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---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- wer
model-index:
- name: model_broadclass_onSet1
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_onSet1
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: 0.9014
- 0 Precision: 0.5217
- 0 Recall: 1.0
- 0 F1-score: 0.6857
- 0 Support: 24
- 1 Precision: 1.0
- 1 Recall: 0.7692
- 1 F1-score: 0.8696
- 1 Support: 39
- 2 Precision: 1.0
- 2 Recall: 0.5652
- 2 F1-score: 0.7222
- 2 Support: 23
- 3 Precision: 1.0
- 3 Recall: 0.75
- 3 F1-score: 0.8571
- 3 Support: 12
- Accuracy: 0.7755
- Macro avg Precision: 0.8804
- Macro avg Recall: 0.7711
- Macro avg F1-score: 0.7837
- Macro avg Support: 98
- Weighted avg Precision: 0.8829
- Weighted avg Recall: 0.7755
- Weighted avg F1-score: 0.7884
- Weighted avg Support: 98
- Wer: 0.9368
- Mtrix: [[0, 1, 2, 3], [0, 24, 0, 0, 0], [1, 9, 30, 0, 0], [2, 10, 0, 13, 0], [3, 3, 0, 0, 9]]
## 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: 70
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:-----------:|:--------:|:----------:|:---------:|:-----------:|:--------:|:----------:|:---------:|:-----------:|:--------:|:----------:|:---------:|:-----------:|:--------:|:----------:|:---------:|:--------:|:-------------------:|:----------------:|:------------------:|:-----------------:|:----------------------:|:-------------------:|:---------------------:|:--------------------:|:------:|:---------------------------------------------------------------------------------------:|
| 2.395 | 4.16 | 100 | 2.2004 | 0.2449 | 1.0 | 0.3934 | 24 | 0.0 | 0.0 | 0.0 | 39 | 0.0 | 0.0 | 0.0 | 23 | 0.0 | 0.0 | 0.0 | 12 | 0.2449 | 0.0612 | 0.25 | 0.0984 | 98 | 0.0600 | 0.2449 | 0.0964 | 98 | 0.9879 | [[0, 1, 2, 3], [0, 24, 0, 0, 0], [1, 39, 0, 0, 0], [2, 23, 0, 0, 0], [3, 12, 0, 0, 0]] |
| 2.2919 | 8.33 | 200 | 2.1576 | 0.2449 | 1.0 | 0.3934 | 24 | 0.0 | 0.0 | 0.0 | 39 | 0.0 | 0.0 | 0.0 | 23 | 0.0 | 0.0 | 0.0 | 12 | 0.2449 | 0.0612 | 0.25 | 0.0984 | 98 | 0.0600 | 0.2449 | 0.0964 | 98 | 0.9879 | [[0, 1, 2, 3], [0, 24, 0, 0, 0], [1, 39, 0, 0, 0], [2, 23, 0, 0, 0], [3, 12, 0, 0, 0]] |
