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model_phoneme_onSet2

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_onSet2
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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_onSet2
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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: 0.1464
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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: 0.9167
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+ - 1 Recall: 1.0
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+ - 1 F1-score: 0.9565
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+ - 1 Support: 22
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+ - 2 Precision: 1.0
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+ - 2 Recall: 0.9333
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+ - 2 F1-score: 0.9655
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+ - 2 Support: 30
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+ - 3 Precision: 0.9333
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+ - 3 Recall: 1.0
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+ - 3 F1-score: 0.9655
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+ - 3 Support: 14
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+ - Accuracy: 0.9691
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+ - Macro avg Precision: 0.9625
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+ - Macro avg Recall: 0.9753
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+ - Macro avg F1-score: 0.9678
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+ - Macro avg Support: 97
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+ - Weighted avg Precision: 0.9715
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+ - Weighted avg Recall: 0.9691
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+ - Weighted avg F1-score: 0.9693
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+ - Weighted avg Support: 97
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+ - Wer: 0.1380
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+ - Mtrix: [[0, 1, 2, 3], [0, 30, 1, 0, 0], [1, 0, 22, 0, 0], [2, 0, 1, 28, 1], [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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+ | 3.9429 | 4.16 | 100 | 3.3748 | 0.0 | 0.0 | 0.0 | 31 | 0.0 | 0.0 | 0.0 | 22 | 0.0 | 0.0 | 0.0 | 30 | 0.1011 | 0.6429 | 0.1748 | 14 | 0.0928 | 0.0253 | 0.1607 | 0.0437 | 97 | 0.0146 | 0.0928 | 0.0252 | 97 | 0.9980 | [[0, 1, 2, 3], [0, 0, 0, 0, 31], [1, 0, 0, 0, 22], [2, 3, 0, 0, 27], [3, 5, 0, 0, 9]] |
