README.md

---
language: multilingual
license: apache-2.0
datasets:
- timit
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 ANN regressor to predict speaker age from audio input. The model was trained on the TIMIT 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: Artificial Neural Network optimized through Optuna
- Performance:
  - TIMIT test set: 4.95 years Mean Absolute Error (MAE)

## Features
1. SpeechBrain ECAPA-TDNN embeddings (192 dimensions)

## Training Data
The model was trained on the TIMIT dataset:
- High-quality studio recordings
- Single channel, 16kHz sampling rate
- Carefully controlled recording conditions
- Age annotations provided in the original dataset
## Installation

```bash
pip install git+https://github.com/griko/voice-age-regression.git[ann-ecapa-timit]
```

## Usage

```python
from age_regressor import AgeRegressionPipeline

# Load the pipeline
regressor = AgeRegressionPipeline.from_pretrained(
    "griko/age_reg_ann_ecapa_timit"
)

# 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 carefully controlled studio recordings 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
```