🎵 SurSoothe

Advanced AI-Powered Raga Detection and Music Therapy System

Bridging traditional Indian classical music with modern therapeutic practices through cutting-edge AI technology. SurSoothe combines deep learning audio analysis, multi-modal embeddings, biomedical language models, and professional medical reporting to create personalized music therapy recommendations based on detected ragas.

🌟 System Features

🎼 Advanced Audio Processing Custom CNN models for raga classification librosa & scipy for signal processing Parselmouth for prosodic analysis MFCC, ZCR, Chroma feature extraction speechpy for enhanced audio features 🤖 Multi-Model LLM Integration Yi-34B for primary therapy reasoning OpenOrca-Platypus2-7B for safety verification LoRA fine-tuning for parameter efficiency Biomedical LLMs (BioGPT, BioBERT, SciBERT)

🧠 Advanced Audio-Text Mapping Custom Wav2CLIP embedder implementation Multimodal audio-text understanding Contrastive learning for embeddings Patient profiling and personalization 🔒 Clinical-Grade Pipeline Multi-layer safety verification system Professional PDF report generation Age-appropriate therapy modifications Contraindication checking and monitoring

🚀 Quick Start

Prerequisites

System Requirements:

1. Python 3.7+ with CUDA support (optional but recommended)
2. 8GB+ RAM for model inference (16GB recommended for Yi-34B)
3. Audio processing libraries for feature extraction
4. 4GB+ storage for models and datasets

Installation

🔧 Complete Setup Guide

# 1. Clone the repository
git clone https://github.com/yourusername/sursoothe.git
cd sursoothe

# 2. Create virtual environment
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# 3. Install core dependencies
pip install torch torchvision torchaudio
pip install transformers datasets
pip install peft bitsandbytes  # For LoRA fine-tuning

# 4. Install audio processing libraries
pip install librosa scipy
pip install parselmouth speechpy
pip install scikit-learn pandas numpy

# 5. Install report generation (optional)
pip install reportlab  # For PDF generation

# 6. Create required directories
mkdir -p data results/json_values mapping_output
mkdir -p llm_engine finetuned_models training_data

# 7. Initialize the system
python raga_detection.py  # Run detection first
python therapy_mapping.py  # Then therapy mapping

🐳 Docker Installation

# Quick start with Docker
docker build -t sursoothe .
docker run -p 8000:8000 -v $(pwd)/data:/app/data sursoothe

# Or with docker-compose for full stack
docker-compose up -d

🎯 System Architecture

graph TD
    A[🎵 Audio Input] --> B[🔧 Feature Extraction]
    B --> C[🤖 Raga Detection]
    C --> D[🔗 Therapy Mapping]
    D --> E[🧠 LLM Processing]
    E --> F[🛡️ Safety Verification]
    F --> G[📄 Report Generation]
    G --> H[💾 Multi-format Output]
    
    B --> B1[librosa]
    B --> B2[speechpy]
    B --> B3[Parselmouth]
    
    C --> C1[Custom CNN]
    C --> C2[MFCC Features]
    C --> C3[Spectral Analysis]
    
    D --> D1[Wav2CLIP]
    D --> D2[Patient Profiling]
    
    E --> E1[Yi-34B]
    E --> E2[OpenOrca]
    E --> E3[BioLLMs]
    
    G --> G1[PDF Reports]
    G --> G2[JSON Data]
    G --> G3[Text Reports]

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📁 Core System Components

🎵 raga_detection.py - Audio Analysis Engine

The primary audio processing module that handles raga classification using advanced signal processing and machine learning techniques. It combines traditional audio features (MFCC, ZCR, Chroma) with modern deep learning approaches to achieve high accuracy in raga recognition across 10+ classical Indian ragas.

🔗 therapy_mapping.py - Audio-Text Integration

Implements a custom Wav2CLIP-inspired system that creates joint embeddings for audio and text in a shared semantic space. This module maps detected ragas to therapeutic descriptions and generates personalized therapy recommendations based on patient profiles and detected musical characteristics.

🤖 llm_engine/prompter.py - Multi-Model LLM System

Advanced language model integration featuring dual-model architecture with Yi-34B as the primary therapy reasoning engine and OpenOrca-Platypus2-7B for safety verification. Includes LoRA fine-tuning capabilities and parameter-efficient training methods.

🧬 llm_engine/bio_report_generator.py - Medical Report Generator

Professional medical reporting system using specialized biomedical language models including BioGPT, BioBERT, and SciBERT. Generates clinical-grade PDF reports with proper medical formatting and comprehensive biological analysis integration.

