speech-accent-detection

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

• Loss: 0.0441
• Accuracy: 0.9955

Model description

I used the wav2vec2 model's weights and fine-tune over my dataset.

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 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: 10
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
0.8005	1.0	2205	0.6526	0.8270
0.0508	2.0	4410	0.3466	0.9374
0.3054	3.0	6615	0.2946	0.9524
0.0882	4.0	8820	0.1832	0.9737
0.0006	5.0	11025	0.1539	0.9757
0.0003	6.0	13230	0.0677	0.9896
0.3011	7.0	15435	0.1219	0.9859
0.0001	8.0	17640	0.0695	0.9916
0.0001	9.0	19845	0.0397	0.9955
0.0	10.0	22050	0.0441	0.9955

Framework versions

• Transformers 4.48.0
• Pytorch 2.5.1+cu124
• Datasets 3.2.0
• Tokenizers 0.21.0