wav2vec2-large-robust-24-ft-age-gender
| Property | Value |
|---|---|
| Parameter Count | 318M |
| License | CC-BY-NC-SA 4.0 |
| Paper | Research Paper |
| Training Datasets | aGender, Mozilla Common Voice, TIMIT, VoxCeleb2 |
What is wav2vec2-large-robust-24-ft-age-gender?
This is a specialized audio processing model based on Wav2Vec2 architecture, designed for age and gender recognition from speech inputs. It's built upon the Wav2Vec2-Large-Robust foundation and fine-tuned with 24 transformer layers to provide precise age estimates (0-100 years) and gender classification (child, female, male) from raw audio signals.
Implementation Details
The model processes raw audio input through a sophisticated pipeline, utilizing the full power of 24 transformer layers to extract relevant features. It outputs age predictions as a normalized value between 0 and 1 (corresponding to 0-100 years) and gender probabilities through softmax classification.
- Implements full 24-layer transformer architecture
- Uses PyTorch framework with F32 tensor type
- Provides both classification outputs and embedding features
- Supports 16kHz audio input sampling rate
Core Capabilities
- Age prediction with continuous value output
- Three-way gender classification (child/female/male)
- Feature embedding extraction from the last transformer layer
- Robust performance across different audio conditions
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its comprehensive approach to both age and gender recognition using a single architecture, leveraging the robust Wav2Vec2 foundation with all 24 transformer layers fine-tuned on diverse datasets.
Q: What are the recommended use cases?
The model is ideal for applications requiring demographic analysis from voice data, such as customer service analytics, voice-based user experience customization, and research applications. However, due to its licensing (CC-BY-NC-SA 4.0), it's restricted to non-commercial use.
