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Open-weight model · Audio classification
by Prithiv Sakthi prithivMLmods/Common-Voice-Gender-Detection
Wav2Vec2: Self-Supervised Learning for Speech Recognition: https://arxiv.org/pdf/2006.11477 male female Common-Voice-Gender-Detection is designed for: Speech Analytics – Assist in analyzing speaker demographics in call centers or customer service recordings.
What it takes to serve Common-Voice-Gender-Detection (95M parameters): the memory its weights need at each precision, and the cheapest way to rent enough data-center GPUs to hold them.
| Precision | Weights | Memory needed | Cheapest setup | Per hour | Also fits |
|---|---|---|---|---|---|
| 16-bit | 0.2 GB | 0.2 GB | 1x MI300X (192 GB) Vultr |
$1.85 | 1x H100 $1.99 · 1x MI325X $2.00 |
| 8-bit | 0.1 GB | 0.1 GB | 1x MI300X (192 GB) Vultr |
$1.85 | 1x H100 $1.99 · 1x MI325X $2.00 |
| 4-bit | 0.0 GB | 0.1 GB | 1x MI300X (192 GB) Vultr |
$1.85 | 1x H100 $1.99 · 1x MI325X $2.00 |
Memory is the weights at that precision plus 20% for the runtime and a short context; a long context needs more. Prices are the lowest on-demand hourly rates in the SAVRN Index, read Sep 18, 2026.
By Prithiv Sakthi, published under apache-2.0, revision ebbf41293c8d.
Common-Voice-Gender-Detection is a fine-tuned version of
facebook/wav2vec2-base-960hfor binary audio classification, specifically trained to detect speaker gender as female or male. This model leverages theWav2Vec2ForSequenceClassificationarchitecture for efficient and accurate voice-based gender classification.[!note] Wav2Vec2: Self-Supervised Learning for Speech Recognition : https://arxiv.org/pdf/2006.11477
Classification Report:
precision recall f1-score support
female 0.9705 0.9916 0.9809 2622
male 0.9943 0.9799 0.9870 3923
accuracy 0.9846 6545
macro avg 0.9824 0.9857 0.9840 6545
weighted avg 0.9848 0.9846 0.9846 6545
Class 0: female
Class 1: male
pip install gradio transformers torch librosa hf_xet
15 files, 1.5 GB in total. The weights are 8 files totalling 1.5 GB in bin, pt, pth, safetensors.
| File | Type | Size | SHA-256 |
|---|---|---|---|
| checkpoint-3272/model.safetensors | Weights | 378.3 MB | ea5368dcba37 |
| checkpoint-3272/optimizer.pt | Weights | 723.1 MB | 44aa1b652ac3 |
| checkpoint-3272/rng_state.pth | Weights | 14.2 KB | 0e8cdd31ecd6 |
| checkpoint-3272/scaler.pt | Weights | 988 B | f12b5512f185 |
| checkpoint-3272/scheduler.pt | Weights | 1.1 KB | 90f3c79c26aa |
| checkpoint-3272/training_args.bin | Weights | 5.3 KB | 850a9a60f019 |
| model.safetensors | Weights | 378.3 MB | ea5368dcba37 |
| training_args.bin | Weights | 5.3 KB | 850a9a60f019 |
| checkpoint-3272/config.json | Configuration | 2.4 KB | — |
| checkpoint-3272/preprocessor_config.json | Configuration | 215 B | — |
| checkpoint-3272/trainer_state.json | Configuration | 58.4 KB | — |
| config.json | Configuration | 2.4 KB | — |
| preprocessor_config.json | Configuration | 215 B | — |
| README.md | Documentation | 4.4 KB | — |
| .gitattributes | Repository | 1.5 KB | — |
Released by Prithiv Sakthi through its official repository on Hugging Face. Read the license.
| Precision | Weights in memory |
|---|---|
| As published | 1.5 GB |
| 16-bit | 0.2 GB |
| 8-bit | 0.1 GB |
| 4-bit | 0.0 GB |
Weights only, from the published parameter count; the key-value cache and runtime add to this.
About 0.2 GB at 16-bit and 0.1 GB at 4-bit: the weights (95M parameters) plus a working margin. A long context needs more.
At 16-bit, 1x MI300X from $1.85 an hour; at 4-bit, 1x MI300X from $1.85 an hour, at the lowest on-demand prices the SAVRN Index lists.
Yes. Common-Voice-Gender-Detection is released under Apache License 2.0. The Apache License 2.0 is a permissive open-source license. It permits commercial use, modification and redistribution. It requires keeping the license and copyright notices and any NOTICE file, stating significant changes, and it includes an express patent grant from contributors.
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