GGUF quantizations of the CED family (Consistent Ensemble Distillation, Xiaomi) - SOTA-tier audio-tagging models that classify everyday sounds (baby cry, footsteps, glass breaking, alarms, dog bark,...) into the 527-class AudioSet ontology. These files run with ced.cpp, a standalone C++/ggml port (no Python, no PyTorch at inference), and with LocalAI via the ced backend. Converted from the mispeech/ced- checkpoints (Apache-2.0). CED is a plain AST/DeiT Vision Transformer over a log-mel spectrogram; the port is numerically equal to the PyTorch reference. One self-contained GGUF per size + quant (config, 527 labels, and the mel filterbank/window are all embedded). Pick by your accuracy/size…
Open-weight model · Audio classification
Common-Voice-Gender-Detection-ONNX
by Prithiv Sakthi prithivMLmods/Common-Voice-Gender-Detection-ONNX
This is an ONNX version of prithivMLmods/Common-Voice-Gender-Detection. It was automatically converted and uploaded using this space.
Parameters—
Context—
Weights1.1 GB
Licenseapache-2.0
AccessOpen weights
Monthly Downloads6.1k
Model Card
By Prithiv Sakthi, published under apache-2.0, revision 8d7fc28d2688.
This is an ONNX version of prithivMLmods/Common-Voice-Gender-Detection. It was automatically converted and uploaded using this space. Wav2Vec2: Self-Supervised Learning for Speech Recognition: https://arxiv.org/pdf/2006.11477 Common-Voice-Gender-Detection is designed for: 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…
Read Prithiv Sakthi's full model card
This is an ONNX version of prithivMLmods/Common-Voice-Gender-Detection. It was automatically converted and uploaded using this space.
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
Intended Use
Common-Voice-Gender-Detection is designed for:
- 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.
Configuration
- Architecture
- Wav2Vec2ForSequenceClassification
- Layers
- 12
- Hidden size
- 768
- Feed-forward size
- 3,072
- Attention heads
- 12
- Vocabulary size
- 32
- Stored precision
- float32
- Model type
- wav2vec2
Identity and Version
- Repository
- prithivMLmods/Common-Voice-Gender-Detection-ONNX
- Publisher
- Prithiv Sakthi
- Task
- Audio classification
- Modality
- Audio
- Library
- transformers.js
- Parameters
- Not stated by the source
- Languages
- en
- Revision
- 8d7fc28d26889a8763098f9e49fed002b15d2d3f
- First published
- 2025-06-01
- Last updated
- 2025-06-01
Files and Weights
13 files, 1.1 GB in total. The weights are 8 files totalling 1.1 GB in onnx.
Weights8 files · 1.1 GB
Configuration3 files · 3.1 KB
Documentation1 file · 1.7 KB
Repository1 file · 1.5 KB
Every file
| File | Type | Size | SHA-256 |
|---|---|---|---|
| onnx/model.onnx | Weights | 378.6 MB | 6c745f536d32 |
| onnx/model_bnb4.onnx | Weights | 84.6 MB | fb4db6cf5d56 |
| onnx/model_fp16.onnx | Weights | 189.5 MB | f619ba9f6f40 |
| onnx/model_int8.onnx | Weights | 95.4 MB | 8ef0097a3d52 |
| onnx/model_q4.onnx | Weights | 90.0 MB | 7af242f161f9 |
| onnx/model_q4f16.onnx | Weights | 66.5 MB | 53a784d96b37 |
| onnx/model_quantized.onnx | Weights | 95.4 MB | a0934c2f8934 |
| onnx/model_uint8.onnx | Weights | 95.4 MB | a0934c2f8934 |
| config.json | Configuration | 2.6 KB | — |
| preprocessor_config.json | Configuration | 215 B | — |
| quantize_config.json | Configuration | 312 B | — |
| README.md | Documentation | 1.7 KB | — |
| .gitattributes | Repository | 1.5 KB | — |
License and Download
- License
- apache-2.0
- Access
- Open weights, no gate
- Download size
- 1.1 GB
Download from Prithiv Sakthi
Released by Prithiv Sakthi through its official repository on Hugging Face. Read the license.
Built From
- Derived from prithivMLmods/Common-Voice-Gender-Detection
- Described by arXiv:2006.11477
- Quantized from prithivMLmods/Common-Voice-Gender-Detection
Memory Requirements
| Precision | Weights in memory |
|---|---|
| As published | 1.1 GB |
Weights only, from the published parameter count; the key-value cache and runtime add to this.
Questions About Common-Voice-Gender-Detection-ONNX
Can I use Common-Voice-Gender-Detection-ONNX commercially?
Yes. Common-Voice-Gender-Detection-ONNX 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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