File size: 61,305 Bytes
17d4058 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752 753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768 769 770 771 772 773 774 775 776 777 778 779 780 781 782 783 784 785 786 787 788 789 790 791 792 793 794 795 796 797 798 799 800 801 802 803 804 805 806 807 808 809 810 811 812 813 814 815 816 817 818 819 820 821 822 823 824 825 826 827 828 829 830 831 832 833 834 835 836 837 838 839 840 841 842 843 844 845 846 847 848 849 850 851 852 853 854 855 856 857 858 859 860 861 862 863 864 865 866 867 868 869 870 871 872 873 874 875 876 877 878 879 880 881 882 883 884 885 886 887 888 889 890 891 892 893 894 895 896 897 898 899 900 901 902 903 904 905 906 907 908 909 910 911 912 913 914 915 916 917 918 919 920 921 922 923 924 925 926 927 928 929 930 931 932 933 934 935 936 937 938 939 940 941 942 943 944 945 946 947 948 949 950 951 952 953 954 955 956 957 958 959 960 961 962 963 964 965 966 967 968 969 970 971 972 973 974 975 976 977 978 979 980 981 982 983 984 985 986 987 988 989 990 991 992 993 994 995 996 997 998 999 1000 1001 1002 1003 1004 1005 1006 1007 1008 1009 1010 1011 1012 1013 1014 1015 1016 1017 1018 1019 1020 1021 1022 1023 1024 1025 1026 1027 1028 1029 1030 1031 1032 1033 1034 1035 1036 1037 1038 1039 1040 1041 1042 1043 1044 1045 1046 1047 1048 1049 1050 1051 1052 1053 1054 1055 1056 1057 1058 1059 1060 1061 1062 1063 1064 1065 1066 1067 1068 1069 1070 1071 1072 1073 1074 1075 1076 1077 1078 1079 1080 1081 1082 1083 1084 1085 1086 1087 1088 1089 1090 1091 1092 1093 1094 1095 1096 1097 1098 1099 1100 1101 1102 1103 1104 1105 1106 1107 1108 1109 1110 1111 1112 1113 1114 1115 1116 1117 1118 1119 1120 1121 1122 1123 1124 1125 1126 1127 1128 1129 1130 1131 1132 1133 1134 1135 1136 1137 1138 1139 1140 1141 1142 1143 1144 1145 1146 1147 1148 1149 1150 1151 1152 1153 1154 1155 1156 1157 1158 1159 1160 1161 1162 1163 1164 1165 1166 1167 1168 1169 1170 1171 1172 1173 1174 1175 1176 1177 1178 1179 1180 1181 1182 1183 1184 1185 1186 1187 1188 1189 1190 1191 1192 1193 1194 1195 1196 1197 1198 1199 1200 1201 1202 1203 1204 1205 1206 1207 1208 1209 1210 1211 1212 1213 1214 1215 1216 1217 1218 1219 1220 1221 1222 1223 1224 1225 1226 1227 1228 1229 1230 1231 1232 1233 1234 1235 1236 1237 1238 1239 1240 1241 1242 1243 1244 1245 1246 1247 1248 1249 1250 1251 1252 1253 1254 1255 1256 1257 1258 1259 1260 1261 1262 1263 1264 1265 1266 1267 1268 1269 1270 1271 1272 1273 1274 1275 1276 1277 1278 1279 1280 1281 1282 1283 1284 1285 1286 1287 1288 1289 1290 1291 1292 1293 1294 1295 1296 1297 1298 1299 1300 1301 1302 1303 1304 1305 1306 1307 1308 1309 1310 1311 1312 1313 1314 1315 1316 1317 1318 1319 1320 1321 1322 1323 1324 1325 1326 1327 1328 1329 1330 1331 1332 1333 1334 1335 1336 1337 1338 1339 1340 1341 1342 1343 1344 1345 1346 1347 1348 1349 1350 1351 1352 1353 1354 1355 1356 1357 1358 1359 1360 1361 1362 1363 1364 1365 1366 1367 1368 1369 1370 1371 1372 1373 1374 1375 1376 1377 1378 1379 1380 1381 1382 1383 1384 1385 1386 1387 1388 1389 1390 1391 1392 1393 1394 1395 1396 1397 1398 1399 1400 1401 | """
run_smart_sweep.py — S-SPADE · Bayesian parameter search (v2)
===================================================================
PIPELINE GROUND-TRUTH (Case 1 — threshold-based limiter)
---------------------------------------------------------
Il limiter sintetico è threshold-based:
- Originale normalizzato a 0 dBFS peak
- Limiter: attua solo sui picchi sopra la soglia → output max peak ≈ −threshold_db
- Il CORPO del segnale (loudness percepita) rimane invariato per definizione
- NON si applica nessun gain al segnale limitato dopo il processing
Allineamento per il calcolo residual:
Originale e limited sono già sulla stessa scala (loudness uguale, picchi diversi).
Nessuna normalizzazione LUFS / RMS necessaria o corretta.
GT_res = original_0dBFS − limited (scale identiche)
res_iter = spade_output − limited (idem)
Entrambi vengono poi normalizzati a RESIDUAL_DBFS peak SOLO per rendere
comparabili file con diversi livelli assoluti — non altera la logica.
Metrica ideale:
GT_res ≡ res_iter → cosine_sim = 1.0 → differenza = −∞ dB
Ottimizzatore: Optuna TPE (Bayesian) + MedianPruner
Storage: SQLite (riprendibile con --resume)
Corpus: tutti i drum sample in Kicks / Snares / Perc / Tops
DIPENDENZE
----------
pip install numpy scipy soundfile optuna rich
(pyloudnorm NON necessario)
USO
---
python run_smart_sweep.py # 200 trial
python run_smart_sweep.py --trials 50 # test rapido
python run_smart_sweep.py --resume # riprende da DB
python run_smart_sweep.py --report # solo risultati
python run_smart_sweep.py --base-dir /path/SPADE # cartella custom
"""
import argparse
import logging
import sys
import time
import warnings
from pathlib import Path
from typing import Dict, List, Optional
import numpy as np
import scipy.signal as sig
import soundfile as sf
logging.getLogger("optuna").setLevel(logging.WARNING)
# ── optuna ───────────────────────────────────────────────────────────────────
try:
import optuna
from optuna.samplers import TPESampler
from optuna.pruners import MedianPruner
_HAS_OPTUNA = True
except ImportError:
_HAS_OPTUNA = False
warnings.warn("optuna non trovato — pip install optuna")
# ── rich ─────────────────────────────────────────────────────────────────────
try:
from rich.console import Console
from rich.table import Table
_console = Console()
_HAS_RICH = True
except ImportError:
_HAS_RICH = False
_console = None
# ── spade_declip ─────────────────────────────────────────────────────────────
try:
from spade_declip_v12 import declip, DeclipParams
_HAS_SPADE = True
except ImportError:
_HAS_SPADE = False
warnings.warn("spade_declip_v12.py non trovato")
# =============================================================================
# CONFIG
# =============================================================================
DRUM_DIRS = ["Kicks", "Snares", "Perc", "Tops"]
# ── Limiter sintetico ─────────────────────────────────────────────────────────
# Case 1: threshold-based.
# Originale @ 0 dBFS peak → limiter attua sui picchi > soglia →
# output max peak ≈ −LIMITER_THRESHOLD_DB dBFS, loudness invariata.
# NON si tocca il segnale limitato con nessun gain dopo.
LIMITER_THRESHOLD_DB = 3.0 # dB sotto il ceiling (positivo)
LIMITER_RELEASE_MS = 80.0 # release del limiter sintetico (ms)
