generated_from_trainer

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model_broadclass_onSet0.1

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:

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:

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
2.343 4.16 100 2.2083 0.3163 1.0 0.4806 31 0.0 0.0 0.0 25 0.0 0.0 0.0 27 0.0 0.0 0.0 15 0.3163 0.0791 0.25 0.1202 98 0.1001 0.3163 0.1520 98 0.9847 [[0, 1, 2, 3], [0, 31, 0, 0, 0], [1, 25, 0, 0, 0], [2, 27, 0, 0, 0], [3, 15, 0, 0, 0]]
2.2769 8.33 200 2.1649 0.3163 1.0 0.4806 31 0.0 0.0 0.0 25 0.0 0.0 0.0 27 0.0 0.0 0.0 15 0.3163 0.0791 0.25 0.1202 98 0.1001 0.3163 0.1520 98 0.9847 [[0, 1, 2, 3], [0, 31, 0, 0, 0], [1, 25, 0, 0, 0], [2, 27, 0, 0, 0], [3, 15, 0, 0, 0]]
1.9687 12.49 300 1.8723 0.3163 1.0 0.4806 31 0.0 0.0 0.0 25 0.0 0.0 0.0 27 0.0 0.0 0.0 15 0.3163 0.0791 0.25 0.1202 98 0.1001 0.3163 0.1520 98 0.9847 [[0, 1, 2, 3], [0, 31, 0, 0, 0], [1, 25, 0, 0, 0], [2, 27, 0, 0, 0], [3, 15, 0, 0, 0]]
1.8046 16.65 400 1.6982 0.3163 1.0 0.4806 31 0.0 0.0 0.0 25 0.0 0.0 0.0 27 0.0 0.0 0.0 15 0.3163 0.0791 0.25 0.1202 98 0.1001 0.3163 0.1520 98 0.9847 [[0, 1, 2, 3], [0, 31, 0, 0, 0], [1, 25, 0, 0, 0], [2, 27, 0, 0, 0], [3, 15, 0, 0, 0]]
1.5645 20.82 500 1.5862 0.3163 1.0 0.4806 31 0.0 0.0 0.0 25 0.0 0.0 0.0 27 0.0 0.0 0.0 15 0.3163 0.0791 0.25 0.1202 98 0.1001 0.3163 0.1520 98 0.9847 [[0, 1, 2, 3], [0, 31, 0, 0, 0], [1, 25, 0, 0, 0], [2, 27, 0, 0, 0], [3, 15, 0, 0, 0]]
1.5322 24.98 600 1.5736 0.3163 1.0 0.4806 31 0.0 0.0 0.0 25 0.0 0.0 0.0 27 0.0 0.0 0.0 15 0.3163 0.0791 0.25 0.1202 98 0.1001 0.3163 0.1520 98 0.9847 [[0, 1, 2, 3], [0, 31, 0, 0, 0], [1, 25, 0, 0, 0], [2, 27, 0, 0, 0], [3, 15, 0, 0, 0]]
1.5468 29.16 700 1.4736 0.3163 1.0 0.4806 31 0.0 0.0 0.0 25 0.0 0.0 0.0 27 0.0 0.0 0.0 15 0.3163 0.0791 0.25 0.1202 98 0.1001 0.3163 0.1520 98 0.9847 [[0, 1, 2, 3], [0, 31, 0, 0, 0], [1, 25, 0, 0, 0], [2, 27, 0, 0, 0], [3, 15, 0, 0, 0]]
1.0542 33.33 800 1.0068 0.3163 1.0 0.4806 31 0.0 0.0 0.0 25 0.0 0.0 0.0 27 0.0 0.0 0.0 15 0.3163 0.0791 0.25 0.1202 98 0.1001 0.3163 0.1520 98 0.9847 [[0, 1, 2, 3], [0, 31, 0, 0, 0], [1, 25, 0, 0, 0], [2, 27, 0, 0, 0], [3, 15, 0, 0, 0]]
0.9664 37.49 900 0.9831 0.3483 1.0 0.5167 31 1.0 0.12 0.2143 25 1.0 0.0370 0.0714 27 0.8 0.2667 0.4 15 0.3980 0.7871 0.3559 0.3006 98 0.7632 0.3980 0.2990 98 0.9758 [[0, 1, 2, 3], [0, 31, 0, 0, 0], [1, 21, 3, 0, 1], [2, 26, 0, 1, 0], [3, 11, 0, 0, 4]]
