File size: 2,255 Bytes
90884df | 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 | #!/usr/bin/env python3
"""Train the bounded local captured-condition prefix adapter."""
from __future__ import annotations
import argparse
import json
from pathlib import Path
def parser() -> argparse.ArgumentParser:
result = argparse.ArgumentParser(
description=(
"Local one-second captured-condition renderer-latent prefix "
"reconstruction only; not song-intro/native-AR prepend, arbitrary "
"WAV, native RVQ, FIM, or generalization."
)
)
for name in (
"config",
"dataset-root",
"snapshot",
"base-manifest",
"diffusers-root",
"output-root",
):
result.add_argument(f"--{name}", required=True)
return result
def main(argv: list[str] | None = None) -> int:
args = parser().parse_args(argv)
from music3lab.editing.flow_prepend_pilot import train_flow_prepend_pilot
metrics = train_flow_prepend_pilot(
config_path=Path(args.config),
dataset_root=Path(args.dataset_root),
snapshot=Path(args.snapshot),
base_manifest=Path(args.base_manifest),
diffusers_root=Path(args.diffusers_root),
output_root=Path(args.output_root),
)
print(
json.dumps(
{
"measured_improvement_gate": metrics[
"measured_improvement_gate"
],
"median_prefix_latent_nmse_improvement_fraction": metrics[
"median_prefix_latent_nmse_improvement_fraction"
],
"median_prefix_audio_ruler_improvement_fraction": metrics[
"median_prefix_audio_ruler_improvement_fraction"
],
"right_suffix_latent_exact": metrics[
"right_suffix_latent_exact"
],
"composited_suffix_audio_exact": metrics[
"composited_suffix_audio_exact"
],
"output_root": metrics["output_root"],
"semantic_digest": metrics["semantic_digest"],
},
sort_keys=True,
)
)
return 0 if metrics["measured_improvement_gate"] else 2
if __name__ == "__main__":
raise SystemExit(main())
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