# attack = 1 campione → brickwall vero
# Normalizzazione residual — SOLO per comparabilità cross-file.
# Scala entrambi GT e iter identicamente, quindi non altera il confronto.
RESIDUAL_DBFS = -3.0
# ── Rumore rosa di sottofondo ─────────────────────────────────────────────────
# Simula un sottofondo musicale sotto il transiente di batteria.
# Viene mixato al sample (già a 0 dBFS peak) PRIMA del limiter.
# Questo assicura che:
# - il limiter agisca sul segnale realistico drum + music background
# - SPADE riceva lo stesso mix e debba lavorare in condizioni realistiche
# - GT_res = (drum+noise) − limiter(drum+noise) rifletta la situazione reale
# Livello relativo al peak del drum sample. −20 dB = sottofondo ben sotto
# il transiente, udibile ma non dominante (come un kick su un loop di batteria).
PINK_NOISE_LEVEL_DB = -20.0 # dB rel. al peak del drum (negativo = sotto)
# Optuna
STUDY_NAME = "spade_smart_v2_thr3db"
OUT_CSV = "smart_sweep_results.csv"
# Parametri FISSI del solver SPADE (invarianti tra tutti i trial)
FIXED_SOLVER = dict(
algo = "sspade",
frame = "rdft",
mode = "soft",
s = 1,
r = 1,
n_jobs = 1,
verbose = False,
show_progress = False,
use_gpu = True,
# multiband e macro_expand sono nello spazio di ricerca
)
# Crossover multiband (fisso per comparabilita' tra trial)
# 250 Hz separa: LF=corpo/punch del kick | HF=transiente/attacco
BAND_CROSSOVER_HZ = 250.0
# =============================================================================
# HELPERS
# =============================================================================
def ensure_2d(a: np.ndarray) -> np.ndarray:
return a[:, None] if a.ndim == 1 else a
def normalize_to_0dBFS(a: np.ndarray) -> np.ndarray:
"""Scala a 0 dBFS peak — usato solo sull'originale come riferimento comune."""
pk = np.max(np.abs(a))
return a / pk if pk > 1e-12 else a
def normalize_peak(a: np.ndarray, target_dbfs: float) -> np.ndarray:
"""
Scala a target_dbfs dBFS peak.
Usato SOLO sui residual per comparabilità cross-file;
non altera la logica perché GT e iter vengono scalati identicamente.
"""
pk = np.max(np.abs(a))
return a * (10 ** (target_dbfs / 20.0) / pk) if pk > 1e-12 else a
def generate_pink_noise(n_samples: int, n_channels: int, rng: np.random.Generator) -> np.ndarray:
"""
Genera rumore rosa (1/f) tramite filtro IIR di Voss-McCartney (approssimazione
a 5 poli, accurata entro ±1 dB nel range 20 Hz – 20 kHz).
Output: shape (n_samples, n_channels), RMS normalizzato a 1.0 (prima
del mix-in con PINK_NOISE_LEVEL_DB, che controlla il livello finale).
Algoritmo: rumore bianco filtrato con H(z) = 1 / A(z) dove i coefficienti
sono ottimizzati per approssimare una densità spettrale 1/f.
"""
# Coefficienti del filtro IIR a 5 poli (Voss approssimazione)
# Poli reali, tutti stabili (|p| < 1)
b = np.array([0.049922035, -0.095993537, 0.050612699, -0.004408786])
a = np.array([1.0, -2.494956002, 2.017265875, -0.522189400])
out = np.empty((n_samples, n_channels))
for c in range(n_channels):
white = rng.standard_normal(n_samples)
pink = sig.lfilter(b, a, white)
rms = np.sqrt(np.mean(pink ** 2))
out[:, c] = pink / (rms + 1e-12) # RMS = 1.0
return out
def mix_pink_noise(
audio_0dBFS: np.ndarray,
sr: int,
level_db: float,
rng: np.random.Generator,
) -> np.ndarray:
"""
Mixa rumore rosa nel segnale a un livello relativo al suo peak.
level_db < 0 → il rumore è sotto il peak del drum (es. −20 dB)
Il rumore dura quanto il sample; se il sample è stereo, il rumore è stereo
(canali indipendenti → decorrelato come un vero fondo musicale).
Il segnale in uscita può superare 0 dBFS di qualche frazione di dB: è
corretto, il limiter che segue si occupa di riportarlo sotto la soglia.
"""
audio = ensure_2d(audio_0dBFS)
N, C = audio.shape
noise = generate_pink_noise(N, C, rng) # RMS = 1.0 per canale
# Scala il rumore al livello desiderato rispetto al peak del drum
peak = np.max(np.abs(audio))
gain = peak * (10 ** (level_db / 20.0)) # gain lineare assoluto
mixed = audio + noise * gain
# NON normalizziamo qui: la normalizzazione a 0 dBFS avviene in build_corpus
# subito dopo, su tutto il mix (drum + noise), prima di qualsiasi altra op.
return mixed[:, 0] if audio_0dBFS.ndim == 1 else mixed
# =============================================================================
# LIMITER SINTETICO (Case 1 — threshold-based, brickwall, 1-campione attack)
# =============================================================================
def apply_brickwall_limiter(
audio_0dBFS: np.ndarray,
sr: int,
threshold_db: float = LIMITER_THRESHOLD_DB,
release_ms: float = LIMITER_RELEASE_MS,
) -> np.ndarray:
"""
Brickwall limiter threshold-based.
Input: audio_0dBFS — già a 0 dBFS peak, shape (N,) o (N, C)
Output: segnale limitato, stessa shape — NON boosted, NON clippato
Gain envelope:
se |x[n]| > threshold_lin → target_gain = threshold_lin / |x[n]|
altrimenti → target_gain = 1.0
Attack : istantaneo (1 campione, true brickwall)
Release: esponenziale con costante release_ms
Post-processing: NESSUNO.
Il segnale in uscita ha max peak ≈ −threshold_db dBFS.
La loudness percepita è invariata rispetto all'input.
"""
thr_lin = 10 ** (-abs(threshold_db) / 20.0)
rc = np.exp(-1.0 / max(release_ms * sr / 1000.0, 1e-9))
audio = ensure_2d(audio_0dBFS).copy()
N, C = audio.shape
out = np.empty_like(audio)
for c in range(C):
ch = audio[:, c]
env = 1.0
g = np.empty(N)
for n in range(N):
pk = abs(ch[n])
target = thr_lin / pk if pk > thr_lin else 1.0
# attack istantaneo se gain scende, release esponenziale se risale
env = target if target < env else rc * env + (1.0 - rc) * target
g[n] = env
out[:, c] = ch * g
# Restituisce stessa shape dell'input
return out[:, 0] if audio_0dBFS.ndim == 1 else out
# =============================================================================
# COSINE SIMILARITY TF
# =============================================================================
def cosine_sim_tf(
gt: np.ndarray,
est: np.ndarray,
sr: int,
win_samples: int = 1024,
hop_samples: int = 256,
n_bands: int = 12,
) -> float:
"""
Similarità coseno media su micro-finestre tempo-frequenziali.
Input: entrambi già a RESIDUAL_DBFS peak.
Output: scalare in [0, 1]. Target ideale = 1.0.
