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README.md

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+---
+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')
+```