Gender Classification Model

This model combines the SpeechBrain ECAPA-TDNN speaker embedding model with an SVM classifier to predict speaker gender from audio input. The model was trained and evaluated on the VoxCeleb2, Mozilla Common Voice v10.0, and TIMIT datasets

Model Details

Input: Audio file (will be converted to 16kHz, mono, single channel)
Output: Gender prediction ("male" or "female")
Speaker embedding: 192-dimensional ECAPA-TDNN embedding from SpeechBrain
Classifier: Support Vector Machine optimized through Optuna (200 trials)
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

Training Data

The model was trained on 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)
No speaker overlap between sets
Audio preprocessing:
- Converted to WAV format, single channel, 16kHz sampling rate, 256 kp/s bitrate
- Applied SileroVAD for voice activity detection, taking the first voiced segment

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

Model was trained on celebrity voices from YouTube interviews
Performance may vary on different audio qualities or recording conditions
Designed for binary gender classification only

Citation

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

TBD