00180d0b1c
- Implementación de detector híbrido (Whisper + Chat + Audio + VLM) - Sistema de detección de gameplay real vs hablando - Scene detection con FFmpeg - Soporte para RTX 3050 y RX 6800 XT - Guía completa en 6800xt.md para próxima IA - Scripts de filtrado visual y análisis de contexto - Pipeline automatizado de generación de videos
214 lines
6.7 KiB
Python
214 lines
6.7 KiB
Python
#!/usr/bin/env python3
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"""
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Detector de MOMENTOS CON ALMA:
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Busca risas, emoción, pérdida de control y chat reaccionando fuerte.
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"""
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import json
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import logging
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import re
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import numpy as np
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logging.basicConfig(level=logging.INFO)
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logger = logging.getLogger(__name__)
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def detect_moments_with_soul(chat_data, transcripcion_json, min_duration=20, max_duration=60, top=25):
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"""
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Detecta momentos con alma: risas, emoción, chat excitado.
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"""
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logger.info("=== Buscando MOMENTOS CON ALMA ===")
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with open(transcripcion_json, 'r', encoding='utf-8') as f:
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trans_data = json.load(f)
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segments = trans_data.get("segments", [])
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# === ANÁLISIS DEL CHAT: Encontrar momentos de emoción colectiva ===
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duration = max(int(c['content_offset_seconds']) for c in chat_data['comments']) + 1
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activity = np.zeros(duration, dtype=np.int32)
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for comment in chat_data['comments']:
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second = int(comment['content_offset_seconds'])
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if second < duration:
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activity[second] += 1
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# Suavizar
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activity_smooth = np.convolve(activity, np.ones(5)/5, mode='same')
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# Encontrar picos EMOCIONALES (percentil alto)
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threshold = np.percentile(activity_smooth[activity_smooth > 0], 90)
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peak_seconds = np.where(activity_smooth > threshold)[0]
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logger.info(f"Picos de chat emocional: {len(peak_seconds)} segundos")
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# === ANÁLISIS DE TRANSCRIPCIÓN: Buscar risas y emoción ===
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laughter_patterns = [
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r'\b(ja){2,}\b', # jajaja
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r'\b(je){2,}\b', # jejeje
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r'\b(ji){2,}\b', # jijiji
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r'\b(jo){2,}\b', # jojojo
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r'\b(ri|ri)(sa|se|se){2,}\b', # risas, rise
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r'\bcarcajadas?\b',
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r'\bme (estoy|toy) muriendo\b',
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r'\bno puedo\b.*\b(reír|risa|jaja)',
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r'\b(jajaja|jejeje|jijiji)\b',
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]
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emotion_patterns = [
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r'!{2,}', # múltiples exclamaciones = emoción
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r'¡{2,}', # exclamaciones invertidas
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r'\b[A-Z]{5,}\b', # palabras en mayúsculas = grito
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r'\b(PUTA|DIOS|MIERDA|CARAJO|HOSTIA)\b',
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r'\b(vamos|vamo|vale|siu){2,}\b', # repetición emocional
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r'\b(estoy|toy) (llorando|llorando|muerto)\b',
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]
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# Analizar segmentos para encontrar momentos con alma
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soul_moments = []
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for i, seg in enumerate(segments):
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text = seg["text"]
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text_lower = text.lower()
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start = seg["start"]
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end = seg["end"]
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soul_score = 0
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reasons = []
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# Buscar risas
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for pattern in laughter_patterns:
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if re.search(pattern, text_lower, re.IGNORECASE):
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soul_score += 30
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reasons.append("risa")
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break
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# Buscar emoción
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for pattern in emotion_patterns:
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if re.search(pattern, text, re.IGNORECASE):
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soul_score += 20
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if not reasons:
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reasons.append("emoción")
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break
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# Verificar si hay chat emocional en este momento
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chat_activity = activity_smooth[int(start):int(end)].mean() if int(end) < len(activity_smooth) else 0
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if chat_activity > threshold * 1.5: # Chat MUY activo
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soul_score += 25
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if not reasons:
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reasons.append("chat loco")
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# Texto muy largo con repeticiones = posible pérdida de control
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if len(text) > 50:
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words = text_lower.split()
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unique_ratio = len(set(words)) / len(words) if words else 1
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if unique_ratio < 0.5: # Mucha repetición
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soul_score += 15
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if not reasons:
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reasons.append("repetición emocional")
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if soul_score >= 20: # Umbral más alto para momentos de calidad
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soul_moments.append({
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"start": start,
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"end": end,
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"score": soul_score,
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"text": text.strip()[:100],
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"reasons": reasons
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})
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if not soul_moments:
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logger.warning("No se encontraron momentos con alma")
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return []
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# Ordenar por score
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soul_moments.sort(key=lambda x: -x["score"])
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# Agrupar en intervalos sin solapamiento
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intervals = []
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for moment in soul_moments:
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start = int(moment["start"])
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end = int(moment["end"])
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# Extender para dar contexto
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duration = max(min_duration, min(end - start, max_duration))
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end = start + duration
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# Verificar solapamiento
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overlaps = False
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for s, e in intervals:
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if not (end < s or start > e):
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overlaps = True
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break
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if not overlaps:
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intervals.append((start, int(end)))
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if len(intervals) >= top:
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break
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intervals.sort()
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logger.info(f"Momentos con alma detectados: {len(intervals)}")
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return intervals, soul_moments
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def main():
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import argparse
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parser = argparse.ArgumentParser()
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parser.add_argument("--chat", required=True)
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parser.add_argument("--transcripcion", required=True)
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parser.add_argument("--output", default="highlights_alma.json")
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parser.add_argument("--top", type=int, default=25)
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parser.add_argument("--min-duration", type=int, default=20)
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parser.add_argument("--max-duration", type=int, default=60)
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args = parser.parse_args()
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with open(args.chat, 'r') as f:
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chat_data = json.load(f)
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intervals, moments = detect_moments_with_soul(
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chat_data,
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args.transcripcion,
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args.min_duration,
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args.max_duration,
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args.top
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)
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# Guardar
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with open(args.output, 'w') as f:
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json.dump(intervals, f)
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logger.info(f"Guardado en {args.output}")
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# Imprimir resumen
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print(f"\n{'='*70}")
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print(f"MOMENTOS CON ALMA".center(70))
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print(f"{'='*70}")
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print(f"Total: {len(intervals)} clips")
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print(f"Duración total: {sum(e-s for s,e in intervals)}s ({sum(e-s for s,e in intervals)/60:.1f} min)")
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print(f"{'-'*70}")
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for i, (start, end) in enumerate(intervals, 1):
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duration = end - start
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h = start // 3600
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m = (start % 3600) // 60
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sec = start % 60
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for moment in moments:
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if abs(moment["start"] - start) < 5:
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reasons_emoji = {
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"risa": "😂",
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"emoción": "🔥",
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"chat loco": "💬",
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"repetición emocional": "🤪"
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}
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emojis = "".join(reasons_emoji.get(r, "") for r in moment["reasons"])
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text_preview = moment["text"][:55].replace('\n', ' ')
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print(f"{i:2d}. {h:02d}:{m:02d}:{sec:02d} - {duration}s {emojis} - {text_preview}...")
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break
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print(f"{'='*70}")
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if __name__ == "__main__":
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main()
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