2664 lines
94 KiB
Python
2664 lines
94 KiB
Python
"""
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spectral_quality.py - Calidad Espectral Avanzada y Análisis (BLOQUE 4)
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Tareas T181-T195: Medición LUFS, análisis espectral, mastering, validación
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Este módulo proporciona:
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- T181: Medición LUFS real usando FFMPEG
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- T182: Integración multi-plataforma streaming normalization
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- T183: Tuning de Club Sub-Bass M/S separation
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- T184: Evaluación correlación de fase y prevención cancelaciones
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- T185: Integración librosa sin lockeos temporales
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- T186: Algoritmo extracción transientes (Onsets) para realinear percusiones
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- T187: Test calidad automático run_mix_quality_check
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- T188: Módulo On-The-Fly limpieza frecuencias problemáticas
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- T189: analyze_mixdown_cleanup purga clips vacíos del arrangement
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- T190: get_mastering_chain_config carga Audio Effect Racks Master Buss
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- T191: Overlap Safety Audit identifica tracks con bandas enmascaradas
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- T192: Diagnóstico de Bus RCA
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- T193: Reentrenamiento preferencias rate_generation feed to Memory
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- T194: Monitor de uso e index cache incremental
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- T195: Actualización asíncrona footprint espectral
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"""
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import os
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import sys
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import json
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import time
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import wave
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import struct
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import socket
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import logging
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import asyncio
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import hashlib
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import tempfile
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import threading
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import subprocess
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from pathlib import Path
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from typing import Dict, Any, List, Optional, Tuple, Union, Callable
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from dataclasses import dataclass, field
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from collections import defaultdict, deque
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from concurrent.futures import ThreadPoolExecutor
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from enum import Enum
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# Logging configuración
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logging.basicConfig(
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level=logging.INFO,
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format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
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)
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logger = logging.getLogger("SpectralQuality")
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# ============================================================================
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# T181: Medición LUFS Real con FFMPEG (T082-T083)
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# ============================================================================
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@dataclass
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class LUFSMeasurement:
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"""Resultado de medición LUFS"""
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integrated_lufs: float
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short_term_lufs: float
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momentary_lufs: float
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loudness_range: float
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true_peak_db: float
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sample_peak_db: float
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platform: str
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compliance: bool
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warnings: List[str] = field(default_factory=list)
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class FFMPEGLUFSAnalyzer:
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"""Analizador LUFS usando FFMPEG local (T081-T083)"""
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PLATFORM_TARGETS = {
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"streaming": {"target": -14.0, "true_peak": -1.0, "range": 8.0},
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"club": {"target": -8.0, "true_peak": -0.5, "range": 12.0},
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"youtube": {"target": -14.0, "true_peak": -1.0, "range": 8.0},
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"soundcloud": {"target": -10.0, "true_peak": -1.0, "range": 10.0},
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"spotify": {"target": -14.0, "true_peak": -1.0, "range": 8.0},
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"apple_music": {"target": -16.0, "true_peak": -1.0, "range": 8.0},
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"tidal": {"target": -14.0, "true_peak": -1.0, "range": 8.0},
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}
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def __init__(self, ffmpeg_path: Optional[str] = None):
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self.ffmpeg_path = ffmpeg_path or self._find_ffmpeg()
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self._cache = {}
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self._cache_lock = threading.Lock()
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def _find_ffmpeg(self) -> str:
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"""Encuentra FFMPEG en el sistema"""
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# Buscar en PATH
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for cmd in ["ffmpeg", "ffmpeg.exe"]:
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try:
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result = subprocess.run(
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[cmd, "-version"],
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capture_output=True,
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timeout=5,
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check=True
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)
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return cmd
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except (subprocess.TimeoutExpired, subprocess.CalledProcessError, FileNotFoundError):
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continue
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# Buscar en ubicaciones comunes Windows
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common_paths = [
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r"C:\ffmpeg\bin\ffmpeg.exe",
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r"C:\Program Files\ffmpeg\bin\ffmpeg.exe",
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r"C:\ProgramData\chocolatey\bin\ffmpeg.exe",
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r"C:\Users\%USERNAME%\AppData\Local\Microsoft\WinGet\Packages\Gyan.FFmpeg_Microsoft.Winget.Source_8wekyb3d8bbwe\ffmpeg.exe",
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]
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for path in common_paths:
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expanded = os.path.expandvars(path)
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if os.path.exists(expanded):
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return expanded
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logger.warning("[SPECTRAL] FFMPEG no encontrado, usando fallback de estimación")
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return None
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def measure_lufs(
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self,
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audio_path: str,
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platform: str = "streaming",
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use_cache: bool = True
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) -> LUFSMeasurement:
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"""
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T082-T083: Mide LUFS real usando FFMPEG
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Args:
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audio_path: Ruta al archivo de audio
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platform: Plataforma objetivo (streaming, club, youtube, etc.)
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use_cache: Usar caché de mediciones
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"""
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# Verificar caché
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cache_key = f"{audio_path}:{platform}"
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if use_cache:
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with self._cache_lock:
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if cache_key in self._cache:
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return self._cache[cache_key]
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# Si no hay FFMPEG, usar estimación basada en RMS
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if not self.ffmpeg_path:
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return self._estimate_lufs_fallback(audio_path, platform)
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try:
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# Usar FFMPEG ebur128 filter para medición LUFS
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cmd = [
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self.ffmpeg_path,
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"-i", audio_path,
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"-af", "ebur128=peak=true:metadata=1",
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"-f", "null",
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"-"
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]
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result = subprocess.run(
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cmd,
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capture_output=True,
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text=True,
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timeout=60,
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encoding='utf-8',
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errors='ignore'
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)
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stderr = result.stderr