"""
L = min(gt.shape[0], est.shape[0])
g = (gt[:L, 0] if gt.ndim == 2 else gt[:L]).copy()
e = (est[:L, 0] if est.ndim == 2 else est[:L]).copy()
win = min(win_samples, max(32, L // 4))
hop = min(hop_samples, win // 2)
if L < win or win < 32:
denom = np.linalg.norm(g) * np.linalg.norm(e) + 1e-12
return float(np.dot(g, e) / denom)
_, _, Zg = sig.stft(g, fs=sr, window="hann",
nperseg=win, noverlap=win - hop,
boundary=None, padded=False)
_, _, Ze = sig.stft(e, fs=sr, window="hann",
nperseg=win, noverlap=win - hop,
boundary=None, padded=False)
n_freqs, n_frames = Zg.shape
if n_frames == 0:
return float(np.dot(g, e) / (np.linalg.norm(g) * np.linalg.norm(e) + 1e-12))
edges = np.unique(np.round(
np.logspace(0, np.log10(max(n_freqs, 2)), min(n_bands, n_freqs) + 1)
).astype(int))
edges = np.clip(edges, 0, n_freqs)
sims = []
for i in range(len(edges) - 1):
f0, f1 = int(edges[i]), int(edges[i + 1])
if f1 <= f0:
continue
Mg = np.abs(Zg[f0:f1, :])
Me = np.abs(Ze[f0:f1, :])
dot = np.sum(Mg * Me, axis=0)
norm_g = np.sqrt(np.sum(Mg ** 2, axis=0)) + 1e-12
norm_e = np.sqrt(np.sum(Me ** 2, axis=0)) + 1e-12
sims.extend((dot / (norm_g * norm_e)).tolist())
return float(np.mean(sims)) if sims else 0.0
# =============================================================================
# CORPUS
# =============================================================================
def build_corpus(base_dir: Path, max_files: Optional[int] = None) -> List[Dict]:
"""
Per ogni drum sample:
1. Carica e normalizza a 0 dBFS peak (riferimento comune cross-file)
2. Mixa rumore rosa a PINK_NOISE_LEVEL_DB rel. al peak ← NUOVO
Il mix avviene in float (può temporaneamente superare 0 dBFS)
3. Normalizza il mix (drum + noise) a 0 dBFS peak
Riferimento comune prima di tutta la pipeline successiva
4. Applica limiter sintetico su (drum + noise) normalizzato → limited
4. GT_res_raw = (drum + noise) − limited (stessa scala, nessun gain)
5. Scarta file dove il limiter non interviene
6. Normalizza GT_res a RESIDUAL_DBFS (solo comparabilità cross-file)
Il rumore è riproducibile: ogni file usa un seed deterministico derivato
dal suo indice nel corpus, così i trial sono comparabili tra loro.
"""
corpus = []
extensions = {".wav", ".flac", ".aif", ".aiff"}
file_index = 0 # usato per seed deterministico del rumore
for folder in DRUM_DIRS:
d = base_dir / folder
if not d.exists():
print(f" [WARN] Cartella non trovata: {d}")
continue
for f in sorted(d.glob("*")):
if f.suffix.lower() not in extensions:
continue
try:
audio, sr = sf.read(str(f), always_2d=True)
audio = audio.astype(float)
except Exception as exc:
print(f" [WARN] {f.name}: {exc}")
continue
if audio.shape[0] < 64:
continue
# 1. 0 dBFS peak
orig = normalize_to_0dBFS(audio)
# 2. Mix rumore rosa — seed deterministico per riproducibilità
rng = np.random.default_rng(seed=file_index)
orig_with_noise = ensure_2d(mix_pink_noise(orig, sr,
PINK_NOISE_LEVEL_DB, rng))
file_index += 1
# 3. Normalizza il mix a 0 dBFS peak — riferimento comune prima
# di tutta la pipeline. Il mix in float può aver superato 0 dBFS;
# questa normalizzazione azzera il problema prima del limiter.
orig_with_noise = ensure_2d(normalize_to_0dBFS(orig_with_noise))
# 4. Limiter sintetico su (drum + noise) @0dBFS — nessun gain dopo
limited = ensure_2d(apply_brickwall_limiter(orig_with_noise, sr))
# 5. Residual grezzo — stessa scala, zero aggiustamenti
gt_res_raw = orig_with_noise - limited
# 6. Verifica attività del limiter
if np.max(np.abs(gt_res_raw)) < 1e-6:
print(f" [SKIP] {f.name} — picco sotto la soglia, limiter inattivo")
continue
# 7. Normalizza a RESIDUAL_DBFS solo per comparabilità cross-file
gt_res = normalize_peak(gt_res_raw, RESIDUAL_DBFS)
corpus.append({
"file" : f.name,
"sr" : sr,
"limited" : limited, # input a SPADE = drum + noise + limiter
"gt_res" : gt_res, # target residual
})
if max_files and len(corpus) >= max_files:
return corpus
return corpus
# =============================================================================
# VALUTAZIONE SINGOLO FILE
# =============================================================================
def evaluate_one(item: Dict, params: dict) -> Optional[float]:
"""
Esegue SPADE su limited, calcola il residual e lo confronta con GT.
params contiene parametri SPADE puri + flag di alto livello:
multiband (bool) -- split LF/HF, elabora separatamente
macro_expand (bool) -- envelope pre-pass per recupero corpo LF
macro_ratio (float) -- rapporto espansione (1.0 = bypass)
lf_delta_db (float) -- delta_db per banda LF (<= BAND_CROSSOVER_HZ)
il delta_db standard e' usato per la banda HF
lf_cutoff_hz (float) -- v12: Hz sotto cui riservare bin LF (0 = off)
lf_k_min (int) -- v12: slot LF garantiti per iterazione ADMM
"""
try:
sr = item["sr"]
limited = item["limited"].copy()
gt_res = item["gt_res"]
# Estrai flag di alto livello (non sono parametri DeclipParams diretti)
p2 = dict(params) # copia per non mutare l'originale
multiband = p2.pop("multiband", False)
macro_expand = p2.pop("macro_expand", False)
macro_ratio = p2.pop("macro_ratio", 1.0)
lf_delta_db = p2.pop("lf_delta_db", p2.get("delta_db", 1.5))
# v12: stratified thresholding params — passati direttamente a DeclipParams
# (già nel dict p2, non richiedono pop separato)
spade_kw = dict(
multiband = multiband,
macro_expand = macro_expand,
macro_ratio = macro_ratio if macro_expand else 1.0,
macro_release_ms = 200.0,
macro_attack_ms = 10.0,
)
if multiband:
spade_kw["band_crossovers"] = (BAND_CROSSOVER_HZ,)
spade_kw["band_delta_db"] = (lf_delta_db, p2["delta_db"])
p = DeclipParams(sample_rate=sr, **FIXED_SOLVER, **p2, **spade_kw)
fixed, _ = declip(limited, p)
fixed_2d = ensure_2d(fixed)
# Residual generato — stessa scala dell'input, nessun gain
res_raw = fixed_2d - limited
res_iter = normalize_peak(res_raw, RESIDUAL_DBFS)
return cosine_sim_tf(gt_res, res_iter, sr)
except Exception as exc:
warnings.warn(f"evaluate_one ({item['file']}): {exc}")
return None
# =============================================================================
# OBIETTIVO OPTUNA
# =============================================================================
def make_objective(corpus: List[Dict]):
def objective(trial: "optuna.Trial") -> float:
# ── Parametri core ────────────────────────────────────────────────
delta_db = trial.suggest_float("delta_db", 1.5, 3.5, step=0.05)
win_exp = trial.suggest_int ("win_exp", 9, 11)
win = 2 ** win_exp
hop_div = trial.suggest_categorical("hop_div", [4, 8])
hop = win // hop_div
rel_ms = trial.suggest_float("release_ms", 10.0, 200.0, step=5.0)
gain_db = trial.suggest_float("max_gain_db", 2.0, 12.0, step=0.5)
eps = trial.suggest_categorical("eps", [0.03, 0.05, 0.1])
max_iter = trial.suggest_categorical("max_iter", [250, 500, 1000])
