Enterprise-grade secure ASR diarization pipeline combining automatic speech recognition with speaker diarization. HIPAA-compliant with modular architecture and comprehensive security.

• 🔒 Enterprise Security: API key authentication, input validation, rate limiting
• 🎯 High-Quality Processing: DER ~8-20%, WER ~1-5% with robust speaker attribution
• 🩺 HIPAA Compliance: Secure file handling, audit logging, encrypted storage
• 🏗️ Modular Architecture: Clean separation into focused modules
• 🐳 Production Ready: Container-ready with security enhancements

• GPU: NVIDIA GPU with CUDA 13.0+ (8GB+ VRAM recommended)
• OS: Linux (Ubuntu 24.04+, CentOS 8+)
• Python: 3.12+
• Models: Access required for nvidia/parakeet-tdt-1.1b and pyannote/speaker-diarization-community-1

git clone https://github.com/SunPCSolutions/DiarASR.git
cd DiarASR
python3 -m venv .venv
source .venv/bin/activate
pip install -r app/requirements.txt
cp .env.example .env
# Edit .env with your API keys and HuggingFace token

git clone https://github.com/SunPCSolutions/DiarASR.git
cd DiarASR
# Create required directories and set ownership for container user (1001:1001)
mkdir -p cache tmp
sudo chown -R 1001:1001 cache tmp
cp .env.example .env
# Edit .env with your API keys and HuggingFace token
docker-compose up --build -d

import os
from app.app import process_audio

os.environ['HF_TOKEN'] = 'your-huggingface-token'
os.environ['API_KEYS'] = 'your-api-key'

result = process_audio(
    audio_path='audio.wav',
    diarize=True,
    min_speakers=2,
    max_speakers=4
)

for segment in result['segments']:
    print(f"{segment['speaker']}: {segment['text']}")

# Start server
export API_KEYS="your-api-key"
export HF_TOKEN="hf_xxx"
uvicorn app:app --host 0.0.0.0 --port 8003

# Make request
curl -H "X-API-Key: your-api-key" \
  -X POST "http://localhost:8003/transcribe_diarize/" \
  -F "[email protected]"

See docs/API_PARAMETERS.md for complete API documentation.

Required HuggingFace Access:

• nvidia/parakeet-tdt-1.1b - ASR model
• pyannote/speaker-diarization-community-1 - Diarization model

Set HF_TOKEN environment variable with your HuggingFace token.

• ASR Accuracy: ~1-5% WER (Parakeet TDT-1.1B)
• Diarization Quality: ~8-20% DER (Pyannote Community-1)
• Processing Speed: ~12x real-time with GPU
• Memory Usage: <8GB VRAM

• API key authentication
• Multi-layer input validation
• Rate limiting (10 req/min)
• Encrypted temporary storage
• HIPAA-compliant processing
• Comprehensive audit logging

• docs/API_PARAMETERS.md - Complete API reference
• memory-bank/systemPatterns.md - Architecture details
• memory-bank/techContext.md - Technical context

MIT License - see LICENSE file for details.

Our greatest appreciation to the creators of:

• Pyannote.audio (Hervé Bredin et al.) for speaker diarization
• NVIDIA Parakeet TDT (NVIDIA NeMo team) for ASR
• FastAPI (Sebastián Ramírez) for the web framework
• PyTorch (Facebook AI Research) for deep learning

Please cite these works if used in your research.