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# Parsear salida de FFMPEG
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measurement = self._parse_ebur128_output(stderr, platform)
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# Guardar en caché
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if use_cache:
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with self._cache_lock:
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self._cache[cache_key] = measurement
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return measurement
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except subprocess.TimeoutExpired:
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logger.error(f"[SPECTRAL] Timeout midiendo LUFS: {audio_path}")
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return self._estimate_lufs_fallback(audio_path, platform)
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except Exception as e:
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logger.error(f"[SPECTRAL] Error midiendo LUFS: {e}")
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return self._estimate_lufs_fallback(audio_path, platform)
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def _parse_ebur128_output(self, output: str, platform: str) -> LUFSMeasurement:
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"""Parsea la salida de FFMPEG ebur128"""
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integrated = -14.0
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short_term = -14.0
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momentary = -14.0
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range_lu = 8.0
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true_peak = -1.0
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sample_peak = -0.5
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warnings = []
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lines = output.split('\n')
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for line in lines:
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if 'I:' in line and 'LUFS' in line:
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try:
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# Formato típico: " I: -14.2 LUFS"
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parts = line.split()
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for i, part in enumerate(parts):
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if part == 'I:':
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integrated = float(parts[i + 1])
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elif part == 'S:':
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short_term = float(parts[i + 1])
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elif part == 'M:':
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momentary = float(parts[i + 1])
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except (ValueError, IndexError):
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pass
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elif 'Loudness Range:' in line:
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try:
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range_lu = float(line.split(':')[1].strip().split()[0])
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except (ValueError, IndexError):
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pass
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elif 'Peak:' in line or 'true_peak' in line.lower():
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try:
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if 'dBTP' in line:
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true_peak = float(line.split('dBTP')[0].split()[-1])
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except (ValueError, IndexError):
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pass
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# Verificar compliance con plataforma
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target = self.PLATFORM_TARGETS.get(platform, self.PLATFORM_TARGETS["streaming"])
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compliance = (
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abs(integrated - target["target"]) <= 1.0 and
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true_peak <= target["true_peak"]
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)
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if abs(integrated - target["target"]) > 1.0:
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warnings.append(f"LUFS integrated {integrated:.1f} fuera de rango objetivo {target['target']:.1f}")
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if true_peak > target["true_peak"]:
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warnings.append(f"True peak {true_peak:.1f}dB excede límite {target['true_peak']:.1f}dB")
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return LUFSMeasurement(
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integrated_lufs=integrated,
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short_term_lufs=short_term,
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momentary_lufs=momentary,
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loudness_range=range_lu,
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true_peak_db=true_peak,
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sample_peak_db=sample_peak,
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platform=platform,
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compliance=compliance,
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warnings=warnings
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)
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def _estimate_lufs_fallback(
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self,
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audio_path: str,
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platform: str
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) -> LUFSMeasurement:
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"""Estimación fallback usando RMS cuando FFMPEG no está disponible"""
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try:
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# Leer audio y calcular RMS
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with wave.open(audio_path, 'rb') as wf:
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n_channels = wf.getnchannels()
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sample_width = wf.getsampwidth()
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n_frames = wf.getnframes()
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frames = wf.readframes(n_frames)
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if sample_width == 2:
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fmt = f"<{n_frames * n_channels}h"
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samples = struct.unpack(fmt, frames)
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elif sample_width == 4:
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fmt = f"<{n_frames * n_channels}i"
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samples = struct.unpack(fmt, frames)
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else:
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samples = []
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if samples:
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# Calcular RMS
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rms = (sum(s**2 for s in samples) / len(samples)) ** 0.5
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max_val = 32768.0 if sample_width == 2 else 2147483648.0
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rms_db = 20 * (rms / max_val)
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# Estimar LUFS (aproximación: LUFS ≈ RMS - 0.691 dB para señales complejas)
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estimated_lufs = rms_db - 0.691
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target = self.PLATFORM_TARGETS.get(platform, self.PLATFORM_TARGETS["streaming"])
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compliance = abs(estimated_lufs - target["target"]) <= 2.0
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return LUFSMeasurement(
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integrated_lufs=estimated_lufs,
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short_term_lufs=estimated_lufs,
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momentary_lufs=estimated_lufs,
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loudness_range=8.0,
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true_peak_db=rms_db + 3.0, # Estimación conservadora
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sample_peak_db=rms_db + 3.0,
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platform=platform,
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compliance=compliance,
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warnings=["Medición estimada (FFMPEG no disponible)"]
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)
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except Exception as e:
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logger.error(f"[SPECTRAL] Error en fallback LUFS: {e}")
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# Valores por defecto seguros
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return LUFSMeasurement(
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integrated_lufs=-14.0,
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short_term_lufs=-14.0,
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momentary_lufs=-14.0,
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loudness_range=8.0,
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true_peak_db=-1.0,
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sample_peak_db=-0.5,
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platform=platform,
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compliance=True,
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warnings=["No se pudo medir audio - valores por defecto"]
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)
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# ============================================================================
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# T182: Integración Multi-Plataforma Streaming Normalization
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# ============================================================================
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@dataclass
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class PlatformNormalizationReport:
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"""Reporte de normalización por plataforma (T092)"""
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platform: str
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current_lufs: float
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target_lufs: float
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delta_db: float
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normalization_applied: float
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will_be_attenuated: bool
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will_be_amplified: bool
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headroom_db: float
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recommendation: str
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class StreamingNormalizationAnalyzer:
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"""Analiza cómo el track será normalizado en diferentes plataformas"""
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PLATFORM_SETTINGS = {
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"spotify": {"target": -14.0, "mode": "auto"},
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"apple_music": {"target": -16.0, "mode": "auto"},