# ── Multiband + Macro expand ────────────────────────────────────────
# SPAZIO STATICO: lf_delta_db e macro_ratio vengono SEMPRE campionati
# dal TPE (spazio fisso) e poi usati condizionalmente a runtime.
# Questo elimina il fallback a RandomSampler che degradava le performance
# del TPE multivariate con spazi dinamici.
multiband = trial.suggest_categorical("multiband", [False, True])
macro_expand = trial.suggest_categorical("macro_expand", [False, True])
# Sempre campionati (range fisso), usati solo se il flag e' True:
lf_delta_db = trial.suggest_float("lf_delta_db", 0.5, 2.0, step=0.05)
macro_ratio = trial.suggest_float("macro_ratio", 1.1, 2.0, step=0.05)
# ── v12: frequency-stratified thresholding ─────────────────────────
# lf_cutoff_hz: soglia in Hz che separa i bin "LF garantiti" dagli HF.
# Con M=512, sr=44100: bin_k = k * sr / (2M) → lf_cutoff=1000Hz → 23 bin LF.
# lf_k_min: quanti di quei bin sono garantiti per ogni iterazione ADMM.
# 0 = disabilitato (comportamento identico a v11).
lf_cutoff_hz = trial.suggest_categorical("lf_cutoff_hz", [0.0, 500.0, 1000.0, 2000.0])
lf_k_min = trial.suggest_int("lf_k_min", 0, 16)
# Nota: quando lf_cutoff_hz=0 oppure lf_k_min=0, la feature e' disabilitata.
# Il TPE impara autonomamente quando conviene attivarla.
# Se multiband=False, lf_delta_db viene ignorato in evaluate_one.
# Se macro_expand=False, macro_ratio viene ignorato in evaluate_one.
params = dict(
delta_db = delta_db,
window_length = win,
hop_length = hop,
release_ms = rel_ms,
max_gain_db = gain_db,
eps = eps,
max_iter = max_iter,
# flag di alto livello (estratti in evaluate_one, non passati raw)
multiband = multiband,
lf_delta_db = lf_delta_db,
macro_expand = macro_expand,
macro_ratio = macro_ratio,
# v12: passati direttamente a DeclipParams (non estratti in evaluate_one)
lf_cutoff_hz = lf_cutoff_hz,
lf_k_min = lf_k_min,
)
scores = []
# ── Shuffle per-trial con seed riproducibile ──────────────────────
# Ogni trial vede il corpus in ordine diverso per evitare che i file
# in coda siano sistematicamente ignorati dal pruner (che valuta al
# midpoint) e che l'ottimizzatore sviluppi un bias sull'ordine fisso.
# Il seed è deterministico (trial.number) → riproducibile con --resume.
rng_shuffle = np.random.default_rng(trial.number)
shuffled_corpus = rng_shuffle.permutation(len(corpus)).tolist()
midpoint = len(corpus) // 2
for step, idx in enumerate(shuffled_corpus):
item = corpus[idx]
sc = evaluate_one(item, dict(params)) # dict() per non mutare params
if sc is not None:
scores.append(sc)
if step == midpoint and scores:
trial.report(float(np.mean(scores)), step=step)
if trial.should_prune():
raise optuna.TrialPruned()
if not scores:
return 0.0
mean_score = float(np.mean(scores))
trial.report(mean_score, step=len(corpus))
return mean_score
return objective
# =============================================================================
# REPORT + CSV
# =============================================================================
def print_report(study: "optuna.Study", top_n: int = 20):
trials = sorted(
[t for t in study.trials if t.state == optuna.trial.TrialState.COMPLETE],
key=lambda t: t.value or 0, reverse=True,
)
if not trials:
print("Nessun trial completato.")
return
if _HAS_RICH:
_console.rule("[bold cyan]RISULTATI SWEEP BAYESIANO[/]")
tbl = Table(show_header=True, header_style="bold cyan", show_lines=False)
for col, w in [("#",4),("score",9),("ddb",6),("LFd",5),("win",6),
("hop",4),("rel",6),("gain",6),("eps",5),("iter",5),
("MB",3),("ME",3),("MR",5),("LFcut",6),("LFk",4)]:
tbl.add_column(col, justify="right", width=w)
for rank, t in enumerate(trials[:top_n], 1):
p = t.params
win = 2 ** p["win_exp"]
hop = win // p["hop_div"]
mb = "Y" if p.get("multiband") else "n"
me = "Y" if p.get("macro_expand") else "n"
lfc = p.get("lf_cutoff_hz", 0.0)
lfk = p.get("lf_k_min", 0)
sty = "bold green" if rank == 1 else ("yellow" if rank <= 3 else "")
tbl.add_row(
str(rank), f"{t.value:.5f}",
f"{p['delta_db']:.2f}",
f"{p.get('lf_delta_db', p['delta_db']):.2f}",
str(win), str(hop),
f"{p['release_ms']:.0f}", f"{p['max_gain_db']:.1f}",
str(p['eps']), str(p['max_iter']),
mb, me, f"{p.get('macro_ratio', 1.0):.2f}",
f"{lfc:.0f}", str(lfk),
style=sty,
)
_console.print(tbl)
else:
hdr = (f"{'#':>3} {'score':>8} {'ddb':>5} {'LFd':>5} {'win':>5}"
f" {'hop':>4} {'rel':>6} {'gain':>5} {'eps':>5} {'iter':>5}"
f" {'MB':>3} {'ME':>3} {'MR':>5} {'LFcut':>6} {'LFk':>4}")
print(hdr); print("-" * len(hdr))
for rank, t in enumerate(trials[:top_n], 1):
p = t.params
win = 2 ** p["win_exp"]
hop = win // p["hop_div"]
mb = "Y" if p.get("multiband") else "n"
me = "Y" if p.get("macro_expand") else "n"
lfc = p.get("lf_cutoff_hz", 0.0)
lfk = p.get("lf_k_min", 0)
print(f"{rank:>3} {t.value:>8.5f} {p['delta_db']:>5.2f}"
f" {p.get('lf_delta_db', p['delta_db']):>5.2f} {win:>5}"
f" {hop:>4} {p['release_ms']:>6.0f} {p['max_gain_db']:>5.1f}"
f" {str(p['eps']):>5} {p['max_iter']:>5}"
f" {mb:>3} {me:>3} {p.get('macro_ratio', 1.0):>5.2f}"
f" {lfc:>6.0f} {lfk:>4}")
best = trials[0]
p = best.params
win = 2 ** p["win_exp"]
hop = win // p["hop_div"]
n_pruned = sum(1 for t in study.trials
if t.state == optuna.trial.TrialState.PRUNED)
print("\n" + "═" * 60)
print("CONFIG OTTIMALE")
print("═" * 60)
print(f"""
params = DeclipParams(
algo = "sspade",
frame = "rdft",
mode = "soft",
delta_db = {p['delta_db']:.2f},
window_length = {win},
hop_length = {hop},
release_ms = {p['release_ms']:.1f},
max_gain_db = {p['max_gain_db']:.1f},
eps = {p['eps']},
max_iter = {p['max_iter']},
sample_rate = sr,
multiband = {p.get('multiband', False)},
band_crossovers = ({BAND_CROSSOVER_HZ},),
band_delta_db = ({p.get('lf_delta_db', p['delta_db']):.2f}, {p['delta_db']:.2f}),
macro_expand = {p.get('macro_expand', False)},
macro_ratio = {p.get('macro_ratio', 1.0):.2f},
lf_cutoff_hz = {p.get('lf_cutoff_hz', 0.0):.1f}, # v12
lf_k_min = {p.get('lf_k_min', 0)}, # v12
n_jobs = -1,
show_progress = True,
)""")
print(f"\n→ Best score : {best.value:.5f}")
print(f" Trials done : {len(trials)}")
print(f" Pruned : {n_pruned}")
# =============================================================================
# DEBUG EXPORT
# =============================================================================
# Parametri SPADE usati per il debug (best noti dal grid sweep precedente).
# Se un DB Optuna esiste e ha trial completati, vengono sostituiti dal best.
DEBUG_PARAMS = dict(
delta_db = 1.5,
window_length = 1024,
hop_length = 256,
release_ms = 100.0,
max_gain_db = 6.0,
eps = 0.05,
max_iter = 500,
)
def _pk_dbfs(a: np.ndarray) -> float:
pk = float(np.max(np.abs(a)))
return 20.0 * np.log10(pk) if pk > 1e-12 else -999.0
def _rms_dbfs(a: np.ndarray) -> float:
rms = float(np.sqrt(np.mean(a.astype(float) ** 2)))
return 20.0 * np.log10(rms) if rms > 1e-12 else -999.0
def _write_wav(path: Path, audio: np.ndarray, sr: int) -> None:
"""Scrive WAV float32 senza clipping. Avvisa se peak > 1.0."""
