---
language: en
license: apache-2.0
datasets:
- ravdess
libraries:
- speechbrain
tags:
- emotion-classification
- speech-emotion-recognition
- speaker-characteristics
- audio-classification
- voice-analysis
---

# Emotion Classification Model

This model is a 7-class SVM classifier trained on the RAVDESS dataset using SpeechBrain ECAPA-TDNN embeddings as features. 

## Model Details
- Input: Audio file (will be converted to 16kHz, mono, single channel)
- Output: Predicted emotion (7 classes) [angry, disgust, fearful, happy, neutral/calm, sad, surprised]
- Features:
  - SpeechBrain ECAPA-TDNN embedding [192 features]
- Performance:
  - RAVDESS 5-fold cross-validation: 86.24% accuracy

## Installation

You can install the package directly from GitHub:

```bash
pip install git+https://github.com/griko/voice-emotion-classification.git
```

## Usage

```python
from pipelines.emotion_classifier import EmotionClassificationPipeline

# Load the model
classifier = EmotionClassificationPipeline.from_pretrained("griko/emotion_7_cls_svm_ecapa_ravdess")

# Use it for prediction
result = classifier("path/to/audio.wav")
print(result) # ['angry'] or ['disgust'] or ['fearful'] or ['happy'] or ['neutral/calm'] or ['sad'] or ['surprised']

# Batch prediction
results = classifier(["audio1.wav", "audio2.wav"])
print(results) # ['angry', 'disgust']
```

## Input Requirements

- Audio files should be in WAV format
- Audio will be automatically resampled to 16kHz if needed
- Audio will be converted to mono if needed

## Limitations
- Model was trained on actor voices from RAVDESS dataset
- Performance may vary on different audio qualities or recording conditions

## Citation
If you use this model in your research, please cite:
```bibtex
@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}, 
}
```