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"youtube": {"target": -14.0, "mode": "auto"},
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"tidal": {"target": -14.0, "mode": "manual_opt"},
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"soundcloud": {"target": -10.0, "mode": "auto"},
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"bandcamp": {"target": -14.0, "mode": "none"},
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"deezer": {"target": -15.0, "mode": "auto"},
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"amazon_music": {"target": -14.0, "mode": "auto"},
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"club_play": {"target": -8.0, "mode": "manual_opt"},
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}
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def __init__(self):
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self.lufs_analyzer = FFMPEGLUFSAnalyzer()
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def analyze_all_platforms(
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self,
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audio_path: str,
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current_lufs: Optional[float] = None
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) -> Dict[str, PlatformNormalizationReport]:
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"""
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T092: Genera reporte de normalización para todas las plataformas
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"""
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if current_lufs is None:
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measurement = self.lufs_analyzer.measure_lufs(audio_path, "streaming")
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current_lufs = measurement.integrated_lufs
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reports = {}
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for platform, settings in self.PLATFORM_SETTINGS.items():
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target = settings["target"]
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delta = current_lufs - target
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# Calcular normalización aplicada
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if settings["mode"] == "auto":
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normalization = -delta if delta > 0 else 0
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elif settings["mode"] == "manual_opt":
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normalization = max(0, -delta)
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else:
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normalization = 0
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# Generar recomendación
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if delta > 2:
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recommendation = f"Reducir ganancia en {delta:.1f}dB para match perfecto"
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elif delta < -2:
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recommendation = f"Aumentar ganancia en {abs(delta):.1f}dB para aprovechar headroom"
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else:
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recommendation = "Niveles óptimos para esta plataforma"
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reports[platform] = PlatformNormalizationReport(
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platform=platform,
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current_lufs=current_lufs,
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target_lufs=target,
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delta_db=delta,
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normalization_applied=normalization,
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will_be_attenuated=delta > 0 and settings["mode"] != "none",
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will_be_amplified=delta < 0 and settings["mode"] != "none",
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headroom_db=target - current_lufs,
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recommendation=recommendation
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)
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return reports
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|
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def get_best_platform_match(
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self,
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audio_path: str,
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current_lufs: Optional[float] = None
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) -> Tuple[str, float]:
|
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"""Encuentra la plataforma donde el track suena mejor"""
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reports = self.analyze_all_platforms(audio_path, current_lufs)
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best_platform = min(
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reports.items(),
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key=lambda x: abs(x[1].delta_db)
|
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)
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|
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return best_platform[0], abs(best_platform[1].delta_db)
|
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|
|
|
|
# ============================================================================
|
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# T183: Tuning de Club Sub-Bass M/S Separation (T084)
|
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# ============================================================================
|
|
|
|
@dataclass
|
|
class ClubTuningConfig:
|
|
"""Configuración de tuning para club (T084)"""
|
|
sub_bass_freq: float # Frecuencia debajo de la cual sumar a mono
|
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side_hp_freq: float # High-pass para lados en M/S
|
|
mono_sub: bool # Sub-bass en mono
|
|
headroom_db: float # Headroom para club
|
|
eq_bands: List[Dict[str, Any]]
|
|
dynamic_eq: bool # EQ dinámico habilitado
|
|
|
|
|
|
class ClubTuningEngine:
|
|
"""Configuración optimizada para reproducción en club"""
|
|
|
|
def __init__(self):
|
|
self.presets = {
|
|
"standard": ClubTuningConfig(
|
|
sub_bass_freq=80.0,
|
|
side_hp_freq=100.0,
|
|
mono_sub=True,
|
|
headroom_db=3.0,
|
|
eq_bands=[
|
|
{"freq": 30, "gain": 0, "q": 0.7, "type": "highpass"},
|
|
{"freq": 60, "gain": 2, "q": 1.0, "type": "lowshelf"},
|
|
{"freq": 120, "gain": -1, "q": 2.0, "type": "bell"},
|
|
{"freq": 400, "gain": 0, "q": 1.5, "type": "bell"},
|
|
{"freq": 3000, "gain": 1, "q": 1.2, "type": "highshelf"},
|
|
{"freq": 10000, "gain": 0, "q": 0.7, "type": "lowpass"},
|
|
],
|
|
dynamic_eq=True
|
|
),
|
|
"warehouse": ClubTuningConfig(
|
|
sub_bass_freq=100.0,
|
|
side_hp_freq=120.0,
|
|
mono_sub=True,
|
|
headroom_db=4.0,
|
|
eq_bands=[
|
|
{"freq": 40, "gain": 0, "q": 0.5, "type": "highpass"},
|
|
{"freq": 80, "gain": 3, "q": 0.8, "type": "lowshelf"},
|
|
{"freq": 150, "gain": -2, "q": 2.5, "type": "bell"},
|
|
{"freq": 300, "gain": 1, "q": 1.0, "type": "bell"},
|
|
{"freq": 2500, "gain": 2, "q": 1.5, "type": "highshelf"},
|
|
],
|
|
dynamic_eq=True
|
|
),
|
|
"festival": ClubTuningConfig(
|
|
sub_bass_freq=70.0,
|
|
side_hp_freq=90.0,
|
|
mono_sub=True,
|
|
headroom_db=2.0,
|
|
eq_bands=[
|
|
{"freq": 25, "gain": 0, "q": 0.7, "type": "highpass"},
|
|
{"freq": 55, "gain": 4, "q": 0.6, "type": "lowshelf"},
|
|
{"freq": 100, "gain": -1, "q": 2.0, "type": "bell"},
|
|
{"freq": 3500, "gain": 3, "q": 1.0, "type": "highshelf"},
|
|
],
|
|
dynamic_eq=False
|
|
)
|
|
}
|
|
|
|
def get_club_tuning_config(
|
|
self,
|
|
venue_type: str = "standard",
|
|
sub_bass_freq: Optional[float] = None
|
|
) -> ClubTuningConfig:
|
|
"""
|
|
T084: Retorna configuración de tuning para club
|
|
|
|
Args:
|
|
venue_type: Tipo de venue (standard, warehouse, festival)
|
|
sub_bass_freq: Frecuencia de sub-bass personalizada
|
|
"""
|
|
config = self.presets.get(venue_type, self.presets["standard"]).__dict__.copy()
|
|
|
|
if sub_bass_freq:
|
|
config["sub_bass_freq"] = sub_bass_freq
|
|
|
|
return ClubTuningConfig(**config)
|
|
|
|
def apply_ms_separation(
|
|
self,
|
|
audio_data: List[float],
|
|
config: ClubTuningConfig
|
|
) -> Tuple[List[float], List[float]]:
|
|
"""
|
|
Aplica separación M/S para sub-bass mono
|
|
"""
|
|
# Implementación simplificada de separación M/S
|
|
n = len(audio_data) // 2 * 2 # Asegurar par
|
|
|
|
mid = []
|
|
side = []
|
|
|
|
for i in range(0, n, 2):
|
|
left = audio_data[i]
|
|
right = audio_data[i + 1]
|
|
|
|
m = (left + right) / 2
|
|
s = (left - right) / 2
|
|
|
|
mid.append(m)
|
|
side.append(s)
|
|
|
|
return mid, side
|
|
|
|
|
|
# ============================================================================
|
|
# T184: Evaluación Correlación de Fase y Prevención Cancelaciones
|
|
# ============================================================================
|
|
|
|
@dataclass
|
|
class PhaseCorrelationReport:
|
|
"""Reporte de correlación de fase"""
|
|
correlation_coefficient: float
|
|
phase_issues_detected: bool
|
|
frequency_bands: Dict[str, float]
|
|
cancellation_risk: str # "low", "medium", "high"
|
|
recommendations: List[str]
|
|
mono_compatibility: float # 0-100%
|
|
|
|
|
|
class PhaseCorrelationAnalyzer:
|
|
"""Analiza correlación de fase para prevenir cancelaciones"""
|
|
|
|
def __init__(self):
|
|
self.critical_bands = {
|
|
"sub_bass": (20, 60),
|
|
"bass": (60, 120),
|
|
"low_mids": (120, 250),
|
|
"mids": (250, 2000),
|
|
"highs": (2000, 20000)
|
|
}
|
|
|
|
def analyze_phase_correlation(
|
|
self,
|
|
audio_path: str,
|
|
segment_duration: float = 5.0
|
|
) -> PhaseCorrelationReport:
|
|
"""
|
|
T088-T089: Analiza correlación de fase entre canales L/R
|
|
"""
|
|
try:
|
|
with wave.open(audio_path, 'rb') as wf:
|
|
n_channels = wf.getnchannels()
|
|
|
|
if n_channels != 2:
|
|
return PhaseCorrelationReport(
|
|
correlation_coefficient=1.0,
|
|
phase_issues_detected=False,
|
|
frequency_bands={},
|
|
cancellation_risk="low",
|
|
recommendations=["Audio mono - no hay problema de fase"],
|
|
mono_compatibility=100.0
|
|
)
|
|
|
|
sample_rate = wf.getframerate()
|
|
sample_width = wf.getsampwidth()
|
|
segment_frames = int(sample_rate * segment_duration)
|
|
|
|
# Leer segmento representativo
|
|
frames = wf.readframes(min(segment_frames, wf.getnframes()))
|
|
|
|
if sample_width == 2:
|
|
fmt = f"<{len(frames) // 2}h"
|
|
samples = struct.unpack(fmt, frames)
|
|
max_val = 32768.0
|
|
elif sample_width == 4:
|
|
fmt = f"<{len(frames) // 4}i"
|
|
samples = struct.unpack(fmt, frames)
|
|
max_val = 2147483648.0
|
|
else:
|
|
return self._default_report()
|
|
|
|
# Separar canales
|
|
left = [samples[i] / max_val for i in range(0, len(samples), 2)]
|
|
right = [samples[i + 1] / max_val for i in range(0, len(samples), 2)]
|
|
|
|
# Calcular correlación
|
|
n = min(len(left), len(right))
|
|
left = left[:n]
|
|
right = right[:n]
|
|
|
|
mean_l = sum(left) / n
|
|
mean_r = sum(right) / n
|
|
|
|
numerator = sum((l - mean_l) * (r - mean_r) for l, r in zip(left, right))
|
|
denom_l = sum((l - mean_l) ** 2 for l in left) ** 0.5
|
|
denom_r = sum((r - mean_r) ** 2 for r in right) ** 0.5
|
|
|
|
if denom_l * denom_r == 0:
|
|
correlation = 1.0
|
|
else:
|
|
correlation = numerator / (denom_l * denom_r)
|
|
|
|
# Evaluar riesgo
|
|
mono_compatibility = ((correlation + 1) / 2) * 100
|
|
|
|
if correlation > 0.8:
|
|
risk = "low"
|
|
recommendations = ["Excelente compatibilidad mono"]
|
|
elif correlation > 0.5:
|
|
risk = "medium"
|
|
recommendations = [
|
|
"Verificar bajos en mono",
|
|
"Considerar M/S processing para sub-bass"
|
|
]
|
|
else:
|
|
risk = "high"
|
|
recommendations = [
|
|
"ALERTA: Problemas de fase significativos",
|
|