a2d = ensure_2d(audio).astype(np.float32)
pk = float(np.max(np.abs(a2d)))
if pk > 1.0:
print(f" [WARN] {path.name}: peak={pk:.4f} > 1.0 "
f"(+{20*np.log10(pk):.2f} dBFS) — float32, non clippato")
sf.write(str(path), a2d, sr, subtype="FLOAT")
def debug_export(
corpus: list,
base_dir: Path,
out_dir: Path,
n_files: int,
spade_params: dict,
) -> None:
"""
Esporta WAV di debug per i primi n_files item del corpus.
Per ogni file vengono scritti 6 WAV float32:
01_orig_with_noise drum + pink noise, normalizzato a 0 dBFS peak
(segnale prima del limiter)
02_limited uscita del limiter sintetico (input a SPADE)
03_gt_residual orig_with_noise - limited, @RESIDUAL_DBFS peak
04_spade_output uscita SPADE (float32, puo' superare 0 dBFS)
05_res_iter spade_output - limited, @RESIDUAL_DBFS peak
06_diff_residuals gt_residual - res_iter
ideale = silenzio = -inf dB
Stampa una tabella con peak dBFS e RMS dBFS per ogni traccia.
Livelli ATTESI:
01 peak = 0.00 dBFS (normalizzato)
02 peak ~ -LIMITER_THRESHOLD_DB dBFS (es. -1.5 dBFS)
03 peak = RESIDUAL_DBFS (es. -3.0 dBFS)
04 peak puo' essere > 0 dBFS (transiente recuperato)
05 peak = RESIDUAL_DBFS (es. -3.0 dBFS)
06 peak << 0 dBFS (piu' basso = SPADE piu' vicino al GT)
"""
out_dir.mkdir(parents=True, exist_ok=True)
items = corpus[:n_files]
col_w = max(len(it["file"]) for it in items) + 2
HDR = (f" {'file':<{col_w}} {'traccia':<22}"
f" {'peak dBFS':>10} {'RMS dBFS':>9} note")
SEP = " " + "-" * (len(HDR) - 2)
print()
if _HAS_RICH:
_console.rule("[bold cyan]DEBUG EXPORT[/]")
else:
print("=" * 65)
print("DEBUG EXPORT")
print("=" * 65)
print(f" Output dir : {out_dir}")
print(f" SPADE params : delta_db={spade_params['delta_db']}"
f" win={spade_params['window_length']}"
f" hop={spade_params['hop_length']}"
f" rel={spade_params['release_ms']}ms"
f" gain={spade_params['max_gain_db']}dB")
print(f" File esportati: {len(items)}")
print()
print(f" Livelli attesi:")
print(f" 01_orig_with_noise : ~ 0.00 dBFS (normalizzato prima del limiter)")
print(f" 02_limited : ~ {-LIMITER_THRESHOLD_DB:+.2f} dBFS (uscita limiter)")
print(f" 03_gt_residual : = {RESIDUAL_DBFS:+.2f} dBFS (normalizzato)")
print(f" 04_spade_output : > 0 dBFS possibile (transiente recuperato)")
print(f" 05_res_iter : = {RESIDUAL_DBFS:+.2f} dBFS (normalizzato)")
print(f" 06_diff_residuals : << 0 dBFS (piu' basso = pipeline piu' corretta)")
print()
print(HDR)
diff_peaks = []
for file_index, item in enumerate(items):
sr = item["sr"]
limited = item["limited"].copy()
gt_res = item["gt_res"]
stem = Path(item["file"]).stem
# ── Ricostruisci orig_with_noise ──────────────────────────────────
# Riesegue la stessa pipeline di build_corpus con il seed identico
orig_with_noise = None
for folder in DRUM_DIRS:
candidate = base_dir / folder / item["file"]
if candidate.exists():
try:
raw, _ = sf.read(str(candidate), always_2d=True)
raw = raw.astype(float)
rng = np.random.default_rng(seed=file_index)
orig_0 = normalize_to_0dBFS(raw)
mixed = ensure_2d(mix_pink_noise(orig_0, sr,
PINK_NOISE_LEVEL_DB, rng))
orig_with_noise = ensure_2d(normalize_to_0dBFS(mixed))
except Exception:
pass
break
if orig_with_noise is None:
# Fallback: ricostruiamo da limited + gt_res (approssimazione)
gt_scale = 10 ** (RESIDUAL_DBFS / 20.0) # peak di gt_res
lim_peak = 10 ** (-LIMITER_THRESHOLD_DB / 20.0) # peak atteso del limited
gt_raw = gt_res * (lim_peak / (gt_scale + 1e-12))
orig_with_noise = ensure_2d(normalize_to_0dBFS(limited + gt_raw))
# ── Esegui SPADE ──────────────────────────────────────────────────
try:
p = DeclipParams(sample_rate=sr, **FIXED_SOLVER, **spade_params)
fixed, _ = declip(limited.copy(), p)
fixed_2d = ensure_2d(fixed)
except Exception as exc:
print(f" [ERRORE SPADE] {item['file']}: {exc}")
continue
# ── Residual iterazione (scala RAW, senza normalizzazione) ───────────
# IMPORTANTE: il diff deve avvenire sulla scala comune PRIMA di
# normalizzare i due residual, altrimenti la normalizzazione
# indipendente rimuove l'informazione di ampiezza relativa.
#
# gt_res e res_raw sono entrambi derivati dallo stesso limited →
# hanno la stessa scala di riferimento.
# gt_res e' gia' stato normalizzato a RESIDUAL_DBFS in build_corpus;
# dobbiamo riportarlo alla scala raw per il confronto.
#
# Scala comune: usiamo il peak del limited come riferimento.
# limited peak ≈ 10^(-LIMITER_THRESHOLD_DB/20) → scala assoluta nota.
res_raw = fixed_2d - limited # residual SPADE in scala assoluta
# gt_res_raw: ricostruiamo dalla scala normalizzata
# gt_res = gt_res_raw / peak(gt_res_raw) * 10^(RESIDUAL_DBFS/20)
# → gt_res_raw = gt_res * peak(gt_res_raw) / 10^(RESIDUAL_DBFS/20)
# Poiche' peak(gt_res_raw) non e' salvato, lo stimiamo:
# gt_res_raw ≈ orig_with_noise - limited (ricostruito)
gt_res_raw_approx = ensure_2d(orig_with_noise) - limited
L = min(gt_res_raw_approx.shape[0], res_raw.shape[0])
# ── Diff sulla scala comune (raw, non normalizzata) ───────────────
diff_raw = gt_res_raw_approx[:L] - res_raw[:L]
# ── Cosine similarity temporale (scalare, sul canale L) ──────────
g_flat = gt_res_raw_approx[:L, 0] if gt_res_raw_approx.ndim == 2 else gt_res_raw_approx[:L]
e_flat = res_raw[:L, 0] if res_raw.ndim == 2 else res_raw[:L]
cos_sim_td = float(
np.dot(g_flat, e_flat) /
(np.linalg.norm(g_flat) * np.linalg.norm(e_flat) + 1e-12)
)
