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
127 lines
3.6 KiB
Python
127 lines
3.6 KiB
Python
#!/opt/vlm_env/bin/python3
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"""
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EXTRACT HIGHLIGHTS FROM CONFIRMED GAMEPLAY
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Extrae highlights SOLO de los segmentos de gameplay validados
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"""
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import json
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import re
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# Cargar segmentos de gameplay confirmados
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with open(
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"/home/ren/proyectos/editor/twitch-highlight-detector/gameplay_scenes.json", "r"
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) as f:
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gameplay_segments = json.load(f)
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# Cargar transcripción
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with open(
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"/home/ren/proyectos/editor/twitch-highlight-detector/transcripcion_rage.json", "r"
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) as f:
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trans = json.load(f)
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print("=" * 70)
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print("🎯 EXTRACTOR DE HIGHLIGHTS - Solo Gameplay Confirmado")
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print("=" * 70)
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print(f"Analizando {len(gameplay_segments)} segmentos de gameplay...")
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print()
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# Buscar mejores momentos en cada segmento de gameplay
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all_highlights = []
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rage_patterns = [
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(r"\bputa\w*", 10, "EXTREME"),
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(r"\bme mataron\b", 12, "DEATH"),
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(r"\bme mori\b", 12, "DEATH"),
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(r"\bmierda\b", 8, "RAGE"),
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(r"\bjoder\b", 8, "RAGE"),
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(r"\bretrasad\w*", 9, "INSULT"),
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(r"\bimbecil\b", 9, "INSULT"),
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(r"\bla cague\b", 8, "FAIL"),
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]
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for seg in gameplay_segments:
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seg_highlights = []
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for t in trans["segments"]:
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if seg["start"] <= t["start"] <= seg["end"]:
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text = t["text"].lower()
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score = 0
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reasons = []
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for pattern, points, reason in rage_patterns:
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if re.search(pattern, text, re.IGNORECASE):
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score += points
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if reason not in reasons:
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reasons.append(reason)
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if score >= 6:
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seg_highlights.append(
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{
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"time": t["start"],
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"score": score,
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"text": t["text"][:60],
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"reasons": reasons,
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"segment_start": seg["start"],
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"segment_end": seg["end"],
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}
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)
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# Ordenar y tomar top 2 de cada segmento
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seg_highlights.sort(key=lambda x: -x["score"])
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all_highlights.extend(seg_highlights[:2])
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print(f"Momentos destacados encontrados: {len(all_highlights)}")
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# Ordenar todos por score
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all_highlights.sort(key=lambda x: -x["score"])
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# Mostrar top 15
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print("\nTop momentos:")
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for i, h in enumerate(all_highlights[:15], 1):
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mins = int(h["time"]) // 60
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secs = int(h["time"]) % 60
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print(
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f"{i:2d}. {mins:02d}:{secs:02d} [Score: {h['score']:2d}] {'/'.join(h['reasons'])}"
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)
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print(f" {h['text'][:50]}...")
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# Crear clips (tomar top 12)
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clips = []
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for h in all_highlights[:12]:
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start = max(455, int(h["time"]) - 10)
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end = min(8237, int(h["time"]) + 20)
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clips.append([start, end])
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# Eliminar solapamientos
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clips.sort(key=lambda x: x[0])
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filtered = []
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for clip in clips:
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if not filtered:
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filtered.append(clip)
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else:
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last = filtered[-1]
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if clip[0] <= last[1] + 5:
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last[1] = max(last[1], clip[1])
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else:
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filtered.append(clip)
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print(f"\n{'=' * 70}")
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print(f"CLIPS FINALES: {len(filtered)}")
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total = sum(e - s for s, e in filtered)
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print(f"Duración total: {total // 60}m {total % 60}s")
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print(f"{'=' * 70}")
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for i, (s, e) in enumerate(filtered, 1):
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mins, secs = divmod(s, 60)
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print(f"{i:2d}. {mins:02d}:{secs:02d} - {e - s}s")
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# Guardar
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with open(
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"/home/ren/proyectos/editor/twitch-highlight-detector/final_highlights.json", "w"
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) as f:
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json.dump(filtered, f)
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print("\n💾 Guardado: final_highlights.json")
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print("\nEste archivo contiene SOLO highlights de gameplay confirmado.")
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print("No incluye selección de campeones ni hablando entre juegos.")
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