"Aplicar corrección de fase en bajos",
|
|
"Revisar grabación/mezcla original"
|
|
]
|
|
|
|
# Análisis por bandas (simplificado)
|
|
bands = {"full_range": correlation}
|
|
|
|
return PhaseCorrelationReport(
|
|
correlation_coefficient=correlation,
|
|
phase_issues_detected=correlation < 0.5,
|
|
frequency_bands=bands,
|
|
cancellation_risk=risk,
|
|
recommendations=recommendations,
|
|
mono_compatibility=mono_compatibility
|
|
)
|
|
|
|
except Exception as e:
|
|
logger.error(f"[SPECTRAL] Error en análisis de fase: {e}")
|
|
return self._default_report()
|
|
|
|
def _default_report(self) -> PhaseCorrelationReport:
|
|
"""Reporte por defecto"""
|
|
return PhaseCorrelationReport(
|
|
correlation_coefficient=0.95,
|
|
phase_issues_detected=False,
|
|
frequency_bands={},
|
|
cancellation_risk="low",
|
|
recommendations=["No se pudo analizar - asumiendo seguro"],
|
|
mono_compatibility=95.0
|
|
)
|
|
|
|
|
|
# ============================================================================
|
|
# T185: Integración Librosa sin Lockeos Temporales
|
|
# ============================================================================
|
|
|
|
class LibrosaAnalyzer:
|
|
"""Analizador espectral usando librosa sin bloqueos (T185)"""
|
|
|
|
def __init__(self):
|
|
self._librosa_available = False
|
|
self._np_available = False
|
|
self._lock = threading.Lock()
|
|
self._executor = ThreadPoolExecutor(max_workers=2)
|
|
self._init_librosa()
|
|
|
|
def _init_librosa(self):
|
|
"""Inicializa librosa de forma segura"""
|
|
try:
|
|
import librosa
|
|
import numpy as np
|
|
self._librosa = librosa
|
|
self._np = np
|
|
self._librosa_available = True
|
|
self._np_available = True
|
|
logger.info("[SPECTRAL] Librosa cargado correctamente")
|
|
except ImportError:
|
|
logger.warning("[SPECTRAL] Librosa no disponible - usando fallback")
|
|
self._librosa_available = False
|
|
|
|
def analyze_spectral_features(
|
|
self,
|
|
audio_path: str,
|
|
timeout: float = 30.0
|
|
) -> Dict[str, Any]:
|
|
"""
|
|
Analiza características espectrales sin bloquear
|
|
|
|
Args:
|
|
audio_path: Ruta al audio
|
|
timeout: Timeout máximo en segundos
|
|
"""
|
|
if not self._librosa_available:
|
|
return self._fallback_analysis(audio_path)
|
|
|
|
# Ejecutar en thread separado para no bloquear
|
|
future = self._executor.submit(self._analyze_with_librosa, audio_path)
|
|
|
|
try:
|
|
return future.result(timeout=timeout)
|
|
except Exception as e:
|
|
logger.error(f"[SPECTRAL] Timeout/error en análisis librosa: {e}")
|
|
return self._fallback_analysis(audio_path)
|
|
|
|
def _analyze_with_librosa(self, audio_path: str) -> Dict[str, Any]:
|
|
"""Análisis real con librosa"""
|
|
try:
|
|
with self._lock:
|
|
y, sr = self._librosa.load(audio_path, duration=30.0)
|
|
|
|
# Características espectrales
|
|
spectral_centroids = self._librosa.feature.spectral_centroid(y=y, sr=sr)[0]
|
|
spectral_rolloff = self._librosa.feature.spectral_rolloff(y=y, sr=sr)[0]
|
|
spectral_bandwidth = self._librosa.feature.spectral_bandwidth(y=y, sr=sr)[0]
|
|
|
|
# MFCCs
|
|
mfccs = self._librosa.feature.mfcc(y=y, sr=sr, n_mfcc=13)
|
|
|
|
# Zero crossing rate
|
|
zcr = self._librosa.feature.zero_crossing_rate(y)[0]
|
|
|
|
# RMS/Energía
|
|
rms = self._librosa.feature.rms(y=y)[0]
|
|
|
|
# Tempo
|
|
tempo = self._librosa.beat.tempo(y=y, sr=sr)[0]
|
|
|
|
return {
|
|
"spectral_centroid_mean": float(self._np.mean(spectral_centroids)),
|
|
"spectral_centroid_std": float(self._np.std(spectral_centroids)),
|
|
"spectral_rolloff_mean": float(self._np.mean(spectral_rolloff)),
|
|
"spectral_bandwidth_mean": float(self._np.mean(spectral_bandwidth)),
|
|
"mfccs_mean": [float(m) for m in self._np.mean(mfccs, axis=1)],
|
|
"zcr_mean": float(self._np.mean(zcr)),
|
|
"rms_mean": float(self._np.mean(rms)),
|
|
"rms_max": float(self._np.max(rms)),
|
|
"estimated_tempo": float(tempo),
|
|
"analysis_method": "librosa",
|
|
"sample_rate": int(sr)
|
|
}
|
|
except Exception as e:
|
|
logger.error(f"[SPECTRAL] Error en análisis librosa: {e}")
|
|
return self._fallback_analysis(audio_path)
|
|
|
|
def _fallback_analysis(self, audio_path: str) -> Dict[str, Any]:
|
|
"""Análisis fallback cuando librosa no está disponible"""
|
|
try:
|
|
with wave.open(audio_path, 'rb') as wf:
|
|
n_channels = wf.getnchannels()
|
|
sample_rate = wf.getframerate()
|
|
sample_width = wf.getsampwidth()
|
|
n_frames = wf.getnframes()
|
|
|
|
frames = wf.readframes(n_frames)
|
|
|
|
if sample_width == 2:
|
|
fmt = f"<{n_frames * n_channels}h"
|
|
samples = struct.unpack(fmt, frames)
|
|
max_val = 32768.0
|
|
else:
|
|
return {"error": "Formato no soportado"}
|
|
|
|
# Calcular métricas básicas
|
|
normalized = [s / max_val for s in samples]
|
|
|
|
# Centroid espectral aproximado usando energía
|
|
rms = (sum(s**2 for s in normalized) / len(normalized)) ** 0.5
|
|
|
|
# Contar zero crossings
|
|
zcr = sum(1 for i in range(1, len(normalized))
|
|
if normalized[i-1] * normalized[i] < 0)
|
|
zcr_rate = zcr / (len(normalized) / sample_rate)
|
|
|
|
return {
|
|
"spectral_centroid_mean": 1000.0, # Estimación genérica
|
|
"rms_mean": rms,
|
|
"zcr_mean": zcr_rate,
|
|
"estimated_tempo": 128.0, # Default
|
|
"analysis_method": "fallback",
|
|
"sample_rate": sample_rate
|
|
}
|
|
except Exception as e:
|
|
logger.error(f"[SPECTRAL] Error en fallback: {e}")
|
|
return {"error": str(e)}
|
|
|
|
|
|
# ============================================================================
|
|
# T186: Algoritmo Extracción Transientes (Onsets) para T075
|
|
# ============================================================================
|
|
|
|
@dataclass
|
|
class TransientAnalysis:
|
|
"""Resultado de análisis de transientes"""
|
|
onset_times: List[float]
|
|
onset_strengths: List[float]
|
|
estimated_positions: List[int] # Posiciones en beats/samples
|
|
confidence: float
|
|
recommended_offsets: Dict[str, float] # Offset recommendations por elemento
|
|
|
|
|
|
class TransientExtractor:
|
|
"""Extrae transientes para alineación de percusiones (T075)"""
|
|
|
|
def __init__(self):
|
|
self.librosa_analyzer = LibrosaAnalyzer()
|
|
|
|
def extract_transients(
|
|
self,
|
|
audio_path: str,
|
|
reference_tempo: float = 128.0,
|
|
sensitivity: float = 0.5
|
|
) -> TransientAnalysis:
|
|
"""
|
|
T075: Extrae transientes para realineación de percusiones
|
|
|
|
Args:
|
|
audio_path: Ruta al audio
|
|
reference_tempo: BPM de referencia
|
|
sensitivity: Sensibilidad de detección (0.0-1.0)
|
|
"""
|
|
if not self.librosa_analyzer._librosa_available:
|
|
return self._fallback_transients(audio_path, reference_tempo)
|
|
|
|
try:
|
|
import librosa
|
|
import numpy as np
|
|
|
|
y, sr = librosa.load(audio_path, duration=60.0)
|
|
|
|
# Calcular onset envelope
|
|
onset_env = librosa.onset.onset_strength(y=y, sr=sr)
|
|
|
|
# Detectar onsets
|
|
wait_frames = int((60.0 / reference_tempo) * sr / 512) # Aprox 1/4 beat
|
|
|
|
onsets = librosa.onset.onset_detect(
|
|
onset_envelope=onset_env,
|
|
sr=sr,
|
|
wait=wait_frames,
|
|
pre_max=wait_frames // 2,
|
|
post_max=wait_frames // 2,
|
|
delta=sensitivity * 0.1
|
|
)
|
|
|
|
# Convertir a tiempos
|
|
onset_times = librosa.frames_to_time(onsets, sr=sr)
|
|
onset_strengths = [onset_env[o] for o in onsets]
|
|
|
|
# Calcular posiciones en beats
|
|
beat_duration = 60.0 / reference_tempo
|
|
estimated_positions = [int(t / beat_duration * 4) for t in onset_times] # 16th notes
|
|
|
|
# Confidence basado en claridad de onsets
|
|
if len(onset_strengths) > 1:
|
|
strength_variance = np.std(onset_strengths) / np.mean(onset_strengths)
|
|
confidence = min(1.0, strength_variance * 2)
|
|
else:
|
|
confidence = 0.5
|
|
|
|
# Recomendaciones de offset
|
|
recommendations = self._calculate_micro_timing(onset_times, reference_tempo)
|
|
|
|
return TransientAnalysis(
|
|
onset_times=list(onset_times),
|
|
onset_strengths=[float(s) for s in onset_strengths],
|
|
estimated_positions=estimated_positions,
|
|
confidence=confidence,
|
|
recommended_offsets=recommendations
|
|
)
|
|
|
|
except Exception as e:
|
|
logger.error(f"[SPECTRAL] Error extrayendo transientes: {e}")
|
|
return self._fallback_transients(audio_path, reference_tempo)
|
|
|
|
def _calculate_micro_timing(
|
|
self,
|
|
onset_times: List[float],
|
|
tempo: float
|
|
) -> Dict[str, float]:
|
|
"""Calcula micro-timing offsets estilo 'push'"""
|
|
beat_duration = 60.0 / tempo
|
|
|
|
# Calcular desviación promedio de posiciones teóricas
|
|
deviations = []
|
|
for t in onset_times:
|
|
beat_pos = t / beat_duration
|
|
nearest_beat = round(beat_pos)
|
|
deviation = (beat_pos - nearest_beat) * beat_duration * 1000 # ms
|
|
deviations.append(deviation)
|
|
|
|
if deviations:
|
|
avg_deviation = sum(deviations) / len(deviations)
|
|
|
|
# Técnica "push": kick adelante (-5ms), bass atrás (+8ms)
|
|
return {
|
|
"kick_offset_ms": -5.0 if avg_deviation > 0 else -3.0,
|
|
"bass_offset_ms": 8.0 if avg_deviation < 5 else 5.0,
|
|
"snare_offset_ms": 0.0,
|
|
"hat_offset_ms": 2.0,
|
|
"average_deviation_ms": avg_deviation
|
|
}
|
|
|
|
return {
|
|
"kick_offset_ms": -5.0,
|
|
"bass_offset_ms": 8.0,
|
|
"snare_offset_ms": 0.0,
|
|
"hat_offset_ms": 2.0,
|
|
"average_deviation_ms": 0.0
|
|
}
|
|
|
|
def _fallback_transients(
|
|
self,
|
|
audio_path: str,
|
|
tempo: float
|
|
) -> TransientAnalysis:
|
|
"""Fallback cuando librosa no está disponible"""
|
|
beat_duration = 60.0 / tempo
|
|
|
|
# Generar transientes en posiciones teóricas
|
|
num_beats = 32
|
|
onset_times = [i * beat_duration for i in range(num_beats)]
|
|
onset_strengths = [0.5 + 0.3 * (i % 4 == 0) for i in range(num_beats)]
|
|
|
|
return TransientAnalysis(
|
|
onset_times=onset_times,
|
|
onset_strengths=onset_strengths,
|
|
estimated_positions=[i * 4 for i in range(num_beats)],
|
|
confidence=0.6,
|
|
recommended_offsets={
|
|
"kick_offset_ms": -5.0,
|
|
"bass_offset_ms": 8.0,
|
|
"snare_offset_ms": 0.0,
|
|
"hat_offset_ms": 2.0,
|
|
"average_deviation_ms": 0.0,
|
|
"note": "Fallback - librosa no disponible"
|
|
}
|
|
)
|
|
|
|
|
|
# ============================================================================
|
|
# T187: Test Calidad Automático run_mix_quality_check (T085)
|
|
# ============================================================================
|
|
|
|
@dataclass
|
|
class MixQualityReport:
|
|
"""Reporte de calidad de mezcla (T085)"""
|
|
lufs_integrated: float
|
|
true_peak_db: float
|
|
rms_balance: float
|
|
correlation_mono: float
|
|
headroom_db: float
|
|
overall_score: float
|
|
passed: bool
|
|
issues: List[str]
|
|
recommendations: List[str]
|
|
|
|
|
|
class AutomaticQualityChecker:
|
|
"""Test de calidad automático tras cada generación"""
|
|
|
|
THRESHOLDS = {
|
|
"lufs_club_range": (-10.0, -6.0),
|
|
"lufs_streaming_range": (-16.0, -12.0),
|
|
"true_peak_max": -0.5,
|
|
"rms_balance_max": 2.0, # dB de diferencia L/R
|
|
"correlation_mono_min": 0.5,
|
|
"headroom_min": 2.0
|
|
}
|
|
|
|
def __init__(self):
|
|
self.lufs_analyzer = FFMPEGLUFSAnalyzer()
|
|
self.phase_analyzer = PhaseCorrelationAnalyzer()
|
|
|
|
def run_mix_quality_check(
|
|
self,
|
|
audio_path: str,
|
|
platform: str = "club",
|
|
auto_fix: bool = False
|
|
) -> MixQualityReport:
|
|
"""
|
|
T085: Ejecuta quality check completo del mix
|
|
|
|
Args:
|
|
audio_path: Ruta al audio a verificar
|
|
platform: Plataforma objetivo (club, streaming)
|
|
auto_fix: Aplicar correcciones automáticas si es posible
|
|
"""
|
|
issues = []
|
|
recommendations = []
|
|
|
|
# 1. Medir LUFS
|
|
lufs_measurement = self.lufs_analyzer.measure_lufs(audio_path, platform)
|
|
|
|
# 2. Analizar correlación de fase
|
|
phase_report = self.phase_analyzer.analyze_phase_correlation(audio_path)
|
|
|
|
# 3. Verificar balance RMS L/R
|
|
rms_balance = self._check_rms_balance(audio_path)
|
|
|
|
# Evaluar según plataforma
|
|
if platform == "club":
|
|
lufs_range = self.THRESHOLDS["lufs_club_range"]
|
|
else:
|
|
lufs_range = self.THRESHOLDS["lufs_streaming_range"]
|
|
|
|
# Detectar issues
|
|
if not (lufs_range[0] <= lufs_measurement.integrated_lufs <= lufs_range[1]):
|
|
issues.append(
|
|
f"LUFS {lufs_measurement.integrated_lufs:.1f} fuera de rango "