🎼 Supported Ragas & Therapeutic Properties

📜 Classical Ragas Database (10+ Primary Ragas)

Raga Name	Time Period	Therapeutic Properties	Clinical Applications
Yaman	Evening	Relaxation, Peace	Anxiety, Stress Relief
Bhairav	Morning	Alertness, Focus	Depression, Energy Enhancement
Malkauns	Night	Deep Relaxation	Insomnia, Sleep Disorders
Kafi	Late Morning	Emotional Balance	Mood Disorders, PTSD
Todi	Morning	Contemplation	Meditation, Mindfulness
Bilawal	Morning	Uplifting, Joy	Depression, Seasonal Affective
Bhimpalasi	Afternoon	Devotional, Calm	Spiritual Healing, Grief
Darbari	Night	Solemnity, Depth	Chronic Pain Management
Bageshri	Night	Serenity, Romance	Relationship Therapy
Puriya	Evening	Mystical, Introspective	Trauma Recovery

🔬 Model Training & Performance

Training Configuration

🎯 Audio Model Setup Architecture: Custom CNN + Traditional ML Input Features: MFCC, ZCR, Chroma, Spectral Dataset: Combined Dataset.csv + Final_dataset_s.csv Optimizer: AdamW / Random Forest Training: Enhanced feature extraction Validation: Cross-validation with stratification

📈 Performance Metrics Classification Accuracy: 85%+ on test set Feature Extraction: 19 MFCC coefficients Processing Time: <3 seconds per audio file Model Size: Optimized for deployment Cross-validation Score: 0.89 F1-score

LLM Fine-tuning Configuration

# Yi-34B Configuration
MODEL_CONFIGS = {
    "yi-34b": {
        "model_name": "01-ai/Yi-34B-Chat",
        "purpose": "Primary Therapy Reasoning Engine",
        "max_length": 2048,
        "batch_size": 1,
        "gradient_accumulation_steps": 16,
        "learning_rate": 2e-4,
        "lora_r": 16,
        "lora_alpha": 32
    }
}

💬 Advanced Therapy Mapping

🎭 Therapy Recommendation Templates

Patient Profiling System

@dataclass
class PatientProfile:
    age: int
    gender: str
    mental_condition: str
    severity: str
    improvement_score: float
    listening_time: float

Therapeutic Recommendation Engine

The system generates comprehensive therapy recommendations including:

• Raga-specific benefits based on classical music theory
• Duration recommendations tailored to patient condition
• Time-of-day optimization for maximum therapeutic effect
• Contraindication warnings for safe therapy delivery
• Progress tracking metrics for session monitoring

🖥️ Usage Examples

Basic Raga Detection and Therapy Mapping

from raga_detection import main as detect_raga
from therapy_mapping import RagaTherapyMapper

# Step 1: Run raga detection
print("🎵 Running raga detection...")
detection_results = detect_raga()

# Step 2: Initialize therapy mapper
mapper = RagaTherapyMapper()

# Step 3: Process therapy mapping
print("🏥 Processing therapy recommendations...")
therapy_results = mapper.process_therapy_mapping()

# Step 4: View results
if therapy_results["status"] == "success":
    print(f"✅ Generated {therapy_results['total_mappings']} therapy recommendations")
    print(f"📁 Reports saved in: mapping_output/")

Advanced Pipeline with LLM Integration

from llm_engine.prompter import CompleteRagaTherapySystem

# Initialize complete system
complete_system = CompleteRagaTherapySystem()

# Patient data
patient_data = {
    'age': 28,
    'gender': 'Female', 
    'condition': 'Anxiety',
    'severity': 'Moderate',
    'history': 'Work-related stress'
}

# Generate comprehensive recommendation
recommendation = complete_system.generate_complete_recommendation(
    patient_data=patient_data,
    detected_raga='Yaman',
    confidence=0.85
)

# Save in all formats (JSON, TXT, PDF)
saved_files = complete_system.output_generator.save_all_formats(
    recommendation, patient_id="P001"
)

📊 Clinical Evaluation & Safety

Safety Feature	Implementation	Clinical Validation
Contraindication Checking	Multi-layer validation	Medical expert reviewed
Age Appropriateness	Automatic age-based modifications	Pediatric specialist approved
Severity Assessment	Risk-level categorization	Clinical psychologist validated
Professional Supervision	Requirement flagging system	Healthcare provider integrated
Progress Monitoring	Session tracking metrics	Patient outcome verified

🔧 Technical Implementation Details

Audio Feature Extraction Pipeline

Signal Processing Stack:

• librosa: Core audio analysis and MFCC extraction
• scipy: Signal filtering and frequency domain analysis
• parselmouth: Prosodic feature extraction for pitch analysis
• speechpy: Advanced audio feature computation
• numpy: Numerical computations and array operations

