| # High-Level Design (HLD) |
|
|
| ## System Overview |
|
|
| Music Generation LLM is a symbolic music generation system that learns patterns from |
| classical MIDI music and generates new compositions. It operates entirely on symbolic |
| note representations (MIDI events), not raw audio waveforms. |
|
|
| ## Architecture Diagram |
|
|
| ``` |
| ┌─────────────────────────────────────────────────────────┐ |
| │ USER INTERFACE │ |
| │ CLI: train / generate / both │ |
| └────────────────────────┬────────────────────────────────┘ |
| │ |
| ┌────────────────────────▼────────────────────────────────┐ |
| │ ORCHESTRATOR (s00_main) │ |
| │ Parses args, wires components │ |
| └──────┬─────────────────┬────────────────────┬───────────┘ |
| │ │ │ |
| ┌──────▼──────┐ ┌───────▼───────┐ ┌────────▼────────┐ |
| │ DATA │ │ MODEL │ │ GENERATION │ |
| │ PIPELINE │ │ PIPELINE │ │ PIPELINE │ |
| ├─────────────┤ ├───────────────┤ ├─────────────────┤ |
| │ HF Download │ │ MusicTrans- │ │ Autoregressive │ |
| │ MIDI Parse │ │ former Build │ │ w/ KV-cache │ |
| │ REMI Token │ │ Train Loop │ │ Top-p/k Sample │ |
| │ DataLoader │ │ AMP + Ckpt │ │ MIDI Export │ |
| └─────────────┘ └───────────────┘ └─────────────────┘ |
| ``` |
|
|
| ## Component Responsibilities |
|
|
| ### Data Pipeline (s02 + s03) |
| - Download 4,796 MIDI files from HuggingFace |
| - Parse with `pretty_midi` library |
| - Tokenize using REMI scheme (Note On/Off, Velocity, TimeShift, Bar, Position) |
| - Create PyTorch DataLoaders with padding and random cropping |
|
|
| ### Model (s04) |
| - LLaMA-style Transformer (6 layers, 256 dim, 8 heads) |
| - Grouped Query Attention with 4 KV heads (50% memory reduction) |
| - SwiGLU feed-forward with RMSNorm |
| - RoPE for positional encoding |
| - Gradient checkpointing support |
|
|
| ### Training (s05) |
| - AdamW optimizer with cosine LR warmup |
| - Mixed precision (BF16/FP16) via PyTorch AMP |
| - Gradient accumulation (effective batch = 32) |
| - Early stopping with patience |
| - TensorBoard logging |
|
|
| ### Generation (s06) |
| - Autoregressive decoding with KV-cache |
| - Temperature scaling + Top-k + Top-p (nucleus) sampling |
| - Repetition penalty for diverse output |
| - Direct MIDI file export |
|
|
| ## Data Flow |
|
|
| ``` |
| MIDI Files → REMI Tokens → Training → Trained Model → Generation → MIDI Output |
| ``` |
|
|
| ## Constraints & Decisions |
|
|
| | Decision | Rationale | |
| |------------------------------|----------------------------------------------| |
| | Symbolic (MIDI) not audio | 100x smaller data, trainable on consumer GPU | |
| | GQA over standard MHA | 50% KV-cache memory reduction | |
| | Gradient checkpointing | ~50% memory savings, ~20% speed cost | |
| | BF16 mixed precision | 50% memory, maintains numeric stability | |
| | REMI tokenization | Best published results for symbolic music | |
| | 5M params (not 100M+) | Fits in <=4GB VRAM, fast training | |
|
|
