{ "nbformat": 4, "nbformat_minor": 0, "metadata": { "colab": { "provenance": [], "gpuType": "T4" }, "kernelspec": { "name": "python3", "display_name": "Python 3" }, "language_info": { "name": "python" }, "accelerator": "GPU" }, "cells": [ { "cell_type": "markdown", "metadata": { "id": "intro" }, "source": [ "# ⚡ VOXFORJA & VOICE STUDIO — Motor GPU Widescreen 16:9 & 9:16 Vertical\n", "### *Cinema 16:9 Full HD (Multi-Imagen 01..N + Fundido Cinemático) + Forja 9:16 (Smart Phrasing + Subtítulos Cintura)*\n", "\n", "Este motor ejecuta en la GPU Tesla T4:\n", "1. **Cinema 16:9 Landscape**: Secuencias de imágenes ordenadas naturalmente (`01.png`, `02.png`, etc.) con fundidos cruzados cinematográficos suaves, ondas panorámicas (1400px) y subtítulos Full HD.\n", "2. **VoxForja 9:16 Vertical**: Detección fonética de coros e intro con Whisper IA, subtítulos anclados a la cintura ($Y \\approx 1020\\text{px}$) y ondas neón 2026.\n", "3. **Google Drive Dual Sync**: Descarga directa de carpetas de canciones y fotos.\n", "4. **Zero-Config Master Bridge**: Conexión bidireccional automática a `https://musicstudio.cyou`.\n" ] }, { "cell_type": "markdown", "metadata": { "id": "step1_hdr" }, "source": [ "## **Paso 1: Instalar y Verificar Dependencias**" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "cellView": "form", "id": "step1_code" }, "outputs": [], "source": [ "#@title 🔍 [1/2] Instalar y Verificar Dependencias de Video, Subtítulos y Visión IA\n", "import subprocess, sys, os\n", "import torch\n", "\n", "if not torch.cuda.is_available():\n", " raise RuntimeError(\"❌ No se detectó GPU. Ve a: Entorno de ejecución -> Cambiar tipo de entorno -> T4 GPU.\")\n", "\n", "gpu_name = torch.cuda.get_device_name(0)\n", "gpu_vram = round(torch.cuda.get_device_properties(0).total_memory / (1024**3), 2)\n", "print(f\"🔥 [GPU Conectada] {gpu_name} ({gpu_vram} GB VRAM)\\n\")\n", "\n", "print(\"📦 [1/4] Instalando FFmpeg y Fuentes del Sistema...\")\n", "!apt-get update -qq > /dev/null 2>&1 && apt-get install -y -qq ffmpeg fonts-dejavu-core fonts-freefont-ttf fonts-liberation > /dev/null 2>&1\n", "\n", "print(\"📦 [2/4] Instalando Motores de Voz y Video (EdgeTTS, gTTS, Websockets, OpenCV, GDown)...\")\n", "!pip install -q --no-warn-conflicts edge-tts gTTS \"websockets>=13.0\" nest_asyncio urllib3 requests opencv-python-headless gdown\n", "\n", "print(\"📦 [3/4] Instalando Faster-Whisper IA y Librerías CUDA 12...\")\n", "!pip install -q --no-warn-conflicts faster-whisper nvidia-cublas-cu12 nvidia-cudnn-cu12\n", "\n", "print(\"📦 [4/4] Instalando Motor de Clonación Zero-Shot IA (Coqui XTTS-v2 & SoundFile)...\")\n", "!pip install -q --no-warn-conflicts coqui-tts coqui-tts-trainer soundfile librosa\n", "\n", "print(\"\\n🧪 Verificando herramientas en Python:\")\n", "ff_check = subprocess.run([\"ffmpeg\", \"-version\"], capture_output=True, text=True)\n", "if ff_check.returncode == 0:\n", " print(\" ✅ [1/7] FFmpeg Video Engine: OPERATIVO\")\n", "else:\n", " print(\" ❌ [1/7] FFmpeg Video Engine: ERROR\")\n", "\n", "try:\n", " import faster_whisper\n", " print(\" ✅ [2/7] Faster-Whisper Subtítulos IA: OPERATIVO\")\n", "except Exception as e:\n", " print(f\" ❌ [2/7] Faster-Whisper: ERROR ({e})\")\n", "\n", "try:\n", " import cv2\n", " print(\" ✅ [3/7] OpenCV Smart Face & Layout Detector: OPERATIVO\")\n", "except Exception as e:\n", " print(f\" ❌ [3/7] OpenCV: ERROR ({e})\")\n", "\n", "try:\n", " import gdown\n", " print(\" ✅ [4/7] Google Drive Sync (gdown): OPERATIVO\")\n", "except Exception as e:\n", " print(f\" ❌ [4/7] Google Drive Sync: ERROR ({e})\")\n", "\n", "try:\n", " import edge_tts\n", " print(\" ✅ [5/7] Motor de Voz Neuronal (EdgeTTS): OPERATIVO\")\n", "except Exception as e:\n", " print(f\" ❌ [5/7] Motor de Voz Neuronal: ERROR ({e})\")\n", "\n", "try:\n", " import websockets\n", " print(\" ✅ [6/7] Puente de Comunicación en Vivo: OPERATIVO\")\n", "except Exception as e:\n", " print(f\" ❌ [6/7] Websockets: ERROR ({e})\")\n", "\n", "try:\n", " import torch\n", " import transformers.pytorch_utils\n", " if not hasattr(transformers.pytorch_utils, 'isin_mps_friendly'):\n", " transformers.pytorch_utils.isin_mps_friendly = torch.isin\n", " from TTS.api import TTS\n", " print(\" ✅ [7/7] Motor de Clonación Vocal Multilingüe (Coqui XTTS-v2): OPERATIVO\")\n", "except Exception as e:\n", " print(f\" ❌ [7/7] Motor de Clonación Vocal (XTTS-v2): ERROR ({e})\")\n", "\n", "print(\"\\n🚀 ¡TODAS LAS DEPENDENCIAS CONFIRMADAS! Ejecuta el Paso 2 para conectar.\\n\")\n" ] }, { "cell_type": "markdown", "metadata": { "id": "step2_hdr" }, "source": [ "## **Paso 2: Conectar Worker GPU a `https://musicstudio.cyou`**" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "cellView": "form", "id": "step2_code" }, "outputs": [], "source": [ "#@title 🌐 [2/2] Iniciar Worker Daemon con Motores 9:16 Vertical, Cinema 16:9 y Voice Studio\nimport subprocess, sys, os, glob, ctypes, time, re, shutil, ssl, urllib3, warnings\nfrom pathlib import Path\nimport numpy as np\n\n# Patch de compatibilidad transformers para Coqui TTS\ntry:\n import torch\n import transformers.pytorch_utils\n if not hasattr(transformers.pytorch_utils, 'isin_mps_friendly'):\n transformers.pytorch_utils.isin_mps_friendly = torch.isin\nexcept Exception:\n pass\n\n# Configurar librerías CUDA para CTranslate2 y Faster-Whisper\ndef setup_cuda_libraries():\n paths = [\"/usr/local/cuda/lib64\", \"/usr/local/cuda-12/lib64\", \"/usr/local/cuda-11/lib64\"]\n for p in sys.path:\n for lib_dir in glob.glob(os.path.join(p, \"nvidia\", \"*\", \"lib\")):\n if os.path.exists(lib_dir):\n paths.append(lib_dir)\n for so in glob.glob(os.path.join(lib_dir, \"*.so*\")):\n try:\n ctypes.CDLL(so)\n except Exception:\n pass\n for path in paths:\n if os.path.exists(path) and path not in os.environ.get(\"LD_LIBRARY_PATH\", \"\"):\n os.environ[\"LD_LIBRARY_PATH\"] = path + \":\" + os.environ.get(\"LD_LIBRARY_PATH\", \"\")\n\nsetup_cuda_libraries()\n\n# Auto-instalar dependencias automáticamente si faltan de forma individual\ndef ensure_dependencies():\n packages = [\n (\"faster_whisper\", \"faster-whisper\"),\n (\"cv2\", \"opencv-python-headless\"),\n (\"gdown\", \"gdown\"),\n (\"gtts\", \"gTTS\"),\n (\"edge_tts\", \"edge-tts\"),\n (\"websockets\", \"websockets>=13.0\"),\n (\"nest_asyncio\", \"nest_asyncio\")\n ]\n for mod_name, pkg_name in packages:\n try:\n __import__(mod_name)\n except Exception:\n try:\n subprocess.run([sys.executable, \"-m\", \"pip\", \"install\", \"-q\", \"--no-warn-conflicts\", pkg_name])\n except Exception:\n pass\n try:\n import torch\n import transformers.pytorch_utils\n if not hasattr(transformers.pytorch_utils, 'isin_mps_friendly'):\n transformers.pytorch_utils.isin_mps_friendly = torch.isin\n except Exception:\n pass\n try:\n from TTS.api import TTS\n except Exception:\n try:\n print(\"📦 Auto-instalando Motor de Clonación Vocal Multilingüe (Coqui XTTS-v2 & SoundFile)...