Streaming server for the AniMUL model https://spacecruft.org/deepcrayon/AniMUL-stream
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AniMUL Stream

Real-time audio stream analysis server for the AniMUL model, a fine-tune of Qwen3-Omni trained on nature sounds.

AniMUL Stream Screenshot

Overview

AniMUL Stream processes live audio streams (SRT/RTMP) and identifies sounds in real-time using the AniMUL multimodal language model. Perfect for:

  • Wildlife monitoring with forest microphones
  • Bird call identification
  • Nature sound logging
  • Real-time biodiversity surveys

Architecture

Forest Microphones → OBS Studio → MediaMTX → SRT Stream
                                                  ↓
                                        AniMUL Stream Backend
                                                  ↓
                                            vLLM (GPU)
                                                  ↓
                                        WebSocket → Browser

Features

  • Real-time Processing: Continuous analysis of live audio streams
  • Sliding Window: Overlapping audio chunks to avoid missing sounds
  • WebSocket Updates: Instant results delivered to connected clients
  • Simple UI: Clean web interface for monitoring and control
  • vLLM Integration: OpenAI-compatible API for the AniMUL model

Quick Start

Prerequisites

  • Python 3.13+
  • Node.js 20+
  • vLLM server with AniMUL-v1 model
  • SRT audio stream source

Development Setup

# Clone repository
git clone https://spacecruft.org/deepcrayon/AniMUL-stream.git
cd AniMUL-stream

# Backend
cd backend
python3.13 -m venv venv
source venv/bin/activate
pip install -e ".[dev]"
cp ../scripts/animul-stream.env.example .env
uvicorn server.main:app --reload --port 8080

# Frontend (new terminal)
cd frontend
npm install
npm run dev

See docs/QUICKSTART.md for detailed setup instructions.

Documentation

API

Endpoint Method Description
/api/health GET Health check
/api/stream GET Stream status
/api/stream/start POST Start processing
/api/stream/stop POST Stop processing
/ws/stream WebSocket Real-time results

License

Apache License 2.0 - See LICENSE-apache.txt

Copyright © 2026 Jeff Moe.

Author

Jeff Moe

moe@spacecruft.org

Loveland, Colorado, USA