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aed1d05 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 | """
Simple API for MIDI Generation
Provides a clean interface for DAW integration and batch generation.
"""
import json
import os
from dataclasses import dataclass
from pathlib import Path
from typing import Optional, Union
import torch
from modelw.tokenizer import MIDITokenizer
from modelw.model import MIDITransformer, MIDITransformerConfig
from modelw.generate import MIDIGenerator, GenerationConfig
@dataclass
class MIDIPrompt:
"""Prompt specification for MIDI generation."""
tempo: int = 120
instrument: str = "piano"
mood: str = "happy"
# Optional fine-grained control
time_signature: str = "4/4"
key: Optional[str] = None
duration_bars: int = 16
class ModelW:
"""
Main API for MODEL-W MIDI generation.
Example:
model = ModelW.load("./checkpoints")
midi = model.generate(tempo=120, instrument="piano", mood="happy")
midi.write("output.mid")
"""
def __init__(
self,
model: MIDITransformer,
tokenizer: MIDITokenizer,
device: str = "auto",
default_config: Optional[GenerationConfig] = None,
):
if device == "auto":
device = "cuda" if torch.cuda.is_available() else "cpu"
self.device = device
self.model = model.to(device)
self.tokenizer = tokenizer
self.config = default_config or GenerationConfig()
self._generator = MIDIGenerator(model, tokenizer, self.config, device)
@classmethod
def load(
cls,
checkpoint_path: Union[str, Path],
device: str = "auto",
) -> "ModelW":
"""
Load a trained model from checkpoint.
Args:
checkpoint_path: Path to checkpoint directory or .pt file
device: Device to load model on ("auto", "cuda", "cpu")
Returns:
ModelW instance ready for generation
"""
checkpoint_path = Path(checkpoint_path)
# Handle directory or file
if checkpoint_path.is_dir():
model_path = checkpoint_path / "best_model.pt"
tokenizer_path = checkpoint_path / "tokenizer"
else:
model_path = checkpoint_path
tokenizer_path = checkpoint_path.parent / "tokenizer"
# Load tokenizer
tokenizer = MIDITokenizer.load(tokenizer_path)
# Load model
if device == "auto":
device = "cuda" if torch.cuda.is_available() else "cpu"
checkpoint = torch.load(model_path, map_location=device)
config_dict = checkpoint.get("config", {})
if isinstance(config_dict, MIDITransformerConfig):
config = config_dict
else:
config = MIDITransformerConfig(**config_dict)
config.vocab_size = tokenizer.vocab_size
model = MIDITransformer(config)
model.load_state_dict(checkpoint["model_state_dict"])
return cls(model, tokenizer, device)
def generate(
self,
tempo: int = 120,
instrument: str = "piano",
mood: str = "happy",
max_length: int = 1024,
temperature: float = 0.9,
top_p: float = 0.92,
output_path: Optional[str] = None,
):
"""
Generate a single MIDI file.
Args:
tempo: BPM (40-240)
instrument: Instrument type (piano, guitar, bass, strings, etc.)
mood: Mood/emotion (happy, sad, energetic, calm, etc.)
max_length: Maximum sequence length
temperature: Sampling temperature (higher = more random)
top_p: Nucleus sampling threshold
output_path: Optional path to save MIDI file
Returns:
pretty_midi.PrettyMIDI object
"""
# Update config
self.config.max_length = max_length
self.config.temperature = temperature
self.config.top_p = top_p
# Generate
results = self._generator.generate_batch([
{"tempo": tempo, "instrument": instrument, "mood": mood}
], show_progress=False)
# Decode
midi = self.tokenizer.decode(results[0]["tokens"], output_path)
return midi
def generate_batch(
self,
prompts: list[dict],
output_dir: Optional[str] = None,
**kwargs,
) -> list:
"""
Generate multiple MIDI files.
Args:
prompts: List of prompt dicts with tempo, instrument, mood
output_dir: Directory to save MIDI files
**kwargs: Generation parameters
Returns:
List of pretty_midi.PrettyMIDI objects
"""
# Update config
for k, v in kwargs.items():
if hasattr(self.config, k):
setattr(self.config, k, v)
# Generate
results = self._generator.generate_batch(prompts)
# Decode
midis = []
output_path = Path(output_dir) if output_dir else None
for i, result in enumerate(results):
save_path = None
if output_path:
output_path.mkdir(parents=True, exist_ok=True)
save_path = output_path / f"generated_{i:04d}.mid"
midi = self.tokenizer.decode(result["tokens"], save_path)
midis.append(midi)
return midis
def generate_dataset(
self,
num_samples: int,
output_dir: str,
**kwargs,
) -> dict:
"""
Generate a large dataset of MIDI files.
Args:
num_samples: Number of samples to generate
output_dir: Output directory
**kwargs: Generation parameters
Returns:
Generation statistics
"""
return self._generator.generate_dataset(
num_samples=num_samples,
output_dir=output_dir,
**kwargs,
)
@property
def available_instruments(self) -> list[str]:
"""List of available instrument types."""
return [
t[6:-1].lower() for t in self.tokenizer.instrument_tokens
]
@property
def available_moods(self) -> list[str]:
"""List of available mood types."""
return [
t[6:-1].lower() for t in self.tokenizer.mood_tokens
]
def __repr__(self) -> str:
num_params = self.model.get_num_params()
return f"ModelW(params={num_params/1e6:.1f}M, device={self.device})"
# Simple functional API
_default_model: Optional[ModelW] = None
def load_model(checkpoint_path: str, device: str = "auto") -> ModelW:
"""Load a MODEL-W checkpoint."""
global _default_model
_default_model = ModelW.load(checkpoint_path, device)
return _default_model
def generate(
tempo: int = 120,
instrument: str = "piano",
mood: str = "happy",
output_path: Optional[str] = None,
**kwargs,
):
"""
Generate MIDI with the default loaded model.
Must call load_model() first.
"""
if _default_model is None:
raise RuntimeError("No model loaded. Call load_model() first.")
return _default_model.generate(
tempo=tempo,
instrument=instrument,
mood=mood,
output_path=output_path,
**kwargs,
)
if __name__ == "__main__":
# Quick test
import fire
def main(
checkpoint: str,
tempo: int = 120,
instrument: str = "piano",
mood: str = "happy",
output: str = "output.mid",
):
"""Generate MIDI from command line."""
model = ModelW.load(checkpoint)
print(f"Loaded {model}")
print(f"\nGenerating: tempo={tempo}, instrument={instrument}, mood={mood}")
midi = model.generate(tempo, instrument, mood, output_path=output)
print(f"Saved to {output}")
print(f"Notes: {len(midi.instruments[0].notes)}")
print(f"Duration: {midi.get_end_time():.1f}s")
fire.Fire(main)
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