1 F1-score: 0.9600
- 1 Support: 39
- 2 Precision: 1.0
- 2 Recall: 1.0
- 2 F1-score: 1.0
- 2 Support: 19
- 3 Precision: 0.8667
- 3 Recall: 1.0
- 3 F1-score: 0.9286
- 3 Support: 13
- Accuracy: 0.9691
- Macro avg Precision: 0.9574
- Macro avg Recall: 0.9808
- Macro avg F1-score: 0.9674
- Macro avg Support: 97
- Weighted avg Precision: 0.9722
- Weighted avg Recall: 0.9691
- Weighted avg F1-score: 0.9693
- Weighted avg Support: 97
- Wer: 0.1293
- Mtrix: [[0, 1, 2, 3], [0, 26, 0, 0, 0], [1, 1, 36, 0, 2], [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 |
|:-------------:|:-----:|:----:|:---------------:|:-----------:|:--------:|:----------:|:---------:|:-----------:|:--------:|:----------:|:---------:|:-----------:|:--------:|:----------:|:---------:|:-----------:|:--------:|:----------:|:---------:|:--------:|:-------------------:|:----------------:|:------------------:|:-----------------:|:----------------------:|:-------------------:|:---------------------:|:--------------------:|:------:|:---------------------------------------------------------------------------------------:|
| 2.3399 | 4.16 | 100 | 2.1769 | 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]] |
| 2.3152 | 8.33 | 200 | 2.1458 | 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]] |
| 1.9859 | 12.49 | 300 | 1.9172 | 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]] |
| 1.7126 | 16.65 | 400 | 1.6954 | 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]] |
| 1.6833 | 20.82 | 500 | 1.7553 | 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]] |
| 1.5318 | 24.98 | 600 | 1.5921 | 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]] |
| 1.5868 | 29.16 | 700 | 1.5517 | 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]] |
| 1.5577 | 33.33 | 800 | 1.5089 | 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]] |
| 1.2201 | 37.49 | 900 | 1.1567 | 0.4643 | 1.0 | 0.6341 | 26 | 1.0 | 0.4872 | 0.6552 | 39 | 1.0 | 0.5263 | 0.6897 | 19 | 1.0 | 0.9231 | 0.9600 | 13 | 0.6907 | 0.8661 | 0.7341 | 0.7347 | 97 | 0.8564 | 0.6907 | 0.6971 | 97 | 0.9485 | [[0, 1, 2, 3], [0, 26, 0, 0, 0], [1, 20, 19, 0, 0], [2, 9, 0, 10, 0], [3, 1, 0, 0, 12]] |
| 0.9692 | 41.65 | 1000 | 1.0489 | 0.5102 | 0.9615 | 0.6667 | 26 | 0.9615 | 0.6410 | 0.7692 | 39 | 0.9167 | 0.5789 | 0.7097 | 19 | 1.0 | 0.7692 | 0.8696 | 13 | 0.7320 | 0.8471 | 0.7377 | 0.7538 | 97 | 0.8369 | 0.7320 | 0.7435 | 97 | 0.9374 | [[0, 1, 2, 3], [0, 25, 1, 0, 0], [1, 13, 25, 1, 0], [2, 8, 0, 11, 0], [3, 3, 0, 0, 10]] |
| 0.9214 | 45.82 | 1100 | 0.9620 | 0.9615 | 0.9615 | 0.9615 | 26 | 0.9730 | 0.9231 | 0.9474 | 39 | 0.9048 | 1.0 | 0.9500 | 19 | 1.0 | 1.0 | 1.0 | 13 | 0.9588 | 0.9598 | 0.9712 | 0.9647 | 97 | 0.9602 | 0.9588 | 0.9587 | 97 | 0.9328 | [[0, 1, 2, 3], [0, 25, 1, 0, 0], [1, 1, 36, 2, 0], [2, 0, 0, 19, 0], [3, 0, 0, 0, 13]] |