| 2.0987 | 12.49 | 300 | 2.0882 | 0.2449 | 1.0 | 0.3934 | 24 | 0.0 | 0.0 | 0.0 | 39 | 0.0 | 0.0 | 0.0 | 23 | 0.0 | 0.0 | 0.0 | 12 | 0.2449 | 0.0612 | 0.25 | 0.0984 | 98 | 0.0600 | 0.2449 | 0.0964 | 98 | 0.9879 | [[0, 1, 2, 3], [0, 24, 0, 0, 0], [1, 39, 0, 0, 0], [2, 23, 0, 0, 0], [3, 12, 0, 0, 0]] |
| 1.9079 | 16.65 | 400 | 1.8619 | 0.2449 | 1.0 | 0.3934 | 24 | 0.0 | 0.0 | 0.0 | 39 | 0.0 | 0.0 | 0.0 | 23 | 0.0 | 0.0 | 0.0 | 12 | 0.2449 | 0.0612 | 0.25 | 0.0984 | 98 | 0.0600 | 0.2449 | 0.0964 | 98 | 0.9879 | [[0, 1, 2, 3], [0, 24, 0, 0, 0], [1, 39, 0, 0, 0], [2, 23, 0, 0, 0], [3, 12, 0, 0, 0]] |
| 1.7168 | 20.82 | 500 | 1.6469 | 0.2449 | 1.0 | 0.3934 | 24 | 0.0 | 0.0 | 0.0 | 39 | 0.0 | 0.0 | 0.0 | 23 | 0.0 | 0.0 | 0.0 | 12 | 0.2449 | 0.0612 | 0.25 | 0.0984 | 98 | 0.0600 | 0.2449 | 0.0964 | 98 | 0.9879 | [[0, 1, 2, 3], [0, 24, 0, 0, 0], [1, 39, 0, 0, 0], [2, 23, 0, 0, 0], [3, 12, 0, 0, 0]] |
| 1.551 | 24.98 | 600 | 1.6614 | 0.2449 | 1.0 | 0.3934 | 24 | 0.0 | 0.0 | 0.0 | 39 | 0.0 | 0.0 | 0.0 | 23 | 0.0 | 0.0 | 0.0 | 12 | 0.2449 | 0.0612 | 0.25 | 0.0984 | 98 | 0.0600 | 0.2449 | 0.0964 | 98 | 0.9879 | [[0, 1, 2, 3], [0, 24, 0, 0, 0], [1, 39, 0, 0, 0], [2, 23, 0, 0, 0], [3, 12, 0, 0, 0]] |
| 1.6399 | 29.16 | 700 | 1.5818 | 0.2449 | 1.0 | 0.3934 | 24 | 0.0 | 0.0 | 0.0 | 39 | 0.0 | 0.0 | 0.0 | 23 | 0.0 | 0.0 | 0.0 | 12 | 0.2449 | 0.0612 | 0.25 | 0.0984 | 98 | 0.0600 | 0.2449 | 0.0964 | 98 | 0.9879 | [[0, 1, 2, 3], [0, 24, 0, 0, 0], [1, 39, 0, 0, 0], [2, 23, 0, 0, 0], [3, 12, 0, 0, 0]] |
| 1.3329 | 33.33 | 800 | 1.2267 | 0.2449 | 1.0 | 0.3934 | 24 | 0.0 | 0.0 | 0.0 | 39 | 0.0 | 0.0 | 0.0 | 23 | 0.0 | 0.0 | 0.0 | 12 | 0.2449 | 0.0612 | 0.25 | 0.0984 | 98 | 0.0600 | 0.2449 | 0.0964 | 98 | 0.9879 | [[0, 1, 2, 3], [0, 24, 0, 0, 0], [1, 39, 0, 0, 0], [2, 23, 0, 0, 0], [3, 12, 0, 0, 0]] |
| 1.1996 | 37.49 | 900 | 1.2143 | 0.2449 | 1.0 | 0.3934 | 24 | 0.0 | 0.0 | 0.0 | 39 | 0.0 | 0.0 | 0.0 | 23 | 0.0 | 0.0 | 0.0 | 12 | 0.2449 | 0.0612 | 0.25 | 0.0984 | 98 | 0.0600 | 0.2449 | 0.0964 | 98 | 0.9879 | [[0, 1, 2, 3], [0, 24, 0, 0, 0], [1, 39, 0, 0, 0], [2, 23, 0, 0, 0], [3, 12, 0, 0, 0]] |
| 1.01 | 41.65 | 1000 | 0.9496 | 0.2474 | 1.0 | 0.3967 | 24 | 1.0 | 0.0256 | 0.05 | 39 | 0.0 | 0.0 | 0.0 | 23 | 0.0 | 0.0 | 0.0 | 12 | 0.2551 | 0.3119 | 0.2564 | 0.1117 | 98 | 0.4586 | 0.2551 | 0.1170 | 98 | 0.9777 | [[0, 1, 2, 3], [0, 24, 0, 0, 0], [1, 38, 1, 0, 0], [2, 23, 0, 0, 0], [3, 12, 0, 0, 0]] |