0.9405 41.65 1000 0.9402 0.3827 1.0 0.5536 31 1.0 0.04 0.0769 25 1.0 0.4815 0.65 27 1.0 0.2 0.3333 15 0.4898 0.8457 0.4304 0.4035 98 0.8047 0.4898 0.4248 98 0.9630 [[0, 1, 2, 3], [0, 31, 0, 0, 0], [1, 24, 1, 0, 0], [2, 14, 0, 13, 0], [3, 12, 0, 0, 3]]
0.9341 45.82 1100 0.9330 0.5082 1.0 0.6739 31 0.9231 0.48 0.6316 25 1.0 0.6296 0.7727 27 0.8571 0.4 0.5455 15 0.6735 0.8221 0.6274 0.6559 98 0.8029 0.6735 0.6707 98 0.9497 [[0, 1, 2, 3], [0, 31, 0, 0, 0], [1, 12, 12, 0, 1], [2, 9, 1, 17, 0], [3, 9, 0, 0, 6]]
0.8769 49.98 1200 0.8662 0.6327 1.0 0.775 31 0.9565 0.88 0.9167 25 1.0 0.6296 0.7727 27 0.8889 0.5333 0.6667 15 0.7959 0.8695 0.7607 0.7828 98 0.8557 0.7959 0.7939 98 0.9442 [[0, 1, 2, 3], [0, 31, 0, 0, 0], [1, 2, 22, 0, 1], [2, 9, 1, 17, 0], [3, 7, 0, 0, 8]]
0.8122 54.16 1300 0.7951 0.9062 0.9355 0.9206 31 0.8519 0.92 0.8846 25 1.0 0.8519 0.92 27 0.9375 1.0 0.9677 15 0.9184 0.9239 0.9268 0.9232 98 0.9230 0.9184 0.9185 98 0.9348 [[0, 1, 2, 3], [0, 29, 2, 0, 0], [1, 1, 23, 0, 1], [2, 2, 2, 23, 0], [3, 0, 0, 0, 15]]
0.5747 58.33 1400 0.4843 1.0 1.0 1.0 31 0.96 0.96 0.96 25 1.0 0.9630 0.9811 27 0.9375 1.0 0.9677 15 0.9796 0.9744 0.9807 0.9772 98 0.9802 0.9796 0.9797 98 0.6732 [[0, 1, 2, 3], [0, 31, 0, 0, 0], [1, 0, 24, 0, 1], [2, 0, 1, 26, 0], [3, 0, 0, 0, 15]]
0.2794 62.49 1500 0.2062 1.0 1.0 1.0 31 0.96 0.96 0.96 25 1.0 0.9630 0.9811 27 0.9375 1.0 0.9677 15 0.9796 0.9744 0.9807 0.9772 98 0.9802 0.9796 0.9797 98 0.2236 [[0, 1, 2, 3], [0, 31, 0, 0, 0], [1, 0, 24, 0, 1], [2, 0, 1, 26, 0], [3, 0, 0, 0, 15]]
0.1654 66.65 1600 0.1573 1.0 0.9677 0.9836 31 0.9259 1.0 0.9615 25 1.0 0.9630 0.9811 27 1.0 1.0 1.0 15 0.9796 0.9815 0.9827 0.9816 98 0.9811 0.9796 0.9798 98 0.1303 [[0, 1, 2, 3], [0, 30, 1, 0, 0], [1, 0, 25, 0, 0], [2, 0, 1, 26, 0], [3, 0, 0, 0, 15]]
0.1092 70.82 1700 0.1451 1.0 0.9677 0.9836 31 0.8889 0.96 0.9231 25 1.0 0.9259 0.9615 27 0.9375 1.0 0.9677 15 0.9592 0.9566 0.9634 0.9590 98 0.9621 0.9592 0.9597 98 0.1056 [[0, 1, 2, 3], [0, 30, 1, 0, 0], [1, 0, 24, 0, 1], [2, 0, 2, 25, 0], [3, 0, 0, 0, 15]]
0.085 74.98 1800 0.1126 1.0 1.0 1.0 31 0.9259 1.0 0.9615 25 1.0 0.9259 0.9615 27 1.0 1.0 1.0 15 0.9796 0.9815 0.9815 0.9808 98 0.9811 0.9796 0.9796 98 0.0938 [[0, 1, 2, 3], [0, 31, 0, 0, 0], [1, 0, 25, 0, 0], [2, 0, 2, 25, 0], [3, 0, 0, 0, 15]]
0.0824 79.16 1900 0.1118 1.0 1.0 1.0 31 0.9259 1.0 0.9615 25 1.0 0.9259 0.9615 27 1.0 1.0 1.0 15 0.9796 0.9815 0.9815 0.9808 98 0.9811 0.9796 0.9796 98 0.0859 [[0, 1, 2, 3], [0, 31, 0, 0, 0], [1, 0, 25, 0, 0], [2, 0, 2, 25, 0], [3, 0, 0, 0, 15]]

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