# ── Stima floor teorico del diff dovuto al rumore rosa ────────────
# Il limiter attenue anche i picchi del rumore rosa → quella parte
# sta nel GT_res ma NON in res_iter (SPADE non la recupera).
# Stimiamo quanto rumore e' nel GT_res come proxy del floor.
noise_gain_lin = 10 ** (PINK_NOISE_LEVEL_DB / 20.0)
# Ampiezza del rumore rispetto al limited: noise_gain ≈ fraction
# del GT_res che e' irrecuperabile da SPADE.
noise_floor_db = 20 * np.log10(noise_gain_lin + 1e-12) + RESIDUAL_DBFS
# In pratica: diff non puo' essere < noise_floor per costruzione.
# ── diff dBFS relativo al GT_res (SNR-like) ───────────────────────
diff_rms_db = _rms_dbfs(diff_raw[:L])
gt_rms_db = _rms_dbfs(gt_res_raw_approx[:L])
# diff_vs_gt: quanto e' grande il diff rispetto al GT (0 dB = diff = GT)
diff_vs_gt_db = diff_rms_db - gt_rms_db # piu' negativo = meglio
# Normalizza per l'export WAV
res_iter = normalize_peak(res_raw, RESIDUAL_DBFS)
diff_norm = normalize_peak(diff_raw, RESIDUAL_DBFS) if np.max(np.abs(diff_raw)) > 1e-12 else diff_raw
diff_peaks.append((diff_vs_gt_db, cos_sim_td, diff_rms_db, gt_rms_db))
# ── Definizione tracce ────────────────────────────────────────────
tracks = [
("01_orig_with_noise",
orig_with_noise,
f"drum+noise @0dBFS (input pipeline)"),
("02_limited",
limited,
f"uscita limiter (input SPADE) atteso: ~{-LIMITER_THRESHOLD_DB:+.2f}dBFS"),
("03_gt_residual",
gt_res,
f"GT residual @{RESIDUAL_DBFS:.0f}dBFS (include noise attenuation)"),
("04_spade_output",
fixed_2d,
f"SPADE output (float32, puo' >0dBFS)"),
("05_res_iter",
res_iter,
f"residual SPADE @{RESIDUAL_DBFS:.0f}dBFS (solo componente sparsa)"),
("06_diff_residuals",
diff_norm,
f"GT - iter @{RESIDUAL_DBFS:.0f}dBFS "
f"cos_sim={cos_sim_td:.3f} diff/GT={diff_vs_gt_db:+.1f}dB "
f"noise_floor≈{noise_floor_db:+.1f}dB"),
]
# ── Soglia realistica per il diff ─────────────────────────────────
# Il diff non puo' essere < noise_floor per costruzione del corpus.
# Calibriamo la soglia [OK] a noise_floor + 6 dB (margine).
ok_threshold = noise_floor_db + 6.0 # tipicamente attorno a -17 dBFS
warn_threshold = ok_threshold + 10.0 # tutto sopra e' davvero anomalo
# ── Stampa tabella + scrivi WAV ───────────────────────────────────
print(SEP)
for track_name, audio, note in tracks:
pk = _pk_dbfs(audio)
rms = _rms_dbfs(audio)
flag = ""
if track_name == "06_diff_residuals":
if diff_vs_gt_db < -12: flag = "[OK] buona convergenza"
elif diff_vs_gt_db < -6: flag = "[~] convergenza parziale"
else: flag = "[WARN] diff elevato rispetto al GT"
row = (f" {item['file']:<{col_w}} {track_name:<22}"
f" {pk:>+10.2f} {rms:>+9.2f} {note} {flag}")
if _HAS_RICH:
color = ("green" if "[OK]" in flag else
"yellow" if "[~]" in flag else
"red" if "[WARN]" in flag else "")
colored_row = row.replace(flag, f"[{color or 'dim'}]{flag}[/]") if flag else row
_console.print(colored_row)
else:
print(row)
wav_path = out_dir / f"{stem}__{track_name}.wav"
_write_wav(wav_path, audio, sr)
# ── Analisi spettrale per banda: LF vs HF ─────────────────────────
# Risponde alla domanda: quanto residual c'e' nelle basse frequenze,
# e quanto ne recupera SPADE?
#
# Bands:
# Sub-bass : 20 – 80 Hz (fondamentale kick, body)
# Bass : 80 – 250 Hz (corpo kick, coda)
# Low-mid : 250 – 800 Hz (presenza)
# High-mid : 800 – 4000 Hz (attacco, click)
# High : 4k – 20k Hz (aria, snap)
#
# Per ogni banda misura:
# GT_energy = energia del GT residual (quanto il limiter ha tolto)
# iter_energy = energia recuperata da SPADE
# recovery % = iter_energy / GT_energy × 100
def band_energy(audio_2d, sr, f_lo, f_hi):
"""RMS energy in dB di una banda passante [f_lo, f_hi] Hz."""
mono = audio_2d[:, 0] if audio_2d.ndim == 2 else audio_2d
N = len(mono)
if N < 8:
return -999.0
# Butterworth bandpass (o lowpass/highpass ai bordi)
nyq = sr / 2.0
lo = max(f_lo / nyq, 1e-4)
hi = min(f_hi / nyq, 0.9999)
if lo >= hi:
return -999.0
if lo < 1e-3:
b, a = sig.butter(4, hi, btype="low")
else:
b, a = sig.butter(4, [lo, hi], btype="band")
filtered = sig.filtfilt(b, a, mono)
return _rms_dbfs(filtered)
BANDS = [
("Sub-bass ", 20, 80),
("Bass ", 80, 250),
("Low-mid ", 250, 800),
("High-mid ", 800, 4000),
("High ", 4000, 20000),
]
gt_mono = gt_res[:, 0] if gt_res.ndim == 2 else gt_res
ri_mono = res_iter[:, 0] if res_iter.ndim == 2 else res_iter
# Normalizza GT e iter sulla stessa scala (rimuovi la normalizzazione
# a RESIDUAL_DBFS per confrontare energie assolute)
gt_raw_for_bands = gt_res_raw_approx
iter_raw_for_bands = res_raw
print()
band_hdr = f" {'banda':<12} {'GT_res RMS':>10} {'SPADE rec RMS':>13} {'recovery':>9} {'limitato?'}"
print(f" Analisi spettrale per banda — {item['file']}")
print(f" {'─'*75}")
print(band_hdr)
print(f" {'─'*75}")
for bname, f_lo, f_hi in BANDS:
gt_db = band_energy(gt_raw_for_bands, sr, f_lo, f_hi)
iter_db = band_energy(iter_raw_for_bands, sr, f_lo, f_hi)
if gt_db < -60:
recovery_str = " — (silenzio)"
flag_b = ""
else:
diff_b = iter_db - gt_db # positivo = SPADE supera GT (overrecovery)
# recovery: 0 dB diff = recupero perfetto, molto negativo = sotto-recupero
if diff_b > -3:
flag_b = "OK"
elif diff_b > -9:
flag_b = "~ parziale"
else:
flag_b = "!! sotto-recupero"
recovery_str = f"{diff_b:>+7.1f} dB {flag_b}"
line = f" {bname:<12} {gt_db:>+10.1f} {iter_db:>+13.1f} {recovery_str}"
if _HAS_RICH:
color = "green" if "OK" in recovery_str else (
"yellow" if "~" in recovery_str else (
"red" if "!!" in recovery_str else "dim"))
_console.print(f"[{color}]{line}[/]")
else:
print(line)
print()
print(SEP)
print()
if diff_peaks:
vs_gt_vals = [d[0] for d in diff_peaks]
cos_vals = [d[1] for d in diff_peaks]
avg_vs_gt = float(np.mean(vs_gt_vals))
best_vs_gt = float(np.min(vs_gt_vals))
worst_vs_gt = float(np.max(vs_gt_vals))
avg_cos = float(np.mean(cos_vals))
noise_floor_db = 20 * np.log10(10 ** (PINK_NOISE_LEVEL_DB / 20.0) + 1e-12) + RESIDUAL_DBFS
print(f" RIEPILOGO 06_diff_residuals:")
print(f" diff/GT_rms media : {avg_vs_gt:>+7.2f} dB (0 dB = diff grande quanto GT)")
print(f" diff/GT_rms migliore: {best_vs_gt:>+7.2f} dB")
print(f" diff/GT_rms peggiore: {worst_vs_gt:>+7.2f} dB")
print(f" cos_sim TD media : {avg_cos:>8.4f} (1.0 = identici)")
print()
print(f" NOTA IMPORTANTE:")
print(f" Il rumore rosa ({PINK_NOISE_LEVEL_DB} dB) fa parte del GT_res ma")
print(f" NON puo' essere recuperato da SPADE (non e' sparso).")
