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model_phoneme_onSet1

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_phoneme_onSet1
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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_phoneme_onSet1
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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: 0.0873
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+ - 0 Precision: 1.0
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+ - 0 Recall: 0.9677
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+ - 0 F1-score: 0.9836
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+ - 0 Support: 31
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+ - 1 Precision: 0.9355
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+ - 1 Recall: 0.9667
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+ - 1 F1-score: 0.9508
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+ - 1 Support: 30
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+ - 2 Precision: 0.9565
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+ - 2 Recall: 1.0
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+ - 2 F1-score: 0.9778
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+ - 2 Support: 22
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+ - 3 Precision: 1.0
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+ - 3 Recall: 0.9333
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+ - 3 F1-score: 0.9655
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+ - 3 Support: 15
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+ - Accuracy: 0.9694
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+ - Macro avg Precision: 0.9730
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+ - Macro avg Recall: 0.9669
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+ - Macro avg F1-score: 0.9694
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+ - Macro avg Support: 98
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+ - Weighted avg Precision: 0.9705
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+ - Weighted avg Recall: 0.9694
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+ - Weighted avg F1-score: 0.9695
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+ - Weighted avg Support: 98
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+ - Wer: 0.0999
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+ - Mtrix: [[0, 1, 2, 3], [0, 30, 1, 0, 0], [1, 0, 29, 1, 0], [2, 0, 0, 22, 0], [3, 0, 1, 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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+ | 4.2188 | 4.16 | 100 | 3.4689 | 1.0 | 0.2581 | 0.4103 | 31 | 0.0 | 0.0 | 0.0 | 30 | 0.0 | 0.0 | 0.0 | 22 | 0.1667 | 1.0 | 0.2857 | 15 | 0.2347 | 0.2917 | 0.3145 | 0.1740 | 98 | 0.3418 | 0.2347 | 0.1735 | 98 | 0.9980 | [[0, 1, 2, 3], [0, 8, 0, 0, 23], [1, 0, 0, 0, 30], [2, 0, 0, 0, 22], [3, 0, 0, 0, 15]] |
