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Echelon Diffusion System - Production Architecture

This is the **production-grade audio diffusion system** for Computational Choreography's Echelon engine. It transforms embodied motion into generative music through a sophisticated pipeline of neural networks.

Embodied Trajectory Systems architecture technical paper candidate score 46 .md

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Echelon Diffusion System - Production Architecture

Overview

This is the production-grade audio diffusion system for Computational Choreography's Echelon engine. It transforms embodied motion into generative music through a sophisticated pipeline of neural networks.

┌─────────────────────────────────────────────────────────────────────────────┐
│                    ECHELON AUDIO GENERATION PIPELINE                        │
├─────────────────────────────────────────────────────────────────────────────┤
│                                                                             │
│   Motion Sensors (iPhones)                                                  │
│           │                                                                 │
│           ▼                                                                 │
│   ┌───────────────┐     ┌───────────────┐     ┌───────────────┐           │
│   │   LIM-RPS     │────▶│   Echelon     │────▶│   Diffusion   │           │
│   │   (cc-mcs)    │     │  Conditioning │     │   Model       │           │
│   │               │     │   Encoder     │     │               │           │
│   └───────────────┘     └───────────────┘     └───────────────┘           │
│                                                       │                     │
│                                                       ▼                     │
│                               ┌───────────────────────────────┐            │
│                               │   VQ-VAE Decoder + Vocoder    │            │
│                               │   (Audio Tokens → Waveform)   │            │
│                               └───────────────────────────────┘            │
│                                                       │                     │
│                                                       ▼                     │
│                                               🎵 Audio Output               │
└─────────────────────────────────────────────────────────────────────────────┘

---

Directory Structure

cc-ml/diffusion/
├── __init__.py                 # Package exports
├── ARCHITECTURE.md             # This file
├── requirements.txt            # Dependencies
│
├── configs/                    # YAML configurations
│   ├── diffusion.yaml         # Diffusion model config
│   ├── vqvae.yaml             # VQ-VAE config
│   ├── echelon.yaml           # Echelon integration config
│   └── training.yaml          # Training hyperparameters
│
├── models/                     # Neural network architectures
│   ├── __init__.py
│   │
│   ├── diffusion/             # Diffusion models
│   │   ├── __init__.py
│   │   ├── unet.py            # U-Net backbone
│   │   ├── dit.py             # Diffusion Transformer (NEW)
│   │   ├── conditioning.py    # Conditioning mechanisms
│   │   ├── noise_scheduler.py # Noise schedules (DDPM, cosine)
│   │   ├── samplers.py        # DDPM, DDIM, DPM++ samplers
│   │   └── flow_matching.py   # Conditional Flow Matching (NEW)
│   │
│   ├── vqvae/                 # Audio tokenizer
│   │   ├── __init__.py
│   │   ├── encoder.py         # Audio → Latent
│   │   ├── decoder.py         # Latent → Audio
│   │   ├── codebook.py        # Vector quantization
│   │   ├── vqvae.py           # Complete model
│   │   ├── vocoder.py         # HiFi-GAN vocoder
│   │   ├── dac.py             # Descript Audio Codec (NEW)
│   │   └── losses.py          # Training losses
│   │
│   └── common/                # Shared components (NEW)
│       ├── __init__.py
│       ├── attention.py       # Multi-head attention variants
│       ├── normalization.py   # LayerNorm, RMSNorm, AdaLN
│       ├── activations.py     # SiLU, GELU, Swish
│       ├── embeddings.py      # Positional, sinusoidal, rotary
│       └── blocks.py          # Residual, transformer blocks
│
├── data/                      # Data processing
│   ├── __init__.py
│   ├── audio_loader.py        # Audio I/O
│   ├── beat_tracker.py        # Beat detection
│   ├── segmenter.py           # Phrase segmentation
│   ├── feature_extractor.py   # Mel, MFCC, chroma
│   ├── phrase_database.py     # SQLite + FAISS
│   ├── phrase_embedder.py     # Phrase encoder
│   ├── motion_dataset.py      # Motion-audio pairs (NEW)
│   └── augmentations.py       # Data augmentation (NEW)
│
├── integration/               # Echelon bridge
│   ├── __init__.py
│   ├── echelon_conditioning.py # Latent → Conditioning (DONE)
│   ├── motion_phrase_mapper.py # Motion → Phrase
│   ├── phrase_retriever.py    # FAISS retrieval
│   ├── motion_diffusion_bridge.py # Full pipeline
│   ├── realtime_engine.py     # Real-time streaming (NEW)
│   └── metrics.py             # Evaluation metrics
│
├── training/                  # Training infrastructure (NEW)
│   ├── __init__.py
│   ├── trainer.py             # Base trainer
│   ├── diffusion_trainer.py   # Diffusion training loop
│   ├── vqvae_trainer.py       # VQ-VAE training loop
│   ├── losses.py              # Training losses
│   ├── optimizers.py          # AdamW, Lion, etc.
│   ├── schedulers.py          # LR schedules
│   └── callbacks.py           # Logging, checkpointing
│
├── inference/                 # Production inference (NEW)
│   ├── __init__.py
│   ├── pipeline.py            # End-to-end pipeline
│   ├── streaming.py           # Real-time streaming
│   ├── batched.py             # Batch generation
│   └── export.py              # ONNX/TorchScript export
│
├── scripts/                   # CLI tools
│   ├── train_vqvae.py
│   ├── train_diffusion.py
│   ├── generate_audio.py
│   ├── build_phrase_database.py
│   └── evaluate.py
│
└── tests/                     # Unit tests (NEW)
    ├── test_models.py
    ├── test_data.py
    ├── test_integration.py
    └── test_inference.py

---

Model Specifications

1. VQ-VAE (Audio Tokenizer)

Parameter	Value
Sample Rate	44,100 Hz
Codebook Size	2,048
Embedding Dim	64
Compression	64× (689 tokens/sec)
Latent Rate	~689 Hz

2. Diffusion Model

Parameter	Value
Architecture	U-Net / DiT
Base Channels	256
Channel Mults	[1, 2, 4, 8]
Attention Resolutions	[8, 4]
Conditioning Dim	256
Training Steps	1,000
Inference Steps	50 (DDIM)

3. Echelon Conditioning

Parameter	Value
Input Dim	25 (LatentState)
Window Size	46 (30 past + 1 + 15 future)
Trajectory Dim	64
Dynamics Dim	64
Transition Dim	16
Device Dim	32
Output Dim	256

---

Key Features

### ✅ Production-Ready
- Stateless neural modules
- External state management
- Deterministic inference
- ONNX exportable

### ✅ Real-Time Capable
- ~50ms latency target
- Streaming inference
- Pre-computed conditioning
- Optimized samplers

### ✅ Echelon-Native
- Transition field encoding
- Dual-sensor support
- Section-aware generation
- Embodied conditioning

### ✅ Observable
- Embedding statistics
- NaN detection
- Loss tracking
- Generation metrics

---

Training Data Requirements

Dataset	Size	Purpose
House Music	100+ hours	Base style
Electronic	50+ hours	Variation
Motion-Audio Pairs	20+ hours	Conditioning

---

Version History

Version	Date	Changes
1.0.0	Dec 2024	Initial production release
0.9.0	Nov 2024	Echelon conditioning encoder
0.8.0	Oct 2024	VQ-VAE and base diffusion

Promotion Decision

Promote into a technical note or architecture paper with implementation anchors.

Source Anchor

Comp-Core/core/ml/cc-ml/diffusion/ARCHITECTURE.md

Detected Structure

Method · Evaluation · Code Anchors · Architecture