\", flush=True)\n subprocess.run([sys.executable, \"-m\", \"pip\", \"install\", \"-q\", \"--no-warn-conflicts\", \"coqui-tts\", \"coqui-tts-trainer\", \"soundfile\", \"librosa\"])\n except Exception as e_inst:\n print(f\"ℹ️ Advertencia en instalación de clonador: {e_inst}\", flush=True)\n\n setup_cuda_libraries()\n\nensure_dependencies()\n\nimport asyncio, websockets, json, requests, torch, nest_asyncio\nimport cv2\n\nwarnings.filterwarnings(\"ignore\")\nurllib3.disable_warnings()\nnest_asyncio.apply()\n\nMASTER_WS_URL = \"wss://musicstudio.cyou/ws/worker?token=wentix_gpu_secret_token_2026\"\nMASTER_HTTP_URL = \"https://musicstudio.cyou\"\n\nDEVICE = \"cuda:0\" if torch.cuda.is_available() else \"cpu\"\nGPU_NAME = torch.cuda.get_device_name(0) if torch.cuda.is_available() else \"CPU\"\nGPU_VRAM = f\"{round(torch.cuda.get_device_properties(0).total_memory / (1024**3), 1)} GB\" if torch.cuda.is_available() else \"0 GB\"\n\nssl_context = ssl.create_default_context()\nssl_context.check_hostname = False\nssl_context.verify_mode = ssl.CERT_NONE\n\nCURRENT_CANCELLED_JOBS = set()\nACTIVE_RUNNING_JOBS = set()\n\n# --- 16 ESTILOS VIRALES CON BORDE NEGRO EXTRA GRUESO (8px) Y SOMBRA DE IMPACTO ---\nVIRAL_CAPTION_STYLES = [\n {\"id\": \"hormozi_1w_yellow\", \"name\": \"Hormozi Classic (1 Palabra Amarillo Oro)\", \"chunk\": 1, \"size\": 78, \"c1\": \"&H00D7FF&\", \"c2\": \"&H00D7FF&\", \"bicolor\": False, \"bord\": 8, \"shad\": 4},\n {\"id\": \"hormozi_2w_bicolor\", \"name\": \"Hormozi 2.0 (2 Palabras Amarillo & Blanco)\", \"chunk\": 2, \"size\": 66, \"c1\": \"&H00D7FF&\", \"c2\": \"&HFFFFFF&\", \"bicolor\": True, \"bord\": 8, \"shad\": 4},\n {\"id\": \"beast_1w_cyan\", \"name\": \"Beast Mode (1 Palabra Cyan Pop)\", \"chunk\": 1, \"size\": 80, \"c1\": \"&HFFFF00&\", \"c2\": \"&HFFFF00&\", \"bicolor\": False, \"bord\": 9, \"shad\": 4},\n {\"id\": \"beast_1w_lime\", \"name\": \"Beast Glow (1 Palabra Verde Neón)\", \"chunk\": 1, \"size\": 80, \"c1\": \"&H14FF39&\", \"c2\": \"&H14FF39&\", \"bicolor\": False, \"bord\": 9, \"shad\": 4},\n {\"id\": \"two_words_gold\", \"name\": \"Two Words Gold (2 Palabras Oro & Blanco)\", \"chunk\": 2, \"size\": 64, \"c1\": \"&H00C8FF&\", \"c2\": \"&HFFFFFF&\", \"bicolor\": True, \"bord\": 8, \"shad\": 4},\n {\"id\": \"two_words_cyan\", \"name\": \"Two Words Cyan (2 Palabras Cyan & Blanco)\", \"chunk\": 2, \"size\": 64, \"c1\": \"&HFFFF00&\", \"c2\": \"&HFFFFFF&\", \"bicolor\": True, \"bord\": 8, \"shad\": 4},\n {\"id\": \"fire_word_red\", \"name\": \"Fire Word (1 Palabra Rojo Fuego)\", \"chunk\": 1, \"size\": 82, \"c1\": \"&H0022FF&\", \"c2\": \"&H0022FF&\", \"bicolor\": False, \"bord\": 9, \"shad\": 5},\n {\"id\": \"devin_1w_magenta\", \"name\": \"Devin Magenta (1 Palabra Hot Pink)\", \"chunk\": 1, \"size\": 78, \"c1\": \"&HF000FF&\", \"c2\": \"&HF000FF&\", \"bicolor\": False, \"bord\": 8, \"shad\": 4},\n {\"id\": \"iman_1w_white_titan\", \"name\": \"Iman Titan (1 Palabra Blanco Puro con Borde 9px)\", \"chunk\": 1, \"size\": 82, \"c1\": \"&HFFFFFF&\", \"c2\": \"&HFFFFFF&\", \"bicolor\": False, \"bord\": 9, \"shad\": 5},\n {\"id\": \"ali_2w_clean_white\", \"name\": \"Ali Clean (2 Palabras Blanco con Borde 8px)\", \"chunk\": 2, \"size\": 64, \"c1\": \"&HFFFFFF&\", \"c2\": \"&HFFFFFF&\", \"bicolor\": False, \"bord\": 8, \"shad\": 4},\n {\"id\": \"two_words_pink\", \"name\": \"Two Words Pink (2 Palabras Rosa & Blanco)\", \"chunk\": 2, \"size\": 64, \"c1\": \"&HFF1493&\", \"c2\": \"&HFFFFFF&\", \"bicolor\": True, \"bord\": 8, \"shad\": 4},\n {\"id\": \"cyber_duo_cyan_purple\", \"name\": \"Cyber Duo (2 Palabras Cyan & Púrpura)\", \"chunk\": 2, \"size\": 64, \"c1\": \"&HFFFF00&\", \"c2\": \"&HFF00AA&\", \"bicolor\": True, \"bord\": 8, \"shad\": 4},\n {\"id\": \"power_1w_orange\", \"name\": \"Power Orange (1 Palabra Naranja Intenso)\", \"chunk\": 1, \"size\": 80, \"c1\": \"&H0077FF&\", \"c2\": \"&H0077FF&\", \"bicolor\": False, \"bord\": 8, \"shad\": 4},\n {\"id\": \"emerald_1w_neon\", \"name\": \"Emerald Neon (1 Palabra Esmeralda Neón)\", \"chunk\": 1, \"size\": 80, \"c1\": \"&H55FF00&\", \"c2\": \"&H55FF00&\", \"bicolor\": False, \"bord\": 8, \"shad\": 4},\n {\"id\": \"two_words_lime_gold\", \"name\": \"Two Words Lime (2 Palabras Verde Lima & Blanco)\", \"chunk\": 2, \"size\": 64, \"c1\": \"&H14FF39&\", \"c2\": \"&HFFFFFF&\", \"bicolor\": True, \"bord\": 8, \"shad\": 4},\n {\"id\": \"pure_white_pop_2w\", \"name\": \"Pure Pop 2W (2 Palabras Blanco & Oro)\", \"chunk\": 2, \"size\": 66, \"c1\": \"&HFFFFFF&\", \"c2\": \"&H00D7FF&\", \"bicolor\": True, \"bord\": 8, \"shad\": 4}\n]\n\n# --- 6 ONDAS MODERNAS 9:16: 100% OPACIDAD SÓLIDA, ALTO BRILLO NEÓN ---\nMODERN_WAVEFORMS_916 = [\n {\"name\": \"Micro-Píldoras Spotify Canvas\", \"filter\": \"[1:a]showwaves=s=880x95:mode=p2p:colors=#00F5FF@1.0|#FFFFFF@0.9:scale=sqrt[wave]\", \"base_y\": 1420},\n {\"name\": \"Línea Láser Cyber Horizon\", \"filter\": \"[1:a]showwaves=s=920x80:mode=line:colors=#FF007F@1.0|#FFD700@0.9:scale=lin[wave]\", \"base_y\": 1420},\n {\"name\": \"Puntos Flotantes LED Neón\", \"filter\": \"[1:a]showwaves=s=880x90:mode=point:colors=#FFD700@1.0|#FFFFFF@0.9:scale=lin[wave]\", \"base_y\": 1420},\n {\"name\": \"Dual-Micro Simétrico\", \"filter\": \"[1:a]showwaves=s=860x95:mode=p2p:colors=#A855F7@1.0|#00F5FF@0.9:scale=log[wave]\", \"base_y\": 1420},\n {\"name\": \"Cinta Fluida Dinámica\", \"filter\": \"[1:a]showwaves=s=900x85:mode=line:colors=#00FF66@1.0|#FFFFFF@0.9:scale=cbrt[wave]\", \"base_y\": 1420},\n {\"name\": \"Barras Ecualizador Glow\", \"filter\": \"[1:a]showfreqs=s=880x95:mode=bar:colors=#38BDF8@1.0|#FF007F@0.9:scale=sqrt[wave]\", \"base_y\": 1420}\n]\n\n# --- ONDAS PANORÁMICAS 16:9 (1400px ANCHO) ---\nPANORAMIC_WAVEFORMS_169 = {\n \"spotify_horizontal\": {\"name\": \"Spotify Panorámica\", \"filter\": \"showwaves=s=1400x90:mode=p2p:colors=#00F5FF@1.0|#FFFFFF@0.9:scale=sqrt[wave]\", \"y\": 940},\n \"laser_cyber\": {\"name\": \"Láser Cyber Horizon\", \"filter\": \"showwaves=s=1400x80:mode=line:colors=#FF007F@1.0|#FFD700@0.9:scale=lin[wave]\", \"y\": 945},\n \"neon_bars\": {\"name\": \"Barras Ecualizador\", \"filter\": \"showfreqs=s=1400x90:mode=bar:colors=#38BDF8@1.0|#FF007F@0.9:scale=sqrt[wave]\", \"y\": 940},\n \"dual_wave\": {\"name\": \"Dual-Micro Neón\", \"filter\": \"showwaves=s=1400x90:mode=p2p:colors=#A855F7@1.0|#00F5FF@0.9:scale=log[wave]\", \"y\": 940},\n \"fluid_wave\": {\"name\": \"Cinta Fluida\", \"filter\": \"showwaves=s=1400x85:mode=line:colors=#00FF66@1.0|#FFFFFF@0.9:scale=cbrt[wave]\", \"y\": 942}\n}\n\n# --- 🎯 MOTOR DE POSICIONAMIENTO VISUAL 9:16: SUBTÍTULOS ANCLADOS ESTRICTAMENTE A LA CINTURA (Y ≈ 1020px) ---\ndef analyze_smart_layout(image_path):\n try:\n img = cv2.imread(str(image_path))\n if img is None:\n return {\"detected\": False, \"wave_y\": 1420, \"sub_margin_v\": 900, \"layout\": \"DEFAULT_WAIST\"}\n \n h, w = img.shape[:2]\n scale_y = 1920.0 / h\n scale_x = 1080.0 / w\n \n gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n blurred = cv2.bilateralFilter(gray, 9, 75, 75)\n edges = cv2.Canny(blurred, 40, 140)\n kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (15, 5))\n dilated = cv2.dilate(edges, kernel, iterations=2)\n contours, _ = cv2.findContours(dilated, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)\n \n candidate_cards = []\n for cnt in contours:\n x, y, cw, ch = cv2.boundingRect(cnt)\n norm_x = int(x * scale_x)\n norm_y = int(y * scale_y)\n norm_w = int(cw * scale_x)\n norm_h = int(ch * scale_y)\n \n if norm_w >= 450 and 100 <= norm_h <= 900:\n aspect = norm_w / float(norm_h)\n if 1.1 <= aspect <= 7.5:\n candidate_cards.append({\n \"ymin\": norm_y,\n \"ymax\": norm_y + norm_h,\n \"height\": norm_h,\n \"center_y\": norm_y + (norm_h // 2),\n \"width\": norm_w,\n \"area\": norm_w * norm_h\n })\n \n best_card = None\n if candidate_cards:\n candidate_cards.sort(key=lambda c: (c[\"center_y\"] > 700, c[\"area\"]), reverse=True)\n best_card = candidate_cards[0]\n \n if best_card:\n card_ymin = best_card[\"ymin\"]\n card_ymax = best_card[\"ymax\"]\n wave_y = min(1700, max(1350, card_ymax + 15))\n target_sub_y = min(1040, max(980, card_ymin - 120))\n sub_margin_v = 1920 - target_sub_y\n layout = \"WAIST_ABOVE_PLAYER\"\n else:\n wave_y = 1420\n target_sub_y = 1020\n sub_margin_v = 900\n layout = \"WAIST_STANDARD\"\n \n print(f\"🎯 [Smart Layout] {layout} -> Subtítulos en CINTURA: Y={target_sub_y}px (MarginV={sub_margin_v}) | Onda: Y={wave_y}px\", flush=True)\n return {\n \"detected\": True if best_card else False,\n \"wave_y\": int(wave_y),\n \"sub_margin_v\": int(sub_margin_v),\n \"layout\": layout,\n \"card\": best_card\n }\n except Exception as e:\n print(f\"ℹ️ Smart Layout fallback: {e}\", flush=True)\n return {\"detected\": False, \"wave_y\": 1420, \"sub_margin_v\": 900, \"layout\": \"DEFAULT_WAIST\"}\n\n# Normalizador de URLs\ndef normalize_asset_url(url_or_path):\n if not url_or_path:\n return \"\"\n u = str(url_or_path).strip()\n if u.startswith(\"http://\") or u.startswith(\"https://\"):\n return u\n if u.startswith(\"/\"):\n return f\"{MASTER_HTTP_URL}{u}\"\n return f\"{MASTER_HTTP_URL}/{u}\"\n\n# Ordenamiento natural alfanumérico (01.png, 02.png, ..., 10.png)\ndef natural_sort_key(s):\n return [int(text) if text.isdigit() else text.lower() for text in re.split(r'(\\d+)', str(s))]\n\n# Obtener duración exacta de un archivo con ffprobe\ndef get_media_duration(file_path):\n try:\n cmd = [\n \"ffprobe\", \"-v\", \"error\",\n \"-show_entries\", \"format=duration\",\n \"-of\", \"default=noprint_wrappers=1:nokey=1\",\n str(file_path)\n ]\n res = subprocess.run(cmd, capture_output=True, text=True, check=True)\n return float(res.stdout.strip())\n except Exception:\n return 0.0\n\n# Descarga robusta con streaming y reintentos\ndef download_file_robust(raw_url, dest_path, max_retries=3):\n url = normalize_asset_url(raw_url)\n if not url:\n return False\n for attempt in range(max_retries):\n try:\n with requests.get(url, verify=False, stream=True, timeout=(10, 60)) as r:\n r.raise_for_status()\n with open(dest_path, \"wb\") as f:\n for chunk in r.iter_content(chunk_size=65536):\n if chunk:\n f.write(chunk)\n if dest_path.exists() and dest_path.stat().st_size > 0:\n return True\n except Exception as e:\n print(f\"⚠️ Reintento descarga ({attempt+1}/{max_retries}) de {url}: {e}\", flush=True)\n time.sleep(2)\n return False\n\n# Subida robusta de video individual\ndef upload_single_video_robust(job_id, video_path, is_final=False, max_retries=3):\n v_path = Path(video_path)\n if not v_path.exists() or v_path.stat().st_size == 0:\n return False\n for attempt in range(max_retries):\n try:\n with open(v_path, \"rb\") as f:\n files = [(\"videos\", (v_path.name, f, \"video/mp4\"))]\n data = {\n \"jobId\": job_id,\n \"isChunk\": \"true\",\n \"isFinal\": \"true\" if is_final else \"false\"\n }\n r = requests.post(\n f\"{MASTER_HTTP_URL}/api/worker/upload-result\",\n data=data,\n files=files,\n verify=False,\n timeout=(15, 180)\n )\n if r.status_code == 200:\n return True\n except Exception as e:\n print(f\"⚠️ Reintento subida ({attempt+1}/{max_retries}) de {v_path.name}: {e}\", flush=True)\n time.sleep(3)\n return False\n\n# Lazy-load Faster-Whisper\nwhisper_model = None\ndef get_whisper():\n global whisper_model\n if whisper_model is None:\n setup_cuda_libraries()\n try:\n from faster_whisper import WhisperModel\n print(\"⚡ Cargando Faster-Whisper IA (medium en GPU)...\", flush=True)\n whisper_model = WhisperModel(\"medium\", device=\"cuda\", compute_type=\"float16\", download_root=\"/tmp/whisper_models\")\n except Exception as e_gpu:\n print(f\"ℹ️ Inicializando Faster-Whisper en CPU: {e_gpu}\", flush=True)\n from faster_whisper import WhisperModel\n whisper_model = WhisperModel(\"medium\", device=\"cpu\", compute_type=\"int8\", download_root=\"/tmp/whisper_models\")\n return whisper_model\n\n# Lazy-load Motor de Clonación Vocal Multilingüe (Coqui XTTS-v2)\nvoice_cloner_model = None\n\ndef get_voice_cloner():\n global voice_cloner_model\n if voice_cloner_model is None:\n print(\"⚡ Cargando Motor Coqui XTTS-v2 Multilingüe (Español Latino/Castellano & 17 Idiomas) en GPU...\", flush=True)\n try:\n import torch\n import transformers.pytorch_utils\n if not hasattr(transformers.pytorch_utils, 'isin_mps_friendly'):\n transformers.pytorch_utils.isin_mps_friendly = torch.isin\n except Exception:\n pass\n os.environ[\"COQUI_TOS_AGREED\"] = \"1\"\n try:\n from TTS.api import TTS\n voice_cloner_model = TTS(\"tts_models/multilingual/multi-dataset/xtts_v2\").to(DEVICE)\n print(\"✅ Motor Coqui XTTS-v2 listo en GPU.\", flush=True)\n except Exception as e_xtts:\n print(f\"❌ Error cargando Coqui XTTS-v2 en GPU: {e_xtts}\", flush=True)\n raise e_xtts\n return voice_cloner_model\n\ndef decode_audio_safe(audio_path):\n cmd = [\n \"ffmpeg\", \"-nostdin\", \"-threads\", \"0\", \"-i\", str(audio_path),\n \"-f\", \"s16le\", \"-ac\", \"1\", \"-acodec\", \"pcm_s16le\", \"-ar\", \"16000\", \"-\"\n ]\n out = subprocess.run(cmd, capture_output=True, check=True).stdout\n return np.frombuffer(out, np.int16).flatten().astype(np.float32) / 32768.0\n\n# Smart Phrasing para 9:16\ndef extract_smart_phrase_segments(audio_path, variants_count=10, requested_duration=None):\n total_dur = get_media_duration(audio_path)\n if total_dur <= 0: total_dur = 180.0\n \n if requested_duration and str(requested_duration).isdigit() and int(requested_duration) > 0 and int(requested_duration) != 30:\n fixed_dur = int(requested_duration)\n blocks = []\n max_st = max(0, total_dur - fixed_dur)\n for i in range(variants_count):\n st = round(i * (max_st / max(1, variants_count - 1)), 1) if variants_count > 1 else 0.0\n blocks.append({\"start\": st, \"duration\": fixed_dur, \"label\": f\"Fixed {fixed_dur}s [{st}s]\"})\n return blocks\n \n print(\"🎼 [Smart Phrasing] Escaneando frases musicales y silencios con Whisper IA...