Sistema completo de detección de highlights con VLM y análisis de gameplay
- 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
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renato97
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rage_detector.py
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213
rage_detector.py
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#!/usr/bin/env python3
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"""
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RAGE & FAIL DETECTOR
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Prioriza momentos de muerte, rage, insultos y fails.
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"""
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import json
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import re
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from pathlib import Path
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def detect_rage_highlights(
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transcription_file, chat_file, skip_intro=455, max_duration=8237
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):
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"""Detecta momentos de rage, muerte y fails."""
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print("=" * 60)
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print("RAGE & FAIL DETECTOR")
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print("=" * 60)
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# Cargar transcripción
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with open(transcription_file, "r") as f:
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trans = json.load(f)
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# Cargar chat
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with open(chat_file, "r") as f:
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chat_data = json.load(f)
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# Diccionario de rage completo
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rage_patterns = {
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"extreme_rage": [
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r"\bputa\b",
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r"\bmadre\b",
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r"\bretrasad\w*",
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r"\bimbecil\b",
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r"\bestupid\w*",
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r"\bidiota\b",
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r"\bmierda\b",
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r"\bbasura\b",
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r"\binutil\b",
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r"\bmongol\w*",
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r"\bcancer\b",
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r"\bmaricon\b",
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],
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"death": [
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r"\bme mataron\b",
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r"\bme mori\b",
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r"\bmuerto\b",
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r"\bme matan\b",
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r"\bmatenme\b",
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r"\bfeed\w*",
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r"\bfeeding\b",
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r"\bme destrozaron\b",
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r"\bme comieron\b",
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r"\bme cargaron\b",
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],
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"fail": [
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r"\bla cague\b",
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r"\bla lie\b",
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r"\berror\b",
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r"\bfail\b",
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r"\bperdon\b",
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r"\bperdón\b",
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r"\blo siento\b",
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r"\bmala mia\b",
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r"\bfalle\b",
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r"\bfall[eé]\b",
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r"\bno puede ser\b",
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r"\bcomo\?\b",
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],
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"team_rage": [
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r"\bequipo\b.*\b(mierda|basura|malos)\b",
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r"\bteam\b.*\b(trash|bad)\b",
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r"\breport\w*",
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r"\btroll\w*",
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r"\binting\b",
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r"\bjugadores\b.*\bmalos\b",
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],
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"frustration": [
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r"\b(nooo|noo|no no no)\b",
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r"\bpor que\b",
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r"\bporque\b",
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r"\ben serio\b",
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r"\bno me jodas\b",
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r"\bque (haces|hace)\b",
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r"\bomg\b",
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r"\bdios\b",
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r"\bhostia\b",
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r"\bjoder\b",
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],
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}
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# Analizar cada segmento
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rage_moments = []
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for seg in trans.get("segments", []):
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if seg["start"] < skip_intro:
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continue
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text = seg["text"].lower()
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score = 0
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reasons = []
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for category, patterns in rage_patterns.items():
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for pattern in patterns:
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if re.search(pattern, text, re.IGNORECASE):
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if category == "extreme_rage":
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score += 15
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if "EXTREME" not in reasons:
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reasons.append("EXTREME")
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elif category == "death":
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score += 12
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if "DEATH" not in reasons:
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reasons.append("DEATH")
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elif category == "team_rage":
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score += 10
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if "TEAM_RAGE" not in reasons:
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reasons.append("TEAM_RAGE")
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elif category == "fail":
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score += 8
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if "FAIL" not in reasons:
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reasons.append("FAIL")
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else:
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score += 5
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if "FRUSTRATION" not in reasons:
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reasons.append("FRUSTRATION")
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break
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if score >= 5: # Mínimo score significativo
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rage_moments.append(
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{
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"start": seg["start"],
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"end": seg["end"],
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"score": score,
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"text": seg["text"][:70],
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"reasons": reasons,
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}
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)
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print(f"\nMomentos de rage detectados: {len(rage_moments)}")
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# Ordenar por score
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rage_moments.sort(key=lambda x: -x["score"])
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# Crear clips extendidos
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clips = []
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for moment in rage_moments[:25]: # Top 25
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start = max(skip_intro, int(moment["start"]) - 10)
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end = min(max_duration, int(moment["end"]) + 20)
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if end - start >= 12:
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clips.append(
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{
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"start": start,
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"end": end,
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"score": moment["score"],
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"reasons": moment["reasons"],
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"text": moment["text"],
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}
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)
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# Eliminar solapamientos
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clips.sort(key=lambda x: x["start"])
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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["start"] <= last["end"] + 3:
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# Fusionar
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last["end"] = max(last["end"], clip["end"])
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last["score"] = max(last["score"], clip["score"])
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last["reasons"] = list(set(last["reasons"] + clip["reasons"]))
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else:
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filtered.append(clip)
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# Tomar top 15
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filtered.sort(key=lambda x: -x["score"])
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final = filtered[:15]
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final.sort(key=lambda x: x["start"])
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print(f"\nClips sin solapar: {len(final)}")
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print(f"\nTop momentos RAGE:")
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for i, clip in enumerate(final, 1):
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mins = int(clip["start"]) // 60
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secs = int(clip["start"]) % 60
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dur = clip["end"] - clip["start"]
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print(
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f"{i:2d}. {mins:02d}:{secs:02d} - {dur}s [Score: {clip['score']:2d}] "
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f"{'/'.join(clip['reasons'])}"
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)
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total_dur = sum(c["end"] - c["start"] for c in final)
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print(
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f"\nTotal: {len(final)} clips, {total_dur}s ({total_dur // 60}m {total_dur % 60}s)"
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)
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return [[c["start"], c["end"]] for c in final]
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if __name__ == "__main__":
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import sys
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highlights = detect_rage_highlights(
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"transcripcion_medium.json", "elxokas_chat.json"
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)
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with open("HIGHLIGHTS_RAGE.json", "w") as f:
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json.dump(highlights, f)
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print(f"\nGuardado en HIGHLIGHTS_RAGE.json")
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