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---
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license: bsd-3-clause
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base_model: MIT/ast-finetuned-audioset-10-10-0.4593
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tags:
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- generated_from_trainer
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datasets:
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- audiofolder
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metrics:
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- accuracy
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- f1
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- precision
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- recall
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model-index:
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- name: AST-ASVspoof5-Synthetic-Voice-Detection
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results:
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- task:
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name: Audio Classification
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type: audio-classification
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dataset:
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name: audiofolder
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type: audiofolder
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config: default
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split: validation
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args: default
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metrics:
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- name: Accuracy
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type: accuracy
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value: 0.8333451578573963
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- name: F1
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type: f1
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value: 0.8891604695934469
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- name: Precision
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type: precision
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value: 0.9208988192978341
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- name: Recall
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type: recall
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value: 0.8595369289154868
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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should probably proofread and complete it, then remove this comment. -->
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# AST-ASVspoof5-Synthetic-Voice-Detection
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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 audiofolder dataset.
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It achieves the following results on the evaluation set:
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- Loss: 2.2821
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- Accuracy: 0.8333
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- F1: 0.8892
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- Precision: 0.9209
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- Recall: 0.8595
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## Model description
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More information needed
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## Intended uses & limitations
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More information needed
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## Training and evaluation data
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More information needed
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## Training procedure
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate: 5e-05
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- train_batch_size: 8
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- eval_batch_size: 8
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- seed: 42
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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: 3.0
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
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|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:---------:|:------:|
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| 0.0042 | 1.0 | 22795 | 1.6954 | 0.8470 | 0.8942 | 0.9672 | 0.8314 |
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| 0.0 | 2.0 | 45590 | 1.5632 | 0.8489 | 0.9014 | 0.9157 | 0.8875 |
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| 0.0 | 3.0 | 68385 | 2.2821 | 0.8333 | 0.8892 | 0.9209 | 0.8595 |
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### Framework versions
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- Transformers 4.42.4
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- Pytorch 2.3.1+cu121
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- Datasets 2.20.0
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- Tokenizers 0.19.1
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