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187bf9a | 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 | """Command-line entry points for generation engines."""
from __future__ import annotations
import argparse
from pathlib import Path
from .common import duration_options
from .corpus import endpoint_priors, load_sequences, symbol_stats
from .engines.markov import generate_markov
from .engines.transformer import (
TransformerConfig,
generate_transformer,
load_transformer_checkpoint,
sample_transformer_checkpoint,
train_and_save_checkpoint,
)
from .io import write_samples
from .reports import format_allowed_durations, write_generation_report
def add_common_args(parser: argparse.ArgumentParser) -> None:
parser.add_argument("--db", type=Path, default=Path("audit/themes_audit.sqlite"))
parser.add_argument("--output-dir", type=Path)
parser.add_argument("--length", type=int, default=24)
parser.add_argument("--samples", type=int, default=12)
parser.add_argument("--key", default="C")
parser.add_argument("--endpoint-strength", type=float, default=1.0)
parser.add_argument("--seed", type=int, default=42)
parser.add_argument(
"--min-duration",
default="16th",
help="Shortest generated/training duration label.",
)
parser.add_argument(
"--duration-grid",
default="16th",
help="Require generated/training durations to be multiples of this value.",
)
parser.add_argument(
"--no-triplets",
action="store_true",
help="Exclude regular triplet durations from the generated/training vocabulary.",
)
parser.add_argument(
"--loose-triplets",
action="store_true",
help="Allow triplet durations outside complete beat-aligned groups.",
)
parser.add_argument("--write-abc", action="store_true", help="Also write ABC files next to the MIDIs.")
parser.add_argument(
"--write-musicxml",
action="store_true",
help="Also write MusicXML files next to the MIDIs.",
)
def load_generation_inputs(args: argparse.Namespace, *, min_len: int):
allowed_durations = duration_options(args.min_duration, args.duration_grid, not args.no_triplets)
if not allowed_durations:
raise ValueError(f"No allowed durations remain for min duration {args.min_duration!r}")
sequences = load_sequences(args.db, allowed_durations, min_len=min_len)
if not sequences:
raise ValueError("No training sequences matched the selected duration and length settings")
return allowed_durations, sequences, symbol_stats(sequences), endpoint_priors(args.db)
def base_settings(args: argparse.Namespace, stats: dict, allowed_durations: set[str]) -> dict[str, object]:
return {
"sequences": stats["sequence_count"],
"events": stats["event_count"],
"vocabulary size": stats["vocab_size"],
"generated note length": args.length,
"samples": args.samples,
"output key": args.key,
"minimum duration": args.min_duration,
"duration grid": args.duration_grid,
"triplets allowed": not args.no_triplets,
"triplets grouped": not args.loose_triplets,
"allowed durations": format_allowed_durations(allowed_durations),
"endpoint strength": args.endpoint_strength,
}
def run_markov(args: argparse.Namespace) -> None:
allowed_durations, sequences, stats, priors = load_generation_inputs(args, min_len=max(6, args.max_order + 1))
start_weights, end_weights = priors
generated, diagnostics = generate_markov(
sequences=sequences,
length=args.length,
samples=args.samples,
max_order=args.max_order,
start_weights=start_weights,
end_weights=end_weights,
endpoint_strength=args.endpoint_strength,
enforce_triplet_groups=not args.loose_triplets,
seed=args.seed,
)
write_samples(
generated,
output_dir=args.output_dir,
key_name=args.key,
engine_name="vo_regular baseline",
write_abc=args.write_abc,
write_musicxml_files=args.write_musicxml,
)
settings = base_settings(args, stats, allowed_durations)
settings["max order"] = args.max_order
settings.update(diagnostics)
write_generation_report(
output_dir=args.output_dir,
title="VO-Regular Baseline Generation",
description="This is the key-relative variable-order Markov baseline.",
settings=settings,
stats=stats,
generated=generated,
write_abc=args.write_abc,
write_musicxml=args.write_musicxml,
)
print(f"Wrote {args.output_dir}")
def run_transformer(args: argparse.Namespace) -> None:
cfg = TransformerConfig(
block_size=args.block_size,
d_model=args.d_model,
nhead=args.nhead,
num_layers=args.layers,
dim_feedforward=args.feedforward,
dropout=args.dropout,
batch_size=args.batch_size,
steps=args.steps,
learning_rate=args.learning_rate,
temperature=args.temperature,
top_k=args.top_k,