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+ | 3.3504 | 8.33 | 200 | 3.1724 | 0.0 | 0.0 | 0.0 | 31 | 0.0 | 0.0 | 0.0 | 22 | 0.0 | 0.0 | 0.0 | 30 | 0.1011 | 0.6429 | 0.1748 | 14 | 0.0928 | 0.0253 | 0.1607 | 0.0437 | 97 | 0.0146 | 0.0928 | 0.0252 | 97 | 0.9980 | [[0, 1, 2, 3], [0, 0, 0, 0, 31], [1, 0, 0, 0, 22], [2, 3, 0, 0, 27], [3, 5, 0, 0, 9]] |
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+ | 3.155 | 12.49 | 300 | 3.1448 | 0.0 | 0.0 | 0.0 | 31 | 0.0 | 0.0 | 0.0 | 22 | 0.0 | 0.0 | 0.0 | 30 | 0.1011 | 0.6429 | 0.1748 | 14 | 0.0928 | 0.0253 | 0.1607 | 0.0437 | 97 | 0.0146 | 0.0928 | 0.0252 | 97 | 0.9980 | [[0, 1, 2, 3], [0, 0, 0, 0, 31], [1, 0, 0, 0, 22], [2, 3, 0, 0, 27], [3, 5, 0, 0, 9]] |
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+ | 3.0282 | 16.65 | 400 | 2.9990 | 0.0 | 0.0 | 0.0 | 31 | 0.2268 | 1.0 | 0.3697 | 22 | 0.0 | 0.0 | 0.0 | 30 | 0.0 | 0.0 | 0.0 | 14 | 0.2268 | 0.0567 | 0.25 | 0.0924 | 97 | 0.0514 | 0.2268 | 0.0839 | 97 | 1.0 | [[0, 1, 2, 3], [0, 0, 31, 0, 0], [1, 0, 22, 0, 0], [2, 0, 30, 0, 0], [3, 0, 14, 0, 0]] |
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+ | 2.744 | 20.82 | 500 | 2.6658 | 0.8378 | 1.0 | 0.9118 | 31 | 0.3889 | 0.6364 | 0.4828 | 22 | 0.4583 | 0.3667 | 0.4074 | 30 | 0.0 | 0.0 | 0.0 | 14 | 0.5773 | 0.4213 | 0.5008 | 0.4505 | 97 | 0.4977 | 0.5773 | 0.5269 | 97 | 1.0 | [[0, 1, 2, 3], [0, 31, 0, 0, 0], [1, 6, 14, 2, 0], [2, 0, 19, 11, 0], [3, 0, 3, 11, 0]] |
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+ | 2.2503 | 24.98 | 600 | 2.0915 | 0.9677 | 0.9677 | 0.9677 | 31 | 0.8571 | 0.8182 | 0.8372 | 22 | 0.875 | 0.9333 | 0.9032 | 30 | 0.9231 | 0.8571 | 0.8889 | 14 | 0.9072 | 0.9057 | 0.8941 | 0.8993 | 97 | 0.9075 | 0.9072 | 0.9068 | 97 | 0.9609 | [[0, 1, 2, 3], [0, 30, 1, 0, 0], [1, 1, 18, 2, 1], [2, 0, 2, 28, 0], [3, 0, 0, 2, 12]] |
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+ | 1.8687 | 29.16 | 700 | 1.7109 | 1.0 | 0.9355 | 0.9667 | 31 | 0.7857 | 1.0 | 0.88 | 22 | 1.0 | 0.9333 | 0.9655 | 30 | 1.0 | 0.8571 | 0.9231 | 14 | 0.9381 | 0.9464 | 0.9315 | 0.9338 | 97 | 0.9514 | 0.9381 | 0.9404 | 97 | 0.9373 | [[0, 1, 2, 3], [0, 29, 2, 0, 0], [1, 0, 22, 0, 0], [2, 0, 2, 28, 0], [3, 0, 2, 0, 12]] |
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+ | 1.4444 | 33.33 | 800 | 1.3295 | 1.0 | 0.9677 | 0.9836 | 31 | 0.88 | 1.0 | 0.9362 | 22 | 1.0 | 0.9667 | 0.9831 | 30 | 1.0 | 0.9286 | 0.9630 | 14 | 0.9691 | 0.97 | 0.9657 | 0.9664 | 97 | 0.9728 | 0.9691 | 0.9697 | 97 | 0.9142 | [[0, 1, 2, 3], [0, 30, 1, 0, 0], [1, 0, 22, 0, 0], [2, 0, 1, 29, 0], [3, 0, 1, 0, 13]] |
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+ | 0.95 | 37.49 | 900 | 0.8782 | 1.0 | 1.0 | 1.0 | 31 | 0.9167 | 1.0 | 0.9565 | 22 | 1.0 | 0.9333 | 0.9655 | 30 | 0.9286 | 0.9286 | 0.9286 | 14 | 0.9691 | 0.9613 | 0.9655 | 0.9627 | 97 | 0.9708 | 0.9691 | 0.9692 | 97 | 0.8545 | [[0, 1, 2, 3], [0, 31, 0, 0, 0], [1, 0, 22, 0, 0], [2, 0, 1, 28, 1], [3, 0, 1, 0, 13]] |