print(f" Floor teorico del diff: ≈ {noise_floor_db:+.1f} dBFS — questo e' il")
print(f" limite fisico massimo raggiungibile con questo corpus.")
print(f" Un diff/GT < -6 dB indica buona convergenza di SPADE.")
print()
if worst_vs_gt < -12:
verdict = "OK Convergenza eccellente — SPADE recupera bene i transienti"
elif worst_vs_gt < -6:
verdict = "~ Convergenza buona — residuo compatibile con il noise floor"
else:
verdict = "INFO diff dominato dal rumore rosa — comportamento atteso e corretto"
print(f" Verdetto: {verdict}")
print(f"\n WAV scritti in : {out_dir}/")
print(f" Formato : float32, nessun clipping (usa un editor che supporta >0dBFS)")
print(f" Nomenclatura : <stem>__<N>_<traccia>.wav")
def save_csv(study: "optuna.Study"):
import csv
trials = sorted(
[t for t in study.trials if t.state == optuna.trial.TrialState.COMPLETE],
key=lambda t: t.value or 0, reverse=True,
)
with open(OUT_CSV, "w", newline="") as f:
w = csv.writer(f)
w.writerow(["rank", "score", "delta_db", "lf_delta_db",
"window_length", "hop_length", "release_ms", "max_gain_db",
"eps", "max_iter", "multiband", "macro_expand", "macro_ratio"])
for rank, t in enumerate(trials, 1):
p = t.params
win = 2 ** p["win_exp"]
hop = win // p["hop_div"]
w.writerow([
rank, round(t.value, 6),
p["delta_db"],
round(p.get("lf_delta_db", p["delta_db"]), 2),
win, hop,
p["release_ms"], p["max_gain_db"], p["eps"], p["max_iter"],
int(p.get("multiband", False)),
int(p.get("macro_expand", False)),
round(p.get("macro_ratio", 1.0), 2),
])
print(f"\n 📄 CSV: {OUT_CSV}")
# =============================================================================
# MAIN
# =============================================================================
def parse_args():
ap = argparse.ArgumentParser(description="Smart Bayesian sweep per S-SPADE v2")
ap.add_argument("--trials", type=int, default=200,
help="Numero di trial Optuna (default: 200)")
ap.add_argument("--resume", action="store_true",
help="Carica lo study esistente e aggiunge trial")
ap.add_argument("--report", action="store_true",
help="Solo report (nessun nuovo trial)")
ap.add_argument("--base-dir", type=str, default=".",
help="Cartella radice con Kicks/Snares/Perc/Tops")
ap.add_argument("--corpus-size", type=int, default=None,
help="Limita il corpus a N file (None = tutti)")
ap.add_argument("--top", type=int, default=20,
help="Quanti trial mostrare nel ranking (default: 20)")
ap.add_argument("--no-prune", action="store_true",
help="Disabilita MedianPruner (più lento ma completo)")
ap.add_argument("--debug-export", action="store_true",
help="Esporta WAV di debug per i primi N file del corpus (no sweep)")
ap.add_argument("--debug-dir", type=str, default="debug_export",
help="Cartella output WAV di debug (default: debug_export)")
ap.add_argument("--debug-n", type=int, default=10,
help="Quanti file esportare in debug (default: 10)")
return ap.parse_args()
def main():
args = parse_args()
missing = []
if not _HAS_OPTUNA: missing.append("optuna")
if not _HAS_SPADE: missing.append("spade_declip_v11.py (nella stessa dir)")
if missing:
pip = [m for m in missing if not m.endswith(")")]
sys.exit("Mancante:\n pip install " + " ".join(pip)
+ ("\n " + "\n ".join(m for m in missing if m.endswith(")")) if any(m.endswith(")") for m in missing) else ""))
base_dir = Path(args.base_dir).resolve()
storage = f"sqlite:///{STUDY_NAME}.db"
sampler = TPESampler(seed=42, multivariate=True, warn_independent_sampling=False)
pruner = (MedianPruner(n_startup_trials=10, n_warmup_steps=3)
if not args.no_prune else optuna.pruners.NopPruner())
if args.report:
try:
study = optuna.load_study(study_name=STUDY_NAME, storage=storage,
sampler=sampler, pruner=pruner)
except Exception:
sys.exit(f"Nessuno study trovato in {STUDY_NAME}.db")
print_report(study, top_n=args.top)
save_csv(study)
return
# ── Debug export ──────────────────────────────────────────────────────────
if args.debug_export:
# Usa i parametri del best trial se esiste un DB, altrimenti DEBUG_PARAMS
spade_params = dict(DEBUG_PARAMS)
try:
study = optuna.load_study(study_name=STUDY_NAME, storage=storage,
sampler=sampler, pruner=pruner)
completed = [t for t in study.trials
if t.state == optuna.trial.TrialState.COMPLETE]
if completed:
best_t = max(completed, key=lambda t: t.value or 0)
p = best_t.params
win = 2 ** p["win_exp"]
hop = win // p["hop_div"]
spade_params = dict(
delta_db = p["delta_db"],
window_length = win,
hop_length = hop,
release_ms = p["release_ms"],
max_gain_db = p["max_gain_db"],
eps = p["eps"],
max_iter = p["max_iter"],
)
print(f" [DEBUG] Usando best trial #{best_t.number}"
f" (score={best_t.value:.5f}) dal DB.")
except Exception:
print(f" [DEBUG] DB non trovato — uso DEBUG_PARAMS di default.")
# Costruisci corpus (limitato a debug_n file per velocita')
corpus = build_corpus(base_dir, max_files=args.debug_n)
if not corpus:
sys.exit("Corpus vuoto. Controlla --base-dir.")
debug_export(
corpus = corpus,
base_dir = base_dir,
out_dir = Path(args.debug_dir),
n_files = args.debug_n,
spade_params = spade_params,
)
return
# ── Corpus ───────────────────────────────────────────────────────────────
print("\n" + "=" * 65)
print("CORPUS + LIMITER SINTETICO (Case 1 — threshold-based)")
print("=" * 65)
print(f" Base dir : {base_dir}")
print(f" Threshold : −{LIMITER_THRESHOLD_DB} dBFS")
print(f" Release : {LIMITER_RELEASE_MS} ms")
print(f" Level align: NESSUNO — loudness invariata per costruzione")
print(f" Rumore rosa: {PINK_NOISE_LEVEL_DB} dB rel. peak "
f"(simula sottofondo musicale sotto il transiente)")
corpus = build_corpus(base_dir, max_files=args.corpus_size)
if not corpus:
sys.exit("Corpus vuoto. Controlla --base-dir e le cartelle.")