|
|
f"[{lufs_range[0]:.1f}, {lufs_range[1]:.1f}]"
|
|
)
|
|
recommendations.append(
|
|
f"Ajustar ganancia master en {lufs_range[1] - lufs_measurement.integrated_lufs:.1f}dB"
|
|
)
|
|
|
|
if lufs_measurement.true_peak_db > self.THRESHOLDS["true_peak_max"]:
|
|
issues.append(
|
|
f"True peak {lufs_measurement.true_peak_db:.1f}dB excede "
|
|
f"límite {self.THRESHOLDS['true_peak_max']:.1f}dB"
|
|
)
|
|
recommendations.append("Reducir true peak o aplicar limitador más agresivo")
|
|
|
|
if abs(rms_balance) > self.THRESHOLDS["rms_balance_max"]:
|
|
issues.append(f"Desbalance L/R de {abs(rms_balance):.1f}dB")
|
|
recommendations.append("Verificar paneo y balance de tracks")
|
|
|
|
if phase_report.correlation_coefficient < self.THRESHOLDS["correlation_mono_min"]:
|
|
issues.append("Problemas de correlación de fase detectados")
|
|
recommendations.append("Aplicar corrección de fase en sub-bass")
|
|
|
|
# Calcular score
|
|
score = 100.0
|
|
score -= len(issues) * 15
|
|
score -= abs(rms_balance) * 2
|
|
score = max(0.0, min(100.0, score))
|
|
|
|
# Calcular headroom
|
|
headroom = -lufs_measurement.true_peak_db
|
|
|
|
passed = (
|
|
lufs_range[0] <= lufs_measurement.integrated_lufs <= lufs_range[1] and
|
|
lufs_measurement.true_peak_db <= self.THRESHOLDS["true_peak_max"] and
|
|
abs(rms_balance) <= self.THRESHOLDS["rms_balance_max"] and
|
|
phase_report.correlation_coefficient >= self.THRESHOLDS["correlation_mono_min"]
|
|
)
|
|
|
|
return MixQualityReport(
|
|
lufs_integrated=lufs_measurement.integrated_lufs,
|
|
true_peak_db=lufs_measurement.true_peak_db,
|
|
rms_balance=rms_balance,
|
|
correlation_mono=phase_report.correlation_coefficient,
|
|
headroom_db=headroom,
|
|
overall_score=score,
|
|
passed=passed,
|
|
issues=issues,
|
|
recommendations=recommendations
|
|
)
|
|
|
|
def _check_rms_balance(self, audio_path: str) -> float:
|
|
"""Verifica balance RMS entre canales L/R"""
|
|
try:
|
|
with wave.open(audio_path, 'rb') as wf:
|
|
if wf.getnchannels() != 2:
|
|
return 0.0
|
|
|
|
n_frames = wf.getnframes()
|
|
sample_width = wf.getsampwidth()
|
|
frames = wf.readframes(min(n_frames, 44100 * 10)) # Primeros 10s
|
|
|
|
if sample_width == 2:
|
|
fmt = f"<{len(frames) // 2}h"
|
|
samples = struct.unpack(fmt, frames)
|
|
max_val = 32768.0
|
|
else:
|
|
return 0.0
|
|
|
|
left = [abs(samples[i] / max_val) for i in range(0, len(samples), 2)]
|
|
right = [abs(samples[i + 1] / max_val) for i in range(0, len(samples), 2)]
|
|
|
|
rms_l = (sum(x**2 for x in left) / len(left)) ** 0.5
|
|
rms_r = (sum(x**2 for x in right) / len(right)) ** 0.5
|
|
|
|
if rms_l + rms_r > 0:
|
|
return 20 * (rms_l / (rms_l + rms_r))
|
|
return 0.0
|
|
except Exception as e:
|
|
logger.error(f"[SPECTRAL] Error verificando balance: {e}")
|
|
return 0.0
|
|
|
|
|
|
# ============================================================================
|
|
# T188: Módulo On-The-Fly Limpieza Frecuencias (T094)
|
|
# ============================================================================
|
|
|
|
class DynamicEQCleaner:
|
|
"""Limpieza on-the-fly de frecuencias problemáticas (T094-T095)"""
|
|
|
|
COMMON_PROBLEM_FREQS = {
|
|
"mud": {"freq": 250, "q": 1.5, "gain": -2, "desc": "Lodo frecuencial"},
|
|
"boxiness": {"freq": 400, "q": 2.0, "gain": -1.5, "desc": "Caja de resonancia"},
|
|
"honk": {"freq": 800, "q": 1.8, "gain": -1, "desc": "Resonancia nasal"},
|
|
"harsh": {"freq": 3000, "q": 2.5, "gain": -2, "desc": "Agresividad"},
|
|
"sibilance": {"freq": 6000, "q": 3.0, "gain": -3, "desc": "Sibilancia"},
|
|
"air": {"freq": 12000, "q": 0.7, "gain": 1, "desc": "Brillo/aire"},
|
|
}
|
|
|
|
def __init__(self):
|
|
self.librosa_analyzer = LibrosaAnalyzer()
|
|
self.active_corrections = {}
|
|
|
|
def get_dynamic_eq_config(
|
|
self,
|
|
problem_freqs: Optional[List[str]] = None,
|
|
side_hp_freq: float = 100.0
|
|
) -> Dict[str, Any]:
|
|
"""
|
|
T094-T095: Retorna configuración de EQ dinámico
|
|
|
|
Args:
|
|
problem_freqs: Lista de frecuencias problemáticas a corregir
|
|
(mud, boxiness, honk, harsh, sibilance)
|
|
side_hp_freq: Frecuencia de high-pass para lados en M/S
|
|
"""
|
|
if problem_freqs is None:
|
|
problem_freqs = ["mud", "harsh"]
|
|
|
|
bands = []
|
|
|
|
for freq_id in problem_freqs:
|
|
if freq_id in self.COMMON_PROBLEM_FREQS:
|
|
config = self.COMMON_PROBLEM_FREQS[freq_id].copy()
|
|
config["id"] = freq_id
|
|
bands.append(config)
|
|
|
|
# Agregar M/S high-pass para lados
|
|
bands.append({
|
|
"id": "ms_side_hp",
|
|
"freq": side_hp_freq,
|
|
"q": 0.7,
|
|
"gain": 0,
|
|
"type": "highpass",
|
|
"target": "side_only",
|
|
"desc": "High-pass para lados en M/S"
|
|
})
|
|
|
|
return {
|
|
"bands": bands,
|
|
"side_hp_freq": side_hp_freq,
|
|
"ms_processing": True,
|
|
"dynamic_mode": True,
|
|
"threshold_db": -20,
|
|
"ratio": 2.0,
|
|
"attack_ms": 10,
|
|
"release_ms": 100
|
|
}
|
|
|
|
def analyze_problem_frequencies(
|
|
self,
|
|
audio_path: str
|
|
) -> List[str]:
|
|
"""Analiza y detecta frecuencias problemáticas"""
|
|
features = self.librosa_analyzer.analyze_spectral_features(audio_path)
|
|
|
|
problems = []
|
|
centroid = features.get("spectral_centroid_mean", 1000)
|
|
|
|
# Heurísticas simples basadas en centroid
|
|
if centroid < 300:
|
|
problems.append("mud")
|
|
elif centroid > 4000:
|
|
problems.append("harsh")
|
|
|
|
# Default si no hay detección clara
|
|
if not problems:
|
|
problems = ["mud"]
|
|
|
|
return problems
|
|
|
|
|
|
# ============================================================================
|
|
# T189: Analyze Mixdown Cleanup purga clips vacíos (T093)
|
|
# ============================================================================
|
|
|
|
@dataclass
|
|
class CleanupCandidate:
|
|
"""Candidato para limpieza"""
|
|
track_index: int
|
|
track_name: str
|
|
clip_index: int
|
|
reason: str
|
|
action: str
|
|
can_purge: bool
|
|
|
|
|
|
class MixdownCleanupAnalyzer:
|
|
"""Analiza mixdown para identificar candidatos de limpieza (T093)"""
|
|
|
|
def __init__(self):
|
|
self.candidates = []
|
|
|
|
def analyze_mixdown_cleanup(
|
|
self,
|
|
runtime_socket: Optional[socket.socket] = None,
|
|
min_clip_duration: float = 0.25
|
|
) -> Dict[str, Any]:
|
|
"""
|
|
T093: Analiza mixdown y sugiere limpieza
|
|
|
|
Detecta:
|
|
- Clips vacíos (duración < min_clip_duration)
|
|
- Tracks sin clips
|
|
- Tracks duplicados/muteados permanentemente
|
|
- Devices sin uso
|
|
"""
|
|
candidates = []
|
|
unused_devices = []
|
|
|
|
try:
|
|
# Obtener información de la sesión
|
|
if runtime_socket:
|
|
tracks_info = self._get_tracks_from_runtime(runtime_socket)
|
|
|
|
for track_idx, track in enumerate(tracks_info):
|
|
track_name = track.get("name", f"Track {track_idx}")
|
|
clips = track.get("clips", [])
|
|
|
|
# Verificar clips vacíos
|
|
for clip_idx, clip in enumerate(clips):
|
|
duration = clip.get("duration", 0)
|
|
has_notes = clip.get("has_notes", True)
|
|
|
|
if duration < min_clip_duration:
|
|
candidates.append(CleanupCandidate(
|
|
track_index=track_idx,
|
|
track_name=track_name,
|
|
clip_index=clip_idx,
|
|
reason=f"Clip vacío/demasiado corto ({duration:.2f}s)",
|
|
action="eliminar_clip",
|
|
can_purge=True
|
|
))
|
|
elif not has_notes and duration > 0:
|
|
candidates.append(CleanupCandidate(
|
|
track_index=track_idx,
|
|
track_name=track_name,
|
|
clip_index=clip_idx,
|
|
reason="Clip MIDI sin notas",
|
|
action="revisar_contenido",
|
|
can_purge=False
|
|
))
|
|
|
|
# Tracks sin clips
|
|
if len(clips) == 0:
|
|
candidates.append(CleanupCandidate(
|
|
track_index=track_idx,
|
|
track_name=track_name,
|
|
clip_index=-1,
|
|
reason="Track sin clips",
|
|
action="eliminar_track",
|
|
can_purge=track.get("can_delete", False)
|
|
))
|
|
|
|
# Devices sin uso (silenciosos todo el tiempo)
|
|
devices = track.get("devices", [])
|
|
for dev_idx, device in enumerate(devices):
|
|
if device.get("bypass", False) or device.get("silent", False):
|
|
unused_devices.append({
|
|
"track": track_idx,
|
|
"device": dev_idx,
|
|
"name": device.get("name", "Unknown"),
|
|
"action": "eliminar_device"
|
|
})
|
|
except Exception as e:
|
|
logger.error(f"[SPECTRAL] Error analizando cleanup: {e}")
|
|
|
|
return {
|
|
"candidates": [
|
|
{
|
|
"track_index": c.track_index,
|
|
"track_name": c.track_name,
|
|
"clip_index": c.clip_index,
|
|
"reason": c.reason,
|
|
"action": c.action,
|
|
"can_purge": c.can_purge
|
|
}
|
|
for c in candidates
|
|
],
|
|
"unused_devices": unused_devices,
|
|
"total_candidates": len(candidates),
|
|
"purgeable_count": sum(1 for c in candidates if c.can_purge)
|
|
}
|
|
|
|
def _get_tracks_from_runtime(
|
|
self,
|
|
runtime_socket: socket.socket
|
|
) -> List[Dict[str, Any]]:
|
|
"""Obtiene información de tracks desde el runtime"""
|
|
try:
|
|
cmd = {"cmd": "get_tracks"}
|
|
runtime_socket.send(json.dumps(cmd).encode())
|
|
response = runtime_socket.recv(65536).decode()
|
|
data = json.loads(response)
|
|
return data.get("tracks", [])
|
|
except Exception as e:
|
|
logger.error(f"[SPECTRAL] Error obteniendo tracks: {e}")
|
|
return []
|
|
|
|
|
|
# ============================================================================
|
|
# T190: Get Mastering Chain Config (T081)
|
|
# ============================================================================
|
|
|
|
@dataclass
|
|
class MasteringChainDevice:
|
|
"""Dispositivo en la cadena de mastering"""
|
|
device_type: str
|
|
name: str
|
|
parameters: Dict[str, Any]
|
|
order: int
|
|
|
|
|
|
class MasteringChainConfig:
|
|
"""Configuración de cadena de mastering profesional (T081)"""
|
|
|
|
CHAINS = {
|
|
"techno_club": {
|
|
"devices": [
|
|
{
|
|
"type": "EQ8",
|
|
"name": "Sub-Bass Mono",
|
|
"params": {
|
|
"mode": "ms",
|
|
"bands": [
|
|
{"freq": 30, "q": 0.7, "gain": 0, "type": "highpass"},
|
|
{"freq": 80, "q": 1.0, "gain": 1, "type": "lowshelf", "target": "mid"},
|
|
]
|
|
}
|
|
},
|
|
{
|
|
"type": "Compressor",
|
|
"name": "Glue",
|
|
"params": {
|
|
"threshold": -18,
|
|
"ratio": 2,
|
|
"attack": 30,
|
|
"release": 100,
|
|
"makeup": 2
|
|
}
|
|
},
|
|
{
|
|
"type": "Saturator",
|
|
"name": "Warmth",
|
|
"params": {
|
|
"drive": 3,
|
|
"type": "analog",
|
|
"color": 50
|
|
}
|
|
},
|
|
{
|
|
"type": "Limiter",
|
|
"name": "Final",
|
|
"params": {
|
|
"ceiling": -0.5,
|
|
"gain": 8
|
|
}
|
|
}
|
|
],
|
|
"target_lufs": -8,
|
|
"true_peak": -0.5
|
|
},
|
|
"house_streaming": {
|
|
"devices": [
|
|
{
|
|
"type": "EQ8",
|
|
"name": "Clean Up",
|
|
"params": {
|
|
"bands": [
|
|
{"freq": 30, "q": 0.7, "gain": 0, "type": "highpass"},
|
|
{"freq": 250, "q": 1.5, "gain": -1, "type": "bell"},
|
|
{"freq": 3000, "q": 1.0, "gain": 0.5, "type": "highshelf"},
|
|
]
|
|
}
|
|
},
|
|
{
|
|
"type": "MultibandDynamics",
|
|
"name": "Control",
|
|
"params": {
|
|
"bands": [
|
|
{"freq": 120, "ratio": 2, "threshold": -20},
|
|
{"freq": 1000, "ratio": 1.5, "threshold": -16},
|
|
{"freq": 8000, "ratio": 1.2, "threshold": -12},
|
|
]
|
|
}
|
|
},
|
|
{
|
|
"type": "Limiter",
|
|
"name": "Final",
|
|
"params": {
|
|
"ceiling": -1.0,
|
|
"gain": 4
|
|
}
|
|
}
|
|
],
|
|
"target_lufs": -14,
|
|
"true_peak": -1.0
|
|
},
|
|
"reggaeton": {
|
|
"devices": [
|
|
{
|
|
"type": "EQ8",
|
|
"name": "Bass Focus",
|
|
"params": {
|
|
"bands": [
|
|
{"freq": 40, "q": 0.5, "gain": 2, "type": "lowshelf"},
|
|
{"freq": 200, "q": 1.2, "gain": -2, "type": "bell"},
|
|
{"freq": 5000, "q": 1.0, "gain": 1, "type": "highshelf"},
|
|
]
|
|
}
|
|
},
|
|
{
|
|
"type": "Compressor",
|
|
"name": "Punch",
|
|
"params": {
|
|
"threshold": -12,
|
|
"ratio": 4,
|
|
"attack": 10,
|
|
"release": 50,
|
|
"makeup": 3
|
|
}
|
|
},
|
|
{
|
|
"type": "Saturator",
|
|
"name": "Color",
|
|
"params": {
|
|
"drive": 4,
|
|
"type": "digital",
|
|
"color": 70
|
|
}
|
|
},
|
|
{
|
|
"type": "Limiter",
|
|
"name": "Final",
|
|
"params": {
|
|
"ceiling": -0.3,
|
|
"gain": 6
|
|
}
|
|
}
|
|
],
|
|
"target_lufs": -9,
|
|
"true_peak": -0.3
|
|
}
|
|
}
|
|
|
|
def get_mastering_chain_config(
|
|
self,
|
|
genre: str = "techno",
|
|
platform: str = "club"
|
|
) -> Dict[str, Any]:
|
|
"""
|
|
T081: Retorna configuración completa de cadena de mastering
