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model_broadclass_onSet1.1
This model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.2469
- 0 Precision: 1.0
- 0 Recall: 1.0
- 0 F1-score: 1.0
- 0 Support: 24
- 1 Precision: 1.0
- 1 Recall: 1.0
- 1 F1-score: 1.0
- 1 Support: 39
- 2 Precision: 1.0
- 2 Recall: 1.0
- 2 F1-score: 1.0
- 2 Support: 23
- 3 Precision: 1.0
- 3 Recall: 1.0
- 3 F1-score: 1.0
- 3 Support: 12
- Accuracy: 1.0
- Macro avg Precision: 1.0
- Macro avg Recall: 1.0
- Macro avg F1-score: 1.0
- Macro avg Support: 98
- Weighted avg Precision: 1.0
- Weighted avg Recall: 1.0
- Weighted avg F1-score: 1.0
- Weighted avg Support: 98
- Wer: 0.2423
- Mtrix: [[0, 1, 2, 3], [0, 24, 0, 0, 0], [1, 0, 39, 0, 0], [2, 0, 0, 23, 0], [3, 0, 0, 0, 12]]
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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2.3722 | 4.16 | 100 | 2.1950 | 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]] |
| 2.2944 | 8.33 | 200 | 2.1537 | 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]] |
| 1.9927 | 12.49 | 300 | 1.8879 | 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]] |
| 1.7175 | 16.65 | 400 | 1.6374 | 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]] |
| 1.6065 | 20.82 | 500 | 1.5619 | 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]] |
| 1.5362 | 24.98 | 600 | 1.5019 | 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]] |
| 1.5599 | 29.16 | 700 | 1.4858 | 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]] |
| 1.5344 | 33.33 | 800 | 1.4721 | 0.2759 | 1.0 | 0.4324 | 24 | 1.0 | 0.2821 | 0.4400 | 39 | 0.0 | 0.0 | 0.0 | 23 | 0.0 | 0.0 | 0.0 | 12 | 0.3571 | 0.3190 | 0.3205 | 0.2181 | 98 | 0.4655 | 0.3571 | 0.2810 | 98 | 0.9919 | [[0, 1, 2, 3], [0, 24, 0, 0, 0], [1, 28, 11, 0, 0], [2, 23, 0, 0, 0], [3, 12, 0, 0, 0]] |
| 1.4024 | 37.49 | 900 | 1.3532 | 1.0 | 1.0 | 1.0 | 24 | 1.0 | 1.0 | 1.0 | 39 | 1.0 | 1.0 | 1.0 | 23 | 1.0 | 1.0 | 1.0 | 12 | 1.0 | 1.0 | 1.0 | 1.0 | 98 | 1.0 | 1.0 | 1.0 | 98 | 0.9742 | [[0, 1, 2, 3], [0, 24, 0, 0, 0], [1, 0, 39, 0, 0], [2, 0, 0, 23, 0], [3, 0, 0, 0, 12]] |
| 0.9429 | 41.65 | 1000 | 0.9455 | 0.96 | 1.0 | 0.9796 | 24 | 0.9744 | 0.9744 | 0.9744 | 39 | 1.0 | 0.9565 | 0.9778 | 23 | 1.0 | 1.0 | 1.0 | 12 | 0.9796 | 0.9836 | 0.9827 | 0.9829 | 98 | 0.9800 | 0.9796 | 0.9796 | 98 | 0.9084 | [[0, 1, 2, 3], [0, 24, 0, 0, 0], [1, 1, 38, 0, 0], [2, 0, 1, 22, 0], [3, 0, 0, 0, 12]] |
| 0.8955 | 45.82 | 1100 | 0.8890 | 0.96 | 1.0 | 0.9796 | 24 | 1.0 | 0.9744 | 0.9870 | 39 | 1.0 | 1.0 | 1.0 | 23 | 1.0 | 1.0 | 1.0 | 12 | 0.9898 | 0.99 | 0.9936 | 0.9917 | 98 | 0.9902 | 0.9898 | 0.9898 | 98 | 0.9246 | [[0, 1, 2, 3], [0, 24, 0, 0, 0], [1, 1, 38, 0, 0], [2, 0, 0, 23, 0], [3, 0, 0, 0, 12]] |