| 0.9516 | 45.82 | 1100 | 0.9471 | 0.2927 | 1.0 | 0.4528 | 24 | 1.0 | 0.3846 | 0.5556 | 39 | 1.0 | 0.0435 | 0.0833 | 23 | 0.0 | 0.0 | 0.0 | 12 | 0.4082 | 0.5732 | 0.3570 | 0.2729 | 98 | 0.7043 | 0.4082 | 0.3515 | 98 | 0.9661 | [[0, 1, 2, 3], [0, 24, 0, 0, 0], [1, 24, 15, 0, 0], [2, 22, 0, 1, 0], [3, 12, 0, 0, 0]] |
| 0.9544 | 49.98 | 1200 | 0.9452 | 0.3582 | 1.0 | 0.5275 | 24 | 1.0 | 0.5128 | 0.6780 | 39 | 1.0 | 0.3043 | 0.4667 | 23 | 0.75 | 0.25 | 0.375 | 12 | 0.5510 | 0.7771 | 0.5168 | 0.5118 | 98 | 0.8122 | 0.5510 | 0.5544 | 98 | 0.9540 | [[0, 1, 2, 3], [0, 24, 0, 0, 0], [1, 18, 20, 0, 1], [2, 16, 0, 7, 0], [3, 9, 0, 0, 3]] |
| 0.9538 | 54.16 | 1300 | 0.9259 | 0.4615 | 1.0 | 0.6316 | 24 | 1.0 | 0.6923 | 0.8182 | 39 | 1.0 | 0.5217 | 0.6857 | 23 | 0.8571 | 0.5 | 0.6316 | 12 | 0.7041 | 0.8297 | 0.6785 | 0.6918 | 98 | 0.8506 | 0.7041 | 0.7185 | 98 | 0.9439 | [[0, 1, 2, 3], [0, 24, 0, 0, 0], [1, 11, 27, 0, 1], [2, 11, 0, 12, 0], [3, 6, 0, 0, 6]] |
| 0.952 | 58.33 | 1400 | 0.9052 | 0.4528 | 1.0 | 0.6234 | 24 | 1.0 | 0.6667 | 0.8 | 39 | 1.0 | 0.4348 | 0.6061 | 23 | 0.8889 | 0.6667 | 0.7619 | 12 | 0.6939 | 0.8354 | 0.6920 | 0.6978 | 98 | 0.8524 | 0.6939 | 0.7066 | 98 | 0.9464 | [[0, 1, 2, 3], [0, 24, 0, 0, 0], [1, 12, 26, 0, 1], [2, 13, 0, 10, 0], [3, 4, 0, 0, 8]] |
| 0.8938 | 62.49 | 1500 | 0.9070 | 0.48 | 1.0 | 0.6486 | 24 | 0.9677 | 0.7692 | 0.8571 | 39 | 1.0 | 0.4348 | 0.6061 | 23 | 1.0 | 0.5833 | 0.7368 | 12 | 0.7245 | 0.8619 | 0.6968 | 0.7122 | 98 | 0.8598 | 0.7245 | 0.7324 | 98 | 0.9398 | [[0, 1, 2, 3], [0, 24, 0, 0, 0], [1, 9, 30, 0, 0], [2, 12, 1, 10, 0], [3, 5, 0, 0, 7]] |
| 0.9027 | 66.65 | 1600 | 0.8919 | 0.5714 | 1.0 | 0.7273 | 24 | 1.0 | 0.8462 | 0.9167 | 39 | 1.0 | 0.7391 | 0.85 | 23 | 1.0 | 0.5 | 0.6667 | 12 | 0.8163 | 0.8929 | 0.7713 | 0.7902 | 98 | 0.8950 | 0.8163 | 0.8240 | 98 | 0.9398 | [[0, 1, 2, 3], [0, 24, 0, 0, 0], [1, 6, 33, 0, 0], [2, 6, 0, 17, 0], [3, 6, 0, 0, 6]] |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.0+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2
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