Voice Gender Classifier
| Property | Value |
|---|---|
| Parameter Count | 15.5M |
| License | MIT |
| Framework | PyTorch |
| Dataset | VoxCeleb |
| Accuracy | 98.7% on VoxCeleb1 |
| Paper | ECAPA-TDNN Paper |
What is voice-gender-classifier?
The voice-gender-classifier is an advanced deep learning model designed to classify the gender of speakers from voice recordings. Built upon the state-of-the-art ECAPA-TDNN architecture, this model represents a sophisticated approach to voice-based gender classification, achieving remarkable accuracy through fine-tuned speaker verification techniques.
Implementation Details
The model builds upon a pretrained ECAPA-TDNN architecture, enhanced with a custom linear layer for binary gender classification. It's implemented in PyTorch and trained on the VoxCeleb2 dev set, demonstrating exceptional performance with 98.7% accuracy on the VoxCeleb1 identification test split.
- Utilizes pretrained ECAPA-TDNN speaker verification architecture
- Additional linear layer for binary classification
- Fine-tuned on VoxCeleb2 dataset
- Supports both CPU and GPU inference
Core Capabilities
- High-accuracy gender classification from voice inputs
- Easy integration with PyTorch workflows
- Efficient inference with both CPU and GPU support
- Robust speaker representation learning
Frequently Asked Questions
Q: What makes this model unique?
This model leverages state-of-the-art speaker verification architecture (ECAPA-TDNN) and achieves exceptional accuracy (98.7%) in gender classification, making it particularly reliable for voice-based gender detection tasks.
Q: What are the recommended use cases?
The model is suitable for voice-based gender classification in applications such as voice analytics, demographic analysis, and audio processing systems. However, users should be aware of potential biases due to the limited representation in the training dataset.
