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# -*- coding: utf-8 -*-
"""Evaluate local karaoke lead/backing split candidates with reproducible metrics."""
from __future__ import annotations
import argparse
import json
import sys
from pathlib import Path
from typing import Dict, Iterable, List
import numpy as np
import soundfile as sf
REPO_ROOT = Path(__file__).resolve().parent.parent
if str(REPO_ROOT) not in sys.path:
sys.path.insert(0, str(REPO_ROOT))
from infer.separator import KARAOKE_DEFAULT_MODEL, KaraokeSeparator
from lib.audio_metrics import evaluate_reference_stems
KARAOKE_MIN_LENGTH_COVERAGE = 0.999
def _load_mono(path: Path) -> tuple[np.ndarray, int]:
audio, sr = sf.read(str(path), always_2d=True)
mono = np.asarray(audio, dtype=np.float32).mean(axis=1)
return mono.astype(np.float32), int(sr)
def _rms(audio: np.ndarray) -> float:
audio = np.asarray(audio, dtype=np.float32).reshape(-1)
if audio.size == 0:
return 0.0
return float(np.sqrt(np.mean(np.square(audio), dtype=np.float64) + 1e-12))
def _abs_corr(a: np.ndarray, b: np.ndarray) -> float:
a = np.asarray(a, dtype=np.float32).reshape(-1)
b = np.asarray(b, dtype=np.float32).reshape(-1)
n = min(a.size, b.size)
if n < 8:
return 0.0
a = a[:n]
b = b[:n]
if float(np.std(a)) <= 1e-8 or float(np.std(b)) <= 1e-8:
return 0.0
corr = np.corrcoef(a, b)[0, 1]
if not np.isfinite(corr):
return 0.0
return float(abs(corr))
def score_karaoke_stems(
input_vocals_path: Path,
lead_path: Path,
backing_path: Path,
) -> Dict[str, float]:
"""Score a karaoke split without ground-truth stems.
This is a proxy, not SDR: good candidates reconstruct the input when summed,
keep lead/backing decorrelated, and preserve a plausible backing bed instead
of collapsing everything into either stem.
"""
input_audio, input_sr = _load_mono(Path(input_vocals_path))
lead_audio, lead_sr = _load_mono(Path(lead_path))
backing_audio, backing_sr = _load_mono(Path(backing_path))
if lead_sr != input_sr or backing_sr != input_sr:
raise ValueError("Karaoke scoring expects matching sample rates.")
input_len = input_audio.size
aligned_len = min(input_audio.size, lead_audio.size, backing_audio.size)
if aligned_len <= 0:
raise ValueError("Karaoke scoring received empty audio.")
length_coverage = float(aligned_len / max(1, input_len))
input_audio = input_audio[:aligned_len]
lead_audio = lead_audio[:aligned_len]
backing_audio = backing_audio[:aligned_len]
input_rms = _rms(input_audio)
lead_rms = _rms(lead_audio)
backing_rms = _rms(backing_audio)
reconstruction_error = _rms(input_audio - lead_audio - backing_audio) / (input_rms + 1e-12)
lead_backing_abs_corr = _abs_corr(lead_audio, backing_audio)
lead_input_abs_corr = _abs_corr(lead_audio, input_audio)
lead_ratio = lead_rms / (input_rms + 1e-12)
backing_ratio = backing_rms / (input_rms + 1e-12)
backing_target = 0.24
backing_balance_penalty = abs(np.log2(max(float(backing_ratio), 1e-4) / backing_target))
lead_body_penalty = max(0.0, 0.70 - float(lead_ratio)) + max(0.0, float(lead_ratio) - 1.15)
length_penalty = max(0.0, 1.0 - float(length_coverage))
score = float(
100.0
- 46.0 * float(reconstruction_error)
- 30.0 * float(lead_backing_abs_corr)
- 8.0 * float(backing_balance_penalty)
- 12.0 * float(lead_body_penalty)
- 200.0 * float(length_penalty)
+ 3.0 * float(lead_input_abs_corr)
)
return {
"score": score,
"input_rms": float(input_rms),
"lead_rms": float(lead_rms),
"backing_rms": float(backing_rms),
"lead_ratio": float(lead_ratio),
"backing_ratio": float(backing_ratio),
"length_coverage": float(length_coverage),
"length_penalty": float(length_penalty),
"reconstruction_error": float(reconstruction_error),
"lead_backing_abs_corr": float(lead_backing_abs_corr),
"lead_input_abs_corr": float(lead_input_abs_corr),
}
def score_reference_stems(
reference_lead_path: Path,
reference_backing_path: Path,
lead_path: Path,
backing_path: Path,
) -> Dict[str, object]:
"""Compute true reference-based SI-SDR/SDR when reference stems exist."""
