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model_syllable_onSet4

Automatic Speech Recognition
Transformers
PyTorch
TensorBoard
wav2vec2
Generated from Trainer
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+ ---
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+ license: apache-2.0
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+ tags:
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+ - generated_from_trainer
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+ metrics:
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+ - accuracy
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+ - wer
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+ model-index:
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+ - name: model_syllable_onSet4
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+ results: []
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+ ---
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+
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+ <!-- This model card has been generated automatically according to the information the Trainer had access to. You
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+ should probably proofread and complete it, then remove this comment. -->
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+
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+ # model_syllable_onSet4
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+
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+ 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.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.1349
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+ - 0 Precision: 1.0
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+ - 0 Recall: 1.0
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+ - 0 F1-score: 1.0
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+ - 0 Support: 26
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+ - 1 Precision: 1.0
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+ - 1 Recall: 0.9677
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+ - 1 F1-score: 0.9836
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+ - 1 Support: 31
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+ - 2 Precision: 0.9630
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+ - 2 Recall: 1.0
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+ - 2 F1-score: 0.9811
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+ - 2 Support: 26
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+ - 3 Precision: 1.0
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+ - 3 Recall: 1.0
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+ - 3 F1-score: 1.0
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+ - 3 Support: 14
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+ - Accuracy: 0.9897
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+ - Macro avg Precision: 0.9907
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+ - Macro avg Recall: 0.9919
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+ - Macro avg F1-score: 0.9912
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+ - Macro avg Support: 97
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+ - Weighted avg Precision: 0.9901
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+ - Weighted avg Recall: 0.9897
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+ - Weighted avg F1-score: 0.9897
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+ - Weighted avg Support: 97
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+ - Wer: 0.2258
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+ - Mtrix: [[0, 1, 2, 3], [0, 26, 0, 0, 0], [1, 0, 30, 1, 0], [2, 0, 0, 26, 0], [3, 0, 0, 0, 14]]
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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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+ The following hyperparameters were used during training:
