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README.md

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+---
+language: multilingual
+license: apache-2.0
+datasets:
+- voxceleb2
+libraries:
+- speechbrain
+tags:
+- age-estimation
+- speaker-characteristics
+- speaker-recognition
+- audio-regression
+- voice-analysis
+---
+
+# Age Estimation Model
+
+This model combines the SpeechBrain ECAPA-TDNN speaker embedding model with an SVR regressor to predict speaker age from audio input. The model was trained on the VoxCeleb2 dataset.
+
+## Model Performance Comparison
+
+We provide multiple pre-trained models with different architectures and feature sets. Here's a comprehensive comparison of their performance:
+
+| Model | Architecture | Features | Training Data | Test MAE | Best For |
+|-------|-------------|----------|---------------|-----------|----------|
+| VoxCeleb2 SVR (223) | SVR | ECAPA + Librosa (223-dim) | VoxCeleb2 | 7.88 years | Best performance on VoxCeleb2 |
+| VoxCeleb2 SVR (192) | SVR | ECAPA only (192-dim) | VoxCeleb2 | 7.89 years | Lightweight deployment |
+| TIMIT ANN (192) | ANN | ECAPA only (192-dim) | TIMIT | 4.95 years | Clean studio recordings |
+| Combined ANN (223) | ANN | ECAPA + Librosa (223-dim) | VoxCeleb2 + TIMIT | 6.93 years | Best general performance |
+
+You may find other models [here](https://huggingface.co/griko).
+
+## Model Details
+- Input: Audio file (will be converted to 16kHz, mono, single channel)
+- Output: Predicted age in years (continuous value)
+- Features: SpeechBrain ECAPA-TDNN embedding [192 features]
+- Regressor: Support Vector Regression optimized through Optuna
+- Performance:
+  - VoxCeleb2 test set: 7.89 years Mean Absolute Error (MAE)
+
+## Features
+1. SpeechBrain ECAPA-TDNN embeddings (192 dimensions)
+
+## Training Data
+The model was trained on the VoxCeleb2 dataset:
+- Audio preprocessing:
+  - Converted to WAV format, single channel, 16kHz sampling rate
+  - Applied SileroVAD for voice activity detection, taking the first voiced segment
+- Age data was collected from Wikidata and public sources
+## Installation
+
+```bash
+pip install git+https://github.com/griko/voice-age-regression.git[svr-ecapa-voxceleb2]
+```
+
+## Usage
+
+```python
+from age_regressor import AgeRegressionPipeline
+
+# Load the pipeline
+regressor = AgeRegressionPipeline.from_pretrained(
+    "griko/age_reg_svr_ecapa_voxceleb2"
+)
+
+# Single file prediction
+result = regressor("path/to/audio.wav")
+print(f"Predicted age: {result[0]:.1f} years")
+
+# Batch prediction
+results = regressor(["audio1.wav", "audio2.wav"])
+print(f"Predicted ages: {[f'{age:.1f}' for age in results]} years")
+```
+
+## Limitations
+- Model was trained on celebrity voices from YouTube interviews recordings
+- Performance may vary on different audio qualities or recording conditions
+- Age predictions are estimates and should not be used for medical or legal purposes
+- Age estimations should be treated as approximate values, not exact measurements
+
+## Citation
+If you use this model in your research, please cite:
+```bibtex
+TBD
+```

config.json

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model.joblib

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requirements.txt

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+scikit-learn
+pandas
+soundfile
+speechbrain
+torch
+torchaudio

scaler.joblib

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+oid sha256:41515fd50d331ccbc06750cef97d38c63660d07c3406c7566092916534a54b19
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