reference_lead, reference_lead_sr = _load_mono(Path(reference_lead_path))
reference_backing, reference_backing_sr = _load_mono(Path(reference_backing_path))
lead_audio, lead_sr = _load_mono(Path(lead_path))
backing_audio, backing_sr = _load_mono(Path(backing_path))
if len({reference_lead_sr, reference_backing_sr, lead_sr, backing_sr}) != 1:
raise ValueError("Reference scoring expects matching sample rates.")
return evaluate_reference_stems(
references={"lead": reference_lead, "backing": reference_backing},
estimates={"lead": lead_audio, "backing": backing_audio},
)
def _unique(items: Iterable[str]) -> List[str]:
result: List[str] = []
for item in items:
if item and item not in result:
result.append(item)
return result
def parse_args() -> argparse.Namespace:
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument("--vocals-path", required=True, help="Separated vocals.wav to split into lead/backing.")
parser.add_argument("--output-dir", required=True, help="Directory for candidate stems and reports.")
parser.add_argument(
"--models",
nargs="*",
default=None,
help="Karaoke model filenames. Defaults to the current strict SOTA default.",
)
parser.add_argument("--reference-lead", default=None, help="Optional ground-truth/reference lead stem.")
parser.add_argument("--reference-backing", default=None, help="Optional ground-truth/reference backing stem.")
parser.add_argument("--device", default="cuda")
return parser.parse_args()
def _resolve_existing_path(path_value: str | None, label: str) -> Path | None:
if not path_value:
return None
path = Path(path_value)
if not path.is_absolute():
path = (REPO_ROOT / path).resolve()
if not path.exists():
raise FileNotFoundError(f"{label} not found: {path}")
return path
def _result_sort_key(item: dict) -> tuple:
reference_metrics = item.get("reference_metrics")
if reference_metrics:
return (1, float(reference_metrics["mean_si_sdr"]))
metrics = item["metrics"]
return (
0,
float(metrics["length_coverage"]) >= KARAOKE_MIN_LENGTH_COVERAGE,
float(metrics["score"]),
)
def main() -> int:
args = parse_args()
vocals_path = Path(args.vocals_path)
if not vocals_path.is_absolute():
vocals_path = (REPO_ROOT / vocals_path).resolve()
if not vocals_path.exists():
raise FileNotFoundError(f"Vocals path not found: {vocals_path}")
reference_lead_path = _resolve_existing_path(args.reference_lead, "Reference lead")
reference_backing_path = _resolve_existing_path(args.reference_backing, "Reference backing")
has_references = reference_lead_path is not None or reference_backing_path is not None
if has_references and not (reference_lead_path and reference_backing_path):
raise ValueError("--reference-lead and --reference-backing must be provided together.")