|
|
|
|
Args:
|
|
genre: Género musical (techno, house, reggaeton)
|
|
platform: Plataforma objetivo (club, streaming)
|
|
"""
|
|
chain_key = f"{genre}_{platform}"
|
|
|
|
if chain_key not in self.CHAINS:
|
|
# Fallback a configuración genérica
|
|
chain_key = "techno_club"
|
|
|
|
config = self.CHAINS[chain_key].copy()
|
|
config["genre"] = genre
|
|
config["platform"] = platform
|
|
config["rack_type"] = "Audio Effect Rack"
|
|
config["macro_mappings"] = {
|
|
1: "Input Gain",
|
|
2: "Output Gain",
|
|
3: "Character",
|
|
4: "Width"
|
|
}
|
|
|
|
return config
|
|
|
|
|
|
# ============================================================================
|
|
# T191: Overlap Safety Audit (T096)
|
|
# ============================================================================
|
|
|
|
@dataclass
|
|
class OverlapIssue:
|
|
"""Problema de overlap detectado"""
|
|
track1: int
|
|
track2: int
|
|
track1_name: str
|
|
track2_name: str
|
|
frequency_range: Tuple[float, float]
|
|
overlap_amount_db: float
|
|
severity: str
|
|
recommendation: str
|
|
|
|
|
|
class OverlapSafetyAuditor:
|
|
"""Auditoría de seguridad de overlap entre tracks (T096)"""
|
|
|
|
FREQUENCY_RANGES = {
|
|
"sub": (20, 60),
|
|
"bass": (60, 120),
|
|
"low_mid": (120, 250),
|
|
"mid": (250, 2000),
|
|
"high": (2000, 20000)
|
|
}
|
|
|
|
def __init__(self):
|
|
self.librosa_analyzer = LibrosaAnalyzer()
|
|
self.lufs_analyzer = FFMPEGLUFSAnalyzer()
|
|
|
|
def run_overlap_safety_audit(
|
|
self,
|
|
audio_paths: Dict[int, str],
|
|
track_names: Optional[Dict[int, str]] = None
|
|
) -> Dict[str, Any]:
|
|
"""
|
|
T096: Ejecuta audit de seguridad de overlap
|
|
|
|
Analiza tracks individuales y detecta bandas frecuenciales enmascaradas
|
|
"""
|
|
issues = []
|
|
warnings = []
|
|
|
|
if track_names is None:
|
|
track_names = {i: f"Track {i}" for i in audio_paths.keys()}
|
|
|
|
# Analizar espectro de cada track
|
|
track_spectra = {}
|
|
for track_idx, path in audio_paths.items():
|
|
try:
|
|
features = self.librosa_analyzer.analyze_spectral_features(path)
|
|
track_spectra[track_idx] = {
|
|
"centroid": features.get("spectral_centroid_mean", 1000),
|
|
"name": track_names.get(track_idx, f"Track {track_idx}")
|
|
}
|
|
except Exception as e:
|
|
logger.error(f"[SPECTRAL] Error analizando track {track_idx}: {e}")
|
|
|
|
# Detectar overlaps problemáticos
|
|
track_indices = list(track_spectra.keys())
|
|
|
|
for i, track1 in enumerate(track_indices):
|
|
for track2 in track_indices[i+1:]:
|
|
t1_data = track_spectra[track1]
|
|
t2_data = track_spectra[track2]
|
|
|
|
# Calcular diferencia de centroid
|
|
centroid_diff = abs(t1_data["centroid"] - t2_data["centroid"])
|
|
|
|
# Si están muy cerca en frecuencia, potencial problema
|
|
if centroid_diff < 500: # Hz
|
|
# Determinar severidad
|
|
if centroid_diff < 200:
|
|
severity = "high"
|
|
recommendation = f"Aplicar EQ diferente a {t1_data['name']} y {t2_data['name']}"
|
|
else:
|
|
severity = "medium"
|
|
recommendation = f"Verificar masking entre {t1_data['name']} y {t2_data['name']}"
|
|
|
|
issues.append({
|
|
"track1": track1,
|
|
"track2": track2,
|
|
"track1_name": t1_data["name"],
|
|
"track2_name": t2_data["name"],
|
|
"frequency_overlap": centroid_diff,
|
|
"severity": severity,
|
|
"recommendation": recommendation
|
|
})
|
|
|
|
# Verificar headroom y clipping potencial
|
|
for track_idx, path in audio_paths.items():
|
|
try:
|
|
measurement = self.lufs_analyzer.measure_lufs(path, "streaming")
|
|
if measurement.true_peak_db > -1.0:
|
|
warnings.append({
|
|
"track": track_idx,
|
|
"track_name": track_names.get(track_idx, f"Track {track_idx}"),
|
|
"issue": f"True peak alto: {measurement.true_peak_db:.1f}dB",
|
|
"recommendation": "Reducir ganancia o aplicar limitador"
|
|
})
|
|
except Exception as e:
|
|
logger.error(f"[SPECTRAL] Error midiendo track {track_idx}: {e}")
|
|
|
|
return {
|
|
"overlap_issues": issues,
|
|
"headroom_warnings": warnings,
|
|
"total_issues": len(issues) + len(warnings),
|
|
"passed": len(issues) == 0 and len(warnings) == 0,
|
|
"tracks_analyzed": len(track_spectra)
|
|
}
|
|
|
|
|
|
# ============================================================================
|
|
# T192: Diagnóstico de Bus RCA (T101-T104)
|
|
# ============================================================================
|
|
|
|
@dataclass
|
|
class BusRoutingIssue:
|
|
"""Problema de enrutamiento de bus"""
|
|
track_index: int
|
|
track_name: str
|
|
current_bus: str
|
|
recommended_bus: str
|
|
issue_type: str
|
|
severity: str
|
|
fix_action: str
|
|
|
|
|
|
class BusRCADiagnostician:
|
|
"""Diagnostica problemas de enrutamiento de buses RCA (T101-T104)"""
|
|
|
|
# Mapeo de roles a buses RCA correctos
|
|
RCA_BUS_MAPPING = {
|
|
"kick": "DRUMS_BUS",
|
|
"bass": "BASS_BUS",
|
|
"sub": "BASS_BUS",
|
|
"snare": "DRUMS_BUS",
|
|
"hat": "DRUMS_BUS",
|
|
"percussion": "DRUMS_BUS",
|
|
"synth": "MUSIC_BUS",
|
|
"pad": "MUSIC_BUS",
|
|
"lead": "MUSIC_BUS",
|
|
"vocal": "VOCALS_BUS",
|
|
"fx": "FX_BUS",
|
|
"atmospheric": "FX_BUS"
|
|
}
|
|
|
|
BUS_HIERARCHY = {
|
|
"DRUMS_BUS": {"color": "#FF6B6B", "sends_to": ["MASTER"]},
|
|
"BASS_BUS": {"color": "#4ECDC4", "sends_to": ["MASTER"], "sidechain_to": "DRUMS_BUS"},
|
|
"MUSIC_BUS": {"color": "#45B7D1", "sends_to": ["MASTER"]},
|
|
"VOCALS_BUS": {"color": "#96CEB4", "sends_to": ["MASTER"]},
|
|
"FX_BUS": {"color": "#FFEAA7", "sends_to": ["MASTER"]},
|
|
"MASTER": {"color": "#DFE6E9", "sends_to": []}
|
|
}
|
|
|
|
def __init__(self):
|
|
self.issues_found = []
|
|
|
|
def diagnose_bus_routing(
|
|
self,
|
|
runtime_socket: Optional[socket.socket] = None,
|
|
tracks_data: Optional[List[Dict]] = None
|
|
) -> Dict[str, Any]:
|
|
"""
|
|
T101-T104: Diagnostica enrutamiento de buses RCA
|
|
|
|
Detecta:
|
|
- Tracks en bus incorrecto
|
|
- Sends excesivos en kicks/bass
|
|
- FX bypassing master
|
|
"""
|
|
issues = []
|
|
|
|
if tracks_data is None and runtime_socket:
|
|
tracks_data = self._get_tracks_data(runtime_socket)
|
|
|
|
if not tracks_data:
|
|
return {"error": "No se pudieron obtener datos de tracks"}
|
|
|
|
for track in tracks_data:
|
|
track_idx = track.get("index", -1)
|
|
track_name = track.get("name", "").lower()
|
|
current_bus = track.get("output_route", "Unknown")
|
|
sends = track.get("sends", [])
|
|
|
|
# Detectar rol del track por nombre
|
|
detected_role = self._detect_role(track_name)
|
|
|
|
if detected_role:
|
|
recommended_bus = self.RCA_BUS_MAPPING.get(detected_role, "MUSIC_BUS")
|
|
|
|
# Verificar si está en bus correcto
|
|
if current_bus != recommended_bus:
|
|
issues.append({
|
|
"track_index": track_idx,
|
|
"track_name": track.get("name", ""),
|
|
"current_bus": current_bus,
|
|
"recommended_bus": recommended_bus,
|
|
"detected_role": detected_role,
|
|
"issue_type": "wrong_bus",
|
|
"severity": "high",
|
|
"fix_action": f"Mover a {recommended_bus}"
|
|
})
|
|
|
|
# Verificar sends excesivos en kick/bass
|
|
if detected_role in ["kick", "bass"] and len(sends) > 1:
|
|
issues.append({
|
|
"track_index": track_idx,
|
|
"track_name": track.get("name", ""),
|
|
"current_bus": current_bus,
|
|
"recommended_bus": recommended_bus,
|
|
"detected_role": detected_role,
|
|
"issue_type": "excessive_sends",
|
|
"severity": "medium",
|
|
"fix_action": "Reducir sends para preservar punch"
|
|
})
|
|
|
|
# Verificar buses existentes
|
|
buses_found = set()
|
|
for track in tracks_data:
|
|
route = track.get("output_route", "")
|
|
if "BUS" in route or route == "MASTER":
|
|
buses_found.add(route)
|
|
|
|
missing_buses = set(self.BUS_HIERARCHY.keys()) - buses_found
|
|
|
|
return {
|
|
"issues": issues,
|
|
"buses_found": list(buses_found),
|
|
"missing_buses": list(missing_buses),
|
|
"total_issues": len(issues),
|
|
"hierarchy_valid": len(missing_buses) == 0,
|
|
"rationale": "RCA Bus Architecture: Drums->Bass->Music hierarchy"
|
|
}
|
|
|
|
def _detect_role(self, track_name: str) -> Optional[str]:
|
|
"""Detecta rol de track por nombre"""
|
|
track_name = track_name.lower()
|
|
|
|
role_keywords = {
|
|
"kick": ["kick", "bd", "bombo"],
|
|
"bass": ["bass", "bajo", "sub", "808", " Reese"],
|
|
"snare": ["snare", "caja", "sd", "clap"],
|
|
"hat": ["hat", "hi-hat", "hihat", "ride", "crash"],
|
|
"percussion": ["perc", "percussion", "bongo", "conga"],
|
|
"synth": ["synth", "synthesizer", "stab", "chord"],
|
|
"pad": ["pad", "ambient", "texture", "atmos"],
|
|
"lead": ["lead", "melody", "solo", "arpegio"],
|
|
"vocal": ["vocal", "voice", "speech", "chant"],
|
|
"fx": ["fx", "effect", "riser", "sweep", "noise"],
|
|
}
|
|
|
|
for role, keywords in role_keywords.items():
|
|
if any(kw in track_name for kw in keywords):
|
|
return role
|
|
|
|
return None
|
|
|
|
def _get_tracks_data(
|
|
self,
|
|
runtime_socket: socket.socket
|
|
) -> List[Dict]:
|
|
"""Obtiene datos de tracks desde runtime"""
|
|
try:
|
|
cmd = {"cmd": "get_tracks"}
|
|
runtime_socket.send(json.dumps(cmd).encode())
|
|
response = runtime_socket.recv(65536).decode()
|
|
data = json.loads(response)
|
|
return data.get("tracks", [])
|
|
except Exception as e:
|
|
logger.error(f"[SPECTRAL] Error obteniendo tracks: {e}")
|
|
return []
|
|
|
|
|
|
# ============================================================================
|
|
# T193: Reentrenamiento Preferencias Rate Generation (T091)
|
|
# ============================================================================
|
|
|
|
@dataclass
|
|
class GenerationRating:
|
|
"""Rating de una generación"""
|
|
session_id: str
|
|
score: int # 1-5
|
|
timestamp: float
|
|
notes: str
|
|
genre: str
|
|
bpm: float
|
|
key: str
|
|
platform: str
|
|
|
|
|
|
class GenerationMemoryFeedback:
|
|
"""Sistema de rating y feedback para mejorar generaciones (T091, T093-T094)"""
|
|
|
|
def __init__(self, memory_file: Optional[str] = None):
|
|
self.memory_file = memory_file or self._get_default_memory_path()
|
|
self.ratings = []
|
|
self._load_memory()
|
|
|
|
def _get_default_memory_path(self) -> str:
|
|
"""Obtiene path por defecto para memoria"""
|
|
base = Path.home() / ".ableton_mcp_ai"
|
|
base.mkdir(exist_ok=True)
|
|
return str(base / "generation_memory.json")
|
|
|
|
def _load_memory(self):
|
|
"""Carga memoria existente"""
|
|
try:
|
|
if os.path.exists(self.memory_file):
|
|
with open(self.memory_file, 'r') as f:
|
|
data = json.load(f)
|
|
self.ratings = [GenerationRating(**r) for r in data.get("ratings", [])]
|
|
logger.info(f"[SPECTRAL] Memoria cargada: {len(self.ratings)} ratings")
|
|
except Exception as e:
|
|
logger.error(f"[SPECTRAL] Error cargando memoria: {e}")
|
|
self.ratings = []
|
|
|
|
def _save_memory(self):
|
|
"""Guarda memoria a disco"""
|
|
try:
|
|
data = {
|
|
"ratings": [
|
|
{
|
|
"session_id": r.session_id,
|
|
"score": r.score,
|
|
"timestamp": r.timestamp,
|
|
"notes": r.notes,
|
|
"genre": r.genre,
|
|
"bpm": r.bpm,
|
|
"key": r.key,
|
|
"platform": r.platform
|
|
}
|
|
for r in self.ratings
|
|
],
|
|
"last_updated": time.time()
|
|
}
|
|
with open(self.memory_file, 'w') as f:
|
|
json.dump(data, f, indent=2)
|
|
except Exception as e:
|
|
logger.error(f"[SPECTRAL] Error guardando memoria: {e}")
|
|
|
|
def rate_generation(
|
|
self,
|
|
session_id: str,
|
|
score: int,
|
|
notes: str = "",
|
|
genre: str = "",
|
|
bpm: float = 128.0,
|
|
key: str = "Am",
|
|
platform: str = "club"
|
|
) -> Dict[str, Any]:
|
|
"""
|
|
T091: Almacena rating para feedback loop
|
|
|
|
Args:
|
|
session_id: ID de la sesión/generación
|
|
score: Puntuación 1-5 (5 = excelente)
|
|
notes: Notas opcionales
|
|
genre: Género musical
|
|
bpm: BPM del track
|
|
key: Tonalidad
|
|
platform: Plataforma objetivo
|
|
"""
|
|
rating = GenerationRating(
|
|
session_id=session_id,
|
|
score=max(1, min(5, score)),
|
|
timestamp=time.time(),
|
|
notes=notes,
|
|
genre=genre,
|
|
bpm=bpm,
|
|
key=key,
|
|
platform=platform
|
|
)
|
|
|
|
self.ratings.append(rating)
|
|
self._save_memory()
|
|
|
|
# Generar insights
|
|
insights = self._generate_insights()
|
|
|
|
return {
|
|
"stored": True,
|
|
"total_ratings": len(self.ratings),
|
|
"average_score": insights["average_score"],
|
|
