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
gender_cls_svm_ecapa_voxceleb
by Gregory Koushnir griko/gender_cls_svm_ecapa_voxceleb
This model combines the SpeechBrain ECAPA-TDNN speaker embedding model with an SVM classifier to predict speaker gender from audio input.
Parameters—
Context—
Weights16.7 MB
Licenseapache-2.0
AccessOpen weights
Monthly Downloads113k
Model Card
By Gregory Koushnir, published under apache-2.0, revision 25f3e5a3c1c1.
This model combines the SpeechBrain ECAPA-TDNN speaker embedding model with an SVM classifier to predict speaker gender from audio input. The model was trained and evaluated on the VoxCeleb2, Mozilla Common Voice v10.0, and TIMIT datasets - Mozilla Common Voice v10.0 English validated test set: 92.3% accuracy The model was trained on VoxCeleb2 dataset: - Converted to WAV format, single channel, 16kHz sampling rate, 256 kp/s bitrate - Applied SileroVAD for voice activity detection, taking the first voiced segment You can install the package directly from GitHub: - Model was trained on celebrity voices from YouTube interviews - Performance may vary on different audio qualities or recording…
Read Gregory Koushnir's full model card
Gender Classification Model
This model combines the SpeechBrain ECAPA-TDNN speaker embedding model with an SVM classifier to predict speaker gender from audio input. The model was trained and evaluated on the VoxCeleb2, Mozilla Common Voice v10.0, and TIMIT datasets
Model Details
- Input: Audio file (will be converted to 16kHz, mono, single channel)
- Output: Gender prediction ("male" or "female")
- Speaker embedding: 192-dimensional ECAPA-TDNN embedding from SpeechBrain
- Classifier: Support Vector Machine optimized through Optuna (200 trials)
- Performance:
- VoxCeleb2 test set: 98.9% accuracy, 0.9885 F1-score
- Mozilla Common Voice v10.0 English validated test set: 92.3% accuracy
- TIMIT test set: 99.6% accuracy
Training Data
The model was trained on VoxCeleb2 dataset: - Training set: 1,691 speakers (845 females, 846 males) - Validation set: 785 speakers (396 females, 389 males) - Test set: 1,647 speakers (828 females, 819 males) - No speaker overlap between sets - Audio preprocessing: - Converted to WAV format, single channel, 16kHz sampling rate, 256 kp/s bitrate - Applied SileroVAD for voice activity detection, taking the first voiced segment
Installation
You can install the package directly from GitHub:
pip install git+https://github.com/griko/voice-gender-classification.git
Usage
from voice_gender_classification import GenderClassificationPipeline
# Load the pipeline
classifier = GenderClassificationPipeline.from_pretrained(
"griko/gender_cls_svm_ecapa_voxceleb"
)
# Single file prediction
result = classifier("path/to/audio.wav")
print(result) # ["female"] or ["male"]
# Batch prediction
results = classifier(["audio1.wav", "audio2.wav"])
print(results) # ["female", "male", "female"]
Limitations
- Model was trained on celebrity voices from YouTube interviews
- Performance may vary on different audio qualities or recording conditions
- Designed for binary gender classification only
Citation
If you use this model in your research, please cite:
@misc{koushnir2025vanpyvoiceanalysisframework,
title={VANPY: Voice Analysis Framework},
author={Gregory Koushnir and Michael Fire and Galit Fuhrmann Alpert and Dima Kagan},
year={2025},
eprint={2502.17579},
archivePrefix={arXiv},
primaryClass={cs.SD},
url={https://arxiv.org/abs/2502.17579},
}
Identity and Version
- Repository
- griko/gender_cls_svm_ecapa_voxceleb
- Publisher
- Gregory Koushnir
- Task
- Audio classification
- Modality
- Audio
- Library
- Not stated by the source
- Parameters
- Not stated by the source
- Languages
- Not stated by the source
- Revision
- 25f3e5a3c1c172dceeb723d8061e3e80ba6c8d64
- First published
- 2024-11-09
- Last updated
- 2025-02-26
Files and Weights
6 files, 16.7 MB in total.
Configuration1 file · 5.5 KB
Documentation1 file · 2.7 KB
Other3 files · 16.7 MB
Repository1 file · 1.5 KB
Every file
| File | Type | Size | SHA-256 |
|---|---|---|---|
| config.json | Configuration | 5.5 KB | — |
| README.md | Documentation | 2.7 KB | — |
| requirements.txt | Other | 63 B | — |
| scaler.joblib | Other | 25.2 KB | 4e44e58d1e66 |
| svm_model.joblib | Other | 16.7 MB | 74badd2f209f |
| .gitattributes | Repository | 1.5 KB | — |
License and Download
- License
- apache-2.0
- Access
- Open weights, no gate
Download from Gregory Koushnir
Released by Gregory Koushnir through its official repository on Hugging Face. Read the license.
Built From
- Described by arXiv:2502.17579
- Trained on (disclosed) voxceleb2
Questions About gender_cls_svm_ecapa_voxceleb
Can I use gender_cls_svm_ecapa_voxceleb commercially?
Yes. gender_cls_svm_ecapa_voxceleb 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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This is a spoken language recognition model trained on the VoxLingua107 dataset using SpeechBrain. The model uses the ECAPA-TDNN architecture that has previously been used for speaker recognition. However, it uses more fully connected hidden layers after the embedding layer, and cross-entropy loss was used for training. We observed that this improved the performance of extracted utterance embeddings for downstream tasks. The system is trained with recordings sampled at 16kHz (single channel). The code will automatically normalize your audio (i.e., resampling + mono channel selection) when calling classifyfile if needed. The model can classify a speech utterance according to the language…
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