naat-classifier-model – AI Model by sahilhasnain07 | AlphaNeural AI

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sahilhasnain07/
naat-classifier-model

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audio-classification

generated_from_trainer

facebook/wav2vec2-base

endpoints_compatible

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naat-classifier-model

This model is a fine-tuned version of facebook/wav2vec2-base on the audiofolder dataset. It achieves the following results on the evaluation set:

Loss: 0.1137
Accuracy: 0.9773

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: 8
eval_batch_size: 8
seed: 42
optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 0.1
num_epochs: 10

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
0.3383	1.0	749	0.2574	0.9446
0.1287	2.0	1498	0.1934	0.9493
0.0620	3.0	2247	0.1921	0.9413
0.0264	4.0	2996	0.1394	0.9746
0.3313	5.0	3745	0.1415	0.9586
0.0112	6.0	4494	0.1009	0.9740
0.1507	7.0	5243	0.1112	0.9760
0.0070	8.0	5992	0.1137	0.9773
0.1192	9.0	6741	0.1203	0.9746
0.0038	10.0	7490	0.1258	0.9740

Framework versions

Transformers 5.0.0
Pytorch 2.10.0+cu128
Datasets 4.0.0
Tokenizers 0.22.2