Common-Voice-Gender-Detection (ONNX)

Common-Voice-Gender-Detection is a fine-tuned version of facebook/wav2vec2-base-960h for binary audio classification, specifically trained to detect speaker gender as female or male. This model leverages the Wav2Vec2ForSequenceClassification architecture for efficient and accurate voice-based gender classification.

[!note] Wav2Vec2: Self-Supervised Learning for Speech Recognition : https://arxiv.org/pdf/2006.11477

Intended Use

• Speech Analytics – Assist in analyzing speaker demographics in call centers or customer service recordings.
• Conversational AI Personalization – Adjust tone or dialogue based on gender detection for more personalized voice assistants.
• Voice Dataset Curation – Automatically tag or filter voice datasets by speaker gender for better dataset management.
• Research Applications – Enable linguistic and acoustic research involving gender-specific speech patterns.
• Multimedia Content Tagging – Automate metadata generation for gender identification in podcasts, interviews, or video content.