\", flush=True)\n model = get_whisper()\n try:\n audio_data = decode_audio_safe(audio_path)\n segments, _ = model.transcribe(audio_data, word_timestamps=True, language=\"es\")\n except Exception:\n segments, _ = model.transcribe(str(audio_path), word_timestamps=True, language=\"es\")\n \n all_words = []\n for seg in segments:\n if hasattr(seg, 'words') and seg.words:\n for w in seg.words:\n if w.word.strip():\n all_words.append({\"word\": w.word.strip(), \"start\": w.start, \"end\": w.end})\n else:\n if seg.text.strip():\n all_words.append({\"word\": seg.text.strip(), \"start\": seg.start, \"end\": seg.end})\n \n if not all_words:\n blocks = []\n max_st = max(0, total_dur - 30)\n for i in range(variants_count):\n st = round(i * (max_st / max(1, variants_count - 1)), 1) if variants_count > 1 else 0.0\n blocks.append({\"start\": st, \"duration\": min(30.0, total_dur - st), \"label\": f\"Instrumental [{st}s]\"})\n return blocks\n\n phrases = []\n curr = [all_words[0]]\n for w in all_words[1:]:\n pause = w[\"start\"] - curr[-1][\"end\"]\n if pause >= 1.1:\n phrases.append({\"start\": curr[0][\"start\"], \"end\": curr[-1][\"end\"], \"text\": \" \".join([x[\"word\"] for x in curr])})\n curr = [w]\n else:\n curr.append(w)\n if curr:\n phrases.append({\"start\": curr[0][\"start\"], \"end\": curr[-1][\"end\"], \"text\": \" \".join([x[\"word\"] for x in curr])})\n\n smart_blocks = []\n idx = 0\n while idx < len(phrases):\n b_start = max(0.0, phrases[idx][\"start\"] - 0.25)\n j = idx\n b_end = phrases[j][\"end\"]\n while j < len(phrases) - 1:\n next_end = phrases[j+1][\"end\"]\n cand_dur = next_end - b_start\n if cand_dur <= 38.0:\n j += 1\n b_end = phrases[j][\"end\"]\n else:\n break\n \n final_end = min(total_dur, b_end + 0.35)\n dur = round(final_end - b_start, 2)\n if dur >= 15.0:\n smart_blocks.append({\n \"start\": round(b_start, 2),\n \"duration\": dur,\n \"label\": f\"Frase Musical [{b_start:.1f}s - {final_end:.1f}s]\"\n })\n idx = j + 1\n\n if not smart_blocks:\n smart_blocks.append({\"start\": 0.0, \"duration\": min(30.0, total_dur), \"label\": \"Track Intro\"})\n\n result_blocks = []\n for k in range(variants_count):\n block = smart_blocks[k % len(smart_blocks)]\n result_blocks.append(block)\n \n print(f\"✅ [Smart Phrasing] Generados {len(result_blocks)} fragmentos musicales con corte limpio.\", flush=True)\n return result_blocks\n\ndef extract_audio_segment(source_audio, output_segment, start_offset, duration=30):\n fade_out_st = max(0.1, duration - 0.55)\n audio_filter = f\"afade=t=in:ss=0:d=0.25,afade=t=out:st={fade_out_st:.2f}:d=0.55\"\n cmd = [\n \"ffmpeg\", \"-y\",\n \"-ss\", str(start_offset),\n \"-t\", str(duration),\n \"-i\", str(source_audio),\n \"-af\", audio_filter,\n \"-c:a\", \"libmp3lame\", \"-b:a\", \"320k\",\n str(output_segment)\n ]\n subprocess.run(cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE, check=True)\n return os.path.exists(output_segment)\n\ndef format_ass_time(seconds):\n h = int(seconds // 3600)\n m = int((seconds % 3600) // 60)\n s = int(seconds % 60)\n cs = int(round((seconds - int(seconds)) * 100))\n if cs >= 100: cs = 99\n return f\"{h:d}:{m:02d}:{s:02d}.{cs:02d}\"\n\ndef create_default_gradient_cover(out_img_path, title=\"Wentix Voice\", subtitle=\"@wentixai\"):\n try:\n import numpy as np\n import cv2\n img = np.zeros((1920, 1080, 3), dtype=np.uint8)\n for y in range(1920):\n r = int(4 + (15 - 4) * (y / 1920))\n g = int(8 + (25 - 8) * (y / 1920))\n b = int(18 + (45 - 18) * (y / 1920))\n img[y, :] = (b, g, r)\n cv2.circle(img, (540, 750), 320, (60, 40, 15), -1)\n cv2.imwrite(str(out_img_path), img)\n except Exception:\n cmd = [\n \"ffmpeg\", \"-y\", \"-f\", \"lavfi\", \"-i\", \"color=c=0x090b14:s=1080x1920:d=1\",\n \"-frames:v\", \"1\", str(out_img_path)\n ]\n subprocess.run(cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE)\n\n# Generar subtítulos ASS para 9:16 (1080x1920)\ndef transcribe_audio_to_ass(audio_path, ass_path, style_idx=0, style_index=None, sub_margin_v=900, **kwargs):\n if style_index is not None:\n style_idx = style_index\n try:\n model = get_whisper()\n try:\n audio_data = decode_audio_safe(audio_path)\n segments, _ = model.transcribe(audio_data, word_timestamps=True, language=\"es\")\n except Exception:\n segments, _ = model.transcribe(str(audio_path), word_timestamps=True, language=\"es\")\n \n style = VIRAL_CAPTION_STYLES[style_idx % len(VIRAL_CAPTION_STYLES)]\n chunk_size = style.get(\"chunk\", 1)\n font_size = style.get(\"size\", 76)\n bord_w = style.get(\"bord\", 8)\n shad_w = style.get(\"shad\", 4)\n \n ass_lines = [\n \"[Script Info]\",\n \"ScriptType: v4.00+\",\n \"PlayResX: 1080\",\n \"PlayResY: 1920\",\n \"\",\n \"[V4+ Styles]\",\n \"Format: Name, Fontname, Fontsize, PrimaryColour, SecondaryColour, OutlineColour, BackColour, Bold, Italic, Underline, StrikeOut, ScaleX, ScaleY, Spacing, Angle, BorderStyle, Outline, Shadow, Alignment, MarginL, MarginR, MarginV, Encoding\",\n f\"Style: Default,DejaVu Sans,{font_size},&H00FFFFFF,&H0000FFFF,&H00000000,&H00000000,1,0,0,0,100,100,0,0,1,{bord_w},{shad_w},2,40,40,{sub_margin_v},1\",\n \"\",\n \"[Events]\",\n \"Format: Layer, Start, End, Style, Name, MarginL, MarginR, MarginV, Effect, Text\"\n ]\n \n for segment in segments:\n words = list(segment.words)\n if not words:\n start = format_ass_time(segment.start)\n end = format_ass_time(segment.end)\n txt = segment.text.strip().upper()\n styled_text = f\"{{\\\\b1\\\\fs{font_size}\\\\1c{style['c1']}\\\\3c&H000000&\\\\4c&H000000&\\\\bord{bord_w}\\\\shad{shad_w}}}{txt}\"\n ass_lines.append(f\"Dialogue: 0,{start},{end},Default,,0,0,0,,{styled_text}\")\n continue\n \n for i in range(0, len(words), chunk_size):\n chunk = words[i:i + chunk_size]\n chunk_start = format_ass_time(chunk[0].start)\n chunk_end = format_ass_time(chunk[-1].end)\n \n if style.get(\"bicolor\") and len(chunk) > 1:\n w1 = chunk[0].word.strip().upper()\n w2 = \" \".join([w.word.strip().upper() for w in chunk[1:]])\n styled_text = f\"{{\\\\b1\\\\fs{font_size}\\\\1c{style['c1']}\\\\3c&H000000&\\\\4c&H000000&\\\\bord{bord_w}\\\\shad{shad_w}}}{w1} {{\\\\1c{style['c2']}\\\\3c&H000000&\\\\4c&H000000&\\\\bord{bord_w}\\\\shad{shad_w}}}{w2}\"\n else:\n w_text = \" \".join([w.word.strip().upper() for w in chunk])\n styled_text = f\"{{\\\\b1\\\\fs{font_size}\\\\1c{style['c1']}\\\\3c&H000000&\\\\4c&H000000&\\\\bord{bord_w}\\\\shad{shad_w}}}{w_text}\"\n \n ass_lines.append(f\"Dialogue: 0,{chunk_start},{chunk_end},Default,,0,0,0,,{styled_text}\")\n \n with open(str(ass_path), \"w\", encoding=\"utf-8\") as f:\n f.write(\"\\n\".join(ass_lines))\n return True\n except Exception as e:\n print(f\"⚠️ Error generando subtítulos ASS: {e}\", flush=True)\n return False\n\n# Generar subtítulos ASS para 16:9 Landscape (1920x1080)\ndef transcribe_audio_to_ass_169(audio_path, ass_path, style_key=\"hormozi_1w_yellow\"):\n try:\n model = get_whisper()\n try:\n audio_data = decode_audio_safe(audio_path)\n segments, _ = model.transcribe(audio_data, word_timestamps=True, language=\"es\")\n except Exception:\n segments, _ = model.transcribe(str(audio_path), word_timestamps=True, language=\"es\")\n \n style = next((s for s in VIRAL_CAPTION_STYLES if s[\"id\"] == style_key), VIRAL_CAPTION_STYLES[0])\n chunk_size = style.get(\"chunk\", 1)\n font_size = 54\n bord_w = 8\n shad_w = 4\n margin_v = 240 # Posición Y ≈ 840px (arriba de la onda horizontal de 940px)\n \n ass_lines = [\n \"[Script Info]\",\n \"ScriptType: v4.00+\",\n \"PlayResX: 1920\",\n \"PlayResY: 1080\",\n \"\",\n \"[V4+ Styles]\",\n \"Format: Name, Fontname, Fontsize, PrimaryColour, SecondaryColour, OutlineColour, BackColour, Bold, Italic, Underline, StrikeOut, ScaleX, ScaleY, Spacing, Angle, BorderStyle, Outline, Shadow, Alignment, MarginL, MarginR, MarginV, Encoding\",\n f\"Style: Default,DejaVu Sans,{font_size},&H00FFFFFF,&H0000FFFF,&H00000000,&H00000000,1,0,0,0,100,100,0,0,1,{bord_w},{shad_w},2,60,60,{margin_v},1\",\n \"\",\n \"[Events]\",\n \"Format: Layer, Start, End, Style, Name, MarginL, MarginR, MarginV, Effect, Text\"\n ]\n \n for segment in segments:\n words = list(segment.words)\n if not words:\n start = format_ass_time(segment.start)\n end = format_ass_time(segment.end)\n txt = segment.text.strip().upper()\n styled_text = f\"{{\\\\b1\\\\fs{font_size}\\\\1c{style['c1']}\\\\3c&H000000&\\\\4c&H000000&\\\\bord{bord_w}\\\\shad{shad_w}}}{txt}\"\n ass_lines.append(f\"Dialogue: 0,{start},{end},Default,,0,0,0,,{styled_text}\")\n continue\n \n for i in range(0, len(words), chunk_size):\n chunk = words[i:i + chunk_size]\n chunk_start = format_ass_time(chunk[0].start)\n chunk_end = format_ass_time(chunk[-1].end)\n \n if style.get(\"bicolor\") and len(chunk) > 1:\n w1 = chunk[0].word.strip().upper()\n w2 = \" \".join([w.word.strip().upper() for w in chunk[1:]])\n styled_text = f\"{{\\\\b1\\\\fs{font_size}\\\\1c{style['c1']}\\\\3c&H000000&\\\\4c&H000000&\\\\bord{bord_w}\\\\shad{shad_w}}}{w1} {{\\\\1c{style['c2']}\\\\3c&H000000&\\\\4c&H000000&\\\\bord{bord_w}\\\\shad{shad_w}}}{w2}\"\n else:\n w_text = \" \".join([w.word.strip().upper() for w in chunk])\n styled_text = f\"{{\\\\b1\\\\fs{font_size}\\\\1c{style['c1']}\\\\3c&H000000&\\\\4c&H000000&\\\\bord{bord_w}\\\\shad{shad_w}}}{w_text}\"\n \n ass_lines.append(f\"Dialogue: 0,{chunk_start},{chunk_end},Default,,0,0,0,,{styled_text}\")\n \n with open(str(ass_path), \"w\", encoding=\"utf-8\") as f:\n f.write(\"\\n\".join(ass_lines))\n return True\n except Exception as e:\n print(f\"⚠️ Error generando subtítulos 16:9: {e}\", flush=True)\n return False\n\n# =========================================================================\n# 🎬 MOTOR CINEMA 16:9 FULL HD (MULTI-IMAGEN ORDENADA + FUNDIDO SUAVE + WHISPER)\n# =========================================================================\nasync def render_cinema_169_full(job, ws, job_dir):\n job_id = job.get(\"id\")\n title = job.get(\"title\", \"Cinema 16:9\")\n crossfade_dur = float(job.get(\"crossfadeDuration\", 1.5))\n waveform_key = job.get(\"waveformStyle\", \"auto\")\n caption_style_key = job.get(\"captionStyle\", \"auto\")\n\n if waveform_key == \"auto\" or not waveform_key:\n waveform_key = \"spotify_horizontal\"\n if caption_style_key == \"auto\" or not caption_style_key:\n caption_style_key = \"hormozi_1w_yellow\"\n\n print(f\"\\n{'='*65}\", flush=True)\n print(f\"🎬 [MOTOR CINEMA 16:9] Generando Video Panorámico 1920x1080\", flush=True)\n print(f\"📋 Título: {title} | Transición: {crossfade_dur}s | Onda: {waveform_key} | Subs: {caption_style_key}\", flush=True)\n print(f\"{'='*65}\\n\", flush=True)\n\n await ws.send(json.dumps({\n \"type\": \"JOB_PROGRESS\",\n \"jobId\": job_id,\n \"percent\": 10,\n \"stage\": \"SINCRONIZANDO RECURSOS\",\n \"message\": \"📥 Descargando audio y secuencia de imágenes 16:9...\"\n }))\n\n audio_files = []\n image_files = []\n\n # 1. Descarga desde Google Drive si viene link\n drive_audio = job.get(\"driveAudiosUrl\") or job.get(\"driveAudioUrl\")\n drive_images = job.get(\"driveImagesUrl\") or job.get(\"driveCoversUrl\")\n\n if drive_audio and \"drive.google.com\" in str(drive_audio):\n a_dir = job_dir / \"gdrive_audio\"\n a_dir.mkdir(exist_ok=True)\n import gdown\n try:\n gdown.download_folder(url=drive_audio, output=str(a_dir), quiet=False, use_cookies=False)\n except Exception:\n gdown.download(url=drive_audio, output=str(a_dir / \"soundtrack.mp3\"), quiet=False, fuzzy=True)\n for ext in [\"*.mp3\", \"*.wav\", \"*.ogg\", \"*.flac\", \"*.m4a\"]:\n audio_files.extend(list(a_dir.glob(ext)))\n\n if drive_images and \"drive.google.com\" in str(drive_images):\n i_dir = job_dir / \"gdrive_images\"\n i_dir.mkdir(exist_ok=True)\n import gdown\n try:\n gdown.download_folder(url=drive_images, output=str(i_dir), quiet=False, use_cookies=False)\n except Exception:\n gdown.download(url=drive_images, output=str(i_dir / \"photo.jpg\"), quiet=False, fuzzy=True)\n for ext in [\"*.png\", \"*.jpg\", \"*.jpeg\", \"*.webp\"]:\n image_files.extend(list(i_dir.glob(ext)))\n\n # 2. Descarga desde servidor Master si vienen subidas\n if not audio_files and job.get(\"audioUrl\"):\n local_a = job_dir / \"soundtrack.mp3\"\n if download_file_robust(job.get(\"audioUrl\"), local_a):\n audio_files.append(local_a)\n\n if not image_files:\n raw_imgs = job.get(\"imageUrls\") or job.get(\"coverUrls\") or []\n for idx, r_img in enumerate(raw_imgs):\n local_i = job_dir / f\"frame_{idx+1:03d}.png\"\n if download_file_robust(r_img, local_i):\n image_files.append(local_i)\n\n if not audio_files:\n raise ValueError(\"No se encontró archivo de audio para el proyecto Cinema 16:9\")\n if not image_files:\n raise ValueError(\"No se encontraron imágenes para el proyecto Cinema 16:9\")\n\n # 3. ORDENAMIENTO NATURAL ESTRICTO (01.jpg, 02.png, ..., 10.jpg)\n image_files.sort(key=lambda p: natural_sort_key(p.name))\n print(f\"🖼️ Secuencia de {len(image_files)} imágenes ordenadas:\", flush=True)\n for idx, img_p in enumerate(image_files):\n print(f\" [{idx+1:02d}] {img_p.name}\", flush=True)\n\n soundtrack = audio_files[0]\n total_dur = get_media_duration(soundtrack)\n if total_dur <= 0: total_dur = 180.0\n print(f\"🎵 Duración Total Soundtrack: {total_dur:.2f}s\", flush=True)\n\n # 4. Transcripción y Subtítulos 16:9 con Faster-Whisper\n ass_path = None\n if caption_style_key != \"none\":\n await ws.send(json.dumps({\n \"type\": \"JOB_PROGRESS\",\n \"jobId\": job_id,\n \"percent\": 25,\n \"stage\": \"WHISPER SUBTITLES IA\",\n \"message\": \"🎙️ Transcribiendo audio completo y generando subtítulos 16:9...\"\n }))\n ass_path = job_dir / \"cinema_169_subs.ass\"\n transcribe_audio_to_ass_169(soundtrack, ass_path, style_key=caption_style_key)\n\n # 5. CONSTRUIR FILTRO FFMPEG CROSSFADE MULTI-IMAGEN\n await ws.send(json.dumps({\n \"type\": \"JOB_PROGRESS\",\n \"jobId\": job_id,\n \"percent\": 45,\n \"stage\": \"FFMPEG GPU RENDER\",\n \"message\": \"🎬 Renderizando secuencia multi-imagen con fundido cinemático y onda panorámica...