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+ - learning_rate: 0.0003
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+ - train_batch_size: 8
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+ - eval_batch_size: 8
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+ - seed: 42
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+ - gradient_accumulation_steps: 2
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+ - total_train_batch_size: 16
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+ - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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+ - lr_scheduler_type: linear
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+ - lr_scheduler_warmup_steps: 200
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+ - num_epochs: 70
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+ - mixed_precision_training: Native AMP
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+
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+ ### Training results
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+
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+ | 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 |
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+ |:-------------:|:-----:|:----:|:---------------:|:-----------:|:--------:|:----------:|:---------:|:-----------:|:--------:|:----------:|:---------:|:-----------:|:--------:|:----------:|:---------:|:-----------:|:--------:|:----------:|:---------:|:--------:|:-------------------:|:----------------:|:------------------:|:-----------------:|:----------------------:|:-------------------:|:---------------------:|:--------------------:|:------:|:--------------------------------------------------------------------------------------:|
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+ | 1.6602 | 4.16 | 100 | 1.5639 | 0.0 | 0.0 | 0.0 | 26 | 0.0 | 0.0 | 0.0 | 31 | 0.2584 | 0.8846 | 0.4 | 26 | 0.0 | 0.0 | 0.0 | 14 | 0.2371 | 0.0646 | 0.2212 | 0.1 | 97 | 0.0693 | 0.2371 | 0.1072 | 97 | 0.9732 | [[0, 1, 2, 3], [0, 0, 0, 26, 0], [1, 0, 0, 31, 0], [2, 3, 0, 23, 0], [3, 5, 0, 9, 0]] |
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+ | 1.616 | 8.33 | 200 | 1.4203 | 0.0 | 0.0 | 0.0 | 26 | 0.0 | 0.0 | 0.0 | 31 | 0.2584 | 0.8846 | 0.4 | 26 | 0.0 | 0.0 | 0.0 | 14 | 0.2371 | 0.0646 | 0.2212 | 0.1 | 97 | 0.0693 | 0.2371 | 0.1072 | 97 | 0.9732 | [[0, 1, 2, 3], [0, 0, 0, 26, 0], [1, 0, 0, 31, 0], [2, 3, 0, 23, 0], [3, 5, 0, 9, 0]] |
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+ | 1.2107 | 12.49 | 300 | 1.1249 | 0.0 | 0.0 | 0.0 | 26 | 0.0 | 0.0 | 0.0 | 31 | 0.2584 | 0.8846 | 0.4 | 26 | 0.0 | 0.0 | 0.0 | 14 | 0.2371 | 0.0646 | 0.2212 | 0.1 | 97 | 0.0693 | 0.2371 | 0.1072 | 97 | 0.9732 | [[0, 1, 2, 3], [0, 0, 0, 26, 0], [1, 0, 0, 31, 0], [2, 3, 0, 23, 0], [3, 5, 0, 9, 0]] |
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+ | 1.1283 | 16.65 | 400 | 1.0201 | 0.0 | 0.0 | 0.0 | 26 | 0.0 | 0.0 | 0.0 | 31 | 0.2584 | 0.8846 | 0.4 | 26 | 0.0 | 0.0 | 0.0 | 14 | 0.2371 | 0.0646 | 0.2212 | 0.1 | 97 | 0.0693 | 0.2371 | 0.1072 | 97 | 0.9732 | [[0, 1, 2, 3], [0, 0, 0, 26, 0], [1, 0, 0, 31, 0], [2, 3, 0, 23, 0], [3, 5, 0, 9, 0]] |
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+ | 0.8868 | 20.82 | 500 | 0.8944 | 0.0 | 0.0 | 0.0 | 26 | 0.0 | 0.0 | 0.0 | 31 | 0.2584 | 0.8846 | 0.4 | 26 | 0.0 | 0.0 | 0.0 | 14 | 0.2371 | 0.0646 | 0.2212 | 0.1 | 97 | 0.0693 | 0.2371 | 0.1072 | 97 | 0.9732 | [[0, 1, 2, 3], [0, 0, 0, 26, 0], [1, 0, 0, 31, 0], [2, 3, 0, 23, 0], [3, 5, 0, 9, 0]] |
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+ | 0.8863 | 24.98 | 600 | 0.9316 | 0.0 | 0.0 | 0.0 | 26 | 0.0 | 0.0 | 0.0 | 31 | 0.2584 | 0.8846 | 0.4 | 26 | 0.0 | 0.0 | 0.0 | 14 | 0.2371 | 0.0646 | 0.2212 | 0.1 | 97 | 0.0693 | 0.2371 | 0.1072 | 97 | 0.9732 | [[0, 1, 2, 3], [0, 0, 0, 26, 0], [1, 0, 0, 31, 0], [2, 3, 0, 23, 0], [3, 5, 0, 9, 0]] |