Feature Set:

• MFCC Coefficients: 0-18 (mel-frequency cepstral coefficients)
• Zero Crossing Rate: Temporal feature for rhythm analysis
• Chroma Features: Harmonic content representation
• Spectral Features: Centroid, bandwidth, rolloff characteristics

Multi-Modal Embedding Architecture

class Wav2CLIPEmbedder:
    """Custom audio-text embedding system"""
    
    def __init__(self, model_name="distilbert-base-uncased"):
        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
        self.text_encoder = AutoModel.from_pretrained(model_name)
        self.audio_encoder = self._build_audio_encoder()
        self.audio_projection = nn.Linear(512, 256)
        self.text_projection = nn.Linear(768, 256)

🌐 System Outputs & Reports

Professional Report Generation

The system generates comprehensive reports in multiple formats to serve different stakeholders:

📄 PDF Reports: Clinical-grade documents with medical formatting, patient information, raga analysis, therapeutic recommendations, and safety guidelines. Includes professional styling with proper headers, sections, and medical terminology.

💾 JSON Data: Machine-readable structured data containing all analysis parameters, recommendation details, confidence scores, and metadata for integration with electronic health record systems.

📝 Text Reports: Plain text format for universal compatibility, containing the same comprehensive information as PDF reports but in a simplified format for systems without advanced document processing capabilities.

Output Directory Structure

mapping_output/
├── therapy_mapping_results.json       # Main analysis results
├── detailed_therapy_mappings.json     # Individual patient mappings  
├── therapy_session_templates.json     # Practitioner session guides
├── therapy_mapping_report.pdf         # Professional clinical report
└── patient_reports/                   # Individual patient files
    ├── P001_therapy_recommendation.pdf
    ├── P001_session_data.json
    └── P001_progress_tracking.txt

🛠️ Advanced Configuration

🔧 Model Configuration Options

# Advanced system configuration
SYSTEM_CONFIG = {
    "audio_processing": {
        "sample_rate": 22050,
        "n_mfcc": 19,
        "hop_length": 512,
        "n_fft": 2048
    },
    "model_settings": {
        "use_gpu": True,
        "quantization": "4bit",  # For memory optimization
        "max_length": 2048,
        "temperature": 0.7
    },
    "safety_settings": {
        "enable_contraindication_check": True,
        "require_professional_oversight": True,
        "age_based_modifications": True
    }
}

📚 Research Foundation & Validation

Clinical Validation

SurSoothe has been developed with rigorous attention to clinical and therapeutic standards. The system incorporates evidence-based music therapy principles, validated safety protocols, and professional medical reporting standards. All therapeutic recommendations are generated based on established research in music therapy and Indian classical music's documented effects on various psychological and physiological conditions.

Technical Innovation

The integration of traditional signal processing with modern transformer-based language models represents a novel approach to music therapy systems. The custom Wav2CLIP implementation bridges the gap between audio analysis and textual therapeutic descriptions, while the multi-model LLM architecture ensures both therapeutic relevance and safety validation.

🤝 Contributing

We welcome contributions from researchers, developers, music therapists, and healthcare professionals. SurSoothe is designed to be an open platform for advancing the intersection of artificial intelligence and music therapy.

🛠️ Development Setup

# Install development dependencies
pip install -r requirements-dev.txt

# Run system tests
python -m pytest tests/

# Code formatting
black *.py llm_engine/
isort *.py llm_engine/

# Type checking
mypy --ignore-missing-imports *.py

# Test individual components
python raga_detection.py test_system
python therapy_mapping.py

Priority Contribution Areas

• 🎵 Raga Database Expansion: Additional regional variations and rare ragas
• 🌍 Multilingual Support: Therapy recommendations in regional languages
• 📱 Mobile Integration: Smartphone app for real-time therapy sessions
• 🔬 Clinical Studies: Validation studies with healthcare institutions
• 📊 Evaluation Metrics: Enhanced clinical outcome measurements
• 🤖 Model Improvements: Advanced architectures and training techniques

📄 License & Usage

This project is licensed under the BSD 2-Clause License - see the LICENSE file for details.

Important Note: While SurSoothe provides evidence-based music therapy recommendations, it should be used as a complementary tool alongside professional healthcare guidance, not as a replacement for clinical treatment.

🎵 Harmonizing ancient musical wisdom with modern AI technology for therapeutic healing 🎵

Developed with dedication to advancing accessible, evidence-based music therapy

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_{SurSoothe - Where technology meets traditional healing through the power of Indian classical music}