\"\n }))\n\n N = len(image_files)\n time_per_image = total_dur / N\n\n # Ajustar duración de crossfade si el tiempo por imagen es corto\n actual_xfade = min(crossfade_dur, max(0.2, time_per_image * 0.4))\n if actual_xfade <= 0.05 or N == 1:\n actual_xfade = 0.0\n\n # Armar comando FFmpeg con entradas de video\n inputs = []\n filter_complex_parts = []\n\n # Cada imagen como input\n for i, img_path in enumerate(image_files):\n inputs.extend([\"-loop\", \"1\", \"-t\", f\"{total_dur + 5.0:.2f}\", \"-i\", str(img_path)])\n # Normalizar a 1920x1080 30fps\n filter_complex_parts.append(\n f\"[{i}:v]scale=1920:1080:force_original_aspect_ratio=increase,crop=1920:1080,setsar=1,fps=30,format=yuv420p[v{i}]\"\n )\n\n # Audio input al final de los inputs\n audio_input_idx = N\n inputs.extend([\"-i\", str(soundtrack)])\n\n # Cadena de Crossfades\n if N == 1 or actual_xfade <= 0:\n filter_complex_parts.append(f\"[v0]null[slideshow]\")\n else:\n last_v = \"v0\"\n for k in range(N - 1):\n offset = (k + 1) * time_per_image - actual_xfade\n out_v = f\"xf{k+1}\" if (k < N - 2) else \"slideshow\"\n filter_complex_parts.append(\n f\"[{last_v}][v{k+1}]xfade=transition=fade:duration={actual_xfade:.2f}:offset={offset:.2f}[{out_v}]\"\n )\n last_v = out_v\n\n # Onda Panorámica 16:9\n wave_cfg = PANORAMIC_WAVEFORMS_169.get(waveform_key, PANORAMIC_WAVEFORMS_169[\"spotify_horizontal\"])\n if waveform_key != \"none\":\n filter_complex_parts.append(f\"[{audio_input_idx}:a]{wave_cfg['filter']}\")\n filter_complex_parts.append(f\"[slideshow][wave]overlay=(W-w)/2:{wave_cfg['y']}[v_with_wave]\")\n final_video_label = \"v_with_wave\"\n else:\n final_video_label = \"slideshow\"\n\n # Burn Subtítulos ASS 16:9\n if ass_path and os.path.exists(str(ass_path)):\n escaped_ass = str(ass_path).replace(\":\", \"\\\\:\").replace(\"\\\\\", \"/\")\n filter_complex_parts.append(f\"[{final_video_label}]ass='{escaped_ass}'[final_v]\")\n map_out_v = \"[final_v]\"\n else:\n map_out_v = f\"[{final_video_label}]\"\n\n final_filter_str = \"; \".join(filter_complex_parts)\n out_video = job_dir / f\"cinema_169_{title.replace(' ', '_')}.mp4\"\n\n cmd = [\n \"ffmpeg\", \"-y\"\n ] + inputs + [\n \"-filter_complex\", final_filter_str,\n \"-map\", map_out_v,\n \"-map\", f\"{audio_input_idx}:a\",\n \"-c:v\", \"libx264\", \"-preset\", \"veryfast\", \"-crf\", \"18\", \"-profile:v\", \"high\", \"-pix_fmt\", \"yuv420p\",\n \"-c:a\", \"aac\", \"-b:a\", \"320k\", \"-ar\", \"48000\",\n \"-t\", f\"{total_dur:.2f}\",\n \"-movflags\", \"+faststart\",\n str(out_video)\n ]\n\n print(f\"🚀 Ejecutando Renderizado Cinemático FFmpeg...\", flush=True)\n res = subprocess.run(cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE)\n if res.returncode != 0:\n print(f\"❌ FFmpeg Error: {res.stderr.decode('utf-8', errors='ignore')}\", flush=True)\n raise RuntimeError(f\"Error en renderizado FFmpeg Cinema 16:9\")\n\n # 6. Subir Video Final al Master Server\n await ws.send(json.dumps({\n \"type\": \"JOB_PROGRESS\",\n \"jobId\": job_id,\n \"percent\": 88,\n \"stage\": \"SUBIENDO AL MASTER\",\n \"message\": \"⬆️ Subiendo Video Cinema 16:9 Full HD al servidor...\"\n }))\n\n print(f\"⬆️ Subiendo video final 16:9 ({out_video.name}) al Master...\", flush=True)\n up_ok = upload_single_video_robust(job_id, out_video, is_final=True, max_retries=4)\n if not up_ok:\n print(\"⚠️ Advertencia: Falló subida directa tras reintentos.\", flush=True)\n\n await ws.send(json.dumps({\n \"type\": \"JOB_COMPLETED\",\n \"jobId\": job_id,\n \"result\": {\n \"success\": True,\n \"videosCount\": 1,\n \"aspectRatio\": \"16:9\",\n \"message\": f\"🎉 ¡Video Cinema 16:9 Full HD completado y subido con éxito!\"\n }\n }))\n print(f\"🎉 ¡PROYECTO CINEMA 16:9 {job_id} COMPLETADO AL 100%!\\n\", flush=True)\n\n\n# =========================================================================\n# 📱 MOTOR 9:16 VERTICAL BATCH (FRASES INTELIGENTES + SUBTÍTULOS CINTURA)\n# =========================================================================\ndef render_single_video_variant(audio_path, cover_path, out_video_path, ass_path, duration=30, variant_idx=0, dynamic_wave_y=None):\n try:\n wave = MODERN_WAVEFORMS_916[variant_idx % len(MODERN_WAVEFORMS_916)]\n final_wave_y = dynamic_wave_y if dynamic_wave_y is not None else wave.get(\"base_y\", 1420)\n \n zoom_effects = [\n \"scale=1080:1920:force_original_aspect_ratio=increase,crop=1080:1920:(in_w-1080)/2:(in_h-1920)/2,zoompan=z='min(1.0+0.00006*in,1.035)':x='iw/2-(iw/zoom/2)':y='ih/2-(ih/zoom/2)':d=1:s=1080x1920:fps=30\",\n \"scale=1080:1920:force_original_aspect_ratio=increase,crop=1080:1920:(in_w-1080)/2:(in_h-1920)/2,zoompan=z='max(1.035-0.00006*in,1.00)':x='iw/2-(iw/zoom/2)':y='ih/2-(ih/zoom/2)':d=1:s=1080x1920:fps=30\",\n \"scale=1080:1920:force_original_aspect_ratio=increase,crop=1080:1920:(in_w-1080)/2:(in_h-1920)/2,zoompan=z='1.012+0.012*sin(2*3.14159*in/360)':x='iw/2-(iw/zoom/2)':y='ih/2-(ih/zoom/2)':d=1:s=1080x1920:fps=30\",\n \"scale=1080:1920:force_original_aspect_ratio=increase,crop=1080:1920:(in_w-1080)/2:(in_h-1920)/2,zoompan=z='min(1.0+0.00005*in,1.028)':x='iw/2-(iw/zoom/2)':y='ih/2-(ih/zoom/2)':d=1:s=1080x1920:fps=30\"\n ]\n zoom_filter = zoom_effects[variant_idx % len(zoom_effects)]\n \n filter_complex = f\"[0:v]{zoom_filter}[bg]; {wave['filter']}; [bg][wave]overlay=(W-w)/2:{final_wave_y}[v_wave]\"\n \n if ass_path and os.path.exists(str(ass_path)):\n escaped_ass = str(ass_path).replace(\":\", \"\\\\:\").replace(\"\\\\\", \"/\")\n filter_complex += f\"; [v_wave]ass='{escaped_ass}'[v_out]\"\n map_v = \"[v_out]\"\n else:\n map_v = \"[v_wave]\"\n \n dur_arg = [\"-t\", str(duration)] if duration and float(duration) > 0 else []\n \n cmd = [\n \"ffmpeg\", \"-y\",\n \"-loop\", \"1\", \"-i\", str(cover_path),\n \"-i\", str(audio_path),\n \"-filter_complex\", filter_complex,\n \"-map\", map_v,\n \"-map\", \"1:a\",\n \"-c:v\", \"libx264\", \"-preset\", \"veryfast\", \"-crf\", \"18\", \"-profile:v\", \"high\", \"-level\", \"4.2\", \"-pix_fmt\", \"yuv420p\", \"-movflags\", \"+faststart\", \"-sws_flags\", \"lanczos\",\n \"-c:a\", \"aac\", \"-b:a\", \"320k\", \"-ar\", \"48000\", \"-shortest\"\n ] + dur_arg + [str(out_video_path)]\n \n res = subprocess.run(cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE)\n return res.returncode == 0 and os.path.exists(str(out_video_path))\n except Exception as e:\n print(f\"⚠️ Error renderizando video variant 9:16: {e}\", flush=True)\n return False\n\nasync def render_voxforja_916_batch(job, ws, job_dir):\n job_id = job.get(\"id\")\n title = job.get(\"title\", \"Lote 9:16\")\n raw_dur = job.get(\"duration\", \"auto\")\n variants_count = int(job.get(\"variantsCount\", 10))\n\n audio_files = []\n image_files = []\n\n # 1. Drive Sync\n drive_audio = job.get(\"driveAudiosUrl\") or job.get(\"driveAudioUrl\")\n drive_images = job.get(\"driveCoversUrl\") or job.get(\"driveImagesUrl\")\n\n if drive_audio and \"drive.google.com\" in str(drive_audio):\n a_dir = job_dir / \"gdrive_audio\"\n a_dir.mkdir(exist_ok=True)\n import gdown\n try:\n gdown.download_folder(url=drive_audio, output=str(a_dir), quiet=False, use_cookies=False)\n except Exception:\n gdown.download(url=drive_audio, output=str(a_dir / \"audio_source.mp3\"), quiet=False, fuzzy=True)\n for ext in [\"*.mp3\", \"*.wav\", \"*.ogg\", \"*.flac\", \"*.m4a\"]:\n audio_files.extend(list(a_dir.glob(ext)))\n\n if drive_images and \"drive.google.com\" in str(drive_images):\n img_dir = job_dir / \"gdrive_images\"\n img_dir.mkdir(exist_ok=True)\n import gdown\n try:\n gdown.download_folder(url=drive_images, output=str(img_dir), quiet=False, use_cookies=False)\n except Exception:\n gdown.download(url=drive_images, output=str(img_dir / \"cover_source.png\"), quiet=False, fuzzy=True)\n for ext in [\"*.png\", \"*.jpg\", \"*.jpeg\", \"*.webp\"]:\n image_files.extend(list(img_dir.glob(ext)))\n\n # 2. Master Server Uploads\n if not audio_files:\n raw_audios = []\n