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abhishtagatya
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wav2vec2-base-960h-asv19-deepfake

Audio Classification
Transformers
TensorBoard
Safetensors
wav2vec2
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/Mar12_20-12-21_e261ad2481f6/events.out.tfevents.1710274341.e261ad2481f6.36692.0 +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/wav2vec2-base-960h](https://huggingface.co/facebook/wav2vec2-base-960h) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.1594
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- - Accuracy: 0.9610
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- - Far: 0.0098
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- - Frr: 0.0423
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- - Eer: 0.0261
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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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- | 0.4231 | 0.39 | 2500 | 0.3092 | 0.8974 | 1.0 | 0.0 | 0.5 |
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- | 0.2276 | 0.79 | 5000 | 0.0955 | 0.9762 | 0.0530 | 0.0205 | 0.0367 |
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- | 0.0979 | 1.18 | 7500 | 0.1456 | 0.9555 | 0.0122 | 0.0482 | 0.0302 |
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- | 0.0784 | 1.58 | 10000 | 0.1613 | 0.9586 | 0.0094 | 0.0451 | 0.0272 |
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- | 0.0618 | 1.97 | 12500 | 0.1594 | 0.9610 | 0.0098 | 0.0423 | 0.0261 |
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  ### Framework versions
 
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  This model is a fine-tuned version of [facebook/wav2vec2-base-960h](https://huggingface.co/facebook/wav2vec2-base-960h) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.0962
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+ - Accuracy: 0.9845
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+ - Far: 0.0090
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+ - Frr: 0.0162
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+ - Eer: 0.0126
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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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+ | 0.3383 | 0.79 | 5000 | 0.2361 | 0.8974 | 1.0 | 0.0 | 0.5 |
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+ | 0.0829 | 1.58 | 10000 | 0.1134 | 0.9739 | 0.0122 | 0.0277 | 0.0199 |
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+ | 0.0441 | 2.36 | 15000 | 0.0922 | 0.9841 | 0.0118 | 0.0163 | 0.0140 |
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+ | 0.0484 | 3.15 | 20000 | 0.1215 | 0.9798 | 0.0086 | 0.0215 | 0.0151 |
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+ | 0.0335 | 3.94 | 25000 | 0.0962 | 0.9845 | 0.0090 | 0.0162 | 0.0126 |
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  ### Framework versions
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