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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: 0.1896
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- - Accuracy: 0.96
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- - F1: 0.9601
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- - Recall: 0.96
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- - Precision: 0.9606
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- - Mcc: 0.9501
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- - Auc: 0.9939
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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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- | 1.5599 | 1.0 | 200 | 1.5446 | 0.415 | 0.3951 | 0.4150 | 0.6762 | 0.3213 | 0.8445 |
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- | 1.1707 | 2.0 | 400 | 1.0171 | 0.7575 | 0.7502 | 0.7575 | 0.7665 | 0.7023 | 0.9487 |
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- | 0.7857 | 3.0 | 600 | 0.7125 | 0.8375 | 0.8311 | 0.8375 | 0.8453 | 0.8008 | 0.9667 |
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- | 0.5713 | 4.0 | 800 | 0.5097 | 0.88 | 0.8794 | 0.8800 | 0.8929 | 0.8536 | 0.9874 |
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- | 0.4225 | 5.0 | 1000 | 0.3919 | 0.9075 | 0.9076 | 0.9075 | 0.9116 | 0.8853 | 0.9894 |
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- | 0.5846 | 6.0 | 1200 | 0.3119 | 0.9325 | 0.9327 | 0.9325 | 0.9355 | 0.9163 | 0.9883 |
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- | 0.3004 | 7.0 | 1400 | 0.2308 | 0.9475 | 0.9477 | 0.9475 | 0.9487 | 0.9346 | 0.9925 |
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- | 0.3011 | 8.0 | 1600 | 0.1974 | 0.955 | 0.9551 | 0.9550 | 0.9557 | 0.9439 | 0.9940 |
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- | 0.138 | 9.0 | 1800 | 0.1851 | 0.96 | 0.9601 | 0.96 | 0.9606 | 0.9501 | 0.9932 |
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- | 0.1582 | 10.0 | 2000 | 0.1896 | 0.96 | 0.9601 | 0.96 | 0.9606 | 0.9501 | 0.9939 |
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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: 0.0526
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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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  | 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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