| 0.8708 | 49.98 | 1200 | 0.8304 | 1.0 | 1.0 | 1.0 | 24 | 0.975 | 1.0 | 0.9873 | 39 | 1.0 | 0.9565 | 0.9778 | 23 | 1.0 | 1.0 | 1.0 | 12 | 0.9898 | 0.9938 | 0.9891 | 0.9913 | 98 | 0.9901 | 0.9898 | 0.9897 | 98 | 0.9272 | [[0, 1, 2, 3], [0, 24, 0, 0, 0], [1, 0, 39, 0, 0], [2, 0, 1, 22, 0], [3, 0, 0, 0, 12]] |
| 0.8671 | 54.16 | 1300 | 0.8028 | 0.96 | 1.0 | 0.9796 | 24 | 1.0 | 1.0 | 1.0 | 39 | 1.0 | 0.9565 | 0.9778 | 23 | 1.0 | 1.0 | 1.0 | 12 | 0.9898 | 0.99 | 0.9891 | 0.9893 | 98 | 0.9902 | 0.9898 | 0.9898 | 98 | 0.9211 | [[0, 1, 2, 3], [0, 24, 0, 0, 0], [1, 0, 39, 0, 0], [2, 1, 0, 22, 0], [3, 0, 0, 0, 12]] |
| 0.8383 | 58.33 | 1400 | 0.7804 | 1.0 | 1.0 | 1.0 | 24 | 1.0 | 1.0 | 1.0 | 39 | 1.0 | 1.0 | 1.0 | 23 | 1.0 | 1.0 | 1.0 | 12 | 1.0 | 1.0 | 1.0 | 1.0 | 98 | 1.0 | 1.0 | 1.0 | 98 | 0.9170 | [[0, 1, 2, 3], [0, 24, 0, 0, 0], [1, 0, 39, 0, 0], [2, 0, 0, 23, 0], [3, 0, 0, 0, 12]] |
| 0.7872 | 62.49 | 1500 | 0.7745 | 0.96 | 1.0 | 0.9796 | 24 | 1.0 | 0.9744 | 0.9870 | 39 | 1.0 | 1.0 | 1.0 | 23 | 1.0 | 1.0 | 1.0 | 12 | 0.9898 | 0.99 | 0.9936 | 0.9917 | 98 | 0.9902 | 0.9898 | 0.9898 | 98 | 0.9439 | [[0, 1, 2, 3], [0, 24, 0, 0, 0], [1, 1, 38, 0, 0], [2, 0, 0, 23, 0], [3, 0, 0, 0, 12]] |
| 0.7538 | 66.65 | 1600 | 0.7141 | 0.96 | 1.0 | 0.9796 | 24 | 1.0 | 0.9744 | 0.9870 | 39 | 1.0 | 1.0 | 1.0 | 23 | 1.0 | 1.0 | 1.0 | 12 | 0.9898 | 0.99 | 0.9936 | 0.9917 | 98 | 0.9902 | 0.9898 | 0.9898 | 98 | 0.9267 | [[0, 1, 2, 3], [0, 24, 0, 0, 0], [1, 1, 38, 0, 0], [2, 0, 0, 23, 0], [3, 0, 0, 0, 12]] |
| 0.6439 | 70.82 | 1700 | 0.5818 | 1.0 | 1.0 | 1.0 | 24 | 1.0 | 1.0 | 1.0 | 39 | 1.0 | 1.0 | 1.0 | 23 | 1.0 | 1.0 | 1.0 | 12 | 1.0 | 1.0 | 1.0 | 1.0 | 98 | 1.0 | 1.0 | 1.0 | 98 | 0.8574 | [[0, 1, 2, 3], [0, 24, 0, 0, 0], [1, 0, 39, 0, 0], [2, 0, 0, 23, 0], [3, 0, 0, 0, 12]] |
| 0.5295 | 74.98 | 1800 | 0.3775 | 1.0 | 1.0 | 1.0 | 24 | 1.0 | 1.0 | 1.0 | 39 | 1.0 | 1.0 | 1.0 | 23 | 1.0 | 1.0 | 1.0 | 12 | 1.0 | 1.0 | 1.0 | 1.0 | 98 | 1.0 | 1.0 | 1.0 | 98 | 0.4633 | [[0, 1, 2, 3], [0, 24, 0, 0, 0], [1, 0, 39, 0, 0], [2, 0, 0, 23, 0], [3, 0, 0, 0, 12]] |
| 0.4184 | 79.16 | 1900 | 0.2507 | 1.0 | 1.0 | 1.0 | 24 | 1.0 | 1.0 | 1.0 | 39 | 1.0 | 1.0 | 1.0 | 23 | 1.0 | 1.0 | 1.0 | 12 | 1.0 | 1.0 | 1.0 | 1.0 | 98 | 1.0 | 1.0 | 1.0 | 98 | 0.2529 | [[0, 1, 2, 3], [0, 24, 0, 0, 0], [1, 0, 39, 0, 0], [2, 0, 0, 23, 0], [3, 0, 0, 0, 12]] |
Framework versions
- Transformers 4.25.1
- Pytorch 1.13.0+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2
