Common-Voice-Gender-Detection
> 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.
Params
95 M
Context
—
Downloads 30d
191 K
Likes
35
Download history
tracking started — chart appears after 7 days of snapshots (5 recorded)191 K downloads in the last 30 days
Can you run it?
Estimated VRAM at 8K context unless noted. Pick your hardware to see the verdict per quantization.
| File | Quant | Size | Est. VRAM | Verdict on RTX 4090 · 24 GB |
|---|---|---|---|---|
| model.safetensors | f32 | 0.8 GB | 1.3 GB | ✅ Runs comfortably |
Estimate: file size × 1.1 + KV cache at 8K + 0.5 GB overhead. Not a benchmark — how we calculate this.
Specifications
- Architecture
- Wav2Vec2ForSequenceClassification
- Parameters
- 95 M
- Tensor type
- F32
- Vocabulary
- 32
- Layers / heads
- 12 / 12
- Licence
- apache-2.0
- First seen on the Hub
- 2025-05-31
- Base model
- wav2vec2-base-960h
- Training datasets
- undisclosed
- Added to our catalog
- 2026-10-20
Family
Base model and the most-downloaded derivatives in the catalog.
Compare with any audio-classification model