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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 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 | #!/usr/bin/env python3
"""Run the bounded continuous flow-latent encoder pilot."""
from __future__ import annotations
import argparse
from pathlib import Path
from typing import Sequence
from music3lab.codec.flow_encoder import canonical_json_bytes
from music3lab.codec.runner import (
generate_teacher_dataset,
load_teacher_dataset,
recover_teacher_dataset,
train_flow_encoder,
verify_pilot_bundle,
)
def _common(parser: argparse.ArgumentParser) -> None:
parser.add_argument("--config", type=Path, required=True)
parser.add_argument("--snapshot", type=Path, required=True)
parser.add_argument("--base-manifest", type=Path, required=True)
parser.add_argument("--diffusers-root", type=Path, required=True)
def _parser() -> argparse.ArgumentParser:
parser = argparse.ArgumentParser(
description=(
"Predict continuous Music 3 Flow-VAE renderer latents from "
"waveforms; this does not produce native RVQ tokens."
)
)
commands = parser.add_subparsers(dest="command", required=True)
generate = commands.add_parser("generate-teachers")
_common(generate)
generate.add_argument("--dataset-root", type=Path, required=True)
recover = commands.add_parser("recover-teachers")
_common(recover)
recover.add_argument("--quarantine-root", type=Path, required=True)
recover.add_argument("--producer-commit", required=True)
recover.add_argument("--dataset-root", type=Path, required=True)
train = commands.add_parser("train")
_common(train)
train.add_argument("--dataset-root", type=Path, required=True)
train.add_argument("--external-wav", type=Path, required=True)
train.add_argument("--output-root", type=Path, required=True)
run = commands.add_parser("run")
_common(run)
run.add_argument("--dataset-root", type=Path, required=True)
run.add_argument("--external-wav", type=Path, required=True)
run.add_argument("--output-root", type=Path, required=True)
verify_data = commands.add_parser("verify-teachers")
verify_data.add_argument("--dataset-root", type=Path, required=True)
verify_run = commands.add_parser("verify")
verify_run.add_argument("--output-root", type=Path, required=True)
return parser
def _generate(arguments: argparse.Namespace) -> dict[str, object]:
manifest = generate_teacher_dataset(
config_path=arguments.config,
snapshot=arguments.snapshot,
base_manifest=arguments.base_manifest,
diffusers_root=arguments.diffusers_root,
output_root=arguments.dataset_root,
)
return {
"kind": "continuous_flow_latent_teachers",
"native_rvq": False,
"dataset_root": str(arguments.dataset_root.absolute()),
"manifest_semantic_digest": manifest.semantic_digest,
"split_counts": {
key: value.count for key, value in manifest.splits.items()
},
}
def _recover(arguments: argparse.Namespace) -> dict[str, object]:
manifest = recover_teacher_dataset(
config_path=arguments.config,
quarantine_root=arguments.quarantine_root,
producer_project_git_commit=arguments.producer_commit,
snapshot=arguments.snapshot,
base_manifest=arguments.base_manifest,
diffusers_root=arguments.diffusers_root,
output_root=arguments.dataset_root,
)
return {
"kind": "continuous_flow_latent_teachers",
"native_rvq": False,
"dataset_root": str(arguments.dataset_root.absolute()),
"manifest_semantic_digest": manifest.semantic_digest,
"producer_project_git_commit": manifest.producer_project_git_commit,
"publication_project_git_commit": (
manifest.publication_project_git_commit
),
"recovered_from_complete_quarantine": True,
"replay_exact_count": manifest.replay_exact_count,
}
def _train(arguments: argparse.Namespace) -> dict[str, object]:
metrics = train_flow_encoder(
config_path=arguments.config,
dataset_root=arguments.dataset_root,
snapshot=arguments.snapshot,
base_manifest=arguments.base_manifest,
diffusers_root=arguments.diffusers_root,
external_wav=arguments.external_wav,
output_root=arguments.output_root,
)
return {
"kind": "continuous_flow_latent_encoder_pilot",
"native_rvq_capability": metrics.native_rvq_capability,
"measured_improvement_gate": metrics.measured_improvement_gate,
"metrics_semantic_digest": metrics.semantic_digest,
"output_root": str(arguments.output_root.absolute()),
}
def main(argv: Sequence[str] | None = None) -> int:
arguments = _parser().parse_args(argv)
if arguments.command == "generate-teachers":
result = _generate(arguments)
elif arguments.command == "recover-teachers":
result = _recover(arguments)
elif arguments.command == "train":
result = _train(arguments)
elif arguments.command == "run":
dataset = _generate(arguments)
result = {"dataset": dataset, "pilot": _train(arguments)}
elif arguments.command == "verify-teachers":
dataset = load_teacher_dataset(arguments.dataset_root)
result = {
"kind": "continuous_flow_latent_teachers",
"native_rvq": False,
"manifest_semantic_digest": dataset.manifest.semantic_digest,
"split_counts": {
key: value.count
for key, value in dataset.manifest.splits.items()
},
}
elif arguments.command == "verify":
metrics = verify_pilot_bundle(arguments.output_root)
result = {
"kind": "continuous_flow_latent_encoder_pilot",
"native_rvq_capability": metrics.native_rvq_capability,
"measured_improvement_gate": metrics.measured_improvement_gate,
"metrics_semantic_digest": metrics.semantic_digest,
}
else:
raise AssertionError("unreachable command")
print(canonical_json_bytes(result).decode("utf-8"), end="")
return 0
if __name__ == "__main__":
raise SystemExit(main())
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