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abhishtagatya
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hubert-base-960h-asv19-deepfake

Audio Classification
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hubert
deepfake
audio-spoof
generated_from_trainer
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End of training

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  1. README.md +11 -11
  2. model.safetensors +1 -1
  3. runs/Mar14_11-42-30_4dfb58ae0f65/events.out.tfevents.1710416551.4dfb58ae0f65.13449.0 +3 -0
  4. training_args.bin +1 -1
README.md CHANGED
@@ -20,11 +20,11 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [facebook/hubert-base-ls960](https://huggingface.co/facebook/hubert-base-ls960) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.0517
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- - Accuracy: 0.9907
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- - Far: 0.0059
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- - Frr: 0.0096
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- - Eer: 0.0078
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  ## Model description
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@@ -51,17 +51,17 @@ The following hyperparameters were used during training:
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  - total_train_batch_size: 4
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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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- - num_epochs: 2.0
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  ### Training results
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy | Far | Frr | Eer |
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  |:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:------:|:------:|
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- | 0.3474 | 0.39 | 2500 | 0.1761 | 0.8974 | 1.0 | 0.0 | 0.5 |
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- | 0.1166 | 0.79 | 5000 | 0.0269 | 0.9948 | 0.0247 | 0.0029 | 0.0138 |
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- | 0.0531 | 1.18 | 7500 | 0.0435 | 0.9909 | 0.0078 | 0.0093 | 0.0086 |
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- | 0.0477 | 1.58 | 10000 | 0.0540 | 0.9899 | 0.0055 | 0.0106 | 0.0081 |
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- | 0.0472 | 1.97 | 12500 | 0.0517 | 0.9907 | 0.0059 | 0.0096 | 0.0078 |
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  ### Framework versions
 
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  This model is a fine-tuned version of [facebook/hubert-base-ls960](https://huggingface.co/facebook/hubert-base-ls960) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.0624
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+ - Accuracy: 0.9918
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+ - Far: 0.0024
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+ - Frr: 0.0088
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+ - Eer: 0.0056
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  ## Model description
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  - total_train_batch_size: 4
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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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+ - num_epochs: 4.0
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  ### Training results
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy | Far | Frr | Eer |
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  |:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:------:|:------:|
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+ | 0.0726 | 0.79 | 5000 | 0.1522 | 0.9648 | 0.0027 | 0.0389 | 0.0208 |
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+ | 0.0338 | 1.58 | 10000 | 0.0995 | 0.9852 | 0.0024 | 0.0162 | 0.0093 |
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+ | 0.0256 | 2.36 | 15000 | 0.0434 | 0.9940 | 0.0027 | 0.0063 | 0.0045 |
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+ | 0.0294 | 3.15 | 20000 | 0.0756 | 0.9899 | 0.0020 | 0.0111 | 0.0065 |
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+ | 0.0175 | 3.94 | 25000 | 0.0624 | 0.9918 | 0.0024 | 0.0088 | 0.0056 |
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  ### Framework versions
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