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---
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language: multilingual
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license: apache-2.0
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datasets:
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- voxceleb2
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libraries:
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- speechbrain
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tags:
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- height-estimation
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- speaker-characteristics
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- speaker-recognition
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- audio-classification
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- voice-analysis
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---
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# Height Estimation Model
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This model combines the SpeechBrain ECAPA-TDNN speaker embedding model with an SVR regressor to predict speaker height from audio input. The model was trained on the VoxCeleb2 and evaluated on the VoxCeleb2 and TIMIT datasets.
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## Model Details
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- Input: Audio file (will be converted to 16kHz, mono, single channel)
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- Output: Predicted height in centimeters (continuous value)
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- Speaker embedding: 192-dimensional ECAPA-TDNN embedding from SpeechBrain
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- Regressor: Support Vector Regression optimized through Optuna
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- Performance:
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- VoxCeleb2 test set: 6.01 cm Mean Absolute Error (MAE)
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- TIMIT test set: 6.02 cm Mean Absolute Error (MAE)
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## Training Data
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The model was trained on height enriched VoxCeleb2 dataset (for details read the paper):
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- Audio preprocessing:
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- Converted to WAV format, single channel, 16kHz sampling rate, 256 kp/s bitrate
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- Applied SileroVAD for voice activity detection, taking the first voiced segment
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## Installation
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You can install the package directly from GitHub:
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```bash
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pip install git+https://github.com/griko/voice-height-regression.git
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```
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## Usage
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```python
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from voice_height_regressor import HeightRegressionPipeline
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# Load the pipeline
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regressor = HeightRegressionPipeline.from_pretrained(
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"griko/height_reg_svr_ecapa_voxceleb"
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)
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# Single file prediction
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result = regressor("path/to/audio.wav")
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print(f"Predicted height: {result[0]:.1f} cm")
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# Batch prediction
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results = regressor(["audio1.wav", "audio2.wav"])
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print(f"Predicted heights: {[f'{h:.1f}' for h in results]} cm")
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```
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## Limitations
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- Model was trained on celebrity voices from YouTube interviews
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- Performance may vary on different audio qualities or recording conditions
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- Height predictions are estimates and should not be used for medical or legal purposes
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## Citation
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If you use this model in your research, please cite:
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```bibtex
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@misc{koushnir2025vanpyvoiceanalysisframework,
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title={VANPY: Voice Analysis Framework},
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author={Gregory Koushnir and Michael Fire and Galit Fuhrmann Alpert and Dima Kagan},
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year={2025},
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eprint={2502.17579},
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archivePrefix={arXiv},
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primaryClass={cs.SD},
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url={https://arxiv.org/abs/2502.17579},
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}
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``` |