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+ | 3.3407 | 8.33 | 200 | 3.1569 | 1.0 | 0.2581 | 0.4103 | 31 | 0.0 | 0.0 | 0.0 | 30 | 0.0 | 0.0 | 0.0 | 22 | 0.1667 | 1.0 | 0.2857 | 15 | 0.2347 | 0.2917 | 0.3145 | 0.1740 | 98 | 0.3418 | 0.2347 | 0.1735 | 98 | 0.9980 | [[0, 1, 2, 3], [0, 8, 0, 0, 23], [1, 0, 0, 0, 30], [2, 0, 0, 0, 22], [3, 0, 0, 0, 15]] |
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+ | 3.1051 | 12.49 | 300 | 3.1500 | 1.0 | 0.2581 | 0.4103 | 31 | 0.0 | 0.0 | 0.0 | 30 | 0.0 | 0.0 | 0.0 | 22 | 0.1667 | 1.0 | 0.2857 | 15 | 0.2347 | 0.2917 | 0.3145 | 0.1740 | 98 | 0.3418 | 0.2347 | 0.1735 | 98 | 0.9980 | [[0, 1, 2, 3], [0, 8, 0, 0, 23], [1, 0, 0, 0, 30], [2, 0, 0, 0, 22], [3, 0, 0, 0, 15]] |
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+ | 2.8593 | 16.65 | 400 | 2.7590 | 0.6889 | 1.0 | 0.8158 | 31 | 0.0 | 0.0 | 0.0 | 30 | 0.3962 | 0.9545 | 0.5600 | 22 | 0.0 | 0.0 | 0.0 | 15 | 0.5306 | 0.2713 | 0.4886 | 0.3439 | 98 | 0.3069 | 0.5306 | 0.3838 | 98 | 1.0 | [[0, 1, 2, 3], [0, 31, 0, 0, 0], [1, 13, 0, 17, 0], [2, 1, 0, 21, 0], [3, 0, 0, 15, 0]] |
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+ | 2.2351 | 20.82 | 500 | 2.0930 | 0.9118 | 1.0 | 0.9538 | 31 | 1.0 | 0.5333 | 0.6957 | 30 | 0.6286 | 1.0 | 0.7719 | 22 | 0.8462 | 0.7333 | 0.7857 | 15 | 0.8163 | 0.8466 | 0.8167 | 0.8018 | 98 | 0.8652 | 0.8163 | 0.8082 | 98 | 0.9631 | [[0, 1, 2, 3], [0, 31, 0, 0, 0], [1, 3, 16, 9, 2], [2, 0, 0, 22, 0], [3, 0, 0, 4, 11]] |
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+ | 1.8803 | 24.98 | 600 | 1.7480 | 1.0 | 1.0 | 1.0 | 31 | 0.9375 | 1.0 | 0.9677 | 30 | 1.0 | 0.9545 | 0.9767 | 22 | 1.0 | 0.9333 | 0.9655 | 15 | 0.9796 | 0.9844 | 0.9720 | 0.9775 | 98 | 0.9809 | 0.9796 | 0.9796 | 98 | 0.9552 | [[0, 1, 2, 3], [0, 31, 0, 0, 0], [1, 0, 30, 0, 0], [2, 0, 1, 21, 0], [3, 0, 1, 0, 14]] |
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+ | 1.5034 | 29.16 | 700 | 1.3694 | 1.0 | 1.0 | 1.0 | 31 | 1.0 | 1.0 | 1.0 | 30 | 1.0 | 1.0 | 1.0 | 22 | 1.0 | 1.0 | 1.0 | 15 | 1.0 | 1.0 | 1.0 | 1.0 | 98 | 1.0 | 1.0 | 1.0 | 98 | 0.9429 | [[0, 1, 2, 3], [0, 31, 0, 0, 0], [1, 0, 30, 0, 0], [2, 0, 0, 22, 0], [3, 0, 0, 0, 15]] |
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+ | 1.0229 | 33.33 | 800 | 0.8522 | 1.0 | 1.0 | 1.0 | 31 | 1.0 | 0.9667 | 0.9831 | 30 | 0.9565 | 1.0 | 0.9778 | 22 | 1.0 | 1.0 | 1.0 | 15 | 0.9898 | 0.9891 | 0.9917 | 0.9902 | 98 | 0.9902 | 0.9898 | 0.9898 | 98 | 0.8848 | [[0, 1, 2, 3], [0, 31, 0, 0, 0], [1, 0, 29, 1, 0], [2, 0, 0, 22, 0], [3, 0, 0, 0, 15]] |