output_dir = Path(args.output_dir)
if not output_dir.is_absolute():
output_dir = (REPO_ROOT / output_dir).resolve()
output_dir.mkdir(parents=True, exist_ok=True)
models = _unique(args.models or [KARAOKE_DEFAULT_MODEL])
results = []
for model_name in models:
candidate_dir = output_dir / Path(model_name).stem
candidate_dir.mkdir(parents=True, exist_ok=True)
separator = KaraokeSeparator(model_filename=model_name, device=args.device)
try:
lead_path, backing_path = separator.separate(str(vocals_path), str(candidate_dir))
metrics = score_karaoke_stems(vocals_path, Path(lead_path), Path(backing_path))
reference_metrics = None
if reference_lead_path and reference_backing_path:
reference_metrics = score_reference_stems(
reference_lead_path=reference_lead_path,
reference_backing_path=reference_backing_path,
lead_path=Path(lead_path),
backing_path=Path(backing_path),
)
results.append(
{
"model": model_name,
"lead_path": str(lead_path),
"backing_path": str(backing_path),
"metrics": metrics,
"reference_metrics": reference_metrics,
}
)
if reference_metrics:
print(
f"{model_name}: mean_si_sdr={reference_metrics['mean_si_sdr']:.3f}, "
f"diagnostic_score={metrics['score']:.3f}"
)
else:
print(f"{model_name}: score={metrics['score']:.3f}, backing_ratio={metrics['backing_ratio']:.3f}")
finally:
separator.unload_model()
results.sort(key=_result_sort_key, reverse=True)
summary = {
"vocals_path": str(vocals_path),
"output_dir": str(output_dir),
"reference_lead_path": str(reference_lead_path) if reference_lead_path else None,
"reference_backing_path": str(reference_backing_path) if reference_backing_path else None,
"ranking": [
{
"rank": index + 1,
"model": item["model"],
**item["metrics"],
**(
{
"reference_mean_si_sdr": item["reference_metrics"]["mean_si_sdr"],
"reference_mean_sdr": item["reference_metrics"]["mean_sdr"],
}
if item.get("reference_metrics")
else {}
),
}
for index, item in enumerate(results)
],
"results": results,
"score_note": (
"SI-SDR/SDR are only computed when reference stems are provided. "
"Without references, score is a diagnostic proxy for reconstruction, "
"decorrelation, plausible backing level, lead/input coherence, and full-length coverage."
),
}
summary_path = output_dir / "karaoke_model_report.json"
summary_path.write_text(json.dumps(summary, ensure_ascii=False, indent=2), encoding="utf-8")
markdown_lines = [
"# Karaoke Model Report",
"",
f"- vocals: `{vocals_path}`",
f"- output: `{output_dir}`",
"",
]
if has_references:
markdown_lines.extend(
[
f"- reference lead: `{reference_lead_path}`",
f"- reference backing: `{reference_backing_path}`",
"",
"| rank | model | mean_si_sdr | mean_sdr | diag_score | len | recon_err | corr |",
"|---:|---|---:|---:|---:|---:|---:|---:|",
]
)
else:
markdown_lines.extend(
[
"| rank | model | score | len | recon_err | corr | lead_in_corr | lead_ratio | backing_ratio |",
"|---:|---|---:|---:|---:|---:|---:|---:|---:|",
]
)
for index, item in enumerate(results, start=1):
metrics = item["metrics"]
reference_metrics = item.get("reference_metrics")
if reference_metrics:
markdown_lines.append(
f"| {index} | `{item['model']}` | {reference_metrics['mean_si_sdr']:.3f} | "
f"{reference_metrics['mean_sdr']:.3f} | {metrics['score']:.3f} | "
f"{metrics['length_coverage']:.4f} | {metrics['reconstruction_error']:.4f} | "
f"{metrics['lead_backing_abs_corr']:.4f} |"
)
else:
markdown_lines.append(
f"| {index} | `{item['model']}` | {metrics['score']:.3f} | "
f"{metrics['length_coverage']:.4f} | "
f"{metrics['reconstruction_error']:.4f} | {metrics['lead_backing_abs_corr']:.4f} | "
f"{metrics['lead_input_abs_corr']:.4f} | "
f"{metrics['lead_ratio']:.3f} | {metrics['backing_ratio']:.3f} |"
)
markdown_path = output_dir / "karaoke_model_report.md"
markdown_path.write_text("\n".join(markdown_lines) + "\n", encoding="utf-8")
print(f"Summary written to: {summary_path}")
print(f"Report written to: {markdown_path}")
return 0
if __name__ == "__main__":
raise SystemExit(main())
|