max_retries=args.max_retries,
)
allowed_durations, sequences, stats, priors = load_generation_inputs(args, min_len=max(6, cfg.block_size // 4))
start_weights, end_weights = priors
if args.load_checkpoint:
checkpoint = load_transformer_checkpoint(args.load_checkpoint, requested_device=args.device)
generated, diagnostics = sample_transformer_checkpoint(
checkpoint=checkpoint,
length=args.length,
samples=args.samples,
start_weights=start_weights,
end_weights=end_weights,
endpoint_strength=args.endpoint_strength,
enforce_triplet_groups=not args.loose_triplets,
seed=args.seed,
temperature=args.temperature,
top_k=args.top_k,
max_retries=args.max_retries,
)
elif args.save_checkpoint:
checkpoint = train_and_save_checkpoint(
sequences=sequences,
cfg=cfg,
seed=args.seed,
requested_device=args.device,
path=args.save_checkpoint,
)
generated, diagnostics = sample_transformer_checkpoint(
checkpoint=checkpoint,
length=args.length,
samples=args.samples,
start_weights=start_weights,
end_weights=end_weights,
endpoint_strength=args.endpoint_strength,
enforce_triplet_groups=not args.loose_triplets,
seed=args.seed,
temperature=args.temperature,
top_k=args.top_k,
max_retries=args.max_retries,
)
diagnostics["saved checkpoint"] = str(args.save_checkpoint)
else:
generated, diagnostics = generate_transformer(
sequences=sequences,
length=args.length,
samples=args.samples,
start_weights=start_weights,
end_weights=end_weights,
endpoint_strength=args.endpoint_strength,
enforce_triplet_groups=not args.loose_triplets,
seed=args.seed,
cfg=cfg,
device=args.device,
)
write_samples(
generated,
output_dir=args.output_dir,
key_name=args.key,
engine_name="transformer baseline",
write_abc=args.write_abc,
write_musicxml_files=args.write_musicxml,
)
settings = base_settings(args, stats, allowed_durations)
settings.update(
{
"block size": cfg.block_size,
"d model": cfg.d_model,
"heads": cfg.nhead,
"layers": cfg.num_layers,
"feedforward": cfg.dim_feedforward,
"dropout": cfg.dropout,
"batch size": cfg.batch_size,
"learning rate": cfg.learning_rate,
"temperature": cfg.temperature,
"top k": cfg.top_k,
}
)
settings.update(diagnostics)
write_generation_report(
output_dir=args.output_dir,
title="Transformer Baseline Generation",
description="This is the first key-relative tiny transformer baseline.",
settings=settings,
stats=stats,
generated=generated,
write_abc=args.write_abc,
write_musicxml=args.write_musicxml,
)
print(f"Wrote {args.output_dir}")
def build_parser() -> argparse.ArgumentParser:
parser = argparse.ArgumentParser(description="Generate short key-relative theme samples.")
subparsers = parser.add_subparsers(dest="engine", required=True)
markov = subparsers.add_parser("markov", help="Run the vo_regular variable-order Markov engine.")
add_common_args(markov)
markov.set_defaults(output_dir=Path("outputs/vo_regular_baseline"), func=run_markov)
markov.add_argument("--max-order", type=int, default=4)
transformer = subparsers.add_parser("transformer", help="Run the tiny PyTorch transformer engine.")
add_common_args(transformer)
transformer.set_defaults(output_dir=Path("outputs/transformer_baseline"), func=run_transformer)
transformer.add_argument("--block-size", type=int, default=64)
transformer.add_argument("--d-model", type=int, default=96)
transformer.add_argument("--nhead", type=int, default=4)
transformer.add_argument("--layers", type=int, default=3)
transformer.add_argument("--feedforward", type=int, default=192)
transformer.add_argument("--dropout", type=float, default=0.1)
transformer.add_argument("--batch-size", type=int, default=64)
transformer.add_argument("--steps", type=int, default=800)
transformer.add_argument("--learning-rate", type=float, default=3e-4)
transformer.add_argument("--temperature", type=float, default=1.0)
transformer.add_argument("--top-k", type=int, default=16)
transformer.add_argument("--max-retries", type=int, default=100)
transformer.add_argument("--device", default="auto", help="auto, cpu, cuda, or mps.")
transformer.add_argument("--save-checkpoint", type=Path, help="Train once and save a reusable checkpoint.")
transformer.add_argument("--load-checkpoint", type=Path, help="Generate from a saved checkpoint instead of training.")
return parser
def main(argv: list[str] | None = None) -> None:
parser = build_parser()
args = parser.parse_args(argv)
try:
args.func(args)
except RuntimeError as exc:
parser.exit(1, f"error: {exc}\n")
if __name__ == "__main__":
main()
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