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+ | 0.4811 | 37.49 | 900 | 0.3999 | 1.0 | 1.0 | 1.0 | 31 | 0.9677 | 1.0 | 0.9836 | 30 | 1.0 | 1.0 | 1.0 | 22 | 1.0 | 0.9333 | 0.9655 | 15 | 0.9898 | 0.9919 | 0.9833 | 0.9873 | 98 | 0.9901 | 0.9898 | 0.9897 | 98 | 0.5576 | [[0, 1, 2, 3], [0, 31, 0, 0, 0], [1, 0, 30, 0, 0], [2, 0, 0, 22, 0], [3, 0, 1, 0, 14]] |
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+ | 0.2314 | 41.65 | 1000 | 0.1075 | 1.0 | 1.0 | 1.0 | 31 | 1.0 | 1.0 | 1.0 | 30 | 1.0 | 1.0 | 1.0 | 22 | 1.0 | 1.0 | 1.0 | 15 | 1.0 | 1.0 | 1.0 | 1.0 | 98 | 1.0 | 1.0 | 1.0 | 98 | 0.1378 | [[0, 1, 2, 3], [0, 31, 0, 0, 0], [1, 0, 30, 0, 0], [2, 0, 0, 22, 0], [3, 0, 0, 0, 15]] |
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+ | 0.1292 | 45.82 | 1100 | 0.0855 | 1.0 | 1.0 | 1.0 | 31 | 0.9677 | 1.0 | 0.9836 | 30 | 1.0 | 1.0 | 1.0 | 22 | 1.0 | 0.9333 | 0.9655 | 15 | 0.9898 | 0.9919 | 0.9833 | 0.9873 | 98 | 0.9901 | 0.9898 | 0.9897 | 98 | 0.1038 | [[0, 1, 2, 3], [0, 31, 0, 0, 0], [1, 0, 30, 0, 0], [2, 0, 0, 22, 0], [3, 0, 1, 0, 14]] |
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+ | 0.0809 | 49.98 | 1200 | 0.1364 | 1.0 | 0.9677 | 0.9836 | 31 | 0.9355 | 0.9667 | 0.9508 | 30 | 0.9565 | 1.0 | 0.9778 | 22 | 1.0 | 0.9333 | 0.9655 | 15 | 0.9694 | 0.9730 | 0.9669 | 0.9694 | 98 | 0.9705 | 0.9694 | 0.9695 | 98 | 0.1309 | [[0, 1, 2, 3], [0, 30, 1, 0, 0], [1, 0, 29, 1, 0], [2, 0, 0, 22, 0], [3, 0, 1, 0, 14]] |
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+ | 0.0605 | 54.16 | 1300 | 0.0987 | 1.0 | 0.9677 | 0.9836 | 31 | 0.9375 | 1.0 | 0.9677 | 30 | 1.0 | 1.0 | 1.0 | 22 | 1.0 | 0.9333 | 0.9655 | 15 | 0.9796 | 0.9844 | 0.9753 | 0.9792 | 98 | 0.9809 | 0.9796 | 0.9797 | 98 | 0.1073 | [[0, 1, 2, 3], [0, 30, 1, 0, 0], [1, 0, 30, 0, 0], [2, 0, 0, 22, 0], [3, 0, 1, 0, 14]] |
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+ | 0.0558 | 58.33 | 1400 | 0.0994 | 1.0 | 0.9677 | 0.9836 | 31 | 0.9355 | 0.9667 | 0.9508 | 30 | 0.9565 | 1.0 | 0.9778 | 22 | 1.0 | 0.9333 | 0.9655 | 15 | 0.9694 | 0.9730 | 0.9669 | 0.9694 | 98 | 0.9705 | 0.9694 | 0.9695 | 98 | 0.1048 | [[0, 1, 2, 3], [0, 30, 1, 0, 0], [1, 0, 29, 1, 0], [2, 0, 0, 22, 0], [3, 0, 1, 0, 14]] |
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+ | 0.038 | 62.49 | 1500 | 0.0666 | 1.0 | 0.9677 | 0.9836 | 31 | 0.9375 | 1.0 | 0.9677 | 30 | 1.0 | 1.0 | 1.0 | 22 | 1.0 | 0.9333 | 0.9655 | 15 | 0.9796 | 0.9844 | 0.9753 | 0.9792 | 98 | 0.9809 | 0.9796 | 0.9797 | 98 | 0.0979 | [[0, 1, 2, 3], [0, 30, 1, 0, 0], [1, 0, 30, 0, 0], [2, 0, 0, 22, 0], [3, 0, 1, 0, 14]] |
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+ | 0.0415 | 66.65 | 1600 | 0.0938 | 1.0 | 0.9677 | 0.9836 | 31 | 0.9355 | 0.9667 | 0.9508 | 30 | 0.9565 | 1.0 | 0.9778 | 22 | 1.0 | 0.9333 | 0.9655 | 15 | 0.9694 | 0.9730 | 0.9669 | 0.9694 | 98 | 0.9705 | 0.9694 | 0.9695 | 98 | 0.1004 | [[0, 1, 2, 3], [0, 30, 1, 0, 0], [1, 0, 29, 1, 0], [2, 0, 0, 22, 0], [3, 0, 1, 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