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ast-finetuned-audioset-10-10-0.4593-finetuned-common_voice

Audio Classification
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Safetensors
audio-spectrogram-transformer
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  1. README.md +17 -17
  2. model.safetensors +1 -1
README.md CHANGED
@@ -20,13 +20,13 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [MIT/ast-finetuned-audioset-10-10-0.4593](https://huggingface.co/MIT/ast-finetuned-audioset-10-10-0.4593) on the None dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.0148
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- - Accuracy: 0.9925
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- - F1: 0.9925
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- - Recall: 0.9925
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- - Precision: 0.9926
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- - Mcc: 0.9906
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- - Auc: 1.0000
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  ## Model description
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Recall | Precision | Mcc | Auc |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:------:|:---------:|:------:|:------:|
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- | 0.2357 | 1.0 | 200 | 0.0914 | 0.96 | 0.9603 | 0.96 | 0.9637 | 0.9508 | 0.9997 |
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- | 0.2769 | 2.0 | 400 | 0.1397 | 0.965 | 0.9653 | 0.9650 | 0.9673 | 0.9567 | 0.9982 |
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- | 0.0736 | 3.0 | 600 | 0.0468 | 0.9875 | 0.9875 | 0.9875 | 0.9880 | 0.9845 | 0.9998 |
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- | 0.0672 | 4.0 | 800 | 0.0554 | 0.985 | 0.9849 | 0.985 | 0.9854 | 0.9814 | 0.9999 |
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- | 0.0024 | 5.0 | 1000 | 0.1224 | 0.97 | 0.9703 | 0.9700 | 0.9724 | 0.9630 | 0.9997 |
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- | 0.0 | 6.0 | 1200 | 0.0109 | 0.995 | 0.9950 | 0.9950 | 0.9951 | 0.9938 | 1.0000 |
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- | 0.0 | 7.0 | 1400 | 0.0255 | 0.9925 | 0.9925 | 0.9925 | 0.9926 | 0.9906 | 1.0000 |
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- | 0.0 | 8.0 | 1600 | 0.0169 | 0.9925 | 0.9925 | 0.9925 | 0.9926 | 0.9906 | 1.0000 |
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- | 0.0 | 9.0 | 1800 | 0.0154 | 0.9925 | 0.9925 | 0.9925 | 0.9926 | 0.9906 | 1.0000 |
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- | 0.0 | 10.0 | 2000 | 0.0148 | 0.9925 | 0.9925 | 0.9925 | 0.9926 | 0.9906 | 1.0000 |
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  ### Framework versions
 
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  This model is a fine-tuned version of [MIT/ast-finetuned-audioset-10-10-0.4593](https://huggingface.co/MIT/ast-finetuned-audioset-10-10-0.4593) on the None dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.5704
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+ - Accuracy: 0.8775
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+ - F1: 0.8773
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+ - Recall: 0.8775
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+ - Precision: 0.8773
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+ - Mcc: 0.8469
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+ - Auc: 0.9835
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  ## Model description
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Recall | Precision | Mcc | Auc |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:------:|:---------:|:------:|:------:|
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+ | 1.0165 | 1.0 | 200 | 1.2394 | 0.5575 | 0.4931 | 0.5575 | 0.4662 | 0.4677 | 0.8879 |
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+ | 0.8119 | 2.0 | 400 | 0.5971 | 0.7575 | 0.7576 | 0.7575 | 0.7668 | 0.6992 | 0.9592 |
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+ | 0.1652 | 3.0 | 600 | 0.5719 | 0.815 | 0.8161 | 0.8150 | 0.8273 | 0.7711 | 0.9713 |
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+ | 0.0829 | 4.0 | 800 | 0.8850 | 0.8 | 0.8017 | 0.8 | 0.8346 | 0.7590 | 0.9730 |
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+ | 0.0102 | 5.0 | 1000 | 0.7974 | 0.8375 | 0.8386 | 0.8375 | 0.8590 | 0.8024 | 0.9778 |
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+ | 0.0004 | 6.0 | 1200 | 0.5919 | 0.86 | 0.8607 | 0.86 | 0.8632 | 0.8254 | 0.9815 |
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+ | 0.0 | 7.0 | 1400 | 0.5652 | 0.88 | 0.8798 | 0.8800 | 0.8803 | 0.8502 | 0.9833 |
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+ | 0.0 | 8.0 | 1600 | 0.5665 | 0.875 | 0.8749 | 0.875 | 0.8749 | 0.8438 | 0.9834 |
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+ | 0.0 | 9.0 | 1800 | 0.5695 | 0.8775 | 0.8773 | 0.8775 | 0.8773 | 0.8469 | 0.9835 |
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+ | 0.0 | 10.0 | 2000 | 0.5704 | 0.8775 | 0.8773 | 0.8775 | 0.8773 | 0.8469 | 0.9835 |
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  ### Framework versions
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