Gender Classification from Audio

A HuggingFace pipeline for gender classification from audio using SpeechBrain ECAPA embeddings and SVM.

Model Details

• Architecture: SpeechBrain ECAPA-TDNN embeddings (192-dim) + SVM classifier
• Training Data: VoxCeleb2 dataset
  • Training set: 1,691 speakers (845 females, 846 males)
  • Validation set: 785 speakers (396 females, 389 males)
  • Test set: 1,647 speakers (828 females, 819 males)
• Performance:
  • VoxCeleb2 test set: 98.9% accuracy, 0.9885 F1-score
  • Mozilla Common Voice v10.0 English validated test set: 92.3% accuracy
  • TIMIT test set: 99.6% accuracy
• Audio Processing:
  • Input format: Any audio file format supported by soundfile
  • Automatically converted to: 16kHz, mono, single channel, 256 Kbps

Installation

You can install the package directly from GitHub:

pip install git+https://github.com/griko/voice-gender-classification.git

Usage

from voice_gender_classification import GenderClassificationPipeline

# Load the pipeline
classifier = GenderClassificationPipeline.from_pretrained(
    "griko/gender_cls_svm_ecapa_voxceleb"
)

# Single file prediction
result = classifier("path/to/audio.wav")
print(result)  # ["female"] or ["male"]

# Batch prediction
results = classifier(["audio1.wav", "audio2.wav"])
print(results)  # ["female", "male", "female"]

Limitations

• Designed for binary gender classification only
• Model was trained on celebrity voices from YouTube interviews
• Performance may vary on:
  • Different audio qualities
  • Different recording conditions
  • Multiple simultaneous speakers

Citation

If you use this model in your research, please cite:

@misc{koushnir2025vanpyvoiceanalysisframework,
      title={VANPY: Voice Analysis Framework}, 
      author={Gregory Koushnir and Michael Fire and Galit Fuhrmann Alpert and Dima Kagan},
      year={2025},
      eprint={2502.17579},
      archivePrefix={arXiv},
      primaryClass={cs.SD},
      url={https://arxiv.org/abs/2502.17579}, 
}

License

This project is licensed under the Apache 2.0 License - see the LICENSE file for details.

Acknowledgments

• VoxCeleb2 dataset for providing the training data
• SpeechBrain team for their excellent speech processing toolkit