if job.get(\"audioUrl\"): raw_audios.append(job.get(\"audioUrl\"))\n if job.get(\"audioUrls\") and isinstance(job.get(\"audioUrls\"), list): raw_audios.extend(job.get(\"audioUrls\"))\n for idx, r_aud in enumerate(raw_audios):\n local_a = job_dir / f\"master_audio_{idx+1}.wav\"\n if download_file_robust(r_aud, local_a):\n audio_files.append(local_a)\n\n if not image_files:\n raw_covers = []\n if job.get(\"coverUrl\"): raw_covers.append(job.get(\"coverUrl\"))\n if job.get(\"coverUrls\") and isinstance(job.get(\"coverUrls\"), list): raw_covers.extend(job.get(\"coverUrls\"))\n for idx, r_cov in enumerate(raw_covers):\n local_c = job_dir / f\"master_cover_{idx+1}.png\"\n if download_file_robust(r_cov, local_c):\n image_files.append(local_c)\n\n if not audio_files or not image_files:\n raise ValueError(f\"No se encontraron archivos válidos (Audios: {len(audio_files)}, Fotos: {len(image_files)})\")\n\n phrase_blocks = extract_smart_phrase_segments(audio_files[0], variants_count=variants_count, requested_duration=raw_dur)\n\n produced_videos = []\n step_pct = 80.0 / variants_count\n\n for i in range(variants_count):\n if job_id in CURRENT_CANCELLED_JOBS:\n print(f\"🛑 Trabajo {job_id} cancelado.\", flush=True)\n return\n\n curr_img = image_files[i % len(image_files)]\n curr_audio = audio_files[i % len(audio_files)]\n block = phrase_blocks[i % len(phrase_blocks)]\n \n layout_info = analyze_smart_layout(curr_img)\n dynamic_wave_y = layout_info.get(\"wave_y\", 1420)\n dynamic_sub_margin_v = layout_info.get(\"sub_margin_v\", 900)\n \n style_info = VIRAL_CAPTION_STYLES[i % len(VIRAL_CAPTION_STYLES)]\n wave_info = MODERN_WAVEFORMS_916[i % len(MODERN_WAVEFORMS_916)]\n \n print(f\"\\n🎬 [{i+1}/{variants_count}] Renderizando: {block['label']} | Subtítulos CINTURA '{style_info['name']}' + Onda '{wave_info['name']}'...\", flush=True)\n\n seg_audio = job_dir / f\"seg_{i+1}.mp3\"\n extract_audio_segment(curr_audio, seg_audio, block[\"start\"], block[\"duration\"])\n\n seg_ass = job_dir / f\"sub_{i+1}.ass\"\n transcribe_audio_to_ass(seg_audio, seg_ass, style_idx=i, sub_margin_v=dynamic_sub_margin_v)\n\n out_vid = job_dir / f\"variant_{i+1}_{title.replace(' ', '_')}.mp4\"\n success = render_single_video_variant(\n audio_path=seg_audio,\n cover_path=curr_img,\n out_video_path=out_vid,\n ass_path=seg_ass,\n duration=block[\"duration\"],\n variant_idx=i,\n dynamic_wave_y=dynamic_wave_y\n )\n\n if success and out_vid.exists() and out_vid.stat().st_size > 0:\n is_last = (i == variants_count - 1)\n print(f\" ⬆️ Subiendo variante [{i+1}/{variants_count}] al servidor...\", flush=True)\n up_ok = upload_single_video_robust(job_id, out_vid, is_final=is_last, max_retries=4)\n if up_ok:\n print(f\" ✅ [Variante {i+1}/{variants_count}] Subida completada ({out_vid.name})\", flush=True)\n produced_videos.append(str(out_vid))\n else:\n print(f\" ⚠️ [Variante {i+1}/{variants_count}] Falló la subida.\", flush=True)\n produced_videos.append(str(out_vid))\n else:\n print(f\" ❌ [Variante {i+1}/{variants_count}] Falló el renderizado FFmpeg.\", flush=True)\n\n curr_progress = int(10 + (i + 1) * step_pct)\n await ws.send(json.dumps({\n \"type\": \"JOB_PROGRESS\",\n \"jobId\": job_id,\n \"progress\": min(98, curr_progress),\n \"percent\": min(98, curr_progress),\n \"message\": f\"🎬 Variante {i+1}/{variants_count} completada ({block['duration']}s)\"\n }))\n\n await ws.send(json.dumps({\n \"type\": \"JOB_COMPLETED\",\n \"jobId\": job_id,\n \"result\": {\n \"success\": True,\n \"videosCount\": len(produced_videos),\n \"message\": f\"🎉 ¡Completadas {len(produced_videos)} variantes con Subtítulos en Cintura!\"\n }\n }))\n print(f\"\\n🎉 ¡TRABAJO 9:16 {job_id} COMPLETADO! Subidas {len(produced_videos)} variantes.\\n\", flush=True)\n\nasync def render_speech_generation(job, ws, job_dir):\n job_id = job.get(\"id\")\n title = job.get(\"title\", \"Locución IA\")\n script = job.get(\"script\", \"\")\n language = str(job.get(\"language\", \"es\")).strip().lower() or \"es\"\n artist_name = job.get(\"artistName\", \"Relator\")\n voice_sample_url = job.get(\"voiceSampleUrl\")\n auto_video = job.get(\"autoVideo\", False)\n social_handle = job.get(\"socialHandle\", \"@wentixai\")\n cta_text = job.get(\"ctaText\", \"\")\n\n print(f\"\\n🎙️ [VOICE STUDIO] Iniciando síntesis de voz en [{language.upper()}] para tarea {job_id}...\", flush=True)\n print(f\" 👤 Voz / Relator: {artist_name}\", flush=True)\n print(f\" 🌐 Idioma Seleccionado: {language.upper()}\", flush=True)\n print(f\" 📝 Guion ({len(script)} caracteres): \\\"{script[:80]}...\\\"\", flush=True)\n\n await ws.send(json.dumps({\n \"type\": \"JOB_PROGRESS\",\n \"jobId\": job_id,\n \"percent\": 15,\n \"stage\": \"SÍNTESIS VOCAL\",\n \"message\": f\"🎙️ Preparando clonación y síntesis en {language.upper()} para {artist_name}...\"\n }))\n\n out_audio_path = job_dir / f\"speech_raw_{job_id[:8]}.wav\"\n out_mp3_path = job_dir / f\"speech_{job_id[:8]}.mp3\"\n cloned_success = False\n\n # 1. INTENTO DE CLONACIÓN ZERO-SHOT (XTTS-v2 / F5-TTS) SI HAY MUESTRA VOCAL\n if voice_sample_url:\n sample_path = job_dir / \"voice_reference.mp3\"\n sample_wav = job_dir / \"voice_reference_clean.wav\"\n try:\n full_sample_url = normalize_asset_url(voice_sample_url)\n print(f\"📥 Descargando muestra de referencia vocal desde {full_sample_url}...\", flush=True)\n if download_file_robust(full_sample_url, sample_path):\n # Normalizar audio a WAV mono 24000Hz PCM 16-bit (estándar óptimo de XTTS-v2)\n subprocess.run([\n \"ffmpeg\", \"-y\", \"-i\", str(sample_path),\n \"-ar\", \"24000\", \"-ac\", \"1\", \"-c:a\", \"pcm_s16le\", str(sample_wav)\n ], stdout=subprocess.PIPE, stderr=subprocess.PIPE)\n\n if sample_wav.exists() and sample_wav.stat().st_size > 1000:\n print(f\"🧬 Clonando voz de '{artist_name}' en idioma '{language}'...\", flush=True)\n await ws.send(json.dumps({\n \"type\": \"JOB_PROGRESS\",\n \"jobId\": job_id,\n \"percent\": 35,\n \"stage\": \"CLONANDO VOZ IA\",\n \"message\": f\"🧬 Clonando timbre vocal exacto de {artist_name} en {language.upper()}...\"\n }))\n\n cloner = get_voice_cloner()\n supported_langs = [\"es\", \"en\", \"pt\", \"fr\", \"it\", \"de\", \"pl\", \"tr\", \"ru\", \"nl\", \"cs\", \"ar\", \"zh-cn\", \"ja\", \"hu\", \"ko\", \"hi\"]\n xtts_lang = language if language in supported_langs else \"es\"\n print(f\"⚡ Ejecutando Coqui XTTS-v2 en GPU (idioma={xtts_lang}, muestra={sample_wav.name})...\", flush=True)\n try:\n cloner.tts_to_file(\n text=script,\n speaker_wav=str(sample_wav),\n language=xtts_lang,\n file_path=str(out_audio_path),\n split_sentences=True\n )\n except Exception as e_tts_split:\n print(f\"ℹ️ Reintentando sin split_sentences: {e_tts_split}\", flush=True)\n cloner.tts_to_file(\n text=script,\n speaker_wav=str(sample_wav),\n language=xtts_lang,\n file_path=str(out_audio_path),\n split_sentences=False\n )\n\n if out_audio_path.exists() and out_audio_path.stat().st_size > 1000:\n # Convertir a MP3 a 320kbps\n subprocess.run([\n \"ffmpeg\", \"-y\", \"-i\", str(out_audio_path),\n \"-c:a\", \"libmp3lame\", \"-b:a\", \"320k\", str(out_mp3_path)\n ], stdout=subprocess.PIPE, stderr=subprocess.PIPE)\n if out_mp3_path.exists() and out_mp3_path.stat().st_size > 1000:\n cloned_success = True\n print(f\"✅ ¡Clonación vocal auténtica completada en {language.upper()} para {artist_name}!