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unispeech-sat-base-finetuned-common_voice

Audio Classification
Transformers
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Safetensors
unispeech-sat
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  1. README.md +17 -17
  2. model.safetensors +1 -1
README.md CHANGED
@@ -19,13 +19,13 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [microsoft/unispeech-sat-base](https://huggingface.co/microsoft/unispeech-sat-base) on the None dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.0526
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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: 0.9927
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- - Mcc: 0.9907
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- - Auc: 0.9994
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  ## Model description
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@@ -58,16 +58,16 @@ The following hyperparameters were used during training:
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Recall | Precision | Mcc | Auc |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:------:|:---------:|:------:|:------:|
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- | 0.2855 | 1.0 | 200 | 0.1316 | 0.9675 | 0.9676 | 0.9675 | 0.9687 | 0.9596 | 0.9977 |
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- | 0.0865 | 2.0 | 400 | 0.0724 | 0.9825 | 0.9825 | 0.9825 | 0.9833 | 0.9783 | 0.9999 |
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- | 0.0536 | 3.0 | 600 | 0.0436 | 0.9925 | 0.9925 | 0.9925 | 0.9926 | 0.9906 | 0.9981 |
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- | 0.1822 | 4.0 | 800 | 0.0470 | 0.99 | 0.9900 | 0.99 | 0.9901 | 0.9875 | 0.9999 |
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- | 0.0343 | 5.0 | 1000 | 0.1409 | 0.975 | 0.9751 | 0.975 | 0.9765 | 0.9691 | 0.9997 |
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- | 0.1201 | 6.0 | 1200 | 0.1419 | 0.9675 | 0.9677 | 0.9675 | 0.9696 | 0.9598 | 0.9975 |
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- | 0.102 | 7.0 | 1400 | 0.1631 | 0.9725 | 0.9727 | 0.9725 | 0.9746 | 0.9661 | 0.9967 |
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- | 0.0105 | 8.0 | 1600 | 0.0528 | 0.9925 | 0.9925 | 0.9925 | 0.9927 | 0.9907 | 0.9987 |
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- | 0.0039 | 9.0 | 1800 | 0.0708 | 0.9875 | 0.9875 | 0.9875 | 0.9880 | 0.9845 | 0.9984 |
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- | 0.0058 | 10.0 | 2000 | 0.0526 | 0.9925 | 0.9925 | 0.9925 | 0.9927 | 0.9907 | 0.9994 |
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  ### Framework versions
 
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  This model is a fine-tuned version of [microsoft/unispeech-sat-base](https://huggingface.co/microsoft/unispeech-sat-base) on the None dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.0641
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+ - Accuracy: 0.9875
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+ - F1: 0.9875
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+ - Recall: 0.9875
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+ - Precision: 0.9878
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+ - Mcc: 0.9844
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+ - Auc: 0.9999
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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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+ | 0.2181 | 1.0 | 200 | 0.0440 | 0.9925 | 0.9925 | 0.9925 | 0.9926 | 0.9906 | 0.9981 |
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+ | 0.0083 | 2.0 | 400 | 0.0609 | 0.9875 | 0.9875 | 0.9875 | 0.9880 | 0.9845 | 0.9987 |
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+ | 0.0035 | 3.0 | 600 | 0.0888 | 0.98 | 0.9799 | 0.9800 | 0.9806 | 0.9752 | 0.9991 |
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+ | 0.2407 | 4.0 | 800 | 0.1593 | 0.9725 | 0.9726 | 0.9725 | 0.9740 | 0.9660 | 0.9997 |
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+ | 0.0859 | 5.0 | 1000 | 0.1234 | 0.9775 | 0.9777 | 0.9775 | 0.9790 | 0.9722 | 0.9999 |
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+ | 0.2073 | 6.0 | 1200 | 0.0851 | 0.9825 | 0.9826 | 0.9825 | 0.9832 | 0.9783 | 0.9999 |
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+ | 0.0036 | 7.0 | 1400 | 0.0550 | 0.9925 | 0.9925 | 0.9925 | 0.9927 | 0.9907 | 0.9999 |
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+ | 0.0036 | 8.0 | 1600 | 0.0600 | 0.9925 | 0.9925 | 0.9925 | 0.9927 | 0.9907 | 1.0000 |
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+ | 0.0013 | 9.0 | 1800 | 0.0645 | 0.99 | 0.9900 | 0.99 | 0.9903 | 0.9876 | 1.0000 |
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+ | 0.0048 | 10.0 | 2000 | 0.0641 | 0.9875 | 0.9875 | 0.9875 | 0.9878 | 0.9844 | 0.9999 |
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  ### Framework versions
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