| 0.9305 | 49.98 | 1200 | 0.9736 | 0.8125 | 1.0 | 0.8966 | 26 | 1.0 | 0.8205 | 0.9014 | 39 | 0.9048 | 1.0 | 0.9500 | 19 | 1.0 | 0.9231 | 0.9600 | 13 | 0.9175 | 0.9293 | 0.9359 | 0.9270 | 97 | 0.9311 | 0.9175 | 0.9175 | 97 | 0.9253 | [[0, 1, 2, 3], [0, 26, 0, 0, 0], [1, 5, 32, 2, 0], [2, 0, 0, 19, 0], [3, 1, 0, 0, 12]] |
| 0.8982 | 54.16 | 1300 | 0.9586 | 0.7812 | 0.9615 | 0.8621 | 26 | 0.9688 | 0.7949 | 0.8732 | 39 | 0.9 | 0.9474 | 0.9231 | 19 | 1.0 | 1.0 | 1.0 | 13 | 0.8969 | 0.9125 | 0.9259 | 0.9146 | 97 | 0.9092 | 0.8969 | 0.8970 | 97 | 0.9283 | [[0, 1, 2, 3], [0, 25, 1, 0, 0], [1, 6, 31, 2, 0], [2, 1, 0, 18, 0], [3, 0, 0, 0, 13]] |
| 0.8382 | 58.33 | 1400 | 0.8864 | 0.9615 | 0.9615 | 0.9615 | 26 | 0.9722 | 0.8974 | 0.9333 | 39 | 0.95 | 1.0 | 0.9744 | 19 | 0.8667 | 1.0 | 0.9286 | 13 | 0.9485 | 0.9376 | 0.9647 | 0.9495 | 97 | 0.9509 | 0.9485 | 0.9483 | 97 | 0.8904 | [[0, 1, 2, 3], [0, 25, 1, 0, 0], [1, 1, 35, 1, 2], [2, 0, 0, 19, 0], [3, 0, 0, 0, 13]] |
| 0.7314 | 62.49 | 1500 | 0.7880 | 0.96 | 0.9231 | 0.9412 | 26 | 0.9474 | 0.9231 | 0.9351 | 39 | 0.95 | 1.0 | 0.9744 | 19 | 0.9286 | 1.0 | 0.9630 | 13 | 0.9485 | 0.9465 | 0.9615 | 0.9534 | 97 | 0.9488 | 0.9485 | 0.9481 | 97 | 0.8020 | [[0, 1, 2, 3], [0, 24, 2, 0, 0], [1, 1, 36, 1, 1], [2, 0, 0, 19, 0], [3, 0, 0, 0, 13]] |
| 0.448 | 66.65 | 1600 | 0.3458 | 0.9615 | 0.9615 | 0.9615 | 26 | 0.9730 | 0.9231 | 0.9474 | 39 | 1.0 | 1.0 | 1.0 | 19 | 0.8667 | 1.0 | 0.9286 | 13 | 0.9588 | 0.9503 | 0.9712 | 0.9594 | 97 | 0.9610 | 0.9588 | 0.9590 | 97 | 0.2561 | [[0, 1, 2, 3], [0, 25, 1, 0, 0], [1, 1, 36, 0, 2], [2, 0, 0, 19, 0], [3, 0, 0, 0, 13]] |
| 0.1921 | 70.82 | 1700 | 0.1970 | 0.9615 | 0.9615 | 0.9615 | 26 | 0.9730 | 0.9231 | 0.9474 | 39 | 1.0 | 1.0 | 1.0 | 19 | 0.8667 | 1.0 | 0.9286 | 13 | 0.9588 | 0.9503 | 0.9712 | 0.9594 | 97 | 0.9610 | 0.9588 | 0.9590 | 97 | 0.1581 | [[0, 1, 2, 3], [0, 25, 1, 0, 0], [1, 1, 36, 0, 2], [2, 0, 0, 19, 0], [3, 0, 0, 0, 13]] |
| 0.1499 | 74.98 | 1800 | 0.1463 | 0.9615 | 0.9615 | 0.9615 | 26 | 0.9730 | 0.9231 | 0.9474 | 39 | 1.0 | 1.0 | 1.0 | 19 | 0.8667 | 1.0 | 0.9286 | 13 | 0.9588 | 0.9503 | 0.9712 | 0.9594 | 97 | 0.9610 | 0.9588 | 0.9590 | 97 | 0.1384 | [[0, 1, 2, 3], [0, 25, 1, 0, 0], [1, 1, 36, 0, 2], [2, 0, 0, 19, 0], [3, 0, 0, 0, 13]] |
| 0.1099 | 79.16 | 1900 | 0.1459 | 0.9630 | 1.0 | 0.9811 | 26 | 1.0 | 0.9231 | 0.9600 | 39 | 1.0 | 1.0 | 1.0 | 19 | 0.8667 | 1.0 | 0.9286 | 13 | 0.9691 | 0.9574 | 0.9808 | 0.9674 | 97 | 0.9722 | 0.9691 | 0.9693 | 97 | 0.1293 | [[0, 1, 2, 3], [0, 26, 0, 0, 0], [1, 1, 36, 0, 2], [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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