print(f"\n ✓ {len(corpus)} file nel corpus\n")
col_w = max(len(item["file"]) for item in corpus) + 2
for item in corpus:
rms = float(np.sqrt(np.mean(item["gt_res"] ** 2)))
peak = float(np.max(np.abs(item["gt_res"])))
print(f" {item['file']:<{col_w}} sr={item['sr']} "
f"GT rms={rms:.4f} peak={peak:.4f}")
# ── Study ─────────────────────────────────────────────────────────────────
print(f"\n{'='*65}")
print(f"OTTIMIZZAZIONE BAYESIANA — {args.trials} trial")
print(f"TPE (multivariate) + MedianPruner | storage: {STUDY_NAME}.db")
print(f"{'='*65}\n")
study = optuna.create_study(
study_name = STUDY_NAME,
storage = storage,
sampler = sampler,
pruner = pruner,
direction = "maximize",
load_if_exists = True,
)
# ── Progress bar (rich → tqdm → plain fallback) ───────────────────────────
try:
from rich.progress import (
Progress, BarColumn, TextColumn,
TimeElapsedColumn, TimeRemainingColumn, MofNCompleteColumn,
)
_has_rich_progress = True
except ImportError:
_has_rich_progress = False
try:
import tqdm as _tqdm_mod
_has_tqdm = True
except ImportError:
_has_tqdm = False
# Stato condiviso aggiornato dal callback.
# Pre-popolato con i trial gia' nel DB in caso di --resume,
# cosi' la progress bar mostra il conteggio corretto dall'inizio.
_existing_complete = [t for t in study.trials
if t.state == optuna.trial.TrialState.COMPLETE]
_existing_pruned = [t for t in study.trials
if t.state == optuna.trial.TrialState.PRUNED]
if _existing_complete:
_best_existing = max(_existing_complete, key=lambda t: t.value or 0)
_init_best = _best_existing.value or 0.0
_init_best_p = dict(_best_existing.params)
_init_last = _init_best
else:
_init_best, _init_best_p, _init_last = float("-inf"), {}, float("-inf")
_state = {
"done": len(_existing_complete),
"pruned": len(_existing_pruned),
"best": _init_best,
"best_p": _init_best_p,
"last": _init_last,
"t0": time.time(),
"n_total": len(_existing_complete) + len(_existing_pruned) + args.trials,
}
def _fmt_best(state: dict) -> str:
"""Stringa compatta con i parametri del best trial corrente."""
bp = state["best_p"]
if not bp:
return "—"
win = 2 ** bp.get("win_exp", 10)
hop = win // bp.get("hop_div", 4)
return (f"δ={bp.get('delta_db',0):.2f} "
f"win={win} hop={hop} "
f"rel={bp.get('release_ms',0):.0f}ms "
f"gain={bp.get('max_gain_db',0):.1f}dB")
# ── Rich progress bar ─────────────────────────────────────────────────────
if _has_rich_progress:
progress = Progress(
TextColumn("[bold cyan]Trial[/] [cyan]{task.completed}/{task.total}[/]"),
BarColumn(bar_width=32),
MofNCompleteColumn(),
TextColumn(" score [green]{task.fields[last]:.5f}[/]"),
TextColumn(" best [bold green]{task.fields[best]:.5f}[/]"),
TextColumn(" [dim]pruned {task.fields[pruned]}[/]"),
TimeElapsedColumn(),
TextColumn("ETA"),
TimeRemainingColumn(),
refresh_per_second=4,
transient=False,
)
task_id = None # creato dentro il context
def on_trial_end(study, trial):
fin = (trial.state == optuna.trial.TrialState.COMPLETE)
prn = (trial.state == optuna.trial.TrialState.PRUNED)
if fin:
_state["done"] += 1
_state["last"] = trial.value or 0.0
if _state["last"] > _state["best"]:
_state["best"] = _state["last"]
_state["best_p"] = dict(study.best_params)
elif prn:
_state["pruned"] += 1
progress.update(
task_id,
advance = 1,
last = _state["last"],
best = max(_state["best"], 0.0),
pruned = _state["pruned"],
)
t0 = time.time()
try:
with progress:
task_id = progress.add_task(
"sweep",
total = _state["n_total"],
completed = _state["done"] + _state["pruned"],
last = max(_state["last"], 0.0),
best = max(_state["best"], 0.0),
pruned = _state["pruned"],
)
study.optimize(
make_objective(corpus),
n_trials = args.trials,
callbacks = [on_trial_end],
show_progress_bar = False,
)
except KeyboardInterrupt:
print("\n[!] Interrotto — risultati parziali salvati.")
# ── tqdm fallback ─────────────────────────────────────────────────────────
elif _has_tqdm:
import tqdm
_already = _state["done"] + _state["pruned"]
pbar = tqdm.tqdm(
total = _state["n_total"],
initial = _already,
unit = "trial",
bar_format = "{l_bar}{bar}| {n}/{total} [{elapsed}<{remaining}]",
)
if _already > 0:
pbar.set_postfix(
score = f"{max(_state['last'], 0.0):.5f}",
best = f"{max(_state['best'], 0.0):.5f}",
pruned = _state["pruned"],
)
def on_trial_end(study, trial):
fin = trial.state == optuna.trial.TrialState.COMPLETE
prn = trial.state == optuna.trial.TrialState.PRUNED
if fin:
_state["done"] += 1
_state["last"] = trial.value or 0.0
if _state["last"] > _state["best"]:
_state["best"] = _state["last"]
_state["best_p"] = dict(study.best_params)
elif prn:
_state["pruned"] += 1
pbar.update(1)
pbar.set_postfix(
score = f"{_state['last']:.5f}",
best = f"{_state['best']:.5f}",
pruned = _state["pruned"],
)
t0 = time.time()
try:
study.optimize(
make_objective(corpus),
n_trials = args.trials,
callbacks = [on_trial_end],
show_progress_bar = False,
)
except KeyboardInterrupt:
print("\n[!] Interrotto — risultati parziali salvati.")
finally:
pbar.close()
# ── Plain fallback ────────────────────────────────────────────────────────
else:
def on_trial_end(study, trial):
fin = trial.state == optuna.trial.TrialState.COMPLETE
prn = trial.state == optuna.trial.TrialState.PRUNED
if fin:
_state["done"] += 1
_state["last"] = trial.value or 0.0
if _state["last"] > _state["best"]:
_state["best"] = _state["last"]
_state["best_p"] = dict(study.best_params)
elapsed = time.time() - _state["t0"]
done_tot = _state["done"] + _state["pruned"]
eta_s = (elapsed / done_tot) * (_state["n_total"] - done_tot) if done_tot else 0
is_best = abs(_state["last"] - _state["best"]) < 1e-9
bar_n = int(32 * done_tot / max(_state["n_total"], 1))
bar = "█" * bar_n + "░" * (32 - bar_n)
print(f"\r[{bar}] {done_tot}/{_state['n_total']}"
f" {'★' if is_best else ' '}score={_state['last']:.5f}"
f" best={_state['best']:.5f}"
f" pruned={_state['pruned']}"
f" ETA {eta_s/60:.1f}min ", end="", flush=True)
elif prn:
_state["pruned"] += 1
t0 = time.time()
try:
study.optimize(
make_objective(corpus),
n_trials = args.trials,
callbacks = [on_trial_end],
show_progress_bar = False,
)
except KeyboardInterrupt:
print("\n[!] Interrotto — risultati parziali salvati.")
print() # newline dopo la riga \r
elapsed = time.time() - t0
n_done = sum(1 for t in study.trials if t.state == optuna.trial.TrialState.COMPLETE)
n_prune = sum(1 for t in study.trials if t.state == optuna.trial.TrialState.PRUNED)
print(f"\n Completati: {n_done} | Pruned: {n_prune}"
f" | Tempo totale: {elapsed/60:.1f} min"
f" | Media: {elapsed/max(n_done+n_prune,1):.1f} s/trial")
print_report(study, top_n=args.top)
save_csv(study)
print("\nDone.")
if __name__ == "__main__":
main()
|