---
license: mit
tags:
  - pytorch
  - audio
  - emotion-recognition
  - audio-classification
  - customer-service
---

# Mantis

## Model Description
Mantis is an audio-based emotion recognition model designed for customer service intelligence. It classifies emotional states from speech audio using a HuBERT + CNN hybrid architecture, enabling real-time sentiment monitoring in call center environments.

## Model Architecture
- **Architecture**: HuBERT (feature extractor) + CNN (classifier head)
- **Framework**: PyTorch
- **Task**: Audio Emotion Classification
- **Input**: Raw audio waveforms / mel spectrograms
- **Output**: Emotion class (e.g., neutral, happy, angry, sad, frustrated)

## Training Details
- **Dataset**: Trained on emotion speech datasets (e.g., RAVDESS, IEMOCAP, or proprietary customer service audio)
- **Approach**: HuBERT pre-trained representations fed into a custom CNN classifier
- **Fine-tuning**: End-to-end fine-tuning for customer service emotion categories

## Performance
Evaluated on held-out emotion speech samples with strong accuracy across key emotion classes relevant to customer service.

## Files
| File | Description |
|------|-------------|
| `emotion_model.pth` | Final trained HuBERT-CNN emotion recognition model |

## Usage

```python
import torch
from huggingface_hub import hf_hub_download

# Download model
model_path = hf_hub_download(repo_id='devanshty/Mantis', filename='emotion_model.pth')

# Load model (adjust to your model class)
model = torch.load(model_path, map_location='cpu')
model.eval()

# Run inference on audio features
# (preprocess audio to match training pipeline)
```

## Download & Use

```python
from huggingface_hub import hf_hub_download
model_path = hf_hub_download(repo_id='devanshty/Mantis', filename='emotion_model.pth')
```