Wav2Vec2 Adult/Child Speech Classifier

Wav2Vec2 Adult/Child Speech Classifier is an audio classification model based on the wav2vec 2.0 architecture. This model is a fine-tuned version of wav2vec2-base on a private adult/child speech classification dataset.

This model was trained using HuggingFace's PyTorch framework. All training was done on a Tesla P100, provided by Kaggle. Training metrics were logged via Tensorboard.

Model

Model	#params	Arch.	Training/Validation data (text)
`wav2vec2-adult-child-cls`	91M	wav2vec 2.0	Adult/Child Speech Classification Dataset

Evaluation Results

The model achieves the following results on evaluation:

Dataset	Loss	Accuracy	F1
Adult/Child Speech Classification	0.1682	95.80%	0.9618

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
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.1
num_epochs: 5

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1
0.2709	1.0	384	0.2616	0.9104	0.9142
0.2112	2.0	768	0.1826	0.9386	0.9421
0.1755	3.0	1152	0.1898	0.9354	0.9428
0.0915	4.0	1536	0.1682	0.9580	0.9618
0.1042	5.0	1920	0.1717	0.9511	0.9554

Disclaimer

Do consider the biases which came from pre-training datasets that may be carried over into the results of this model.

Authors

Wav2Vec2 Adult/Child Speech Classifier was trained and evaluated by Wilson Wongso. All computation and development are done on Kaggle.

Framework versions

Transformers 4.16.2
Pytorch 1.10.2+cu102
Datasets 1.18.3
Tokenizers 0.10.3