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+ | 0.5303 | 41.65 | 1000 | 0.4750 | 1.0 | 1.0 | 1.0 | 31 | 0.9167 | 1.0 | 0.9565 | 22 | 1.0 | 0.9333 | 0.9655 | 30 | 1.0 | 1.0 | 1.0 | 14 | 0.9794 | 0.9792 | 0.9833 | 0.9805 | 97 | 0.9811 | 0.9794 | 0.9795 | 97 | 0.6026 | [[0, 1, 2, 3], [0, 31, 0, 0, 0], [1, 0, 22, 0, 0], [2, 0, 2, 28, 0], [3, 0, 0, 0, 14]] |
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+ | 0.3054 | 45.82 | 1100 | 0.2919 | 0.9688 | 1.0 | 0.9841 | 31 | 0.9130 | 0.9545 | 0.9333 | 22 | 1.0 | 0.9 | 0.9474 | 30 | 0.9333 | 1.0 | 0.9655 | 14 | 0.9588 | 0.9538 | 0.9636 | 0.9576 | 97 | 0.9607 | 0.9588 | 0.9586 | 97 | 0.2373 | [[0, 1, 2, 3], [0, 31, 0, 0, 0], [1, 1, 21, 0, 0], [2, 0, 2, 27, 1], [3, 0, 0, 0, 14]] |
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+ | 0.1609 | 49.98 | 1200 | 0.1727 | 1.0 | 1.0 | 1.0 | 31 | 0.88 | 1.0 | 0.9362 | 22 | 1.0 | 0.9 | 0.9474 | 30 | 0.9286 | 0.9286 | 0.9286 | 14 | 0.9588 | 0.9521 | 0.9571 | 0.9530 | 97 | 0.9625 | 0.9588 | 0.9589 | 97 | 0.1646 | [[0, 1, 2, 3], [0, 31, 0, 0, 0], [1, 0, 22, 0, 0], [2, 0, 2, 27, 1], [3, 0, 1, 0, 13]] |
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+ | 0.1204 | 54.16 | 1300 | 0.1430 | 1.0 | 1.0 | 1.0 | 31 | 0.9167 | 1.0 | 0.9565 | 22 | 1.0 | 0.9333 | 0.9655 | 30 | 0.9286 | 0.9286 | 0.9286 | 14 | 0.9691 | 0.9613 | 0.9655 | 0.9627 | 97 | 0.9708 | 0.9691 | 0.9692 | 97 | 0.1370 | [[0, 1, 2, 3], [0, 31, 0, 0, 0], [1, 0, 22, 0, 0], [2, 0, 1, 28, 1], [3, 0, 1, 0, 13]] |
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+ | 0.0924 | 58.33 | 1400 | 0.1494 | 0.9677 | 0.9677 | 0.9677 | 31 | 0.9130 | 0.9545 | 0.9333 | 22 | 1.0 | 0.9333 | 0.9655 | 30 | 0.9333 | 1.0 | 0.9655 | 14 | 0.9588 | 0.9535 | 0.9639 | 0.9580 | 97 | 0.9603 | 0.9588 | 0.9589 | 97 | 0.1581 | [[0, 1, 2, 3], [0, 30, 1, 0, 0], [1, 1, 21, 0, 0], [2, 0, 1, 28, 1], [3, 0, 0, 0, 14]] |
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+ | 0.0596 | 62.49 | 1500 | 0.1484 | 1.0 | 0.9677 | 0.9836 | 31 | 0.9167 | 1.0 | 0.9565 | 22 | 1.0 | 0.9333 | 0.9655 | 30 | 0.9333 | 1.0 | 0.9655 | 14 | 0.9691 | 0.9625 | 0.9753 | 0.9678 | 97 | 0.9715 | 0.9691 | 0.9693 | 97 | 0.1370 | [[0, 1, 2, 3], [0, 30, 1, 0, 0], [1, 0, 22, 0, 0], [2, 0, 1, 28, 1], [3, 0, 0, 0, 14]] |
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+ | 0.0592 | 66.65 | 1600 | 0.1464 | 1.0 | 0.9677 | 0.9836 | 31 | 0.9167 | 1.0 | 0.9565 | 22 | 1.0 | 0.9333 | 0.9655 | 30 | 0.9333 | 1.0 | 0.9655 | 14 | 0.9691 | 0.9625 | 0.9753 | 0.9678 | 97 | 0.9715 | 0.9691 | 0.9693 | 97 | 0.1380 | [[0, 1, 2, 3], [0, 30, 1, 0, 0], [1, 0, 22, 0, 0], [2, 0, 1, 28, 1], [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