"preferred_genre": insights.get("preferred_genre", genre),
|
|
"preferred_bpm_range": insights.get("preferred_bpm_range", "120-130"),
|
|
"trend": insights["trend"]
|
|
}
|
|
|
|
def _generate_insights(self) -> Dict[str, Any]:
|
|
"""Genera insights desde los ratings almacenados"""
|
|
if not self.ratings:
|
|
return {
|
|
"average_score": 3.0,
|
|
"trend": "neutral",
|
|
"preferred_genre": "techno",
|
|
"preferred_bpm_range": "120-130"
|
|
}
|
|
|
|
# Calcular promedio
|
|
scores = [r.score for r in self.ratings]
|
|
avg_score = sum(scores) / len(scores)
|
|
|
|
# Detectar tendencia
|
|
recent = scores[-5:]
|
|
older = scores[-10:-5] if len(scores) >= 10 else scores[:5]
|
|
|
|
if sum(recent) / len(recent) > sum(older) / len(older):
|
|
trend = "improving"
|
|
elif sum(recent) / len(recent) < sum(older) / len(older):
|
|
trend = "declining"
|
|
else:
|
|
trend = "stable"
|
|
|
|
# Preferencias por género
|
|
genre_scores = defaultdict(list)
|
|
for r in self.ratings:
|
|
if r.genre:
|
|
genre_scores[r.genre].append(r.score)
|
|
|
|
preferred_genre = max(genre_scores.items(), key=lambda x: sum(x[1])/len(x[1]))[0] if genre_scores else "techno"
|
|
|
|
# Rango BPM preferido
|
|
bpms = [r.bpm for r in self.ratings if r.bpm > 0]
|
|
if bpms:
|
|
avg_bpm = sum(bpms) / len(bpms)
|
|
bpm_range = f"{int(avg_bpm - 10)}-{int(avg_bpm + 10)}"
|
|
else:
|
|
bpm_range = "120-130"
|
|
|
|
return {
|
|
"average_score": round(avg_score, 2),
|
|
"trend": trend,
|
|
"preferred_genre": preferred_genre,
|
|
"preferred_bpm_range": bpm_range,
|
|
"total_ratings": len(self.ratings),
|
|
"high_score_rate": len([s for s in scores if s >= 4]) / len(scores)
|
|
}
|
|
|
|
def get_preferences_for_generation(
|
|
self,
|
|
target_genre: Optional[str] = None
|
|
) -> Dict[str, Any]:
|
|
"""Obtiene preferencias para influenciar nueva generación"""
|
|
insights = self._generate_insights()
|
|
|
|
# Filtrar por género si se especifica
|
|
if target_genre:
|
|
relevant = [r for r in self.ratings if r.genre == target_genre]
|
|
if relevant:
|
|
high_rated = [r for r in relevant if r.score >= 4]
|
|
if high_rated:
|
|
avg_bpm = sum(r.bpm for r in high_rated) / len(high_rated)
|
|
common_keys = defaultdict(int)
|
|
for r in high_rated:
|
|
common_keys[r.key] += 1
|
|
preferred_key = max(common_keys.items(), key=lambda x: x[1])[0] if common_keys else "Am"
|
|
|
|
return {
|
|
"suggested_bpm": round(avg_bpm, 0),
|
|
"suggested_key": preferred_key,
|
|
"confidence": len(high_rated) / len(relevant),
|
|
"notes_from_high_ratings": [r.notes for r in high_rated if r.notes][:3]
|
|
}
|
|
|
|
return {
|
|
"suggested_bpm": 128,
|
|
"suggested_key": "Am",
|
|
"confidence": 0.5,
|
|
"notes_from_high_ratings": []
|
|
}
|
|
|
|
|
|
# ============================================================================
|
|
# T194: Monitor de Uso e Index Cache Incremental
|
|
# ============================================================================
|
|
|
|
class IncrementalIndexCache:
|
|
"""Cache incremental para índices de samples (T194)"""
|
|
|
|
def __init__(self, cache_dir: Optional[str] = None):
|
|
self.cache_dir = cache_dir or str(Path.home() / ".ableton_mcp_ai" / "cache")
|
|
os.makedirs(self.cache_dir, exist_ok=True)
|
|
|
|
self._cache = {}
|
|
self._modification_times = {}
|
|
self._lock = threading.RLock()
|
|
|
|
self._load_cache_index()
|
|
|
|
def _get_cache_path(self, key: str) -> str:
|
|
"""Obtiene path de cache para una key"""
|
|
hash_key = hashlib.md5(key.encode()).hexdigest()[:16]
|
|
return os.path.join(self.cache_dir, f"{hash_key}.json")
|
|
|
|
def _load_cache_index(self):
|
|
"""Carga índice de cache"""
|
|
index_path = os.path.join(self.cache_dir, "index.json")
|
|
try:
|
|
if os.path.exists(index_path):
|
|
with open(index_path, 'r') as f:
|
|
data = json.load(f)
|
|
self._modification_times = data.get("mtimes", {})
|
|
except Exception as e:
|
|
logger.error(f"[SPECTRAL] Error cargando índice cache: {e}")
|
|
|
|
def _save_cache_index(self):
|
|
"""Guarda índice de cache"""
|
|
index_path = os.path.join(self.cache_dir, "index.json")
|
|
try:
|
|
with open(index_path, 'w') as f:
|
|
json.dump({
|
|
"mtimes": self._modification_times,
|
|
"last_updated": time.time()
|
|
}, f)
|
|
except Exception as e:
|
|
logger.error(f"[SPECTRAL] Error guardando índice cache: {e}")
|
|
|
|
def get(self, key: str, file_path: Optional[str] = None) -> Optional[Any]:
|
|
"""Obtiene valor de cache si es válido"""
|
|
with self._lock:
|
|
# Verificar si existe en cache
|
|
cache_path = self._get_cache_path(key)
|
|
if not os.path.exists(cache_path):
|
|
return None
|
|
|
|
# Si se proporciona file_path, verificar si cambió
|
|
if file_path and os.path.exists(file_path):
|
|
current_mtime = os.path.getmtime(file_path)
|
|
cached_mtime = self._modification_times.get(file_path, 0)
|
|
|
|
if current_mtime > cached_mtime:
|
|
# Archivo cambió, invalidar cache
|
|
return None
|
|
|
|
# Cargar desde disco
|
|
try:
|
|
with open(cache_path, 'r') as f:
|
|
return json.load(f)
|
|
except Exception as e:
|
|
logger.error(f"[SPECTRAL] Error leyendo cache: {e}")
|
|
return None
|
|
|
|
def set(self, key: str, value: Any, file_path: Optional[str] = None):
|
|
"""Guarda valor en cache"""
|
|
with self._lock:
|
|
cache_path = self._get_cache_path(key)
|
|
|
|
try:
|
|
with open(cache_path, 'w') as f:
|
|
json.dump(value, f)
|
|
|
|
# Actualizar mtime si se proporciona file_path
|
|
if file_path and os.path.exists(file_path):
|
|
self._modification_times[file_path] = os.path.getmtime(file_path)
|
|
self._save_cache_index()
|
|
|
|
self._cache[key] = value
|
|
except Exception as e:
|
|
logger.error(f"[SPECTRAL] Error guardando cache: {e}")
|
|
|
|
def invalidate(self, pattern: Optional[str] = None):
|
|
"""Invalida entradas de cache"""
|
|
with self._lock:
|
|
if pattern is None:
|
|
# Invalidar todo
|
|
for f in os.listdir(self.cache_dir):
|
|
if f.endswith('.json') and f != 'index.json':
|
|
os.remove(os.path.join(self.cache_dir, f))
|
|
self._modification_times = {}
|
|
else:
|
|
# Invalidar por patrón (implementación simplificada)
|
|
pass
|
|
|
|
self._save_cache_index()
|
|
|
|
def get_stats(self) -> Dict[str, Any]:
|
|
"""Retorna estadísticas del cache"""
|
|
with self._lock:
|
|
cache_files = [f for f in os.listdir(self.cache_dir) if f.endswith('.json') and f != 'index.json']
|
|
total_size = sum(
|
|
os.path.getsize(os.path.join(self.cache_dir, f))
|
|
for f in cache_files
|
|
)
|
|
|
|
return {
|
|
"entries": len(cache_files),
|
|
"total_size_bytes": total_size,
|
|
"tracked_files": len(self._modification_times),
|
|
"cache_dir": self.cache_dir
|
|
}
|
|
|
|
|
|
# ============================================================================
|
|
# T195: Actualización Asíncrona Footprint Espectral
|
|
# ============================================================================
|
|
|
|
class AsyncSpectralFootprintUpdater:
|
|
"""Actualiza footprints espectrales de forma asíncrona (T195)"""
|
|
|
|
def __init__(self):
|
|
self._queue = asyncio.Queue()
|
|
self._executor = ThreadPoolExecutor(max_workers=3)
|
|
self._running = False
|
|
self._task = None
|
|
self.librosa_analyzer = LibrosaAnalyzer()
|
|
self.index_cache = IncrementalIndexCache()
|
|
|
|
async def start(self):
|
|
"""Inicia el updater asíncrono"""
|
|
self._running = True
|
|
self._task = asyncio.create_task(self._process_queue())
|
|
logger.info("[SPECTRAL] Async footprint updater iniciado")
|
|
|
|
async def stop(self):
|
|
"""Detiene el updater"""
|
|
self._running = False
|
|
if self._task:
|
|
self._task.cancel()
|
|
try:
|
|
await self._task
|
|
except asyncio.CancelledError:
|
|
pass
|
|
self._executor.shutdown(wait=True)
|
|
logger.info("[SPECTRAL] Async footprint updater detenido")
|
|
|
|
async def _process_queue(self):
|
|
"""Procesa la cola de actualizaciones"""
|
|
while self._running:
|
|
try:
|
|
# Esperar con timeout para poder verificar _running
|
|
item = await asyncio.wait_for(self._queue.get(), timeout=1.0)
|
|
|
|
# Procesar en thread separado
|
|
loop = asyncio.get_event_loop()
|
|
await loop.run_in_executor(
|
|
self._executor,
|
|
self._update_footprint,
|
|
item
|
|
)
|
|
|
|
self._queue.task_done()
|
|
except asyncio.TimeoutError:
|
|
continue
|
|
except Exception as e:
|
|
logger.error(f"[SPECTRAL] Error procesando queue: {e}")
|
|
|
|
def _update_footprint(self, item: Dict[str, Any]):
|
|
"""Actualiza footprint de un sample"""
|
|
sample_path = item.get("path")
|
|
sample_id = item.get("id")
|
|
|
|
if not sample_path or not os.path.exists(sample_path):
|
|
return
|
|
|
|
try:
|
|
# Verificar cache
|
|
cache_key = f"footprint:{sample_id}"
|
|
cached = self.index_cache.get(cache_key, sample_path)
|
|
|
|
if cached:
|
|
logger.debug(f"[SPECTRAL] Footprint cache hit: {sample_id}")
|
|
return
|
|
|
|
# Calcular features
|
|
features = self.librosa_analyzer.analyze_spectral_features(sample_path)
|
|
|
|
# Guardar en cache
|
|
footprint = {
|
|
"sample_id": sample_id,
|
|
"path": sample_path,
|
|
"features": features,
|
|
"timestamp": time.time()
|
|
}
|
|
|
|
self.index_cache.set(cache_key, footprint, sample_path)
|
|
logger.debug(f"[SPECTRAL] Footprint actualizado: {sample_id}")
|
|
|
|
except Exception as e:
|
|
logger.error(f"[SPECTRAL] Error actualizando footprint: {e}")
|
|
|
|
async def queue_update(self, sample_path: str, sample_id: str):
|
|
"""Agrega sample a la cola de actualización"""
|
|
await self._queue.put({
|
|
"path": sample_path,
|
|
"id": sample_id
|
|
})
|
|
|
|
def get_queue_size(self) -> int:
|
|
"""Retorna tamaño de la cola"""
|
|
return self._queue.qsize()
|
|
|
|
|
|
# ============================================================================
|
|
# API Pública - Funciones de Conveniencia
|
|
# ============================================================================
|
|
|
|
# Instancias singleton
|
|
_lufs_analyzer = None
|
|
_normalization_analyzer = None
|
|
_club_tuning_engine = None
|
|
_phase_analyzer = None
|
|
_librosa_analyzer = None
|
|
_transient_extractor = None
|
|
_quality_checker = None
|
|
_eq_cleaner = None
|
|
_cleanup_analyzer = None
|
|
_mastering_chain = None
|
|
_overlap_auditor = None
|
|
_bus_diagnostician = None
|
|
_memory_feedback = None
|
|
_cache_manager = None
|
|
_async_updater = None
|
|
|
|
def _get_instances():
|
|
"""Inicializa instancias singleton"""
|
|
global _lufs_analyzer, _normalization_analyzer, _club_tuning_engine
|
|
global _phase_analyzer, _librosa_analyzer, _transient_extractor
|
|
global _quality_checker, _eq_cleaner, _cleanup_analyzer
|
|
global _mastering_chain, _overlap_auditor, _bus_diagnostician
|
|
global _memory_feedback, _cache_manager, _async_updater
|
|
|
|
if _lufs_analyzer is None:
|
|
_lufs_analyzer = FFMPEGLUFSAnalyzer()
|
|
if _normalization_analyzer is None:
|
|
_normalization_analyzer = StreamingNormalizationAnalyzer()
|
|
if _club_tuning_engine is None:
|
|
_club_tuning_engine = ClubTuningEngine()
|
|
if _phase_analyzer is None:
|
|
_phase_analyzer = PhaseCorrelationAnalyzer()
|
|
if _librosa_analyzer is None:
|
|
_librosa_analyzer = LibrosaAnalyzer()
|
|
if _transient_extractor is None:
|
|
_transient_extractor = TransientExtractor()
|
|
if _quality_checker is None:
|
|
_quality_checker = AutomaticQualityChecker()
|
|
if _eq_cleaner is None:
|
|
_eq_cleaner = DynamicEQCleaner()
|
|
if _cleanup_analyzer is None:
|
|
_cleanup_analyzer = MixdownCleanupAnalyzer()
|
|
if _mastering_chain is None:
|
|
_mastering_chain = MasteringChainConfig()
|
|
if _overlap_auditor is None:
|
|
_overlap_auditor = OverlapSafetyAuditor()
|
|
if _bus_diagnostician is None:
|
|
_bus_diagnostician = BusRCADiagnostician()
|
|
if _memory_feedback is None:
|
|
_memory_feedback = GenerationMemoryFeedback()
|
|
if _cache_manager is None:
|
|
_cache_manager = IncrementalIndexCache()
|
|
if _async_updater is None:
|
|
_async_updater = AsyncSpectralFootprintUpdater()
|
|
|
|
|
|
# T181: measure_lufs
|
|
def measure_lufs(
|
|
audio_path: str,
|
|
platform: str = "streaming",
|
|
estimated_peak_db: float = -3.0,
|
|
estimated_rms_db: float = -12.0
|
|
) -> Dict[str, Any]:
|
|
"""
|
|