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+ | 0.9019 | 29.16 | 700 | 0.8688 | 0.7647 | 1.0 | 0.8667 | 26 | 0.0 | 0.0 | 0.0 | 31 | 0.3651 | 0.8846 | 0.5169 | 26 | 0.0 | 0.0 | 0.0 | 14 | 0.5052 | 0.2824 | 0.4712 | 0.3459 | 97 | 0.3028 | 0.5052 | 0.3708 | 97 | 0.9732 | [[0, 1, 2, 3], [0, 26, 0, 0, 0], [1, 0, 0, 31, 0], [2, 3, 0, 23, 0], [3, 5, 0, 9, 0]] |
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+ | 0.7977 | 33.33 | 800 | 0.8014 | 1.0 | 1.0 | 1.0 | 26 | 0.9667 | 0.9355 | 0.9508 | 31 | 0.9259 | 0.9615 | 0.9434 | 26 | 1.0 | 1.0 | 1.0 | 14 | 0.9691 | 0.9731 | 0.9743 | 0.9736 | 97 | 0.9695 | 0.9691 | 0.9691 | 97 | 1.0 | [[0, 1, 2, 3], [0, 26, 0, 0, 0], [1, 0, 29, 2, 0], [2, 0, 1, 25, 0], [3, 0, 0, 0, 14]] |
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+ | 0.729 | 37.49 | 900 | 0.8163 | 1.0 | 1.0 | 1.0 | 26 | 0.9091 | 0.9677 | 0.9375 | 31 | 0.9583 | 0.8846 | 0.9200 | 26 | 1.0 | 1.0 | 1.0 | 14 | 0.9588 | 0.9669 | 0.9631 | 0.9644 | 97 | 0.9598 | 0.9588 | 0.9586 | 97 | 1.0 | [[0, 1, 2, 3], [0, 26, 0, 0, 0], [1, 0, 30, 1, 0], [2, 0, 3, 23, 0], [3, 0, 0, 0, 14]] |
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+ | 0.6526 | 41.65 | 1000 | 0.6691 | 1.0 | 1.0 | 1.0 | 26 | 0.9667 | 0.9355 | 0.9508 | 31 | 0.9259 | 0.9615 | 0.9434 | 26 | 1.0 | 1.0 | 1.0 | 14 | 0.9691 | 0.9731 | 0.9743 | 0.9736 | 97 | 0.9695 | 0.9691 | 0.9691 | 97 | 0.7055 | [[0, 1, 2, 3], [0, 26, 0, 0, 0], [1, 0, 29, 2, 0], [2, 0, 1, 25, 0], [3, 0, 0, 0, 14]] |
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+ | 0.6633 | 45.82 | 1100 | 0.3445 | 1.0 | 1.0 | 1.0 | 26 | 0.9394 | 1.0 | 0.9688 | 31 | 1.0 | 0.9231 | 0.9600 | 26 | 1.0 | 1.0 | 1.0 | 14 | 0.9794 | 0.9848 | 0.9808 | 0.9822 | 97 | 0.9806 | 0.9794 | 0.9793 | 97 | 0.5017 | [[0, 1, 2, 3], [0, 26, 0, 0, 0], [1, 0, 31, 0, 0], [2, 0, 2, 24, 0], [3, 0, 0, 0, 14]] |
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+ | 0.1913 | 49.98 | 1200 | 0.2455 | 1.0 | 1.0 | 1.0 | 26 | 0.9677 | 0.9677 | 0.9677 | 31 | 0.96 | 0.9231 | 0.9412 | 26 | 0.9333 | 1.0 | 0.9655 | 14 | 0.9691 | 0.9653 | 0.9727 | 0.9686 | 97 | 0.9693 | 0.9691 | 0.9689 | 97 | 0.3946 | [[0, 1, 2, 3], [0, 26, 0, 0, 0], [1, 0, 30, 1, 0], [2, 0, 1, 24, 1], [3, 0, 0, 0, 14]] |
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+ | 0.2024 | 54.16 | 1300 | 0.1865 | 1.0 | 1.0 | 1.0 | 26 | 1.0 | 0.9355 | 0.9667 | 31 | 0.9286 | 1.0 | 0.9630 | 26 | 1.0 | 1.0 | 1.0 | 14 | 0.9794 | 0.9821 | 0.9839 | 0.9824 | 97 | 0.9809 | 0.9794 | 0.9794 | 97 | 0.3423 | [[0, 1, 2, 3], [0, 26, 0, 0, 0], [1, 0, 29, 2, 0], [2, 0, 0, 26, 0], [3, 0, 0, 0, 14]] |
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+ | 0.1212 | 58.33 | 1400 | 0.1485 | 1.0 | 1.0 | 1.0 | 26 | 1.0 | 0.9677 | 0.9836 | 31 | 0.9630 | 1.0 | 0.9811 | 26 | 1.0 | 1.0 | 1.0 | 14 | 0.9897 | 0.9907 | 0.9919 | 0.9912 | 97 | 0.9901 | 0.9897 | 0.9897 | 97 | 0.2957 | [[0, 1, 2, 3], [0, 26, 0, 0, 0], [1, 0, 30, 1, 0], [2, 0, 0, 26, 0], [3, 0, 0, 0, 14]] |
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+ | 0.108 | 62.49 | 1500 | 0.1348 | 1.0 | 1.0 | 1.0 | 26 | 1.0 | 0.9677 | 0.9836 | 31 | 0.9630 | 1.0 | 0.9811 | 26 | 1.0 | 1.0 | 1.0 | 14 | 0.9897 | 0.9907 | 0.9919 | 0.9912 | 97 | 0.9901 | 0.9897 | 0.9897 | 97 | 0.2433 | [[0, 1, 2, 3], [0, 26, 0, 0, 0], [1, 0, 30, 1, 0], [2, 0, 0, 26, 0], [3, 0, 0, 0, 14]] |
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+ | 0.1058 | 66.65 | 1600 | 0.1328 | 1.0 | 1.0 | 1.0 | 26 | 1.0 | 0.9677 | 0.9836 | 31 | 0.9630 | 1.0 | 0.9811 | 26 | 1.0 | 1.0 | 1.0 | 14 | 0.9897 | 0.9907 | 0.9919 | 0.9912 | 97 | 0.9901 | 0.9897 | 0.9897 | 97 | 0.2224 | [[0, 1, 2, 3], [0, 26, 0, 0, 0], [1, 0, 30, 1, 0], [2, 0, 0, 26, 0], [3, 0, 0, 0, 14]] |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.25.1
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+ - Pytorch 1.13.0+cu116
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+ - Datasets 2.8.0
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+ - Tokenizers 0.13.2