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DistilWav2Vec2 Adult/Child Speech Classifier 52M

DistilWav2Vec2 Adult/Child Speech Classifier is an audio classification model based on the wav2vec 2.0 architecture. This model is a distilled version of wav2vec2-adult-child-cls 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 #params Arch. Training/Validation data (text)
distil-wav2vec2-adult-child-cls-52m 52M 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 0.1301 96.03% 0.9639

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: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_ratio: 0.1
  • num_epochs: 3

Training results

Training Loss Epoch Step Validation Loss Accuracy F1
0.212 1.0 96 0.1561 0.9561 0.9596
0.1523 2.0 192 0.1408 0.9575 0.9616
0.0844 3.0 288 0.1301 0.9603 0.9639


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


DistilWav2Vec2 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
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Model size
52M params
Tensor type
Hosted inference API
This model can be loaded on the Inference API on-demand.