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deepfake_audio_detection – AI Model by Shahzaib-Arshad | AlphaNeural AI
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Shahzaib-Arshad
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deepfake_audio_detection
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transformers
tensorboard
safetensors
wav2vec2
audio-classification
generated_from_trainer
facebook/wav2vec2-base
finetune
apache-2.0
endpoints_compatible
us
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deepfake_audio_detection
This model is a fine-tuned version of
facebook/wav2vec2-base
on an unknown dataset. It achieves the following results on the evaluation set:
eval_loss: 0.0065
eval_accuracy: 0.9988
eval_runtime: 58.7898
eval_samples_per_second: 85.049
eval_steps_per_second: 2.671
epoch: 2.0
step: 626
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
learning_rate: 3e-05
train_batch_size: 32
eval_batch_size: 32
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 128
optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.1
num_epochs: 3
Framework versions
Transformers 4.48.3
Pytorch 2.5.1+cu124
Datasets 3.5.0
Tokenizers 0.21.0