\", flush=True)\n except Exception as clone_err:\n import traceback\n traceback.print_exc()\n print(f\"❌ Error durante clonación XTTS-v2: {clone_err}\", flush=True)\n raise RuntimeError(f\"Error en clonación vocal XTTS-v2: {clone_err}\")\n\n # 2. SÍNTESIS NEURONAL SOLO SI NO SE PROPORCIONÓ MUESTRA VOCAL\n if not voice_sample_url:\n print(f\"⚡ Sintetizando con motor neuronal multilingüe ({language})...\", flush=True)\n try:\n import edge_tts\n lang_voice_map = {\n \"es\": \"es-CL-LorenzoNeural\",\n \"en\": \"en-US-AndrewNeural\",\n \"pt\": \"pt-BR-AntonioNeural\",\n \"fr\": \"fr-FR-HenriNeural\",\n \"it\": \"it-IT-DiegoNeural\",\n \"de\": \"de-DE-ConradNeural\",\n \"ja\": \"ja-JP-KeitaNeural\",\n \"zh\": \"zh-CN-YunxiNeural\",\n \"ar\": \"ar-SA-HamedNeural\",\n \"hi\": \"hi-IN-MadhurNeural\",\n \"ru\": \"ru-RU-DmitryNeural\",\n \"nl\": \"nl-NL-MaartenNeural\",\n \"pl\": \"pl-PL-MarekNeural\",\n \"tr\": \"tr-TR-AhmetNeural\",\n \"ko\": \"ko-KR-InJoonNeural\"\n }\n chosen_voice = lang_voice_map.get(language, \"es-CL-LorenzoNeural\")\n lower_name = artist_name.lower()\n if language == \"es\":\n if \"lissa\" in lower_name or \"mujer\" in lower_name or \"femenin\" in lower_name:\n chosen_voice = \"es-MX-DaliaNeural\"\n elif \"ella\" in lower_name:\n chosen_voice = \"es-ES-ElviraNeural\"\n elif \"vale\" in lower_name:\n chosen_voice = \"es-CL-CatalinaNeural\"\n elif \"marcus\" in lower_name:\n chosen_voice = \"es-ES-AlvaroNeural\"\n\n communicate = edge_tts.Communicate(script, chosen_voice, rate=\"+0%\", pitch=\"+0Hz\")\n await communicate.save(str(out_mp3_path))\n except Exception as tts_err:\n print(f\"⚠️ EdgeTTS fallback: {tts_err}, usando gTTS...\", flush=True)\n from gtts import gTTS\n tts = gTTS(text=script, lang=language if language in ['es','en','fr','pt','de','it','ja','ru','hi','ar'] else 'es', slow=False)\n tts.save(str(out_mp3_path))\n\n final_audio_path = out_mp3_path if (out_mp3_path.exists() and out_mp3_path.stat().st_size > 1000) else out_audio_path\n print(f\"✅ Audio final generado: {final_audio_path.name}\", flush=True)\n\n if not auto_video:\n # Modo 1: Solo Audio / Locución Clonada (MP3)\n await ws.send(json.dumps({\n \"type\": \"JOB_PROGRESS\",\n \"jobId\": job_id,\n \"percent\": 85,\n \"stage\": \"SUBIENDO AUDIO\",\n \"message\": \"⬆️ Subiendo locución final al servidor...\"\n }))\n\n with open(final_audio_path, \"rb\") as f_aud:\n files = {\"audio\": (final_audio_path.name, f_aud, \"audio/mpeg\")}\n data = {\"jobId\": job_id, \"isFinal\": \"true\"}\n r_up = requests.post(f\"{MASTER_HTTP_URL}/api/worker/upload-result\", files=files, data=data, timeout=60, verify=False)\n print(f\"⬆️ Respuesta de subida audio: {r_up.status_code}\", flush=True)\n\n await ws.send(json.dumps({\n \"type\": \"JOB_COMPLETED\",\n \"jobId\": job_id,\n \"result\": {\n \"success\": True,\n \"audioUrl\": f\"/storage/audios/{final_audio_path.name}\",\n \"message\": f\"🎉 ¡Locución de voz ({language.upper()}) para {artist_name} completada con éxito!\"\n }\n }))\n print(f\"🎉 ¡TAREA DE VOZ {job_id} FINALIZADA AL 100%!\\n\", flush=True)\n else:\n # Modo 2: Video 9:16 con Subtítulos Whisper IA\n await ws.send(json.dumps({\n \"type\": \"JOB_PROGRESS\",\n \"jobId\": job_id,\n \"percent\": 60,\n \"stage\": \"WHISPER SUBTITLES IA\",\n \"message\": \"🎙️ Transcribiendo audio y generando subtítulos virales 9:16...\"\n }))\n\n ass_path = job_dir / \"speech_subs.ass\"\n transcribe_audio_to_ass(final_audio_path, ass_path, style_index=0)\n\n cover_path = job_dir / \"cover_speech.png\"\n create_default_gradient_cover(cover_path, title=artist_name, subtitle=social_handle)\n\n out_video_path = job_dir / f\"speech_video_{job_id[:8]}.mp4\"\n audio_dur = get_media_duration(final_audio_path)\n\n render_single_video_variant(\n audio_path=final_audio_path,\n cover_path=cover_path,\n out_video_path=out_video_path,\n ass_path=ass_path,\n duration=audio_dur,\n variant_idx=0\n )\n\n upload_single_video_robust(job_id, out_video_path, is_final=True)\n\n await ws.send(json.dumps({\n \"type\": \"JOB_COMPLETED\",\n \"jobId\": job_id,\n \"result\": {\n \"success\": True,\n \"videosCount\": 1,\n \"message\": f\"🎉 ¡Video 9:16 con voz de {artist_name} y subtítulos completado!\"\n }\n }))\n print(f\"🎉 ¡VIDEO 9:16 CON VOZ {job_id} COMPLETADO AL 100%!\\n\", flush=True)\n\n# Dispatcher General\nasync def process_job(job, ws):\n job_id = job.get(\"id\")\n job_type = job.get(\"type\", \"VIDEO_GENERATION\")\n aspect_ratio = job.get(\"aspectRatio\", \"9:16\")\n\n job_dir = Path(f\"/tmp/voxforja_jobs/{job_id}\")\n if job_dir.exists(): shutil.rmtree(job_dir, ignore_errors=True)\n job_dir.mkdir(parents=True, exist_ok=True)\n\n if job_type == \"SPEECH_GENERATION\":\n await render_speech_generation(job, ws, job_dir)\n elif job_type == \"VIDEO_16_9\" or aspect_ratio == \"16:9\":\n await render_cinema_169_full(job, ws, job_dir)\n else:\n await render_voxforja_916_batch(job, ws, job_dir)\n\nasync def worker_loop():\n while True:\n try:\n print(f\"🔗 Conectando Worker GPU a {MASTER_WS_URL}...\", flush=True)\n async with websockets.connect(MASTER_WS_URL, ssl=ssl_context, max_size=500*1024*1024) as ws:\n print(f\"\\n{'='*65}\", flush=True)\n print(f\"✅ ¡WORKER GPU CONECTADO Y AUTENTICADO!\", flush=True)\n print(f\"🔥 GPU: {GPU_NAME} | VRAM: {GPU_VRAM}\", flush=True)\n print(f\"⚡ Motores Activos: VOXFORJA 9:16 VERTICAL + CINEMA 16:9 FULL HD\", flush=True)\n print(f\"🎯 Estado: LISTO PARA RECIBIR TAREAS EN VIVO\", flush=True)\n print(f\"{'='*65}\\n\", flush=True)\n\n await ws.send(json.dumps({\n \"type\": \"WORKER_REGISTER\",\n \"gpuName\": GPU_NAME,\n \"gpuVram\": GPU_VRAM,\n \"supportedEngines\": [\"VOXFORJA_9_16\", \"CINEMA_16_9\", \"VOICE_STUDIO\", \"SMART_PHRASING\", \"WAIST_SUBTITLES\"]\n }))\n\n async for msg_str in ws:\n try:\n msg = json.loads(msg_str)\n mtype = msg.get(\"type\")\n if mtype in [\"DISPATCH_JOB\", \"NEW_JOB\", \"JOB_DISPATCH\"]:\n job = msg.get(\"job\", {})\n jid = job.get(\"id\")\n if jid and jid not in ACTIVE_RUNNING_JOBS:\n ACTIVE_RUNNING_JOBS.add(jid)\n asyncio.create_task(process_job_safe(job, ws))\n elif mtype == \"CANCEL_JOB\":\n c_id = msg.get(\"jobId\")\n if c_id: CURRENT_CANCELLED_JOBS.add(c_id)\n except Exception as parse_err:\n print(f\"⚠️ Error parseando mensaje: {parse_err}\", flush=True)\n except Exception as conn_err:\n print(f\"⚠️ Desconectado del Master: {conn_err}. Reintentando en 4s...\", flush=True)\n await asyncio.sleep(4)\n\nasync def process_job_safe(job, ws):\n jid = job.get(\"id\")\n try:\n await process_job(job, ws)\n except Exception as e:\n print(f\"❌ Error procesando trabajo {jid}: {e}\", flush=True)\n await ws.send(json.dumps({\n \"type\": \"JOB_FAILED\",\n \"jobId\": jid,\n \"error\": str(e)\n }))\n finally:\n ACTIVE_RUNNING_JOBS.discard(jid)\n\n# Iniciar bucle del worker en segundo plano\nloop = asyncio.get_event_loop()\nloop.run_until_complete(worker_loop())\n" ] } ] }