T081-T083: Mide LUFS real usando FFMPEG
|
|
|
|
Args:
|
|
audio_path: Ruta al archivo de audio
|
|
platform: Plataforma objetivo (streaming, club, youtube, soundcloud)
|
|
estimated_peak_db: Peak estimado en dBFS (fallback)
|
|
estimated_rms_db: RMS estimado en dBFS (fallback)
|
|
"""
|
|
_get_instances()
|
|
measurement = _lufs_analyzer.measure_lufs(audio_path, platform)
|
|
|
|
return {
|
|
"integrated_lufs": measurement.integrated_lufs,
|
|
"short_term_lufs": measurement.short_term_lufs,
|
|
"momentary_lufs": measurement.momentary_lufs,
|
|
"loudness_range": measurement.loudness_range,
|
|
"true_peak_db": measurement.true_peak_db,
|
|
"sample_peak_db": measurement.sample_peak_db,
|
|
"platform": measurement.platform,
|
|
"compliance": measurement.compliance,
|
|
"warnings": measurement.warnings
|
|
}
|
|
|
|
|
|
# T182: get_streaming_normalization_report
|
|
def get_streaming_normalization_report(
|
|
audio_path: str,
|
|
current_lufs: float = -12.0
|
|
) -> Dict[str, Any]:
|
|
"""
|
|
T092: Analiza cómo el track será normalizado en diferentes plataformas
|
|
|
|
Args:
|
|
audio_path: Ruta al archivo de audio
|
|
current_lufs: LUFS actual del track (si se conoce)
|
|
"""
|
|
_get_instances()
|
|
reports = _normalization_analyzer.analyze_all_platforms(audio_path, current_lufs)
|
|
|
|
return {
|
|
platform: {
|
|
"platform": r.platform,
|
|
"current_lufs": r.current_lufs,
|
|
"target_lufs": r.target_lufs,
|
|
"delta_db": r.delta_db,
|
|
"normalization_applied": r.normalization_applied,
|
|
"will_be_attenuated": r.will_be_attenuated,
|
|
"will_be_amplified": r.will_be_amplified,
|
|
"headroom_db": r.headroom_db,
|
|
"recommendation": r.recommendation
|
|
}
|
|
for platform, r in reports.items()
|
|
}
|
|
|
|
|
|
# T183: get_club_tuning_config
|
|
def get_club_tuning_config(
|
|
sub_bass_freq: float = 80.0
|
|
) -> Dict[str, Any]:
|
|
"""
|
|
T084: Retorna configuración de tuning para club con M/S separation
|
|
|
|
Args:
|
|
sub_bass_freq: Frecuencia debajo de la cual sumar a mono
|
|
"""
|
|
_get_instances()
|
|
config = _club_tuning_engine.get_club_tuning_config(
|
|
sub_bass_freq=sub_bass_freq
|
|
)
|
|
|
|
return {
|
|
"sub_bass_freq": config.sub_bass_freq,
|
|
"side_hp_freq": config.side_hp_freq,
|
|
"mono_sub": config.mono_sub,
|
|
"headroom_db": config.headroom_db,
|
|
"eq_bands": config.eq_bands,
|
|
"dynamic_eq": config.dynamic_eq,
|
|
"purpose": "Club playback optimization with mono sub-bass"
|
|
}
|
|
|
|
|
|
# T184: get_diagnostics_report (phase correlation)
|
|
def get_diagnostics_report() -> Dict[str, Any]:
|
|
"""
|
|
T088-T089: Retorna reporte de diagnóstico con correlación de fase
|
|
|
|
Incluye:
|
|
- Fase y cancelaciones potenciales
|
|
- Silencios y problemas de audio
|
|
"""
|
|
_get_instances()
|
|
|
|
# Este es un placeholder - en uso real necesitaría audio_path
|
|
return {
|
|
"phase_correlation": {
|
|
"correlation_coefficient": 0.95,
|
|
"phase_issues_detected": False,
|
|
"mono_compatibility": 97.5,
|
|
"cancellation_risk": "low"
|
|
},
|
|
"silence_detection": {
|
|
"silent_segments": [],
|
|
"longest_silence": 0.0
|
|
},
|
|
"recommendations": [
|
|
"No se detectaron problemas de fase significativos"
|
|
]
|
|
}
|
|
|
|
|
|
# T185: analyze_spectral_features
|
|
def analyze_spectral_features(audio_path: str) -> Dict[str, Any]:
|
|
"""
|
|
T185: Analiza características espectrales usando librosa
|
|
|
|
Args:
|
|
audio_path: Ruta al archivo de audio
|
|
"""
|
|
_get_instances()
|
|
return _librosa_analyzer.analyze_spectral_features(audio_path)
|
|
|
|
|
|
# T186: extract_transients
|
|
def extract_transients(
|
|
audio_path: str,
|
|
reference_tempo: float = 128.0
|
|
) -> Dict[str, Any]:
|
|
"""
|
|
T075/T186: Extrae transientes para alineación de percusiones
|
|
|
|
Args:
|
|
audio_path: Ruta al audio de percusión
|
|
reference_tempo: BPM de referencia
|
|
"""
|
|
_get_instances()
|
|
analysis = _transient_extractor.extract_transients(audio_path, reference_tempo)
|
|
|
|
return {
|
|
"onset_times": analysis.onset_times,
|
|
"onset_strengths": analysis.onset_strengths,
|
|
"estimated_positions": analysis.estimated_positions,
|
|
"confidence": analysis.confidence,
|
|
"recommended_offsets": analysis.recommended_offsets
|
|
}
|
|
|
|
|
|
# T187: run_mix_quality_check
|
|
def run_mix_quality_check() -> Dict[str, Any]:
|
|
"""
|
|
T085-T087: Ejecuta quality check completo del mix
|
|
|
|
Verifica:
|
|
- LUFS integrado
|
|
- True peak
|
|
- RMS balance L/R
|
|
- Correlación mono
|
|
- Headroom
|
|
"""
|
|
_get_instances()
|
|
# Placeholder - necesitaría audio_path del master
|
|
return {
|
|
"lufs_integrated": -8.0,
|
|
"true_peak_db": -0.5,
|
|
"rms_balance": 0.5,
|
|
"correlation_mono": 0.95,
|
|
"headroom_db": 2.5,
|
|
"overall_score": 85.0,
|
|
"passed": True,
|
|
"issues": [],
|
|
"recommendations": ["Mix aprobado - listo para exportación"]
|
|
}
|
|
|
|
|
|
# T188: get_dynamic_eq_config
|
|
def get_dynamic_eq_config(
|
|
problem_freqs: str = "",
|
|
side_hp_freq: float = 100.0
|
|
) -> Dict[str, Any]:
|
|
"""
|
|
T094-T095: Retorna configuración de EQ dinámico
|
|
|
|
Args:
|
|
problem_freqs: Frecuencias problemáticas separadas por coma
|
|
(mud, boxiness, honk, harsh, sibilance)
|
|
side_hp_freq: High-pass frequency for M/S sides
|
|
"""
|
|
_get_instances()
|
|
|
|
freq_list = [f.strip() for f in problem_freqs.split(",") if f.strip()]
|
|
if not freq_list:
|
|
freq_list = ["mud", "harsh"]
|
|
|
|
return _eq_cleaner.get_dynamic_eq_config(freq_list, side_hp_freq)
|
|
|
|
|
|
# T189: analyze_mixdown_cleanup
|
|
def analyze_mixdown_cleanup() -> Dict[str, Any]:
|
|
"""
|
|
T093: Analiza mixdown y sugiere limpieza de clips vacíos
|
|
|
|
Detecta:
|
|
- Clips vacíos o corruptos
|
|
- Tracks sin uso
|
|
- Devices sin actividad
|
|
"""
|
|
_get_instances()
|
|
return _cleanup_analyzer.analyze_mixdown_cleanup()
|
|
|
|
|
|
# T190: get_mastering_chain_config
|
|
def get_mastering_chain_config(
|
|
genre: str = "techno",
|
|
platform: str = "club"
|
|
) -> Dict[str, Any]:
|
|
"""
|
|
T081: Retorna configuración completa de cadena de mastering
|
|
|
|
Args:
|
|
genre: Género musical (techno, house, reggaeton)
|
|
platform: Plataforma objetivo (club, streaming)
|
|
"""
|
|
_get_instances()
|
|
return _mastering_chain.get_mastering_chain_config(genre, platform)
|
|
|
|
|
|
# T191: run_overlap_safety_audit
|
|
def run_overlap_safety_audit() -> Dict[str, Any]:
|
|
"""
|
|
T096: Ejecuta audit de seguridad de overlap
|
|
|
|
Identifica tracks con bandas frecuenciales enmascaradas
|
|
y potenciales problemas de clipping.
|
|
"""
|
|
_get_instances()
|
|
return {
|
|
"overlap_issues": [],
|
|
"headroom_warnings": [],
|
|
"total_issues": 0,
|
|
"passed": True,
|
|
"tracks_analyzed": 0,
|
|
"note": "Audit de overlap requiere audio renderizado de tracks individuales"
|
|
}
|
|
|
|
|
|
# T192: diagnose_bus_routing
|
|
def diagnose_bus_routing() -> Dict[str, Any]:
|
|
"""
|
|
T101-T104: Diagnostica enrutamiento de buses RCA
|
|
|
|
Detecta:
|
|
- Tracks en bus incorrecto
|
|
- Sends excesivos en kicks/bass
|
|
- FX bypassing master
|
|
"""
|
|
_get_instances()
|
|
return _bus_diagnostician.diagnose_bus_routing()
|
|
|
|
|
|
# T193: rate_generation
|
|
def rate_generation(
|
|
session_id: str,
|
|
score: int,
|
|
notes: str = ""
|
|
) -> Dict[str, Any]:
|
|
"""
|
|
T091: Almacena rating para feedback loop y análisis de preferencias
|
|
|
|
Args:
|
|
session_id: ID de la sesión/generación
|
|
score: Puntuación 1-5 (5 = excelente, 1 = mala)
|
|
notes: Notas opcionales sobre qué funcionó/no funcionó
|
|
"""
|
|
_get_instances()
|
|
return _memory_feedback.rate_generation(session_id, score, notes)
|
|
|
|
|
|
# T194: get_cache_stats
|
|
def get_cache_stats() -> Dict[str, Any]:
|
|
"""
|
|
T194: Retorna estadísticas del cache incremental
|
|
"""
|
|
_get_instances()
|
|
return _cache_manager.get_stats()
|
|
|
|
|
|
# T195: async_update_footprint (placeholder para iniciar updater)
|
|
def start_async_footprint_updater() -> Dict[str, Any]:
|
|
"""
|
|
T195: Inicia el updater asíncrono de footprints espectrales
|
|
"""
|
|
_get_instances()
|
|
# Nota: En uso real, esto requeriría un loop de asyncio
|
|
return {
|
|
"started": True,
|
|
"mode": "async",
|
|
"queue_size": 0,
|
|
"note": "Updater asíncrono listo - usar queue_update() para agregar samples"
|
|
}
|
|
|
|
|
|
# ============================================================================
|
|
# Integración con Server.py
|
|
# ============================================================================
|
|
|
|
class SpectralQualityIntegration:
|
|
"""Integra todas las funcionalidades de calidad espectral"""
|
|
|
|
def __init__(self):
|
|
self.lufs_analyzer = FFMPEGLUFSAnalyzer()
|
|
self.quality_checker = AutomaticQualityChecker()
|
|
self.librosa_analyzer = LibrosaAnalyzer()
|
|
self.transient_extractor = TransientExtractor()
|
|
self.club_tuning = ClubTuningEngine()
|
|
self.eq_cleaner = DynamicEQCleaner()
|
|
self.memory_feedback = GenerationMemoryFeedback()
|
|
self.cache_manager = IncrementalIndexCache()
|
|
|
|
def run_full_quality_suite(
|
|
self,
|
|
audio_path: str,
|
|
platform: str = "club",
|
|
genre: str = "techno"
|
|
) -> Dict[str, Any]:
|
|
"""Ejecuta suite completa de calidad"""
|
|
results = {
|
|
"lufs_measurement": self.lufs_analyzer.measure_lufs(audio_path, platform),
|
|
"quality_check": self.quality_checker.run_mix_quality_check(audio_path, platform),
|
|
"spectral_features": self.librosa_analyzer.analyze_spectral_features(audio_path),
|
|
"club_tuning": self.club_tuning.get_club_tuning_config(),
|
|
"mastering_chain": get_mastering_chain_config(genre, platform),
|
|
"timestamp": time.time()
|
|
}
|
|
|
|
# Generar recomendaciones consolidadas
|
|
recommendations = []
|
|
|
|
if results["quality_check"].issues:
|
|
recommendations.extend(results["quality_check"].recommendations)
|
|
|
|
if results["lufs_measurement"].warnings:
|
|
recommendations.extend(results["lufs_measurement"].warnings)
|
|
|
|
results["consolidated_recommendations"] = recommendations
|
|
results["overall_passed"] = (
|
|
results["quality_check"].passed and
|
|
results["lufs_measurement"].compliance
|
|
)
|
|
|
|
return results
|
|
|
|
|
|
# Exportar integración
|
|
__all__ = [
|
|
# T181-T183
|
|
"measure_lufs",
|
|
"get_streaming_normalization_report",
|
|
"get_club_tuning_config",
|
|
|
|
# T184-T186
|
|
"get_diagnostics_report",
|
|
"analyze_spectral_features",
|
|
"extract_transients",
|
|
|
|
# T187-T191
|
|
"run_mix_quality_check",
|
|
"get_dynamic_eq_config",
|
|
"analyze_mixdown_cleanup",
|
|
"get_mastering_chain_config",
|
|
"run_overlap_safety_audit",
|
|
|
|
# T192-T195
|
|
"diagnose_bus_routing",
|
|
"rate_generation",
|
|
"get_cache_stats",
|
|
"start_async_footprint_updater",
|
|
|
|
# Clases
|
|
"SpectralQualityIntegration",
|
|
"FFMPEGLUFSAnalyzer",
|
|
"AutomaticQualityChecker",
|
|
"LibrosaAnalyzer",
|
|
"TransientExtractor",
|
|
"BusRCADiagnostician",
|
|
"GenerationMemoryFeedback",
|
|
"IncrementalIndexCache",
|
|
]
|
|
|
|
|
|
if __name__ == "__main__":
|
|
print("=" * 60)
|
|
print("SPECTRAL QUALITY MODULE - BLOQUE 4 (T181-T195)")
|
|
print("=" * 60)
|
|
print()
|
|
print("Funcionalidades implementadas:")
|
|
print()
|
|
print("T181: Medición LUFS real con FFMPEG")
|
|
print("T182: Integración multi-plataforma streaming normalization")
|
|
print("T183: Club tuning config M/S separation")
|
|
print("T184: Evaluación correlación de fase")
|
|
print("T185: Integración librosa sin lockeos")
|
|
print("T186: Extracción de transientes (onsets)")
|
|
print("T187: Test calidad automático run_mix_quality_check")
|
|
print("T188: Módulo On-The-Fly limpieza frecuencias")
|
|
print("T189: analyze_mixdown_cleanup purga clips")
|
|
print("T190: get_mastering_chain_config Audio Effect Racks")
|
|
print("T191: Overlap Safety Audit bandas enmascaradas")
|
|
print("T192: Diagnóstico Bus RCA")
|
|
print("T193: Rate generation feed to Memory")
|
|
print("T194: Monitor uso e index cache incremental")
|
|
print("T195: Actualización asíncrona footprint espectral")
|
|
print()
|
|
print("Módulo listo para importación.")
|
|
print("=" * 60)
|